Identification of most representative hub-genes for diagnosis, prognosis, and therapies of hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths globally. To reduce HCC-related mortality, early diagnosis and therapeutic improvement are essential. Hub differentially expressed genes (HubGs) may serve as potential diagnostic and prognostic biomarkers, also offering therapeutic targets for precise therapies. Therefore, we aimed to identify top-ranked hub genes for the diagnosis, prognosis, and therapy of HCC. METHODS: Through a systematic literature review, 202 HCC-related HubGs were derived from 59 studies, yet consistent detection across these was lacking. Then, we identified top-ranked HubGs (tHubGs) by integrated bioinformatics analysis, highlighting their functions, pathways, and regulators that might be more representative of the diagnosis, prognosis, and therapies of HCC. RESULTS: In this study, eight HubGs (CDK1, AURKA, CDC20, CCNB2, TOP2A, PLK1, BUB1B, and BIRC5) were identified as the tHubGs through the protein-protein interaction (PPI) network and survival analysis. Their differential expression in different stages of HCC, validated using The Cancer Genome Atlas (TCGA) Program database, suggests their potential as early HCC markers. The enrichment analyses revealed some important roles in HCC-related biological processes (BPs), molecular functions (MFs), cellular components (CCs), and signaling pathways. Moreover, the gene regulatory network analysis highlighted key transcription factors (TFs) and microRNAs (miRNAs) that regulate these tHubGs at transcriptional and post-transcriptional. Finally, we selected three drugs (CD437, avrainvillamide, and LRRK2-IN-1) as candidate drugs for HCC treatment as they showed strong binding with all of our proposed and published protein receptors. CONCLUSIONS: The findings of this study may provide valuable resources for early diagnosis, prognosis, and therapies for HCC.

Comparative Seed Proteome Profile Reveals No Alternation of Major Allergens in High-Yielding Mung Bean Cultivars.

Mung bean contains up to 32.6% protein and is one of the great sources of plant-based protein. Because many allergens also function as defense-related proteins, it is important to determine their abundance levels in the high-yielding, disease-resistant cultivars. In this study, for the first time, we compared the seed proteome of high-yielding mung bean cultivars developed by a conventional breeding approach. Using a label-free quantitative proteomic platform, we successfully identified and quantified a total of 1373 proteins. Comparative analysis between the high-yielding disease-resistant cultivar (MC5) and the other three cultivars showed that a total of 69 common proteins were significantly altered in their abundances across all cultivars. Bioinformatic analysis of these altered proteins demonstrated that PDF1 (a defensin-like protein) exhibited high sequence similarity and epitope matching with the established peanut allergens, indicating a potential mung bean allergen that showed a cultivar-specific response. Conversely, known mung bean allergen proteins such as PR-2/PR-10 (Vig r 1), Vig r 2, Vig r 4, LTP1, ß-conglycinin, and glycinin G4 showed no alternation in the MC5 compared to other cultivars. Taken together, our findings suggest that the known allergen profiles may not be impacted by the conventional plant breeding method to develop improved mung bean cultivars.

A computational approach to identify phytochemicals as potential inhibitor of acetylcholinesterase: Molecular docking, ADME profiling and molecular dynamics simulations.

Inhibition of acetylcholinesterase (AChE) is a crucial target in the treatment of Alzheimer's disease (AD). Common anti-acetylcholinesterase drugs such as Galantamine, Rivastigmine, Donepezil, and Tacrine have significant inhibition potential. Due to side effects and safety concerns, we aimed to investigate a wide range of phytochemicals and structural analogues of these compounds. Compounds similar to the established drugs, and phytochemicals were investigated as potential inhibitors for AChE in treating AD. A total of 2,270 compound libraries were generated for further analysis. Initial virtual screening was performed using Pyrx software, resulting in 638 molecules showing higher binding affinities compared to positive controls Tacrine (-9.0 kcal/mol), Donepezil (-7.3 kcal/mol), Galantamine (-8.3 kcal/mol), and Rivastigmine (-6.4 kcal/mol). Subsequently, ADME properties were assessed, including blood-brain barrier permeability and Lipinski's rule of five violations, leading to 88 compounds passing the ADME analysis. Among the rivastigmine analogous, [3-(1-methylpiperidin-2-yl)phenyl] N,N-diethylcarbamate showed interaction with Tyr123, Tyr336, Tyr340, Phe337, Trp285 residues of AChE. Tacrine similar compounds, such as 4-amino-2-styrylquinoline, exhibited bindings with Tyr123, Phe337, Tyr336, Trp285, Trp85, Gly119, and Gly120 residues. A phytocompound (bisdemethoxycurcumin) showed interaction with Trp285, Tyr340, Trp85, Tyr71, and His446 residues of AChE with favourable binding. These findings underscore the potential of these compounds as novel inhibitors of AChE, offering insights into alternative therapeutic avenues for AD. A 100ns simulation analysis confirmed the stability of protein-ligand complex based on the RMSD, RMSF, ligand properties, PCA, DCCM and MMGBS parameters. The investigation suggested 3 ligands as a potent inhibitor of AChE which are [3-(1-methylpiperidin-2-yl)phenyl] N,N-diethylcarbamate, 4-Amino-2-styrylquinoline and bisdemethoxycurcumin. Furthermore, investigation, including in-vitro and in-vivo studies, is needed to validate the efficacy, safety profiles, and therapeutic potential of these compounds for AD treatment.

Unveiling the molecular basis of paracetamol-induced hepatotoxicity: Interaction of N-acetyl-p-benzoquinone imine with mitochondrial succinate dehydrogenase.

Background and aim: N-acetyl-p-benzoquinoneimine (NAPQI), a toxic byproduct of paracetamol (Acetaminophen, APAP), can accumulate and cause liver damage by depleting glutathione and forming protein adducts in the mitochondria. These adducts disrupt the respiratory chain, increasing superoxide production and reducing ATP. The goal of this study was to provide computational proof that succinate dehydrogenase (SDH), a subunit of complex II in the mitochondrial respiratory chain, is a favorable binding partner for NAPQI in this regard. Method: Molecular docking, molecular dynamics simulation, protein-protein interaction networks (PPI), and KEGG metabolic pathway analysis were employed to identify binding characteristics, interaction partners, and their associations with metabolic pathways. A lipid membrane was added to the experimental apparatus to mimic the natural cellular environment of SDH. This modification made it possible to develop a context for investigating the role and interactions of SDH within a cellular ecosystem that was more realistic and biologically relevant. Result: The molecular binding affinity score for APAP and NAPQI with SDH was predicted -6.5 and -6.7 kcal/mol, respectively. Furthermore, RMSD, RMSF, and Rog from the molecular dynamics simulations study revealed that NAPQI has slightly higher stability and compactness compared to APAP at 100 ns timeframe with mitochondrial SDH. Conclusion: This study serves to predict the mechanistic process of paracetamol toxicity by using different computational approaches. In addition, this study will provide information about the drug target against APAP hepatotoxicity.

Insights into pelvic insufficiency fracture following pelvic radiotherapy for cervical cancer: a comparative review.

BACKGROUND: Radiotherapy (RT)-induced pelvic insufficiency fractures (PIF) are prevalent in patients with cervical cancer. Inconclusive studies on PIF after cervical irradiation create uncertainty. This review examined PIF after RT in cervical patients, including its pathobiology, likely locations of fractures, incidence, clinical symptoms, and predisposing factors. We further discussed study limitations and therapeutic possibilities of PIF. METHODS: The following online resources were searched for relevant articles: Google Scholar and PubMed. The keywords 'pelvic insufficiency fractures', 'cervical carcinoma' and 'cervical cancer', as well as 'chemoradiotherapy', 'chemoradiation', and 'radiotherapy', were some of the terms that were used during the search. RESULTS: Patients with PIF report pelvic pain after radiation treatment for cervical cancer; the incidence of PIF ranges from 1.7 to 45.2%. Evidence also supports that among all patients treated with pelvic radiation, those who experienced pelvic insufficiency fractures invariably had at least one sacral fracture, making it the most frequently fractured bone in the body. Menopausal status, weight, BMI, age, and treatments and diagnosis modalities can influence PIF during radiotherapy. CONCLUSIONS: In conclusion, our comparative review of the literature highlights significant heterogeneity in various aspects of PIF following radiation for patients with cervical cancer. This diversity encompasses prevalence rates, associated risk factors, symptoms, severity, diagnosis methods, preventive interventions, and follow-up periods. Such diversity underscores the complexity of PIF in this population and emphasizes the critical need for further research to elucidate optimal management strategies and improve patient outcomes.

Exploring the reproductive features of Clupisoma garua from Ganges River, Bangladesh: insights into eco-climatic factors for sustainable management and advancements in aquaculture practices.

The Garua bacha, Clupisoma garua, holds considerable commercial and nutritional significance as a fish species. This study investigates the relationships between GSI (gonadosomatic index) and eco-climatic conditions, and provides comprehensive insights into several facets of reproduction, such as size at first sexual maturity (Lm), spawning season, peak spawning period, and proposes sustainable management strategies for C. garua in the Ganges River, northwestern, Bangladesh. Since January to December 2017, 570 female individuals have been gathered from the Ganges River using cast nets, gill nets, and square lift nets on a monthly basis. The total length (TL) of the specimens varied from 3.90 to 26.30 cm. Lm was estimated as 14.30 cm based on the results of the TL vs. GSI, MGSI (modified gonadosomatic index), DI (Dobriyal index), 14.00 cm through TL-FL regressions, and 15.18 cm depending on Lmax for this species. Furthermore, the spawning period was observed from May to August, peaking in June and July, based on greater GSI, MGSI, and DI values. Throughout the peak spawning season, there was not a significant differ from a value of 100 in the relative weight (WR) of the female species. During the spawning season, a substantial association between temperature and GSI (p = 0.0038); rainfall and GSI (p = 0.0043); DO and GSI (p = 0.0043); pH and GSI (p = 0.0002); and alkalinity and GSI (p = 0.0001) was detected. Analyzing a 55-year data series (1964-2018), it became clear that an increase in average air temperature of 0.0289 °C and a decrease in rainfall of 2.988 mm per year might possibly delay the spawning season of this species. As a consequence, the results of this study provide crucial information for developing management strategies to safeguard C. garua populations in the Ganges River and its adjacent ecosystems.

Influence of hydrometeorological risk factors on child diarrhea and enteropathogens in rural Bangladesh.

BACKGROUND: A number of studies have detected relationships between weather and diarrhea. Few have investigated associations with specific enteric pathogens. Understanding pathogen-specific relationships with weather is crucial to inform public health in low-resource settings that are especially vulnerable to climate change. OBJECTIVES: Our objectives were to identify weather and environmental risk factors associated with diarrhea and enteropathogen prevalence in young children in rural Bangladesh, a population with high diarrheal disease burden and vulnerability to weather shifts under climate change. METHODS: We matched temperature, precipitation, surface water, and humidity data to observational longitudinal data from a cluster-randomized trial that measured diarrhea and enteropathogen prevalence in children 6 months-5.5 years from 2012-2016. We fit generalized additive mixed models with cubic regression splines and restricted maximum likelihood estimation for smoothing parameters. RESULTS: Comparing weeks with 30°C versus 15°C average temperature, prevalence was 3.5% higher for diarrhea, 7.3% higher for Shiga toxin-producing Escherichia coli (STEC), 17.3% higher for enterotoxigenic E. coli (ETEC), and 8.0% higher for Cryptosporidium. Above-median weekly precipitation (median: 13mm; range: 0-396mm) was associated with 29% higher diarrhea (adjusted prevalence ratio 1.29, 95% CI 1.07, 1.55); higher Cryptosporidium, ETEC, STEC, Shigella, Campylobacter, Aeromonas, and adenovirus 40/41; and lower Giardia, sapovirus, and norovirus prevalence. Other associations were weak or null. DISCUSSION: Higher temperatures and precipitation were associated with higher prevalence of diarrhea and multiple enteropathogens; higher precipitation was associated with lower prevalence of some enteric viruses. Our findings emphasize the heterogeneity of the relationships between hydrometeorological variables and specific enteropathogens, which can be masked when looking at composite measures like all-cause diarrhea. Our results suggest that preventive interventions targeted to reduce enteropathogens just before and during the rainy season may more effectively reduce child diarrhea and enteric pathogen carriage in rural Bangladesh and in settings with similar meteorological characteristics, infrastructure, and enteropathogen transmission.

Factors affecting the performance of a pharmaceutical wastewater treatment plant: Characterization of effluent and environmental risk.

Pharmaceutical industries produce a huge volume of emerging pollutants (EPs) that pose a threat to the aqueous environment. Biological processes have shown their inefficacy in treating many pharmaceutical products. The study assessed physicochemical parameters, EPs, heavy metals in pharmaceutical industrial wastewater, and the removal efficiency (RE) of an aerobic biological treatment plant. The study also assessed the contamination levels and risk using several indices, such as the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI), heavy metal pollution index (HPI), heavy metal evaluation index (HEI), and risk quotients index (RQs). The study found that the treated water quality was poor, having antibiotics, nonsteroidal anti-inflammatory drugs, and others, along with several transformation products (TPs) and heavy metals, which were unsafe for consumption with high environmental risk. The analysis results showed that the RE for TSS, BOD5, COD, TDS, and EC were found to be 91.80%, 86.81%, 72.29%, 72.20%, and 65.60%, respectively, where the values of BOD5, COD, NO3-, and PO43- in the effluent were still higher than the permissible limits of the ECR (2023). However, the RE for heavy metals was in the order of Cu (84.62%) > Fe (65.04%) > Mn (63.3%) > Zn (60.58%) > Cd (53.85%) > Ni (54.12%) > Pb (42.42%) > Cr (38%), where Cr and Cd concentrations were still higher than the permissible limit of DoE (2019). The Pearson correlation and PCA suggested that EC, TDS, TSS, DO, BOD5, and COD were the most correlating and contributing variables. This study argued that metal-ligand behaviors mainly affect the removal efficiency of the treatment plant by lowering the removal rate of heavy metals and pharmaceutical products.

Treatment Heterogeneity of Water, Sanitation, Hygiene, and Nutrition Interventions on Child Growth by Environmental Enteric Dysfunction and Pathogen Status for Young Children in Bangladesh.

Background: Water, sanitation, hygiene (WSH), nutrition (N), and combined (N+WSH) interventions are often implemented by global health organizations, but WSH interventions may insufficiently reduce pathogen exposure, and nutrition interventions may be modified by environmental enteric dysfunction (EED), a condition of increased intestinal permeability and inflammation. This study investigated the heterogeneity of these treatments' effects based on individual pathogen and EED biomarker status with respect to child linear growth. Methods: We applied cross-validated targeted maximum likelihood estimation and super learner ensemble machine learning to assess the conditional treatment effects in subgroups defined by biomarker and pathogen status. We analyzed treatment (N+WSH, WSH, N, or control) randomly assigned in-utero, child pathogen and EED data at 14 months of age, and child LAZ at 28 months of age. We estimated the difference in mean child length for age Z-score (LAZ) under the treatment rule and the difference in stratified treatment effect (treatment effect difference) comparing children with high versus low pathogen/biomarker status while controlling for baseline covariates. Results: We analyzed data from 1,522 children, who had median LAZ of -1.56. We found that myeloperoxidase (N+WSH treatment effect difference 0.0007 LAZ, WSH treatment effect difference 0.1032 LAZ, N treatment effect difference 0.0037 LAZ) and Campylobacter infection (N+WSH treatment effect difference 0.0011 LAZ, WSH difference 0.0119 LAZ, N difference 0.0255 LAZ) were associated with greater effect of all interventions on growth. In other words, children with high myeloperoxidase or Campylobacter infection experienced a greater impact of the interventions on growth. We found that a treatment rule that assigned the N+WSH (LAZ difference 0.23, 95% CI (0.05, 0.41)) and WSH (LAZ difference 0.17, 95% CI (0.04, 0.30)) interventions based on EED biomarkers and pathogens increased predicted child growth compared to the randomly allocated intervention. Conclusions: These findings indicate that EED biomarker and pathogen status, particularly Campylobacter and myeloperoxidase (a measure of gut inflammation), may be related to impact of N+WSH, WSH, and N interventions on child linear growth.

An Ensemble Feature Selection Approach-Based Machine Learning Classifiers for Prediction of COVID-19 Disease.

The respiratory disease of coronavirus disease 2019 (COVID-19) has wreaked havoc on the economy of every nation by infecting and killing millions of people. This deadly disease has taken a toll on the life of the entire human race, and an exact cure for it is still not developed. Thus, the control and cure of this disease mainly depend on restricting its transmission rate through early detection. The detection of coronavirus infection facilitates the isolation and exclusive care of infected patients. This research paper proposes a novel data mining system that combines the ensemble feature selection method and machine learning classifier for the effective identification of COVID-19 infection. Different feature selection approaches including chi-square test, recursive feature elimination (RFE), genetic algorithm (GA), particle swarm optimization (PSO), and random forest are evaluated for their effectiveness in enhancing the classification accuracy of the machine learning classifiers. The classifiers that are considered in this research work are decision tree, naïve Bayes, K-nearest neighbor (KNN), multilayer perceptron (MLP), and support vector machine (SVM). Two COVID-19 datasets were used for testing from which the best features supporting the dataset were extracted by the proposed system. The performance of the machine learning classifiers based on the ensemble feature selection methods is analyzed.

A cluster-randomized trial of water, sanitation, handwashing and nutritional interventions on stress and epigenetic programming.

A regulated stress response is essential for healthy child growth and development trajectories. We conducted a cluster-randomized trial in rural Bangladesh (funded by the Bill & Melinda Gates Foundation, ClinicalTrials.gov NCT01590095) to assess the effects of an integrated nutritional, water, sanitation, and handwashing intervention on child health. We previously reported on the primary outcomes of the trial, linear growth and caregiver-reported diarrhea. Here, we assessed additional prespecified outcomes: physiological stress response, oxidative stress, and DNA methylation (N = 759, ages 1-2 years). Eight neighboring pregnant women were grouped into a study cluster. Eight geographically adjacent clusters were block-randomized into the control or the combined nutrition, water, sanitation, and handwashing (N + WSH) intervention group (receiving nutritional counseling and lipid-based nutrient supplements, chlorinated drinking water, upgraded sanitation, and handwashing with soap). Participants and data collectors were not masked, but analyses were masked. There were 358 children (68 clusters) in the control group and 401 children (63 clusters) in the intervention group. We measured four F2-isoprostanes isomers (iPF(2α)-III; 2,3-dinor-iPF(2α)-III; iPF(2α)-VI; 8,12-iso-iPF(2α)-VI), salivary alpha-amylase and cortisol, and methylation of the glucocorticoid receptor (NR3C1) exon 1F promoter including the NGFI-A binding site. Compared with control, the N + WSH group had lower concentrations of F2-isoprostanes isomers (differences ranging from -0.16 to -0.19 log ng/mg of creatinine, P < 0.01), elevated post-stressor cortisol (0.24 log µg/dl; P < 0.01), higher cortisol residualized gain scores (0.06 µg/dl; P = 0.023), and decreased methylation of the NGFI-A binding site (-0.04; P = 0.037). The N + WSH intervention enhanced adaptive responses of the physiological stress system in early childhood.

Global disparities in COVID-19 vaccine booster dose (VBD) acceptance and hesitancy: An updated narrative review.

The global deployment of COVID-19 vaccine booster dose (VBD) has been recognized as a promising therapeutic alliance to provide repeated immunity against the arrival of new variants. Despite scientific evidence supports the effectiveness of periodic doses, COVID-19 vaccine booster reluctance continues to thrive. This narrative review aimed to examine global COVID-19 vaccine booster dose (VBD) acceptance and summarize an up-to-date assessment of potential antecedents associated with VBD acceptance. A comprehensive search was performed in several reputable databases such as Medline (via PubMed), Scopus, Google scholar, and Web of Science from June 10th, 2023, to August 1st, 2023. All relevant descriptive and observational studies on COVID-19 VBD acceptance and hesitancy were included in this review. A total of fifty-eight (58) studies were included, with Asia representing the highest count with thirty-one (53%) studies, Europe with eleven (19 %), the United States with nine (16 %), and other regions (Africa and multi-ethnic) with seven (12 %). Worldwide, the pooled COVID-19 VBD acceptance rate was 77.09 % (95 % CI: 76.28-78.18), VBD willingness (n) = 164189, and the total sample (N) = 212,990. The highest and the lowest VBD acceptance rate was reported in Europe and American regions, respectively, 85.38 % (95 % CI: 85.02-85.73, (n) = 32,047, (N = 37,533) vs. 66.92 % (95 % CI: 66.56-67.4), (n) = 29335, (N) = 43,832. However, Asia and multi-ethnic areas reported moderately high VBD acceptance rate 79.13 % (95 % CI: 78.77-79.23, (n) = 93,994, (N) = 11,8779) and 72.16 % (95 % CI: 71.13-72.93, (n) = 9276, (N) = 12,853), respectively. The most common and key antecedents of COVID-19 VBD acceptance and hesitancy across the countries were "equal safety", "efficacy", "effectiveness", "post-vaccination side effects", "community protection" "family protection", "risk-benefit ratio", "booster necessity", "trust", and "variants control". Disparities in the uptake of COVID-19 VBD were observed globally, with the highest rates found in Europe, and the lowest rates in American regions. Multiple potential antecedents including safety, efficacy, and post-vaccination side effects were associated with VBD acceptance and hesitancy.

Stress biomarkers and child development in young children in Bangladesh.

BACKGROUND: Hundreds of millions of children in low- and middle-income countries are exposed to chronic stressors, such as poverty, poor sanitation and hygiene, and sub-optimal nutrition. These stressors can have physiological consequences for children and may ultimately have detrimental effects on child development. This study explores associations between biological measures of chronic stress in early life and developmental outcomes in a large cohort of young children living in rural Bangladesh. METHODS: We assessed physiologic measures of stress in the first two years of life using measures of the hypothalamic-pituitary-adrenal (HPA) axis (salivary cortisol and glucocorticoid receptor gene methylation), the sympathetic-adrenal-medullary (SAM) system (salivary alpha-amylase, heart rate, and blood pressure), and oxidative status (F2-isoprostanes). We assessed child development in the first two years of life with the MacArthur-Bates Communicative Development Inventories (CDI), the WHO gross motor milestones, and the Extended Ages and Stages Questionnaire (EASQ). We compared development outcomes of children at the 75th and 25th percentiles of stress biomarker distributions while adjusting for potential confounders using generalized additive models, which are statistical models where the outcome is predicted by a potentially non-linear function of predictor variables. RESULTS: We analyzed data from 684 children (49% female) at both 14 and 28 months of age; we included an additional 765 children at 28 months of age. We detected a significant relationship between HPA axis activity and child development, where increased HPA axis activity was associated with poor development outcomes. Specifically, we found that cortisol reactivity (coefficient -0.15, 95% CI (-0.29, -0.01)) and post-stressor levels (coefficient -0.12, 95% CI (-0.24, -0.01)) were associated with CDI comprehension score, post-stressor cortisol was associated with combined EASQ score (coefficient -0.22, 95% CI (-0.41, -0.04), and overall glucocorticoid receptor methylation was associated with CDI expression score (coefficient -0.09, 95% CI (-0.17, -0.01)). We did not detect a significant relationship between SAM activity or oxidative status and child development. CONCLUSIONS: Our observations reveal associations between the physiological evidence of stress in the HPA axis with developmental status in early childhood. These findings add to the existing evidence exploring the developmental consequences of early life stress.

The Enterics for Global Health (EFGH) Shigella Surveillance Study in Bangladesh.

Background: Shigella is an important cause of diarrhea in Bangladeshi children <5 years of age, with an incidence rate of 4.6 per 100 person-years. However, the report was more than a decade old, and data on Shigella consequences are similarly outdated and heterogeneously collected. Methods: Facility-based disease surveillance is planned to be carried out under the Enterics for Global Health (EFGH) Shigella Surveillance Study consortium for 2 years with aims to optimize and standardize laboratory techniques and healthcare utilization and coverage survey, clinical and anthropometric data collection, safety monitoring and responsiveness, and other related activities. The EFGH is a cohesive network of multidisciplinary experts, capable of operating in concert to conduct the study to generate data that will pave the way for potential Shigella vaccine trials in settings with high disease burden. The study will be conducted within 7 country sites in Asia, Africa, and Latin America. Conclusions: We outline the features of the Bangladesh site as part of this multisite surveillance network to determine an updated incidence rate and document the consequences of Shigella diarrhea in children aged 6-35 months, which will help inform policymakers and to implement the future vaccine trials.

Data Management in Multicountry Consortium Studies: The Enterics For Global Health (EFGH) Shigella Surveillance Study Example.

Background: Rigorous data management systems and planning are essential to successful research projects, especially for large, multicountry consortium studies involving partnerships across multiple institutions. Here we describe the development and implementation of data management systems and procedures for the Enterics For Global Health (EFGH) Shigella surveillance study-a 7-country diarrhea surveillance study that will conduct facility-based surveillance concurrent with population-based enumeration and a health care utilization survey to estimate the incidence of Shigella--associated diarrhea in children 6 to 35 months old. Methods: The goals of EFGH data management are to utilize the knowledge and experience of consortium members to collect high-quality data and ensure equity in access and decision-making. During the planning phase before study initiation, a working group of representatives from each EFGH country site, the coordination team, and other partners met regularly to develop the data management systems for the study. Results: This resulted in the Data Management Plan, which included selecting REDCap and SurveyCTO as the primary database systems. Consequently, we laid out procedures for data processing and storage, study monitoring and reporting, data quality control and assurance activities, and data access. The data management system and associated real-time visualizations allow for rapid data cleaning activities and progress monitoring and will enable quicker time to analysis. Conclusions: Experiences from this study will contribute toward enriching the sparse landscape of data management methods publications and serve as a case study for future studies seeking to collect and manage data consistently and rigorously while maintaining equitable access to and control of data.

Diarrhea Case Surveillance in the Enterics for Global Health Shigella Surveillance Study: Epidemiologic Methods.

Background: Shigella is a leading cause of acute watery diarrhea, dysentery, and diarrhea-attributed linear growth faltering, a precursor to stunting and lifelong morbidity. Several promising Shigella vaccines are in development and field efficacy trials will require a consortium of potential vaccine trial sites with up-to-date Shigella diarrhea incidence data. Methods: The Enterics for Global Health (EFGH) Shigella surveillance study will employ facility-based enrollment of diarrhea cases aged 6-35 months with 3 months of follow-up to establish incidence rates and document clinical, anthropometric, and financial consequences of Shigella diarrhea at 7 country sites (Mali, Kenya, The Gambia, Malawi, Bangladesh, Pakistan, and Peru). Over a 24-month period between 2022 and 2024, the EFGH study aims to enroll 9800 children (1400 per country site) between 6 and 35 months of age who present to local health facilities with diarrhea. Shigella species (spp.) will be identified and serotyped from rectal swabs by conventional microbiologic methods and quantitative polymerase chain reaction. Shigella spp. isolates will undergo serotyping and antimicrobial susceptibility testing. Incorporating population and healthcare utilization estimates from contemporaneous household sampling in the catchment areas of enrollment facilities, we will estimate Shigella diarrhea incidence rates. Conclusions: This multicountry surveillance network will provide key incidence data needed to design Shigella vaccine trials and strengthen readiness for potential trial implementation. Data collected in EFGH will inform policy makers about the relative importance of this vaccine-preventable disease, accelerating the time to vaccine availability and uptake among children in high-burden settings.

Essential oil-loaded biopolymeric particles on food industry and packaging: A review.

Essential oils (EOs) are liquid extracts derived from various parts of herbal or medicinal plants. They are widely accepted in food packaging due to their bioactive components, which exhibit remarkable antioxidant and antimicrobial properties against various pathogenic and food spoilage microorganisms. However, the functional efficacy of EOs is hindered by the high volatility of their bioactive compounds, leading to rapid release. Combining biopolymers with EOs forms a complex network within the polymeric matrix, reducing the volatility of EOs, controlling their release, and enhancing thermal and mechanical stability, favoring their application in food packaging or processing industries. This study presents a comprehensive overview of techniques used to encapsulate EOs, the natural polymers employed to load EOs, and the functional properties of EOs-loaded biopolymeric particles, along with their potential antioxidant and antimicrobial benefits. Additionally, a thorough discussion is provided on the widespread application of EOs-loaded biopolymers in the food industries. However, research on their utilization in confectionery processing, such as biscuits, chocolates, and others, remains limited. Further studies can be conducted to explore and expand the applications of EOs-loaded biopolymeric particles in food processing industries.

Determinants of anxiety and depression among type 2 diabetes mellitus patients: A hospital-based study in Bangladesh amid the COVID-19 pandemic.

Anxiety and depression are common psychological disorders in patients with type 2 diabetes mellitus (T2DM), which was upsurging worldwide amid the COVID-19 pandemic. This study aimed to explore factors associated with anxiety and depression among T2DM patients in Bangladesh during the COVID-19 pandemic. A cross-sectional study was conducted among T2DM patients using face-to-face interviews. Anxiety and depressive symptoms were measured using the CAS and PHQ-9 scales. Outcomes were assessed including sociodemographic, lifestyle, anthropometric, and challenges of getting routine medical and healthcare access-related questions. The prevalence of anxiety and depressive symptoms were 29.8% and 22.7%, respectively. Regression analysis reported that males older than 50 years, illiterate, unemployed or retired, urban residents, below the recommended level of moderate to vigorous physical activity (MVPA), low dietary diversity score (DDS) and obese respondents were associated with higher odds of anxiety and depressive symptoms. Moreover, respondents with transport difficulties, unaffordable medicine, medicine shortages, close friends or family members diagnosed with COVID-19 and financial problems during COVID-19 had higher odds of anxiety and depressive symptoms than their counterparts, respectively. Our study suggests providing psychological support, such as home-based psychological interventions, and highlighting policy implications to ensure the well-being of T2DM patients in Bangladesh during the pandemic.

Decolorization, degradation and detoxification of mutagenic dye Methyl orange by novel biofilm producing plant growth-promoting rhizobacteria.

Discharge of untreated dyeing wastewater nearby water-bodies is one of major causes of water pollution. Generally, bacterial strains isolated from industrial effluents and/or contaminated soils are used for the bioremediation of Methyl orange (MO), a mutagenic recalcitrant mono-azo dye, used in textiles and biomedical. However, MO degradation by biofilm producing plant growth-promoting rhizobacteria (BPPGPR) was not studied yet. In this study, 19 out of 21 BPPGPR strains decolorized 96.3-99.9% and 89.5-96.3% MO under microaerophilic and aerobic conditions, respectively from Luria-Bertani broth (LBB) followed by yeast-extract peptone and salt-optimized broth plus glycerol media within 120 h of incubation at 28 °C. Only selected BPPGPR including Pseudomonas fluorescens ESR7, P. veronii ESR13, Stenotrophomonas maltophilia ESR20, Staphylococcus saprophyticus ESD8, and P. parafulva ESB18 were examined for process optimization of MO decolorization using a single factor optimization method. This study showed that under optimal conditions (e.g., LBB, 100 mg L-1 MO, pH 7, incubation of 96 h, 28 °C), these strains could remove 99.1-99.8% and 97.6-99.5% MO under microaerophilic and aerobic conditions, respectively. Total azoreductase and laccase activities responsible for biodegradation were also remarkably activated in the biodegraded samples under optimal conditions, while these activities were repressed under unfavorable conditions (e.g., 40 °C and 7.5% NaCl). This study confirmed that MO was degraded and detoxified by these bacterial strains through breakage of azo bond. So far, this is the first report on bioremediation of MO by the BPPGPR strains. These BPPGPR strains are highly promising to be utilized for the bioremediation of dyeing wastewater in future.

Unsupervised novelty detection for time series using a deep learning approach.

In the Smart Homes and IoT devices era, abundant available data offers immense potential for enhancing system intelligence. However, the need for effective anomaly detection models to identify and rectify unusual data and behaviors within Smart Home Systems (SHS) remains a critical challenge. This research delves into the relatively unexplored domain of novelty anomaly detection, particularly in the context of unlabeled datasets. Introducing the novel DeepMaly method, this approach provides a practical tool for SHS developers. Functioning seamlessly in an unsupervised manner, DeepMaly distinguishes between seasonal and actual anomalies through a unique process of training on unlabeled pristine features extracted from time series data. Leveraging a combination of Long Short-Term Memory (LSTM) and Deep Convolutional Neural Network (DCNN), the model is primed to detect anomalies in real-time. The research culminates in a comprehensive data prediction and classification process into normal and abnormal data based on specified anomaly thresholds and fraction percentages. Notably, this function operates seamlessly unsupervised, eliminating the need for labeled datasets. The study concludes with a complete data forecasting and sorting method that divides data into normal and abnormal categories based on defined anomaly thresholds and fraction percentages. Working in an unsupervised mode reduces the requirement for labeled datasets. The results highlight the model's prowess in new detection, which has been successfully applied to benchmark datasets. However, there is a restriction since deep learning algorithms can recognize noise as abnormalities. Finally, the investigation enhances SHS anomaly detection, providing a crucial tool for real-time anomaly identification in the ever-changing IoT and Smart Homes scene.