The recent covid crisis has proven important lessons for academia and industry regarding digital reorganization. Among fascinating lessons from these times is the huge potential of data analytics and artificial intelligence. The crisis exponentially accelerated the adoption of analytics and artificial intelligence, and this momentum is predicted to continue into the 2020s and over. Moreover, drug development is a costly and time-consuming business, and only a minority of approved drugs return the revenue that exceeds the research and development costs. As a result, there is a huge drive to make drug discovery cheaper and faster. With modern algorithms and hardware, it is not too surprising that the new technologies of artificial intelligence and other computational simulation tools can help drug developers. In only two years of covid research, many novel molecules have been designed/identified using artificial intelligence methods with astonishing results in terms of time and effectiveness. This paper will review the most significant research on artificial intelligence in the de novo drug design for COVID-19 pharmaceutical research.

The interactions between work and personal life are important for ensuring well-being especially during COVID-19 where the lines between work and home are blurred. Work-life interference/imbalance can result in work-related burnout, which has been shown to have negative effects on faculty members’ physical and psychological health. Although our understanding of burnout has advanced considerably in recent years, little is known about the effects of burnout on nursing faculty turnover intentions and career satisfaction. Thus, this study aimed to test a hypothesized model examining the effects of work-life inference on nursing faculty burnout (emotional exhaustion and cynicism), turnover intentions and ultimately, career satisfaction. A predictive cross-sectional design was used. An online national survey of nursing faculty members was administered throughout Canada in Summer 2021. Nursing faculty who held full-time or part-time positions in Canadian academic settings were invited via email to participate in the study. Data was collected from an anonymous survey housed on Qualtrics. Descriptive statistics and reliability estimates were computed. The hypothesized model was tested using structural equation modeling. Data suggest that work-life interference significantly increase burnout which contribute to both higher turnover intentions and lower career satisfaction. Turnover intentions in turn was negatively associated with career satisfaction. The findings add to the growing body of literature linking burnout to turnover and dissatisfaction, highlighting key antecedents and/or drivers of burnout among nurse academics. These results provide suggestions for suitable areas for the development of interventions and policies within the organizational structure to reduce the risk of burnout during and post-COVID-19 and improve faculty retention.

The confinement experienced due to the COVID-19 pandemic has prompted a rethink of the teaching-learning process to which teachers have responded without planning and instead using their resources. This study aims to analyze the relationships between work-family interactions, technostress, and perceived organizational support in teachers during the confinement period in Spain that began in March 2020. An online survey was administered to 640 pre-school, primary, and secondary school teachers. Positive reciprocal work-family interactions and their relationship with organizational support were found, with differences according to gender, with women showing a more negative perception of the impact on the family. There were no marked levels of technostress in the overall sample, although higher levels of perceived ineffectiveness and skepticism were found in teachers aged 46 years or older. Teachers in private and subsidized schools showed a higher level of perceived support than those in public schools. There is a need to continue this work to verify the values of these dimensions in other contexts and to apply institutional measures and public policies to improve these indicators in this group.

The emergence of the COVID-19 pandemic has propelled the use of alcohol-based hand sanitizers to the fore as a SARS-CoV-2 control measure. To be effective these products must comply with relevant quality parameters such as alcohol concentration, methanol limits and purity. The current study was designed to determine the quality of alcohol-based hand sanitizer products in the Nairobi metropolitan area. For this purpose, 74 commercially marketed samples were collected and subjected to analysis by gas chromatography. Only three samples (4.1%) complied with the regulatory specifications for alcohol content, methanol limits and pH. Five samples (6.8%) complied with the specification for alcohol content but did not meet methanol or pH limits. A total of 44 (59.5%) samples had methanol levels that exceeded threshold limits. Eleven samples (14.9%) were found with methanol substitution (i.e., methanol, instead of ethanol or isopropanol, was the main alcohol component). The results show that users of alcohol-based hand sanitizers are being exposed to substandard and falsified products which in addition to being non-efficacious pose harm due to unacceptable levels of toxic impurities. Regular, routine post-market surveillance is needed to prevent such products from reaching the market.

The present paper proposes a fractal analysis of the Covid-19 dynamics in 45 European countries. We introduce a new idea of using the box-counting dimension of the epidemiologic curves as a means of classifying the Covid-19 pandemic in the countries taken into consideration. The classification can be a useful tool in deciding upon the quality and accuracy of the data available. We also investigated the reproduction rate, which proves to have significant fractal features, thus enabling another perspective on this epidemic characteristic. Moreover, we studied the correlation between two meteorological parameters: global radiation and daily mean temperature and two Covid-19 indicators: daily new cases and reproduction rate. The fractal dimension differences between the analysed time series graphs could represent a preliminary analysis criterion, increasing research efficiency. Daily global radiation was found to be stronger linked with Covid-19 new cases than air temperature (with a greater correlation coefficient -0.386, as compared with -0.318), and consequently it is recommended as the first-choice meteorological variable for prediction models.

Advancements in deep learning and availability of medical imaging data have led to use of CNN based architectures in disease diagnostic assisted systems. In spite of the abundant use of reverse transcription-polymerase chain reaction (RT-PCR) based tests in COVID-19 diagnosis, CT images offer an applicable supplement with its high sensitivity rates. Here, we study classification of COVID-19 pneumonia (CP) and non-COVID-19 pneumonia (NCP) in chest CT scans using efficient deep learning methods to be readily implemented by any hospital. We report our deep network framework design that encompasses Convolutional Neural Networks (CNNs) and bidirectional Long Short Term Memory (biLSTM) architectures. Our study achieved high specificity (CP: 98.3%, NCP: 96.2% Healthy: 89.3%) and high sensitivity (CP: 84.0%, NCP: 93.9% Healthy: 94.9%) in classifying COVID-19 pneumonia, non-COVID-19 pneumonia and healthy patients. Next, we provide visual explanations for the CNN predictions with gradient-weighted class activation mapping (Grad-CAM). The results provided a model explainability by showing that Ground Glass Opacities (GGO), indicators of COVID-19 pneumonia disease, were captured by our CNN network. Finally, we have implemented our approach in three hospitals proving its compatibility and efficiency.

Social media has become the most influential battleground for political and societal debate. The reach, the speed, and the fact that everybody can participate make this medium very popular but also highly manipulative, subject to biases and extremely precious to control. Misinformation spreads in an unforeseeable way. Sometimes it just follows usual non-malicious social interaction patterns, but often a targeted and designed-for-purpose manner. In this paper, we propose a novel method to identify the most effective opportunities to moderate the social media debate at a large scale using the properties of social signed graphs, such as structural balance. After building the topic universe for a particular political debate in a given time frame, we extract the most active and influential users and mine their position regarding the topic based on their interactions with other users. The result is a signed graph where each edge represents either support or challenge between a pair of users in the context of the topic. This graph can be modelled as an Ising model, with the goal of minimizing its energy state by using the advantages of Adiabatic Quantum Computing. Applying this principle, we use a Quantum Annealing implementation to solve the problem of finding the edges violating the structural balance of this graph, which in classic computing is known to be an NP-hard problem. We then provide a non-intrusive moderation technique for those violations to mitigate or increase the structural imbalance, depending on the desired strategy. The methodology is shown in the context of the COVID-19 skeptics’ debate in Twitter and implemented using a D-Wave 2000Q-6 quantum annealer.

Let us all take a moment to talk, once again, about this new coronavirus pandemic that the world has been facing since November 2019 and about its global response. After a short period marked by the pandemic underestimation risk by most governments, the Western world went nuts and overreacted, most probably so as not to be accused of inaction. In many cases, the overall benefits of the chosen policies were not sufficiently questioned, which resulted in many side effects on global health .The medical motto “primum non nocere”, a moral principle everyone should at least consider following, was evidently not taken into account. It has been overlooked, and the virus has become an obsession, to the extent that nearly everything else, even the most valuable things in life, is still now under appreciated if not simply ignored. This review highlighted facts against this simplistic, one-dimensional view.

Mental health and emotional responses to the effects of COVID-19 lockdown in sub-Saharan Africa (SSA) are of serious public health concern and may negatively affect the mental health status of people. Hence, this study assessed the prevalence of mental health symptoms as well as emotional reactions among sub-Saharan Africans (SSAs) and associated factors among SSAs during the COVID-19 lockdown period. This was a web-based cross-sectional, a study on mental health and emotional features from 2005 respondents in seven SSA countries. This study was conducted between April 27 and May 17, 2020 corresponding to the lockdown period in most SSA countries. Respondents aged 18 years and above and the self-reported symptoms were feeling anxious, being worried, angry, bored and frustrated. These were the main outcomes and were treated as dichotomous variables. Univariate and multivariate logistic regression analyses were used to identify the factors associated with these symptoms. We found that over half (52.2%) of the participants reported any of the mental health symptoms in SSA and the prevalence of feeling bored was 70.5% followed by feeling anxious (59.1%), being worried (57.5%), frustrated (51.5%) and angry (22.3%) during the COVID-19 pandemic. Multivariate analysis revealed that males, those aged >28 years, Central and Southern Africans, those who were not married, the unemployed, those living with more than six persons in a household, had higher odds of mental health and emotional symptoms. Similarly, people who perceived low risk of contracting the infection, and those who thought the pandemic would not continue after the lockdown had higher odds of mental health and emotional symptoms. Health care workers had lower odds for feeling angry than non-healthcare workers. During COVID-19 lockdown periods in SSA, about one in two participants reported mental health and emotional symptoms. Public health measures can be effectively used to identify target groups for prevention and treatment of mental health and emotional symptoms. Such interventions should be an integral component of SSA governments’ response and recovery strategies of any future pandemic.

This research is aimed at determining the characteristics of the current level of entrepreneurial potential of the Three Seas Initiative (3SI) countries, the ability to overcome the consequences of extraordinary events, such as COVID-19 and prospects for the return to an accelerated development once the destabiliser of the economic system, the coronavirus pandemic, has ceased. Eurostat, World Bank and the World Economic Forum data for 2015-2019 were used for the purpose of the research. The research was divided into three stages, i.e. assessment of economic development on the basis of a synthetic ratio of economic anchor development, for which a relative benchmark method based on spatial median (so-called L1 median or Weber point) was used, identification of conditions for the development of entrepreneurial capacity and statistical analysis showing the correlation between economic anchor measures and selected factors of the 3SI countries economic development. Our study found that the entrepreneurial capacity of the 3SI countries in 2015-2019 was determined by nine characteristics, belonging to six areas, i.e. local economy, demographic situation, social situation, trade exchange, innovation and tourism economy. The entrepreneurial potential of the 3SI countries was spatially diversified, and its development was determined, among others, by the entrepreneurial activity of residents (entrepreneurship index) and the conditions for running a business.

Objectives: There are limited data regarding the efficacy of methylprednisolone in patients with acute respiratory distress syndrome (ARDS) due to coronavirus disease 2019 (COVID-19) requiring invasive mechanical ventilation. We aimed to determine whether methylprednisolone increases the number of ventilator-free days (VFDs) among these patients. Design: Retrospective single-center study Setting: Intensive care unit Patients: All patients with ARDS due to confirmed SARS-CoV-2 infection and requiring invasive mechanical ventilation between 1 March and 29 May 2020 were included Interventions: None Measurements and Main Results: The primary outcome was ventilator-free days (VFDs) during the first 28 days, defined as being alive and free from mechanical ventilation. The primary outcome was analyzed with competing-risks regression based on Fine and Gray’s proportional subhazards model. Death before day 28 was considered to be the competing event. A total of 77 patients met the inclusion criteria. Thirty-two patients (41.6%) received methylprednisolone. The median dose was 1 mg.kg-1 (IQR: 1-1.3 mg.kg-1) and median duration of 5 days (IQR:5-7 days). Patients who received methylprednisolone had a mean 18.8 VFDs (95% CI, 16.6-20.9) during the first 28 days vs. 14.2 VFDs (95% CI, 12.6-16.7) in patients who did not receive methylprednisolone (difference, 4.61; 95% CI, 1.10-8.12; P = 0.001). In the multivariable competing-risks regression analysis and after adjusting for potential confounders (ventilator settings, prone position, organ failure support, severity of the disease, tocilizumab, and inflammatory markers), methylprednisolone was independently associated with a higher number of VFDs (subhazards ratio: 0.10, 95%CI: 0.02-0.45; p=0.003). Hospital mortality did not differ between the two groups (31.2% vs. 28.9%, p=0.82). Hospital length of stay was significantly shorter in the methylprednisolone group (24 days [IQR:15-41 days] vs. 37 days [IQR:23-52 days], p=0.046). The incidence of positive blood cultures was higher in patients who received methylprednisolone (37.5% vs. 17.8%, p=0.052). However, 91% of patients who received methylprednisolone also received tocilizumab. The number of days with hyperglycemia was similar in the two groups. Conclusions: Methylprednisolone was independently associated with increased VFDs and shortened hospital length of stay. The combination of methylprednisolone and tocilizumab was associated with a higher rate of positive blood cultures. Further trials are needed to evaluate the benefits and safety of methylprednisolone in moderate or severe COVID-19 ARDS.

A mechanical ventilation system is a big support for breathing complications, in which an external solution is quite necessary to keep oxygen compensation in the patients. Its knowledge is well widespread and different equipment has been developed. However, they are very expensive and their quantity in medical centers is not sufficient, especially in Peru. Hence, it has been required to develop new methods to provide oxygen by a low cost equipment; Protofy, a research group from Spain, designed one of the first low cost mechanical ventilation systems which was medically validated by its government. In this sense, a redesign of the mechanical ventilation system was carried out according to the local requirements and available technology, a different airbag resuscitator with different properties and geometry, but maintaining its working concept based on a cam compression mechanism. Sensors and a display were added to improve the performance with a control algorithm for the rotation frequency and to show the ventilation curves over time to the medical staff. It was necessary to develop a mathematical model to relate the behavior between ventilation curves for a patient and physical variables of the design, especially in the epidemic COVID 19, that many countries are dealing with at the time research is being conducted. The mechanical ventilation system was redesigned, fabricated, and tested measuring its ventilation curves over time. Results indicate that this redesign provides a sturdy equipment able to work during a longer lifetime than the original. The replicability of the ventilation curves behavior is assured, while the mechanism dimensions are adapted for a particular airbag resuscitator. The mathematical model of the whole system can predict satisfactorily the ventilation curves over time and was used to provide the air pressure, volume, and flow as a function of the rotation angle measured by sensors.

We aimed to explore student and staff perceptions and experiences of a pilot COVID-19 asymptomatic testing service (P-ATS) in a UK university campus setting. This was a mixed-method study comprised of an online survey, and thematic analysis of qualitative data from interviews and focus groups conducted at the end of the 12-week P-ATS programme. Ninety-nine students (84.8% female, 70% first year; 93.9% P-ATS participants) completed an online survey, 41 individuals attended interviews or focus groups, including 31 students (21 first year; 10 final year) and 10 staff. All types of testing and logistics were highly acceptable (virus: swab, saliva; antibody: finger prick) and 94.9% would participate again. Reported adherence to weekly virus testing was high (92.4% completed ≥6 tests; 70.8% submitted all 10 swabs; 89.2% completed ≥1 saliva sample) and 76.9% submitted ≥3 blood samples. Students tested to ‘keep campus safe’, ‘contribute to national efforts to control COVID-19’, and ‘protect others’. 31.3% had high anxiety as measured by the Generalized Anxiety Disorder scale (GAD-7) (27.1% of first year). Students with lower levels of anxiety and greater satisfaction with university communications around P-ATS were more likely to adhere to virus and antibody tests. Increased adherence to testing was associated with higher perceived risk of COVID-19 to self (virus) and others (antibody). Qualitative findings revealed 5 themes and 13 sub-themes: ‘emotional responses to COVID-19’, ‘university life during COVID-19’, ‘influences on testing participation’, ‘testing physical and logistical factors’ and ‘testing effects on mental wellbeing’. Asymptomatic COVID-19 testing (virus/antibodies) is highly acceptable to students and staff in a university campus setting. Clear communications and support for mental wellbeing is likely to be important for testing uptake and adherence. Strategies are needed to facilitate social connections and mitigate the mental health impacts of COVID-19 and self-isolation.

Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2) is the virus that caused COVID-19 around the world. The disease starts off as a flu-like symptom and then eventually spreads in the human body.Infections are easily transmitted from human to human which makes it more severe among individuals. SARS-CoV-2 virus is in the same family of viruses SARS and MERS.As of today, there is no approved vaccine that can cure COVID-19. However, potential treatments showing promising improvement results among affected COVID-19 patients. In this review, the effectiveness of the current treatments for COVID-19 has been explored.

Neuroinflammation, defined as inflammatory reactions mediated by cytokines, chemokines, reactive oxygen species, and secondary messengers in the central nervous system (CNS) including the brain and spinal cord is the basis of many neurological disorders. Recently, erythropoietin (EPO) has been considered and studied as a modulator of neuroinflammation. On this article minireview of pathophysiology of neuroinflammation and the neuroprotective effects of EPO is discussed and a case of subacute huge subdural hematoma with double mydriasis operated urgently, treated with low daily dose (vs high dose once or twice a month in the literature) of EPO and recovered fully and discharged home with good consciousness is reported. In addition, the probable unfavorable outcome of erythropoietin administration in patients with neuroinflammation in COVID-19 is considered.

The COVID-19 appears as a catastrophic health risk with psychological, emotional, social and relational implications. From the early stages of the virus spread, the elderly population was identified as the most vulnerable and the health authorities have rightly focused on such frailest population. Conversely, less attention was paid to emotional and psychological dimension of children and adolescents. Actually, they were less at risk quoad vitam or quoad valetudinem, nevertheless they had to face a reality of anxiety, fears and uncertainties. The current study investigated state anxiety and emotion awareness in a healthy sample of older adolescents, 84 females and 64 males, aged 17 to 19, during the pandemic lockdown, using Self-rating Anxiety Scale and the Italian Emotion Awareness Questionnaire. An unexpected anxious phenomenology, impacting the anxiety ideo-affective domain, was found, while the somatic symptomatology appeared to be less severe. The highest anxiety symptom were the breathing difficulties. These findings supported the hypothesis that the COVID-19 pandemic may be a risk condition for an increased state anxiety in older adolescents and suggest the need to provide 1. an effective, empathic communication system with the direct participation of older adolescents, 2. a psychological counseling service for stress management of adolescents.

We report the results of our study of approved drugs as potential treatments for COVID 19, based on the application of various bioinformatics predictive methods. The drugs studied include chloroquine, ivermectin, remdesivir, sofosbuvir, boceprevir, and α-difluoromethylornithine (DMFO). Our results indicate that these small molecules selectively bind to stable, kinetically active residues and residues adjoining them on the surface of proteins and inside protein pockets, and that some prefer hydrophobic over other active sites. Our approach is not restricted to viruses and can facilitate rational drug design, as well as improve our understanding of molecular interactions, in general.

Background: Coronavirus disease 2019 (COVID-19) is an ongoing pandemic and life-threatening highly infectious disease. The people of Bangladesh are at high risk of COVID-19 and have already experienced various socio-economic, health and psychological (mental) consequences. Particularly, mental health problems are dominantly reported in the literature and should be controlled. The main objective of this epidemiological study is to assess the mental distress and identify its determinants using online-based survey. Such information is urgently needed to develop feasible strategies for Bangladesh. Methods: An online survey was conducted for this study from May 01 to May 05, 2020. A total of 240 respondents provided self-reported online responses. Respondent’s mental distress was measured by the General Health Questionnaire 12 (GHQ-12) and by the self-rated mental health (SRMH) question. Various kinds of statistical analyses ranging from simple to multivariable logistic recession were performed using SPSS 23.0. Results: About 31.3% and 48.3% of respondents were mentally distressed by GHQ-12 and SRMH question, respectively. Logistic regression analysis revealed that mental distress was significantly higher among those respondents, whose usual activity was affected by the coronavirus (OR = 6.40, 95% CI: 1.87 - 21.90, p<0.001) and whose financial stress was increased due to lockdown (OR = 2.12, 95% CI: 1.01 – 4.46, p<0.05) on GHQ-12. Female sex (OR = 1.97, 95% CI: 1.03 – 3.75, p<0.05) and respondents with poor mental health before the outbreak (OR = 3.38, 95% CI: 1.18 – 9.72, p<0.05) were also significantly affected by mental distress on SRMH. Conclusions: At least thirty percent of the respondents were found to be mentally distressed. Some of the study findings, particularly significant determinants, should be considered while developing strategies to reduce the burden of mental distress among study respondents or similar group in Bangladesh.

A recent manuscript (Zhou, P. et al. “A pneumonia outbreak associated with a new coronavirus of probable bat origin”, Nature 579, 270–273 (2020). https://doi.org/10.1038/s41586-020-2012-7) from Wuhan Institute of Virology claimed the identification of a bat coronavirus, RaTG13, which showed 96.2% genome homology with SARS-CoV-2. In this paper, we raise the puzzling observations surrounding the identification, characterization, unique genome features of this RaTG13 strain, as well as its 100% nucleotide identity in partial RdRp gene with another bat coronavirus strain BtCoV/4991. And the paper presented premature hypothesis of potential bat origin of SARS-CoV-2 while RaTG13 strain was not successfully isolated. We also present the concerns on the methodology, data quality and experiment procedures described in this paper. We call for the authors to provide additional data, to share related samples to be verified and further characterized by other scientists.

Abstract: A primary action of vitamin D is regulation of gene transcription. Many cell types possess genes that make antimicrobial peptides (AMPS) (endogenous antibiotics), recently discovered to be regulated by vitamin D. Two examples are cathelicidin and beta defensins, both bioactive against many different bacteria, fungi, mycobacteria, parasites and viruses. The signal transduction pathway is triggered by sensing microorganisms via cell surface receptors, causing intracellular production of calcitriol (1,25(OH)2D) and vitamin D receptors, leading to upregulation of AMP production. Serum 25(OH)D concentrations required to sustain adequate AMP production to eradicate infections are unknown. Vitamin D3 is photosynthesized in skin in amounts ranging from 10,000 (250 mcg) to 25,000 (625 mcg) International Units (IU) from 7-dehydrocholesterol after whole-body exposure to one minimal erythemal dose (MED) of ultraviolet B (UVB) radiation, and is impacted by many factors including geographic localities, seasonal changes and skin pigmentation. We and others have reported extended daily oral dosing with these amounts of vitamin D3 safe. We routinely observe serum 25(OH)D concentrations below 20ng/ml on new admissions, which have been reported insufficient to sustain AMP production. In contrast serum 25(OH)D concentrations above 100ng/ml have been reported after serial UVB treatments for psoriasis. Little vitamin D naturally occurs in food, and insufficient sun exposure may be causing worldwide deficiency. We review evidence suggesting that higher daily intakes of vitamin D3 than the currently recommended 600 (15 mcg) IU/day may be necessary to sustain AMP production in the face of an overwhelming infection, particularly in non-Hispanic blacks, a high risk population suffering the worst outcomes from COVID-19. We propose that increased vitamin D supplementation could provide a safe and cost-effective way to protect all populations from infections, in particular those from pandemic COVID-19.

On 9 March 2020, two cases of COVID-19 were reported in Burkina Faso. As of 10 April 2020, a total number of 484 cases (404 cases in the Kadiogo province) were reported nationwide. Real-time forecasts of COVID-19 are important to inform decision-making in the country. Here, we propose an approach that tests the performance of four models (Exponential Growth model, the Generalized Growth model (GGM), the Generalized Logistic Growth, and Richards Growth model) to select the model that best fit data and to generate short-term forecasting (5-, 10-, and 15-day forecasts from 11 to 25 April 2020) in Kadiogo, the epicenter of the outbreak. Using daily number of confirmed COVID-19 cases, the results suggests that GGM performed the best out of the 4 models. Overall, our GGM predictions suggested an average total number of cumulative cases of 514 (95% CI, 464–559), 629 (95% CI, 559–691), and 750 (95% CI, 661–840) between 11 to 15 April, 16 to 20 April, and 20 to 25 April 2020, respectively. COVID-19 in this province was best approximated by sub exponential growth rather than exponential or logistic growth. Current data suggest that COVID-19 cases would continue to increase over the next 15-days.

The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems to timely handle public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. On the one hand, blockchain can combat pandemics by enabling early detection of outbreaks, protecting user privacy, and ensuring reliable medical supply chain during the outbreak tracking. On the other hand, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Motivated by these, in this paper we present an extensive survey on the use of blockchain and AI for combating coronavirus (COVID-19) epidemics based on the rapidly emerging literature. First, we introduce a new conceptual architecture which integrates blockchain and AI specific for COVID-19 fighting. Particularly, we highlight the key solutions that blockchain and AI can provide to combat the COVID-19 outbreak. Then, we survey the latest research efforts on the use of blockchain and AI for COVID-19 fighting in a wide range of applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.

A literature review was conducted to summarize the frequency of the D-allele of the angiotensin-converting enzyme-1 in all countries with available data. Using an ecological study design limited to high income countries, we found that the country-level frequency of the D-allele was associated with increased COVID-19 incidence and mortality.

Reliable methods to confirm the diagnosis of COVID-19 are essential to the successful management and containment of the virus. Current diagnostic options are limited in type, supply, and reliability. This article explores the controversial unreliability of existing diagnostic methods and maintains that more reliable diagnostic methods, combinations, and sequencing are necessary to effectively assist in reducing the occurrence of discharge of the patient on false negative test results. This reduction would in effect reduce transmission of the disease.

New coronavirus (SARS-CoV-2) treatments and vaccines are under development to combat the COVID-19 disease. Several approaches are being used by scientists for investigation including 1) various small molecule approaches targeting RNA polymerase, 3C-like protease, and RNA endonuclease and 2) exploration of antibodies obtained from convalescent plasma from patients who have recovered from COVID-19. The coronavirus genome is highly prone to mutations that lead to genetic drift and escape from immune recognition; thus, it is imperative that sub-strains with different mutations are also accounted for during vaccine development. As the disease has grown to become a pandemic, new B-cell and T-cell epitopes predicted from SARS coronavirus have been reported. Using the epitope information along with variants of the virus, we have found several variants which might cause drifts. Among such variants, 23403A>G variant (p.D614G) in spike protein B-cell epitope is observed frequently in European countries such as the Netherlands, Switzerland and France.

Abstract: COVID-19 Long Haulers, an estimated 3% to 12% of people infected globally with coronavirus having latter devasting symptoms 12 weeks after the initial infection is on the rise. We conducted a collaborative study with the long covid patient organization in Greece in order to estimate the prevalence, symptoms and problems that adult long haulers experience and then propose a management plan for these patients. Symptoms were obtained from 208 patients using unstructured qualitative free text entries in an anonymized online questionnaire. The majority of respondents (68.8%) were not hospitalized and had been diagnosed more than six months ago with lingering symptoms (66,8%). Eighteen different symptoms (fatigue, tachycardia, shortness of breath, parosmia etc) were mentioned in both hospitalized and community patients. Interestingly, patients with initial mild symptoms suffer from the same persistent symptoms as those who were hospitalized. Awareness of long covid sequelae seems to be low even among medical doctors. Treatment options incorporating targeted rehabilitation programs are either not available or still excluded from the management plan of long covid patients. Since long COVID is a multi-systemic entity, we propose a holistic interventional approach using a multidisciplinary medical team in order to securely and effectively diagnose and treat these specific patients. Academic and medical community must collaborate with long covid patients’ organizations so as to provide personalized medicine.

Patients with chronic kidney disease (CKD), particularly those with end-stage kidney disease (ESKD) undergoing dialysis or kidney transplantation, face ongoing challenges in relation to the risk of COVID-19. In addition to their underlying condition, factors such as immune-evasive variants, immunosuppressive therapies, and the need for dialysis contribute to poor COVID-19 outcomes in this population. Severe COVID-19 can lead to the deterioration of CKD/ESKD, resulting in acute kidney injury, septic shock, and respiratory failure. Moreover, patients with CKD/ESKD have shown impaired response to COVID-19 vaccination, with seroconversion failures associated with the burden of immunosuppressive treatments, further heightening their vulnerability to severe disease. Despite the current endemic, it is crucial to acknowledge the ongoing significance of COVID-19 as a serious threat to patients burdened with chronic diseases like CKD and ESRD. Understanding the impact of COVID-19 on these patients is essential, as they remain at risk of severe disease progression. This paper presents the results of a literature review to enhance our understanding of COVID-19-related risks in patients with CKD/ESKD. Expert insights and illustrative scenarios provide practical perspectives and highlight the importance of risk stratification based on clinical factors for guiding targeted measures and treatments to mitigate COVID-19 risks in these vulnerable patients.

Enclosed public spaces are the hotspots for airborne disease transmission. To measure and maintain indoor air quality in terms of airborne transmission, an open source, low cost and distributed array of Particulate Matter Sensors has been developed and named as Dynamic Aerosol Transport for Indoor Ventilation or DATIV system. This system can use multiple Particulate Matter Sensors (PMS) simultaneously and can be remotely controlled using a Raspberry Pie based operating system. The data acquisition system can be easily operated using the GUI within any common browser installed on a remote device such as a PC or Smartphone with corresponding IP address. The software architecture and validation measurements are presented together with possible future developments.

The worldwide competition to vaccinate against COVID-19 has highlighted the importance of immunization measures in containing the virus. Saudi Arabia, like the rest of the globe, must assess socio-demographic subtleties, chronic illnesses, vaccination effects, and public health policy implications as it navigates this complicated environment. This cross-sectional study examined Saudi COVID-19 vaccine in many ways. It examined the socio-demographic makeup of vaccine recipients, the prevalence of chronic conditions in this population, vaccine administration patterns, post-vaccination side effects, and vaccine-infection rates. A questionnaire-based online survey of 401 participants ensured gender, nationality, age, weight, and educational diversity. We analyzed vaccine manufacturer choice for each dosage, side effects after the first and second doses, and chronic diseases in the sample. Pfizer/BioNTech vaccines are preferred for all three doses, reflecting worldwide vaccination acceptance patterns. Side effects were not significantly different between vaccination kinds. Interestingly, immunization reduced infection rates, demonstrating the efficacy of COVID-19 vaccines. The data fills important information gaps and illuminates Saudi COVID-19 vaccination kinetics. These data support the global preference for mRNA vaccines, demonstrating the faith in this breakthrough technology. They also sponsor COVID-19 vaccine safety and effectiveness studies. The dramatic drop in infection rates among vaccinated people shows how vaccinations suppress the pandemic. These findings inform Saudi Arabia's immunization efforts. As booster doses become more common, long-term vaccination research is crucial. Vaccine reluctance, especially in certain demographics, must be addressed. Due to vaccines' protective potential, equitable vaccine distribution and large immunization programs are essential for herd immunity and pandemic management. This thorough cross-sectional study helps policymakers and healthcare practitioners build successful and inclusive COVID-19 immunization regimens in Saudi Arabia.

The expression of non-coding RNAs varies greatly depending on the cell type and physiological state. Changes in expression affect the regulatory properties of these molecules. We investigated RNA-seq data from sorted blood cells of healthy control donors and severe COVID-19 patients. Our aim was to display a distinguishable piRNA differential expression portrait for each type of cell and to study the behavior of piRNA in the blood cells of COVID-19 patients. Overall piRNA expression in sorted control cells (erythrocytes, monocytes, lymphocytes, eosinophils, basophils, neutrophils) differs depending on cell type. Four of the most abundant piRNA were expressed relatively equally in all cell types, they included piR-49145, piR-49144, piR-49143 and piR-33151. By reviewing piRNA expression in each group of sorted cells from severe COVID-19 patients we observed 3 upregulated and 10 downregulated piRNAs in erythrocytes. Monocytes were presented with a larger amount of statistically significant piRNA, 4 upregulated and 35 downregulated. In lymphocytes, all 19 piRNAs were upregulated.

Before the emergence of SARS-CoV-1, MERS-CoV, and most recently SARS-CoV-2, al-ready four other coronaviruses have been circulating in the human population. The alpha coronaviruses NL63 and 229E and the beta coronaviruses OC34 and HKU1. These circulating coronaviruses all cause mild respiratory illness during the winter seasons and most people are already infected in early life. Could antibodies and/or T cells, es-pecially against the beta coronaviruses have offered some form of protection against SARS-CoV-2? Related is the question whether survivors of SARS-CoV-1 or MERS-CoV would be relatively protected against SARS-CoV-2. More importantly, would humoral and cellular immunological memory, generated during the SARS-CoV-2 pandemic, ei-ther by infection or vaccination offer protection against future coronaviruses? Or rather than protection, could antibody dependent enhancement have taken place, a mecha-nism by which circulating corona antibodies enhance the severity of COVID-19? An-other related phenomenon, the original antigen sin, would also predict that the effec-tivity of the immune response to future coronaviruses would be impaired because of reactivation of memory against irrelevant epitopes. Current available evidence indicates that latter scenarios are highly unlikely and that especially cytotoxic memory T cells directed against conserved epitopes of human coronaviruses could at least offer partial protection against future coronaviruses.

Background: In America, vaccine-related deaths constitute a significant contributor to child mortality. An essential means of reducing this is through broad vaccine coverage. The COVID-19 pandemic has posed a potential disruption to vaccine coverage due to its effects on the healthcare system. Objectives: This study aims to evaluate the impact of the COVID-19 pandemic on DTP3 vaccination coverage in the Americas, investigating trends from 2012 to 2022 to identify significant changes, regional disparities, and the overall effect of the pandemic on progress towards global immunization targets. Methods: This study used the coverage data for the third dose of the Diphtheria, Tetanus, and Pertussis Vaccine (DTP3) pulled from UNICEF databases spanning 2012 to 2022. We conducted a JoinPoint regression to identify points of significant trend changes. The annual percentage change (APC) and 95% confidence intervals (95% CI) were calculated for America and its regions. We also used segmented regression analysis. Using the Chi-square test, we compared DTP3 vaccination coverage for each country between 2019 and 2022. Results: Overall, America saw a decrease in vaccine coverage during this period, with an APC of -1.4 (95% CI -1.8.; -1.0). This trend varied across regions. In North America, the decrease was negligible (-0.1% APC). South America showed the steepest decrease, with an APC of -2.5%. Central America also signif-icantly declined, with an APC of -1.3%. Our findings suggest a concerning trend of declining DTP vaccination rates in the Americas, exacerbated in certain regions, in the wake of the COVID-19 pandemic. The absolute decrease in vaccine coverage in the Americas was -4 % between 2019 and 2022, with the most significant drop observed in Central America (-7 %). However, six countries reported increased vaccination rates post-COVID-19, led by Brazil, with a 7% increase. Conversely, twenty-two countries registered a decline in DTP3 vaccine coverage, with the average decrease being -7.37%. This decline poses a significant challenge to achieving the WHO's target of 90% coverage for the third dose of DTP by 2030, as evidenced by the reduction in the number of countries meeting this target from 2019 to 2022. Conclusions: The COVID-19 pandemic has impacted vaccine coverage in America, leading to a decrease, especially across Central America.

Long Covid or post-acute sequelae of SARS-CoV-2 infection (PASC) can manifest as long-term symptoms in multiple organ systems, including respiratory, cardiovascular, neurological, and metabolic systems. In patients with severe COVID-19, immune dysregulation is significant, and the relationship between metabolic regulation and immune response is of great interest in determining the pathophysiological mechanisms. We aimed to characterize the metabolomic footprint for recovering severe COVID-19 patients in three consecutive time points and compare metabolite levels to controls. Our findings add proof of dysregulated amino acid metabolism in the acute phase, and dyslipidemia, glycoprotein level alterations, and energy metabolism disturbances in severe COVID-19 patients 3-4 months post hospitalization.

Background: The newly introduced COVID-19 vaccines have reduced disease severity and hospitalizations. However, they do not significantly prevent infection or transmission. In the same context, measuring IgM and IgG antibody levels is important, but it doesn't provide information about the status of the mucosal immune response. This article describes a comprehensive mapping of IgA epitopes of the S protein, its cross-reactivity, and the development of an ELISA-peptide assay. Methods: IgA epitope mapping was conducted using Spot-synthesis and sera from RT-qPCR COVID-19-positive patients. Specific and cross-reacting epitopes were identified, and an evolutionary analysis from the early Wuhan strain to the Omicron variant was performed using bioinformatics tools and a microarray of peptides. The selected epitopes were chemically synthesized and evaluated using ELISA-IgA. Results: 40 IgA epitopes were identified, with 23 in S1 and 17 in the S2 subunit. Among these, at least 23 epitopes showed cross-reactivity with DENV and other organisms and 24 with other associated coronaviruses. Three MAP4 polypeptides were validated by ELISA, demonstrating a sensitivity of 90-99.96% and a specificity of 100%. Among the six IgA-RBD epitopes, only the SC/18 epitope of the Omicron variants (BA.2 and BA.2.12.1) presented a single IgA epitope. Conclusions: This research unveiled the IgA epitome of the S protein and identified many epitopes that exhibit cross-reactivity with DENV and other coronaviruses. The S protein of variants from Wuhan to Omicron retains many conserved IgA epitopes, except for one epitope (#SCov/18). The cross-reactivity with DENV suggests limitations in using the whole S protein or the S1/S2/RBD segment for IgA serological diagnostic tests for COVID-19. The expression of these identified specific epitopes as diagnostic biomarkers could facilitate monitoring mucosal immunity to COVID-19, potentially leading to more accurate diagnoses and alternative mucosal vaccines.

Globally, we are undergoing compelling changes in virtually every branch of the society and every aspect of our daily lives over the last couple of years due to the COVID-19 pandemic. In the words of Melinda Gates, the famous American Philanthropist, the pandemic has literally “magnified every existing inequality”. There has been much talk and an ongoing debate on the origin of SARS-CoV-2 (or, the novel coronavirus) as to whether have evolved purely naturally or has the contaminating element of human intervention somewhere in its trajectory. Proponents of the pure ‘natural evolution theory’, some of whom are bigshots in the field of virology have seem to take up great effort to stamp their authority right from the very early days of the pandemic on the slow natural evolution of the virus from its ancestrors by several publications in high-profile journals. These well-cited papers however provide rationals that are somewhat contradictory and are backed up with data which maybe argued as far from being complete and/or all-inclusive. As time progressed, alternative theories to that of the pure natural evolution of the virus emerged with strong counter-logics and gradually compiling genomic and epidemiological data along with detailed structural studies of relevant molecular interactions. All said and done, till date, compelling evidences lack on either end of the spectrum making the COVID origin debate still very mucg wide open. Regardless of whether purely natural or human-intervented the damage caused by the virus is unmistakable and has been an alarming concern to medical science lately. To that end, a section of the scientific community is devoting committed efforts using immuno-informatics to design and develop possible multi-epitope combination vaccines to cover a plethora of viral antigens and their plausible future mutations, alongside with the vaccines that are currently available.

Background and aim. The study aimed to assess the efficacy of the treatment of Kan Jang®, a fixed combination of Andrographis paniculata (Burm. F.) Wall. ex. Nees and Eleutherococcus senticosus (Rupr. & Maxim.) Maxim in patients with mild symptoms of COVID-19. Methods. One hundred forty patients received six capsules of Kan Jang® (n = 68, daily dose of andrographolides – 90 mg) or placebo (n=72) and supportive treatment (paracetamol) for 14 consecutive days in a randomized, quadruple-blinded, placebo-controlled, two-parallel-group design. The efficacy outcomes were the rate of cases turning to severe, the detection rate of SARS-CoV-2 virus over the time of treatment, the duration and the severity of symptoms (sore throat, runny nose, cough, headache, fatigue, loss of smell, taste, pain in muscles) in the acute phase of the disease. Other efficacy measures included improving cognitive and physical performance, quality of life, and the levels of inflammatory blood markers - IL-6, c-reactive protein, and D-dimer. Results. Kan Jang® significantly (p < 0.05) reduced the rate of cases turning to severe (5.36%) compared to placebo (17.86%) and decreased the detection rate of SARS-CoV-2 virus over the time of treatment. The statistical difference in the rates of patients with clinical deterioration in the Kan Jang treatment and placebo control groups was significant (p = 0.0176) both in 112 patients included per protocol (IPP) analysis and in 140 patients included per intention to treat (ITT) analysis (p = 0.0236); the absolute risk reduction of cases by Kan Jang treatment was 12.5%, and the number Needed to Treat by Kan Jang was 8. The patient's recovery time (Number of sick days at the home/clinic) was shorter in the Kan Jang group compared with the placebo group. The rate of resolution of inflammatory symptoms in the Kan Jang® group was significantly higher compared with the placebo group, and relief of the severity of cough, sore throat/pain, runny nose, and muscle soreness. Kan Jang® significantly decreased the Wisconsin Upper Respiratory Symptoms scores compared to placebo in the sample size of 140 patients. However, the relief of fatigue and headache and the decrease of IL-6 in the blood was observed only in a subset of the 86 patients infected during the second three waves of the pandemic. Kan Jang® significantly increased physical activity and workout; however, it did not affect cognitive functions (attention and memory), Quality of life Score, inflammatory markers D-dimer, and c-reactive protein compared with the placebo group. Conclusions. Overall, the results of this study suggest that Kan Jang® is effective in treating mild and moderate COVID-19 irrespective of SARS-Cov-2 variants infection.

Background: This work presents Com@Rehab, a technological patient-centred activity for individuals needing a physical rehabilitation approach and with a specific loss of functionality. Within this scope, this paper focuses on the description of the activity in virtual reality (VR), its components, design and user profile, and its versatility and future adaptability regarding different ages, pathologies and rehabilitation contexts. Methods: The VR activity has been customised according to the patient's clinical needs while replicating an activity of daily living, namely showering. In addition, to validate and get feedback on the developed system, a validation questionnaire was applied to a group of healthy individuals to test the efficiency of the technology that supports the VR activity. Results: Preliminary results show that 94% of the participants recommended the experience, the performance of the various components of the system was successfully implemented, participants quickly adhered to the VR technology, and the UI assistant’s functionality should be improved. Conclusion: This solution creates a more effective, interactive, humanised rehabilitation approach based on a Virtual Reality (VR) activity. The Com@Rehab Communication Module improves human-human and human-machine communication while contributing to health literacy.

Abstract: This is a retrospective study of patients admitted to Jackson Hospital, Montgomery, Alabama with a diagnosis of COVID-19 from January 1, 2021, to February 15, 2022. The independent variable used in the models were patient sex, age, race, BMI category, daily D-dimer categories, categories of anticoagulation doses (Enoxaparin 40 mg VS. Enoxaparin 80 mg VS Enoxaparin 1mg/kg or equivalent), bleeding episodes, and vaccination status. The three different categories of anticoagulation doses were considered for the purpose of the study. Results: The study reviewed a total of one hundred (100) hospitalized patients. Intermediate-dose anticoagulation was found to be the optimal dose as only fourteen percent (14%) patients died compared to a thirty-six percent (36%) and fifty percent (57%) death rate among those treated with low-dose and high-dose anticoagulation, respectively. The multivariate linear regression model predicting patient oxygen requirements revealed D-Dimer and bleeding status to be statistically significant predictors with a p value of <0.01. For the patients who had a D-dimer value ≥ 2 µg/mL, the oxygenation requirement was predicted to be 31 L higher than those with a D-dimer <2 µg/mL (99% CI; p<0.01). When mean D-dimer and corresponding oxygen requirements were calculated per hospitalization days category, the D-dimer levels and oxygen requirements were noted to follow the same trends indicating both values tended to increase and decrease simultaneously. Conclusion: The study concludes daily D-dimer trends can predict COVID-19 patient survival or daily oxygen requirements indicating D-dimer can be the miracle molecule for COVID-19 prognosis.

All vaccines exhibit both specific and non-specific effects. The specific effects are measured by the efficacy against the target pathogen, while the non-specific effects can be detected by the change in all-cause mortality . All-cause mortality data (gender, age band, vaccination history, month of death) between January 2021 and May 2022 was compiled by the Office for National Statistics. COVID–19 vaccination gave good protection on many occasions but less so for younger ages. Each gender and age group shows its own unique vaccination benefit/disbenefit time profile. Individuals are free to make vaccination decisions. For example, women aged 18-39 show a cohort who do not progress beyond the first or second dose. The all-cause mortality outcomes for the Omicron variant showed a very poor response to vaccination with 70% of sex/age/vaccination stage/month combinations increasing all-cause mortality, probably due to unfavorable antigenic distance between the first-generation vaccines and this variant, and additional non-specific effects. The all-cause mortality outcomes of COVID–19 vaccination is far more nuanced than have been widely appreciated, and virus vector appear better than the mRNA vaccines in this specific respect. The latter are seemingly more likely to increase all-cause mortality especially in younger age groups. An extensive discussion/literature review is included to provide potential explanations for the observed unexpected vaccine effects.

Mining and analysis of the Big Data of Twitter conversations have been of significant interest to the scientific community in the fields of healthcare, epidemiology, big data, data science, computer science, and their related areas, as can be seen from several works in the last few years that focused on sentiment analysis and other forms of text analysis of Tweets related to Ebola, E-Coli, Dengue, Human papillomavirus (HPV), Middle East Respiratory Syndrome (MERS), Measles, Zika virus, H1N1, influenza-like illness, swine flu, flu, Cholera, Listeriosis, cancer, Liver Disease, Inflammatory Bowel Disease, kidney disease, lupus, Parkinson's, Diphtheria, and West Nile virus. The recent outbreaks of COVID-19 and MPox have served as "catalysts" for Twitter usage related to seeking and sharing information, views, opinions, and sentiments involving both these viruses. While there have been a few works published in the last few months that focused on performing sentiment analysis of Tweets related to either COVID-19 or MPox, none of the prior works in this field thus far involved analysis of Tweets focusing on both COVID-19 and MPox at the same time. With an aim to address this research gap, a total of 61,862 Tweets that focused on Mpox and COVID-19 simultaneously, posted between May 7, 2022, to March 3, 2023, were studied to perform sentiment analysis and text analysis. The findings of this study are manifold. First, the results of sentiment analysis show that almost half the Tweets (the actual percentage is 46.88%) had a negative sentiment. It was followed by Tweets that had a positive sentiment (31.97%) and Tweets that had a neutral sentiment (21.14%). Second, this paper presents the top 50 hashtags that were used in these Tweets. Third, it presents the top 100 most frequently used words that are featured in these Tweets. The findings of text analysis show that some of the commonly used words involved directly referring to either or both viruses. In addition to this, the presence of words such as "Polio", "Biden", "Ukraine", "HIV", "climate", and "Ebola" in the list of the top 100 most frequent words indicate that topics of conversations on Twitter in the context of COVID-19 and MPox also included a high level of interest related to other viruses, President Biden, and Ukraine. Finally, a comprehensive comparative study that involves a comparison of this work with 49 prior works in this field is presented to uphold the scientific contributions and relevance of the same.

COVID-19 has harmful impact on health. It is especially important for endurance athletes (EAs). Sleep and psychology influence sport performance. Aims of this study were: (1) investigation of the consequences of mild COVID-19 on sleep and psychology and (2) assessment of the conse-quences of the infection on cardiopulmonary exercise test (CPET) results. 49 EAs (males= 43; 87.76%, females= 6; 12.24%, age= 39.9±7.8 years, height= 178.4±6.8 cm, weight= 76.3±10.4 kg; BMI= 24.0±2.6 kg·m−2) underwent maximal cycling or running CPET pre- and post- COVID-19 and completed a survey. Exercise performance was deteriorated after COVID-19 (maximal oxy-gen uptake; VO2max= 47.81±7.81 vs 44.97±7.00 ml·kg·min−1 respectively pre- and post- infection; p<0.001). Waking up at night affected heart rate (HR) at the respiratory compensation point (RCP) (p=0.028). Sleep time influenced pulmonary ventilation (p=0.013), breathing frequency (p=0.010), and blood lactate concentration (Lac) (p=0.013) at RCP. Maximal power/speed (p=0.046) and HR (p=0.070) linked with the quality of sleep. Stress management and relaxation techniques linked with VO2max (p=0.046), maximal power/speed (p=0.033), and maximal Lac (p=0.045). Cardiorespiratory fitness was deteriorated after mild COVID-19 and was correlated with sleep and mental health. Medical Professionals should encourage EAs to maintain proper mental health and sleep after COVID-19 infection to facilitate recovery.

Abstract: Our goal is to create point-of-care (POC) strategies that accelerate decision making, increase efficiency, improve outcomes, and enhance standards of care in island communities faced with global warming, rising oceans, population migration, and intensifying weather disasters. We assessed needs in the Bantayan Archipelago and mainland Cebu Province, Visayas Islands, Philippines, to map POC diagnostics, rescue times, and spatial care paths. Significant deficiencies were lack of cardiac troponin testing for rapid diagnosis of acute myocardial infarction, absence of blood gas and pH testing for support of critically ill patients, and geographic gaps prolonging patient transfers and delaying treatment. Strengths comprised primary care that can be facilitated by POC testing, logical inter-island transfers for which decision making and triage could be accelerated with onboard diagnostic testing, and healthcare small-world networks amenable to POC advances, such as pre-hospital testing, that avoid overloading emergency rooms. Healthcare resources must be distributed to archipelago islands, not concentrated in large metropolitan areas inaccessible for emergency interventions. We conclude that a point-of-need focus will help improve public health, decrease disparities in mortality among rural islanders versus urban dwellers, and pave the way for heightened resilience in anticipation of the adverse impact of global warming on vulnerable coastal areas.

The 2019-2020 Australian bushfires followed by the COVID-19 pandemic brought the significant mental health implications of working in healthcare to the fore. The importance of appropriate support services to ensure the resilience and recovery of healthcare workers has been highlighted. In response to healthcare staff experiences during the bushfires, the SEED Wellness Program was created in 2020 in the Illawarra Shoalhaven Local Health District, in NSW Australia. SEED used a participatory action methodology to engage and collaborate with healthcare staff teams in workplace-based restorative activities. Guided by Practice Theory, this study aimed to identify and describe SEED wellness practices that supported healthcare staff. Thirty-three healthcare workers participated in focus groups or individual interviews between June 2021 and March 2022. The analysis involved inductive thematic individual and collective exploration of SEED practices, including co-analysis with participants. Eight core practices that supported participants’ wellbeing were identified including responsive and compassionate leading, engaging staff at every stage of the recovery process, creating a sense of connection with others, and collective caring. The study found that workplace wellness initiatives are optimised when place-based and grounded in local knowledge, needs, and resources incorporating a collective and supportive team approach. Moreover, to ensure engagement in, and sustainability of these initiatives, both bottom-up and top-down commitment is required.

Hybrid organic-inorganic (dendritic polymer-silica) xerogels containing silver nanoparticles (Ag-Nps) were developed as antibacterial leather coatings. The preparation method is environmentally friendly, based on two biomimetic reactions. Silica gelation and spontaneous (Ag-Np) formation were both mediated by hyperbranched poly (ethylene imine) (PEI) scaffolds of variable Mw (2000-750000). The formation of precursor hydrogels was monitored by dynamic light scattering (DLS). The chemical composition of the xerogels was assessed by Infra-Red Spectroscopy (IR) and Energy-dispersive X-ray spectroscopy (EDS) while the uniformity of the coatings was established by scanning electron microscopy (SEM). Release properties of coated leather samples and overall behaviour in water in comparison to untreated analogs were investigated by UV-Vis spectroscopy. Antibacterial activity was tested towards Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus, and antibiofilm properties against Staphylococcus aureus, Staphylococcus epidermidis, Escherichia coli, Acinetobacter baumannii, Enterococcus faecalis while SARS-Cov-2 clinical isolate was employed for the first estimation of their antiviral potential. Toxicity was evaluated using the Jurkat E6.1-cell line. Finally, water contact angle measurements were implemented to determine the enhancement of the leather surface hydrophilicity caused by these composite layers. The final advanced products are intended for use in medical applications.

The COVID-19 was described as a respiratory illness, however further studies recognize it as a complex heterogeneous multisystemic disorder. Global efforts have been proposed to combat COVID-19, emerging diverse therapeutic options, in which discovering new drug therapies, development of vaccines and drug repurposing have been considered the most promising approaches to fight the virus. This study aimed to repurpose known drugs for use against the COVID-19, finding better therapeutic options. Seventeen biological databases were used in this study. The genetic algorithm (GA) was performed for a set of drug target classes and COVID-19 proteins as input, whose drug candidates are obtained according to the target similarities found in the target-target similarity predictive network, resulting in a drug-target interaction network. Thus, recommended drugs correspond to the union of the drug subsets found during each GA execution. Twenty-eight drugs were indicated to be the best therapeutic targets for the virus, in special, the Cyclosporine drug was administered as adjuvant to steroid treatment for COVID-19 patients which showed positive outcomes, reducing mortality in moderate and severe cases. The drugs found have used to treat other diseases, evidencing that the COVID-19 is a multisystemic disorder and suggests that the viruses’ mechanism of action presents some comorbidity with other human diseases. Evidence shows that the drugs found in this research might act together to fight the virus in a broader fashion, however further studies including in vitro and in vivo experiments are needed to find the best combination of these drugs.

Technological advances such as blockchain, artificial intelligence, big data, social media, geographic information systems represent a building block of the digital transformation that supports the resilience of the food supply chain (FSC) and increases its efficiency. This paper reviews the literature surrounding digitalization in FSCs. A bibliometric and key-route main path analysis was carried out to objectively and analytically uncover the knowledge development in digitalization within the context of sustainable FSCs. The research began with the selection of 2140 articles published nearly over five decades. Then, the articles were examined according to several bibliometric metrics such as year of publication, countries, institutions, sources, authors, and keywords frequency. A keyword co-occurrence network was generated to cluster the relevant literature. Findings of the review and bibliometric analysis indicate that research at the intersection of technology and the FSC has gained substantial interest from scholars. On the basis of keyword co-occurrence network, the literature is focused on the role of information communication technology for agriculture and food security, food waste and circular economy, and the merge of the Internet of Things and blockchain in the FSC. The analysis of the key-route main path uncovers three critical periods marking the development of technology-enabled FSCs. The study offers scholars a better understanding of digitalization within the agri-food industry and the current knowledge gaps for future research. Practitioners may find the review useful to remain ahead of the latest discussions of technology-enabled FSCs. To the authors’ best knowledge, the current study is one of the few endeavors to explore technology-enabled FSCs using a comprehensive sample of journals articles published during the past five decades.

The COVID-19 pandemic has exposed the inadequacies of the current healthcare system and needs a paradigm change, which is holistic, and community based illustrated by the healing wheel. The present paper proposes that existential positive psychology (PP 2.0) represents a promising approach to meet the rising needs in palliative care. This framework has a twofold emphasis on (a) How to transcend and transform suffering as the foundation for wellbeing, and (b) how to cultivate our spiritual and existential capabilities to achieve personal growth and flourishing. We propose that these objectives can be achieved simultaneously through dialectical palliative counselling, as illustrated by Wong’s integrative meaning therapy (Wong, 2020) and Lo’s Conceptual Model of CALM Therapy in palliative care (Lo et al., 2014). We then discuss existential suffering in general and at the last stage of life in particular; we also review recent research and interventions on existential suffering in palliative patients. Finally, we outline the objectives and the strategies of IMT in providing palliative counselling for palliative care and hospice patients.

The epidemiological situation generated by COVID-19 has highlighted the importance of applying non-pharmacological measures. Among these, mass screening of the asymptomatic general population has been established as a priority strategy by carrying out diagnostic tests to limit the spread of the virus. In this article, we aim to evaluate the economic impact of mass COVID-19 screenings of an asymptomatic population through a Cost-Benefit Analysis based on the estimated total costs of mass screening versus health gains and associated health costs avoided. Excluding the value of monetized health, the Benefit-Cost ratio was estimated at approximately 0.45. However, if monetized health is included in the calculation, the ratio is close to 1.20. The monetization of health is the critical element that tips the scales in favour of the desirability of screening. Screenings with the highest return are those that maximize the percentage of positives detected.

To safeguard against meat supply shortages during pandemics or other catastrophes, this study analyzed the potential to provide the average household’s entire protein consumption using either soybean production or distributed meat production at the household level in the U.S. with: 1) pasture-fed rabbits, 2) pellet and hay-fed rabbits, or 3) pellet-fed chickens. Only using the average backyard resources, soybean cultivation can provide 80%-160% of household protein and 0%-50% of a household’s protein needs can be provided by pasture-fed rabbits using only the yard grass as feed. If external supplementation of feed is available, raising 52 chickens while also harvesting the concomitant eggs or alternately 107 grain-fed rabbits can meet 100% of an average household’s protein requirements. These results show that resilience to future pandemics and challenges associated with growing meat demands can be incrementally addressed through backyard distributed protein production. Backyard production of chicken meat, eggs, and rabbit meat reduces environmental costs of protein due to savings in production, transportation, and refrigeration of meat products and even more so with soybeans. Generally, distributed production of protein was found to be economically competitive with centralized production of meat if distributed labor costs were ignored.

One of the critical tools for early detection and subsequent evaluation of the incidence of lung diseases is chest radiography. At a time when the speed and reliability of results, especially for COVID-19 positive patients, is important, the development of applications that would facilitate the work of untrained staff involved in the evaluation is also crucial. Our model takes the form of a simple and intuitive application, into which you only need to upload X-rays: tens or hundreds at once. In just a few seconds, the physician will determine the patient's diagnosis, including the percentage accuracy of the estimate. While the original idea was a mere binary classifier that could tell if a patient was suffering from pneumonia or not, in this paper we present a model that distinguishes between a bacterial disease, a viral infection, or a finding caused by COVID-19. The aim of this research is to demonstrate whether pneumonia can be detected or even spatially localized using a uniform, supervised classification.

Background: A malfunction of the innate immune response in COVID-19 is associated with eosinopenia particularly in more severe cases. This study tested the hypothesis that this eosinopenia is COVID-19 specific and is associated with systemic activation of eosinophils.. Methods: Blood of 15 healthy controls and 75 adult patients with suspected COVID-19 at the ER were included before PCR testing and analyzed by point-of-care automated flow cytometry (CD10, CD11b, CD16 and CD62L) in the absence or presence of a formyl peptide (fNLF). 45 SARS-CoV-2 PCR positive patients were grouped based on disease severity. PCR negative patients with proven bacterial (n=20) or other viral (n=10) infections were used as disease controls. Eosinophils were identified with the use of the FlowSom algorithm. Results: Low blood eosinophil numbers (<100 cells/microL; p<0.005) were found both in patients with COVID-19 and with other infectious diseases albeit less pronounced. Two discrete eosinophil populations were identified in healthy controls both before and after activation with fNLF based on the expression of CD11b. Before activation, the CD11bbright population consisted of 5.4% (CI95% = 3.8, 13.4) of total eosinophils. After activation, this population of CD11bbright cells comprised nearly half the population (42.21%, CI95% = 35.9, 54.1). Eosinophils in COVID-19 had a similar percentage of CD11bbright cells before activation (7.6%, CI95% = 4.5, 13.6), but were clearly refractory to activation with fNLF as a much lower percentage of cells end up in the CD11bbright fraction after activation (23.7%, CI95% = 18.5, 27.6; p<0.001). Conclusion: Low eosinophil numbers in COVID-19 are associated with refractoriness in responsiveness to fNLF, which is likely caused by migration of fully functional cells to the tissue. Such homing might be beneficial as eosinophils have been implicated in viral killing.

This paper presents a discrete compartmental Susceptible-Exposed-Infected-Recovered/Dead (SEIR/D) model to address the expansion of Covid-19 pandemic. When time passes, the status of the cells is determined by binary rules that update following both a neighbourhood and a delay pattern. The model assumes the environmental parameters have a crucial impact on the expansion of the disease so a grid is assigned to each parameter to model the single effect caused by this parameter. The expansion is then the weighted sum of all the grids. This proposal shows how the grid architecture, along with an update rule and a neighbourhood pattern is a valuable tool to model the pandemic expansion. This model has already been analyzed in previous works and compared with the corresponding continuous models solved by ordinary differential equations (ODE), coming to find the homologous parameters between both approaches. Thus, it has been possible to prove that the combination neighborhood-update rule is responsible for the rate of expansion and recovering/death of the illness. The delays (between Susceptible and Asymptomatic, Asymptomatic and Infected, Infected and Recovered/Dead) may have a crucial impact on both the peak of Infected and the Recovery/Death rate. This theoretical model has been successfully tested in the case of the dissemination of information through mobile social networks and in the case of plant pests.

Nowadays, the development of civilization requires a vision of balancing the interests of employees and employers in the sphere of work as never before. Work-life balance is directly linked to social sustainability. The aim of this article is to analyse various dimensions of mutual enrichment of the professional and private life of an individual and to describe how positive experiences in professional and non-professional life influence the improvement of satisfaction, health and achievements, thus enabling the sustainable development of the individual. The conducted research was of a qualitative nature. Thematic exploration was used to analyse the findings of 34 in-depth interviews with experienced HR managers and employees at various levels of enterprises in Poland. The research shows that the work and personal life of the respondents interact, complement, and enrich in different ways, depending on the stage of the employee’s life. Habits developed by practicing a specific sport discipline or other type of hobby are helpful in the effective implementation of professional tasks. Also, non-professional interests, including communing with culture and art have a positive impact on professional activities. On the other hand, the respondents emphasized that thanks to their professional activities, specific to the type of work they perform, they are sometimes more extroverted, meticulous, organized and consistent when performing activities outside of work and in other aspects of private life.

An accurate outbreak prediction of COVID-19 can successfully help to get insight into the spread and consequences of infectious diseases. Recently, machine learning (ML) based prediction models have been successfully employed for the prediction of the disease outbreak. The present study aimed to engage an artificial neural network-integrated by grey wolf optimizer for COVID-19 outbreak predictions by employing the Global dataset. Training and testing processes have been performed by time-series data related to January 22 to September 15, 2020 and validation has been performed by time-series data related to September 16 to October 15, 2020. Results have been evaluated by employing mean absolute percentage error (MAPE) and correlation coefficient (r) values. ANN-GWO provided a MAPE of 6.23, 13.15 and 11.4% for training, testing and validating phases, respectively. According to the results, the developed model could successfully cope with the prediction task.

The prolonged lockdown imposed to contain the COVID-19 pandemic prevented many people from direct contact with nature and greenspaces, raising alarms for a possible worsening of mental health. This study investigates the effectiveness of a simple and affordable remedy for improving psychological well-being, based on audio-visual stimuli brought by a short computer video showing forest environments, with an urban video as a control. Randomly selected participants were assigned the forest or urban video, to look at and listen early in the morning, and filled questionnaires. In particular, the State-Trait Anxiety Inventory (STAI) Form Y, collected in baseline condition and at the end of the study, and the Part II of the Sheehan Patient Rated Anxiety Scale (SPRAS), collected every day immediately before and after watching the video. The virtual exposure to forest environments showed effective to reduce perceived anxiety levels in in people forced by lockdown in limited spaces and environmental deprivation. Although significant, the effects were observed only in the short term, highlighting the limitation of the virtual experiences. The reported effects might also represent a benchmark to disentangle the determinants of health effects due to real forest experiences, for example, the inhalation of biogenic volatile organic compounds (BVOC).

Many individuals are wearing face masks improperly at ‘half mask’ and potentially breathing out microbes that can contaminate the air as well as anything below the nose. This note provides the first report that bacteria and fungi breathed out during nasal air exhalation are able to be cultured after landing on blood agar plates. The CFU’s are higher after both 10 breaths and extremely significant for 20 breaths compared to the control plates exposed to the air. Implications of this finding are that going ‘nose commando’ may be able to continue the spread of respiratory diseases such as COVID-19.

Previously we have shown that the combination of radiotherapy with human-umbilical-cord-derived mesenchymal stem-cell therapy significantly reduces the size of the xenotumours in mice, both in the directly irradiated tumour and in the distant non-irradiated tumour or in its metastasis. We have also shown that exosomes secreted from mesenchymal stem-cells pre-irradiated with 2 Gy are quantitatively, functionally and qualitatively different from the exosomes secreted from non-irradiated mesenchymal cells and also that proteins, exosomes and microvesicles secreted by mesenchymal cells suffer a dramatic change when cells are activated or non-activated, with the amount of protein present in the exosomes of the pre-irradiated cells being 1.5-fold times greater compared to those from non-irradiated cells. This finding correlates with a dramatic increase in the anti-tumour activity of the exosomes secreted by pre-irradiated mesenchymal-cells. After the proteomic analysis of the load of the exosomes released from both irradiated and non-irradiated cells, we conclude that annexin A1 is the most important and significant difference between the exosomes released by the cells in either status. Knowing the role of annexin A1 in the control of hypoxia and inflammation which is characteristic of acute-distress-respiratory syndrome, we have designed a hypothetical therapeutic strategy, based on the transplantation of mesenchymal stem cells stimulated with radiation, to alleviate the symptoms of patients who, due to pneumonia caused by COVID-19, require the care of an intensive care unit for patients with life-threatening conditions. With this hypothesis, we would seek to improve the patients’ respiratory capacity and increase the expectations of their cure.

Corona virus disease (COVID-19) has infected over more than 10 million people around the globe and killed at least 500K worldwide by the end of June 2020. As this disease continues to evolve and scientists and researchers around the world now trying to find out the way to combat this disease in most effective way. Chest X-rays are widely available modality for immediate care in diagnosing COVID-19. Precise detection and diagnosis of COVID-19 from these chest X-rays would be practical for the current situation. This paper proposes one shot cluster based approach for the accurate detection of COVID-19 chest x-rays. The main objective of one shot learning (OSL) is to mimic the way humans learn in order to make classification or prediction on a wide range of similar but novel problems. The core constraint of this type of task is that the algorithm should decide on the class of a test instance after seeing just one test example. For this purpose we have experimented with widely known Generalized Regression and Probabilistic Neural Networks. Experiments conducted with publicly available chest x-ray images demonstrate that the method can detect COVID-19 accurately with high precision. The obtained results have outperformed many of the convolutional neural network based existing methods proposed in the literature.

To mitigate the spread of the COVID-19 coronavirus, some countries have enforced more stringent non-pharmaceutical interventions in contrast to those widely adopted (for e.g. the state of Kuwait). In addition to standard practices such as enforcing curfews, social distancing, and closure of non-essential service industries, other non-conventional policies such as the total confinement of highly populated areas has also been implemented. In this paper, we model the movement of a host population using a mechanistic approach based on random walks, which are either diffusive or super-diffusive. Infections are realised through a contact process, whereby a susceptible host may be infected if in close spatial proximity of the infectious host. Our focus is only on the short-time scale prior to the infectious period, so that no further transmission is assumed. We find that the level of infection depends heavily on the population dynamics, and increases in the case of slow population diffusion, but remains stable for a high or super-diffusive population. Also, we find that the confinement of homogeneous or overcrowded sub-populations has minimal impact in the short term. Finally, we discuss the possible implications of our findings for total confinement in the context of the current situation in Kuwait.

The critical hypoxia in COVID-19 patients during this pandemic, has taken away many lives all around the globe. The mechanism has been poorly understood and initially, word got around that it was a SARS (Severe Acute Respiratory Syndrome) pneumonia. The atypical images in lung computerized axial tomography (CAT) scans were alarming. This immediately alerted everyone including poor countries to purchase lacking sophisticated ventilator equipment. However, in some countries, even 88% of the patients on ventilators lost their lives. New observations and pathological findings are gradually clarifying the disease. What seems evident is that it is not only one disease but several, with different responses in different countries and different altitudes. The critical hypoxia and «gasping» present in some patients are not totally understood. It was mentioned that it could be like a High-Altitude Pulmonary Edema (HAPE). Hereby, as high-altitude medicine and hypoxia physiology specialists, we compare the pathophysiology with that of high-altitude exposure in order to understand the mechanisms involved. Some differences in lung radiological images along with transmission and viral attack mechanisms are discussed. The oxygen transport triad used at high-altitude can be applied on this pathology in order to propose even the use of erythropoietin (EPO) early in the treatment. The immune system is the most important long-term survival tool, so we suggest a short-term strategy: the use of special Earth open-circuit astronaut-resembling suits with effective outside air filtering re-breathing mechanisms in order to return to work and daily activities, without contamination risk. Thereby, the curve can be flattened without quarantine and the economy could recover.

The coronavirus disease 2019 (COVID-19) is the fastest transmittable virus caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The detection of COVID-19 using artificial intelligence techniques and especially deep learning will help to detect this virus in early stages which will reflect in increasing the opportunities of fast recovery of patients worldwide. This will lead to release the pressure off the healthcare system around the world. In this research, classical data augmentation techniques along with CGAN based on a deep transfer learning model for COVID-19 detection in chest CT scan images will be presented. The limited benchmark datasets for covid-19 especially in chest CT images is the main motivation of this research. The main idea is to collect all the possible images for covid-19 that exists until the very writing of this research and use the classical data augmentations along with CGAN to generate more images to help in the detection of the COVID-19. In this study, five different deep convolutional neural network-based models (AlexNet, VGGNet16, VGGNet19, GoogleNet, and ResNet50) have been selected for the investigation to detect the coronavirus infected patient using chest CT radiographs digital images. The classical data augmentations along with CGAN improve the performance of classification in all selected deep transfer models. The Outcomes show that ResNet50 is the most appropriate deep learning model to detect the COVID-19 from limited chest CT dataset using the classical data augmentation with testing accuracy of 82.91%.

The severity and outcome of coronavirus disease 2019 (COVID-19) largely depends on a patient’s age. Over 80% of hospitalizations are of those over 65 years of age with a 23-fold greater risk of death. In the clinic, COVID-19 patients most commonly present with fever, cough and dyspnea. Particularly in those over 65, it can progress to pneumonia, lung consolidation, cytokine release syndrome, endotheliitis, coagulopathy, multiple organ failure and death. Comorbidities such as cardiovascular disease, diabetes, obesity and hypertension increase the chances of fatal disease, but they alone do not explain the variability in COVID-19 symptoms. Here, we present the molecular differences between the young, middle-aged and elderly that may determine whether COVID-19 is a mild or life-threatening illness. We also discuss several biological age clocks that could be used in conjunction with genetic tests to identify both the mechanisms of the disease and individuals most at risk. Finally, based on these mechanisms, we discuss treatments that could increase survival in the elderly, not simply by inhibiting the virus, but by restoring patients’ ability to clear the infection.

The outbreak of coronavirus disease 2019 (COVID-19) was identified in Wuhan, China, in December 2019. As of April 17, 2020, more than 2 million cases of COVID-19 have been reported worldwide. Northern Italy is one of the world’s centers of active coronavirus cases. In this study, we predicted the spread of COVID-19 and its burden on hospital care under different conditions of social distancing in Lombardy and Emilia-Romagna, the two regions of Italy most affected by the epidemic. To do this, we used a Susceptible-Exposed-Infectious-Recovered (SEIR) deterministic model, which encompasses compartments relevant to public health interventions such as quarantine. A new compartment L was added to the model for isolated infected population, i.e., individuals tested positives that do not need hospital care. We found that in Lombardy restrictive containment measures should be prolonged at least until early July to avoid a resurgence of hospitalizations; on the other hand, in Emilia-Romagna the number of hospitalized cases could be kept under a reasonable amount with a higher contact rate. Our results suggest that territory-specific forecasts under different scenarios are crucial to enhance or take new containment measures during the epidemic.

In the context of the COVID-19 epidemic, and on the basis of the Theory of Dynamical Systems, we propose a simple model for the expansion of contagious diseases, with a particular focus on viral respiratory tracts. The infection develops through contacts between contagious and exposed people, with a rate proportional to contact duration and turnover, inversely proportional to the efficiency of protection measures, and balanced by the average immunological response. The obvious initial exponential increase is readily hindered by the size reduction of the exposed population. The system converges towards a stable attractor whose value is expressed in terms of the ratio C/D of contamination vs decay factors. Decreasing this ratio below a critical value leads to a tipping point beyond which the epidemic is over. By contrast, significant values of C/D may bring the system through a bifurcating hierarchy of stable cycles up to a chaotic behaviour.

The coronavirus (covid-19) pandemic is putting healthcare systems across the world under unprecedented and increasing pressure according to the World Health Organization (WHO). With the advances in computer algorithms and especially Artificial Intelligence, the detection of this type of virus in the early stages will help in fast recovery and help in releasing the pressure off healthcare systems. In this paper, a GAN with deep transfer learning for coronavirus detection in chest x-ray images is presented. The lack of benchmark datasets for covid-19 especially in chest x-rays images is the main motivation of this research. The main idea is to collect all the possible images for covid-19 that exists until the writing of this research and use the GAN network to generate more images to help in the detection of the virus from the available x-rays images with the highest accuracy possible. The dataset used in this research was collected from different sources and it is available for researchers to download and use it. The number of images in the collected dataset is 307 images for four different types of classes. The classes are the covid-19, normal, pneumonia bacterial, and pneumonia virus. The dataset is divided into 90% for the GAN and the training and the validation phase, while 10% used in the testing phase. The GAN helps in generating more images from the original dataset to be 30 times larger than the originally collected dataset. The GAN also help in overcoming the overfitting problem and made the proposed model more robust. Three deep transfer models are selected in this research for investigation. The models are the Alexnet, Googlenet, and Restnet18. Those models are selected based on their small number of layers on their architectures, which will reflect in reducing the complexity of the models and the consumed memory and time. Using a combination of GAN and deep transfer models prove it is efficiency according to validation, testing accuracy, and performance measurements such as precision, recall, and F1 score. Three case scenarios are tested through the paper, the first scenario which includes 4 classes from the dataset, while the second scenario includes 3 classes and the third scenario includes 2 classes. All the scenarios include the covid-19 class as it is the main target of this research to be detected. In the first scenario, the Googlenet is selected to be the main deep transfer model as it achieves 80.6% in testing accuracy. In the second scenario, the Alexnet is selected to be the main deep transfer model as it achieves 85.2% in testing accuracy, while in the third scenario which includes 2 classes(covid-19, and normal), Googlenet is selected to be the main deep transfer model as it achieves 100% in testing accuracy and 99.9% in the validation accuracy. All the performance measurement strengthen the obtained results through the research. Finally, this research may be considered one of the first trails to use GAN and deep transfer models together to help in detecting coronaviruses (covid-19) within the absence of a benchmark dataset around the world, especially in x-rays chest images.

COVID-19 (2019-nCoV) is a pandemic disease with an estimated mortality rate of 3.4% (estimated by the WHO as of March 3, 2020). Until now there is no antiviral drug and vaccine for COVID-19. The current overwhelming situation by COVID-19 patients in hospitals is likely to increase in the next few months. About 15 percent of patients with serious disease in COVID-19 require immediate health services. Rather than waiting for new anti-viral drugs or vaccines that take a few months to years to develop and test, several researchers and public health agencies are attempting to repurpose medicines that are already approved for another similar disease and have proved to be fairly effective. This study aims to identify FDA approved drugs that can be used for drug repurposing and identify biomarkers among high- risk and asymptomatic groups. In this study gene-disease association related to COVID-19 reported mild, severe symptoms and clinical outcomes were determined. The high-risk group was studied related to SARS-CoV-2 viral entry and life cycle by using Disgenet and compared with curated COVID-19 gene data sets from the CTD database. The overlapped gene sets were enriched and the selected genes were constructed for protein-protein interaction networks. Through interactome, key genes were identified for COVID-19 and also for high risk and asymptomatic groups. The key hub genes involved in COVID-19 were VEGFA, TNF, IL-6, CXCL8, IL10, CCL2, IL1B, TLR4, ICAM1, MMP9. The identified key genes were used for drug-gene interaction for drug repurposing. The chloroquine, lenalidomide, pentoxifylline, thalidome, sorafenib, pacitaxel, rapamycin, cortisol, statins were proposed to be probable drug repurposing candidates for the treatment of COVID-19. However, these predicted drug candidates need to be validated through randomized clinical trials. Also, a key gene involved in high risk and the asymptomatic group were identified, which can be used as probable biomarkers for early identification.

The current global pandemic of coronavirus disease 2019 (COVID-19) caused by the coronavirus SARS-CoV-2 has already had a major adverse impact on the world due to the exponentially increasing deaths due to the disease and the extreme actions taken by the world community to prevent its spread. It is important to explore novel methods of reducing the illnesses and fatality rates of the coronavirus-infected patients. Since the weakness of the immune system is one of the major contributing factors for the illnesses caused by such viruses, and since inflammation is a major contributing factor for the mortality of COVID-19 patients, interventions that boost the immune system and/or are anti-inflammatory may reduce the COVID-19 incidence and the mortality due to the disease. A large variety of interventions are known to improve the immune response and/or reduce inflammation. However, all the interventions would not be applicable or acceptable to everyone and so the interventions would need to be individualized based on individual circumstances and preferences. This approach, known as “Individualized Interventions to Improve the Immune Response”, or the I⁴R approach, should be studied in pilot clinical trials urgently, in order to potentially reduce the harm caused by the current coronavirus pandemic.

Purpose: Acute kidney injury (AKI) is a severe symptom of the 2019 novel coronavirus disease (COVID-19), especially for patients in a critical condition.This study explored the potential mechanism of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on AKI at the single-cell level. Methods: 15 normal human kidney samples were collected and analyzed using single-cell RNA sequencing (scRNA-seq). Subsequently, we analyzed the components and proportions of kidney cells expressing the host cellular receptor ACE2 and the key protease TMPRSSs family, and analyzed the expression differences in Occidental and Asian populations. Results: We drafted the currently available world's largest human kidney cell atlas with 42,589 cells and identified 19 clusters through unsupervised hierarchical clustering analysis. ACE2 and TMPRSSs genes were significantly co-expressed in podocytes and proximal convoluted tubules as potential host cells targeted by SARS-CoV-2. Comparative analysis showed that ACE2 expression in kidney cells was no less than that in the lung, esophagus, small intestine and colon, suggesting that the kidney may be an important target organ for SARS-CoV-2. In addition, given the high expression of ACE2 and kidney disease-related genes in Occidental donors relative to Asian donors, Occidental populations with SARS-CoV-2 infection might be a higher risk of of kidney injury.

Depression and cognitive impairment are recognized complications of COVID-19. This study aimed to assess cognitive performance in clinically diagnosed post-COVID depression (PCD) patients using neuropsychological testing. The study involved 71 post-COVID patients, with matched control groups: recovered COVID-19 individuals without complications (n=18) and individuals without prior COVID-19 history (n=19). A post-COVID depression group (PCD, n=25) was identified based on psychiatric diagnosis, a comparison group (noPCD, n=46) included participants with neurological COVID-19 complications, excluding clinical depression. The PCD patients showed significantly less scores in the MoCA test, decreased immediate memory recall in the Word Memory Test, decreased processing speed and higher accuracy in the Trail Making Test, and near to significant worse executive control and processing speed in the Stroop task compared to controls and the noPCD patients. The number of post-COVID symptoms negatively correlated with immediate word memory recall and processing speed among all post-COVID patients. In PCD patients, negative correlations between number of post-COVID symptoms and delayed recall, between time after recovery and immediate recall, and positive correlation between the number of acute symptoms and processing speed in the incongruent condition of the Stroop task were found. No differences between groups in Sniffin’s stick olfactory test was found. Overall, our study revealed cognitive impairment in PCD patients similar to those in major depressive disorder.

A severe consequence of SARS-CoV-2 infection that manifests as systemic inflammation and multi-organ involvement is called Multisystem Inflammatory Syndrome in Children (MIS-C). This review examines the possible relationship between gut barrier integrity, the microbiome, dysregulation of interleukin-6 (IL-6) signaling, and MIS-C. Clinical and biochemical features of MIS-C are similar to those of other hyperinflammatory syndromes, suggesting a dysregulated immune response. One possible explanation for the systemic inflammation seen in MIS-C patients is SARS-CoV-2-induced dysregulation of the IL-6 signaling pathway. In addition, new data suggest a reciprocal link between gut barrier integrity and IL-6. SARS-CoV-2 exhibits bacteriophage-like behavior, highlighting the role of bacteria as a reservoir for the virus and emphasizing the importance of understanding the bacteriophagic mechanism of the virus in fecal-oral transmission. Increased translocation of viral products and bacterial toxins may result from disrupting the intestinal barrier and cause systemic inflammation. On the other hand, systemic inflammation can weaken the integrity of the intestinal barrier, which feeds back into the loop of immunological dysregulation. In the context of MIS-C, understanding the interaction between SARS-CoV-2 infection, IL-6, and gut barrier integrity may shed light on the etiology of the disease and guide treatment options.

HIV early detection (CD4 counts ≥350 cells/μL) is correlated with higher life expectancy among people living with HIV (PLHIV). Several factors including physical, cultural, structural, and financial barriers have contributed to HIV late detection. This is the first-of-its-kind study on the regional differences in early detection of HIV within Tajikistan and any country in the Central Asia region. Utilizing Tajikistan Ministry of Health's national HIV data (N=10,700) spanning 2010 to 2023, we developed median regression models with median CD4 cell count as the outcome and as predictors: time (years), region, age, gender, and area (urban/rural status). Individuals younger than 19 years old are more likely to be detected early for HIV, whereas those older than 39 years are more prone to late detection. Given their active engagement in age-related and sexual activities, those older than 39 years face a higher risk of HIV transmission to others if diagnosed late. Therefore, they require further attention and targeted interventions. Females are detected earlier compared to their male counterparts, regardless of region of residence. Rural populations are detected earlier in most years compared to their urban counterparts. The COVID-19 pandemic accelerated HIV early detection in 2021 but most regions have returned to near pre-pandemic levels of detection in 2022 and 2023. These fluctuations in early detection may signify a shift in attention and resource allocation, potentially affecting efforts to address access gaps during the initial year of the COVID-19 pandemic. There are significant health inequities among different demographic and geographic groups which warrant further attention.

Estimating the infection risks in indoor environments comprises the assessment of the behavior of virus-laden aerosols, i.e. spreading, mixing, removal by air purifiers etc. A promising experimental approach is based on using non-hazardous surrogate aerosols of similar size, e.g. salt particles, to mimic the virus aerosol behavior. This manuscript addresses the issue how a successful transfer of such experiments can be accomplished. Corresponding experiments in two very different environments, a large community hall and a seminar room, with optional use of air purifiers in various constellations were conducted. While high particle concentrations are advantageous in terms of avoiding influence of background aerosol concentrations, it is shown that appropriate consideration of aggregation and settling are vital to theoretically describe the experimentally determined course of particle concentrations. A corresponding model equation for a well-mixed situation is derived and the required parameters are thoroughly determined in separate experiments independently. It is demonstrated that clean air delivery rates (CADR) of air purifiers determined with this approach may differ substantially from common approaches not explicitly taking into account aggregation.

This study delves into the dynamics of relational energy (RE) within organizational context, examining some of its antecedents and decedents. Specifically, it investigates the influence of psychological capital (PsyCap) and humor on RE, and subsequently, the latter’s impact on job performance (JB) mediated by job engagement (JE). A research model based on structural equation modelling carried out with 481 employees in private service industries demonstrates several key relationships. It reveals that both PsyCap and affiliative humor positively affect RE, while aggressive humor exerts a negative influence. Furthermore, RE shows positive association with JE and JP, with JE serving as a mediator. To the authors’ knowledge, this is the first study to present an integrated model encompassing this exact combination of influencers and consequences of RE as well as to be investigated within Western Balkans cultural context. Therefore, it represents a novel approach. Additionally, the research addresses crucial questions regarding the existence and strategic significance of RE within organizational interactions. The findings offer valuable insights for organizations seeking to enhance employee engagement, performance, and wellbeing – even during health crises such as COVID-19 – by fostering RE. The study advances the understanding of RE in organizational settings and provides a foundation for future research in this domain.

The SARS-CoV-2 pandemic continues to pose a global threat. While its virulence has subsided, it has persisted due to the continual emergence of new mutations. Although many high-risk conditions related to COVID-19 have been identified, the understanding of protective factors remains limited. Intriguingly, epidemiological evidence suggests a low incidence of the COVID-19-infected CD patients. The present study explores whether their genetic background, namely the associated HLA-DQs, offers protection against severe COVID-19 outcomes. We hypothesize that the HLA-DQ 2/8 alleles may shield CD patients from SARS-CoV-2 and its subsequent effects, possibly due to memory CD4 T cells primed by previous exposure to human-associated common cold coronaviruses (CCC) and higher affinity to those allele's groove. In this context, we examined potential cross-reactivity between SARS-CoV-2 epitopes and human-associated CCC and assessed the binding affinity (BA) of these epitopes to HLA-DQ 2/8. Using computational methods, we analyzed sequence similarity between SARS-CoV-2 and four distinct CCC. Of 924 unique immunodominant 15-mer epitopes with at least 67% identity, 37 exhibited significant BA to HLA-DQ 2/8, suggesting a protective effect. We present various mechanisms that might explain the protective role of HLA-DQ2/8 in COVID-19-afflicted CD patients. If substantiated, these insights could enhance our understanding of gene-environment enigma and viral-host relationship, guiding potential therapeutic innovations against the ongoing SARS-CoV-2 pandemic.

Central Nervous System (CNS) cancers pose a formidable challenge in the medical world, characterized by aggressive behaviors and limited therapeutic options. The emergence of nanomedicine has offered new hope in CNS cancer treatment. The COVID-19 pandemic has further accelerated the need for innovative therapeutic strategies, emphasizing the significance of nanomedicine in this era. This mini review explores the current status of nanomedicine in CNS cancer therapy, with a particular focus on the post-COVID-19 landscape. We discuss the unique challenges presented by CNS cancers, the potential of nanomedicine in overcoming these challenges, and the recent developments in the field. Additionally, we highlight the lessons learned from the pandemic and how they can be applied to improve CNS cancer therapeutics. We also shed light on the evolving regulatory landscape and the prospects of personalized nanomedicine in CNS cancer treatment. The integration of nanomedicine in CNS cancer therapy is becoming increasingly promising, and its role in the post-COVID-19 era is poised to shape the future of neuro-oncology.

The work presented in this paper presents several novel findings from a comprehensive analysis of about 50,000 Tweets about online learning during COVID-19, posted on Twitter between November 9, 2021, and July 13, 2022. First, the results of sentiment analysis from VADER, Afinn, and TextBlob show that a higher percentage of these tweets were positive. The results of gender-specific sentiment analysis indicate that for positive tweets, negative tweets, and neutral tweets, between males and females, males posted a higher percentage of the tweets. Second, the results from subjectivity analysis show that the percentage of least opinionated, neutral opinionated, and highly opinionated tweets were 56.568%, 30.898%, and 12.534%, respectively. The gender-specific results for subjectivity analysis indicate that for each subjectivity class, males posted a higher percentage of tweets as compared to females. Third, toxicity detection was performed on the tweets to detect different categories of toxic content - toxicity, obscene, identity attack, insult, threat, and sexually explicit. The gender-specific analysis of the percentage of tweets posted by each gender in each of these categories revealed several novel insights. For instance, for the sexually explicit category, females posted a higher percentage of tweets as compared to males. Fourth, gender-specific tweeting patterns for each of these categories of toxic content were analyzed to understand the trends of the same. The results unraveled multiple paradigms of tweeting behavior, for instance, the intensity of obscene content in tweets about online learning by males and females has decreased since May 2022. Fifth, the average activity of males and females per month was calculated. The findings indicate that the average activity of females has been higher in all months as compared to males other than March 2022. Finally, country-specific tweeting patterns of males and females were also performed which presented multiple novel insights, for instance, in India a higher percentage of the tweets about online learning during COVID-19 were posted by males as compared to females.

Pre-exposure prophylaxis for COVID-19 with Tixagevimab/Cilgavimab in immunocompromised patients has reduced the risk of breakthrough infection, disease, hospitalization, and COVID-19 related mortality. However, the advent of the Kraken variant (XBB.1.5) has limited the use of this monoclonal antibody, based on poor efficacy in in vitro studies The objective of the study was to evaluate the risk of breakthrough infection, symptomatic disease, hospitalization, intensive care admission and COVID-19 related death in kidney transplanted recipients receiving pre-exposure prophylaxis with Tixagevimab/Cilgavimab for COVID-19 in the era of the Kraken variant (XBB.1.5). In a prospective, observational study, we enrolled kidney transplant patients undergoing pre-exposure prophylaxis for COVID-19 with Tixagevimab/Cilgavimab at the Division of Infectious Diseases of Federico II University of Naples between February 2023 and August 2023. Each patient subsequently underwent a six-month follow-up with symptom monitoring and surveillance nasopharyngeal swab for SARS-CoV-2 RNA detection every 30 days, regardless of symptoms. Thirty-four kidney transplant patients were enrolled and in the follow-up period, only one tested positive for the nasopharyngeal swab for SARS-CoV-2 research with asymptomatic infection and virological recovery on the eighth day from the diagnosis of infection. Therefore, no patient developed disease and no patient needed hospitalization and no death occurred. No adverse drug reaction to Tixagevimab/Cilgavimab occurred. Our data, although derived from a limited and uncontrolled sample, show the potential of Tixagevimab/Cilgavimab as a valid and viable therapeutic strategy in pre-exposure prophylaxis for immunocompromised patients. These findings highlight the importance of conducting clinical studies on this topic.

Discovery and development of effective strategies to treat emerging and often drug resistant strains of viral and microbial pathogens presents one of the greatest challenges of the 21st century. Despite nearly a century of progress in developments in antimicrobial and antiviral therapies, infectious diseases still account for a substantial proportion of deaths worldwide, including over 100 million globally in the recent pandemic to date. In recent decades, the plague of antimicrobial resistance also represents an additional and alarming signal for both human and animal healthcare and signals a renewed sense of urgency in addressing this issue. This has made the search for novel classes of antibiotics, antivirals and therapeutics found to be capable of bypassing the microbial and viral resistance mechanisms necessary to in order to replenish our current arsenal of antimicrobial and antiviral drugs and update our therapeutic regimens. In addition, the tremendous impact exerted by viral infections and related pathologies on our lives during the recent decade has forced scientists to acknowledge the opportunities and challenges associated with tackling infectious diseases by developing effective antiviral agents endowed with novel mechanisms of action. The discovery of new antimicrobial/antiviral agents, as well as the repurposing of existing drugs and therapeutic options will be crucial to fight the ever-increasing resistance of “superbugs”, pathogenic fungi, viruses, and parasites. Proponents of modern biophoton therapy proposed in this paper can trace its origins to the Russian scientist Alexander Gurwitsch, who a century ago observed ultra-weak emissions of light emerging from an onion root and soon found additional living tissues to be emanating similar energetic phenomena, thus energizing research which continues to the present day and projected toward the development of useful applications of the new-found biological photonic phenomena. The purpose of the present paper is to introduce the past experiences, potential applications and highly successful outcomes of biophotonic therapy as an option to treat infectious diseases including HIV, Hepatitis C, MRSA, and others, with emphasis on efficacy of using biophotonic therapies to treat and ameliorate the drug resistant variants of the above infectious agents, including potential applications for the current COVID-19 pandemic and in preparation for the emergence of the next epidemic or pandemic.

The use of food waste disposers in gastronomical facilities influence the municipal wastewater composition. Ground food waste poses problems in the operation of the sewerage network and generates high electric energy consumption in wastewater treatment plants (WWTP). The study aimed to determine, for five towns with a PE of 4,000 to 220,000, the volumes of catering waste discharged to the WWTPs. The towns differed in the number of inhabitants, beds in hotel and catering places. The calculations were made based on data received from the operators of WWTP. The pollutant concentrations in 2019, were compared with data from the "pandemic" year - 2020. The loads of catering waste entering the sewerage system in 2019 ranged from 32.7 to 1062 tons. In town with the largest tourist base, the BOD value in 2020 accounted for 62.3% of 2019 value. In the largest town, the annual energy consumption for food waste treatment could be up to 2,539,770 kWh. If the waste was fermented, it could be obtained up to 1,376,650 m3 of methane. There is a strong need for implementing a collection system for food waste from catering facilities and its fermentation to produce methane, which can be used for energy purposes.

Nebulized thrombolysis offers locally targeted therapy with potentially lower bleeding risk than systemic administration for coronavirus disease 2019 (COVID-19) respiratory failure. In a proof-of-concept safety study, adult patients with COVID-19-induced respiratory failure and a <300mmHg PaO2/FiO2 (P/F) ratio, requiring invasive mechanical ventilation (IMV) or non-invasive respiratory support (NIRS) received nebulized rt-PA in two cohorts (C1 and C2), alongside standard of care during the first two UK COVID-19 waves. Matched historical controls (MHC; n=18) were used in C1. Safety co-primary endpoints were treatment-related bleeds and fibrinogen reduction to <1.0–1.5 g/L. A dose escalation strategy for improved efficacy with the least safety concerns was determined in C1 for use in C2; patients were stratified by ventilation type to receive 40–60 mg rt-PA per day for ≤14 days. Nine patients in C1 (IMV, 6/9; NIRS, 3/9) and 26 in C2 (IMV, 12/26; NIRS, 14/26) received nebulized rt-PA for a mean (SD) of 6.7 (4.6) and 9.1(4.6) days, respectively. Four bleeding events (one severe and three mild) in three patients were considered treatment-related. No significant fibrinogen reductions were reported. Greater improvement in mean P/F ratio from baseline to end of study was observed in C1 compared with MHC [C1; 154 to 299 vs MHC; 154 to 212). In C2, there was no difference in the baseline P/F ratio of NIRS and IMV patients. However, a larger improvement in P/F ratio was observed in NIRS patients [NIRS; 126 to 240 vs IMV; 120 to 188) and they required fewer treatment days (NIRS; 7.86 vs IMV; 10.5). Nebulized rt-PA appears to be well-tolerated, showing a trend of improved oxygenation and faster recovery in patients with acute COVID-19-induced respiratory failure requiring respiratory support; this effect was more pronounced in the NIRS group. Further investigation is required to study the potential of this novel treatment approach.

Invasive fungal infections, notably candidemia, have been associated with COVID-19. The epidemiology of candidemia has significantly changed during the COVID-19 pandemic. We aim to identify the microbiological profile, resistance rates, and outcomes of COVID-19 associated candidemia (CAC) compared to patients with candidemia not associated with COVID-19. We retrospectively collected data on patients with candidemia admitted to the American University of Beirut Medical Center between 2004 and 2022. We compared the epidemiology of candidemia during and prior to the COVID-19 pandemic. Additionally, we compared the outcomes of critically ill patients with CAC to those with candidemia without COVID-19 from March 2020 till March 2022. Among 245 candidemia episodes, 156 occurred prior to the pandemic and 89 during the pandemic. Of the latter, 39 (43.8%) were CAC, most of which (82%) were reported from intensive care units (ICU). Non-albicans Candida (NAC) spp. were predominant throughout the study period (67.7%). Candida auris infection was the most common cause of NAC spp. in CAC. C. glabrata had decreased susceptibility rates to fluconazole and caspofungin during the pandemic period (46.1% and 38.4% respectively). Mortality rate in the overall ICU population during the pandemic was 76.6%, much higher than the previously reported mortality of candidemia from previous studies at our center. There was no significant difference in 30-day mortality between CAC and non-CAC (75.0% vs 78.1%; P =0.76). Performing ophthalmic examination (P = 0.002), CVC removal during the 48 hours following the candidemia (P = 0.008) and identifying the Candida spp. (P = 0.028) were significantly associated with a lower case-fatality rate. The epidemiology of candidemia has been significantly affected by the COVID-19 pandemic at our center. Rigorous infection control measures and proper antifungal stewardship are essential to combat highly resistant species like C. auris.

Background. Chest CT is widely regarded as a dependable imaging technique for detecting pneumonia in COVID-19 patients, but there is growing interest in microwave radiometry (MWR) of the lungs as a possible substitute for diagnosing lung involvement. Aim. The aim of the study is to examine the utility of the MWR approach as a screening tool for diagnosing pneumonia with complications in patients with COVID-19. Methods. Our study involved two groups of participants. The control group consisted of 50 individuals (24 male and 26 female) between the ages of 20 to 70 years who underwent clinical evaluations and had no known medical conditions. The main group included 142 participants (67 men and 75 women) between the ages of 20 to 87 years who were diagnosed with COVID-19 complicated by pneumonia and were admitted to the emergency department between June 2020 to June 2021. Skin and lung temperatures were measured at 14 points, including 2 additional reference points, using a previously established method. Lung temperature data were obtained with the MWR2020 (RTM-01-RES) (MMWR LTD, Edinburgh, UK), a CE Class I device. All participants underwent clinical evaluations, laboratory tests, chest CT scans, MWR of the lungs, and reverse transcriptase polymerase chain reaction (RT-PCR) testing for SARS-CoV-2. Results. The MWR exhibits a high predictive capacity as demonstrated by its sensitivity of 98.6% and specificity of 84.0%. Conclusions. MWR of the lungs can be a valuable substitute for chest CT in diagnosing pneumonia in patients with COVID-19, especially in situations where chest CT is unavailable or impractical.

During a sanitary crisis, excess mortality measures the number of all-cause deaths, beyond what we would have expected if that crisis had not occurred. The high number of COVID-19 deaths started a debate in Italy with two opposite positions: those convinced that COVID-19 deaths were not, by default, excess deaths, because many COVID-19 deaths were not correctly registered, with the most part attributable to other causes and to the overall crisis conditions, and those who presented the opposite hypothesis. We analyzed the curve of the all-cause excess mortality, during the period January 5, 2020 – August 28, 2022, compared to the curve of the daily confirmed COVID-19 deaths, investigating the association between excess mortality and the recurrence of COVID-19 waves in Italy. We compared the two curves looking for the corresponding highest peaks and we found that 5 out of the 6 highest peaks (83.3%) of the excess mortality curve have occurred, on average, just a week before the concomitant COVID-19 waves hit their highest peaks of daily deaths (Mean 6.4 days; SD 2.4 days). This temporal correspondence between the moments when the excess mortality peaked and the highest peaks of the COVID-19 deaths provides further evidence that the all-cause excess mortality wave has been mostly driven by COVID-19 deaths

Mounting evidence supports the connections between exposure to environment types––such as green spaces and biodiversity––and human health. However, the mechanistic links that connect biodiversity (the variety of life) and human health, plus the level of supporting evidence, are less clear. Here, we undertook a scoping review to map the links between biodiversity and human health and summarise the levels of associated evidence using an established weight of evidence framework. Distinct from other reviews, we provide additional context regarding the environment-microbiome-health axis, evaluate the environmental buffering pathway (e.g., biodiversity impacts on air pollution), and draw upon expert opinion to provide case studies on three underrepresented linkages. The case studies include (1) biodiversity and Indigenous Peoples’ health, (2) biodiversity and urban social equity, and (3) biodiversity and COVID-19. We observed a moderate level of evidence to support the environmental microbiota-human health pathway and a moderate-high level of evidence to support broader nature pathways (e.g., green space) to various health outcomes, from stress reduction to enhanced wellbeing and improved social cohesion. However, studies of broader nature pathways did not typically include specific biodiversity metrics, indicating clear research gaps. Further research is required to understand the connections and causative pathways between biodiversity (e.g., using metrics such as taxonomy, diversity/richness, structure, and function) and health outcomes. There are well-established frameworks to assess the effects of broad classifications of nature on human health. These can assist future research in linking biodiversity metrics to human health outcomes. Our case studies on underrepresented linkages highlight the roles of biodiversity and its loss on urban lived experiences, infectious diseases, and Indigenous Peoples’ sovereignty and livelihoods. More research and awareness of these socioecological interconnections are needed.

Tracking and early identification of suspected cases are essential to control and prevent potential COVID-19 outbreaks. One of the most popular techniques used to track this disease is the use of Infrared cameras to identify individuals with elevated body temperatures. However, they are limited by their inability to be implemented in open public settings such as public parks or even outdoor recreational centers. This limits their ability to effectively track possible COVID-19 patients as open public recreational places such as parks, concert venues and other public venues are hotspots for the spreading of the virus. Other technological solutions such as thermal scanners require an individual to perform the actual testing as they are not individual standalone technologies. This method of testing can potentially cause the transmission of the virus between the tester and the individual getting tested. As can be seen, an alternative solution is essential to solving this issue. In this study, we aim to present the system, design and potential scope of a non-invasive system that can diagnose and identify potential COVID-19 patients using thermal and optical images of the individual using drone technology. The proposed system (COVIDRONE) combines multi-modal machine intelligence, computer vision and real-time monitoring to enable scalable monitoring. The system will also involve the use of machine learning algorithms for better and more accurate diagnosis. We envisage that development of such technologies may help in developing technological solutions to combat infectious disease threats in the future pandemics.

Background: The fulminant course of COVID-19, triggered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presents with a high mortality rate and still lacks a causative treatment. C-reactive protein (CRP) has been shown to increase dramatically during the disease progression and correlates with deleterious outcomes. Selective CRP apheresis can reduce circulating CRP levels fast and effective. Methods: Seven hospitalized patients with documented severe COVID-19 progression, elevated CRP plasma levels (> 100 mg/L) and signs of respiratory failure were treated with CRP apheresis. 2-12 CRP apheresis sessions were performed generally in 24 h time intervals and depending on CRP plasma levels. Results: CRP apheresis reduced CRP plasma levels by up to 84% within a few hours, without exhibiting side effects in any patient. Despite signs of severe lung infiltration in all patients, only one patient died. The other patients showed improvements within the chest x-ray after CRP apheresis and were able to recover regardless of intubation and/or ECMO (4 patients). All remaining six patients were discharged from the hospital in good clinical condition. All patients had comorbidities. Conclusions: This case series presents a mortality rate of only 14%, which is dramatically lower than expected from the presented CRP levels as well as comorbidities and ventilation require-ments. Our clinical observations regarding the here presented seven patients support the hypoth-esis that CRP is a candidate to be therapeutically targeted in the early stage of severe COVID-19.

Results from a behavioral economic laboratory experiment are used to enhance our understanding of public health decisions made during the COVID-19 pandemic. The identification of systematic biases from optimal decision theory found in controlled experiments could help inform public policy design for future public health crises. The laboratory and the shelter-in-place decisions made during COVID-19 included elements of risk, uncertainty and ambiguity. The lab findings found individuals adopt different decision rules depending on both personal attributes and on the context and environment in which the decision task is conducted. Key observations to consider in the context of the COVID-19 decision environment include the importance of past experience, the ability to understand and calculate the odds of each action, the size and differences in economic payoffs given the choice, the value of information received, and how past statistical independent outcomes influence future decisions. The academic space encompassing both public health and behavioral economics is small, yet important, particularly in the current crisis. The objective of continued research in this area would be to develop a more representative model of decision-making processes, particularly during crisis, that would serve to enhance future public health policy design.

Background University students’ psychological health is linked to their academic satisfaction. This study aimed to investigate students’ psychological health and academic satisfaction in the context of COVID-19 and academic year-end stress. Methods Standardized self-filled scales for anxiety, depression, stress, psychological well-being, and an ad-hoc COVID-19 stress scale were used in this cross-sectional study. Participants were first- to third-year students of eight different health-related tracks in Geneva, Switzerland. Descriptive statistics and hierarchical regression analyses were applied. Results In June 2020, out of 2835 invited students, 433 (15%) completed the survey. Academic satisfaction was a stronger mental health predictor than COVID-19, which mainly predicted stress and anxiety. Lower academic satisfaction scores were significantly associated with stress (β = -.53, p < .001), depression (β = -.26, p < .001), anxiety (β = -.20, p < .001), while higher scores with psychological well-being (β = .48, p < .001). Being female was strongly associated with anxiety and stress but not with depression or psychological well-being. Lower age was associated with stress only. The nature of the academic training had a lesser impact on mental health and the academic year none. Compared to students starting the academic year, year-end students reported significantly lower academic satisfaction, higher depression, and particularly higher anxiety and stress. There was, however, no difference in psychological well-being. Conclusion Students suffer more from anxiety, stress, depression, and lower satisfaction with studies at the end of the academic year than at the beginning. Academic satisfaction plays a more substantial role than COVID-19 in predicting students’ overall mental health status. Training institutions should address the underlying factors that can enhance students’ academic satisfaction, especially during the COVID-19 period, in addition to ensuring that they have a continuous and adequate learning experience, as well as access to psychosocial services that help them cope with mental distress and enhance their psychological well-being.

The COVID-19 pandemic is one of the worst catastrophes that we have faced globally in recent years. It has not only taken its toll on the economic sector but also on the education sector. The social distancing norms that are in place as a direct response to the pandemic have turned conventional classroom teaching into a problematic minefield; as such, students all over the world have been forced into unprecedented situations that have served only to worsen the situation. The current pandemic has given rise to one of the worst crises the 21^st Century has ever seen, resulting in a surge of unemployment. Many companies have taken the route of firing employees or making redundancies, as they cannot afford the monthly reimbursement for staff. While this issue primarily concerns full-time workers, it also carries significant consequences for students – a considerable number of students are required to earn their daily living costs, and, without a job, they cannot pay their educational fees, accommodation costs, or living expenses. This comprehensive study briefly discusses the multitude of problems faced by students in the UK regarding higher education, as a direct result of the coronavirus pandemic. It contains six individual sections: a detailed introduction; the methodological procedures employed; educational disruptions, covering issues from hindrances in field research to examinations and student evaluations; personal problems experienced by students, such as accommodation and loss of income; concerns arising from the global pandemic; and finally, a conclusion and summary of the study’s findings.

Background Oil palm (OP), Elaeis guineensis , is by far the most important global oil crop, supplying about 40% of total traded vegetable oil. Palm oils are key dietary components consumed daily by over three billion people, mostly in Asia, and also have a wide range of important non-food uses including in cleansing and sanitising products. Main body Oil palm is a perennial crop with a >25-year life cycle and an exceptionally small land footprint compared to annual oilseed crops. Oil palm crops globally produce an annual 81 million tonnes (Mt) of oil from about 19 million hectares (Mha). In contrast, the second and third largest vegetable oil crops, soybean and rapeseed, yield a combined 84 Mt oil but occupy over 163 Mha of increasingly scarce arable land. Despite this advantage, oil palm has acquired a poor environmental reputation, especially in Europe and North America, although soybean planting is now responsible for more deforestation. Oil palm crops face other challenges in the 2020s. On the demand side, these include changing consumer purchasing habits, threats to global trade systems, and diminishing demand for liquid fuels as transport systems become increasingly electrified. On the supply side, major issues include stagnant yields in ageing plantations, sluggish replanting of improved varieties, labour shortages, diseases and climatic/ environmental threats. The latter include the increasing incidence of new and existing pests/diseases and a general lack of climatic resilience, especially relating to elevated temperatures and increasingly erratic rainfall patterns. This review surveys the oil palm sector in the 2020s and beyond, its major challenges and options for future progress. Conclusions Oil palm crops face many future challenges, including emerging threats from climate change and new pests and diseases, that require more effective international collaboration. Nevertheless, new breeding technologies are providing the promise of improvements, such as much higher yielding varieties, improved oil profiles, enhanced disease resistance and modified crop architecture to enable harvesting mechanisation. The industry also needs to redouble its efforts to engage with global consumers in a constructive dialogue aimed at addressing its image problem and explaining the many benefits of its products.

Washing hands, social distancing and staying at home are the preventive measures set in place to contain the spread of the COVID-19, a disease caused by SARS-CoV-2. These measures, although straightforward to follow, highlight the tip of an imbalanced socio-economic and socio-technological iceberg. Here, a System Dynamic (SD) model of COVID-19 preventive measures and their correlation with the 17 Sustainable Development Goals (SDGs) is presented. The result demonstrates a better informed view of the COVID-19 vulnerability landscape. This novel qualitative approach refreshes debates on the future of SDGS amid the crisis and provides a powerful mental representation for decision makers to find leverage points that aid in preventing long-term disruptive impacts of this health crisis on people, planet and economy. There is a need for further tailor-made and real-time qualitative and quantitative scientific research to calibrate the criticality of meeting the SDGS targets in different countries according to ongoing lessons learned from this health crisis.

This paper reports on the optimization of thin-film coating assisted self-sustainable off-grid hybrid power generation systems for cattle farming in rural areas of Bangladesh. Bangladesh is a lower middle-income country with declining rates of poverty among its 160 million people due to persistent economic growth in conjunction with balanced agricultural improvements. Most of the rural households adopt a mixed farming system by cultivating crops and simultaneously rearing livestock. Among the animals raised, cattle are considered as the most valuable asset for the small/medium-scale farmers in terms of their meat and milk production. Currently, along with the major health issue, the COVID-19 pandemic is hindering the world’s economic growth and has thrust millions into unemployment; Bangladesh is also in this loop. However, natural disasters such as COVID-19 pandemic and floods, largely constrain rural smallholder cattle farmers from climbing out of their poverty. In particular, small and medium-scale cattle farmers face many issues that obstruct them from taking advantage of market opportunities and imposing a greater burden on their families and incomes. An appropriate measure can give a way to make those cattle farmers’ businesses both profitable and sustainable. Optimization of thin-film coating assisted self-sustainable off-grid hybrid power generation system for cattle farming is a new and forward-looking approach for sustainable development of the livestock sector. In this study, we design and optimize a thin-film coating assisted hybrid (photovoltaic-battery-generator) power system by using the Hybrid Optimization of Multiple Energy Resources (HOMER, Version 3.14.0) simulation tool. An analysis of the results has suggested that the off-grid hybrid system is more feasible for small and medium-scale cattle farming systems with long-term sustainability to overcome the significant challenges faced by smallholder cattle farmers in Bangladesh.

Decisions affecting the COVID-19 pandemic, by the individual and those with highest authority, are being made on the basis of unreliable data. Data about cases and deaths are collected daily but represent only a sample of reality. Statistics convert sample data into more reliable estimates. However, statistics have no magical powers; reliability requires dependable data. It is futile to rail against this darkness; COVID-19 is not a scientific experiment. However, we must do better both with data collection and data analysis. In this review, I focus on one element of the data, the asymptomatic case of COVID-19. Without reliable information about this number, decision makers are significantly blinded. By its nature, the asymptomatic case is hidden but contaminating to understanding COVID-19. The true case rate and death rate per case are unknowable without knowing the fraction of cases that are asymptomatic. The best estimate of asymptomatic cases is in the CDC document: COVID-19 Pandemic Planning Scenarios. For four different scenarios the estimates range from 10% to 70%, with the best estimate of 40% for asymptomatic cases. However, even the definition of the asymptomatic case is problematic. In simplest terms, two elements are required: an infection and no symptoms. How is “no symptoms” to be usefully defined? It appears to be analogous to pontificating about black swans from studying only white swans. It implies infection, but how is infection defined? Is it presence of the virus, replication of the virus, or presence of antibodies? Is asymptomatic disease an oxymoron? Without extensive, purposeful screening for specifically defined, essential symptoms and appropriate virus and antibody testing over time, the class of asymptomatic cases remains unknown. Current estimates range from <20% to ˃80%. If low, it can be ignored; if high, it dramatically and proportionately lowers the case rate and the death rate per case. Consequentially, the asymptomatic rate dramatically affects our societal and political responses. In this focused review, we assess the limitations of the published estimates, bring attention to the importance of obtaining accurate data, and exhort that high priority be given in the scientific community to understanding the issue, asymptomatic COVID-19 cases.

While the COVID-19 outbreak was reported to first originate from Wuhan, China, it has been declared as a Public Health Emergency of International Concern (PHEIC) on 30 January 2020 by WHO, and it has spread to over 180 countries by the time of this paper was being composed. As the disease spreads around the globe, it has evolved into a worldwide pandemic, endangering the state of global public health and becoming a serious threat to the global community. To combat and prevent the spread of the disease, all individuals should be well-informed of the rapidly changing state of COVID-19. In the endeavor of accomplishing this objective, a COVID-19 real-time analytical tracker has been built to provide the latest status of the disease and relevant analytical insights. The real-time tracker is designed to cater to the general audience without advanced statistical aptitude. It aims to communicate insights through various straightforward and concise data visualizations that are supported by sound statistical foundations and reliable data sources. This paper aims to discuss the major methodologies which are utilized to generate the insights displayed on the real-time tracker, which include real-time data retrieval, normalization techniques, ARIMA time-series forecasting, and logistic regression models. In addition to introducing the details and motivations of the utilized methodologies, the paper additionally features some key discoveries that have been derived in regard to COVID-19 using the methodologies.

SARS-CoV-2 is a new coronavirus that has caused a worldwide pandemic. It produces severe acute respiratory disease (COVID-19), which is fatal in many cases, characterised by cytokine release syndrome (CRS). According to the World Health Organization (WHO), those who smoke are likely to be more vulnerable to infection. Here, in order to clarify the epidemiologic relationship between smoking and COVID-19, we present a systematic literature review until 28 April 2020 and a meta-analysis. It includes 18 recent COVID-19 clinical and epidemiological studies based on smoking patient status from 720 initial studies in China, USA, and Italy. The percentage of hospitalised current smokers was 7.7% (95%CI: 6.9-8.4) in China, 2.3% (95%CI: 1.7-2.9) in the USA and 7.6% (95%CI: 4.2-11.0) in Italy. These percentages were compared to the smoking prevalence of each country and statistically significant differences were found in them all (p <0.0001). By means of the meta-analysis, we offer epidemiological evidence showing that smokers were statistically less likely to be hospitalised (OR=0.18, 95%CI: 0.14-0.23, p<0.01). CRS and exacerbated inflammatory response are associated with aggravation of hospitalise patients. In this scenario, we hypothesise that nicotine, not smoking, could ameliorate the cytokine storm and severe related inflammatory response through the cholinergic-mediated anti-inflammatory pathway.

Background: A significant proportion of SARS-CoV-2-infected patients develops respiratory failure. Thromboembolism is also prevalent in coronavirus disease 2019 (Covid-19). Vitamin K plays a role in coagulation and possibly also in lung diseases. We therefore hypothesized that vitamin K is implicated in Covid-19 pathogenesis. Methods: 134 Covid-19 patients and 184 controls were included. Inactive vitamin K-dependent matrix Gla protein (i.e.dp-ucMGP) and prothrombin (i.e. PIVKA-II) were measured, which are inversely related to respectively extrahepatic and hepatic vitamin K status. Desmosine was measured to quantify elastic fiber degradation. Lung involvement and arterial calcifications severity were assessed by computed tomography. Results Dp-ucMGP was elevated in Covid-19 patients compared to controls (P=0.001). Higher dp-ucMGP was found in Covid-19 patients with poor compared to better outcomes (P=0.002). PIVKA-II was normal in 81.8%, mildly elevated in 14.0% and moderately elevated in 4.1% of Covid-19 patients not using vitamin K antagonists. Dp-ucMGP in Covid-19 patients was correlated with desmosine (P<0.001), thoracic aortic calcification (P<0.001) but not with pneumonia severity. Conclusions: Extrahepatic vitamin K status was severely reduced in Covid-19 patients, as reflected by elevated inactive MGP, and related to poor outcome. Procoagulant prothrombin activity remained preserved in the majority of Covid-19 patients, which is compatible with the increased thrombogenicity that is frequently observed in severe Covid-19. Impaired MGP activation was linked to accelerated elastic fiber degradation and premorbid vascular calcifications. A trial should assess whether increasing MGP and protein S activity by vitamin K administration improves Covid-19 outcomes.

Mankind faces a coronavirus pandemic originating from a seafood market in Wuhan, China since December 2019. The pathogen was named novel coronavirus (n-CoV) and bats are the identified key reservoir. The coronavirus disease 2019 (COVID-19) quickly spread over China across the globe, turned into a pandemic with exponentially increasing numbers of cases and significant mortality rate. China reacted with lockdowns and strict control measurements to prevent spreading the virus. The treatment of severe cases was hampered by lack of specific vaccines. Vaccine-development and production is a painstaking process and can only be enforced by international cooperation. Different supportive treatment options surfaced due to combinations of antiviral agents with antibiotic drugs. Elderly, male, immune-suppressed patients with co-morbidities showed a high mortality rate. Health literacy, strong immune system, adequate serum 25-hydroxyvitamin D (25(OH)D) concentrations and healthy life style choices can support fast recovery. Antibiotic resistance needs to be addressed by development of new generation antimicrobials against nosocomial infections in preparation for future outbreaks. Plant-biosynthesis of nanomaterials and antiseptics may help in prevention and recovery rate. Prevalence of COVID-19 maybe inversely related to BCG vaccination, endemicity of malaria, humidy and temperature but directly with latitude. Recommendations to prevent the spread of COVID-19 should be followed strictly.

We conducted many model simulations to understand the causes of the damages of coronavirus (COVID-19) to lung tissue and constructed a diagram showing apparent viral reproduction, immune response and damage accumulation curves. We found that lung damages include virus-caused damage, tissue damage caused by immune responses and tissue damage caused by accumulated wastes. The virus-caused damage is proportional to the phase lag between the viral reproduction curve and the delayed adaptive immune response curve, while waste-induced damage is attributed to imbalance in removing viral, cellular and metabolic by-products. We found that treatment strategies should slow down viral reproduction and speed up immune response, and improve blood micro-circulation in the lungs. Consistent with the strategies, measures are taken to void direct lung infection, strengthen innate responses, promote immune responses, dilute viral concentration in lung tissue, maintain waste removal balance, protect heart and kidneys, control other infections, avoid allergic reactions and other inflammation, etc. We show that medical, dietary, emotional, lifestyle, environmental, mechanical factors, etc. may be simultaneously used to mitigate lung damages and prove that multiple factor health optimization method is magnitudes more powerful than a single factor treatment. Such a method does not depend on molecular specificity and can be used in parallel to antiviral drugs.

Public health practices including handwashing and vaccinations help reduce the spread and impact of infections. Nevertheless, the global burden of infection is high, and additional measures are necessary. Acute respiratory tract infections, for example, are responsible for approximately 2.65 million deaths per year. The role nutrition plays in supporting the immune system is well-established. A wealth of mechanistic and clinical data show that vitamins, including vitamins A, B6, B12, C, D, E, and folate; trace elements, including zinc, iron, selenium, magnesium, and copper; and the omega-3 fatty acids eicosapentaenoic acid and docosahexaenoic acid play important and complementary roles in supporting the immune system. Inadequate intake and status of these nutrients are widespread, leading to a decrease in resistance to infections and as a consequence an increase in disease burden. Against this background the following conclusions are made: 1) Supplementation with the above micronutrients and omega-3 fatty acids is a safe, effective, and low-cost strategy to help support optimal immune function; 2) Supplementation above the RDA, but within recommended upper safety limits, for specific nutrients such as vitamins C and D is warranted; and 3) Public health officials are encouraged to include nutritional strategies in their recommendations to improve public health.

Objectives: To examine the long term impact of COVID-19 on sleep patterns and development of sleep disorders. Methods: Using the centralized Massachusetts General Brigham (MGB) Research Patient Data Registry (RPDR), SARS-CoV2 positive patients were surveyed about their sleep patterns before and after the viral infection. Information related to co-morbid conditions and medications were obtained through chart review. Results: Two hundred and forty five completed surverys were analysed. Average age was 53.3 ± 16.3 years, and participants were predominantly Non-Hispanic White (84.1%) and female (74.3%). Average BMI (kg/m2) was 29.9 ± 6.9, and a greater proportion was non-smokers (63.2%). After COVID-19, there was an increase in the percentage of participants reporting difficulty initiating (31 ± 46% vs. 39 ± 49%, P=0.01), and maintaining sleep (43 ± 49% vs. 57 ± 49%, P<0.001), and use of sleep aids (24 ± 43% vs. 30 ± 45% P=0.003) with an incidence rate of 24.3%, 37.4%, and 12.3% respectively. In addition, there was an increase in daytime fatigue and the need for napping (58 ± 49% vs. 36 ± 48%, P <0.0001) with an incidence of 8% and 23% respectively. The sleep symptoms persisted beyond 12 months among 28% of the participants and were predominantly seen among women. Conclusions: Infection with SARS-CoV2 has negative effects on sleep, and a significant proportion of adults experience insomnia and daytime sleepiness beyond 12 months after recovering from the initial infection.