Search Results (7,185)

Ensitrelvir and nirmatrelvir are the two most widely used oral SARS-CoV-2 main protease inhibitors, yet their toxicological profiles have never been compared under a single computational panel. An identical panel of 105 toxicity endpoints per compound was generated with ADMETlab 3.0, ProTox 3.0, Deep-PK, admetSAR 3.0 and Pred-hERG 5.0, yielding 210 endpoint-level predictions across 17 toxicological domains. Each endpoint formed a matched pair classified as shared positive, shared negative, discriminant or crossed, and predictions were compared against primary clinical and nonclinical literature under a three-level admissibility hierarchy that excluded prescribing information, regulatory review documents and commercial databases. Of 93 class-assignable pairs, 73 assigned both to the same class, 62 negative, and 20 differed. Four domains were shared positive: genotoxicity, with broad genotoxicity and micronucleus probabilities of 1.000 for both molecules, respiratory toxicity, nephrotoxicity and neurotoxicity; ototoxicity was high for both in a single tool. The largest separation was hepatic: ensitrelvir returned drug-induced liver injury 1.000 and human hepatotoxicity 0.994, against 0.314 and 0.456 for nirmatrelvir. Target class therefore does not determine predicted toxicological profile. Genotoxicity, respiratory, renal and neural endpoints are class-level validation priorities, whereas hepatic and hERG endpoints require compound-specific testing. These findings are hypothesis-generating prioritization markers, not confirmed toxicity. Full article

Background: This paper aims to systematically review, based on a set of 125 papers, smart-home automation with IoT from the perspective of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The main evidence synthesis was conducted on empirical studies published from 2024 to 2026, with background studies as a supplement. Methods: IEEE Xplore, Scopus, SpringerLink, ACM Digital Library, and other databases were used in a systematic search. There were 81 empirical studies published from January 2024 to March 2026 that were included in the systematic evidence synthesis; 44 additional foundational and background works were added separately to provide a theoretical and contextual base, resulting in a corpus of 125 works. Due to significant methodological variation, results were summarized qualitatively rather than by meta-analysis, employing a structured framework of evidence tiers. Results: Real-world validation was limited across the reviewed corpus; reported savings were typically in the 15–25% range, with the majority of reported savings from simulations, testbeds, and/or short-term evaluations. Based on our review, six major gaps are identified: seamless interoperability, lack of real-world validation, weak security and privacy, generalizability, standardized benchmarks, and user trust. Discussion: Overall, the research roadmap and recommendations are given in an action-oriented, future-looking way for both industry and policymakers. Full article

In Bangladesh, adolescent girls face multiple challenges that negatively influence diet quality. Understanding the determinants of their diet quality is essential for informing effective nutrition interventions. This rapid review synthesised evidence on determinants of diet quality among Bangladeshi adolescent girls using the Socio-Ecological Model (SEM). We searched MEDLINE (Ovid), CINAHL Complete and Web of Science. Eligible studies examined determinants of diet quality-related indicators, such as dietary diversity, nutrient intake, or food choices among adolescent girls in Bangladesh. Fifteen studies met the inclusion criteria. We conducted a narrative synthesis. The studies reported the determinants across five SEM levels. At the individual level, adolescent girls’ diet quality was associated with their taste preferences, perceived body image, knowledge of nutrition and health, self-efficacy, food choice motives, dieting concerns, and misconceptions during menstruation. At the interpersonal level, family decision-making dynamics, gender-biased food allocation, household wealth and food security status, expenditure on food, low parental education, family and peer influence, and women’s empowerment were associated with diet quality. At the organisational level, lack of dedicated lunchrooms, availability of unhealthy food near schools, nutrition education by community organisations, and advice from healthcare providers were associated with diet quality. Community-level determinants included cultural norms, rural-urban disparities, and geographic variations. At the policy/macro level, food prices and seasonal food availability emerged as critical determinants. Diet quality among adolescent girls in Bangladesh is shaped by complex multi-level factors spanning individual to policy-level factors. These insights can guide context-appropriate interventions to improve diet quality in this population. Full article

The use of agro-waste materials as an alternative to synthetic dyes has gained considerable interest in the development of sustainable textile coloration. This study investigates the utilization of binary colorants from agro-waste sources such as harmal seeds and thuja leaves for silk, with statistical selection of variables. A central composite design (C.C.D.) coupled with microwave radiation was employed to select the significant dyeing conditions for excellent yield. It has been found that the pH 3 extract obtained from 6 g of binary powder after irradiation for 3 min, when applied at 75 °C for 40 min, produced an excellent yield of up to 6.15 (K/S). Two-way ANOVA also revealed the significance of selected dyeing variables for silk coloration using a binary colorant. The use of optimum amounts of chemical and bio-mordants produced colorfast, stable shades with fastness ratings of 4/5-5 for light, washing, and rubbing. The improvement in functional properties such as antioxidant and antibacterial properties of both the extracts and dyed fabric after MW treatment reveals that this bi-colorant has potential benefits for the community. It has been concluded that sustainable mordanting of silk has added more value in the coloration of silk with binary colorants by producing colorfast shades upon dyeing using statistically selected variables. Full article

Background: Dengue fever remains a major global health problem, and early diagnosis is challenging where confirmatory testing is limited. Machine-learning studies using routine hematological data have focused mainly on discrimination, whereas calibration, decision-analytic performance, interpretability, and robust validation have received less attention. This study aimed to develop and compare tree-based machine-learning models for dengue classification, benchmark them against L2-penalized logistic regression (LR), and evaluate discrimination, calibration, potential decision-analytic benefit, and interpretability. Methods: This retrospective secondary analysis used an open-access dataset from Bangladesh comprising 1523 patients, 18 demographic and hematological predictors, and a binary dengue test outcome. Data were divided into stratified training (80%) and test (20%) sets. The Synthetic Minority Over-sampling Technique was applied only within the training workflow. Random Forest (RF), XGBoost, and LightGBM were optimized using Optuna with stratified five-fold cross-validation. L2-penalized LR was evaluated using the same predictors and training–test partition. Held-out test-set performance was assessed using AUROC, AUPRC, accuracy, sensitivity, specificity, predictive values, F1-score, and Brier score. Calibration, decision curve analysis, SHAP values, and permutation importance were also examined. Results: LightGBM, RF, and XGBoost yielded AUROCs of 0.709, 0.704, and 0.702, respectively, indicating closely similar discrimination. The primary SMOTE-trained LR yielded a numerically lower AUROC of 0.608 (95% CI: 0.536–0.677) and a higher Brier score of 0.310 than the tree-based models (0.174–0.176); however, in sensitivity analysis without SMOTE, the LR AUROC increased numerically to 0.655 and the Brier score decreased to 0.194. At the training-derived threshold of 0.558, LightGBM achieved a sensitivity of 0.914 and a specificity of 0.458, reflecting a high-sensitivity, low-specificity profile. The LightGBM calibration curve suggested closer agreement in the low-to-moderate predicted-probability range, with greater deviation at higher probabilities. Decision curve analysis suggested potential net benefit across a range of threshold probabilities but did not establish clinical utility. Platelet count, monocyte percentage, and neutrophil percentage were consistently among the leading predictors across the tree-based models. Conclusions: Tree-based models showed moderate discrimination, with high sensitivity but limited specificity, and yielded numerically higher AUROCs and lower Brier scores than the primary SMOTE-trained LR within this internal-validation framework. They should not replace etiological testing or be used as standalone diagnostic tools; their observed operating characteristics are more compatible with a potential adjunctive screening or triage-support role. External and prospective validation across independent populations and settings is required before clinical use or superiority over simpler statistical models can be established. Full article

Peste des petits ruminants (PPR) is a highly contagious transboundary viral disease of sheep and goats that causes major economic losses and livelihood disruption across Africa, the Middle East, and Asia. Goats are often more severely affected than sheep, particularly in endemic smallholder systems, where high turnover, nutritional stress, and limited veterinary infrastructure sustain viral transmission. Despite effective live attenuated vaccines and the ongoing FAO–WOAH Global Eradication Programme, PPR remains widely distributed, indicating that vaccination alone is insufficient without understanding of host, viral, and environmental determinants of disease persistence. This review synthesizes current knowledge on three interrelated pillars of PPR prevention and control in goats: epidemiology, immunology, and host genetics. We summarize the epidemiological drivers of PPR transmission and how these factors shape disease burden in endemic settings. We review the immunobiology of PPR virus (PPRV) infection and vaccination, focusing on innate antiviral sensing, adaptive immune protection, virus-induced immunosuppression, and field determinants of vaccine performance. Finally, we examine the emerging evidence for host genetic resilience to PPR, with emphasis on immunogenomic and transcriptomic findings and the relevance of indigenous breeds such as the Black Bengal goat as genomic resources. Current evidence suggests that genetic resilience to PPR is likely polygenic and remains insufficiently characterized, though genomic and transcriptomic tools offer opportunities to identify markers of reduced susceptibility, milder disease, or improved vaccine response. Sustainable control of PPR in goats will require integrated strategies combining mass vaccination, surveillance, improved husbandry, and host-focused immunogenomic research to strengthen herd resilience and accelerate progress toward global eradication. Full article

E-commerce website performance—encompassing both technical delivery quality and information quality—is associated with customer retention: platforms exhibiting poor technical metrics (session friction, high cart abandonment) or poor information quality (low engagement, weak content relevance) tend to show elevated churn. This study operationalizes website performance prediction by mapping behavioural and transactional signals to a Technical Metrics sub-vector $T_i$ and an Information Quality sub-vector $Q_i$ (theoretically assigned proxy indicators), constructing a Website Performance Influence Score (WPIS), and training an XGBoost prediction engine that achieves $92.1\pm 0.3\%$ accuracy across five stratified train–test splits, with all preprocessing parameters fit exclusively on each split’s training fold. Using a publicly available E-Commerce Customer Behavior dataset of 50,000 records, the model attains F1-scores of 0.95 (low-risk) and 0.86 (high-risk) and an AUC-ROC of 0.93 for the churned class. Feature importance, cross-validated against SHAP attributions, identifies customer service interactions, customer lifetime value, discount usage rate, and cart abandonment rate as the dominant performance-failure signals, with the engineered Information Quality Degradation Index (IQD${}_i$) and Technical Performance Failure Score (TPF${}_i$) ranking sixth and ninth by gain. A Strategic MIS Decision Support Layer stratifies customers into risk tiers and maps each tier to illustrative, scenario-based intervention strategies. These results support the hypothesis that ensemble learning captures non-linear interactions between technical and information-quality dimensions, offering a reproducible, theoretically grounded foundation for data-driven MIS decision-making in digital commerce. Full article

Conventional evaluations of rainfall prediction models rely on average accuracy, often masking the conditions, locations and causes of model failure and reduced reliability. This study proposes a paradigm shift from conventional average-accuracy benchmarking toward failure-aware forecast-error diagnosis in the Bengawan Solo River Basin, a tropical monsoon river basin in Indonesia with moderate topographic gradients (grid elevations span ≈ 300–650 m). Methodologically, forecasts from previously published models are treated as fixed inputs and their errors are modelled as the response variable, so the analysis diagnoses when and where models fail rather than retraining them. By treating forecast errors as response variables, rather than as random residuals, this study analyses 345,180 model–grid records–month records from ten individual models (RF, XGB, LGBM, SVR, MLP, LSTM, GRU, TCN, CNN, Transformer) and one best ensemble model (Ensemble_Q, a stacking of RF, XGB, SVR, MLP, LGBM, LSTM, GRU, TCN, CNN, Transformer) against observed CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) precipitation, seasonal phase, ENSO and IOD regimes (El Niño–Southern Oscillation and Indian Ocean Dipole, respectively), the MJO index (Madden–Julian Oscillation) as an additional analysis, and elevation as a topographic control, using log-error models, high-error logistic regression, interaction tests, and block bootstrap validation (N = 1000), false discovery rate, and spatial statistics. Results indicate that prediction errors are not random but are systematically controlled: the Transition II phase increases log-error by 245% (pooled log-error model) and raises the odds of a high-error event roughly 40-fold relative to the dry season; La Niña conditions amplify errors by 41% and the odds of a high-error event by 3.3 times (though this ENSO signal is largely entangled with co-occurring Negative-IOD months), and every 100 m increase in elevation increases errors by 26%, with errors forming distinct spatial clusters (Moran’s I = 0.78; p = 0.001). Ensemble_Q outperforms the baseline on an aggregate basis (mean absolute error, MAE = 54.10 mm) but still experiences error amplification under these conditions, while spatial deep-learning architectures (TCN, CNN, Transformer) prove most vulnerable to elevation gradients. All major patterns persisted across variations in thresholds, model subsets, ENSO definitions, multiplicity corrections, and bootstrapping. These findings confirm that superior mean accuracy does not guarantee operational reliability, and that conditional failure diagnosis is an essential complement to benchmarking rainfall predictions in tropical monsoon regions. Full article

Freshwater scarcity and rising wastewater generation have intensified global reliance on desalination and reuse, with reverse osmosis (RO) providing 65–70% of installed desalination capacity and achieving energy reductions from 15 kWhm⁻³ in the 1970s to 1.8–2.5 kWhm⁻³ today. However, fouling caused by organics, inorganics, microorganisms, and colloids remains the major operational challenge, accounting for ≈25% of RO costs and over USD 15 billion annually. This review synthesises fouling mechanisms and mitigation strategies in renewable energy (RE)-driven RO wastewater-treatment systems, where intermittency exacerbates fouling through start–stop cycles and low-shear conditions. Analysis of recent literature highlights that mixed fouling reduces flux by 10–30%, increases transmembrane pressure, and deteriorates permeate quality. Advances in pretreatment (coagulation, MF/UF), antifouling membranes (hydrophilic coatings, zwitterionic surfaces), and cleaning protocols (osmotic backwashing, nanobubbles) have improved performance, yet complete prevention remains elusive. Persistent gaps include predictive fouling models, standardised performance metrics, and scalable green chemistries for silica and combined fouling control. Future directions emphasise integrated solutions combining advanced materials, AI-driven monitoring, and renewable-aware operational strategies, alongside circular economy approaches for brine valorisation. These innovations are critical for achieving sustainable, low-carbon RO systems for global water security. Full article

Climate diplomacy in Bangladesh may reflect a structural gap between two tiers of media coverage. This qualitative study draws on four focus group discussions with 24 district journalists in Cox’s Bazar, Ukhia, Shatkhira, and Shunamganj from four of the country’s most climate-vulnerable districts and semi-structured interviews with 10 Dhaka-based national journalists (N = 34), conducted between August and December 2024 and analyzed through inductive thematic analysis supplemented by concordance-based coding. National participants engage with international diplomatic forums but report limited operational knowledge of ground-level climate impacts, while district-based participants hold granular, project-level knowledge of climate impacts, financial flows, and displacement yet describe themselves as structurally excluded from diplomatic representation. District participants also raised institutional barriers such as NGO and INGO opacity, restricted access to government information, and mismanagement of disaster relief that were largely absent from the Dhaka data and characterized climate financing as politically routed along lines of power rather than community need. These findings suggest that weak diplomatic representation may reflect an information-pathway failure rather than, or in addition to, a capacity deficit, with implications for media-inclusion research in climate governance. Full article

Rising antimicrobial resistance and healthcare-associated infections have intensified the demand for antibacterial medical textiles that avoid metal-based or synthetic biocides. The present study is about an entirely plant-derived antibacterial and antioxidant finish for cotton fabric, using Fagonia arabica extract and clove oil as bioactive agents and tragacanth gum as natural polymeric binder. These bioactive loadings were applied via pad-dry-cure at varying percentages (1–5%). FTIR spectroscopy verified the successful bioactive deposition, while polarized optical microscopy showed that the structure of the fibers changed as a function of the amount of extract used, with Fagonia arabica extract causing swelling of the fibers and clove oil forming a coalescing surface film to provide complete web-like fiber coverage at the highest loading. Essential comfort characteristics were maintained together with functionalization, as all treated samples showed hydrophilic wettability with absorption times ranging from 1.8 to 7.5 s depending on different formulations. The Fagonia arabica–clove oil formulation (5% each) demonstrated the best antibacterial activity (in terms of zone of inhibition) and antioxidant activity radical-scavenging capacity using ABTS radical assay as compared to the clove-oil-only and Fagonia-only formulations. Beyond their antibacterial and antioxidant activities, the formulation of Fagonia arabica–clove oil proved to be effective (more than 69%) in all concentrations in repelling mosquitoes, indicating an additional protective function that is useful in environments where insects transmit infections. These results prove the feasibility of a completely natural bioactive-and-binder strategy that can provide relevant antibacterial, antioxidant, and mosquito repellency activity without sacrificing comfort, providing a scalable and sustainable paradigm for antimicrobial, antioxidant, and mosquito-repellent medical textiles. Full article

Extreme temperatures, drought, and salinity are among the most detrimental abiotic stressors limiting global plant productivity, and their frequency has intensified under climate change. These escalating pressures underscore the need for sustainable biological strategies that enhance plant resilience to climate-induced abiotic stresses. Plant growth-promoting rhizobacteria (PGPR) have emerged as a promising, eco-friendly solution due to their ability to optimize rhizospheric processes that strengthen plant adaptive capacity. PGPR improve nutrient acquisition, maintain ionic homeostasis, modulate phytohormone signaling, and regulate ethylene levels through ACC deaminase activity. They also stimulate antioxidant defenses, promote osmolyte and exopolysaccharide synthesis, and enhance root system development—key traits that collectively alleviate drought, salinity, and heat stress. Recent research demonstrates that co-inoculation, multi-strain microbial consortia, and synthetic communities designed using multi-omics approaches significantly enhance PGPR stability, colonization, and functional effectiveness under field conditions. Additionally, nanotechnology-enabled formulations and smart delivery systems are emerging as innovative tools to improve PGPR survival and targeted release in harsh environments. This review synthesizes current insights into PGPR-mediated stress mitigation, highlights technological innovations that support their application, and outlines pathways for integrating PGPR into climate-resilient, sustainable agricultural systems to safeguard crop productivity amid escalating environmental stress. Full article

Poultry supply chains need to comply with Shariah requirements when supplying halal meat, which requires continuous quality improvement and multi-stakeholder inspection. However, current traceability systems have centralized opaque characteristics, fragmented records, and slow detection of anomalies, leading to food safety vulnerabilities and impractical recall protocols. To overcome these challenges, this paper presents an intelligent blockchain and Internet of Things (IoT)-based traceability system with a permissioned Hyperledger Fabric consortium network. A hybrid off-chain storage architecture supports scalable monitoring without ledger congestion: TimescaleDB stores high-frequency IoT sensor data (e.g., temperature and GPS), MinIO warehouses compliance documentation, while only immutable cryptographic hashes are stored on-chain to guarantee the integrity of the data. Halal governance is digitalized using role-based smart contracts that trigger real-time alerts, batch blocking, and automated recall upon environmental threshold breaches. Performance evaluation via Hyperledger Caliper indicates the system achieves 275 write transactions per second, a 300 TPS read throughput, an optimized read latency of 5 ms, and a write latency of less than 36 ms. Validated as a laboratory concept, this decentralized framework demonstrates proactive quality assurance, mitigates ledger bloat, and enhances halal-integrity trust. Full article

Nipah virus (NiV) is a zoonotic virus that causes severe encephalitis and respiratory disease with a mortality rate often exceeding 70%. Currently, there are no licensed vaccines or therapeutics for NiV disease. This study aimed to identify and experimentally evaluate small-molecule inhibitors targeting the NiV-G (attachment) glycoprotein using integrated in silico and in vitro approaches. A high-throughput virtual screening of >215,000 compounds was conducted against the NiV-G glycoprotein, using docking and molecular dynamics (MD) simulations. The next potential candidates were evaluated using an established, BSL-2-compatible recombinant Cedar virus (rCedV)-based green fluorescent protein (GFP) reporter virus expressing the NiV-F (fusion) and G glycoproteins (rCedV-NiV-B-GFP). During MD simulations, quercetin demonstrated the most stable binding, maintaining a consistent RMSD (3.25 ± 0.3 Å). In vitro testing showed dose-dependent inhibition of rCedV-NiV-B-GFP infection. Although quercetin alone was less potent (IC₅₀ = 24 µM; 95% CI: 7.6–93.6 µM) than a reference NiV-neutralizing monoclonal antibody mAb-7B7 (IC₅₀ = 0.027 μg/mL; approximately 0.00018 μM; 95% CI: 0.0108 to 0.0735), combination with ascorbic acid enhanced neutralization (IC₅₀ = 4.4 µM; 95% CI: 2.9–6.8 µM). Quercetin demonstrated a high Selectivity Index of >20.8. The study identifies quercetin as a promising small-molecule inhibitor of NiV cellular infection, potentially through a stable interaction with NiV-G at the ephrin-B2/B3 binding interface identified computationally. These findings highlight the importance of combining computational screening with BSL-2 cell-based bioassays to accelerate NiV countermeasure discovery. Full article

Peripheral blood mononuclear cells (PBMCs) are the most immunologically active and readily accessible fraction of whole blood, and they initiate host immune responses following infection and vaccination. Since PBMC transcriptomes capture diverse host–pathogen interactions, they offer a promising tool to uncover the genetic drivers of viral resistance and tolerance. As a detailed case study of this principle, we examine porcine reproductive and respiratory syndrome (PRRS), which remains one of the most economically important viral diseases of swine worldwide. Following PRRS virus (PRRSV) exposure, pigs rely on two complementary defense strategies: resistance, the capacity to limit viral replication, and tolerance, the capacity to sustain performance despite infection. Since resistance and tolerance phenotypes are difficult to measure directly through experimental challenge, indirect immune-trait measurements collected after vaccination offer a practical alternative. Most transcriptomic studies of the host response to PRRSV have focused on respiratory tissues, reflecting the virus’s tropism for pulmonary macrophages. However, intramuscularly delivered modified-live PRRSV vaccine reaches the bloodstream, bypassing the lung, so PBMCs, as the frontline defense system, mount the earliest measurable innate response. This review synthesizes the current literature on PBMC transcriptome models for deciphering innate resistance and tolerance to viral disease, using PRRS as our principal worked example; presents our own approach to profiling PBMCs after PRRSV vaccination; and outlines how the field has advanced since the original candidate-gene and QTL studies of the 2010s, including the recent FDA approval of the first CD163 gene-edited PRRSV-resistant pig line, and the emergence of single-cell and multi-tissue PBMC atlases, before considering how the same PBMC-based approach could extend to other host–virus interactions. Full article