Genomic characterization of carbapenemase-producing Enterobacterales from Dhaka food markets unveils the spread of high-risk antimicrobial-resistant clones and plasmids co-carrying bla NDM and mcr-1.1.

Background: The transmission of carbapenemase-producing Enterobacterales (CPE) in the external environment, especially through food, presents a significant public health risk. Objectives: To investigate the prevalence and genetic characteristics of CPE in food markets of Dhaka, Bangladesh, using WGS. Methods: CPE isolates were obtained from different food and water samples collected from food markets in the southern part of Dhaka, Bangladesh. The isolates subsequently underwent molecular typing, WGS employing both short- and long-read sequencers, and plasmid analysis. Results: This study unveiled an extensive spread of CPE, with no significant difference in contamination rates observed in samples (N = 136), including meat (n = 8), fish (n = 5), vegetables (n = 36) or various food-washed water (n = 65) from markets near hospitals or residential areas. Thirty-eight Enterobacterales from 33 samples carried carbapenemase genes (bla NDM-1, -4, -7, bla KPC-2, bla OXA-181 or bla IMI-1). Among these, the high-risk Escherichia coli ST410 clone was the most prevalent and distributed across various locations. Furthermore, the identification of IncHI2 plasmids co-harbouring resistance genes like bla NDM-5 and mcr-1.1, without discernible epidemiological connections, is a unique finding, suggesting their widespread dissemination. Conclusions: The analysis unveils a dynamic landscape of CPE dissemination in food markets, underscored by the proliferation of novel IncHI2 hybrid plasmids carrying both colistin- and carbapenem-resistance genes. This illuminates the ever-evolving landscape of antimicrobial resistance in Dhaka, urging us to confront its emergent challenges.

Mitigating Sociodemographic Bias in Opioid Use Disorder Prediction: Fairness-Aware Machine Learning Framework.

BACKGROUND: Opioid use disorder (OUD) is a critical public health crisis in the United States, affecting >5.5 million Americans in 2021. Machine learning has been used to predict patient risk of incident OUD. However, little is known about the fairness and bias of these predictive models. OBJECTIVE: The aims of this study are two-fold: (1) to develop a machine learning bias mitigation algorithm for sociodemographic features and (2) to develop a fairness-aware weighted majority voting (WMV) classifier for OUD prediction. METHODS: We used the 2020 National Survey on Drug and Health data to develop a neural network (NN) model using stochastic gradient descent (SGD; NN-SGD) and an NN model using Adam (NN-Adam) optimizers and evaluated sociodemographic bias by comparing the area under the curve values. A bias mitigation algorithm, based on equality of odds, was implemented to minimize disparities in specificity and recall. Finally, a WMV classifier was developed for fairness-aware prediction of OUD. To further analyze bias detection and mitigation, we did a 1-N matching of OUD to non-OUD cases, controlling for socioeconomic variables, and evaluated the performance of the proposed bias mitigation algorithm and WMV classifier. RESULTS: Our bias mitigation algorithm substantially reduced bias with NN-SGD, by 21.66% for sex, 1.48% for race, and 21.04% for income, and with NN-Adam by 16.96% for sex, 8.87% for marital status, 8.45% for working condition, and 41.62% for race. The fairness-aware WMV classifier achieved a recall of 85.37% and 92.68% and an accuracy of 58.85% and 90.21% using NN-SGD and NN-Adam, respectively. The results after matching also indicated remarkable bias reduction with NN-SGD and NN-Adam, respectively, as follows: sex (0.14% vs 0.97%), marital status (12.95% vs 10.33%), working condition (14.79% vs 15.33%), race (60.13% vs 41.71%), and income (0.35% vs 2.21%). Moreover, the fairness-aware WMV classifier achieved high performance with a recall of 100% and 85.37% and an accuracy of 73.20% and 89.38% using NN-SGD and NN-Adam, respectively. CONCLUSIONS: The application of the proposed bias mitigation algorithm shows promise in reducing sociodemographic bias, with the WMV classifier confirming bias reduction and high performance in OUD prediction.

Prediction and assessment of deleterious and disease causing nonsynonymous single nucleotide polymorphisms (nsSNPs) in human FOXP4 gene: An in - silico study.

In humans, FOXP gene family is involved in embryonic development and cancer progression. The FOXP4 (Forkhead box protein P4) gene belongs to this FOXP gene family. FOXP4 gene plays a crucial role in oncogenesis. Single nucleotide polymorphisms are biological markers and common determinants of human diseases. Mutations can largely affect the function of the corresponding protein. Therefore, the molecular mechanism of nsSNPs in the FOXP4 gene needs to be elucidated. Initially, the SNPs of the FOXP4 gene were extracted from the dbSNP database and a total of 23124 SNPs was found, where 555 nonsynonymous, 20525 intronic, 1114 noncoding transcript, 334 synonymous were obtained and the rest were unspecified. Then, a series of bioinformatics tools (SIFT, PolyPhen2, SNAP2, PhD SNP, PANTHER, I-Mutant2.0, MUpro, GOR IV, ConSurf, NetSurfP 2.0, HOPE, DynaMut2, GeneMANIA, STRING and Schrodinger) were used to explore the effect of nsSNPs on FOXP4 protein function and structural stability. First, 555 nsSNPs were analyzed using SIFT, of which 57 were found as deleterious. Following, PolyPhen2, SNAP2, PhD SNP and PANTHER analyses, 10 nsSNPs (rs372762294, rs141899153, rs142575732, rs376938850, rs367607523, rs112517943, rs140387832, rs373949416, rs373949416 and rs376160648) were common and observed as deleterious, damaging and diseases associated. Following that, using I-Mutant2.0 and MUpro servers, 7 nsSNPs were found to be the most unstable. GOR IV predicted that these seven nsSNPs affect protein structure by altering the protein contents of alpha helixes, extended strands, and random coils. Following DynaMut2, 5 nsSNPs showed a decrease in the ΔΔG value compared with the wild-type and were found to be responsible for destabilizing the corresponding protein. GeneMANIA and STRING network revealed interaction of FOXP4 with other genes. Finally, molecular dynamics simulation analysis revealed consistent fluctuation in RMSD and RMSF values, Rg and hydrogen bonds in the mutant proteins compared with WT, which might alter the functional and structural stability of the corresponding protein. As a result, the aforementioned integrated comprehensive bioinformatic analyses provide insight into how various nsSNPs of the FOXP4 gene change the structural and functional properties of the corresponding protein, potentially proceeding with the pathophysiology of human diseases.

Zoonotic linkage and environmental contamination of Methicillin-resistant Staphylococcus aureus (MRSA) in dairy farms: A one health perspective.

Methicillin-Resistant Staphylococcus aureus (MRSA) is a ubiquitous public health challenge, with its prevalence in human, animal, and environmental interfaces posing significant concerns. This study aimed to characterize and detect the zoonotic linkages of MRSA within the cow-environment-human interfaces in dairy farms to address the One Health perspective. A comprehensive investigation, involving 636 samples (an equal number of raw milk and cow nasal swab samples, along with varying numbers of human nasal swab and environmental samples), revealed an overall MRSA prevalence of 13.4% (n = 271/636). Notably, environmental samples exhibited the highest prevalence (19.3%), emphasizing the potential role of farm surroundings in MRSA transmission, while the lowest prevalence was found in raw milk at 11.8% (n = 31/263). The prevalence in cow nasal swabs and human nasal swabs was 13.3% (n = 35/263) and 15.1% (n = 8/53), respectively. Multiplex PCR analysis revealed the presence of different Staphylococcal enterotoxin (SEa, SEb, SEc, and SEd), and exfoliative toxin-producing genes (Eta, Etb) within the MRSA isolates underlining their potential to induce public health threats. All MRSA isolates exhibited complete resistance to Oxacillin (100%) and Amoxicillin (100%), while the highest sensitivity was observed for Vancomycin (85.8%). Furthermore, these MRSA strains demonstrated varying degrees of resistance to other commonly used antimicrobial drugs, including Cefoxitin (75.3%), Ceftarolin (71.2%), Sulfamethoxazole-Trimethoprim (63.5%), Ciprofloxacin (60%), and Gentamicin (49.5%). Detection of MRSA in cow, human, and environmental samples within the same farm vicinity highlights the risk of zoonotic transmission of MRSA from cows to humans through environmental interfaces. Phylogenetic analysis of the mecA gene in MRSA isolates from all sources within the same farm revealed a high similarity index (>84%) among them suggesting a shared evolutionary origin. Moreover, the MRSA isolates from milk samples showed a close evolutionary relationship with isolates from Kenya and Brazil, while the isolates from humans and the environment displayed noticeable resemblance to isolates from several Asian countries. The findings emphasize the importance of collaborative efforts under the One Health framework to address this multifaceted issue and ensure the safety of our food supply and public health.

Alpha-Synuclein Interaction with UBL3 Is Upregulated by Microsomal Glutathione S-Transferase 3, Leading to Increased Extracellular Transport of the Alpha-Synuclein under Oxidative Stress.

Aberrant aggregation of misfolded alpha-synuclein (α-syn), a major pathological hallmark of related neurodegenerative diseases such as Parkinson's disease (PD), can translocate between cells. Ubiquitin-like 3 (UBL3) is a membrane-anchored ubiquitin-fold protein and post-translational modifier. UBL3 promotes protein sorting into small extracellular vesicles (sEVs) and thereby mediates intercellular communication. Our recent studies have shown that α-syn interacts with UBL3 and that this interaction is downregulated after silencing microsomal glutathione S-transferase 3 (MGST3). However, how MGST3 regulates the interaction of α-syn and UBL3 remains unclear. In the present study, we further explored this by overexpressing MGST3. In the split Gaussia luciferase complementation assay, we found that the interaction between α-syn and UBL3 was upregulated by MGST3. While Western blot and RT-qPCR analyses showed that silencing or overexpression of MGST3 did not significantly alter the expression of α-syn and UBL3, the immunocytochemical staining analysis indicated that MGST3 increased the co-localization of α-syn and UBL3. We suggested roles for the anti-oxidative stress function of MGST3 and found that the effect of MGST3 overexpression on the interaction between α-syn with UBL3 was significantly rescued under excess oxidative stress and promoted intracellular α-syn to extracellular transport. In conclusion, our results demonstrate that MGST3 upregulates the interaction between α-syn with UBL3 and promotes the interaction to translocate intracellular α-syn to the extracellular. Overall, our findings provide new insights and ideas for promoting the modulation of UBL3 as a therapeutic agent for the treatment of synucleinopathy-associated neurodegenerative diseases.

Responsible AI for cardiovascular disease detection: Towards a privacy-preserving and interpretable model.

BACKGROUND AND OBJECTIVE: Cardiovascular disease (CD) is a major global health concern, affecting millions with symptoms like fatigue and chest discomfort. Timely identification is crucial due to its significant contribution to global mortality. In healthcare, artificial intelligence (AI) holds promise for advancing disease risk assessment and treatment outcome prediction. However, machine learning (ML) evolution raises concerns about data privacy and biases, especially in sensitive healthcare applications. The objective is to develop and implement a responsible AI model for CD prediction that prioritize patient privacy, security, ensuring transparency, explainability, fairness, and ethical adherence in healthcare applications. METHODS: To predict CD while prioritizing patient privacy, our study employed data anonymization involved adding Laplace noise to sensitive features like age and gender. The anonymized dataset underwent analysis using a differential privacy (DP) framework to preserve data privacy. DP ensured confidentiality while extracting insights. Compared with Logistic Regression (LR), Gaussian Naïve Bayes (GNB), and Random Forest (RF), the methodology integrated feature selection, statistical analysis, and SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) for interpretability. This approach facilitates transparent and interpretable AI decision-making, aligning with responsible AI development principles. Overall, it combines privacy preservation, interpretability, and ethical considerations for accurate CD predictions. RESULTS: Our investigations from the DP framework with LR were promising, with an area under curve (AUC) of 0.848 ± 0.03, an accuracy of 0.797 ± 0.02, precision at 0.789 ± 0.02, recall at 0.797 ± 0.02, and an F1 score of 0.787 ± 0.02, with a comparable performance with the non-privacy framework. The SHAP and LIME based results support clinical findings, show a commitment to transparent and interpretable AI decision-making, and aligns with the principles of responsible AI development. CONCLUSIONS: Our study endorses a novel approach in predicting CD, amalgamating data anonymization, privacy-preserving methods, interpretability tools SHAP, LIME, and ethical considerations. This responsible AI framework ensures accurate predictions, privacy preservation, and user trust, underscoring the significance of comprehensive and transparent ML models in healthcare. Therefore, this research empowers the ability to forecast CD, providing a vital lifeline to millions of CD patients globally and potentially preventing numerous fatalities.

An explainable machine learning framework for predicting the risk of buprenorphine treatment discontinuation for opioid use disorder among commercially insured individuals.

OBJECTIVES: Buprenorphine is an effective evidence-based medication for opioid use disorder (OUD). Yet premature discontinuation undermines treatment effectiveness, increasing the risk of mortality and overdose. We developed and evaluated a machine learning (ML) framework for predicting buprenorphine care discontinuity within 12 months following treatment initiation. METHODS: This retrospective study used United States (US) 2018-2021 MarketScan commercial claims data of insured individuals aged 18-64 who initiated buprenorphine between July 2018 and December 2020 with no buprenorphine prescriptions in the previous six months. We measured buprenorphine prescription discontinuation gaps of ≥30 days within 12 months of initiating treatment. We developed predictive models employing logistic regression, decision tree classifier, random forest, extreme gradient boosting, Adaboost, and random forest-extreme gradient boosting ensemble. We applied recursive feature elimination with cross-validation to reduce dimensionality and identify the most predictive features while maintaining model robustness. For model validation, we used several statistics to evaluate performance, such as C-statistics and precision-recall curves. We focused on two distinct treatment stages: at the time of treatment initiation and one and three months after treatment initiation. We employed SHapley Additive exPlanations (SHAP) analysis that helped us explain the contributions of different features in predicting buprenorphine discontinuation. We stratified patients into risk subgroups based on their predicted likelihood of treatment discontinuation, dividing them into decile subgroups. Additionally, we used a calibration plot to analyze the reliability of the models. RESULTS: A total of 30,373 patients initiated buprenorphine and 14.98% (4551) discontinued treatment. C-statistic varied between 0.56 and 0.76 for the first-stage models including patient-level demographic and clinical variables. Inclusion of proportion of days covered (PDC) measured after one month and three months following treatment initiation significantly increased the models' discriminative power (C-statistics: 0.60 to 0.82). Random forest (C-statistics: 0.76, 0.79 and 0.82 with baseline predictors, one-month PDC and three-months PDC, respectively) outperformed other ML models in discriminative performance in all stages (C-statistics: 0.56 to 0.77). Most influential risk factors of discontinuation included early stage medication adherence, age, and initial days of supply. CONCLUSION: ML algorithms demonstrated a good discriminative power in identifying patients at higher risk of buprenorphine care discontinuity. The proposed framework may help healthcare providers optimize treatment strategies and deliver targeted interventions to improve buprenorphine care continuity.

Effects of Commercial Probiotics on the Growth Performance, Intestinal Microbiota and Intestinal Histomorphology of Nile Tilapia (Oreochromis niloticus) Reared in Biofloc Technology (BFT).

Though different types of commercial probiotics are supplemented in biofloc technology (BFT), very little information is available on their effects on the farmed fish. Therefore, this study focused on evaluating the effects of three most commonly used commercial probiotics on the growth performance, intestinal histomorphology, and intestinal microbiota of Nile tilapia (Oreochromis niloticus) reared in BFT. Tilapia fry, with an average weight of 3.02 ± 0.50 g, were stocked at a density of 60 fry/0.2 m3, and cultured for 90 days. Three commercial probiotics were administered, with three replications for each: a single-genus multi-species probiotic (Bacillus spp.) (T1), a multi-genus multi-species probiotic (Bacillus sp., Lactobacillus sp., Nitrosomonas sp., Nitrobacter sp.) (T2), and a multi-species probiotic (Bacillus spp.) combined with enzymes including amylase, protease, cellulase, and xylanase (T3). The results showed significant variations in growth and feed utilization, with T3 outperforming other treatments in terms of weight gain, liver weight, and intestine weight. Adding Bacillus spp. with enzymes (T3) to water significantly increased the histomorphological parameters (villi length, villi depth, crypt depth, muscle thickness, intestinal thickness) as well as microbes (total viable count and total lactic acid bacteria) of intestine of fish compared to T1 and T2, leading to improved digestion and absorption responses. It is concluded that the supplementation of commercial probiotics has potential benefits on farmed fish species in BFT.

In silico functional, structural and pathogenicity analysis of missense single nucleotide polymorphisms in human MCM6 gene.

Single nucleotide polymorphisms (SNPs) are one of the most common determinants and potential biomarkers of human disease pathogenesis. SNPs could alter amino acid residues, leading to the loss of structural and functional integrity of the encoded protein. In humans, members of the minichromosome maintenance (MCM) family play a vital role in cell proliferation and have a significant impact on tumorigenesis. Among the MCM members, the molecular mechanism of how missense SNPs of minichromosome maintenance complex component 6 (MCM6) contribute to DNA replication and tumor pathogenesis is underexplored and needs to be elucidated. Hence, a series of sequence and structure-based computational tools were utilized to determine how mutations affect the corresponding MCM6 protein. From the dbSNP database, among 15,009 SNPs in the MCM6 gene, 642 missense SNPs (4.28%), 291 synonymous SNPs (1.94%), and 12,500 intron SNPs (83.28%) were observed. Out of the 642 missense SNPs, 33 were found to be deleterious during the SIFT analysis. Among these, 11 missense SNPs (I123S, R207C, R222C, L449F, V456M, D463G, H556Y, R602H, R633W, R658C, and P815T) were found as deleterious, probably damaging, affective and disease-associated. Then, I123S, R207C, R222C, V456M, D463G, R602H, R633W, and R658C missense SNPs were found to be highly harmful. Six missense SNPs (I123S, R207C, V456M, D463G, R602H, and R633W) had the potential to destabilize the corresponding protein as predicted by DynaMut2. Interestingly, five high-risk mutations (I123S, V456M, D463G, R602H, and R633W) were distributed in two domains (PF00493 and PF14551). During molecular dynamics simulations analysis, consistent fluctuation in RMSD and RMSF values, high Rg and hydrogen bonds in mutant proteins compared to wild-type revealed that these mutations might alter the protein structure and stability of the corresponding protein. Hence, the results from the analyses guide the exploration of the mechanism by which these missense SNPs of the MCM6 gene alter the structural integrity and functional properties of the protein, which could guide the identification of ways to minimize the harmful effects of these mutations in humans.

Hazard-mapping and health risk analysis of iron and arsenic contamination in the groundwater of Sylhet district.

This study investigates groundwater contamination by arsenic and iron and its health implications within the Sylhet district in Bangladesh. Utilizing geographic information system (GIS) and inverse distance weighting (IDW) methods, hazard maps have been developed to evaluate contamination risk across various upazilas. The findings show significant arsenic and iron pollution, particularly in the northwestern part of the district. In about 50% of the area, especially in Jaintiapur, Zakiganj, Companiganj, and Kanaighat where arsenic levels surpass 0.05 mg/L which is the standard limit of Bangladesh. Iron levels peak at 13.83 mg/L, severely impacting 45% of the region, especially in Gowainghat, northeastern Jaintiapur, Zakigonj, and Golabganj. The study employs USEPA health risk assessment methods to calculate the hazard quotient (HQ) and hazard index (HI) for both elements via oral and dermal exposure. Results indicate that children face greater noncarcinogenic and carcinogenic risks than adults, with oral HI showing significant risk in Balagonj and Bishwanath. Dermal adsorption pathways exhibit comparatively lower risks. Cancer risk assessments demonstrate high carcinogenic risks from oral arsenic intake in all areas. This comprehensive analysis highlights the urgent need for effective groundwater management and policy interventions in the Sylhet district to mitigate these health risks and ensure safe drinking water.

Comparison of hypoxia- and hyperoxia-induced alteration of epigene expression pattern in lungs of Pleurodeles waltl and Mus musculus.

Epigenetics is an emerging field of research because of its involvement in susceptibility to diseases and aging. Hypoxia and hyperoxia are known to be involved widely in various pathophysiologies. Here, we compared the differential epigene expression pattern between Pleurodeles waltl and Mus musculus (commonly known as Iberian ribbed newt and mouse, respectively) exposed to hypoxia and hyperoxia. Adult healthy newts and mice were exposed to normobaric hypoxia (8% O2) and hyperoxia (80% O2) for 2 hours. We collected the lungs and analyzed the expression of hypoxia-inducible factor 1 alpha (Hif1α) and several key epigenes from DNA methyltransferase (DNMT) family, histone deacetylase (HDAC) family, and methyl-CpG binding domain (MBD) family. The exposure to hypoxia significantly increased the mRNA levels of DNA methyltransferase 3 alpha (Dnmt3α), methyl-CpG binding domain protein 2 (Mbd2), Mbd3, and histone deacetylase 2 (Hdac2) in lungs of newts, but decreased the mRNA levels of DNA methyltransferase 1 (Dnmt1) and Dnmt3α in lungs of mice. The exposure to hyperoxia did not significantly change the expression of any gene in either newts or mice. The differential epigene expression pattern in response to hypoxia between newts and mice may provide novel insights into the prevention and treatment of disorders developed due to hypoxia exposure.

Artificial intelligence in the diagnosis of glaucoma and neurodegenerative diseases.

Artificial Intelligence is a rapidly expanding field within computer science that encompasses the emulation of human intelligence by machines. Machine learning and deep learning - two primary data-driven pattern analysis approaches under the umbrella of artificial intelligence - has created considerable interest in the last few decades. The evolution of technology has resulted in a substantial amount of artificial intelligence research on ophthalmic and neurodegenerative disease diagnosis using retinal images. Various artificial intelligence-based techniques have been used for diagnostic purposes, including traditional machine learning, deep learning, and their combinations. Presented here is a review of the literature covering the last 10 years on this topic, discussing the use of artificial intelligence in analysing data from different modalities and their combinations for the diagnosis of glaucoma and neurodegenerative diseases. The performance of published artificial intelligence methods varies due to several factors, yet the results suggest that such methods can potentially facilitate clinical diagnosis. Generally, the accuracy of artificial intelligence-assisted diagnosis ranges from 67-98%, and the area under the sensitivity-specificity curve (AUC) ranges from 0.71-0.98, which outperforms typical human performance of 71.5% accuracy and 0.86 area under the curve. This indicates that artificial intelligence-based tools can provide clinicians with useful information that would assist in providing improved diagnosis. The review suggests that there is room for improvement of existing artificial intelligence-based models using retinal imaging modalities before they are incorporated into clinical practice.

SalivaDirect: an alternative to a conventional RNA extraction protocol for molecular detection of SARS-CoV-2 in a clinical setting.

IMPORTANCE: Affordable and accessible tests for COVID-19 allow for timely disease treatment and pandemic management. SalivaDirect is a faster and easier method to implement than NPS sampling. Patients can self-collect saliva samples at home or in other non-clinical settings without the help of a healthcare professional. Sample processing in SalivaDirect is less complex and more adaptable than in conventional nucleic acid extraction methods. We found that SalivaDirect has good diagnostic performance and is ideal for large-scale testing in settings where supplies may be limited or trained healthcare professionals are unavailable.

In Silico Functional and Structural Analysis of Non-synonymous Single Nucleotide Polymorphisms (nsSNPs) in Human Paired Box 4 Gene.

In human genome, members of Paired box (PAX) transcription factor family are highly sequence-specific DNA-binding proteins. Among PAX gene family members, PAX4 gene has significant role in growth, proliferation, differentiation, and insulin secretion of pancreatic ß-cells. Single nucleotide polymorphisms (SNPs) in PAX4 gene progress in the pathogenesis of various human diseases. Hence, the molecular mechanism of how these SNPs in PAX4 gene significantly progress diseases pathogenesis needs to be elucidated. For the reason, a series of bioinformatic analyzes were done to identify the SNPs of PAX4 gene that contribute in diseases pathogenesis. From the analyzes, 4145 SNPs (rsIDs) in PAX4 gene were obtained, where, 362 missense (8.73%), 169 synonymous (4.08%), and 2323 intron variants (56.04%). The rest SNPs were unspecified. Among the 362 missense variants, 118 nsSNPs were found as deleterious in SIFT analysis. Among those, 25 nsSNPs were most probably damaging and 23 were deleterious as observed in PolyPhen-2 and PROVEAN analyzes, respectively. Following all analyzes, 14 nsSNPs (rs149708455, rs115887120, rs147279315, rs35155575, rs370095957, rs373939873, rs145468905, rs121917718, rs2233580, rs3824004, rs372751660, rs369459316, rs375472849, rs372497946) were common and observed as deleterious, probably damaging, affective and diseases associated. Following structural analyzes, 11 nsSNPs guided proteins were found as most unstable and highly conserved. Among these, R20W, R39Q, R45Q, R60H, G65D, and A223D mutated proteins were highly harmful. Hence, the results from above-mentioned integrated comprehensive bioinformatic analyzes guide how different nsSNPs in PAX4 gene alter structural and functional characteristics of the protein that might progress diseases pathogenesis in human including type 2 diabetes.

In silico identification and functional prediction of differentially expressed genes in South Asian populations associated with type 2 diabetes.

Type 2 diabetes (T2D) is one of the major metabolic disorders in humans caused by hyperglycemia and insulin resistance syndrome. Although significant genetic effects on T2D pathogenesis are experimentally proved, the molecular mechanism of T2D in South Asian Populations (SAPs) is still limited. Hence, the current research analyzed two Gene Expression Omnibus (GEO) and 17 Genome-Wide Association Studies (GWAS) datasets associated with T2D in SAP to identify DEGs (differentially expressed genes). The identified DEGs were further analyzed to explore the molecular mechanism of T2D pathogenesis following a series of bioinformatics approaches. Following PPI (Protein-Protein Interaction), 867 potential DEGs and nine hub genes were identified that might play significant roles in T2D pathogenesis. Interestingly, CTNNB1 and RUNX2 hub genes were found to be unique for T2D pathogenesis in SAPs. Then, the GO (Gene Ontology) showed the potential biological, molecular, and cellular functions of the DEGs. The target genes also interacted with different pathways of T2D pathogenesis. In fact, 118 genes (including HNF1A and TCF7L2 hub genes) were directly associated with T2D pathogenesis. Indeed, eight key miRNAs among 2582 significantly interacted with the target genes. Even 64 genes were downregulated by 367 FDA-approved drugs. Interestingly, 11 genes showed a wide range (9-43) of drug specificity. Hence, the identified DEGs may guide to elucidate the molecular mechanism of T2D pathogenesis in SAPs. Therefore, integrating the research findings of the potential roles of DEGs and candidate drug-mediated downregulation of marker genes, future drugs or treatments could be developed to treat T2D in SAPs.

Fewer polymer chains but higher adhesion: How gradient-stiffness hydrogel layers mediate adhesion through network stretch.

The presence of gradient softer outer layers, commonly observed in biological systems (such as cartilage and ocular tissues), as well as synthetic crosslinked hydrogels, profoundly influences their interactions with opposing surfaces. Our prior research demonstrated that gradient-stiffness hydrogel layers, characterized by increasing elasticity with depth, control contact mechanics, particularly in proximity to the layer thickness. We postulate that the distribution of polymers within these gradient layers imparts extraordinary stretch and adhesion characteristics due to network adaptability and stress-induced reorganization. To investigate this phenomenon, we utilized Atomic Force Microscopy nanoindentation to assess the depth-dependent adhesion behavior of polyacrylamide hydrogels with varying gradient layer thicknesses. Two gradient layer thicknesses were achieved by employing different molding materials: glass and polyoxymethylene (POM). Glass-molded hydrogels exhibited a thinner gradient layer alongside a stiffer bulk layer compared to their POM-molded counterparts. In indentation experiments, the POM-molded hydrogel had larger adhesion compared to glass-molded hydrogel. We find that indenting within the gradient layer engenders increased load-unload hysteresis due to heightened fluid transport in the sparse outer polymer network. Consequently, this led to augmented adhesion and work of separation at shallow depths. We suggest that the prominent stretching capability of the sparse outer polymer network during probe retraction contributes to enhanced adhesion. The Maugis-Dugdale adhesive model only fits well to indentations on the thin layer or indentations which engage significantly with the bulk. These results facilitate a comprehensive characterization of adhesion mechanics in gradient-stiffness hydrogels, which could foster their application across emerging contexts in health science and environmental domains.

Sentiment analysis in multilingual context: Comparative analysis of machine learning and hybrid deep learning models.

This research paper investigates the efficacy of various machine learning models, including deep learning and hybrid models, for text classification in the English and Bangla languages. The study focuses on sentiment analysis of comments from a popular Bengali e-commerce site, "DARAZ," which comprises both Bangla and translated English reviews. The primary objective of this study is to conduct a comparative analysis of various models, evaluating their efficacy in the domain of sentiment analysis. The research methodology includes implementing seven machine learning models and deep learning models, such as Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Convolutional 1D (Conv1D), and a combined Conv1D-LSTM. Preprocessing techniques are applied to a modified text set to enhance model accuracy. The major conclusion of the study is that Support Vector Machine (SVM) models exhibit superior performance compared to other models, achieving an accuracy of 82.56% for English text sentiment analysis and 86.43% for Bangla text sentiment analysis using the porter stemming algorithm. Additionally, the Bi-LSTM Based Model demonstrates the best performance among the deep learning models, achieving an accuracy of 78.10% for English text and 83.72% for Bangla text using porter stemming. This study signifies significant progress in natural language processing research, particularly for Bangla, by enhancing improved text classification models and methodologies. The results of this research make a significant contribution to the field of sentiment analysis and offer valuable insights for future research and practical applications.

Modulation of PI3K/Akt/GSK3ß signaling cascade through G protein-coupled receptor 55 (GPR55) activation: Prenatal lysophosphatidylinositol attenuates valproic acid-induced synaptic abnormalities and mitochondrial dysfunction.

AIMS: Dysregulation of PI3K/Akt/GSK3ß signaling has been implicated in various neurological disorders, including autism spectrum disorder (ASD). G protein-coupled receptor 55 (GPR55) has recently emerged as a potential regulator of this signaling cascade. This study explores the intricate modulation of the PI3K/Akt/GSK3ß signaling cascade via GPR55 activation and its potential therapeutic implications in the context of autism-associated neuronal impairments. MAIN METHODS: Valproic acid (VPA) was administered on embryonic day 12 (E12) to induce ASD, and lysophosphatidylinositol (LPI), a GPR55 agonist, was used prenatally to modulate the receptor activity. Golgi-cox staining was performed to observe neuronal morphology, and Hematoxylin and eosin (H and E) staining was carried out to quantify damaged neurons. Enzyme-linked immunosorbent assay (ELISA) was implemented to identify molecular mediators involved in neuroprotection. KEY FINDINGS: Prenatal VPA exposure resulted in significant abnormalities in synaptic development, which were further evidenced by impairments in social interaction and cognitive function. When LPI was administered, most of the synaptic abnormalities were alleviated, as reflected by higher neuron and dendritic spine count. LPI treatment also reduced cytoplasmic cytochrome c concentration and related neuronal cell death. Mechanistically, GPR55 activation by LPI increases the expression of phospho-Akt and phospho-GSK3ß, leading to the activation of this signaling in the process of rescuing synaptic abnormalities and mitochondria-mediated neuronal apoptosis. SIGNIFICANCE: The observed therapeutic effects of GPR55 activation shed light on its significance as a prospective target for ameliorating mitochondrial dysfunction and dendritic spine loss, offering novel prospects for developing targeted interventions to alleviate the neuropathological causes of ASD.

Nutritional status and dietary diversity of pregnant and nonpregnant reproductive-age Rohingya women.

There are no data on the nutritional status and dietary diversity of the pregnant and nonpregnant reproductive-age Rohingya women who have recently shifted to the Bhasan Char Relocation Camp located on an island in the Bay of Bengal. A cross-sectional survey was conducted in November-December, 2021 to assess the nutritional status and evaluate the dietary diversity of two vulnerable groups of the forcibly displaced Rohingya population: nonpregnant reproductive-age women and pregnant mothers. Multivariable logistic regression was applied to identify the factors associated with nutritional impairments. Overall, 7.6% of the nonpregnant reproductive-age women were underweight (Body Mass Index [BMI] < 18.5 kg/m2), and nearly one-third of them had a BMI ≥ 25 kg/m2. However, 26.7% of the pregnant mothers were undernourished (BMI < 20.0 kg/m2) and almost one-fourth of them were either overweight or obese (BMI ≥ 25.0 kg/m2). The prevalence of thinness (Mid Upper Arm Circumference [MUAC] < 23 cm) was 34.5% among pregnant mothers, and 10.1% of them were severely thin (MUAC < 21 cm). The mean (±SD) of the Women's Dietary Diversity Score (WDDS) was 3.3 (±1.1) for nonpregnant reproductive-age women and 3.7 (±1.3) for pregnant mothers enrolled in this study. Overall, 63.8% of the nonpregnant women of childbearing age and 46% of the pregnant mothers had a low WDDS (WDDS < 4). The WDDS was found to be protective against thinness among nonpregnant reproductive-age women (AOR = 0.61; 95% CI = 0.37, 0.93; p-value = .03) and low BMI in pregnant mothers (AOR = 0.71; 95% CI = 0.55, 0.91; p-value = .01). The results of this survey will assist in early recognition of the nutritional demands, and act as a guide to planning nutrition-based programs among Rohingya reproductive-age women relocated to the Bhasan Char Island.

Comparative evaluation of low-cost ceramic membrane and polymeric micro membrane in algal membrane photobioreactor for wastewater treatment.

Algal-bacterial membrane photobioreactor (AMPBR) is proven as a highly energy-efficient process for treating domestic wastewater. This study compared the application of polymeric micro-membrane (PMM) and a low-cost ceramic membrane (LCM) to the AMPBR process for treating domestic wastewater with low and high organic pollution levels. Experiments were conducted over 57 days using two PMM-AMPBRs and two LCM-AMPBRs, operating on a 12-h dark/light cycle in a continuous mode. Simulated wastewater containing varying levels of chemical oxygen demand (COD) was fed to reactors for a consistent hydraulic residence time (HRT) of 7 d and a flux rate of 100 L/m2/d. PMM and LCM-AMPBRs demonstrated efficient wastewater treatment capabilities, achieving COD removal rates exceeding 94% and 95% for high and low COD loadings, respectively. PMM-AMPBR achieved 54.1% TN removal at low COD loading, while LCM-AMPBR achieved 57.2%. These removal efficiencies decreased to 45.6% and 47.0% under high COD loading. Total Phosphorus (TP) removal reached 29-33% for PMM-AMPBRs and 21-24% for LCM-AMPBRs, irrespective of COD loading. LCM-AMPBRs showed significantly lower fouling frequency than PMM-AMPBRs. The biomass production rate decreased with increasing COD loading and achieved 40 mg/L/d at low COD loading for both AMPBRs. Net energy return (NER) values for both AMPBRs were close to 0.87, indicating them as energy-efficient processes. Considering the cost-effectiveness and comparable performance, LCM-AMPBR could be a viable alternative to PMM-AMPBR for wastewater treatment, particularly under low COD loading conditions.