SARS-CoV-2, influenza A/B virus (IAV/IBV) and respiratory syncytial virus (RSV) are among the common viruses causing acute respiratory infections. Clinical diagnosis to differentiate these viruses is challenging due to similar clinical presentations, thus laboratory-based real-time RT PCR is the gold standard for diagnosis. The study aimed to evaluate the diagnostic performance of STANDARD M10 Flu/RSV/SARS-CoV-2 (SD Biosensor Inc, Seoul, Korea). This retrospective study was conducted with archived respiratory samples positive and negative for SARS-CoV-2, IAV, IBV or RSV. The evaluation study involved a total of 322 respiratory samples comprising 215 positive samples (49 SARS-CoV-2, 48 IAV, 53 IBV, 65 RSV) and 107 negative samples. All samples were tested with both STANDARD M10 and either Xpert Xpress SARS-CoV-2 or Xpert Xpress Flu/RSV (Cepheid, USA). The sensitivity, specificity, positive predictive value and negative predictive value rates of STANDARD M10 were very similar to Xpert Xpress SARS-CoV-2 or Xpert Xpress Flu/RSV ranges for each virus (98-100%). The overall agreement was 99.4% with 99.1% agreement for positive samples and 100% agreement for negative samples. In conclusion, the STANDARD M10 point-of-care test is suitable for rapid simultaneous detection of SARS-CoV-2, IAV, IBV and RSV.

A series of defective ZSM-5 zeolites (~300 nm, SiO₂/Al₂O₃ ratio of 55, 100, 400 and 950) were intentionally prepared and systematically studied by XRD, SEM, ²⁹Si MAS NMR, argon physisorption, NH₃-TPD and FT-IR technologies. The nature, the amount and the accessibility of the acid sites of defective ZSM-5 zeolites are greatly different from reported ZSM-5 zeolites with perfect crystal structure. The co-existed strong Brønsted acid sites (Si(OH)Al) and weak Brønsted acid sites (hydroxyl nests) over defective ZSM-5 zeolites might lead to unique catalytic function. Zn(C₂H₅)₂ was grafted onto defective ZSM-5 zeolites through chemical liquid deposition (CLD) method. Interestingly, FT-IR spectroscopy studies find that Zn(C₂H₅)₂ was preferentially grafted on the hydroxyl nests with weak acidity rather than the Si(OH)Al groups with strong acidity over different defective ZSM-5 zeolites. Particularly, home-built operando dual beam FTIR-MS was applied to study the catalytic performance of Zn species locating at different sites of defective ZSM-5 zeolites under n-hexane transformation. Results show that Zn species grafted over hydroxyl nests obtain better dehydrogenation performance than Zn species over framework aluminum. This study provides guidance for the rational design of highly efficient alkane dehydrogenative aromatization catalysts.

Soil carbon is newly refined as ~32,000 Gt C (gigatonnes) over ten times previous totals. Soil organic carbon (SOC) of ~22,000 Gt C plus plants at ~2,400 Gt C in both above- and below-ground stocks hold 99% of Earth’s biomass. Occupying a topographic surface area of 25 Gha with mean depth 21 m, soil has more organic carbon than all plants, oceans, fossil fuels, or the atmosphere combined. Soils are both the greatest biotic carbon store and the most active CO2 source. Basic inventory is required for proper understanding and utilization of Earth’s natural resources, especially with increasing soil degradation and species loss. Values are raised considerably, the disparity due to lack of full soil depth survey, neglect of terrain and of other omissions. Herein new totals are determined for mineral soils, permafrost, and peats (of all forms and ages), to full depth (easily doubling shallow values) and allowing for terrain that is ignored in all terrestrial models (doubling most values again), plus SOC in recalcitrant glomalins (~30%) and friable saprock (+26%). Additionally, soil inorganic carbon (SIC), aquatic sediments and dissolved fractions are factored in. Soil biota (e.g. fungi, bacteria and earthworms) are similarly upgraded. Primary productivity, also upped for terrain, is confirmed at 220 Gt C/yr on land supported by C/O isotope fluxes. Priority issues of species extinction, humic topsoil loss, and excess atmospheric CO2 are all remedied by SOC restoration and biomass recycling via (vermi-)compost for use in 100% organic husbandry and adoption of wider practices of Permaculture based upon scientific observation of Nature.

The genetic transformation of Coffea arabica L. is an alternative strategy for obtaining plants with agronomic traits of interest that is less time-consuming than conventional breeding methods. Given the importance of coffee cultivation in Colombia, this study evaluated the main factors interfering with the genetic transformation of C. arabica using Agrobacterium tumefaciens. An effi-cient and reproducible method was accordingly established that involved the propagation of “early” embryogenic tissue in a liquid proliferation medium, supplemented with 3 mg l−1 BAP for eight months, which was subsequently sonicated for 300 sec in a suspension of LBA4405 OD600 of 0.5 harboring pC1301 and incubated in this same suspension for 1 hr. This plasmid contained the uidA gene under control of the 35S promoter. The Agrobacterium suspen-sion was subsequently removed from the embryogenic tissue using a micropipette, after which the tissue was deposited on filter paper to remove the remaining Agrobacterium suspension. The embryogenic tissue was co-cultured for four days in a solid differentiation medium supple-mented with 100 µM acetosyringone on filter paper. Subsequently, the tissue was post-cultured for four days in liquid differentiation medium under orbital shaking at 100 rpm with 300 mg l−1 Claforan (Hoechst), followed by selection with 50 mg l-1 hygromycin at 26 °C in the dark, with subcultures at 20-day intervals until somatic embryos were formed for subsequent culturing in germination medium. Molecular analysis confirmed the presence of the uidA gene in coffee seedlings transformed with strains LBA4405 and EHA105 and vectors pC1301 and pC2301. This method successfully enables the stable integration of genes of interest in the coffee plant ge-nome

This study explores the integration of genome sequencing and polymerase chain reaction (PCR) based methods for tracking the diversity of Covid-19 variants in wastewater. The research focuses on monitoring various Omicron subvariants during a period of significant viral evolution. Genome sequencing, particularly using Oxford Nanopore Technology (ONT), provides a detailed view of emerging variants, surpassing the limitations of PCR-based detection kits that rely on known sequences. This study highlights the effectiveness of combining these methodologies to enhance early detection and inform public health strategies, especially in regions with limited clinical sequencing capabilities.

Wide-bandgap tin oxide (SnO₂) thin-films are frequently used as an electron-transporting layers in perovskite solar cells due to superior thermal and environmental stabilities. However, its crystallization by conventional thermal methods requires typical temperatures between 300 and 900 ◦C [1] with up-to 60 min crystallization times. These post-processing conditions severely limit the choice of substrates and reduce the large-scale manufacturing capabilities. This work describes the intense pulsed light-induced crystallization of SnO₂ thin films using only 500 μs of exposure time. Thin-film properties are investigated using both impedance spectroscopy and photoconductivity characteristic measurements. Nyquist plot analysis establishes that the process parameters have a significant impact on the electronic and ionic behaviors of the SnO2 films. Most importantly, we demonstrate that light-induced crystallization yields improved topography and excellent electrical properties through enhanced charge transfer, improved interfacial morphology and better ohmic contacts compared to thermally-annealed SnO₂ films.

The dissolution of CO₂ in seawater in the form of bicarbonate ions is an attractive alternative to the storage in geological formations, on the conditions that the storage is stable over long times and does not harm the marine environment. In this work, we focus on the long-term chemical stability of CO₂ absorbed in seawater as bicarbonate, by monitoring the physico-chemical properties of the solutions (pH, dissolved inorganic carbon and alkalinity), in six different sets of experiments on both natural and artificial seawater lasting up to three months. The bicarbonate treatment of natural seawater consists in pouring pre-equilibrated solutions obtained from the reaction of CO₂ and Ca(OH)₂. If the pre-equilibrated mixture does not exceed a critical threshold (ca. 1000-1500 mmol/L), the resulting bicarbonate-rich solutions can be stable for over three months.

Obesity causes insulin resistance (IR) through systemic low-grade inflammation and can lead to type 2 diabetes mellitus (T2DM). However, the mechanisms that cause IR and T2DM in non-obese individuals are unclear. The Goto-Kakizaki (GK) rat develops IR spontaneously and is a model of non-obese T2DM. These rats exhibit hyperglycemia beginning at weaning and exhibit lower body mass than control Wistar rats. Herein, we tested the hypothesis that macrophages of GK rats are permanently in a pro-inflammatory state, which may be associated with a systemic inflammation condition that mimics the pathogenesis of obesity-induced T2DM. Using eighteen-week-old GK and control Wistar rats, we investigated the proportions of M1 (pro-inflammatory) and M2 (anti-inflammatory) macrophages isolated from the peritoneal cavity. Additionally, the production of inflammatory cytokine and reactive oxygen species (ROS) in cultured macrophages under basal and stimulated conditions was assessed. It was found that phorbol myristate acetate (PMA) stimulation increased GK rat macrophage ROS production by 90-fold compared to basal levels. This response was also three times more pronounced than in control cells (36-fold). The production of pro-inflammatory cytokines, such as tumor necrosis factor-alpha (TNF-α), tended to be upregulated in cultured macrophages from GK rats under basal conditions. Macrophages from GK rats produced 1.6 times more granulocyte-macrophage colony-stimulating factor (GM-CSF), 1.5 times more monocyte chemoattractant protein-1 (MCP-1) and 3.3 times more TNF-α than control cells when stimulated with lipopolysaccharide (LPS) (p = 0.0033; p = 0.049; p = 0.002, respectively). Moreover, compared to control cells, GK rats had 60% more M1 (p = 0.0008) and 23% less M2 (p = 0.038) macrophages. This study is the first to report macrophage inflammatory reprogramming towards a pro-inflammatory state in GK rats.

This study delves into the realm of potential therapeutics for breast cancer by conducting a comprehensive docking simulation and virtual screening analysis using Autodock Vina Algorithm. The focus based on the human epidermal growth factor receptor 2 (HER2), a key protein associated with various cancers, particularly in the context of breast cancer.The results reveal compelling insights into the binding interactions of several natural compounds and drugs with HER2. Notably, Acebilustat, Bemcentinib, and Amentoflavone exhibit outstanding binding energies, averaging around -10 kcal/mol, suggesting strong and favorable interactions with the HER2 target.These encouraging findings not only emphasize the potential biological significance of these compounds but also hint at their potential usefulness in fighting breast cancer.Their promising binding affinities with HER2 position them as potential candidates for further investigation and development as therapeutic agents against breast cancer.

Multilineage-differentiating stress-enduring (Muse) cells, a cell subgroup from mesenchymal stem cells (MSCs), are pluripotent cells with unique characteristics such as non-tumorigenic and pluripotent differentiation ability. After homing, Muse cells spontaneously differentiate into tissue component cells and supplement damaged/lost cells to participate in tissue repair. Importantly, Muse cells can survive in injured tissue for an extended period, stabilizing and promoting tissue repair. In addition, it has been confirmed that injection of exogenous Muse cells exerts anti-inflammatory, anti-apoptosis, anti-fibrosis, immunomodulatory, and paracrine protective effects in vivo. The discovery of Muse cells is an important breakthrough in regenerative medicine. The article provides a comprehensive review of the characteristics, sources, and potential mechanisms of Muse cells for tissue repair and regeneration. This review serves as a foundation for the further utilization of Muse cells as a key clinical tool in regenerative medicine.

In this work, a ZIF-67/MWCNTs/Nafion sensor platform was constructed based on the good adsorption capacity of ZIF-67, the electrical conductivity of multiwalled carbon nanotubes (MWCNTs) and the excellent chemical stability of Nafion for the detection of Cu2+ in water. Meanwhile, the modified materials were characterized by scanning electron microscopy (SEM), Transmission electron microscopy (TEM), BET specific surface area test, X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD) and Fourier transform infrared spectrometry (FT-IR). Cyclic voltammetry (CV), electrochemical impedance (EIS), and square wave solvation voltammetry (SWSV) electrochemical methods were used to perform applied test studies on ZIF-67/MWCNTs/Nafion/GCE. The results show that ZIF-67/MWCNTs/Nafion/GCE has high sensitivity (57.5 μA/μM) and a low limit of detection (15.0 nM) for the electrochemical detection of Cu2+ ions in an electrochemical sensing system. It has high adsorption selectivity for Cu2+, and the recovery of Cu2+ in real water reached 98.6%-103%. The modified electrode has good repeatability, reproducibility, anti-interference, and stability, which makes this sensing platform can be practically applied to the detection of domestic water.

Myocarditis has been recognized as a possible rare complication of COVID-19 mRNA vaccination. It concerns between one and five vaccinated people per 100,000 in the general population, with increased incidence in adolescent and young adult men. Most often, cases of myocarditis have been reported in the days following the second dose of vaccine mainly in younger male patients. This rare complication of vaccination usually resolves within days or weeks. However, the pathophysiological events responsible for the increase in frequency of myocarditis after COVID-19 vaccination remain unclear. Several recent reports have highlighted that free spike proteins circulating in the blood of patients at high levels appear to play a major role in myocarditis. Here, we review the most relevant data that partly lift the veil on the molecular mechanisms of the induction of myocarditis following mRNA-based COVID-19 vaccination. We hypothesize that a mechanism of molecular mimicry of the viral spike triggers transient dysregulation of angiotensin-converting enzyme 2, leading to increased soluble angiotensin II binding to the transmembrane receptor angiotensin II type I receptor, similar to what is observed during SARS-CoV-2 infection. We suggest to standardize management of suspected cases of mRNA-based COVID-19 vaccine-induced myocarditis, including angiotensin II and spike antigenemia monitoring.

Severe COVID-19 is a major cause of morbidity and mortality worldwide, especially to those with co-morbidities, the elderly and the immunocompromised. However, the molecular determinants critical for severe COVID-19 progression remain to be fully elucidated. Meta-analysis of transcriptomic RNAseq and single-cell sequencing datasets comparing between severe and mild COVID-19 patients have demonstrated that the early expansion of myeloid-derived suppressor cells (MDSCs) could a key feature of severe COVID-19 progression. Besides serving as potential early prognostic biomarkers for severe COVID-19 progression, several studies have also indicated the functional roles of MDSCs in severe COVID-19 pathogenesis and possibly even long COVID. Given the potential links between MDSCs and severe COVID-19, we examined the existing literature summarizing the characteristics of MDSCs, the evidence of MDSCs in facilitating severe COVID-19 pathogenesis and discuss the potential therapeutic avenues that can be explored to reduce the risk and burden of severe COVID-19. We also provide a web app where users can visualise the temporal changes of specific genes or MDSC-related gene sets during severe COVID-19 progression and disease resolution at https://temporal-severe-covid.streamlit.app/, based on our previous study described by Ong et al., 2021.

When it is in the template RNA, the naturally occurring m¹A epitranscriptomic RNA modification was recently reported to be able to stop the RNA polymerization reaction catalyzed by the RNA dependent RNA polymerase (RdRp) of SARS-CoV-2. In this report, we report that m¹A via its triphosphate form (m¹ATP) can be incorporated into RNA by the same RdRp. These two findings point a new direction for antiviral drug development based on m¹A for combatting COVID-19. More broadly, it is possible that the large pool of epigenetic RNA as well as DNA modifications could serve as a treasury for drug discovery aimed at combating various infectious and other diseases.

Hypertension is one of the most common and significant cardiovascular risk factor. With the emergence of COVID-19, concerns have arisen about its potential to exacerbate cardiovascular morbidity and mortality. Several theories have emerged linking hypertension with COVID-19. However, the relationship between SARS-CoV-2 infection and long-term risk of hypertension remains insufficiently explored. Therefore, our study aimed to investigate the impact of COVID-19 infection on blood pressure and long-term risk of hypertension developing. A comprehensive search was conducted in the PubMed and SCOPUS databases up to 10th August 2023 for relevant papers. The analysis included 35 eligible articles. The majority of the latest studies pointed to increase BP after COVID-19 infection. However, several observations contradicted the influence of SARS-CoV-2 on the hypertension development or suggesting a transient effect. The possible role of the loss of angiotensin-converting enzyme 2, cytokine storm, increased sympathetic activity, and endothelial dysfunction were considered. On the other hand, BP changes following COVID-19 could be a consequence of lifestyle changes, which often accompanied COVID-19 infection. Our findings highlight the urgent need for comprehensive research on the COVID-19 and hypertension link. These insights are crucial for implementing effective prevention and management strategies for individuals with the history of COVID-19 infection.

A land cover map of two arctic catchments, nearby the Abisko Scientific Research Station, was obtained from a classification of a Sentinel-2 satellite image and a ground survey performed in July 2022. The two contiguous catchments, Miellajokka and Stordalen, are covered by various ecotypes, from boreal forest to alpine tundra and peatland. Two classification algorithms, support vector machine and random forest, were tested and gave very similar results. The percentage of correctly classified pixels was over 88% in both cases. The developed workflow relies solely on open source software and acquired ground observations. Space organization was directed by the altitude as demonstrated by the intersection of the land cover with the topography. Comparison between this new land cover map and previous ones based on data acquired between 2008 and 2011 shows some trends of vegetation cover evolution in response to climate change in the considered area. This land cover map is key input data for permafrost modeling, and hence for the quantification of climate change impacts in the studied area.

Alzheimer’s disease(AD) progression correlates with the propagation of hyperphosphorylated Tau (pTau) from the entorhinal cortex to the hippocampus and neocortex. Neutral sphingomyelinase2(nSMase2) is critical in the biosynthesis of extracellular vesicles (EVs), which play a role in pTau propagation. We recently conjugated DPTIP, a potent nSMase2 inhibitor, to hydroxyl-PAMAM-dendrimer nanoparticles which can improve brain delivery. We showed that dendrimer-conjugated-DPTIP (D-DPTIP) robustly inhibited pTau spread in an AAV-pTau propagation model. To further evaluate its efficacy, we tested D-DPTIP in the PS19 transgenic mouse model. Unexpectantly, D-DPTIP showed no beneficial effect. To understand this discrepancy, we assessed D-DPTIP’s brain localization. Using immunofluorescence and fluorescence-activated cell-sorting, D-DPTIP was found to be primarily internalized by microglia where it selectively inhibited microglial nSMase2 activity with no effect on other cell types. Furthermore, D-DPTIP inhibited microglia-derived EV release into plasma without affecting other brain-derived EVs. We hypothesize that microglial targeting allowed D-DPTIP to inhibit tau propagation in the AAV-hTau model, where microglial EVs play a central role in propagation. However, in PS19 mice, where tau propagation is independent of microglial EVs, it had a limited effect. Our findings confirm microglial targeting with hydroxyl-PAMAM dendrimers and highlight the importance of understanding cell-specific mechanisms when designing targeted AD therapies.

Lactic acid bacteria (LAB) have an extracellular proteolytic system that includes a multi-domain, cell envelope protease (CEP) with a subtilisin homologous protease domain. These CEPs have different proteolytic activities despite having similar protein sequences. Structural characterization has previously been limited to CEP homologs of dairy and human derived LAB strains, excluding CEPs of plant derived LAB strains. CEP structures are a challenge to determine experimentally due to their large size and attachment to the cell envelope. This study aims to clarify the prevalence and structural diversity of CEPs by using the structure prediction software AlphaFold 2. Domain boundaries are clarified based on a comparative analysis of 21 three-dimensional structures, revealing novel domain architectures of CEP homologs that are not necessarily restricted to specific LAB species or ecological niches. The C-terminal flanking region of the protease domain is divided into fibronectin type-III-like domains with various structural traits. The analysis also emphasizes the existence of two distinct domains for cell envelope attachment that are preceded by an intrinsically disordered cell wall spanning domain. The domain variants and their combinations provide CEPs with different stability, proteolytic activity and potentially adhesive properties, making CEPs targets for steering proteolytic activity with relevance for both food development and human health.

Since the first description of SARS-CoV-2 in China in 2019, thousands of variants have emerged worldwide. For some of them, the constellation of mutations caused changes in virus biology, pathogenicity, infectivityity and transmissibility resulting in dissemination throughout the world. Gamma variant (P.1) differs from SARS-CoV-2 Wuhan strain (B.1) by 12 amino acids in the Spike (S) protein, and presented mutations related to greater affinity for the receptor angioten-sin-converting enzyme 2 (ACE-2) and/or immune escape. The Gamma variant and subvariants were responsible for the second wave of COVID-19 in the Brazilian city of Manaus, characterized by high mortality and rapid transmission. The ability of variants to induce cytokine production may be closely related to their pathogenicity. Herein we observed that there was no significant difference in the quantity of cytokines among macrophages or neutrophils infected with P.1 and B.1 strains. Also, no significant difference was observed in the absolute number of macrophages and neutrophils infected with these variants. Furthermore, no evidence of SARS-CoV-2 replication was observed in macrophages when infected by the two analyzed variants. Our findings suggest that the difference in the epidemiological outcome observed during the P.1 variant spread when compared to B.1, it is not explained by differences in the quantity of cytokines and absolute number of macrophages or neutrophils. Through bioinformatics analysis of the S protein, we observed that the physicochemical differences between the variants and subvariants of P.1, probably refer to the degree of infectivity, due to the impact caused in the recognition of antibodies and receptor af-finity

Results from an explorative study revealing spatio-temporal patterns of the SARS-CoV-2/COVID-19 epidemic in Germany are presented. We dispense with contestable model assumptions and show the intrinsic spatio-temporal patterns of the epidemic dynamics. The analysis is based on COVID-19 incidence data, which are age-stratified and spatially resolved at the county level, provided by the Federal Government’s Public Health Institute of Germany (RKI) for public use. Although the 400 county-related incidence time series show enormous heterogeneity both with respect to temporal features as well as spatial distributions, the counties’ incidence curves organise into well distinguished clusters that coincide with East and West Germany. The analysis is based on dimensionality reduction, multidimensional scaling, network analysis, and diversity measures. Dynamical changes are captured by means of difference-in-difference methods which are related to fold changes of the effective reproduction numbers. The age-related dynamical patterns suggest a considerably stronger impact of children, adolescents and seniors on the epidemic activity than previously expected. Besides these concrete interpretations, the work mainly aims at providing an atlas for spatio-temporal patterns of the epidemic which serves as a basis to be further explored with the expertise from different disciplines, particularly sociology and policy makers. The study should also be understood as a methodological contribution to getting a handle on the unusual complexity of the COVID-19 pandemic.

The COVID-19 pandemic has led to a significant and enduring influence on global health, in-cluding maternal and fetal well-being. Evidence suggests that placental dysfunction is a potential consequence of SARS-CoV-2 infection during pregnancy, which may result in adverse outcomes such as preeclampsia and preterm birth. However, the molecular mechanisms underlying this association remain unclear, and it is uncertain whether a mature placenta can protect the fetus from SARS-CoV-2 infection. To address the above hiatus, we conducted a transcriptome-based study of the placenta in both maternal and fetal compartments. We collected placental samples from 16 women, immediately after term delivery, of which seven had confirmed SARS-CoV-2 in-fection by PCR before parturition. Notably, we did not detect any viral load in either the maternal or fetal compartments of the placenta, regardless of symptomatic status. We extracted total RNA from placental tissues, separately from maternal and fetal compartments, constructed cDNA li-braries, and sequenced them to assess mRNA and small RNA expression. Our analysis revealed 727 differentially expressed genes (DEG) in the maternal placental tissue, with 608 upregulated and 109 downregulated in SARS-CoV-2-positive women compared with healthy, negative wom-en. In contrast, the fetal compartment did not exhibit any significant changes in gene expression with SARS-CoV-2 infection. Specifically, we observed significant downregulation of seven genes belonging to the pregnancy-specific glycoprotein (PSG), related to the immunoglobulin superfam-ily in the maternal compartment with active SARS-CoV-2 infection (fold change range from -13.70 to -5.28, FDR ≤ 0.05). Additionally, comparing symptomatic women with healthy, we identified 14,223 DEGs, with high expression of the inflammatory cytokine IL6 in the maternal placenta of the symptomatic women. Furthermore, KEGG analysis revealed that pathways relat-ed to viral infection, vascular smooth muscle contraction, and oxytocin signaling were altered significantly in symptomatic women. Overall, our study sheds light on the molecular mechanisms underlying the reported clinical risk of preeclampsia and preterm delivery in women with SARS-CoV-2 infection. Nonetheless, studies with larger sample sizes are warranted to further deepen our understanding of the molecular mechanisms of the placenta’s anti-viral effects in ma-ternal SARS-CoV-2 infection.

Introduction: When analyzing the current situation in Spain, derived from our current lifestyles far from following a Mediterranean lifestyle, there is an alarming prevalence of diabesity, which increases the global risk of suffering from cardiovascular disease (CVD) and decreases the quality of life related to health (QLRH). In order to guarantee, beyond glycemic control of patients with type 2 diabetes (DM2), adequate control of cardiovascular risk factors (CVRF) in DM2, it will be necessary to carry out a community intervention focused on the application of education programs focused on promoting the acquisition of healthy eating habits through the Mediterranean diet (DMED). This dietary pattern, together with physical exercise, has been shown to contribute to improving the QLRH of patients. Objective: To analyze the quality of life related to health, present in patients with poorly controlled type 2 diabetes, to determine the possible relationship between this and the degree of adherence to the Mediterranean diet and to examine whether there are differences between the sexes. Material and methods: Observational descriptive study in 93 patients diagnosed with DM2 with poor glycemic control (1Ac ≥ 7%), carried out in various health centers in Albacete and Cuenca, in which the baseline relationship between adherence to DMed and the HRQoL. They were administered a data collection sheet that included a survey of the degree of adherence to the DMed (MEDAS-14) and QLRH (SF-12v2) in the Primary Care (PC) medical and nursing consultations. The variables were analyzed: age groups, sex, years of evolution of DM2, body mass index (BMI), as well as basal glycemia (GB) and glycosylated hemoglobin (HbA1c). The “MEDAS-14” (adherence to DMED) was the main variable and the “SF-12v2” (QLRH) was the secondary variable. Material and methods: Observational descriptive study in 93 patients diagnosed with DM2 with poor glycemic control (1Ac ≥ 7%), carried out in various health centers in Albacete and Cuenca, in which the baseline relationship between adherence to DMed and the HRQoL. They were administered a data collection sheet that included a survey of the degree of adherence to the DMed (MEDAS-14) and QLRH (SF-12v2) in the Primary Care (PC) medical and nursing consultations. The variables were analyzed: age groups, sex, years of evolution of DM2, body mass index (BMI), as well as basal glycemia (GB) and glycosylated hemoglobin (HbA1c). The “MEDAS-14” (adherence to DMED) was the main variable and the “SF-12v2” (QLRH) was the secondary variable. Results: Patients with poorly controlled DM2 and with low adherence to the MedDM show a non-significantly greater affectation in the physical sphere: fair general state of health, physical function I (limitation to make moderate efforts such as moving a table, vacuuming or walk more than an hour), physical function II (limitation to climb several flights of stairs), physical role (problems at work or daily activities doing less than loved ones), physical role II (they had to stop performing some tasks at work or activities of daily living) and regular body pain. Presenting less affectation in the mental field: emotional role I, emotional role II, mental health I, vitality and mental health II. Without affectation in the social function since they consider that rarely the emotional or physical problems have hindered their social activities. Conclusions: Diabetic patients with poor glycemic control have low adherence to the MedMD (<9 points) and have poor QLRH. Low adherence to the DMed and obesity are related to a greater affectation in the physical dimension and less affectation in the mental dimension without affectation in the "social function".

Despite the fact that COVID-19 treatment and management are now considerably regulated, SARS-CoV-2 is still one of the leading causes of death in 2022. The availability of COVID-19 vaccines, FDA-approved antivirals, and monoclonal antibodies in low-income countries still poses an issue to be addressed. Natural products, particularly traditional Chinese medicines (TCMs) and medicinal plant extracts (or their active component) have challenged the dominance of drug repurposing and synthetic compound libraries in COVID-19 therapeutics. Abundant resources and excellent antiviral performance make natural products a relatively cheap and easily available alternative for COVID-19 therapeutics. Here, we deliberately review the anti-SARS-CoV-2 mechanisms of the natural products, their potency (pharmacological profiles), and application strategies for COVID-19 intervention. In light of their advantages, this review is intended to acknowledge the potential of natural products as COVID-19 therapeutic candidates.

There have been significant collaborative efforts over the past three years to develop therapies against COVID-19. During this journey, there has also been a lot of focus on understanding at-risk groups of patients who either have pre-existing conditions or have developed concomitant health conditions due to the impact of COVID-19 on immune system. There was a high incidence of COVID-19 induced pulmonary fibrosis (PF) observed in patients. PF can cause significant morbidity, long-term disability, and lead to death in the long run. Additionally, being a progressive disease, PF can also impact the patient for a long time after COVID infection and affect the overall quality of life. Although current therapies are being used as the mainstay for treating PF, there is no therapy specifically for COVID-induced PF. As observed in the treatment of other diseases, nanomedicine can show a significant promise in overcoming the limitations of current anti-PF therapies. In this review, we summarize the efforts reported by various groups to develop nanomedicine therapeutics to treat COVID-induced PF.

The severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) Omicron variant of concern has been the dominant cause of worldwide COVID-19 cases since 2022. All the Omicron sublineage viruses have demonstrated high transmissibility and an ability to escape vaccine-induced immunity. While first-generation vaccines, including monovalent vaccines, continue to provide protection against severe disease, hospitalization and mortality, their efficacy against Omicron sub variants remains sparse. These vaccines have also been associated with rapidly waning protection against primary COVID-19 and COVID-19 reinfections conferred by evolving Omicron sublineages.. This led to the development and deployment of updated vaccines and the introduction of the bivalent booster. Through this review, we highlight the brief journey of the variants of concern leading to the dominance of Omicron and the effectiveness of the key vaccines against these variants, including the updated (bivalent) boosters.

COVID-19, the infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is frequently associated with pulmonary thrombotic events, especially in hospitalised patients. Severe SARS-CoV-2 infection is characterized by a proinflammatory state and an associated disbalance in hemostasis. Immune pathology analysis supports the inflammatory nature of pulmonary arterial thrombi composed by white blood cells, especially neutrophils, CD3+ and CD20+ lymphocytes, fibrin, red blood cells and platelets. Immune cells, cytokines, chemokines and the complement system are key drivers of immunothrombosis, as they induce the damage of endothelial cells and initiate pro-inflammatory and pro-coagulant positive feedback loops. Neutrophil extracellular traps induced by COVID-19-associated “cytokine storm”, platelets, red blood cells, and coagulation pathways close the inflammation-endotheliopathy-thrombosis axis, contributing to SARS-CoV-2 associated pulmonary thrombotic events. The hypothesis of immunothrombosis is also supported by the minor role of venous thromboembolism, chest CT imaging data showing peripheral blood clots associated with inflammatory lesions and the high incidence of thrombotic events despite routine thromboprophylaxis. Understanding the complex mechanisms behind COVID-19-induced pulmonary thrombosis will lead to future combination therapies for hospitalised patients with severe disease, that would target the crossroads of inflammatory and coagulation pathways.

Digital Elevation Model (DEM) is a representation of elevation data that represent terrain with or without overlaying objects of the Earth. It is the ideal and most widely used method for determining topography. DEMs are generated from various techniques such as traditional Surveying, Photogrammetry, InSAR, LiDAR, Clinometry and radargrammetry. It has been observed that mostly LiDAR-generated DEMs provide the best accuracy. The unavailability of LiDAR data in most of the region restricts global researchers from high-resolution and accurate DEMs. The recent launch of ICESat-2 with a 13m beam footprint and 0.7m pulse interval, promises elevations at high orbital precision. Its accuracy is of the order of few centimeters in complex topography, because of this ICESat-2 proves to be a good source to generate high-accuracy DEMs. ICESat-2 provides discrete photon data with elevations of points on the Earth’s surface. Traditional interpolation techniques tend to over-smooth the estimated space and still are unable to justify the complicated continuity in the topographical data. Machine learning algorithms are widely being used to extract patterns and spatial extent in geographic data. To estimate a DEM from ICESat-2 LiDAR point data, machine learning regression algorithms are implemented in this study. The present study has been performed for a plain region of Ghaziabad, Uttar Pradesh, India. Studies have shown that Cartosat-1 DEM V3 R1 product provides an accuracy of the order of 2m in predominantly plain regions, hence taken for this region. Current work focuses on comparing various regression-based machine-learning techniques to interpolate DEM from ICESat-2 data The RMSE of the interpolated DEM resulted from the Gradient Boosting Regressor, Random Forest Regressor, Decision Tree Regressor, and Multi-Layer Perceptron Regressor was 7.13m, 7.01m, 7.15m, and 3.76m, respectively when evaluated against the TANDEM-X DEM of the same region. The MLP Regressor is found to perform the best among the four algorithms tested.

Background: After its initial detection in Wuhan, China, in December 2019, SARS-CoV-2 has spread rapidly, causing successive epidemic waves worldwide. This study aims to provide a genomic epidemiology of SARS-CoV-2 in Burkina Faso. Methods: Three hundred and seventy-seven SARS-CoV-2 genomes obtained from PCR-positive nasopharyngeal samples (PCR cycle threshold score <35) collected between May 5, 2020, and January 31, 2022 were analysed. Genomic sequences were assigned to phylogenetic clades using NextClade and to Pango lineages using pangolin. Phylogenetic and phylogeographic analyses were performed to determine the geographical sources and time of virus introduction in Burkina Faso. Results: The analyzed SARS-CoV-2 genomes could be assigned to 10 phylogenetic clades and 27 Pango lineages already described worldwide. Our analyses revealed the important role of cross-border human mobility in the successive SARS-CoV-2 introductions in Burkina Faso from neighboring countries. Conclusion: This study provides additional insights into the genomic epidemiology of SARS-CoV-2 in West Africa. It highlights the importance of land travel in the spread of the virus and the need to rapidly implement preventive policies. Regional cross-border collaborations and the adherence of the general population to government policies are key to prevent new epidemic waves.

Recent lines of evidence suggest the intriguing hypothesis that consuming common culinary herbs of the mint family might help prevent or treat Covid. Individual citizens could easily explore the hypothesis using ordinary kitchen materials. I offer a philosophical framework to account for the puzzling lack of public health messaging about this interesting idea.

Nasal cavity is a primary checkpoint for the invasion of several respiratory pathogens. Numerous pathogens including SARS-CoV-2, S. pneumonia, S. aureus, etc., adhere to the nasal epithelium or mucus to invade and trigger an infection. IgA serves as the first line of defense against foreign antigens and pathogens. They exhibit cross-reactivity against a diverse variety of antigens through immune exclusion, which intercepts the invasion of pathogens through the mucosal lining. Advances in intranasal immunization technology underscore the elevated neutralizing IgA levels at local and distal mucosa in contrast to the parenteral vaccines. This review highlights the adjuvants that induce IgA class switching and the challenges of maintaining nominal IgA levels at the mucosal surface. Finally, the review features the paradigm-shifting of conventional immunization techniques to IgA-inducing vaccines to enhance protection against homologous and heterologous pathogens.

Introduction: The development of effective vaccines has partially mitigated the trend of the SARS-CoV-2 pandemic, however, the need for orally available antiviral drugs persists. This study aims to investigate the activity of molnupiravir in combination with nirmatrelvir or GC376 on SARS-CoV-2 to verify the synergistic effect. Methods: The SARS-CoV-2 strains 20A.EU, BA.1 and BA.2 were used to infect Vero E6 in presence of antiviral compounds alone or in combinations using 5 two-fold serial dilution of compound concentrations ≤EC90. After 48 and 72 h post-infection, viability was performed using MTT reduction assay. Supernatants were collected for plaque-assay titration. All experiments were conducted three times and in triplicate. The synergistic score was calculated using Synergy Finder version 2. Results: All compounds reached micromolar EC90. Molnupiravir and GC376 showed a synergistic activity at 48 h with an HSA score of 19.33 (p<0.0001) and an additive activity at 72 h with an HSA score of 8.61 (p<0.0001). Molnupiravir and nirmatrelvir showed a synergistic activity both at 48 h and 72 h with an HSA score of 14.2 (p=0.01) and 13.08 (p<0.0001), respectively. Conclusion: Molnupiravir associated with one of the two protease-inhibitors nirmatrelvir and GC376 showed good additive-synergic activity in vitro.

Dengue is a viral mosquito-borne disease that rapidly spreads in tropical and subtropical countries, including the Philippines. One of its most distinguishing characteristics is the ability of the Dengue Virus (DENV) to easily surpass the innate responses of the body, thus activating B cells of the adaptive immunity to produce virus-specific antibodies. Moreover, Dengvaxia® is the only licensed vaccine for DENV, but recent studies showed that seronegative individuals become prone to increased disease severity and hospitalization. Owing to this limitation of the dengue vaccine, this study determined and compared consensus and unique B cell epitopes among each DENV (1-4) Philippine isolate to identify potential areas of interest for future vaccine studies and therapeutic developments. An in silico-based epitope prediction of forty (40) DENV 1-4 strains, each serotype represented by ten (10) sequences from The National Center for Biotechnology Information (NCBI), was conducted using Kolaskar and Tongaonkar antigenicity, Emini surface accessibility, and Parker hydrophilicity prediction in Immune Epitope Database (IEDB). Results showed that five (5) epitopes were consensus for DENV-1 with no detected unique epitope, one (1) consensus epitope for DENV-2 with two (2) unique epitopes, one (1) consensus epitope for DENV-3 plus two (2) unique epitopes, and two (2) consensus epitopes and one (1) unique epitope for DENV-4. The findings of this study would contribute to determining potential vaccine and diagnostic marker candidates for further research studies and immunological applications against DENV (1-4) Philippine isolates.

The scientific, private and industrial sectors use a wide variety of technological platforms available to achieve protection against SARS-CoV-2, including vaccines. However, the virus evolves continually into new highly virulent variants, which might overcome the protection provided by vaccines and may re-expose the population to infections. Mass vaccinations should be continued in combination with more or less obligation mandatory non-pharmaceutical interventions. Therefore, the key questions to be answered are: (i) How to identify the primary and secondary infections of SARS-CoV-2? (ii) Why are neutralizing antibodies not long-lasting in both the cases of natural infections and post-vaccinations? (iii) Which are the factors responsible for this decay in neutralizing antibodies? (iv) What strategy could be adapted to develop long-term herd immunity? (v) Is the Spike the only vaccine candidate or a vaccine cocktail is better?

The open-bundle structure of cytochrome c’ as an unfolding intermediate was determined by small-angle neutron scattering experiment (SANS). The four-α-helix bundle structure of Cyt c’ at neutral pH was transited to an open-bundle structure (at pD ~13), which is a joint-clubs consisting of four clubs (α-helices) connected by short loops. The compactly folded structure of Cyt c’ (radius of gyration, Rg = 18 Å for the Cyt c’ dimer) at neutral or mildly alkaline pD transitioned to a remarkably larger “open-bundle” structure at pD ~13 (Rg = 25 Å for the Cyt c’ monomer). Cyt c’ adopts an unstructured random coil structure at pD = 1.7 (Rg = 25 Å for the Cyt c’ monomer). Numerical partial scattering function analysis (joint-clubs) and ab initio modelling gave structures similar to the “open-bundle”, which retains the α-helices but loses the bundle structure.

Physical distancing and wearing a face mask are key interventions to prevent COVID-19. While this remains difficult to practice for millions of firefighters in fire engines responding to emergencies, the delayed forthcoming of evidence on the physical effectiveness of such safety interventions in this setting presents a major problem. In this field experimental study, we provided initial evidence to close this gap. We examined total aerosol burden in the cabin of a fire engine whilst manipulating crew size, natural ventilation, use of FFP2 respirators and use of SCBA full-face masks during 15-minute driving periods. At the same time, we controlled for crew activity and speaking, vehicle speed, cabin air temperature, pressure and humidity. Limiting the crew size, using FFP2 respirators and not donning SCBA full-face masks was associated with a reduction of the arithmetic mean of total aerosol burden of up to 49%. Natural ventilation as tested in this study was associated with both an increase and a decrease of total aerosol burden. This study provided initial evidence on the physical effectiveness of safety interventions in fire engines to reduce potential airborne transmission of SARS-CoV-2 through aerosols. More research about the physical and clinical effectiveness of such safety interventions is needed.

The anisotropy in the CMBR detected by the COBE satellite has sprouted the research and theories of the void that got created, after being washed away by a parallel universe after its collision. This details of the roadmap to the parallel universe, that are previously been entangled, but now, separated, with the concept of 2-time dimensions as a time curve of containing the closed curves embedded on the surface area, thereby time running backward in orientation in the parallel universe, along with the perspective of eternal inflation and divided subspaces giving birth to twin universes are provided here. This time curve being responsible for creating an exponentiality of time frames, growing in layers, giving us the perception of the confabulations being an indication of the previously connected worlds.

Identifying the SARS-CoV-2 virus has been a unique challenge for the scientific community. In this paper, we discuss a practical solution to help guide clinicians with interpretation of the probability that a positive, or negative, COVID-19 test result indicates an infected person, based on their clinical estimate of pre-test probability of infection.The authors conducted a small survey on LinkedIn to confirm that hypothesis that that the clinical pre-test probability of COVID-19 increases relative to local prevalence of disease plus patient age, known contact, and severity of symptoms. We examined results of PPA (Positive Percent Agreement, sensitivity) and NPA (Negative Percent Agreement, specificity) from 73 individual laboratory experiments for molecular tests for SARS-CoV-2 as reported to the FIND database ¹, and for selected methods in FDA EUA submissions^2,3. Authors calculated likelihood ratios to convert pre-test to post-test probability of disease and designed an online calculator to create graphics and text to report results. Despite best efforts, false positive and false negative Covid-19 test results are unavoidable^4,5. A positive or negative test result from one laboratory has a different probability for the presence of disease than the same result from another laboratory. Likelihood ratios and confidence intervals can convert the physician or other healthcare professional’s clinical estimate of pre-test probability to post-test probability of disease. Ranges of probabilities differ depending on proven method PPA and NPA in each laboratory. We recommend that laboratories verify PPA and NPA and utilize a the “Clinician’s Probability Calculator” to verify acceptable test performance and create reports to help guide clinicians with estimation of post-test probability of COVID-19.

Utility of vaccine campaigns to control coronavirus disease 2019 (COVID-19) is not merely dependent on vaccine efficacy and safety. Vaccine acceptance among the general public and the healthcare workers, appears to have a decisive role for successful control of the pandemic. The aim of this review was to provide an up-to-date assessment of COVID-19 vaccination acceptance rates worldwide. A systematic search of the peer-reviewed English survey literature indexed in PubMed was done on December 25, 2020. Results from 30 studies, met the inclusion criteria and formed the basis for final COVID-19 vaccine acceptance estimates. Results of an additional recent survey from Jordan and Kuwait was considered in this review as well. Survey studies on COVID-19 vaccine acceptance rates were found from 33 different countries. Among adults representing the general public, the highest COVID-19 vaccine acceptance rates were found in Ecuador (97.0%), Malaysia (94.3%), Indonesia (93.3%) and China (91.3%). On the other hand, the lowest COVID-19 vaccine acceptance rates were found in Kuwait (23.6%), Jordan (28.4%), Italy (53.7), Russia (54.9%), Poland (56.3%), US (56.9%), and France (58.9%). Only eight surveys among healthcare workers (doctors, nurses) were found, with vaccine acceptance rates ranging from 27.7% in the Democratic Republic of the Congo to 78.1% in Israel. In a majority of survey studies among the general public (62%), the acceptance of COVID-19 vaccination showed a level of ≥ 70%. Low rates of COVID-19 vaccine acceptance were reported in the Middle East, Russia, Africa and several European countries. This could represent a major problem in the global efforts that aim to control the current COVID-19 pandemic. More studies are recommended to address the scope of COVID-19 vaccine hesitancy. Such studies are particularly needed in the Middle East Africa, Eastern Europe, Central Asia, Middle and Latin America.

Rising child obesity rate creates a need for tools quantifying metabolic changes in obese children and adolescents for purposes of comorbidities early detection or prevention. A candidate for such role seem to be miRNAs – in vivo serving as the suppressing factors of the gene expression. The aim of this study was to review the recent discoveries in this field and to conclude directions of research or application of studied molecules. Repeated browsing of databases, followed by screening for eligibility of results, led to final approval of 9 articles. Filtered studies examined the differences in miRNA (miR) expression levels of obese subjects and children suffering from obesity comorbidities. Studies concerning the endothelial dysfunction (ED) identified miR-630 as a possible treatment option. Search for the alternative markers in diagnosis of non-alcoholic fatty liver disease (NAFLD) suggested value of miR-199a-5p and miR-122. MiR-486, miR-146b and miR-15b may serve as a panel of markers grading the development of type 2 diabetes mellitus (T2DM) in children, although further research raised doubts in that matter. Another panel of miRNA molecules was indicated as useful in early detection of alterations leading to metabolic syndrome (MetS). No valid link between studied miRNAs and atherosclerosis (AS) was found. MiRNAs seem to be promising prognostic markers for the diagnosis of NAFLD, T2DM and MetS in children. Further studies are required to support these findings.

In this paper, we present an extended version of the Hadi-Vencheh model for multiple criteria ABC inventory classification. The proposed model is a nonlinear weighted product model (WPM) which determines a common set of weights for all the items. Our proposed nonlinear WPM incorporates multiple criteria with different measure units, without converting the performance of each inventory item in terms of each criterion into a normalized attribute value, thereby providing an improvement over the model proposed by Hadi-Vencheh. Our study mainly includes various criteria for ABC classification, and demonstrates an efficient algorithm for solving nonlinear programming problems in which the feasible solution set does not have to be convex. The algorithm presented in this study improves the solution efficiency of the Canonical Coordinates Method (CCM) algorithm substantially when applied to large scale, nonlinear programming problems. The modified algorithm was tested to compare our proposed model results to the results derived using the Hadi-Vencheh model and demonstrate the algorithm's efficacy. The practical implications of the study are to develop an efficient nonlinear optimization solver by optimizing the quality of existing solutions, thus improving time and space efficiency.

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread rapidly to most parts of the world, causing high numbers of deaths and significant social and economic impacts. SARS-CoV-2 is a novel coronavirus with a suggested zoonotic origin and with the potential for cross-species transmission among animals. Antarctica can be considered the only continent free of SARS-CoV-2 although at the end of the 2019-2020 tourist season, at least one SARS-CoV-2 positive tourist visited the Antarctic Peninsula. Therefore, concerns have been expressed regarding the potential human introduction of this virus to the continent through the activities of research or tourism with potential effects including those related to human health, but also the potential for virus transmission to Antarctic wildlife. This reverse-zoonotic transmission risk to Antarctic wildlife is assessed considering the available information on host susceptibility, dynamics of the infection in humans, and contact interactions between humans and Antarctic wildlife. Measures to reduce the risk are proposed as well as the identification of knowledge gaps related to this issue.

Abstract The COVID-19 pandemic is thought to began in Wuhan, China in December 2019. Mobility analysis identified East-Asia and Oceania countries to be highly-exposed to COVID-19 spread, consistent with the earliest spread occurring in these regions. However, here we show that while a strong positive correlation between case-numbers and exposure level could be seen early-on as expected, at later times the infection-level is found to be negatively correlated with exposure-level. Moreover, the infection level is positively correlated with the population size, which is puzzling since it has not reached the level necessary for population-size to affect infection-level through herd immunity. These issues are resolved if a low-virulence Corona-strain (LVS) began spreading earlier in China outside of Wuhan, and later globally, providing immunity from the later appearing high-virulence strain (HVS). Following its spread into Wuhan, cumulative mutations gave rise to the emergence of an HVS, known as SARS-CoV-2, starting the COVID-19 pandemic. We model the co-infection by an LVS and an HVS, and show that it can explain the evolution of the COVID-19 pandemic and the non-trivial dependence on the exposure level to China and the population-size in each country. We find that the LVS began its spread a few months before the onset of the HVS, and that its spread doubling-time is $\sim 1.59\pm 0.17$ times slower than the HVS. Although more slowly spreading, its earlier onset allowed the LVS to spread globally before the emergence of the HVS. In particular, in countries exposed earlier to the LVS and/or having smaller population-size, the LVS could achieve herd-immunity earlier, and quench the later-spread HVS at earlier stages. We find our two-parameter (the spread-rate and the initial onset time of the LVS) can accurately explain the current infection levels (R^2$\mathrm{=0.74}$; correlation p-value (p) of 5x10^-13$$). Furthermore, countries exposed early should have already achieved herd-immunity. We predict that in those countries cumulative infection levels could rise by no more than 2-3 times the current level through local-outbreaks, even in the absence of any containment measures. We suggest several tests and predictions to further verify the double-strain co-infection model and discuss the implications of identifying the LVS.

A 47-year-old physician suddenly noticed a persistent difficulty maintaining attention. He was awake, alert, and oriented. After two hours he developed fever, ageusia, and anosmia. He denied any previous history of psychiatric illness and was hydrated at the time of the subjective attention impairment. On admission, the patient remained oriented. He reported the persistence of attention problems, anosmia, and mild fatigue. The oxygen saturation 99% while he was breathing ambient air. Laboratory tests were unremarkable. A high-resolution computed tomography of the chest was normal. Nasopharyngeal and throat swabs specimens on reverse transcription-polymerase chain reaction analysis tested positive for SARS-CoV2. On illness day 3, the examination was unchanged, but he continued to complain of difficulties to stay focused. Then, he performed an objective attention test. The test demonstrated a moderate attentional impairment. On day 6, the patient reported a subjective worse in his concentration and performed a second test. Although his physical examination remained normal, the attention performance was worse as compared to day 3. Eight hours after worsening of attention impairment, the patient’s oxygen saturation dropped to 94%. From illness days 9 to 14, the patient evolved with clinical improvement. On day 10, a third objective attention test indicated a mild deficit. On day 16, he did not report any other symptom and the attention test was completely normal. Then, the patient returned to work. Neurological symptoms had been previously described in COVID- 19 patients. However, no previous research had investigated early cognitive deficits preceding the traditional symptoms.

In this paper, we construct the degenerate poly-Frobenius-Genocchi polynomials, called the type 2 degenerate poly-Frobenius-Euler polynomials, by means of polyexponential function. We derive explicit expressions and some identities of those polynomials. In the last section, we introduce type 2 degenerate unipoly-Frobenius-Genocchi polynomials by means of unipoly function and derive explicit multifarious properties.

The worldwide outbreak of a novel coronavirus, SARS-CoV-2 has caused a pandemic of respiratory disease. Due to this emergency, researchers around the globe have been investigating the evolution of the genome of SARS-CoV-2 in order to design vaccines. Here I examined the evolution of GC content of SARS-CoV-2 by comparing the genomes of the members of the group Betacoronavirus. The results of this investigation revealed a highly significant positive correlation between the GC contents of betacoronaviruses and their divergence from SARS-CoV-2. The betacoronaviruses that are distantly related to SARS-CoV-2 have much higher GC contents than the latter. Conversely, the closely related ones have low GC contents, which are only slightly higher than that of SARS-CoV-2. This suggests a systematic reduction in the GC content in the SARS-CoV-2 lineage over time. The declining trend in this lineage predicts a much-reduced GC content in the coronaviruses that will descend/evolve from SARS-CoV-2 in the future. Due to the three consecutive outbreaks (MERS-CoV, SARS-CoV and SARS-CoV-2) caused by the members of the SARS-CoV-2, the scientific community is emphasizing the need for universal vaccines that are effective across many strains including those, that will inevitably emerge in the near future. The reduction in GC contents implies an increase in the rate of GC→AT mutations than that the mutational changes in the reverse direction. Therefore, understanding the evolution of base composition and mutational patterns of SARS-CoV-2 could be useful in designing broad-spectrum vaccines that could identify and neutralize the present and future strains of this virus.

Background: It is unclear why certain countries have been more severely affected by COVID-19 than other countries. Methods: In this ecological study we compared COVID-19 mortality and incidence/100,000 as well as 4 putative explanatory factors by WHO world region. Linear regression was then used to assess the country-level predictors of COVID-19 mortality/100,000 and incidence/100,000 in 204 countries with available data. Results: COVID-19 incidence and mortality/capita were greater in Europe than other regions. This was despite a higher testing rate in Europe than other regions. Europe had an older population than all other regions and a higher prevalence of obesity than Africa, South East Asia and the Eastern Mediterranean. Country level multiple linear regression revealed positive associations between mortality/capita and testing rate, percent of the population 65 years or older, and Europe compared to Western Pacific and South East Asia (all P<0.005). Results for the analyses with cases/100,000 as outcome variable were similar. Conclusion: Our results suggest that older populations as well as other undefined regional and national factors, possibly related to efficacy of control efforts, are responsible for differences in national severity COVID-19 epidemics.

In December 2019, pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection emerged in Wuhan City, Hubei Province, China. Early in 2020, the World Health Organization (WHO) announced a new name for the 2019-nCoV-caused epidemic disease: coronavirus disease 2019 (COVID-19) and declared COVID-19 to be the sixth international public health emergency. Cellular co-infection is a critical determinant of both viral fitness and infection outcome and plays a crucial role in shaping the host immune response to infections. In this study, sixty-eight public next-generation sequencing libraries from SARS-CoV-2 infected patients were retrieved from the NCBI Sequence Read Archive database using SRA-Toolkit. Using an alignment-free method based on K-mer mapping and extension, SARS-CoV-2 was identified in all except three patients. Influenza A H7N9 (3/68), Human immunodeficiency virus 1 (1/68), rhabdovirus isolate (3/68), Human metapneumovirus (1/68), coronaviruses NL63 (1/68), Parvovirus (1/68), Simian virus 40 (1/68), and hepatitis virus (1/68) genome sequences were detected in SARS-CoV-2 infected patients.

A compartmental epidemiological model with seven groups is introduced herein, to account for the dissemination of diseases similar to the Coronavirus disease 2019 (COVID-19). In its simplified version, the model contains ten parameters, four of which relate to characteristics of the virus, whereas another four are transition probabilities between the groups; the last two parameters enable the empirical modelling of the effective transmissibility, associated in this study with the cumulative number of fatalities due to the disease within each country. The application of the model to the fatality data (the main input herein) of five countries (to be specific, of those which had suffered most fatalities by April 30, 2020) enabled the extraction of an estimate for the basic reproduction number $R_0$ for the COVID-19 disease: $R_0=4.91(33)$.

Introduction: Due to the spread of SARS CoV-2 virus responsible for COVID-19 disease, there is an urgent need to analyse COVID-19 epidemic perception in Poland. This study aims to investigate social perception of coronavirus in the Internet media during the epidemic. It is a signal report highlighting the main issues in public perception and medical commutation in real time. Methods: We study the perception of COVID-19 epidemic in Polish society using quantitative analysis of its digital footprints on the Internet on platforms: Google, Twitter, YouTube, Wikipedia and electronic media represented by Event Registry, from January 2020 to 29.04.2020 (before and after official introduction to Poland on 04.03.20). We present trend analysis with a support of natural language processing techniques. Results: We identified seven temporal major clusters of interest on the topic COVID-19: 1) Chinese, 2) Italian, 3) Waiting, 4) Mitigations, 5) Social distancing and Lockdown, 6) Anti-crisis shield, 7) Restrictions releasing. There was an exponential increase of interest when the Polish government “declared war against disease” around 11/12.03.20 with a massive mitigation program. Later on, there was a decay in interest with additional phases related to social distancing and an anti-crisis legislation act with local peaks. We have found that declarations of mitigation strategies by the Polish prime minister or the minister of health gathered the highest attention of Internet users. So enacted or in force events do not affect interest to such extent. Traditional news agencies were ahead of social media (mainly Twitter) in dissemination of information. We have observed very weak or even negative correlations between a colloquial searching term 'antiviral mask' in Google, encyclopaedic definition in Wikipedia “SARS-CoV-2” as well official incidence series, implying different mechanisms governing the search for knowledge, panic related behaviour and actual risk of acquiring infection. Conclusions: Traditional and social media do not only reflect reality, but also create it. Risk perception in Poland is unrelated to actual physical risk of acquiring COVID-19. As traditional media are ahead of social media in time, we advise to choose traditional news media for a quick dissemination of information, however for a greater impact, social media should be used. Otherwise public information campaigns might have less impact on society than expected.

Background: There are no known medicines or vaccines to control the COVID-19 pandemic caused by SARS-CoV-2 (nCoV). Antiviral peptides are superior to conventional drugs and may also be effective against COVID-19. Hence, we investigated the SARS-CoV-2 Spike RBD (nCoV-RBD) that interacts with hACE2 for viral attachment and entry. Methods: Three strategies and bioinformatics approaches were employed to design potential nCoV-RBD - hACE2 interaction-blocking peptides that may restrict viral attachment and entry. Firstly, the key residues interacting with nCoV-RBD - hACE2 are identified and hACE2 sequence based peptides are designed. Second, peptides from five antibacterial peptide databases that block nCoV-RBD are identified; finally, a chimeric peptide design approach is used to design peptides that can bind to key nCoV-RBD residues. The final peptides are selected based on their physiochemical properties, numbers and positions of key residues binding, binding energy, and antiviral properties. Results: We found (i) three amino acid stretches in hACE2 interact with nCoV-RBD; (ii) effective peptides must bind to three key positions of nCoV-RBD: Gly485/Phe486/Asn487, Gln493, and Gln498/Thr500/Asn501; (iii) Phe486, Gln493, and Asn501 are critical residues; (iv) AC20 and AC23 derived from hACE2 may block two key critical positions; (iv) DBP6 identified from databases can block the three sites of the nCoV-RBD interacting with one critical position Gln498; (v) seven chimeric peptides were considered promising among which cnCoVP-3, cnCoVP-4, and cnCoVP-7 are the top three; and (vi) cnCoVP-4 meets all the criteria and is the best peptide. Conclusion: All the ten peptides need experimental validation for their therapeutic efficacy.

Reliable estimates of poplar plantations area are not available at the French national scale due to the unsuitability and low update rate of existing forest databases for this short-rotation species. While supervised classification methods have been shown to be highly accurate in mapping forest cover from remotely sensed images, their performance depends to a great extent on the labelled samples used to build the models. In addition to their high acquisition cost, such samples are often scarce and not fully representative of the variability in class distributions. Consequently, when classification models are applied to large areas with high intra-class variance, they generally yield poor accuracies. In this paper, we propose the use of active learning (AL) to efficiently adapt a classifier trained on a source image to spatially distinct target images with minimal labelling effort and without sacrificing classification performance. The adaptation consists in actively adding to the initial local model, new relevant training samples from other areas, in a cascade that iteratively improves the generalisation capabilities of the classifier, leading to a global model tailored to different areas. This active selection relies on uncertainty sampling to directly focus on the most informative pixels for which the algorithm is the least certain of their class labels. Experiments conducted on Sentinel-2 time series showed that when the same number of training samples was used, active learning outperformed passive learning (random sampling) by up to 5% of overall accuracy and up to 12% of class F-score. In addition, and depending on the class considered, the random sampling required up to 50% more samples to achieve the same performance of an active learning-based model. Moreover, the results demonstrate the suitability of the derived global model to accurately map poplar plantations among other tree species with overall accuracy values up to 14% higher than those obtained with local models. The proposed approach paves the way for national-scale mapping in an operational context.

Outbreaks of coronavirus disease 2019 (COVID-19) have been recorded in different countries across the globe. The virus is highly contagious, hence early detection, isolation, and quarantine of infected patients will play an important role in containing the viral spread. Diagnosis in a mobile lab can aid to find infected patients in time. Here, we develop a field-deployable diagnostic workflow that can reliably detect COVID-19. Instruments used in this workflow could easily fit in a mobile cabin hospital and also be installed in the community. Different steps from sample inactivation to detection were optimized to find the fastest steps and portable instruments in detection of COVID-19. Each step was compared to that of the normal laboratory diagnosis set-up. From the results, our proposed workflow (80 min) was two times faster compared to that of the normal laboratory workflow (183 min) and a maximum of 32 samples could be detected at each run. Additionally, we showed that using 1% Rewocid WK-30 could inactivate the novel coronavirus directly without affecting the overall detection results. Comparison of our workflow using an in-house assay to that of a commercially acquired assay produced highly reliable results. From the 250 hospital samples tested, there was a high concordance 247/250 (98.8%) between the two assays. The in-house assay sensitivity and specificity were 116/116 (100%) and 131/134 (97.8%) compared to that of the commercial assay. Based on these results, we believe that our workflow is fast, reliable, adaptable and most importantly, field deployable.

In the past decades, several new diseases have emerged in new geographical areas, with pathogens including Ebola, Zika, Nipah, and coronaviruses (CoVs). Recently, a new type of viral infection has emerged in Wuhan City, China, and initial genomic sequencing data of this virus does not match with previously sequenced CoVs, suggesting a novel CoV strain (2019-nCoV), which has now been termed as severe acute respiratory syndrome CoV-2 (SARS-CoV-2). Although Coronavirus disease 2019 (COVID-19) is suspected to originate from an animal host (zoonotic origin) followed by human-to-human transmission, the possibility of other routes such as food-borne transmission should not be ruled out. Compared to diseases caused by previously known human CoVs, COVID-19 shows less severe pathogenesis but higher transmission competence, as is evident from the continuously increasing number of confirmed cases globally. Compared to other emerging viruses such as Ebola virus, avian H7N9, SARS-CoV, or MERS-CoV, SARS-CoV-2 has shown relatively low pathogenicity and moderate transmissibility. Codon usage studies suggest that this novel virus may have been transferred from an animal source such as bats. Early diagnosis by real-time PCR and next-generation sequencing has facilitated the identification of the pathogen at an early stage. Since, no antiviral drug or vaccine exists to treat or prevent SARS-CoV-2, potential therapeutic strategies that are currently being evaluated predominantly stem from previous experience with treating SARS-CoV, MERS-CoV, and other emerging viral diseases. In this review, we address epidemiological, diagnostic, clinical, and therapeutic aspects, including perspectives of vaccines and preventive measures that have already been globally recommended.

Based on recent computational and experimental studies, hesperidin, a bioactive flavonoid abundant in citrus peel, stands out for its high binding affinity to the main cellular receptors of SARS-CoV-2, outperforming drugs already recommended for clinical trials. Thus, it is very promising for prophylaxis and treatment of COVID-19, along with other coexistent flavonoids such as naringin, which could help restraining the pro-inflammatory overreaction of the immune system. Controlled hydrodynamic cavitation processes showed the highest speed, effectiveness and efficiency in the integral and green aqueous extraction of flavonoids, essential oils and pectin from citrus peel waste. After freeze-drying, the extracted pectin showed high quality and excellent antioxidant and antibacterial activities, attributed to flavonoids and essential oils adsorbed and concentrated on its surface. This study reviews the recent evidence about hesperidin as a promising molecule, and proposes a feasible and affordable process based on hydrodynamic cavitation for the integral aqueous extraction of citrus peel waste resulting in hesperidin-rich products, either aqueous extracts or pectin tablets. The uptake of this process on a relevant scale is urged, in order to achieve large-scale production and distribution of hesperidin-rich products. Meanwhile, experimental and clinical studies could determine the effective doses either for therapeutic and preventive purposes.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is characterized by a high mortality of elderly men with age-related comorbidities. In most of these patients, uncontrolled local and systemic hyperinflammation induces severe and often lethal outcomes. The aging process is characterized by the gradual development of a chronic subclinical systemic inflammation (inflamm-aging) and by acquired immune system impairment (immune senescence). Here, we advance the hypothesis that some key features of aging contribute to the disproportionate SARS-CoV-2 mortality suffered by elderly men. At least four well-recognized aging-related characteristics that are strongly expressed in older men go some way towards explaining why these patients account for the vast majority of fatalities: i. the presence of subclinical systemic inflammation without overt disease, ii. a blunted acquired immune system and type I interferon response due to the chronic inflammation; iii. the downregulation of ACE2 (SARS-CoV-2 receptor), which triggers inflammation, particularly in patients with age-related comorbid diseases such as type II diabetes; and iv. accelerated biological aging, as measured by epigenetic and senescence markers (e.g. telomere shortening) associated to the chronic inflammatory state. Though typical of the aged, especially of elderly men, it is conceivable that these features are also shared by some subsets of the younger population. The high mortality rate of SARS-CoV-2 infection suggests that clarification of the mechanisms of inflamm-aging and immune senescence can help combat not only age-related disorders but also SARS-CoV-2 infection.

Corona viruses cause extensive SARS epidemics via super spread events (SSE). Due to variation in infection risk and heterogeneity of reproduction numbers specific distinction between SSE’s and typical case events is essential. SARS transmissions unveil a complex scenario in which SSE’s are shaped by multiple factors. Specific screening strategies for infection emergence within potential super spreading groups will help to efficiently control the SARS-2 pandemic and alleviate the partially effective general restriction measures.

This paper investigates whether the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) pandemic – also known as COronaVIrus Disease 19 (COVID-19) – could have been favored by specific weather conditions. It was found that the 2020 winter weather in the region of Wuhan (Hubei, Central China) – where the virus first broke out in December and spread widely from January to February 2020 – was strikingly similar to that of the Northern Italian provinces of Milan, Brescia and Bergamo, where the pandemic has been very severe from February to March. The similarity suggests that this pandemic worsens under weather temperatures between 4°C and 11°C. Based on this result, specific isotherm world maps were generated to locate, month by month, the world regions that share similar temperature ranges. From January to March, this isotherm zone extended mostly from Central China toward Iran, Turkey, West-Mediterranean Europe (Italy, Spain and France) up to the United State of America, coinciding with the geographic regions most affected by the pandemic from January to March. It is predicted that next spring, as the weather gets warm, the pandemic will likely worsen in northern regions (United Kingdom, Germany, East Europe, Russia and North America) while the situation will likely improve in the southern regions (Italy and Spain). However, in autumn, the pandemic could come back and affect the same regions again. The Tropical Zone and the entire Southern Hemisphere, but in restricted southern regions, could avoid a strong pandemic because of the sufficiently warm weather during the entire year. Google-Earth-Pro interactive-maps are provided as supplements.

A SARS-CoV receptor-binding domain (RBD) recombinant protein was developed and manufactured under current good manufacturing practices in 2016. The protein known as RBD219-N1 when formulated on Alhydrogel®, induced high-level neutralizing antibodies and protective immunity with minimal immunopathology in mice after a homologous virus challenge with SARS-CoV (MA15 strain). In this report, we examined published evidence in support of whether the SARS-CoV RBD219-N1 could be repurposed as a heterologous vaccine against Coronavirus Infectious Disease (COVID)-19. Our findings include evidence that convalescent serum from SARS-CoV patients can neutralize SARS-CoV-2. Additionally, a review of published studies using monoclonal antibodies (mAbs) raised against SARS-CoV RBD and that neutralize the SARS-CoV virus in vitro, finds that some of these mAbs bind to the receptor-binding motif (RBM) within the RBD, while others bind to domains outside this region within RBD. This information is relevant and supports the possibility of developing a heterologous SARS-CoV RBD vaccine against COVID-19, especially due to the finding that the overall high amino acid similarity (82%) between SARS-CoV and SARS-CoV-2 spike and RBD domains is not reflected in RBM region (59%). However, the high similarity (94%) in the region outside of RBM offers the potential of conserved neutralizing epitopes between both viruses.

Easy to use satellite-based water quality visualizations are needed for monitoring and understanding coastal and inland waters, but to date, no publicly accessible real-time global visualization system was in place. Here we introduce the Ulyssys Water Quality Viewer (UWQV), a Sentinel Hub EO Browser Custom script designed for qualitative views of aquatic chlorophyll and suspended sediment concentrations. The viewer avoids unmixing of the chlorophyll and suspended sediment spectral signal by visualizing these parameters together, with high concentrations of suspended sediment obscuring chlorophyll if present. Cloud masking uses the Hollstein and Braaten algorithms (existing EO Browser custom script code), additionally water surfaces are masked using the Normalized Differential Water Index. Chlorophyll is estimated using reflectance line height-based indicators such as fluorescence line height and maximum chlorophyll index. Suspended sediment is visualized based on single-band reflectances at 620 or 700 nm. Data sources are Sentinel-2 and Sentinel-3 images, allowing either 20 m spatial resolution or up to daily imaging. This visualization system is easy to operate and interpret, and combined with the data service capacity of the Sentinel Hub, it is expected that UWQV will contribute to monitoring of remote water bodies and to our overall understanding of physical limnology and aquatic ecology.

The epithelial cell tight junction structure is the site of the transepithelial movement of solutes and water between epithelial cells (paracellular permeability). Paracellular permeability can be divided into two distinct pathways, the Pore Pathway mediating the movement of small ions and solutes and the Leak Pathway mediating the movement of large solutes. Claudin proteins form the basic paracellular permeability barrier and mediate the movement of small ions and solutes via the Pore Pathway. The Leak Pathway remains less understood. Several proteins have been implicated in mediating the Leak Pathway, including occludin, ZO proteins, tricellulin, and actin filaments, but the proteins comprising the Leak Pathway remain unresolved. The properties of the Leak Pathway, such as its molecular mechanism, its regulation, and whether or not it has a size limit, remain controversial. This review will trace the evolution of the Leak Pathway concept from its origins, will discuss the current information about the properties of the Leak Pathway, and will discuss recent research suggesting a possible molecular basis for the Leak Pathway. Based on these findings, we propose a model for the molecular mechanism underlying the Leak Pathway and its regulation.

Ischemic neuron loss contributes to brain dysfunction in patients with cardiac arrest (CA). Histidine–tryptophan–ketoglutarate (HTK) solution is a preservative used during organ transplantation. Can HTK also protect neurons from severe hypoxia (SH) following CA? We isolated rat primary cortical neurons and induced SH with or without HTK. Changes in caspase-3, hypoxia-inducible factor 1-alpha (HIF-1α), and NADPH oxidase-4 (NOX4) expression were evaluated at different time points till 72 h. Using a rat asphyxia model, we induced CA-mediated brain damage and then completed resuscitation. HTK or sterile saline was administered into the left carotid artery. Neurological deficit scoring and mortality were evaluated for 3 days. Then the rats were sacrificed for evaluating NOX4 and H2O2 level in blood and brain. In the in vitro study, HTK attenuated SH- and H2O2-mediated cytotoxicity in a volume- and time-dependent manner, associated with persisted HIF-1α expression, reductions in procaspase-3 activation and NOX4 expression. The inhibition of HIF-1α abrogated HTK’s effect on NOX4. In the in vivo study, neurological scores were significantly improved by HTK. H2O2 level, NOX4 activity and NOX4 gene expression were all decreased in the brain specimen of HTK-treated rats. Our results suggest that HTK acts as an effective neuroprotective solution.

Hypothesis: Anti-diabetic drugs modulate p-21 activated kinase (PAK) signaling Introduction: Type 2 diabetes mellitus (T2DM) is a chronic inflammatory disease associated with increased cancer risk. PAK signaling is implicated in cellular homeostasis when regulated, and cancer when unrestrained. Recent reports provided a role for PAK signaling in glucose homeostasis, but the role of PAKs in the pathogenesis of T2DM is unknown. Here we performed a mini-meta analysis to explore if anti-diabetic drugs modify PAK signaling pathways, and provide insight regarding modulation of these pathways to potentially reduce diabetes-associated cancer risk. Methods: PAK interacting partners in T2DM were identified using online STRING database. Correlation studies were performed via systematic literature review to understand the effect of anti-diabetic drugs on PAK signaling. Mini meta analysis correlated multiple clinical studies and revealed the overall clinical response rate and percentage of adverse events in piogliazone (n=53) and metformin (n=91) treated patients with PAK-associated diseases. Results: A total of 30 PAK interacting partners were identified (10: reduced beta-cell mass; 10: beta-cell dysfunction; 10: obesity-insulin resistance) which were highly associated with Wnt, and G-protein signaling. Anti-diabetic drug metformin activated signaling pathways upstream; whereas pioglitazone inhibited pathways downstream of PAK. Overall clinical response upon pioglitazone treatment was 53%. 79% of pioglitazone and 75% of metformin treated patients had adverse events. Pioglitazone reduced molecular-PAK biomarkers of proliferation (Ki67 and CyclinD1), and metformin had the opposite effect. Conclusions: PAK signaling in T2DM likely involves Wnt and G-protein signaling which may be altered by anti-diabetic drugs metformin and pioglitazone. Apart from the therapeutic limitations of adverse events, pioglitazone may be promising in chemoprevention, however long-term multi-centered studies, which initiate pioglitazone treatment early will be required to fully assess the full potential of these drugs.

Hypothesis: Anti-diabetic drugs modulate p-21 activated kinase (PAK) signaling Introduction: Type 2 diabetes mellitus (T2DM) is a chronic inflammatory disease associated with increased cancer risk. PAK signaling is implicated in cellular homeostasis when regulated, and cancer when unrestrained. Recent reports provided a role for PAK signaling in glucose homeostasis, but the role of PAKs in the pathogenesis of T2DM is unknown. Here we explored whether PAK signaling should be targeted via chemoprevention to reduce diabetes-associated cancer risk. Methods: PAK interacting partners in T2DM were identified using online STRING database. Systematic literature review provided the effect of anti-diabetic drugs on PAK signaling. Review of clinical studies revealed the overall clinical response rate and percentage of adverse events in piogliazone (n=53) and metformin (n=91) treated patients with PAK-dependent diseases. Results: A total of 30 PAK interacting partners were identified (10: reduced beta-cell mass; 10: beta-cell dysfunction; 10: obesity-insulin resistance) which were highly associated with Wnt, and G-protein signaling. Anti-diabetic drug metformin activated signaling pathways upstream; whereas pioglitazone inhibited pathways downstream of PAK. Overall clinical response upon pioglitazone treatment was 53%. 79% of pioglitazone and 75% of metformin treated patients had adverse events. Pioglitazone reduced molecular-PAK biomarkers of proliferation (Ki67 and CyclinD1), and metformin had the opposite effect. Conclusions: PAK signaling in T2DM involves Wnt and G-protein signaling which is altered by anti-diabetic drugs metformin and pioglitazone. Apart from the therapeutic limitations of adverse events, pioglitazone is promising in chemoprevention, however long-term multi-centered studies, which initiate pioglitazone treatment early will be required to fully assess the full potential of these drugs.

Public-Private Partnerships (P3 or PPPs) have been utilized and studied more frequently over the past couple of decades for both infrastructure projects and building development projects. Based upon the records of the outcomes of these projects there is a plethora of data that demonstrates the benefits and challenges that this delivery method poses. As the name suggests, these partnerships allow for public goals to become realized while also providing opportunities for the private sector to gain business success. These two often divergent interests are finding mutual benefit but the process of compromises also comes with some disadvantages that should be understood in order to make decision of whether the risk/reward equation works for all stakeholders. In particular, this paper will focus on how PPPs affect the construction aspects of projects and will assert how to understand the risks and evaluate the benefits from a construction perspective.

Benveniste’s experiments – known in the lay press as the “water memory” phenomenon – are generally considered to be a closed case. However, the amount of data generated by twenty years of well-conducted experiments prevents closing the file so simply. An issue, which has been little highlighted so far, merits to be emphasized. Indeed, if Benveniste failed to persuade his peers of the value of his experiments, it was mainly because of a stumbling block, namely the difficulty of convincingly proving the causal relationship between the supposed cause (“informed water”) and the experimental outcomes in different biological models. To progress in the understanding of this phenomenon, we abandon the idea of any role of water in these experiments (“water memory” and its avatars). In other words, we assume that “control” and “test” conditions to be evaluated were physically identical and differed only by their respective designations. We show in this article how simple probabilistic considerations allow to build a model that accounts for all aspects of Benveniste’s experiments. In this model based on probability amplitudes, constructive and destructive interferences emerge – or not – according to the experimental context. The recording of a statistical regularity shapes the intertwined whole constituted by the probability amplitudes of the states of the experimenter’s cognitive structures and of the experimental system. This model provides an alternative explanation to Benveniste’s experiments where water plays no role and where the place of the experimenter is central.

Rett syndrome (RTT) is mainly caused by mutations in methyl CpG-binding protein 2, cyclin-dependent kinase-like 5, or forkhead box protein G1. These RTT-causing proteins harbor an intrinsically disordered region (IDR) whose conformation exhibits spatiotemporal heterogeneity, which not only confer versatility to the protein, but also implicates them in diseases. The IDR generally evolves more rapidly than an ordered structure. In this study, we examined the relationship between pathogenic RTT-associated point mutations in RTT-causing proteins and the evolutionary dynamics of sequence features including structural order–disorder, phosphorylation sites, and evolutionary rates. We also analyzed the molecular properties and evolution of proteins that interact with RTT-causing proteins in terms of phylogenetic profiles, tissue specificity, subcellular localization, expression level, and functions. The results indicate that constrained IDRs may function by forming contacts with other regions in the protein sequence causing pathogenic missense mutations likely to arise in the rapidly evolving IDR and affect molecular networks, leading to disease. The results also provide novel insights into the genetic basis for RTT and the evolution of the neocortex in higher vertebrates.

Densely urbanized areas, with a low percentage of green vegetation, are highly exposed to Heat Waves (HW) which nowadays are increasing in terms of frequency and intensity also in the middle-latitude regions, due to ongoing Climate Change (CC). Their negative effects may combine with those of the UHI (Urban Heat Island), a local phenomenon where air temperatures in the compact built up cores of towns increase more than those in the surrounding rural areas, with significant impact on the quality of urban environment, on citizens health and energy consumption and transport, as it has occurred in the summer of 2003 on France and Italian central-northern areas. In this context this work aims at designing and developing a methodology based on aero-spatial remote sensing (EO) at medium-high resolution and most recent GIS techniques, for the extensive characterization of the urban fabric response to these climatic impacts related to the temperature within the general framework of supporting local and national strategies and policies of adaptation to CC. Due to its extension and variety of built-up typologies, the municipality of Rome was selected as test area for the methodology development and validation. First of all, we started by operating through photointerpretation of cartography at detailed scale (CTR 1: 5000) on a reference area consisting of a transect of about 5x20 km, extending from the downtown to the suburbs and including all the built-up classes of interest. The reference built-up vulnerability classes found inside the transect were then exploited as training areas to classify the entire territory of Rome municipality. To this end, the satellite EO HR (High Resolution) multispectral data, provided by the Landsat sensors were used within a on purpose developed "supervised" classification procedure, based on data mining and “object-classification” techniques. The classification results were then exploited for implementing a calibration method, based on a typical UHI temperature distribution, derived from MODIS satellite sensor LST (Land Surface Temperature) data of the summer 2003, to obtain an analytical expression of the vulnerability model, previously introduced on a semi-empirical basis.

Background. Azithromycin (AZM) is widely used in cystic fibrosis (CF) treatment following clinical trials demonstrating improved lung function and fewer pulmonary exacerbations. While the precise mechanisms remain elusive, immunomodulatory actions are thought to be involved. We previously reported impaired phagocytosis and defective anti-inflammatory M2 macrophage polarization in CF macrophages. This study systematically analyzed the effect of AZM on the functions of unpolarized and M1/M2 polarized macrophages in CF. Methods: Monocytes, isolated from the venous blood of patients with CF (pwCF) and healthy controls (HCs) were differentiated into monocyte-derived macrophages (MDMs) and infected with P. aeruginosa. P. aeruginosa uptake and killing by MDMs in the presence or absence of AZM was studied. M1 and M2 macrophage polarizations were induced and their functions and cytokine release were analyzed. Results: Following AZM treatment, both HC and CF MDMs exhibited a significant increase in P. aeruginosa uptake and killing, however, lysosomal acidification remained unchanged. AZM treatment led to higher activation of ERK1/2 in both HC and CF MDMs. Pharmacological inhibition of ERK1/2 using U0126 significantly reduced P. aeruginosa uptake in HC MDMs. M1 macrophage polarization remained unaffected, however, AZM treatment led to increased IL-6 and IL-10 release in both HC and CF M1 macrophages. AZM also significantly increased the phagocytic index for both pHrodo E. coli and S. aureus in CF M1 macrophages. In CF, AZM treatment promoted anti-inflammatory M2 macrophage polarization, with an increased percentage of CD209+ M2 macrophages, induction of the M2 gene CCL18, along with its secretion in the culture supernatant. However, AZM did not restore endocytosis in CF, another essential feature of M2 macrophages. Conclusions: This study highlights the cellular functions and molecular targets of AZM which may involve improved uptake of both gram-positive and gram-negative bacteria, restored anti-inflammatory macrophage polarization in CF. This may in turn shape the reduced lung inflammation observed in clinical trials. In addition, we confirmed the role of ERK1/2 activation for bacterial uptake.

: Background: Brain-derived neurotrophic factor (BDNF) is a crucial mediator of neuronal plasticity. Here, we investigated the effects of controlled normobaric hypoxia (NH) combined with physical inactivity on BDNF blood levels and executive functions. Methods: Twenty-five healthy adults (25.8±3.3 years, 15 female) were analyzed in a randomized, controlled cross-over study. Each intervention began with a 30 min resting phase under normoxia (NOR), followed by a 90 min continuation of NOR or NH (peripheral oxygen saturation [SpO₂] 85-80%). Serum and plasma samples were collected every 15 min. Heart rate and SpO₂ were continuously measured. Before and after each exposure, cognitive tests were performed and after 24 h another follow-up blood sample was taken. Results: NH decreased SpO₂ (p<.001, η_p²=.747) and increased heart rate (p=.006, η_p²=.116) significantly. The 30-min resting phase under NOR led to a significant BDNF reduction in serum (p <.001, η_p²=.581) and plasma (p <.001, η_p²=.362). Continuation of NOR further significantly reduced BDNF after another 45 min (p=.018) in serum and after 30 min (p=.040) and 90 min (p=.005) in plasma. There was no significant BDNF decline under NH. 24 h Follow-up examination showed a significant decline of serum BDNF, both after NH and NOR. Conclusion: Our results show that NH has the potential to counteract physical inactivity-induced BDNF decline. Therefore, our study emphasizes the need for a physically active lifestyle and its positive effects on BDNF. This study also demonstrates the need for a standardized protocol for future studies to determine BDNF in serum and plasma.

We presented unusual case of double left ventricular false aneurysm complicating late presentation AMI successfully treated with the combination of urgent surgical drainage of tamponade, staged PCI of non-culprit lesion, preventive implantation of ICD-DR, guideline directed heart failure medical therapy and delayed but effective reconstructive operation.

Oedaleus decorus asiaticus, Calliptamus abbreviatus, Angaracris rhodopa, and Myrmeleotettix palpalis are the main grasshoppers that harm the natural grassland in the Hexi Corridor. In this study, the MaxEnt model was employed to identify the key environmental factors affecting the distribution of the four grasshoppers habitats and to assess their distribution under current and future climate change conditions. The aim was to provide a basis for grasshopper monitoring, prediction, and precise control. In this study, 61 occurrences of O. decorus asiaticus, 68 occurrences of A. rhodopa, 58 occurrences of C. abbreviatus, and 92 occurrences of M. palpalis were recorded by GPS, and the distribution of suitable habitats for O. decorus asiaticus, C. abbreviatus, A. rhodopa, and M. palpalis were predicted under current and future climatic scenarios using a MaxEnt model. The average AUC and TSS values of the four grasshoppers were greater than 0.9, and the simulation results were excellent and highly reliable. The mean annual precipitation was the main factor limiting the current range of suitable areas for these four species. Under the current climate, O. asiaticus, C. abbreviatus, and A. rhodopa were mainly distributed in the central and eastern parts of the Hexi Corridor, and M. palpalis was distributed throughout the Hexi Corridor, with a potential distribution area of 1.44×104, 1.43×104, 1.29×104, and 2.12×104 km2, accounting for 15.3, 15.2, 13.7, and 22.5% of the total area of the grasslands in the Hexi Corridor, respectively. The highly suitable areas of O. asiaticus, C. abbreviatus, and A. rhodopa were mainly distributed in the east-central part of Zhangye City, the western part of Wuwei City, and the western and southern part of Jinchang City, with areas of 0.35×104, 0.29×104, and 0.20×104 km2, accounting for 3.7, 3, and 2.2% of the grassland area, respectively. The high habitat of M. palpalis was mainly distributed in the southeast of Jiuquan City, the west, middle, and east of Zhangye City, the west of Wuwei City, and the west and south of Jinchang City, with an area of 0.32×104 km2, accounting for 3.4% of the grassland area. In the 2030s, the range of O. asiaticus, C. abbreviatus, A. rhodopa, and M. palpalis was predicted to decrease.

After prolonged development, most domestic and international oilfields have entered a late stage of high water cut and high recovery, with a comprehensive water cut reaching up to 90%. Carbon-oxygen ratio logging can directly distinguish oil layers from water layers with low salinity, unknown salinity, or significantly varying salinity in cased wells. This makes it highly valuable for detecting oil and gas layers and locating and monitoring oil-water interfaces. It is currently one of the most effective methods for determining residual oil saturation in cased wells. This paper is based on two carbon-oxygen ratio spectrum interpretation models (the fan and ratio chart methods). It compares and analyzes these two methods and optimizes the interpretation parameters in the ratio chart model using an improved genetic algorithm. The results are effectively integrated with reservoir and conventional logging interpretation results, reducing the impact of lithology and physical properties on measurement values. This provides a theoretical foundation for improving the interpretation capability and accuracy of carbon-oxygen ratio logging, allowing for quicker and more accurate identification of water-flooded layers. This technical support aids in monitoring oil-water dynamics, determining water-flooded layers, and enhancing production potential during oilfield development.

Air pollution, particularly fine particulate matter (PM2.5), poses a significant threat to public health, especially in rapidly urbanizing areas like Lagos State. Despite the severity of the issue, there is a lack of comprehensive data on PM2.5 levels and the Air Quality Index (AQI) in Lagos. This study leverages data from AirQo, an innovative air quality monitoring network using low-cost sensors, to provide a detailed assessment of PM2.5 concentrations and AQI across eight locations in Lagos State, Nigeria. This approach offers real-time, continuous monitoring, filling a critical data gap in the region's air quality management efforts. This study uses AirQo data to assess the concentration levels and spatial distribution of PM2.5 and AQI across the selected eight locations. Data on PM2.5 concentrations were obtained from AirQo sensors deployed across the eight strategic locations in Lagos. The spatial distribution of PM2.5 levels and AQI were analyzed, and the results were compared with national and international air quality standards. The findings revealed that PM2.5 levels (minimum – 6.28 µg/m3 (Ikeja) and maximum - 204.68 µg/m3 (Banana Island) in many areas of Lagos exceed annual WHO and national air quality guidelines, indicating significant health risks. The spatial analysis identified pollution hotspots, particularly in densely populated and industrial regions. The data suggested vehicular emissions and industrial activities as major contributors to high PM2.5 levels. This study underscores the severe air quality issues in Lagos State, emphasizing the need for immediate and targeted interventions to mitigate PM2.5 pollution. The use of AirQo sensors proved effective in providing accurate and timely data for air quality assessment. It is recommended that Lagos State implements stricter emission controls, promotes cleaner technologies, and enhances public transportation systems to reduce traffic-related pollution. Additionally, expanding the air quality monitoring network and incorporating more environmental data will provide a more comprehensive understanding of air pollution impacts, thereby facilitating better policy and public health decisions.

In the context of smart agriculture, accurately estimating plant leaf chemical parameters is crucial for optimizing crop management and improving agricultural yield. Hyperspectral imaging, with its ability to capture detailed spectral information across various wavelengths, has emerged as a powerful tool in this regard. However, the complex and high-dimensional nature of hyperspectral data poses significant challenges in extracting meaningful features for precise estimation. To address this challenge, this study proposes an end-to-end estimation network for multiple chemical parameters of Astragalus leaves based on attention mechanism (AM) and multivariate hyperspectral features (AM-MHENet). We leveraging HybridSN and multilayer perception (MLP) to extract prominent features from the hyperspectral data of Astragalus membranaceus var. mongholicus (AMM) leaves and stems, as well as the surface and deep soil surrounding AMM roots. This methodology allows us to capture the most significant characteristics present in these hyperspectral data with high precision. The AM is subsequently used to assign weights and integrate the hyperspectral features extracted from different parts of the AMM. The MLP is then employed to simultaneously estimate the chlorophyll content (CC) and nitrogen content (NC) of AMM leaves. Compared with estimation networks that utilize only hyperspectral data from AMM leaves as input, our proposed end-to-end AM-MHENet demonstrates superior estimation performance. Specifically, AM-MHENet achieves an R2 of 0.983 with an RMSE of 0.73 for the estimation of CC in AMM leaves. For NC estimation, AM-MHENet achieves an R2 value of 0.977 with an RMSE of 0.27. These results underscore AM-MHENet’s effectiveness in significantly enhancing the accuracy of both CC and NC estimation in AMM leaves. Moreover, these findings indirectly suggest a strong correlation between the development of AMM leaves and stems, as well as the surface and deep soil surrounding the roots of AMM, and directly highlight the ability of AM to effectively focus on the relevant spectral features within the hyperspectral data. This findings from this study could offer valuable insights into the simultaneous estimation of multiple chemical parameters in plants, thereby making a contribution to the existing body of research in this field.

The chemical stability of tetramethylammonium (TMA) head groups, both with and without the presence of a choline chloride and ethylene glycol-based deep eutectic solvent (DES), was studied using Density Functional Theory (DFT) calculations and ab initio Molecular Dynamics (MD) simulations. DFT calculations of transition state energetics (ΔEreaction, ΔGreaction, ΔEactivation, and ΔGactivation) for key degradation mechanisms, ylide formation (YF) and nucleophilic substitution (SN2), suggested that the presence of DES enhances the stability of the TMA head groups compared to systems without DES. Ab initio MD simulations across hydration levels (HLs) 1 to 5 indicated that without DES, YF dominates at lower HLs, while SN2 does not occur. In contrast, both mechanisms are suppressed in the presence of DES. Temperature also plays a role: without DES, YF dominates at 298 K, while SN2 becomes prominent at 320 K and 350 K. With DES, both degradation mechanisms are inhibited. These findings suggest DES could improve the chemical stability of TMA head groups in anion exchange membranes.

Neuropathic pain (NeP) is a complex and debilitating condition that impacts millions of people globally. Although various treatment options exist, their effectiveness is often limited, and they can be accompanied by significant side effects. In recent years, there has been increasing interest in targeting the N-methyl-D-aspartate receptor (NMDAR) as a potential therapeutic approach to alleviate different types of neuropathic pain. This narrative review aims to provide a comprehensive examination of NMDAR antagonists, specifically ketamine, memantine, methadone, amantadine, carbamazepine, valproic acid, phenytoin, dextromethorphan, riluzole, and levorphanol, in the management of NeP. By analyzing and summarizing current preclinical and clinical studies, this review seeks to evaluate the efficacy of these pharmacologic agents in providing adequate relief for NeP.

Acute Respiratory Distress Syndrome (ARDS) is a severe lung condition with a high mortality rate for which there are no effective therapeutics. The failure of the alveolar-capillary barrier, composed of lung endothelial (EC) and alveolar epithelial (AEC) cells, is a critical factor leading to excessive inflammation and edema characteristics of acute lung injury (ALI). Phosphodiesterases (PDE) are enzymes well-recognized for their roles in regulating endothelial permeability and inflammation. Although PDE inhibitors are used as therapeutics for inflammatory diseases like COPD (chronic obstructive pulmonary disease), their efficacy in treating ARDS has not yet been established. In this study, we investigate the effects of ensifentrine, an FDA-approved novel dual PDE 3/4 inhibitor, on lung endothelial and epithelial dysfunction caused by methicillin-resistant S. aureus (MRSA), a pathogen involved in bacterial ARDS. Human primary lung endothelial cells and alveolar epithelial cell lines (A549 and immortalized AEC) were treated with heat-killed MRSA, and their responses were assessed in the presence or absence of ensifentrine. Ensifentrine given either pre- or post exposure attenuated MRSA-induced increased lung endothelial permeability. VE-cadherin junctions, which serve to stabilize the EC barrier, were disrupted by MRSA; however, ensifentrine effectively prevented this disruption. Pre-treatment with ensifentrine protected against MRSA-induced EC pro-inflammatory signaling by inhibiting the expression of VCAM-1, ICAM-1, and by reducing the IL-6 release. In AEC, MRSA caused upregulation of ICAM-1, activation of NF-kB and production of IL-8, all of which were inhibited by ensifentrine. These results indicate that dual inhibition of phosphodiesterases 3 and 4 by ensifentrine is barrier protective and attenuates MRSA-induced inflammation in both lung endothelial and epithelial cells. The PDE3/4 inhibitor ensifentrine may represent a promising novel strategy for the treatment of ALI.

This study examines change detection techniques in dense point clouds for the purpose of Cultural Heritage preservation, with a particular focus on the San Pietro Barisano Bell Tower in Matera, Italy. Dense point clouds, obtained via advanced laser scanning, offer detailed 3D representations of heritage structures, facilitating the precise monitoring changes over time. The investigation uses a variety of change detection algorithms, including the Iterative Closest Point (ICP) algorithm, which is renowned for its robust registration capabilities in aligning point clouds with high accuracy. The combination of ICP with deviation analysis and feature-based methods allows for the effective identification of alterations, including deformations, material loss, and surface degradation. This methodology establishes a comprehensive framework for the monitoring of Cultural Heritage, thereby enabling timely and targeted preservation efforts. The results emphasise the substantial contribution of dense point cloud analysis to the enhancement of heritage management and the safeguarding of vulnerable architectural sites.

The manuscript deals with a new two-parameter unit stochastic distribution, obtained by transforming the Gumbel distribution, using generalized logistic mapping, into a unit interval. The distribution thus obtained is called the Gumbel–Logistic Unit (abbr. GLU) distribution, and its key stochastic properties have been investigated in detail. Among others, it is shown that the GLU distribution, unlike the Gumbel one which is always positively asymmetric, can take both asymmetric forms. Also, the procedure for estimating parameters based on quantiles, along with the asymptotic properties of the obtained estimators and the study of their numerical simulation, is presented. Finally, the application of the GLU distribution in modeling some real–world data related to telecommunications is considered.

Photocatalysis is a promising method for methylene blue (MB) degradation due to its effectiveness and environmental compatibility. Among photocatalysts, titanium dioxide (TiO2) has been widely used for MB degradation due to its exceptional photocatalytic activity. However, the wide bandgap limits the degradation efficiency of TiO2 under visible light. Herein, an interstitial nitrogen-doped TiO2 (5%NT/TiO2) used thiourea as the N source is fabricated for the visible light-derived MB degradation. 5%NT/TiO2 exhibits an extended visible light absorption range. Besides, photoelectrochemical measurement shows an improvement in photocurrent response and charge transfer behavior on N/TiO2. Thus, 5%NT/TiO2 has enhanced photocatalytic activity compared to pristine TiO2 and substitutive N-doped TiO2 (5%NAB/TiO2). The accelerated photocatalytic MB degradation process on N/TiO2 should be mainly attributed to the interstitial N doping, which causes the appearance of new energy states and extended optical properties.

Electronic waste generation has been following a continuously rising trend. With electronics containing a myriad of materials some of which are hazardous, toxic, extremely rare or precious combined with more stringent legislative laws which encourage the reuse and optimised recycling of the materials included in e-waste, the need for a holistic approach is inevitable. Such an approach should not only identify the materials found in the various types of e-waste and their respective quantities but also needs to highlight the possible handling techniques and their respective impacts and associated hazards whether to the environment or human health. Performing such a task manually would be exasperating and costly while requiring extensive resources which might not be met with a justifiable economic gain; thus, the use of the advancement in computational sustainability to draw a complete picture and aid in an informed decision-making process is crucial. The e-waste ontology developed in this paper is one way of addressing the problem since it covers the treatment domain, the hazard and the materials contained within the different types of e-waste. The ontology developed can be used directly from the protégé software or by embedding it into a developed Decision Support System.

Background: Tumor mutational burden (TMB) is increasingly recognized as a vital biomarker for predicting the efficacy of immune checkpoint inhibitors (ICIs) in cancer treatment. Despite its growing importance, the effectiveness of TMB as a predictive marker are well established in lung cancer and melanoma but remain ambiguous for breast and prostate cancers. Objective: This study aims to evaluate the role of TMB in predicting response to ICIs across four major cancer types—lung, melanoma, breast, and prostate—and to address the variability in TMB's predictive value in different cancers. Methods: A comprehensive review of the current literature was performed, analysing studies that investigated TMB and its association with ICI therapy outcomes in the specified cancer types. Results: The analysis reveals a strong consensus on the predictive value of TMB in lung cancer and melanoma, where high TMB levels are associated with improved clinical outcomes and better responses to ICIs. In contrast, the evidence for breast and prostate cancers is less conclusive, with variability in results highlighting the need for further research. Specifically, high TMB in these cancers does not consistently predict better responses to ICIs, suggesting that additional biomarkers or refined criteria might be necessary. Conclusion: TMB is a promising biomarker for predicting responses to ICI therapy, particularly in lung cancer and melanoma. However, its predictive value in breast and prostate cancers remains uncertain, underscoring the need for more extensive studies. Future research should focus on standardizing TMB evaluation methods and exploring additional biomarkers to improve treatment personalization and outcomes in these cancer types.

Inflammation is implicated in the etiology of obesity-related diseases. Thromboxane-prostanoid receptor (TPR) is known to play a role in mediating an inflammatory response in a variety of cells. Gut-derived lipopolysaccharide (LPS), a TLR4 agonist, is elevated in obesity. Moreover, free fatty acids (FFAs) are important mediators of obesity-related inflammation. However, the role and mechanisms by which TPR regulates the inflammatory response in human immune cells remain unclear. We sought to determine the link between TPR and obesity and the role/mechanisms by which TPR alters LPS or stearic acid (SA)-induced inflammatory response in PBMCs. Cells were pre-treated with agents blocking TPR signaling, followed by treatment with LPS or stearic acid (SA). Our findings showed that TPR mRNA levels are higher in PBMCs from individuals with obesity. Blockade of TPR as well as ROCK which acts downstream of TPR, attenuated LPS and/or SA-induced pro-inflammatory response. On the other hand, TPR activation using its agonist enhanced the pro-inflammatory effects of LPS and/or SA. Of note, TPR agonist by itself elicits an inflammatory response which was attenuated by blocking TPR or ROCK. Our data suggest that TPR plays a key role in promoting an inflammatory response in human PBMCs and this effect is mediated via TLR4 and/or ROCK signaling.

Alzheimer's disease (AD) is a multifaceted neurodegenerative disorder characterized by cognitive decline and neuronal loss, representing a most challenging health issue. We present a computational analysis of transcriptomic data of AD tissues vs. healthy controls, focused on elucidation of functional roles played by long non-coding RNAs (lncRNAs) throughout the AD progression. We first assembled our own lncRNA transcripts from the raw RNA-Seq data generated from 527 samples of dorsolateral prefrontal cortex, resulting in the identification of 31,574 novel lncRNA genes. Based on co-expression analyses between mRNAs and lncRNAs, a co-expression network is constructed. Maximal subnetworks with dense connections are identified as functional clusters. Pathway enrichment analyses are conducted over mRNAs and lncRNAs in each cluster, which serve as the basis for the inference of functional roles played by lncRNAs involved in each of the key steps in an AD development model that we have previously build based on transcriptomic data of protein-encoding genes. Detailed information is presented about the functional roles by lncRNAs in activities related to stress response, reprogrammed metabolism, cell-polarity, and development. Our analyses have also revealed that lncRNAs have discerning power in distinguishing between AD samples of each stage and healthy controls. This study represents the first of its kind.

The traffic light detection and recognition are crucial for enhancing the security of unmanned systems. This study proposes a YOLOv5-based traffic light detection algorithm to tackle the challenges posed by small targets and complex urban backgrounds. Initially, the mosaic-9 method is employed to enhance the training dataset, thereby boosting the network’s ability to generalize and adapt to real-world scenarios. Furthermore, the network incorporates the Squeezed-and-Excitation (SE) attention mechanism to improve. Moreover, the YOLOv5 algorithm’s loss function has been optimized by substituting it with EIoU_loss, which addresses issues like missed detection and false alarms. Experimental results demonstrate that the model , trained with this enhanced network, achieves a 99.4% mAP on a custom dataset, which is 6.3% higher than the original YOLOv5, while maintaining a detection speed of 74 f/s. Therefore, this algorithm offers higher detection accuracy and effectively meets real-time operational requirements.

In recent years, bioengineered and biotechnological products have gained tremendous attention to identify unique biologic features associated with oncology. To overwhelm the complexity of tumor personalized medicines (PM) or vaccines could be a promising way-out to be considered. Cancer is a heterogeneous disease. This characteristic variability of cancer provides itself to the emergent era of PM i.e., precision oncology. The prime objective of PM is merely to deliver the right treatment to the right patient at the right time. For instance, imatinib was effectively targeted chronic myeloid leukemia. To date, attainment in using PM approaches to cancer treatment have been mixed. The next-generation sequencing (NGS), and the use of several biomarkers (e.g., HER2, BRAF, ALK, etc.), to drive precise cancer treatment through a timely and cost-effective manner. In current review, author summarizes the advancement in the field of PM such as CRISPR-Cas9, NGS, pharmacogenomics, and the benefits of machine learning techniques. The precise communication between healthcare professionals, regulatory bodies, and patients is essential to translate advances in PM into immense benefits for patients with cancer globally.

Energy harvesting is an emerging topic recently. This harvested energy is used to operate wireless sensors for different industrial monitoring and automation. To maximize the harvested energy in spinning systems, the harvester should vibrate at resonance for different spinning frequencies which can be achieved by active fitting. In this research, a novel methodology was developed by actively customizing the length of vibrating element beam to manipulate its natural frequency according to the spinning frequency. Different models were investigated numerically and experimentally to verify the proposed methodology. COMSOL Multiphysics software V5.1 was used to develop the numerical model, then and tip deformation and voltage difference results were benchmarked. The numerical model was verified experimentally by attaching a piezoelectric MIDE PPA 1021 which its vibrating length was varied by changing the supporting length from 0 mm to 12 mm to a spinning object having variable frequency from 0-200 Hz. It is found that the MIDE PPA 1021 beam vibrated at its resonance throughout the specified spinning frequencies. The proposed harvester can be applied in battery-free sensors being used in automotives, wind-mills blades, and rotating machinery.

We analyze a time-delayed SIQR model that considers the transportation-related infections and entry-exit screening. This model aims to determine the measures for preventing and controlling major emergent infectious diseases and the associated costs. We calculate the basic reproduction number (R0), apply the linearization method, and construct an appropriate Lyapunov function to determine the stability of the disease-free equilibrium in the local and global. We collect COVID-19 infection data from two regions in the United States in 2020 for data fitting, obtain a set of optimal parameter values, and show that the model is suitable for simulating the outbreak and spread of emerging infectious diseases. We find that transportation-related infections increase the basic reproduction number, enhancing the impact on disease spread. Entry-exit screening effectively suppress the spread of disease by reducing the basic reproduction number. Furthermore, we investigate the influence of the incubation period on disease and find that a shorter incubation period results in a shorter duration but a larger scale of infection, and the peaks are reduced. We conduct a sensitivity analysis of the R0 and propose three measures to prevent the spread of new infectious diseases based on the most sensitive parameters: wearing masks, implementing urban closures, and administering medication to sick but not yet hospitalized patients promptly. In the case of COVID-19, we use Pontryagin’s minimum principle to get time-varying control measures to control the spread of the outbreak. Optimal control effectively controls the development and deterioration of the disease. Finally, several control measures are compared through cost-effectiveness analysis, and the results show that wearing masks is the most cost-effective measure. Our study provides some practical guidance for controlling the spread of major emerging infectious diseases.

A 2-month incubation study was carried out using two soil types to determine nitrogen mineralization of different inorganic-organic amendments. The following seven treatments (T) were established; T1 = control, T2 = 5 g of dry algae per kg of soil (100%DA), T3 = 136g of agri-mat per kg of soil (100%GA), T4 = 61 g of ground grass per kg of soil (100%GG), T5 = 2.5 g of dry algae + 2.5 g of nitrogen fertilizer (NF) per kg of soil (50%DA50NF), T6 = 50%GA50NF, T7 = 50%GG50NF. Three samples per treatment were drawn at 0, 3, 7, 15, 30, 45, and 60 days for mineral-N determination. A 2-month glasshouse experiment was established afterwards with the following five treatments: T1 = Control, T2 = 50%DA, T3 = 50%GA, T4 = 50%GG, and T5 = 100NF. The results indicated that nitrogen mineralization was significantly higher in organic-inorganic amendments compared to sole organic amendments. The percentage differences ranged from 157% to 195%. The 50%DA treatment increased spinach yield by 20.6% in sandy loam and 36.5% in loam soil. It is difficult to fully recommend the 50%DA treatment without field-scale evaluation, but it is a promising option to be considered.

CAMx-LPiG (Comprehensive Air Quality Model with Extensions – Linear Plume in Grid) is an online hybrid model, based on the Chemistry and Transport Model (CTM) CAMx, that includes a sub-grid scale module to simulate the dispersion of linear road traffic emissions, called LPiG. LPiG is a plume in grid module specifically developed by extending the capabilities of the Lagrangian puff sub-grid model available in CAMx. The online integration of the local scale model within the Eulerian CTM allows for a multiscale simulation of air quality from the regional scale to the urban scale, preserving a coherent description of the chemical state of the atmosphere at all spatial scales and avoiding any double counting of the emissions simulated by the sub-grid module. In this work the model is presented and evaluated against measured NO2 concentrations for the city of Milan for the month of January 2017. The model is able to introduce road traffic induced gradient in NO2 concentration at sub-grid resolution. Moreover, CAMx-LPiG is shown to reduce the bias compared to CAMx stand-alone simulations.

Researchers are exploring solutions to meet the growing demand for protein due to the expected increase in global population by 2050. Interest in alternative protein sources like insects has risen, driven by concerns about environmental impact and the need for sustainable food production. This study aimed to develop and evaluate the physicochemical properties of soy protein-based burger enriched with insect protein from Alphitobius diaperinus. Three formulations were developed: a control (B0), and burgers with 5% (B5) and 10% (B10) insect protein - Whole Buffalo Powder (WBP). Results showed that adding insect protein decreased the burger analogue's pH. There was observed a clear trend of increasing total lipids and SFA, and decreasing MUFA and PUFA, as the WBP concentration increases from 0% to 10%. No significant differences with increasing WBP concentration in the protein content of the burger analogue, as well as the cooking yield, were noted. The WBP addition had a notable effect on the color change, especially decrease in brightness (L*). It was shown that as the WBP concentration increased, there were no significant differences in the texture profile of the burger analogues. The formulation with 5% WBP concentration was the most acceptable in sensory analysis.

The recyclable paper based on photochromic materials not only reduces the pollution in the paper manufacture process, but also reduces the pollution caused by the use of ink, which receives wide attention. In this paper, a serial of PMoA-PWA/ZnO/PVP hybrid films which had different ratio of PMoA/PWA was prepared by the ultrasonic composite method. The results indicated that the hybrid film prepared when the ratio of PMoA to PWA was 3 had the best photochromic performance. In this system, ZnO was the photosensitizer while PMoA/PWA was the chromophore. The photochromic mechanism of PMoA-PWA/ZnO/PVP hybrid film was based on the photogenerated electron transfer mechanism. ZnO generated photoelectron under the excitation of visible light, then PMoA and PWA obtained the photoelectron and produced photoreduction reaction to generate heteropolyblue. The visible light photochromic paper was prepared by loaded PMoA-PWA/ZnO/PVP hybrid film (A3) on A4 paper. Application test showed that the prepared paper had extremely stable, excellent and reversible visible light photochromic properties, whether it was printing patterns or words and could replace the ordinary paper to realize the reuse of paper.

Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. In contemporary times, the transition from conventional electricity grids to Smart Grids constitutes an area where extensive research is conducted on a global scale. Among the research challenges, the investigation of Deep Transfer Learning (DTL) in the field of electricity load forecasting represents a fundamental effort that imparts generality to Artificial Intelligence applications, due to new capabilities, such as knowledge transfer and computational power reduction. In this paper a comprehensive study is conducted for day-ahead electricity load forecasting. For this purpose, three Sequence-to-Sequence (Seq2seq) Deep Learning (DL) models are used, namely the Multilayer Perceptron (MLP), the Convolutional Neural Network (CNN) and the Ensemble Learning Model (ELM), which is consisted of the weighted combination of the outputs of MLP and CNN models. Also, the study focuses on the development of different forecasting strategies based on DTL and emphasizing the way the datasets are trained and fine-tuning for higher forecasting accuracy. In order to implement the forecasting strategies using Deep Learning models, load datasets from three Greek islands, Rhodes, Lesvos, and Chios, are used. The main purpose is to apply DTL for day-ahead predictions (1-24 hours) for each month of the year for Chios dataset after training and fine-tuning the models using the datasets of the three islands in various combinations. After several trials, four DTL strategies are illustrated. In the first strategy (DTL Case 1), each of the three DL models is trained using only Lesvos dataset, while fine-tuning is performed on the dataset of Chios island, in order to create day-ahead predictions for Chios load. In the second strategy (DTL Case 2), data from both Lesvos and Rhodes concurrently are used for the DL model training period, and fine-tuning is performed on the data from Chios. The third DTL strategy (DTL Case 3) contains the training of the DL models using Lesvos dataset, and the testing period performed directly on the Chios dataset without fine-tuning. The fourth strategy is a Multi-task Deep Learning (MTDL) approach, which has been extensively studied in recent years. In MTDL, the three DL models are trained simultaneously on all three datasets and the final predictions are made on the unknown part of the dataset of Chios. In this paper, we explore the performance of DTL and compare the results with those produced with MTDL. The results demonstrated that DTL can be applied with high efficiency for day-ahead load forecasting. Specifically, the two cases with fine-tuning (DTL Case 1 and 2) outperformed MTDL in terms of load prediction accuracy. Regarding the DL models, all three exhibit very high prediction accuracy, especially in the two cases with fine-tuning. The ELM excels compared to the single models. More specifically, for conducting day-ahead predictions, it has been concluded that the MLP model presents best monthly forecasts with a MAPE of 6.24% and 6.01% for the first two cases, the CNN model presents best monthly forecasts with a MAPE of 5.57% and 5.60% respectively and the ELM model achieves best monthly forecasts with a MAPE of 5.29% and 5.31%, respectively, indicating the very high accuracy it can achieve.

Much research has been conducted to determine how hair regeneration is regulated, as this could provide therapeutic, cosmetic, and even psychological interventions for hair loss. The current study focused on the hair growth effect and effective utilization of fatty oil obtained from Bryde's whales through high-throughput DNA microarray approach in conjunction with immunohistochemical observations. The research also examined the mechanisms and factors involved in hair growth. In an experiment using female C57BL/6J mice, the vehicle control group (VC: propylene glycol: ethanol: water), the positive control group (MXD: 3% minoxidil), and the experimental group (WO: 20% Whale Oil) were topically applied to the back of the mouse. The results showed that 3% MXD and 20% WO were more effective than VC in promoting hair growth, especially 20% WO. Furthermore, in hematoxylin and eosin-stained skin tissue, an increase in the number of hair follicles and subcutaneous tissue thickness was observed with 20% WO. Whole-genome transcriptomic analysis also showed that 20% WO may have lower stress- and inflammation-related responses than 3% MXD. Therefore, whale oil can be expected to be used as a safe hair growth agent.

The search of DNA polymorphisms useful for the genetic improvement of dairy farm animals lasts more than 40 years with relevant findings in cattle for milk traits, where the best combination of alleles for dairy processing have been found in casein genes and in the DGAT1. Nowadays, similar results are not reached yet in river buffaloes despite advanced genomic technologies and accurate phenotype records are available. The aim of the present study was to investigate and validate in a larger buffalo population the effect on six milk traits of four single nucleotide polymorphisms (SNP) in the CSN1S1, CSN3, SCD and LPL genes, previously reported to be associated with or affect dairy traits in smaller populations often belonging to one farm. A total of 800 buffaloes were genotyped. Daily milk yield (dMY, kg), protein yield (dPY, kg) and fat yields (dFY, kg), fat and protein contents (dFP, % and dPP, %), somatic cell count (SCC, 103cell/ml) and urea (mg/dl) were individually recorded in each month for the whole lactation from 2010 to 2021. A total of 15,742 individual milk test day records (2,496 lactations) were available on 680 buffalo cows with 3.6±1.7 parity (from 1 to 13) and 6.1±1.2 test day records per lactation on average. Three out four SNP in CSN1S1, CSN3 and LPL were associated with at least one of analyzed traits. In particular, the CSN1S1 (AJ005430:c.578C&gt;T) gave favorable associations with all yield traits (dMY, p=0.022; dPY, p=0.014 ; dFY, p=0.029) and SCS (p=0.032), whereas the CSN3 (HQ677596: c.536C&gt;T) positively associated with SCS (p=0.005) and milk urea (p=0.04). Favorable effect on dMY (p=0.028), dFP (p=0.027) and dPP (p=0.050) were observed for the LPL. Conversely, SCD did not show any association with milk traits. This is the first example of confirmation study carried out in the Mediterranean river buffalo for genes of economic interest in dairy field and it represents a very important indication for the preselection of young bulls destined to breeding programs in the view of a more sustainable dairy production.

Background: Microscopic examination of acid-fast stained sputum smears for detecting mycobacterial acid-fast bacilli (AFB) remains the most economical and readily available method for laboratory diagnosis of tuberculosis (TB). However, this conventional approach has limitations, including low sensitivity and labor-intensive procedures. Methods: An automated microscopy system incorporating artificial intelligence (AI) and machine learning for AFB identification was evaluated. The study was conducted at an Infectious Disease Hospital in Jiangsu Province, China, utilizing an intelligent microscope system (TB-Scan, Wellgen Medical, Kaohsiung). A total of 1,000 sputum smears were included in the analysis, with the system capturing digital microscopic images and employing an image recognition model to automatically identify and classify AFB. Referee technicians provided the gold standard for discrepant results. Results: The automated system demonstrated an overall accuracy of 95.00% (950/1,000), sensitivity of 91.24% (177/194), and specificity of 95.91% (773/806). Notably, the system identified 21 smears as positive that were previously reported as negative, with referee technicians confirming 17 of these as true positive and retracting the test results accordingly. Recalculating the performance, the accuracy increased to 96.70% (967/1,000), sensitivity to 91.94% (194/211), and specificity to 97.97% (773/789), with a false negative rate of 8.06% (17/211) and a false positive rate of 2.03% (16/789). Conclusions: The incorporation of AI and machine learning into an automated microscopy system demonstrated the potential to enhance the sensitivity and efficiency of AFB detection in sputum smears compared to conventional manual microscopy. This approach holds promise for widespread application in TB diagnostics and potentially other fields requiring labor-intensive microscopic examination.

Melioidosis is an endemic infectious disease caused by Burkholderia pseudomallei bacteria, which contaminates soil and water. To better understand the environmental changes that have contributed to melioidosis outbreaks, this study used spatiotemporal analyses to clarify the distribution pattern of melioidosis and the relationship between melioidosis morbidity rate and local environmental indicators (land surface temperature, normalised difference vegetation index, normalised difference water index) and rainfall. A retrospective study was conducted from January 2013 to December 2022, covering data from 219 sub-districts, with each exhibiting varying morbidity rate of melioidosis on a monthly basis. Spatial autocorrelation was determined using local Moran’s I, and the relationship between melioidosis morbidity rate and the environmental indicators was evaluated using geographically weighted Poisson regression. The results revealed clustered spatiotemporal patterns of melioidosis morbidity rate across sub-districts, with hotspots predominantly observed in the northern region. Furthermore, we observed a range of coefficients for the environmental indicators, varying from negative to positive, which provided insights into their relative contributions to melioidosis in each local area and month. These findings highlight the presence of spatial heterogeneity driven by environmental indicators and underscore the importance of public health offices implementing targeted monitoring and surveillance strategies for melioidosis in different locations.

This research presents a comprehensive framework for efficiently generating forest fire datasets from Google Earth Engine data sources. The primary contribution of this work lies in providing a methodology to swiftly extract forest fire factors without the need for permissions or access to private datasets, rendering the dataset openly accessible and shared without barriers. Furthermore, given that the remote sensing data used is a global dataset, it can be applied in any region without restrictions. In this study, Peninsular Malaysia is chosen as a case study to demonstrate the framework's effectiveness. The generated dataset includes essential variables including the climate and environment, landcover, topography, and anthropogenic factors facilitating the analysis of fire occurrences. The methodology empowers data scientists, enabling them to leverage their analytical skills on the extracted dataset without requiring specialized remote sensing knowledge. Additionally, this study also showcases the adoption of large language models, specifically GPT-4 with the Noteable plugin, as a tool for conducting preliminary analyses on the generated dataset. Sample analyses reveal that several key features, including the KBDI, LST, PDSI, climate water deficit, and precipitation, significantly impact forest fire occurrences in Peninsular Malaysia. Despite the successful application of the GPT-4 with Noteable plugin, certain limitations and challenges are identified, highlighting the necessity for further validation of the tool's applicability and limitations. This study encourages future research to (1) adopt the proposed framework in other regions, (2) explore more detailed analyses encompassing all variables, and (3) leverage machine learning for advanced forecasting.

This Systematic Review examines studies focusing on tooth bleaching and its effect on healthy enamel or incipient caries and bacterial adhesion. The aim is to explore the impact of different bleaching agents on incipient caries lesions and healthy enamel. Randomized controlled trials, clinical trials, observational studies, and in vitro studies that compared at least two groups were included. A comprehensive search strategy was used to select studies from Pubmed and Scopus databases. Two evaluators performed data extraction, screening, and quality assessment independently. Only studies written in English were included. From 968 initial records, 28 studies were selected for a full-text evaluation. 7 studies were classified as cluster 1 (bacterial adherence on teeth), 12 studies as cluster 2 (no bacteria involved), 4 studies as cluster 3 (no teeth deployment), and 5 clinical studies were cluster 4. Of the selected studies, 6 (21.43%) supported increased bacterial attachment capacity and cariogenic dynamics, 4 (14.29%) decreased adhesion and cariogenic activity, 7 (25%) no difference, and 11 (39.28%) followed a differentiated methodological approach and could not be categorized. Due to different methodology strategies used in the studies of all clusters, the risk of bias was high. Thus, we cannot reach a confident conclusion.

Lactoperoxidase system (LPS) effect on quality of raw and pasteurized milk under real conditions along the dairy value chain in Ethiopia was evaluated. A total of 250 milk samples were collected from farmers, collectors, and factories. Experimental samples were LPS-activated of morning and overnight milk. The samples were tested with several chemical and microbiological tests such as total bacterial count (TBC), total coliform count (TCC) and Escherichia coli count (EC). Results indicated that all LPS-activated milk samples had a higher quality than all the control samples. For instance, both the morning and overnight farm milk samples had a mean TBCs of 5.79 log and 6.55 log cfu/ml which is significantly (p