This research elucidates the dynamic expression of expansin genes during the wheat grain (Triticum aestivum L.) development process using comprehensive meta-analysis and experimental validation. We leveraged RNA-seq data from multiple public databases, applying stringent criteria for selection, and identified 60,852 differentially expressed genes across developmental stages. From this pool, 28,558 DEGs were found to exhibit significant temporal regulation in at least two different datasets, and were enriched for processes integral to grain development such as carbohydrate metabolism and cell wall organization. Notably, 30% of the 241 known expansin genes showed differential expression during grain growth. Hierarchical clustering and expression level analysis revealed temporal regulation and distinct contributions of expansin subfamilies during the early stages of grain development. Further analysis using co-expression networks underscored the significance of expansin genes, revealing their substantial co-expression with genes involved in cell wall modification. Finally, qPCR validation and grain morphological analysis under field conditions indicated a significant negative correlation between expression of select expansin genes and grain size and weight. This study illuminates the potential role of expansin genes in wheat grain development and provides new avenues for targeted genetic improvements in wheat.

As a fundamental optical approach to interferometry, Sagnac interference has been widely used for reflection manipulation, precision measurements, and spectral engineering in optical systems. Compared to other interferometry configurations, it offers attractive advantages by yielding a reduced system complexity without the need for phase control between different pathways, thus offering a high degree of stability against external disturbance and a low wavelength dependence. The advance of integration fabrication techniques has enabled chip-scale Sagnac interferometers with greatly reduced footprint and improved scalability compared to more conventional approaches implemented by spatial light or optical fiber devices. This facilitates a variety of integrated photonic devices with bidirectional light propagation, showing new features and capabilities compared to unidirectional-light-propagation devices such as Mach-Zehnder interferometers (MZIs) and ring resonators (RRs). Here, we present our latest results for functional integrated photonic devices based on Sagnac interference. We outline the theory of integrated Sagnac interference devices with comparisons to other integrated photonic building blocks such as MZIs, RRs, photonic crystal cavities, and Bragg gratings. We present our latest results for Sagnac interference devices realized in integrated photonic chips, including reflection mirrors, optical gyroscopes, basic filters, wavelength (de)interleavers, and optical analogues of quantum physics.

The location and number data of individual fruit trees are critical for planting area investigation, fruit yield prediction, and smart orchard management and planning. These data are conventionally obtained through manual investigation and statistics with time-consuming and laborious effort. Object detection models in deep learning used widely in computer vision could provide an opportunity for accurate detection of individual fruit trees, which is essential for rapidly obtaining the data and reducing human operations errors. This study proposes an approach to detecting individual fruit trees and mapping their spatial distribution by integrating deep learning with unmanned aerial vehicle (UAV) remote sensing. UAV remote sensing collected high-resolution true-color images of fruit trees in the experimental pomelo tree orchards in Meizhou city, South China. An image dataset of deep learning samples of individual pomelo trees (IPTs) was constructed through visual interpretation and field investigation based on the fruit tree images captured by UAV remote sensing. Four different scales of YOLOv5 (YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x) for object detection were selected to train, validate, and test on the image dataset of pomelo trees. The results show that the average precision (AP@0.5) of the four YOLOv5 models for validation reach 87.8%, 88.5%, 89.1%, and 90.7%, respectively. The larger the model scale, the higher the average accuracy of the detection result of validation. It suggests that YOLOv5x is a preferred high-accuracy model among the YOLOv5 family and is suitable to realize the detection of IPTs. The number of the IPTs in the study area was counted using YOLOv5x, and their spatial distribution map was made using the non-maximum suppression method and ArcGIS software. This study will provide primary data and technical support for smart orchard management in Meizhou city and other fruit-producing areas.

A single farmed fish species assimilates about 20% of the nutrients in the supplied diet. This study evaluated if the culture of complementary ecological-function species can recover nutrients dispersed into the water and transform them into high-valued biomass. A completely randomized experiment was designed with three treatments and four replications of each production system: monoculture of lambari (Astyanax lacustris); integrated aquaculture of lambari and Amazon river prawn (Macrobrachium amazonicum); and integrated aquaculture of lambari, Amazon river prawn, and curimbatá (Prochilodus lineatus). Fingerlings of lambari (0.8 ± 0.8 g) were stocked in twelve earthen-ponds (0.015 ha) at the density of 50 fish m^-2. Eight ponds, were stocked with juveniles of Amazon river prawn (1.1 ± 0.2 g) at the density of 25 prawn m⁻². Four of these eight ponds were stocked with curimbatá fingerlings (0.2 ± 0.1 g) at a density of 13 fish m^-². Only lambari was fed twice a day with an extruded commercial diet. The experiment lasted 60 days when lambari attained commercial size. The inclusion of prawn increased the total species yield from 1.8 to 2.4 t ha^-1 cycle^-1 and reduced the feed conversion ratio (FCR) from 2.5 to 1.8. The inclusion of prawn and curimbatá increased the total yield to 3.2 t ha^-1 cycle^-1 and reduced the FCR to 1.4. Therefore, the integrated culture of lambari, prawn, and curimbatá improves the use of space, water, feed, and benthic species can recover the large quantity of nutrients accumulated in the bottom of lambari pond production, converting them into high-nutritional and monetary-valued biomass.

The present work proposed a low-cost portable device as an enabling technology for Smart Farms using multispectral imaging and Machine Learning in soil texture. Clay is an important factor for the verification and monitoring of soil use due to its fast reaction to chemical and surface changes. The system developed uses the analysis of reflectance in wavebands for clay prediction. The selection of each wavelength is performed through an LED lamp panel. A NoIR microcamera controlled by a Raspberry Pi device is employed to acquire the image and unfold it in RGB histograms. Results showed an good prediction performance with R2 of 0.96, RMSEC of 3.66% and RMSECV of 16.87%. The high portability allows the equipment to be used in a field providing strategic information related to soil sciences.

Supplier selection is one of the most important multi criteria decision making (MCDM) problems for decision makers in competitive market. Organizations of today’s world are seeking new ways to reduce negative effects of their organizations to the environment and to reach a greener system. At this point, green supplier selection concept has gained great importance with its ability on incorporating environmental or green criteria into the classical supplier selection practices. Therefore, in this study, it is aimed at proposing a multi-phase MCDM model based on Best-Worst Method (BWM) and interval type-2 fuzzy technique for order preference by similarity to ideal solution (TOPSIS). A case study in a plastic injection molding facility in Turkey is performed to show the applicability of the proposed integrated methodology. The paper ensures insights into the decision making, methodology, and managerial implications. Results of the case study are examined and suggestions for future research are provided.

The Greenland ice sheet is a major contributor to sea level rise, adding an estimated 0.47 +/− 0.23 mm/yr to global mean sea level between 1991 and 2015 (van den Broeke et al., 2016). Making sea level rise projections for the future and understanding the processes controlling current observed rates of sea level rise are crucially dependent on understanding the present-day state of the ice sheet. Here, we provide an overview of the current state of the mass budget of Greenland based on satellite gravimetry and remote sensing observations of surface elevation change, ice sheet velocity and calving front positions. We also combine these essential climate variables with a regional climate model (RCM) output from an ice sheet model (ISM) to gain insight into poorly understood ice sheet dynamical and surface mass processes. On average from 1992 to 2017 the ice sheet in some locations has lost up −2.65 m/yr in elevation based on ESA Radar altimetry analysis. Calving fronts have retreated all around Greenland since the 1990s and in only two out of 28 study locations have they remained stable. The locations of grounding lines at 5 key glaciers with floating ice tongues have remained stable over the observation period. However a detailed case study at Petermann glacier with an ice fracture model shows the sensitivity of these floating ice shelves to future climate change. GRACE gravimetric mass balance (GMB) data allows us to tie together disparate lines of evidence showing that Greenland has lost about 265 +/− 25 Gt/yr of ice over the period 2002 to 2015. RCM and ISM simulations show that surface mass processes dominate the overall Greenland ice sheet mass budget except for areas of fast ice sheet flow but marked differences between models and between models and observations indicate that not all processes are captured accurately, indicating areas of greater uncertainty and directions of future research for future sea level rise projections.

This paper focuses on the integrated guidance and autopilot design with control input saturation in the end-game phase of hypersonic flight. Firstly, uncertain nonlinear integrated guidance and autopilot model is developed with third actuator dynamics, where the control surface deflection has magnitude constraint. Secondly, neural network is implemented in extended state observer (ESO) design, which is used to estimate the complex model uncertainty, nonlinearity and state coupling. Thirdly, a command filtered back-stepping controller is designed with hybrid sliding surfaces to improve the terminal performance. In the process, different command filters are implemented to avoid the influences of disturbances and repetitive derivation, meanwhile solve the problem of unknown control direction caused by saturation. The stability of closed-loop system is proved by Lyapunov theory, and the principles abided by the controller parameters are concluded through the proof. Finally, series of 6-DOF numerical simulations are presented to show the feasibility and validity of the proposed controller.

Carbon nanotube (CNT) yarns are fiber-like materials that exhibit excellent mechanical, electrical and thermal properties. More importantly, they exhibit a piezoresistive response that can be tapped for sensing purposes. The objective of this study is to determine experimentally the piezoresistive response of CNT yarns that are embedded in a polymeric medium while subjected to either compression or tension, and compare it with that of the free or unconstrained CNT yarns. The rationale for this study is the need to know the response of the CNT yarn while in a medium, which provides a lateral constraint to the CNT yarn thus mimicking the response of integrated CNT yarn sensors. The experimental program will include the fabrication of samples and their electromechanical characterization. The CNT yarns are integrated in polymeric beams and subjected to four-point bending, allowing the determination of their response under tension and compression. The electrical resistance data from an Inductance-Capacitance-Resistance (LCR) device is used with the data acquired from the mechanical testing system to determine the piezoresistive response of the CNT yarns. This data and information will be used for future modeling efforts and to study the phenomena that occur when CNT yarns are integrated in polymeric and composite materials and structures.

The present exploratory study tested the hypothesis that computerized cognitive training (CCT) in home telemonitoring may beneficially affect eyes-closed resting-state electroencephalographic (rsEEG) rhythms in Parkinson's disease patients with cognitive deficits (PDCD). A Eurasian database provided clinical-demographic-rsEEG datasets in 40 PDCD patients, 29 PD patients without cognitive deficits (PDNCD), 40 Alzheimer’s disease patients with cognitive deficits (ADCD), and 40 cognitively normal older adults (Healthy). Sixteen of the 40 PDCD patients completed a cross-over unsupervised CCT program of simple, serious video games (versus a sham program) consisting of 14 daily sessions of approximately 20 minutes each in the patients' homes. Compared to the Healthy, PDNCD, and ADCD groups, the PDCD group was characterized by greater diffuse rsEEG delta (about 2-4 Hz) and theta (about 4-7 Hz) source activity. The PDCD patients who underwent the CCT program showed an improvement in video game performance and a reduction in these delta-theta source activities after this program compared to the control condition. In conclusion, these results suggest that the 2-week CCT program in home telemonitoring may mitigate the abnormal “slowing” of rsEEG rhythms in PDCD patients, possibly enhancing the regulation of brain arousal and quiet vigilance.

The COVID-19 pandemic has had a substantial impact on global health, notably the virus's association with autoimmune illnesses. Autoimmune illnesses occur when the immune system of the body targets healthy cells, causing damage and inflammation. The potential impact of COVID-19 on autoimmune disorders, the hazards of COVID-19 vaccinations for persons with autoimmune diseases, and the potential risks of COVID-19 vaccines are all discussed in this essay. COVID-19 can cause autoimmune illnesses by causing inflammation, organ and tissue damage, and molecular mimicry, in which the immune system mistakenly assaults healthy cells for the virus. However, not everyone infected with COVID-19 develops autoimmune disorders. While some research has found a link between COVID-19 and autoimmune illnesses, this does not always suggest causation. Other factors, such as genetics and environment, may also play a role in the emergence of autoimmune illnesses. COVID-19 can aggravate existing autoimmune disorders by causing the immune system to attack healthy cells, making management more difficult and increasing the risk of consequences. People with autoimmune disorders, on the other hand, may not be at a higher risk of severe COVID-19 than the general population. Some research has found that persons with autoimmune disorders are more likely to have severe COVID-19, whereas others have found no difference. COVID-19 vaccinations may represent dangers to persons with autoimmune illnesses since they contain antigens that can activate the immune system. Furthermore, due to their already overactive immune systems, some vaccines may not perform as well for people with autoimmune disorders, and some people with autoimmune diseases may have severe reactions to immunizations, producing inflammation and other symptoms. COVID-19 may not be any more hazardous to those with autoimmune illnesses than it is to the general population. The connection between COVID-19 and autoimmune disorders is complicated and under investigation. While some research implies that people with autoimmune disorders have a higher risk of severe COVID-19, others demonstrate no difference in death rates between them and the general population. There are also counterarguments that COVID-19 can induce and exacerbate autoimmune disorders, as well as worries about COVID-19 vaccine hazards for people who have autoimmune diseases. More research is required to completely understand the influence of COVID-19 on autoimmune illnesses, as well as the dangers and benefits of vaccinations for these people.

Developmental Coordination Disorder (DCD) is a neurodevelopmental condition characterized by non-progressive central motor impairments. Mild movement disorder features have been observed in DCD. Until now, the etiology of DCD remains unclear. Recent studies suggested a genetic substrate in some patients with DCD, but comprehensive knowledge about associated genes and underlying pathogenetic mechanisms is still lacking. In this study, we first identified genes described in literature in patients with a diagnosis of DCD according to the official diagnostic criteria. Second, we exposed the underlying pathogenetic mechanisms of DCD, by investigating tissue- and temporal gene expression patterns and brain-specific biological mech-anisms. Third, we explored putative shared pathogenetic mechanisms between DCD and frequent movement disorders with a known genetic component, including ataxia, chorea, dystonia, and myoclonus. We identified 12 genes associated with DCD in literature, which are ubiquitously expressed in the central nervous system throughout brain development. These genes are involved in cellular processes, neural signaling and nervous system development. There was a remarkable overlap (62%) in pathogenetic mechanisms between DCD-associated genes and genes linked with movement disorders. Our findings suggest that some patients might have a genetic etiology of DCD, which could be considered part of a pathogenetic movement disorder spectrum.

Capsaicin, a lipophilic, volatile compound, is responsible for the pungent properties of chili peppers. In recent years, a significant increase in investigations into its properties has allowed the production of new formulations and the development of tools with biotechnological, diagnostic, and potential therapeutic applications. In this way, the possible clinical application of such compound is expanding every year. This opinion article aims to provide a synthesis of recent findings regarding the mechanisms by which capsaicin participates in the control of non-communicable diseases such as obesity, diabetes, and dyslipidemia. This opinion review also discusses the recent findings and clinical studies of the use of capsaicin in body weight control.

The way in which a person walks, known as human gait, is a significant indicator of overall health and well-being. Abnormalities in gait can indicate the presence of metabolic disorders, such as diabetes or obesity. However, detecting these disorders can be challenging using traditional methods, which often involve subjective assessments or invasive procedures. In this study, a novel methodology known as Criticality Analysis (CA) was proposed for the detection and monitoring of human gait in people with metabolic disorders taking part in an intervention to increase activity and reduce weight. The CA approach utilised inertial measurement unit gait data, alongside clinical health measures. This allows for the control of nonlinear growth in the system, resulting in lower dimensional, nonlinear, free-scale, stable, controlled, and organised trajectories. These trajectories were then analysed using a Support Vector Machine (SVM) algorithm, which is well-suited for this task due to its ability to handle nonlinear and dynamic data. The combination of the CA approach and the SVM algorithm demonstrated high accuracy and non-invasiveness in detecting metabolic disorders, yielding an average accuracy within the range of 78.2% to 90%. Additionally, the classification technique accuracy, at a group level was observed to reduce during period of the intervention (e.g., from week 2 to week 3) alongside changes in fitness and health, which indicates the potential of using the approach to measure and monitor biological systems. As such, this novel methodology has the potential to be a valuable tool for healthcare professionals in detecting and monitoring metabolic disorders, as well as other unknown diseases associated with the human biological system.

This paper presents a modular approach to generic exergame design that combines custom physical exercises in a meaningful and motivating story. This aims to provide a tool that can be individually tailored and adapted to people with different needs, making it applicable to different diseases and states of disease. The game is based on motion capturing and integrates four example exercises that can be configured via our therapeutic web platform "Blexer-med". To prove the feasibility for a wide range of different users, evaluation tests were performed on 14 patients with various types and degrees of neuromuscular disorders, classified into three groups based on strength and autonomy. Users were free to choose their schedule and frequency. Game scores and three surveys (before, during, and after the intervention) showed similar experiences for all groups, with the most vulnerable having the most fun and satisfaction. The players were motivated by the story and by achieving high scores. The average usage time was 2.5 times per week, 20 minutes per session. Pure exercise time was about half the game time. The concept has proven feasible and forms a reasonable basis for further developments. The full 3D exercise needs further fine-tuning to enhance fun and motivation.

Human gut microbiome is associated with various diseases, including autism spectrum disorders (ASD). Variations of the taxonomical composition in the gut microbiome of children with ASD have been observed repeatedly. However, features and parameters of the CRISPR-Cas systems in the gut microbiome of children with ASD have not been investigated yet. Here we demonstrate such an analysis in comparison with the healthy microbiome. For the identification of CRISPR-Cas systems, we used a combination of the publicly available tools suited for completed genomes with subsequent filtrations. In all considered datasets, the microbiomes of children with ASD contained fewer arrays per Gb of assembly, than the control group, but the arrays included more spacers on average. These patterns were observed systematically in our datasets, although their statistical significance hardly matched the thresholds. CRISPR arrays from the microbiomes of children with ASD differed from the control group neither in the fractions of spacers with protospacers from known genomes, nor in the sets of known bacteriophages providing protospacers. The majority of bacterial protospacers of the gut microbiome systems for both children with ASD and the healthy ones was located in the prophage islands.

Learning disorders (LD) are diagnosed in children whose academic skills of reading, writing or mathematics are impaired and lagged according to their age, schooling and intelligence. Children with LD experience substantial working memory (WM) deficits, even more pronounced if more than one of the academic skills is affected. We compared the task-related EEG power spectral density of children with LD (n= 23), with a control group of children with good academic achievement (n= 22), during the performance of a WM task. sLoreta was used to estimate the current distribution at the sources, and 18 brain regions of interests (ROIs) were chosen with an extended version of the eigenvector centrality mapping technique. In this way, we lessen some drawbacks of the traditional EEG at the sensor space by an analysis at the brain sources level over data-driven selected ROIs. Results: The LD group showed fewer correct responses at the WM task, an overall slower EEG with more theta activity in all ROIs, less upper-alpha power at posterior areas, and less high-frequency beta activity in frontal areas. We explain these EEG patterns in LD children as indices of an inefficient neural resource management related with a delay in the neural development.

Small bowel carcinomas (SBC) are uncommon neoplasms, whose predisposing conditions include hereditary syndromes and immune-mediated intestinal disorders, including coeliac disease (CD) and Crohn’s disease (CrD). Although both CD-associated SBC (CD-SBC) and CrD-associated SBC (CrD-SBC) arise from an inflammatory background, they differ substantially in tumour cell phenotype, frequency of microsatellite instability and nuclear β-catenin expression, as well as in prognosis. For these patients, high tumor-infiltrating lymphocyte density and glandular/medullary histotype represent independent positive prognostic factors. Dysplasia adjacent to SBC is rare and characterized by intestinal phenotype and nuclear β-catenin in CD, while it is frequent and typified by gastro-pancreatobiliary marker expression and preserved membranous β-catenin in CrD. Recent evidence suggests that Epstein-Barr virus-positive dysplasia and SBC, albeit exceptional, do exist and are associated with CrD. In this review we summarize the novel pathological and molecular insights of clinical and therapeutic interest to guide the care of CD-SBC and CrD-SBC.

Background: The aim of this experimental work was to evaluate cheiloscopy potential: to confirm the individual uniqueness of lip prints, to verify the presence of recurrent patterns according to the Suzuki and Tsuchihashi classification and to search for characteristics that can be used in establishing the sex of a subject, with a focus on assessing the method's limitations. Methods: Lip prints were taken from a sample of 172 volunteers using liquid lipstick and transparent adhesive tape and were digitally analyzed. Results: 59 prints (35.3%) were not considered for Suzuki and Tsuchihashi classification due to quality errors and the impossibility of visualizing lip wrinkles in one or more sextants. All the digitized prints revealed themselves to be unique after having been analyzed using superimposition. Qualitatively valid prints indicated a prevalence of Type II in both male and female. A major problem was that the analysis of the same print by different operators often resulted in a different attribution of a Type. Conclusions: The potential of cheiloscopy lies mainly in the matching of prints, given their uniqueness, but the absence of a database greatly limits its use. Additionally, its use for sex identification does not seem to be reliable. It is necessary to establish an acquisition protocol for taking and analyzing prints that could reduce the errors committed by the operator.

Introduction/Aim: Central sensitivity to thyroid hormones refers to the sensitivity of the hypothalamic-pituitary-thyroid (HPT) axis to changes in circulating free thyroxine (fT4). The relationship between fT4 levels and iodine intake is complex. The aim of the present study was to assess central sensitivity to thyroid hormones in pregnancy against iodine intake. Materials & Methods: One thousand one hundred two blood and urine samples were collected from pregnant women (with a mean age+SD of 30.4+4.6 years) during singleton pregnancies; women with known/diagnosed thyroid disease were excluded. Specifically, TSH & fT4 and 24-hour urinary iodine excretion (UI) were measured in each trimester and at two months postpartum. The Thyroid Feedback Quantile-based Index (TFQI) was calculated to assess central sensitivity to thyroid hormones. For the statistical analysis of TFQI by time period, analysis of variance (ANOVA) was used, while Pearson's correlation was used to assess TFQI versus UI. Results: The mean TFQI index ranged from -0.06 (first trimester) to -0.07 (two months postpartum), while the corresponding UI was 141 and 172 μg/L, respectively. The TFQI-UI correlation was negative (Pearson r: -0.325, p: 0.04 and r: -0.399, p: 0.023), for UI values over 250 μg/L, in the first and the third trimester of pregnancy, respectively. Discussion: TFQI is a new index reflecting central sensitivity to thyroid hormones. Lower TFQI indicates higher sensitivity to thyroid hormones. In our sample TFQI was inversely related to iodine intake, in the first trimester of pregnancy (the critical period of organogenesis) and the third trimester (the critical period of fetal growth). Thus, the observed changes in TFQI may reflect different ways of central action of thyroid hormones, according to the phase of pregnancy. The negative association between maternal TFQI (indicative of central sensitivity to thyroid hormone) and urine iodine levels during the third trimester of pregnancy may reflect the combined influence of maternal and fetal thyroid function. Our results are intriguing and have the potential to enhance our comprehension of the changes in the HPT axis’ function via variations in central sensitivity to thyroid hormones and its interplay with nutritional iodine status during pregnancy.

Introduction: People with severe and persistent mental illness (SPMI) present unique challenges in mental healthcare due to the enduring nature and complexity of their conditions. This study evaluated the clinical effectiveness and potential cost benefits of a multidisciplinary community psychosocial rehabilitation team catering to individuals with SPMI in Nova Scotia, Canada. Method: The Canadian billing system of Medical Service Insurance (MSI) was used to compare inpatient service use and emergency department visits during the year before and the year after receipt of one year of community rehabilitation for patients referred to the team from September 2016 to September 2017. Result: Results demonstrated a statistically significant reduction in mean admission rates and length of inpatient admissions in the year following rehabilitation compared to the pre-rehabilitation year. A substantial percentage of patients experienced no inpatient admissions (88% v. 60%) or ED visits (82% v. 67%) in the post-rehabilitation year compared to the pre-rehabilitation year. There was a significant reduction in inpatient days by 90%, translating into substantial cost savings. The findings highlight the potential economic benefits of community rehabilitation for people with SPMI. Conclusion: This uncontrolled study suggests that community rehabilitation is associated with favourable clinical outcomes for individuals with SPMI in terms of reduced inpatient service use and related costs. Further research into community psychosocial rehabilitation services in the Canadian setting is needed, including controlled and cost-effectiveness studies.

The epidemic of obesity has increased worldwide and is associated with comorbidities as diabetes as cardiovascular disease. Strategies that modulate body weight and improves glycemic metabolism has increased, and the bariatric surgeries as Sleeve Gastrectomy (SG) has been highlighted in obesity treatment. However, the mechanism by which SG reduces body weight and improves glycemic control are unknow. C57BL mice were submitted to a 10 weeks high fat-diet protocol and submitted to SG. Food intake, fed/fasted glycemia and hypothalamic anorexigenic and orexigenic gene expression were evaluated. SG reduces body weight, fasting glycemia, insulinemia and leptinemia in obese mice. Moreover, SG reduces food and total calorie intake with no alterations in the expression of anorexigenic and orexigenic gene expression. These data indicates that SG improves obesity associated alterations, at least in part, by reduction on food intake. This effect is not associated with the classic food intake pathway in hypothalamus, indicating the involvement of supplementary pathways in this process.

The emergence of antimicrobial resistance have represented a serious threats for public health and infections due to multidrug-resistant (MDR) microorganisms represent one of the most important causes of death worldwide. The renew of old antimicrobials, such as colistin, have been proposed as valuable therapeutic alternative to the emergence of the MDR microorganisms. Although colistin is well known to presents several adverse toxic effect, its usage in clinical practice have been reconsidered due to the broad spectrum of activity to gram-negative (GN) bacteria and to the important role of “last resort” agent against MDR-GN. Despite the revolutionary prospective of treatment of this old antimicrobial molecule, many questions remain open regarding the emergence of novel phenotypic traits of resistance and the optimal usage of the colistin in clinical practice. In the last years, several forward steps have been done in the understanding of resistance determinants, clinical usage and pharmacological dosage of this molecule, however, different points regarding the role of colistin in clinical practice and the optimal pharmacokinetic/pharmacodynamic targets are not well defined yet. In this review, we summarize the mode of action, the emerging resistance determinants and its optimal administration in the treatment of difficult-to-treat infections due to MDR Gram-negative bacteria.

The objective of this paper was to verify the applicability of statistical learning (SL) compared to human reasoning with respect to the Universal Thermal Climate Index (UTCI), a complex tool for the assessment of outdoor thermal stress. UTCI is an equivalent temperature index based on the 48-dimensional output of an advanced model of human thermoregulation formed by 12 variables at four consecutive 30-minute intervals, which were calculated for 105642 thermal conditions from extreme cold to extreme heat. Comparing the performance of SL algorithms to the results accomplished by an international endeavor involving more than 40 experts from 23 countries, we found that random forests and k-nearest neighbors closely predicted UTCI values, but that clustering applied after dimension reduction algorithms (principal component analysis and t-distributed stochastic neighbor embedding) were inadequate for risk assessment in relation to the UTCI stress categories. This indicates a potential supportive role for SL, as it will not (yet) fully replace the bio-meteorological expert knowledge.

Background/Objectives: Hematopoietic stem cell transplantation (HSCT) in pediatric and young adult (YA) patients can lead to endotheliopathy, such as thrombotic microangiopathy (TMA), sinusoidal obstruction syndrome (SOS), and diffuse alveolar hemorrhage (DAH). Natriuretic peptides have been studied as markers of endotheliopathy and critical illness. We hypothesized that elevation in NT-proBNP was associated with development of endotheliopathy (DAH, SOS, or TMA) in the first 100 days following HSCT in pediatric and YA patients. Methods: IRB exempt status was obtained. This retrospective case-control study reviewed HSCT at our institution from 2016-2020. Cases were selected based on an endotheliopathy diagnosis in the first 100 days after HSCT. Cases were matched with controls. Baseline and near-event NT-proBNP levels were compared between cases and matched controls. Effect of NT-proBNP levels on developing endotheliopathy was estimated using conditional logistic regression. Results: Sixty-two patients were included (31 cases, 31 controls). Near-event NT-proBNP was significantly higher in cases compared to controls (Median: 473 vs. 187 pg/mL, p = 0.03, Wilcoxon rank-sum test), in contrast to comparison in baseline NT-proBNP (Median: 86 vs. 86 pg/mL, p = 0.51). After adjusting for co-variates, association between near-event NT-proBNP and odds of developing endotheliopathy did not achieve statistical significance. However, trends from most common transplant indications suggested an association between elevated near-event NT-proBNP level and endotheliopathy, particularly in acute lymphoblastic leukemia (ALL) patients. Conclusions: NT-proBNP should be studied further as a biomarker for endotheliopathy in pediatric and YA patients undergoing HSCT. This may be particularly relevant for patients undergoing HSCT for ALL.

Familial Mediterranean fever (FMF) is a systemic autoinflammatory disorder caused by inherited mutations in the MEFV (Mediterranean FeVer) gene located on chromosome 16 (16p13.3) and coding pyrin protein. Despite the existing data on MEFV mutations, the exact mechanism of their effect on the development of pathological processes leading to autoinflammation observed in FMF, remains unclear. Induced pluripotent stem cells (iPSCs) are considered an important tool for studying the molecular genetic mechanisms of various diseases due to their ability to differentiate into any cell type, including macrophages, which contribute to the development of FMF. In this study, we developed iPSCs of an Armenian patient with FMF having M694V, p.(Met694Val) (c.2080A>G, rs61752717) pathogenic mutation in exon 10 of the MEFV gene. As a result of direct differentiation, macrophages expressing CD14 and CD45 surface markers were obtained. In addition, we found that the morphology of patient macrophages derived from iPSCs with MEFV mutation was significantly different from that of differentiated from iPSCs of a healthy donor carrying wild-type MEFV gene.

New information suggests female autistic spectrum disorder (ASD) is much more common than previously thought, with a likely prevalence of 6% and with 80% undiagnosed at the age of 18. ASD then becomes a common comorbidity of female mental illness with nearly one in five women who develop a mental illness being autistic. ASD has heretofore been regarded as a pediatric condition and though now thought to be life long it is still not well recognized by adult health services. Most mental illness first presents in the teens and early twenties. Comorbid ASD is more difficult to diagnose due to diagnostic overshadowing, and ASD comorbidity maks the mental illness more severe and mor difficult to treat. Recognized ASD is readily treatable with an approach sympathetic to neurodiversity. Improving the pathway from adolescence to adulthood by increasing knowledge of autism in adult health services would dramatically improve female mental health at surprisingly little extra cost. It is low hanging fruit in a very fraught area of healthcare. This paper will examine the extent of the problem. It will explore solutions and quantify the gains of appropriate therapy.

Interoperable multilayer networks are pivotal and transformative in public digital health. They not only enhance the delivery of healthcare services but also contribute significantly to improving public health outcomes. Through better accessibility, efficiency, and data-driven decision-making, these networks are revolutionizing how we approach public health. The seamless data exchange and integration they facilitate across different healthcare systems and providers can lead to substantial cost savings for public healthcare systems. These networks pave the way for a more efficient and effective healthcare system by reducing redundancies, optimizing resource allocation, and minimizing unnecessary procedures. This study combines the pressing issues of public digital health with the innovative interpretation of multilayer network theory, proposing practical solutions that inspire academics and practitioners alike.Public digital health is not just about improving care access and patient engagement. It is also about ensuring the financial sustainability of our healthcare systems. Innovative multilayer network analysis is critical in this game, improving interoperability and reducing spatial and temporal constraints. These networks streamline processes and improve efficiency by easing complex interactions within healthcare systems. This process, in turn, allows healthcare providers to deliver high-quality care at a lower cost, transforming, whenever possible, in-patients and out-patients into home-patients. Networks are a cornerstone and a beacon of hope for transforming public digital health. They are increasing the Value for Money and enhancing a patient-centric approach, making us optimistic about the future of healthcare.This study shows that revolutionizing digital healthcare methodologies has a profound impact on crucial elements like resilience, interoperability, and patient well-being within public health. The enhancement of interoperability facilitates the smooth flow of data exchange across various platforms and healthcare providers, fostering improved collaboration and decision-making processes and elevating the safety of patients.

This review emphasizes the significance of dietary carotenoids in promoting skin health and appearance. The applications of carotenoids in health-promoting and nutrient products and their potential health effects are discussed. Food technology, health, nutrition, dermatology, and dermoaesthetics have recently focused on colorless carotenoids, particularly phytoene, and phytofluene, which have the unique ability to absorb UV radiation. These carotenoids, being significant components of the human diet, are readily bioavailable and have demonstrated various health-promoting actions in re-cent studies. Their distinct structures and properties, such as light absorption, oxidation sensitivity, stiffness, aggregation tendency, and even fluorescence in the case of phytof-luene, contribute to their potential benefits. Furthermore, it is recommended to assess the levels of these carotenoids to evaluate skin carotenoid status accurately.

On Feb. 6^th, 2020, FDA implemented the enforcement priorities against all flavored, cartridge-based e-cigarettes other than menthol and tobacco flavors. This ban undermined the products’ attraction to vapers, so e-cigarette manufacturers added flavorants of other attractive flavors into tobacco-flavored e-cigarettes and re-established attractions. Both the sales of e-cigarettes and posts on social media suggested that the manufacturers’ strategies are likely “successful”. The re-established attraction causes not only a public health issue, but also threats to the health of individual vapers. Research has shown an increase in toxicity associated with the flavorants commonly used in flavored e-cigarettes that are likely added in tobacco-flavored e-cigarettes based on tobacco-derived and synthetic tobacco free nicotine, and these other flavors are associated with higher clinical symptoms not often induced by only natural traditional tobacco flavors. The additional health risks posed by the flavorants are pronounced even without considering the interactions of toxicology of the different tobacco flavorants, and more research should be done to understand the health risks thoroughly and to take proper actions accordingly for regulation of these emerging products.

Diseases of the lung account for more than 5 million deaths worldwide and are a burden to healthcare. Improving clinical outcomes including mortality and quality of life involves a holistic understanding the etiopathogenesis, which can be provided by multi-omics integration of lung data. An enhanced understanding of large comprehensive datasets provides opportunities to mine those datasets for features that contribute to prevention and amelioration of disease. In this review, we evaluate lung disease models including animal models, organoids and single cell lines as mechanisms to study multiomics in lung health and disease. We provide examples of lung diseases where multi-omics investigations have provided a deeper insight into pathogenesis that has resulted in improved preventive and therapeutic interventions.

Microbiome First Medicine is a suggested 21st century healthcare paradigm that prioritizes the entire human, the human superorganism, beginning with the microbiome. To date, much of medicine has protected and treated patients as if they were a single species. This has resulted in unintended damage to the microbiome and an epidemic of chronic disorders [e.g., noncommunicable diseases and conditions (NCDs)]. Along with NCDs came loss of colonization re-sistance, increased susceptibility to infectious diseases, and increasing multimorbidity and polypharmacy over the life course. To move toward sustainable healthcare, the human micro-biome needs to be front and center. This paper presents microbiome-human physiology from the view of systems biology regulation. It also details the ongoing NCD epidemic including the role of existing drugs and other factors that damage the human microbiome. Examples are provided for two entryway NCDs, asthma and obesity, regarding their extensive network of comorbid NCDs. Finally, the challenges of ensuring safety for the microbiome are detailed. Under Microbiome First Medicine and considering the importance of keystone bacteria and critical windows of development, changes in even a few microbiota-prioritized medical decisions could make a significant difference in health across the life course.

The microbioma is presently one of the hottest areas of scientific and medical research and exerts a marked influence on the host during homeostasis and disease. Fermented foods arise in the human relationship to the microbial environment. Further to the traditionally recognized effects of fermented foods and beverages on the digestive health and well-being there is now strong evidence on their general health benefits, namely the significance on the gut microbiota and brain functionality. We highlight the possibilities in this field, how little is still known, and call for a convergence of interdisciplinary research fields of One Health microbe-nutrition with fermented foods and gut-brain research. A consequence of civilisation, changes in present-day society in diets with more sugar, fat and salt, habits and lifestyle, contributes to the likelihood of an inflammatory microbiome, particularly the global epidemics of obesity and mental health. Although two recent papers claim that probiotics perturb rather than aid in microbiota recovery back to baseline after antibiotic administration in humans, consuming fermented foods has shown to reduce inflammation so improve gut health and the proper function of the body’s immune system.

Chronic kidney disease (CKD) affects worldwide 8-16% of the population. In developed countries, the most important risk factors for CKD are diabetes, hypertension and obesity, calling into question the importance of educating and acting on lifestyles and nutrition. A balanced nutrition and supplementation can indeed support the maintenance of a general health status, including preservation of renal function, and help to manage and curb the main risk factors for renal damage. While the concepts of protein and salt restriction in nephrology are historically acknowledged, the role of some nutrients on renal health and the importance of nutrition as a preventative measure for renal care are less known. In this review, we provide an overview of the demonstrated and potential actions of some selected nutrients, nutraceuticals and xenobiotics on renal health and function. The effects on kidney of fibres, proteins, fatty acids, curcumin, steviol glycosides, green tea, coffee, nitrates, nitrites, and alcohol, both direct and indirect, in CKD and non-CKD condition, are reviewed here. In a view of a functional and personalized nutrition, understanding the renal and systemic effects of dietary components is essential since many chronic conditions and CKD are related to systemic dysfunctions such as chronic low-grade inflammation.

A fully automated bacteria whole genome sequencing (WGS) assay was evaluated to characterize Mycobacterium tuberculosis (MTB) and non-tuberculosis Mycobacterium (NTM) clinical isolates. Results generated were highly reproducible with 100% concordance in species and sub-lineage classification, and 92% concordance between antimicrobial resistance (AMR) genotypic and phenotypic profiles. These findings demonstrate that a fully automated WGS assay, with a short turnaround time of 24.5 hours, provides timely and valuable insights into MTB outbreak investigation while providing reliable genotypic AMR profiling consistent with traditional antimicrobial susceptibility tests (AST). This study establishes a favorable proposition in the adoption of an end-to-end fully automated WGS solutions for decentralized TB diagnostics, thereby aiding in WHO’s vision of tuberculosis eradication.

Background:Tourette syndrome (TS) is a characterized by motor and vocal tics, often accompanied by obsessive-compulsive disorder (OCD) and attention-deficit/hyperactivity disorder (ADHD). Cannabis-based medicines (CBM) present a promising avenue for future therapeutic intervention owing to their interaction with the endocannabinoid system, potentially mitigating tics and associated symptoms. Unlike conventional treatments such as antipsychotics, CBM exhibits improved tolerability and reduced side effects. While the available evidence is still limited, ongoing research underscores the potential of CBM to enhance the quality of life for patients, establishing it as a pivotal area for future therapeutic advancements in TS. Aim: In our systematic review and meta-analysis, we aim to assess the effectiveness of cannabis-based medicine (CBM) in the treatment of Tourette Syndrome. Methods: We searched in electronic databases (PubMed, Google Scholar, Science Direct, Cochrane Collaboration Database of Randomized Trials) was performed to identify cohort studies and randomized controlled trials (RCTs) up to 18th June 2024. Data extraction focused on baseline characteristics of the included studies and efficacy outcomes, including scores on the Yale Global Tic Severity Scale (YGTSS) and Premonitory Urge for Tics Scale (PUTS). We conducted the meta-analysis using Review Manager version 5.4. software. We compared the measurements before and after CBM intake using mean difference (MD) and 95% confidence interval (CI). Result: In total, 1105 articles were identified for screening, with eight studies included in the systematic review and seven in the meta-analysis. These studies involved 306 adult patients with TS treated with cannabis. YGTSS revealed a significant reduction in total scores (MD = -13.29, 95% CI [-21.67 to -4.91], P = 0.002) and PUTS revealed a significant decrease in scores (MD = -4.09, 95% CI [-7.24 to -0.93], P = 0.01). Conclusion: The research emphasizes the potential of cannabis-based medication in alleviating tics and premonitory urges associated with Tourette syndrome. While the initial results are promising, they are based on limited-scale studies. It is imperative to conduct larger, placebo-controlled trials encompassing diverse demographic groups to substantiate the effectiveness, ensure safety, and optimize dosages for long-term therapeutic advantages.

Copper-containing materials are attracting attention as self-desinfecting surfaces, suitable for helping healthcare settings in reducing healthcare associated infections. However, the impact of repeated exposure to disinfectants frequently used in biocleaning protocols on their antibacterial activity remains insufficiently characterized. This study aimed at evaluating the antibacterial effi-ciency of copper (positive control), a brass alloy (AB+®) and stainless steel (negative control) after repeated exposure to a quaternary ammonium compound and/or a mix of peracetic acid/hydrogen peroxide routinely used in healthcare settings. A panel of 6 antibiotic-resistant strains (clinical isolates) was selected for this assessment. After a short (5 min) exposure time, the copper and brass materials retained significantly better antibacterial efficiencies than stainless steel, whatever the bacterial strain or disinfectant treatment considered. Moreover, post-treatment with both disinfectant products, copper-containing materials still reached similar levels of antibacterial efficiency as those obtained before treatment. Antibiotic resistance mechanisms such as efflux pump overexpression did not impair the antibacterial efficiency of copper-containing materials, neither did the presence of one or several genes related to copper homeostasis/resistance. In light of those results, surfaces made out of copper and brass remain interesting tools in the fight against dissemination of antibiotic-resistant strains that might cause healthcare associated infections.

Global health is significantly impacted by antimicrobial resistance. Antibiotic misuse and overuse have resulted in the emergence of drug-resistant bacteria, viruses, parasites and fungi, which makes ordinary infections more difficult to cure and accelerates the spread of disease. The World Health Organisation has declared that antimicrobial resistance could result in up to 10 million deaths yearly by 2050, making it a worldwide health emergency. One of the greatest challenges to global health, food security, and development today, according to the World Health Organisation is the emergence of antibiotic resistance. Even though antibiotic resistance can arise naturally, improper usage of these medications and many other factors are accelerating its global spread. Antimicrobial resistance is frequently referred to as the "Silent Pandemic" and is a problem that requires current action and should be managed more effectively rather than considered as a future situation. This paper address a number of antimicrobial classes, global health, Onehealth and how drug resistance affects human health, and related risk factors. This review will also provide background information on the causes and processes of antimicrobial resistance in brief by identifying these characteristics, targeted interventions can be developed to lower the risk of illnesses that are resistant to antibiotics. It will also offer insights into new and emerging solutions that are being studied and may be created in the future to address the advancement of antimicrobial resistance.

Adolescence is a crucial phase marked by significant physical, psychological, emotional and social changes. India having the world's largest adolescent population, understanding and addressing their health needs are vital for the nation's social, political, and economic progress. The primary aim of this study is to examine the impact of key health policies on adolescent health outcomes in India. To achieve this objective, the research adopts a mixed-method approach, combining qualitative and quantitative analysis of health policies, strategies, and programs implemented since 2005 was conducted. Additionally, data from the most recent three Demographic Health Surveys (DHS) were analyzed and compared to assess changes in adolescent health indicators after the implementation of these policies/strategies. Major adolescent health policies in India were assessed, namely the Adolescent Reproductive and Sexual Health Strategy (ARSH 2005), Rashtriya Kishor Swasthya Karyakram (RKSK 2014), and School Health Programme 2020. All the strategies and programs aim to provide a comprehensive framework for sexual and reproductive health services, expand the scope of adolescent health programming, and address various health aspects. The SWOT analysis findings, highlighted strengths in targeted interventions, monitoring, and promotion, but weaknesses in awareness, societal barriers, and healthcare worker participation. Opportunities include female-friendly clinics and education about early pregnancy, while addressing substance abuse and training volunteers remain challenges. Family planning has improved, with higher contraception usage and a decrease in unmet needs. Violence reduced, and positive health behaviors increased, such as condom use. However, challenges remain, including limited access to health services, concerns about female providers, and low health insurance coverage. Nutrition indicators show a slight increase in overweight/obesity and anemia rates. Overall, progress has been made, but certain health aspects still require attention. Therefore, conducting targeted awareness campaigns, strengthening health worker and NGOs engagement, combating the increasing prevalence of overweight and obesity among adolescents are highly recommended. Further efforts are needed to achieve universal health coverage and improve adolescent health outcomes globally.

Background/Objectives: Congenital Radioulnar Synostosis (CRS) is a rare elbow joint disorder resulting from abnormal fusion of the radius and ulna during fetal development. Methods: Although predominantly sporadic, familial cases exist with autosomal dominant inheritance. Diagnosis relies on clinical evaluation and radiological findings, showcasing synostosis. Surgical decisions hinge on factors like age and symptom severity. Results: Multiple surgical treatments have been described but derotation osteotomy offers predictable results. Challenges include potential complications and cultural considerations influencing final forearm positioning. Conclusions: A nuanced approach is crucial for optimal management, highlighting the need for ongoing research to refine diagnostic and therapeutic strategies for improved outcomes in CRS patients.

Intratumoral immune cytolytic activity (CYT), measured as the geometric mean of the expression of Granzyme-A (GZMA) and Perforin-1 (PRF1), has emerged as a critical factor in cancer immunotherapy, with significant implications for patient prognosis and treatment outcomes. Here, we review how different immune checkpoint pathways, the composition of Tumor Microenvironment (TME), antigen presentation and metabolic pathways, all regulate CYT. We also discuss the various methods through which CYT can be assessed. The detection and analysis of tumor-infiltrating lymphocytes using flow cytometry or immunohistochemistry provide important information on the immune cell populations within the TME. Gene expression profiling and spatial analysis techniques, such as multiplex immunofluorescence and imaging mass cytometry, allow researchers to study CYT in the context of the TME. We further report the significant clinical implications that CYT has, as its increased levels are associated with positive clinical outcomes and a favorable prognosis. Moreover, CYT can be used as a prognostic biomarker and aid in patient stratification. Measuring CYT through different methods offers promising paths for improving treatment responses. Overall, we highlight that the understanding and modulation of CYT levels is critical for improving cancer immunotherapy. Research into CYT and the factors that influence it has the potential to transform cancer treatment and improve patient outcomes.

The advent of AI in medicine has transformed various medical specialties, including orthodontics. AI has shown promising results in enhancing the accuracy of diagnoses, treatment planning, and predicting treatment outcomes. With the growing number of AI applications and commercially available tools, there is an increase in their usage in orthodontic practices worldwide. This review aims to explore the principles of artificial intelligence (AI), its applications in the diagnostic process of modern orthodontic practices, and concerns associated with the implementation of AI algorithms in clinical practice. A comprehensive review of the literature was conducted, focusing on five categories where AI has been applied in orthodontics: dental diagnostics, cephalometric evaluation, skeletal age determination, temporomandibular joint (TMJ) evaluation, and extraction decision making. AI has demonstrated high efficacy in all those fields. However, variations in performance and the necessity of manual supervision indicate that AI should be used cautiously in clinical settings. Nevertheless, the high complexity and potential unpredictability of AI algorithms call for cautious implementation and regular manual validation of results. Continuous AI learning, proper governance, and addressing privacy and ethical concerns are crucial for the successful integration of AI into orthodontic practice.

Chloroquine (CQ) and its derivate hydroxychloroquine (HCQ), the compounds with recognized ability to suppress autophagy, have been tested in experimental works and in clinical trials as adjuvant therapy for the treatment of cancers of different origin to increase the efficacy of cytotoxic agents. Such strategy can be effective to overcome the resistance to standard chemotherapy or anti-angiogenic therapy. This review presents the results of combined application of CQ/HCQ with conventional chemotherapy drugs (doxorubicin, paclitaxel, platinum-based compounds, gemcitabine, tyrosine kinases and PI3K/Akt/mTOR inhibitors, and other agents) for the treatment of different malignancies obtained in experiments on cultured cancer cells and on animal xenograft models, with a few examples of clinical trials. The effects of such approach on viability of cancer cells and tumor growth, as well as autophagy-dependent and independent molecular mechanisms underlying cellular responses of cancer cells to CQ/HCQ are summarized. Although the majority of experimental studies in vitro and in vivo have shown that CQ/HCQ can effectively sensitize the cancer cells to cytotoxic agents and increase the potential of chemotherapy, the results of clinical trials are often inconsistent. Although pharmacological suppression of autophagy remains a promising tool for increasing the efficacy of standard chemotherapy, the development of more specific compounds is required.

Sepsis is a major health problem and accounts for 20 per cent of deaths worldwide. It is the most expensive condition treated in United States hospitals at $27 billion per year or about $20,000 per patient. Treatment consisting largely of fluid resuscitation and antibiotics has only marginal impact. Mortality is about 27 per cent for hospitalized patients and about 42 per cent for patients in intensive care.There are two phases of sepsis – a hyper inflammatory phase and a subsequent hypo inflammatory phase.During the hyper inflammatory phase the metabolic rate increases associated with an increase in body temperature and a rapid escalation of immune system functioning including increased numbers of leucocytes and migration of those leucocytes to infected and damaged tissues and an increased supply and consumption of glucose to fuel this immune system.During the subsequent hypo inflammatory phase the metabolic rate declines and this is associated with a decrease in body temperature and a generalized decrease in the physiological activity of many organs including the immune system akin to hibernation. The activated immune system has priority for the available glucose over most other organs and physiological functions during such potentially life threatening circumstances. Thus adenosine triphosphate (ATP) production by mitochondria (the source of energy at the cellular level for the organism as a whole) also has a lower priority for the available glucose relative to the activated immune system. If glucose availability is threatened then mitochondrial production of ATP is partially or substantially suppressed in favor of glycolysis because glycolysis can rapidly produce large quantities of ATP that are necessary for immune cell function in infected, anaerobic, ischemic, or damaged tissues.However, glycolysis is only a temporary fix as it cannot produce the quantities of ATP necessary on an ongoing basis for the normal functioning of the healthy animal. Mitochondrial production of ATP must be recommenced for full recovery.It appears that the partial or substantial suppression of mitochondrial production of ATP by activation of the immune response becomes relatively fixated in some patients leading to a substantial ATP deficit. This is the fundamental issue of sepsis.This paper reviews the metabolism of glucose and insulin during sepsis and concludes that high dose insulin with mild hyper glycaemia in conjunction with the intravenous administration of thiamine, an inhibitor of the pyruvate dehydrogenase kinase enzymes, to re-establish physiological ATP production by mitochondria, administered early in the hypo metabolic (hypo inflammatory) phase of sepsis should significantly improve survival.

Cervical cancer (CC) is the most common cancer in women worldwide and is almost always associated with repeated infections by human papillomavirus (HPV). Screening by traditional tests combined with biomarker identification techniques for low- or high-grade injuries, are becoming increasingly important in diagnosis and prognosis, avoiding countless deaths. This article explores existing literature on the main serum biomarkers and the identification of biomarkers associated with the oncogenesis of HPV expression in the identification of pre-cancerous lesions and of CC for an effective treatment, with reduction of recurrence, as well as we explain strategies for the development of biomarkers based on DNA, proteins and other markers. We also describe how markers of chromosomal instability host DNA, promoting hypo- or hypermethylation of DNA, as well as polymorphisms and epigenetic events in the p53 gene. Finally, we discuss changes in gene expression using cDNA microarray techniques and changes in the expression of proteins and markers identified through mass spectrometry.

This article develops duality principles applicable to originally non-convex primal variational formulations. More specifically, as a first application, we establish a convex dual approximate variational formulation for a non-linear Kirchhoff-Love plate model. The results are obtained through basic tools of functional analysis, calculus of variations, duality and optimization theory in infinite dimensional spaces. We emphasize such a convex dual approximate formulation obtained may be applied to a large class of similar models in the calculus of variations. Finally, in the last section, we present a duality principle and respective convex dual formulation for a Ginzburg-Landau type equation.

Digital technology makes energy systems more dynamic, efficient, reliable and sustainable. Promoting the digital governance of renewable energy is not only a key part of energy transformation, but also an important initiative to cope with the global energy crisis, which provides a new impetus for realizing the goal of net-zero emissions. In order to achieve the "dual-carbon" goal, China has been actively promoting energy reform and enhancing the wide application of digital technologies in the development and utilization of renewable energy. The digital governance system and governance capacity of renewable energy is an important support for the modernization of the national governance system and governance capacity. Although the digital governance of renewable energy in China has better policy support and advantages, the dilemmas it faces are still relatively prominent, including unsound laws and policies, insufficient governance capacity, and security loopholes in digital technology, etc. Therefore, this paper proposes a problem-oriented approach to the digital governance of renewable energy. Therefore, this paper puts forward problem-oriented optimization suggestions to innovate the legal system of renewable energy digital governance, promote the digital transformation of China's renewable energy industry, provide development space for renewable energy digital governance in a diversified mode, and enhance the digital governance capacity by improving the renewable energy security system.

The rapid, sensitive, and selective detection of pathogenic bacteria is of utmost importance in ensuring food safety and preventing the spread of infectious diseases. Here, we present a novel, reusable, and cost-effective impedimetric sensor based on a dual bacteria-imprinted polymer (DBIP) for the specific detection of Escherichia coli O157:H7 and Staphylococcus aureus. The DBIP sensor stands out with its remarkably short fabrication time of just 20 minutes, achieved through the efficient electro-polymerization of o-phenylenediamine monomer in the presence of dual bacterial templates, followed by in-situ template removal. The key structural feature of the DBIP sensor lies in the cavity-free imprinting sites, indicative of a thin layer of bacterial surface imprinting. This facilitates rapid rebinding of the target bacteria within a mere 15 minutes, while the sensing interface regenerates in just 10 minutes, enhancing the sensor's overall efficiency. A notable advantage of the DBIP sensor is its exceptional selectivity, capable of distinguishing the target bacteria from closely related bacterial strains, including different serotypes. Moreover, the sensor exhibits high sensitivity, showcasing a low detection limit of approximately 9 CFU mL-1. The sensor's reusability further enhances its cost-effectiveness, reducing the need for frequent sensor replacements. The practicality of the DBIP sensor was demonstrated in the analysis of real apple juice samples, yielding good recoveries. The integration of quick fabrication, high selectivity, rapid response, sensitivity, and reusability makes the DBIP sensor a promising solution for monitoring pathogenic bacteria, playing a crucial role in ensuring food safety and safeguarding public health.

Nowadays, many Muslim-majority countries have implemented a dual banking system, namely the sharia and conventional systems. The development of Islamic banks is to fulfill the Muslims' need for the existence of halal transactions in financial institutions. However, in some countries, it turns out that conventional banks still dominate the country's economy. Because of that, it is necessary to see whether there are differences in financial risk and Earnings management between Islamic and conventional banks. The samples are conventional and Islamic banks in Southeast Asia, analyzed by the purposive sampling method from 2010-2019. The analytical tool used is the statistical difference test and economometrics analysis using generalized least square (GLS) regression with panel data (time series and cross-sectional data). These models are intended to forecasting the macroeconomics effects in applying dual banking system in one country or region. The results using non parametrics means difference test showed that the first hypothesis is accepted It means that Earnings management in conventional banks is greater than in Islamic banks. The Random Model Effect (REM) for second and third hypotheses testing on Conventional banks shows the Bankruptcy Risk and NPL do not affect the dependent variable Earnings Management (LLP). While fixed effect model testing on Islamic banks, the second and third hypothesis testing is rejected. Therefor Islamic Banks the value of Bankruptcy Risk (z-score) and the value of Non-Performing Loans (NPL) do not affect Earnings management. It also means that hypothesis 2 and 3 are rejected both in conventional as well as Islamic Banking. Sensitivity analysis for conventional as well as Islamic banking altogether using fixed effect model shows that the second and third hypotheses show that the independent variables (Bankruptcy Risk and NPL) do not affect the dependent variable Earnings Management (LLP). These results can be concluded that Islamic bank are enganged in less earnings management. Therefor in the the long run there are still more research that should conduct in comparing dual banking system in one region.

Cytochrome P450 monooxygenases (P450s) are promising versatile oxidative biocatalysts. However, the practical use of P450s in vitro is limited by their dependence on the co-enzyme NAD(P)H and the complex electron transport system. Using H2O2 simplifies the catalytic cycle of P450s; however, most P450s are inactive in the presence of H2O2. By mimicking the molecular structure and catalytic mechanism of natural peroxygenases and peroxidases, an artificial P450 peroxygenase system has been designed with the assistance of a dual-functional small molecule (DFSM). DFSMs, such as N-(ω-imidazolyl fatty acyl)-l-amino acids, use an acyl amino acid as an anchoring group to bind the enzyme, and the imidazolyl group at the other end functions as a general acid-base catalyst in the activation of H2O2. In combination with protein engineering, the DFSM-facilitated P450 peroxygenase system has been used in various oxidation reactions of non-native substrates, such as alkene epoxidation, thioanisole sulfoxidation, and alkanes and aromatic hydroxylation, which showed unique activities, and regio- and enantioselectivities when compared with native P450s. Moreover, the DFSM-facilitated P450 peroxygenase system can switch to the peroxidase mode by mechanism-guided protein engineering. In this short review, the design, mechanism, evolution, application, and perspective of these novel non-natural P450 peroxygenases for the oxidation of non-native substrates are discussed.

This paper proposes a new structure of the dual-rotor hybrid-excited axial-flux permanent magnet vernier machine (DR-HEAFPMVM) with the modular stator and the consequent-pole PM (CPM) rotor for low-speed, high torque density applications such as in-wheel electric vehicles. The tooth-wound non-overlapping armature windings and direct current (DC) excitation windings are respectively arranged in stator main-teeth and split-teeth to obtain the modulated and adjustable air-gap flux densities, which result in the high torque density and outstanding flux-weakening capability. First, the design consideration, operation principle, and the air-gap flux density distributions of the proposed machine are elaborated based on the air-gap permeance function. Then, the influence of the pole ratios (PRs) and the DC excitation currents on the main electromagnetic performances of the DR-HEAFPMVM, such as the flux-weakening capability, back-electromotive force (back-EMF), on-load electromagnetic torque, loss distribution, and efficiencies, is investigated using the 3-D finite-element method (FEM). Results verify the feasibility of the flux adjustment of the DC excitation windings equipped in the split-tooth, and the design with a pole ratio of 8/1 tends to have higher torque density, higher machine efficiency, and considerable flux-weakening capability compared with the other two PRs.

Current therapeutic protocols for the treatment of HIV infection consist of the combination of diverse anti-retroviral drugs in order to reduce the selection of resistant mutants and to allow for the use of lower doses of each single agent to reduce toxicity. However, avoiding drugs interactions and patient compliance are issues not fully accomplished so far. In this respect the identification of single agents with multitarget potential might represent the ideal solution. Accordingly, a small library of biphenylhydrazo 4-arylthiazoles derivatives has been synthesised and evaluated to investigate the ability of the new derivatives to simultaneously inhibit both associated functions of HIV reverse transcriptase. All compounds were active towards the two functions, although at different concentrations. The substitution pattern on the biphenyl moiety appears relevant to determine the activity.

This paper introduces a novel decision-making approach grounded in insights into human visual perception of change. Modern technologies such as internet of things (IoT) provide us with large amounts of sensor data that need to be processed in real time and decisions made with a high degree of accuracy and reliability. Artificial intelligence (AI) methods are welcome in this context and need to be upgraded to meet actual challenges. While modern computing capabilities facilitate rapid data processing, the real-time demands of vast sensor data necessitate swift responses across the cyber chain, often leading to compromises in solution quality to circumvent combinatorial search complexities. Determining the adequacy of a solution entails varied approaches, often relying on heuristic methodologies. We illustrate our original approach with an example of a selected detail of a differential evolution algorithm, where we have to make a decision to adopt the best solution so far. We propose an approach inspired by human perceptual features that exploits the Weber-Fechner law to emulate human judgements, and offers a promising way to improve decision making in AI applications and real-time requirements fulfillment. Our proposed methodology demonstrates applicability across diverse AI scenarios involving numerical data, effectively mirroring human perception abilities.

Kisspeptin, a key neuropeptide derived from the KISS1 gene, is renowned for its critical role in regulating the hypothalamic-pituitary-gonadal axis and reproductive hormone secretion. Beyond its primary function in reproductive biology, emerging research has illuminated its influence in various cancers, mediating significant effects through its interaction with the G-protein coupled receptor, Kisspeptin receptor. This interaction has been implicated in modulating cellular pro-cesses such as proliferation and metastasis, making it a potential target for therapeutic interven-tion. Our study initially screened ten kisspeptin-10 analogs through cytotoxic effects of kisspep-tin-10 (KP10) and its analogs in several cancer types, including cervical, prostate, breast, and gastric cancers, with a particular focus on cervical cancer where the most profound effects were observed. Further exploration using kinase array assays revealed that these analogs specifically alter key kinases involved in cancer progression. Migration assays demonstrated a substantial decrease in cell motility, and Bioluminiscence Resonance Energy Transfer assays confirmed these analogs strong interactions with the kisspeptin receptor. Overall, our results indicate that these KP10 analogs not only hinder cervical cancer cell proliferation but also curtail migration through targeted modulation of kinase signaling, suggesting their potential as therapeutic ayfgents in managing cervical cancer progression. This comprehensive approach underscores the therapeutic promise of exploiting kisspeptin signaling in cancer treatment strategies.

This study examines HIV/AIDS-associated knowledge and attitudes towards treating disadvantaged communities among predoctoral dental students in U.S. dental schools who had not yet commenced their Community-Based Dental Education (CBDE) training. It also compares the difference in knowledge and attitudes between students with reported community engagement with PLWHA, and those without. Our study comprised 89 predoctoral dental students within their D1-D3 years of dental school who had not yet initiated their Community-Based Dental Education (referred to as pre-CBDE dental students). Responses were collected via an online survey. Participants were 68% females, 94% heterosexual, of mean age (years): 25.9 ± 3.5 SD. The majority (62%) were in their first (D1) and second (D2) years of dental education. Of the thirty knowledge questions, only five received a minimum of 90% correct responses. Similarly, we found no statistical differences in most of the knowledge/attitude sections, between those with and without prior PLWHA exposure. Encouragingly, 90% of our participants reported prior experience working with disadvantaged communities. Early community engagement fosters a sense of professional responsibility towards administering dental care to disadvantaged communities and we propose that it must be encouraged among aspiring dental school students.

There are several well-described molecular mechanisms that influence cell growth and are relat-ed to the development of cancer. Chemokines constitute a fundamental element which are not only involved in local growth but also affect angiogenesis, tumor spread and metastatic disease. Among them, the C-X-C motif chemokine ligand 12 (CXCL12) and its specific receptor the chemo-kine C-X-C motif receptor 4 (CXCR4) have been widely studied. The overexpression in cell mem-branes of CXCR4 has been shown to be associated with the development of different kinds of his-tological malignancies, such as adenocarcinomas, epidermoid carcinomas, mesenchymal tumors or neuroendocrine neoplasms (NENs). The molecular synapsis between CXCL12 and CXCR4 leads to the interaction of G proteins and activation of different intracellular signaling pathways in both gastroenteropancreatic (GEP) and bronchopulmonary (BP) NENs conferring greater ca-pacity for locoregional aggressiveness, epithelial-mesenchymal transition (EMT) and the ap-pearance of metastases. Therefore, it has been hypothesized how to design tools that target this receptor. The aim of this review is to focus on current knowledge of the relationship between CXCR4 and NENs, with a special emphasis on diagnostic and therapeutic molecular targets.

In recent years, pedestrian re-identification has gained a lot of interest due to its importance for many purposes such as security and safety. Many types of solutions have been proposed to solve this problem, where the majority are based on a features extraction such Convolution Neural Networks (CNNs). These approaches assume that only the identities that are in the training data can be recognized. The pedestrians in the training data are called In distribution (ID). However, in real world scenarios, new pedestrians and objects can appear in the scene and the model should detect them as Out Of Distribution (OOD). In our previous study , we proposed a pedestrian re-identification based on von-Mises Fisher (vMF) distribution. Each identity is embedded in the unit sphere as a compact vMF distribution far from other identities distributions. The embedding is done through a base CNN. Recently, proposed a framework called Virtual Outlier Synthetic (VOS), that detects OOD based on synthesising virtual outlier in the embedding space in an online manner. Their approach assumes that the samples from the same object maps to a compact space. This assumption aligns with the vMF based approach. Therefore, in this paper, we revisited the vMF approach and merged with VOS in order to detect OOD data points. We present our approach to merge both frameworks. We conducted several experiments to evaluate our proposed framework. Results showed that our framework was able to detect new pedestrian that do not exist in the training data in the inference phase. It also slightly helped to improve the re-identification performance.

Soil erosion is one of the most important environmental problems which can have various negative consequences, such as land degradation affecting the sustainable development and the agricultural production, especially for developing countries like Tunisia. Moreover, soil erosion is a major problem around the world because of its effects on soil fertility by nutriment loss and siltation in water bodies. Apart from this, soil erosion by water is the most serious type of land loss in several regions both locally and globally. This study evaluated regional soil erosion risk through the derivation of appropriate factors, using the Revised Universal Soil Loss Equation (RUSLE), which was applied to establish a soil erosion risk map of the whole Tunisian territory and to identify the vulnerable areas of the country. RUSLE model take into account all the factors playing a major role in erosion processes, namely the erodibility of soils, topography, land use, rainfall erosivity and anti-erosion farming practices. The equation is thus implemented under Geographic Information System (GIS) “Arc GIS Desktop”. The results indicated that Tunisia has a serious risk of soil water erosion, showing that 6.43% of the total area of the country is affected by a very high soil loss rate estimated at more than 30 t/ha/year and 4.20% are affected by high mean annual soil loss ranging from 20 to 30 t/ha/year. The most eroded areas were identified in west southern, central and western parts of the country. The spatial erosion map can be used as a decision support document to guide decision-makers towards better land management and provide the opportunity to develop management strategies for soil erosion prevention and control in the global scale of Tunisia.

Multiple myeloma (MM) is a genetically heterogenous disease that includes a subgroup of 10-15% of patients facing dismal survival despite most intensive treatment. The aim of this review article is to provide an integrated clinical and biological overview on the high-risk MM discussing the novel therapeutic perspectives aimed to target the neoplastic clone and its microenvironment. The dissection of the molecular determinants of the aggressive phenotypes and drug resistance can foster a better tailored clinical management of high-risk profile and refractoriness to therapy. Among the current clinical difficulties in MM, patient’s management manipulating the tumor niche represents a major challenge given the limited knowledge about the MM-milieu interaction. The angiogenesis and bystander infiltrate constitute pivotal mechanisms of mutual collaboration between MM and the non-tumoral counterpart. Immuno-modulatory and anti-angiogenic therapy hold great efficacy but variable and unpredictable responses in high-risk MM. Therefore, it would be worth to better select the population and the MM stage that could profit to a dual immune/vasculogenesis targeting. The comprehensive knowledge of the genetic heterogeneity and MM high-risk ecosystem enforce a systematic bench-to-bedside approach. Despite significant improvements in the biology knowledge, MM is still a chronic and incurable neoplasia and therapeutic options able to overcome the relapsing/refractory behavior represent an unmet clinical need. Here, we corroborate previous biological findings providing a synthetic outlook of novel druggable targets. We also summarize the existing multi-omics-based risk profiling tools, in order to better personalize the patient-oriented clinical management.

Autoimmune bullous diseases (AIBDs) are a group of conditions marked by the formation of blisters and erosions on the skin and mucous membranes. Knowing that, most of the AIBDs are severe and resistant to treatment, we decided to look upon the nutritional aspect as a light for the future for both - patients and clinicians. In this review, we would like to summarize the evidence of the role of nutrition in pathogenesis and treatment of the following AIBDs: i) pemphigus, ii) bullous pemphigoid and mucous membrane pemphigoid, iii) dermatitis herpetiformis, and iv) epidermolysis bullosa aqusita.

Abstract Issue Addressed: Men’s Sheds (‘Sheds’) have been identified as inherently health promoting and as potential settings to engage ‘hard-to-reach’ men in more structured health promotion initiatives. However, little is known about the sociodemographic or health and wellbeing characteristics of Shed members (‘Shedders’) on which such initiatives might be based. This study captures a baseline cross sectional analysis of Shedders (n=384) who participated in ‘Sheds for Life’, a health promotion initiative tailored to Sheds. Methods: Objective health measure, (body composition, blood pressure, blood lipids) captured via health screening as well as sociodemographic and health and wellbeing measures (physical activity, subjective wellbeing, mental health, social capital, cooking and diet) via questionnaires were assessed. Results: Participants were mostly over 65 years, retired with limited educational attainment. The majority were in the ‘at-risk’ categories for objective health measures, with most being referred to their GP following health screening. Older Shedders were also more likely to meet physical activity guidelines. Mental wellbeing was positively correlated with life satisfaction and increased social capital and these were also positively correlated with physical activity. Conclusions: Findings highlight the potential of Sheds in reaching a ‘hard-to-reach’ and ‘at-risk’ cohort of men. Despite a high prevalence of ‘at-risk’ objective health measures, participants report their health in positive terms. Future health promotion initiatives should capitalise on the inherent health promoting properties of Sheds. So what? Findings raise important implications for prioritising and designing health promotion initiatives in Shed settings.

International migrants comprised 14% of the UK population in 2020, but migrant health in the UK has rarely been studied at a population level using primary care electronic health records (EHRs). Given the difficulty of determining migration status using EHRs, this study developed a migration phenotype and assessed its validity. We developed a phenotyping algorithm using codes for country of birth, visa status, non-English main/first language and non-UK origin. It was applied to a Clinical Practice Research Datalink (CPRD) GOLD database of 16,071,111 primary care patients between 1997 and 2018. We compared the completeness and representativeness of the identified migrant population to Office for National Statistics (ONS) country of birth and 2011 census data by year, age, sex, geographic region of birth and ethnicity. Between 1997-2018, 403,768 migrants (2.51% of the CPRD GOLD population) were identified using the phenotype. 178,749 (1.11%) of these migrants were identified by codes indicating foreign country of birth or visa status, 216,731 (1.35%) a non-English main/first language, and 8,288 (0.05%) non-UK origin. The cohort was similarly distributed compared to ONS migration statistics in terms of sex and region of birth. Recording of migration improved from identifying approximately one-tenth of the expected proportion of migrants according to the ONS in 2004 to a quarter in 2018. Younger migrants were better represented than those aged 50 and over. The migration phenotype identified a large number of migrants and can be used to undertake large-scale migration health research in CPRD GOLD to inform healthcare policy, practice and action. While the cohort was representative of the UK migrant population in terms of sex and region of birth, migration status was under-recorded in earlier years and older ages, and future studies for these groups should therefore be interpreted with caution.

Background: Screening for postpartum depression can lower prevalence and ensure that mothers get adequate treatment and support. Yet, few practitioners screen.The present article is a brief review on barriers to screening and two screening scales that are validated for perinatal women. Findings: Despite recommendations from health organizations, most new mothers are not screened for depression. Providers cite lack of time, opening “Pandora’s box,” and lack of resources for mothers who screen positive as reasons for why they do not screen. The Edinburgh Postnatal Depression Scale and the Patient Health Questionnaires are brief screening scales that have been validated for new mothers and are widely available.Conclusions: Screening is the necessary first step to identifying new mothers who are depressed. Practitioners who screen need a clear plan, and to know how to access available community resources, so they will know what to do when a mother screens positive.

Anxiety in older individuals is understudied despite its prevalence. Investigating its occurrence can be challenging, yet understanding the factors influencing its recurrence is important for improved management. This study aimed to model the recurrence of anxiety symptomatology in an older population within a five-month timeframe. Data included baseline socio-demographic and general health information for older adults aged 60 years or older with at least mild depressive symptoms. A Bayesian network model explored the relationship between baseline data and recurrent anxiety symptomatology. Model evaluation employed the Area Under the Receiver Operating Characteristic Curve (AUC). The Bayesian model was also compared to three machine learning models. The model achieved an AUC of 0.821 on the test data, using a threshold of 0.367. This result surpassed both the Logistic Regression and XGBoost models, but slightly behind SGDClassifier. Key factors associated with recurrence of anxiety symptomatology were: “Not being able to stop or control worrying”; “Becoming easily annoyed or irritable”; “Trouble relaxing”; and “depressive symptomatology severity”. The model demonstrated generalisation abilities and outperformed some machine learning models while being less complex and more explainable. These findings indicate a prioritised sequence of predictors to identify individuals most likely to experience recurrent anxiety symptomatology.

Background: The effect of COVID-19 infection, versus the indirect effect of the pandemic on body composition remains unclear. This study investigates long-term changes in body composition in COVID–19 survivors compared to a contemporary control group. Method: This is a prospective study involving adults who had a pre-pandemic whole-body DXA scan (DXA#1) performed between 2017 and 2019. Participants were asked to return for a repeat whole-body DXA scan (DXA#2) after the pandemic. Detailed data were collected including medical and COVID history. Inflammation markers and fasting lipids were measured. For those participants who had a COVID-19 infection between the 2 DXAs, DXA#2 was acquired at least one year after COVID-19 infection. Results: Overall, 160 adults were enrolled; 32.5% females, 51.8% non-white, with mean age of 43.2 years. Half (n=80) of the participants had documented COVID infection between their 2 DXA scans (COVID+ group), and the other half never had COVID. COVID-negative participants had an increase in annualized trunk fat (g) [922.5 vs. 159.7; P=0.01], total fat (g) [1564.3 vs. 199.9; P=0.2], and LBM (g) [974.9 vs. -64.5; P=0.0002], when compared to COVID+ group. However, among the COVID+ group, no differences were seen in annualized trunk fat, total fat mass, or LBM between those with PASC and without (p>0.05). Conclusion: During the pandemic, both COVID-19-survivors and COVID-negative groups exhibited increases in weight, and total and trunk fat, likely associated with pandemic-linked lifestyle modifications. However, only COVID-19-survivors had a decline in lean body mass over the same period, regardless of PASC symptoms.

Tanning, crucial for leather production, relies heavily on chromium yet poses risks due to chromium's oxidative conversion, leading to significant wastewater and solid waste generation. Physico-chemical methods are typically used for heavy metal removal, but they have drawbacks, prompting interest in eco-friendly biological remediation techniques like biosorption, bioaccumulation, and biotransformation. The EU Directive (2018/850) mandates alternatives to landfilling or incineration for industrial textile waste management, highlighting the importance of environmentally conscious practices for leather products' end-of-life management, with com-posting being the most researched and viable option. This study aimed to isolate microorganisms from tannery wastewater and identify those responsible for different types of tanned leather biodegradation. Using a leather biodegradation assay (ISO 20136) with tannery and municipal wastewaters as inoculum, bacterial shifts during leather biodegradation were observed. Over 10,000 bacterial species were identified in all analyzed samples, with 8 bacterial strains isolated from tannery wastewater. Identification of bacterial genera like Acinetobacter, Brevundimo-nas, and Mycolicibacterium provides insights into potential microbial candidates for enhancing leather biodegradability, wastewater treatment, and heavy metal bioremediation in industrial applications.

The etiology of ‘dual harm’ (the co-occurrence of self-harm and externalized violence in the same individual) is under-researched. Risk factors have mostly been investigated for each behavior separately. We aimed to examine adversities experienced between birth and age 15 years among adolescents and young adults with histories of self-harm and violent criminality, with a specific focus on dual harm. Three nested case-control studies were delineated using national interlinked Danish registers; 58,409 cases in total aged 15-35 were identified: 28,956 with a history of violent criminality (but not self-harm), 25,826 with a history of self-harm (but not violent criminality), and 3987 with dual harm history. Each case was matched by date of birth and gender to 20 controls who had not engaged in either behavior. We estimated exposure prevalence for cases vs. controls for each of the three behavior groups, and incidence rate ratios (IRRs). Experiencing 5 or more childhood adversities was more prevalent among individuals with dual harm history (19.3%; 95% CI 18.0, 20.8%) versus self-harm (10.9%; 10.5, 11.3%) and violence (11.4%; 11.0%, 11.8%) histories. The highest IRRs for dual harm were linked with parental unemployment (5.15; 95% CI 4.71, 5.64), parental hospitalization following self-harm (4.91; 4.40, 5.48) or assault (5.90; 5.07, 6.86), and parental violent criminality (6.11; 5.57, 6.70). Growing up in environments that are characterized by poverty, violence and substance misuse, and experiencing multiple adversities in childhood, appear to be especially strongly linked with elevated dual harm risk. These novel findings indicate potential etiologic pathways to dual harm.

Oil and gas refineries are highly water-intensive industrial settings, with effluent containing a significant level of pollution stemming from diverse organic and inorganic compounds. Besides adhering to discharge standards for industrial effluent, incorporating treated oil refinery effluent (ORE) into the production cycle can play a pivotal role in curbing water consumption. In recent years, there has been research into different approaches to reclaiming ORE. Yet, selecting treatment methods that are technically, economically, and environmentally effective is crucial to preventing resource waste. Therefore, this study aimed to examine the last two decades of literature on methods and technologies used for ORE treatment. Based on the inclusion criteria, the final screening included 82 studies, with acceptable agreement assessed using Cohen's inter-examiner kappa equal to 0.86. The included studies were of biological treatment (n = 27), physicochemical processes (n = 12), advanced purification processes (n = 16), membrane-based technologies (n = 15), and green technologies (n = 13). This comprehensive review showed that the advanced membrane-based techniques are effective in the removal of pollutants from ORE for several reasons, such as reducing the consumption of chemicals, high efficiency, and ease of setup and maintenance. However, combined methods with a focus on membrane-based processes (e.g. UF-RO) are the most promising options for the reclamation of ORE. Since some effluent treatment methods require the use of chemicals and energy to run, future research should focus on environmentally friendly methods and the use of renewable energy.

Dirofilaria immitis and D. repens are the two most widespread and important species of mosquito-borne nematodes, posing a significant threat to veterinary health, particularly affecting canines and felines. While D. immitis causes cardiopulmonary dirofilariasis, D. repens causes subcutaneous infections in dogs and other carnivores. Despite extensive knowledge about the aforementioned parasites, little is known about their natural vectors in Serbia. Thus, the aim of this study was to: i) further map out Dirofilaria sp. hotspots in the Vojvodina Province, ii) detect positive mosquito species which can provide insights in how the nematodes spread and adapt to the environmental conditions, and iii) analyze the blood fed mosquitoes females of species found infected to identify the potential source of parasite infection. A total of 2,902 female mosquitoes were collected across 73 locations during 2021 and 2022. Molecular biology methods, based on conventional PCR, were used to analyze non-blood fed (2,521 specimens) and blood fed (381 specimens) mosquito females, in order to detect filarial nematode presence and identify blood meal sources respectively. When the parasite genome was detected, the amplicon (COI gene, 650 bp fragment) was sent for Sanger sequencing, further confirming the presence of nematodes and species assignation. D. immitis was detected in three Culex pipiens mosquitoes collected in XXXX (location and date), while Setaria tundra was detected in Aedes vexans and Aedes caspius. This work adds two new locations where D. immitis occurs in Vojvodina, and is the first report of S. tundra on the territory of Serbia. Blood meal analysis provided insights into the preferences of mosquitoes that were positive for Dirofilaria sp. and S. tundra.

Parental refusal of childhood vaccines is a growing public health concern. Numerous reasons exist for this refusal, including religious, personal, and philosophical beliefs, and safety concerns. However, parental refusal of childhood vaccines is not simply an individualized problem for the family; this impacts herd immunity and affects the entire community. To improve vaccination rates among the pediatric population, understanding the thought process and decision-making behind parental opposition and refusal of vaccinations is essential. Using a survey developed to assess attitudes towards recommended childhood vaccines and the COVID-19 vaccine, this study examined the correlation between vaccine literacy and hesitancy among parents and legal guardians of elementary school-aged children in the Midwest. Responses were analyzed using Chi-squared tests on “R” software. Significant negative correlations were found between COVID-19 hesitancy and vaccine literacy, and resistance towards all vaccines and vaccine literacy. No significant negative correlations were found between hesitancy towards all vaccines and vaccine literacy, or hesitancy and literacy among different income and education brackets. Our results suggest that vaccine education may lessen vaccine hesitancy among parents and may be an essential factor in improving vaccination rates among the pediatric population.

Abstract: This study aimed to evaluate the long-term impact of paediatric dentistry programmes on children and adolescents to reduce oral health inequalities. It measures and assesses the improvement effects of paediatric dentistry programmes on the oral health of children and adolescents as part of the efforts to decrease oral health disparities in this age group. It included 406 individuals who participated in student and paediatric dentistry programmes between 2013 and 2019 at screening facilities in Gwangjin-gu, Seoul. Frequency analysis was conducted for demographic characteristics, and binary logistic regression analysis was performed to identify factors influencing the prevalence of dental caries as the dependent variable. Data were analysed using PASW Statistics with the statistical significance level set at α=0.05. Regarding oral health indicators based on the frequency of participation in paediatric dentistry programmes for children and adolescents, participants with seven or more sessions had lower prevalence rates of dental caries, malocclusion, and periodontal disease than those with only one session. Second, when comparing oral health indicators in paediatric dentistry programmes between primary and adolescent age groups, individuals under continuous management showed a decrease in permanent teeth affected by dental caries, dental caries prevalence, and malocclusion prevalence (excluding primary school age). Third, binary logistic regression analysis revealed significant influences (p

Background: Exercise rehabilitation programs are important for long-term health and wellbeing among people with cardiac and pulmonary disease. De-spite this, many people struggle to maintain their physical activity once reha-bilitation ends. This repeated measures study tracked changes in physical ac-tivity behaviour and motivation during and after completing a communi-ty-based exercise rehabilitation program. Methods: Cardiac and pulmonary exercise rehabilitation patients (N=31), com-pleted six once-monthly measures of physical activity (MET·min), self-determined motivation, intention, and habit strength for rehabilitation ex-ercise (within rehabilitation sessions) and lifestyle physical activity (outside of rehabilitation sessions). Linear regression and random effects models with es-timated marginal means were used to test for associations between physical activity motivation and behaviour and change during and post-rehabilitation. Results: Overall physical activity decreased after rehabilitation (823 MET·min) despite patients becoming more self-determined for lifestyle physical activity during rehabilitation. More self-determined motivation, stronger intentions, and stronger habits were associated with more lifestyle physical activity be-haviour. However, none of these motivation variables were significantly asso-ciated with rehabilitation exercise behaviour. Conclusions: Among community-based cardiac and pulmonary rehabilitation patients, physical activity levels decreased following exercise rehabilitation programs. The findings revealed clear distinctions in the motivation of reha-bilitation exercise compared to lifestyle physical activity. Exercise rehabilitation programs might improve the longevity of outcomes by integrating approaches to enhance lifestyle physical activity beyond the clinic.

Purpose: The objective of this paper is to explore the association between insufficient lycopene intake and risk of periodontitis among older adults. Methods: Data analysis was conducted using data from the National Health and Nutritional Examination Survey 2009-2014. Overall, 1227 adults aged 65- to 79-years-old were investigated. Results: Overall, 48.7% of older adults in this study were classified with different levels of periodontitis and 49.1% of individuals with a form of periodontitis had insufficient dietary lycopene intake. Sufficient lycopene intake was found to be associated with a lower likelihood of severe periodontitis (OR: .33; 95% CI: 0.17-.65; p=0.002). Non-Hispanic Black (NHB) older adults were more likely to develop severe forms of periodontal disease in comparison to Non-Hispanic White (NHW) older adults (OR: 2.82, 95% CI: 1.46-5.45, p=0.003). Gender was also found to play a role in periodontitis status, with women being less likely to have severe periodontitis status, compared to men (OR: 0.27; 95% CI: 0.14-0.55; p=0.0007,). However, only NHW females were less likely to have severe periodontitis compared to NHW males (OR: 0.26; 95% CI: 0.12-0.56; p= 0.001). Conclusion: Dietary intake of lycopene associated with periodontitis disease for individuals over the age of 65; those who consume a sufficient amount of lycopene are less likely to develop severe periodontitis. In general, more men suffered from periodontitis in comparison to women. Such association also has been found between NHB with severe periodontitis. In addition, reduced risk of moderate periodontitis is associated with sufficient lycopene intake is explored in NHW, future targeted interventions using lycopene in dietary intake as a preventative measure to delay or prevent the onset of periodontal disease needs to be race and gender specific.

Onychomycosis is a common fungal nail infection for which new antibiotics are needed to overcome antifungal resistance and other limitations of conventional treatments. This study reports the development of antifungal nail lacquers containing oregano essential oil (OEO), rosemary essential oil (REO), and/or biogenic silver nanoparticles (bioAgNP). The formulations (F) were tested against dermatophytes by agar diffusion, ex vivo nail infection, and scanning electron microscopy techniques. They were evaluated pharmacotechnically and by FTIR-PAS to access permeation across the nail plate. F-OEO and F-OEO/Ag were promising for the final nail lacquer formulation, they permeated through the nail plate and showed antifungal efficacy against dermatophytes-contaminated nails after 5 days of treatment. The treated nails had decreased hyphae and spores compared to untreated control; the hyphae were atypically flattened which indicates loss of cytoplasmic content due to damage to cytoplasmic membrane. Formulations were stable after centrifugation and thermal stress, maintaining organoleptic and physicochemical characteristics. F-OEO and F-OEO/Ag had pH compatible with the nail, and drying time (59-90 s) within the reference for nail lacquer. For the first time, OEO and bioAgNP were incorporated in a nail lacquer, resulting in a natural and nanotechnological antifungal product for onychomycosis that may combat microbial resistance.

Background: Worldwide, around 73 million induced abortions take place every year. Of these, 45% are unsafe and can lead to complications. The evolution of the legal and practical landscape of abortion in the Democratic Republic of the Congo (DRC) over the last few years necessitates a re-examination of the experience of induced abortion, leading this study to measure the incidence of abortion among young women (15 to 29 years of age), as well as the heterogeneity of this problem according to the residence of these young women (slum vs. non-slum areas). Methodology: We used representative survey data on women aged 15–49 in Kinshasa, collected from December 2021 to April 2022. The survey included questions about the respondents’ and their closest confidants’ experience of induced abortion, including the methods and sources used. We estimated abortion incidence and heterogeneity over one year based on residence in the city of Kinshasa according to sociodemographic characteristics. Results: The incidence rate of induced abortion among the confidants (close friends) in 2021 was 131.5 per 1000 (95% CI: IQR 99.4–163.6). These rates were significantly higher than the corresponding estimates of those surveyed. The incidence of induced abortion was 24.4 per 1000 (95% CI: 15.8–32.9) abortions per 1000 women among the respondents. The incidence rates of induced abortion were much higher among the respondents residing in slums than among those residing in non-slums (29.2 vs. 13.0 per 1000; p < 0.001). Conclusions: More precise estimates of the incidence of abortion indicate that the incidence rate of abortion was higher among young women residing in slums, who were unmarried and had no children. These incidences were higher among confidants than among respondents. There is still a lot of work to be done to fulfill the obligations outlined in the Maputo Protocol. This includes establishing easily accessible services that offer complete abortion care with a focus on women's needs, particularly in slum areas. The aim is to decrease the occurrence of unsafe abortions and their associated effects.

The hotel sector generates related environmental aspects from its construction process to the operation of its activities, products and services. In this way, corporate environmental management becomes directly linked, since it provides an adequate management of its resources, thus minimizing impacts. Thus, this research aims to carry out a socioeconomic and environmental diagnosis of the hotel sector in the municipality of Mossoró-RN. The study was classified in an exploratory, descriptive and explanatory way, with a qualitative-quantitative approach that is based on bibliographic, documentary and field research, carried out in 10 lodging facilities, through the application of questionnaires, on general aspects, initial environmental assessment and environmental aspects, and the data obtained were treated in Microsoft Excel software with the objective of creating a model and demonstrating results on the sustainable level of each establishment in question. The profile of the lodging facilities analyzed is characterized by 50% having from 10 to 30 rooms, 80% having between 10 and 30 employees, 80% being micro-enterprises, with 50% in an area of 750 m² to 2250 m² and 80% operating for more than 10 years. Point out a relevant result on the impacts of companies. The projects analyzed were built without any complaint for environmental crime and without negative impacts on the local population. Actions to reduce consumption were also observed, especially for the rationalization of water and energy. To this end, it points out that the adoption of the Environmental Management System (EMS) as a differential in the management of aspects and impacts of the hotel sector.

COVID-19, caused by the SARS-CoV-2 virus, has spread around the world and killed around 6.9 million people. Rapid and accurate diagnosis is essential for preventing and controlling the disease, reducing transmission and consequently saving lives. RT-PCR is the gold standard test used to detect the disease. However, the test is expensive and the result is time-consuming, which makes mass testing difficult, especially in countries with limited resources. In addition, the test has high analytical specificity and low diagnostic sensitivity, which leads to false-negative results. Several studies in the literature report the presence of hematological and biochemical alterations in infected patients and use these alterations with machine learning algorithms to help diagnose the disease. Therefore, this article presents the results obtained by different neural network architectures based on Adaptive Resonance Theory (ART) for the diagnosis of COVID-19. The study was conducted in two distinct stages: the first consisted of selecting the best ART network among several, using three open-access datasets and comparing the results with the literature. In the second stage, the chosen model was tested on a dataset containing patients from various hospitals in four countries. In addition, the model was subjected to external validation, including data from a country not present during the training and adjustment of the model, in order to validate the robustness and generalization capacity of the model. The results obtained by the ART networks in this study are promising, outperforming not only classical models, but also the deep learning models often used in the literature. Validation on data from different countries strengthens the model’s reliability and effectiveness.

It has been well established that hypertension is considered as a predominant risk factor for the development of cardiovascular diseases. Despite the link between hypertension and cardiovascular diseases, hypertension may directly affect the cardiac function leading in heart failure, mostly with preserved ejection fraction (HFpEF). There are echocardiographic findings indicating hypertensive heart disease (HHD) defined as altered cardiac morphology (left ventricular concentric hypertrophy, left atrium dilatation) and function (systolic or diastolic dysfunction) in patients with persistent hypertension irrespective of cardiac pathologies to which it contributes, such as coronary artery disease and kidney function impairment. In addition to classical echocardiographic parameters, novel indices, like speckle tracking of the left ventricle and left atrium, 3D volume evaluation, myocardial work in echocardiography may provide more accurate and reproducible diagnostic and prognostic data in patients with hypertension. However, their use is still underappreciated. An early detection and prompt therapy of HHD will greatly improve the prognosis. Hence, in the present review, we shed light on the role of echocardiography in the contemporary diagnostic and prognostic approach of HHD.

Detecting gear rim fatigue cracks using vibration signal analysis is often a challenging task, which typically requires a series of signal processing steps to detect and enhance fault features. This task becomes even harder in helicopter planetary gearboxes due to the complex interactions between different gear sets, and the presence of vibration from sources other than the planetary gear set. In this paper, we propose an effectual processing algorithm to isolate and enhance rim crack features and to trend crack growth in planet gears. The algorithm is based on using cepstrum editing (or liftering) of the hunting tooth synchronous averaged signals (angular domain) to extract harmonics and sidebands of the planet gears, and low-pass filtering and minimum entropy deconvolution (MED) to enhance extracted fault features. The algorithm has been successfully applied to a vibration dataset collected from a planet gear rim crack propagation test undertaken in the Helicopter Transmission Test Facility (HTTF) at DSTG Melbourne. In this test, a seeded notch generated by an electric discharge machine (EDM) was used to initiate a fatigue crack that propagated through the gear rim body over 94 load cycles. The proposed algorithm demonstrated a successful isolation of incipient fault features and provided a reliable trending capability to monitor crack progression. Results of a comparative analysis showed that the proposed algorithm outperformed the traditional signal processing approach.

(1) Background If placenta percreta (PP) is suspected, one should take into account ultrasound and MRI data, which allow one to assess not only the depth of invasion of the placenta into the myometrium, but also the degree of involvement of the blood vessels of the uteroplacental region and the pelvis, the formation of adhesions and collaterals between organs as well; (2) Methods: The patient with suspected PP was examined by ultrasound and Magnetic Resonance Imaging (MRI) examination. Histological study was performed on paraffin sections (hematoxylin & eosin, Mallory staining); cytotrophoblast was detected with antibodies to cytokeratin-8; (3) Results MRI and ultrasound examinations showed placental invasion extending beyond the serous lining of the uterus involving the posterior wall of the bladder. Elective abdominal delivery was performed at 37 weeks. Surgical excision of the uterine wall with invaded villi and involved fibrous bladder wall were performed. Histological study revealed invasive cytotrophoblast cells in the uterine wall located up to adventitia of the uterine subserosal vessels only. Thinning of the uterine wall up to the serous membrane was detected; (4) Conclusions: Placental villi and trophoblast extended beyond the uterus is not histology equivalent to placenta percreta. To diagnose PP instrumental visualization methods should be used.

Spironucleosis is considered one of the most significant enteric infectious diseases in young and growing game birds. Despite its importance diagnosing spironucleosis in game birds remains a continuing problem as no 'gold standard' test exists and there is no consensus for the use of the available methods for the diagnosis of spironucleosis in game birds. In this study we describe veterinarians’ perceptions of the adoption of existing methods for the diagnosis of spironucleosis in game birds and identify implications for the development of diagnostic guidelines. A questionnaire was used to evaluate participants’ familiarity with and use of existing diagnostic methods for spironucleosis, and to assess their perceptions of the adoption of these methods in their geographic regions. We surveyed veterinarians in practices in East Anglia (EA) and Northern Ireland (NI), UK with expertise or involvement in the treatment and/or diagnosis of spironucleosis in game birds. Of the 261 participants surveyed, 15 EA and 13 NI veterinarians (10.7%) responded to the questions on existing diagnostic methods. The study results indicate an overall insufficient knowledge and skills in the diagnosis and treatment of spironucleosis among veterinarians surveyed. These findings highlight the need for improved training of veterinarians in the diagnosis of spironucleosis.

(1) Background: Newer generation ophthalmologists practicing in the developed world are not very familiar with some tropical ocular diseases due to the absence of reports in the ophthalmic literature over the past thirty years. Because of world globalization or due to influx of immigrants from Sub Saharan Africa, exotic retinal diseases are being encountered more often in ophthalmology clinics; (2) Methods: Case series of chorioretinitis or optic neuritis with obscure etiology; (3) Results: 4 cases qualified with the diagnosis of ocular onchocerciasis based on their residence near fast rivers in endemic areas, multimodal imaging, long term follow-up showing progressive disease and negative workup for other diseases. Characteristic findings include peripapillary choroiditis with optic neuritis or atrophy, subretinal tracts of the microfilaria, progressive RPE atrophy around heavily pigmented multifocal chorioretinal lesions of varying shapes and subretinal white or crystalline dots. Typical skin findings are often absent in eyes with chorioretinitis rendering the diagnosis more challenging; (4) Conclusions: Familiarity with the myriad ocular findings of onchocerciasis, and a high degree of suspicion in subjects residing in endemic areas can help in the correct diagnosis and implementation of appropriate chemotherapy. Onchocercid chorioretinitis is a slow insiduous progressive and prolonged polymorphous disease.

One of the most prevalent brain diseases, epilepsy is characterized by recurring seizures that happen quite frequently. During seizures, a patient suffers uncontrollable muscle contractions that cause loss of motion and balance, which could lead to harm or even death. Establishing an automatic method for warning patients about impending seizures requires extensive research. It is possible to anticipate seizures by analyzing the Electroencephalogram (EEG) signal from the scalp region of the human brain. Time domain-based features such as Hurst exponent (Hur), Tsallis Entropy (TsEn), improved permutation entropy (impe), and amplitude-aware permutation entropy (AAPE) were extracted from EEG signals. In order to diagnose epileptic seizure children from normal children automatically, this study conducted two sessions, in the first session, the extracted features from the EEG dataset were classified using three machine learning (ML)-based models including support vector machine (SVM), K nearest neighbor (KNN), or decision tree (DT), while in the second session, the dataset was classified using three deep learning (DL)-based recurrent neural network (RNN) classifiers including a gated recurrent unit (GRU), long short-term memory (LSTM), and bidirectional (BiLSTM). The EEG dataset obtained from the Neurology Clinic at the Ibn-Rushd Training Hospital. In this regard, detailed explanations and research from the time domain and entropy characteristics show that using GRU, LSTM, and BiLSTM RNN deep learning classifiers on the All−time−entropy fusion feature improves the final classification results.

Leishmaniases are zoonotic vector-borne diseases caused by a wide variety of Leishmania species with complex transmission cycles involving different reservoirs, potential new hosts and vectors. Similarly, to other eukaryotes, Leishmania use released extracellular vesicles (LEVs) to play important initial interactions that are crucial to modulate the subsequent systemic immune response on the establishment of infection in humans and other important hosts like dogs. Recent studies in endemic areas of Brazil concluded that canine infections were predominantly due to L. amazonensis and not restricted to L. infantum (syn. Leishmania chagasi). Under these premises, the diagnosis of canine leishmaniasis needs to be improved, including the identification of current etiological agent, the clinical differential diagnosis and the histopathologic features. In this way, the dual aim of that study is to register collected observations for the diagnosis of natural canine infections and to insert in vitro results in the field of LEVs that still research gaps to be filled to understand the mechanisms and biological aspects involving the parasite-host interactions. Therefore, the future studies of Parasitology research for both of these fields are very important for the interventions for the prevention, control, elimination and eradication worldwide.

In Switzerland, psychomotor therapy (PMT) is a standard treatment for children with graphomotor impairments, but scientific evidence of its effectiveness is rare. To investigate the effectiveness of PMT, we conducted a randomized field trial (RFT). The sample consisted of 121 first and second graders with graphomotor impairments, some of whom met the criteria of developmental coordination disorder, while the remaining suffered from developmental dysgraphia. The treatments lasted over 5 months. Handwriting fluency and consistency were measured five times on a digitizing tablet. All participating children completed a self-concept interview, and a standardized fine motor performance test twice. Psychomotor therapy significantly improved the fine motor skills of the therapy group compared to those of the waiting group. However, there was no evidence that the treated children improved more than the waiting children in terms of their graphomotor skills such as frequency, automaticity, and consistency of forming letters. Finally, the children of the therapy group showed partial improvements in their handwriting self-concept, while those of the waiting group children remained stable. This short-term RFT demonstrated the effectiveness of PMT in terms of fine motor skills and some aspects of the handwriting self-concept but showed no effects on handwriting fluency and consistency.

Safe, high-quality and economical electric energy transportation is the basic requirements of modern power system operation. Transformer, as one of the core equipment of power system and national grid system, has the characteristics of diverse types, variable models and wide deployment. It is the basic equipment for power system to realize voltage change and electric energy distribution. Because the power system transformer needs to operate with load for a long time, the probability of failure is usually higher than that of other power equipment in general. This paper starts from the transformer fault and fault diagnosis. Firstly, the transformer fault types, traditional transformer fault diagnosis techniques and the advantages of machine learning in transformer fault diagnosis are reviewed. Secondly, the application of information system theory and information entropy in transformer fault diagnosis is introduced. Then it introduces machine learning technology, feature selection and extraction technology in transformer fault diagnosis, and machine learning algorithms such as support vector machine and extreme learning machine for transformer fault diagnosis. Finally, the potential development trend of information system theory, information entropy and machine learning in transformer fault diagnosis is forecasted. The combination of transformer fault prediction and machine learning algorithm is helpful for power system maintenance personnel to accurately predict the running state of power equipment, and also provides a new method and technology for the safe and reliable operation and regular maintenance of power system.

Chorioamnionitis (CA) at term of pregnancy can have infectious and/or inflammatory origin and is associated with adverse outcomes. Triple I (Intrauterine Inflammation, Infection or both, TI) has been proposed to reduce the overdiagnosis of infection and the neonatal overtreatment. The aim of the study is to identify clinical and histological variables predicting adverse outcomes when TI has been suspected and/or confirmed. This retrospective cohort study included 404 pregnancies ≥37 g.a. that were divided into 5 all-inclusive and mutually exclusive groups. TI was defined according to NICHD definition of 2015 and it could be confirmed (TI+) or not (TI-) by histological examination. Signs of infection/inflammation not respecting the definition of TI were classified as “Clinical suspicion”, supported (CS+) or not (CS-) by histology. Histological chorioamnionitis (HCA) without clinical manifestation represented fifth group. Whole Placental Involvement (WPLI), was defined as a histological inflammation involving maternal and fetal side. There were 113 TI+, 30 TI-, 186 CS+, 35 CS- and 40 isolated HCA. WPLI was diagnosed in 133 cases (39.2%). Composite neonatal outcome (CNO) occurred in 114 cases (28.2%) while composite maternal outcome (CMO) occurred in 192 cases (47.5%). TI+ compared to CS+ was more predictive of CNO (p=0.001), CMO (p<0.001) and WPLI (p=0.005). WPLI was related both to CNO (p<0.001) and CMO (p=0.046). TI+ and WPLI showed similar sensitivity but different specificity in predicting CNO. At logistic regression, CNO was independently predicted by TI+ (OR 2.21; p=0.001) and by WPLI (OR 2.23; p=0.001). TI, compared to CS, is a better predictor of CNO and can be useful for the identification of newborns at risk.

The pervasiveness of cardiovascular disease and physician misdiagnosis creates the urgent need for artificial intelligence models to improve diagnosis accuracy. The first objective of this study was to train machine learning models on publicly available data sets containing simple medical information of patients to diagnose cardiovascular disease. The Multilayer Perceptron (MLP) assembled for this task performed optimally with an F1 score of 0.8968. This prompted the creation of an open-source, automated cardiovascular disease diagnosis tool, powered by the MLP. The second objective of this study was to employ a meta-learning methodology called Local Interpretable Model-Agnostic Explanations (LIME) to understand the impact of different features on the model's diagnosis in the form of marginal probabilities. K-Means Clustering was employed to segment the data into ten clusters, after which each data example was passed through LIME. The resulting histograms depict the complex relationship between feature, cluster, and impact on diagnosis. A series of P-values with contrasting orders of magnitude shows the nuances in the MLP's understanding of patients from different clusters. The results of meta-learning analysis reveal that the most important features for cardiovascular disease diagnosis are fasting blood sugar, type of chest pain, and slope of the ST segment on an electrocardiogram. Future experiments should replicate the novel methodology introduced in this study on data sets containing more specialized medical features in order to gain practical medical insights about different types of cardiovascular disease represented by each cluster. Finally, feature engineering pathways should be explored with consideration of these results to create versatile diagnosis models not only for cardiovascular disease, but adaptable to other diseases as well.

As a relatively high percentage of adenoma polyps are missed, a computer-aided diagnosis (CAD) tool based on deep learning can aid the endoscopist in diagnosing colorectal polyps or colorectal cancer in order to decrease polyps missing rate and prevent colorectal cancer mortality. Convolutional Neural Network (CNN) is a deep learning method and has achieved better results in detecting and segmenting specific objects in images in the last decade than conventional models such as regression, support vector machines or artificial neural networks. In recent years, based on the studies in medical imaging criteria, CNN models have acquired promising results in detecting masses and lesions in various body organs, including colorectal polyps. In this review, the structure and architecture of CNN models and how colonoscopy images are processed as input and converted to the output are explained in detail. In most primary studies conducted in the colorectal polyp detection and classification field, the CNN model has been regarded as a black box since the calculations performed at different layers in the model training process have not been clarified precisely. Furthermore, I discuss the differences between the CNN and conventional models, inspect how to train the CNN model for diagnosing colorectal polyps or cancer, and evaluate model performance after the training process.

The Covid-19 pandemic spreads in waves for a year and a half, despite significant worldwide efforts, the development of biochemical diagnostic methods and population vaccination. One of the reasons for the infection spread is the impossibility of early disease detection through biochemical diagnostics, since biochemical processes slowly develop in a body. At the same time, well known that behavioral characteristics of a person, measured based on reflex movements, are capable for inertialess assessment of psychophysiological parameters. Vibraimage technology is the method of head micromovements video processing by inter-frame difference accumulation and converting spatial and temporal characteristics of the inter-frame difference into behavioral and psychophysiological parameters. Here we shown that behavioral parameters measured by vibraimage changed during COVID-19 infection. The identification of changes signs in behavioral parameters detected by AI trained on patients and controls. The best diagnostic accuracy (higher 94%) obtained using instantaneous values of behavioral parameters measured with the following vibraimage settings: 10Hz frequency of basic measurements; 25 inter-frame difference accumulations and averaging the diagnostic results over period of at least 5 seconds. COVID-19 diagnoses by behavioral parameters showed earlier (5-7 days) detection of the disease compared to symptoms and positive results of biochemical RT-PCR testing. Proposed method for COVID-19 diagnosis indicates infected persons within 5 seconds video processing using standard television cameras (web, IP) and computers, allows mass testing/selftesting and will stop the pandemic spread. We assume that head micromovements analysis for diagnosis of various diseases is possible not only with the help of vibraimage technology. Further research of human head micromovement analysis will help stop the COVID-19 pandemic and will contribute to the development of new contactless and environmentally friendly methods for early diagnosis of diseases.

There are numerous challenges involved in the diagnosis and treatment of lung cancer. Globally, majority of people suffers from cancerous disease involving throat cancer, lung cancer, stomach cancer, cancerous brain tumor. Among these the most common ones is the lung cancer or lung carcinoma. The leading cause of the lung cancer is the smoking. Around 80% to 90% of deaths are caused due to Non-small cell lung cancer (NSCLC). The inadequate diagnostic techniques and low chances for the survival of lung cancer patients results from the lack of an early prognosis and incompetency in traditional therapies. However, such challenges involved in the prognosis and treatment of lung cancer are on decline with the progression in magnetic nanoparticle (MNP) technology. Many break-through discoveries and inventions have been made in the field of cancer therapy by using magnetic nanoparticles. The implication of nanotechnology has led to the recent advancement in nanomedicine field. This has encouraged the improvement in different therapeutic and diagnosis strategies employing nanotechnology. The generation of immense technological benefits for nanoparticles systems has been accredited to its remarkable nanoscale physico-chemical properties. This in turns provides the early detection of lung cancer and active drugs delivery for an improved theranostics strategy. The present review provides a general idea of the current progression in the therapeutic and prognosis purpose of magnetic nanoparticles. Further, we disclose the development in the lung cancer theranostics by functionalization of magnetic nanoparticles. The established importance of magnetic nanoparticles in the theranostics centers for lung cancer has been revealed in this paper. The challenges existing in the theranostics of lung cancer are addressed through the functioning of magnetic nanoparticles in the process.

The aim of this study was to develop and validate a predictive early tooth loss multivariable model for periodontitis patients before periodontal treatment. A total of 544 patients seeking periodontal care at a university dental hospital were enrolled in the study. Teeth extracted after periodontal diagnosis and due to periodontal reasons were recorded. Clinical and sociodemographic variables were analyzed, considering the risk of short-term tooth loss. This study followed the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines for development and validation, with two cohorts considered as follows: 455 patients in the development phase and 99 in the validation phase. As a result, it was possible to compute a predictive model based on tooth type and clinical attachment loss. The model explained 25.3% of the total variability and correctly ranked 98.9% of the cases. The final reduced model area under the curve (AUC) was 0.809 (95% Confidence Interval (95% CI): 0.629 - 0.989) for the validation sample and 0.920 (95% CI: 0.891 - 0.950) for the development cohort. The established model presented adequate prediction potential of early tooth loss due to periodontitis. This model may have clinical and epidemiologic relevance towards the prediction of tooth loss burden.

The outbreak of novel Coronavirus (SARS-COV-2 ) disease (COVID-19) in Wuhan has attracted worldwide attention. SARS-COV-2 known to share a similar clinical manifestation that includes various symptoms such as pneumonia, fever, breathing difficulty, and in particular, SARS-COV-2 also causes a severe in ammation state that leads to death. Consequently, massive and rapid research growth has been observed across the globe to elucidate the mechanisms of infections and disease progression in genotype and phenotype scale. Data Science is playing a pivotal role in in-silico analysis to draw hidden and novel insights about the SARS-COV-2 origin, pathogenesis, COVID-19 outbreak forecasting, medical diagnosis, and drug discovery. With the availability of multi-omics, radiological, biomolecular, and medical data urges to develop novel exploratory and predictive models or customise exiting learning models to t the current problem domain. The presence of many approaches generates the need for the systematic surveys to guide both data scientists and medical practitioners. We perform an elaborate study on the state-of-the-art data science method ologies in action to tackle the current pandemic scenario. We consider various active COVID-19 data analytics domains such as phylogeny analysis, SARS-COV-2 genome identication, protein structure prediction, host-viral protein interactomics, clinical imaging, epidemiological analysis, and most importantly (existing) drug discovery. We highlight types of data, their generation pipeline, and the data science models in use. We believe that the current study will give a detailed sketch of the road map towards handling COVID-19 like situation by leveraging data science in the future. We summarise our review focusing on prime challenges and possible future research directions .

Automated medical diagnosis is one of the important machine learning applications in the domain of healthcare. In this regard, most of the approaches primarily focus on optimizing the accuracy of classification models. In this research, we argue that unlike general-purpose classification problems, medical applications, such as chronic kidney disease (CKD) diagnosis, require special treatment. In the case of CKD, apart from model performance, other factors such as the cost of data acquisition may also be taken into account to enhance the applicability of the automated diagnosis system. In this research, we have proposed two techniques for cost-sensitive feature ranking. An ensemble of decision tree models is employed in both the techniques for computing the worth of a feature in the CKD dataset. An automatic threshold selection heuristic is also introduced which is based on the intersection of features’ worth and their accumulated cost. A set of experiments are conducted to evaluate the efficacy of the proposed techniques on both tree-based and non-tree based classification models. The proposed approaches are also evaluated against several comparative techniques. Furthermore, it is demonstrated that the proposed techniques select around 1/4^th of the original CKD features while reducing the cost by a factor of 7.42 of the original feature set. Based on the extensive experimentation it is concluded that the proposed techniques employing feature-cost interaction heuristic tend to select feature subsets that are both useful and cost-effective.

The purpose of feature selection is to find important features from the original high-dimensional space. As atypical feature selection algorithm, Locally linear embedding(LLE)-based feature selection algorithm, which applies the idea of LLE to the graph-preserving feature selection framework, has been received wide attention. However, LLE-based feature selection framework is sensitive to noise and K-nearest neighbors. To address these problems, an improved LLE-based feature selection algorithm, robust LLE (RLLE) vote, is proposed. In this algorithm, $l_1$ and $l_2$ regularization are introduced into the high-dimensional reconstruction model of LLE. Furthermore, RLLE vote also proposes a criterion to measure the difference between the reconstruction features and the original features, and then the importance features can be selected by this criteria. Extensive experiments are carried out on a benchmark fault data set and the bearing data set collected from our own laboratory, and the experimental results demonstrate that RLLE vote achieves the most significant performance compared existing state-of-art methods.

Acute pancreatitis (AP) is an inflammatory condition of the pancreas and is one of the most common ailments of the gastrointestinal system that results in significant morbidity and mortality. The main etiologic causes of AP are alcohol consumption, gallstones, hypertriglyceridemia, and biliary stones. The clinical signs and symptoms, and diagnostic criteria of AP are well established in the literature and multiple studies. Multiple scoring systems have been used to predict the severity, prognosis, and mortality associated with AP. The present review of the literature brings to light the significant and recent contributions in the etiology, risk factors, epidemiology, diagnosis, complications, prognosis and newest modalities in treatment that could be beneficial in the management of AP.

The vibration feature of weak gear fault is often covered in strong background noise, which makes it necessary to establish weak feature enhancement methods. Among the enhancement methods, stochastic resonance (SR) has the unique advantage of transferring noise energy to weak signals and has a great application prospection in weak signal extraction. But the traditional SR potential model cannot form a richer potential structure and may lead to system instability when the noise is too great. To overcome these shortcomings, the article presents a periodic potential underdamping stochastic resonance (PPUSR) method after investigating the potential function and system signal-to-noise ratio (SNR). In addition, system parameters are further optimized by using ant colony algorithm. Through simulation and gear experiments, the effectiveness of the proposed method was verified. We concluded that compared with the traditional underdamped stochastic resonance (TUSR) method, the PPUSR method had a higher recognition degree and better frequency response capability.

For planetary gear has the characteristics of small volume, light weight and large transmission ratio, it is widely used in high speed and high power mechanical system. Poor working conditions result in frequent failures of planetary gear. A method is proposed for diagnosing faults in planetary gear based on permutation entropy of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Adaptive Neuro-fuzzy Inference System (ANFIS) in this paper. The original signal is decomposed into 6 intrinsic mode functions (IMF) and residual components by CEEMDAN. Since the IMF contains the main characteristic information of planetary gear faults, time complexity of IMFs are reflected by permutation entropies to quantify the fault features. The permutation entropies of each IMF component are defined as the input of ANFIS, and its parameters and membership functions are adaptively adjusted according to training samples. Finally, the fuzzy inference rules are determined, and the optimal ANFIS is obtained. The overall recognition rate of the test sample used for ANFIS is 90.75%, and the recognition rate of gear with one missing tooth is relatively high. The recognition rates of different fault gears based on the method can also achieve better results. Therefore, the proposed method can be applied to planetary gear fault diagnosis effectively.

Rare earth elements (REEs) are a type of new material resources, which have attracted significant attention in recent years. REEs have emerged as essential metals in modern-day technology due to their unique functions. Long-term large-scale mining and utilization of rare earths have caused serious environmental pollution and constitutes a global health issue, which raised concerns about human health safety. However, the toxicity profile of suspended particulate matter of REEs in the environment interacts with the human body in remain largely unknown. Studies have shown that REEs can enter the human body through a variety of pathways, leading to a variety of organ and system dysfunctions through changes in genetics, epigenetics, and signaling pathways. Through an extensive literature search and critical analysis, we provide a comprehensive overview of the available evidence, identify knowledge gaps, and make recommendations for future research directions.

The evaluation of nanostructured biomaterials and medicines is associated with 2D cultures that provide insight into the biological mechanisms at the molecular level, while critical aspects of the tumor microenvironment (TME) are provided by the study of animal xenograft models. More realistic models that can histologically reproduce human tumors are provided by tissue engineering methodologies of co-culturing cells of varied phenotypes, to provide 3D tumor spheroids that recapitulate the dynamic TME in 3D matrices. The novel approaches of 3D tumor models are combined with tumor tissue engineering (TTE) scaffolds including hydrogels, bioprinted materials, decellularized tissues, fibrous and nanostructured matrices. This review focuses on the use of nanostructured materials in cancer therapy and regeneration, and the development of realistic models for studying TME molecular and immune characteristics. Tissue regeneration is an important aspect of TTE scaffolds for restoring the normal function of the tissues, while providing cancer treatment. Thus, this article reports recent advancements on the development of 3D TTE models for antitumor drug screening, studying of tumor metastasis, and tissue regeneration. Also, this review identifies the great opportunities of 3D TTE scaffolds in the evaluation of the immunological mechanisms and processes involved in the application of immunotherapies.