Reducing sedentary behavior through personalized digital interventions holds particular promise for individuals with chronic pelvic pain disorders (CPPDs), who face unique barriers to physical activity. We present a self-contained, missing data-resilient framework for real-time forecasting of a physical activity score (PAS) using wearable Fitbit data from 134 females with CPPDs. Comparing online and offline learning approaches, we demonstrate that models leveraging recent activity and daily recurring patterns perform the best. When applied to 15-min sedentary bouts (SBs), the PAS forecasts support timely alerts, yielding approximately one true alert per day vs 0.6 false alerts at a conservative operating point. By integrating real-time imputation, the system supports forecasting of SBs for potential use in just-in-time adaptive interventions. Our work demonstrates a privacy-preserving, scalable pathway for integrating precision forecasting into just-in-time adaptive interventions, laying the groundwork for more equitable and effective digital health solutions for women with CPPDs.

Gene Ontology (GO) enrichment analysis has been widely adopted for interpretating omics data, including in ecological research. Although valuable for summarizing large gene lists into functional categories, it is often treated as the final interpretive step. In ecological omics, where functional annotations are frequently incomplete and experimental validation is often difficult, this reliance can lead to repetitive, generic conclusions that lack environmental and mechanistic specificity. Although previous literature has addressed the statistical and annotation-related limitations of GO enrichment, its role in shaping the broader interpretive process has received less critical attention. Here, we highlight a conceptual weakness: the treatment of enriched GO terms as definitive biological insight. We focus particularly on ecological studies of non-model organisms in complex, and heterogeneous environments. We discuss complementary strategies, including co-expression network analysis, integration with environmental metadata, phylogenetically informed annotation, and AI-assisted functional inference. To promote more context-sensitive interpretation, we introduce the concept of GO-Deep, which frames GO enrichment as a starting point for integrative, multilayered functional insight. By reframing GO enrichment as an entry point rather than a conclusion, ecological omics can move toward more mechanistically informative and environmentally grounded interpretations of high-throughput data.

Latent tuberculosis (TB), caused by dormant, non-replicating Mycobacterium tuberculosis (Mtb), afflicts an estimated 1.7 billion individuals globally and remains a principal barrier to TB eradication. Dormant bacilli are phenotypically tolerant to conventional antibiotics and survive through continued dependence on oxidative phosphorylation (OXPHOS) and the electron transport chain (ETC), making bioenergetic enzymes, including the F1Fo-ATP synthase, cytochrome bcc:aa3 supercomplex, Type II NADH dehydrogenase (NDH-2) and cytochrome bd oxidase, compelling drug targets. This review proposes and rigorously analyses a synergistic, multi-scale computational workflow as a uniquely powerful paradigm for discovering anti-persister agents. The workflow cascades across four integrated stages: (1) dormancy-specific Quantitative Structure-Activity Relationship (QSAR) modelling using hypoxic assay data, with predictive models achieving R[2] values of 0.83-0.98; (2) rational scaffold design and pharmacophore-guided hit expansion against structurally validated bioenergetic targets, incorporating bioisosteric replacement and fragment merging strategies; (3) structure-based molecular docking exploiting high-resolution cryo-EM structures (resolution ≤ 2.67 Å), with clinically validated inhibitors such as Bedaquiline (BDQ) yielding docking scores of -12.3 to -15.8 kcal/mol; and (4) advanced Molecular Dynamics (MD) simulation under physiologically relevant conditions, including Constant pH MD (CpHMD) at phagosomal pH 4.5 and all-atom mycomembrane models. A critically underexplored dimension, multi-target drug discovery, is systematically examined, focusing on the design of dual-pharmacophore inhibitors that simultaneously engage NDH-2 and cytochrome bd oxidase, thereby raising the genetic barrier to resistance. By framing discovery as a logical progression from statistical correlation (QSAR) through structural hypotheses (scaffold design and docking) to dynamic atomistic validation (MD), this review provides a rigorous, quantitatively grounded computational roadmap for the next generation of anti-persister therapeutics.

With the accelerated industrialization, the emission of emerging pollutants has increased in aquatic systems, which poses a risk to the integrity of ecosystems, water security and human health. Examples include pharmaceuticals, PFAS, endocrine-disrupting chemicals (EDCs), synthetic dyes, industrial solvents, microplastics, engineered nanomaterials (ENMs), antibiotic resistance genes (ARGs), and persistent organic pollutants (POPs), all of which are persistent, mobile, bioaccumulating, and have complex toxicity. Their industrial emissions, environmental presence, fate, and mechanism of toxicity are discussed in this review to microorganisms, aquatic organisms, wildlife, and humans. It represents a critical evaluation of the developments in high-resolution mass spectrometry, molecular methods and biosensors, chemometrics, and artificial intelligence for monitoring. The physical, chemical, biological, and hybrid treatment methods (adsorption, membrane filtration, advanced oxidation, electrochemical oxidation, bioremediation, microalgal systems, biochar, and nanotechnology) are discussed. There has been some progress, but gaps remain in the knowledge on environmental fate, environmental transformations, mixture effects, and long-term risk. Future research should be directed towards real-time monitoring and mechanistic toxicity evaluation, predictive modeling, integrated treatment, resource recovery, and sustainable environmental management.

Aging represents a major contributor to the deterioration of female fertility, with oocyte quality being the central determinant of ovarian aging, a key factor underlying infertility. Although several natural compounds with anti-aging activity have been documented, highly effective strategies to reverse ovarian aging remain elusive. Here, we report that the peptide elamipretide preserves ovarian function and sustains female fertility during maternal aging. We found that elamipretide administration increases litter size in aged mice and ameliorates oocyte quality decline. Metabolomic and transcriptomic profiling revealed substantial restoration of multiple biological processes in aged oocytes after elamipretide injection. Elamipretide improved both nuclear and cytoplasmic maturation of aged oocytes, accompanied by enhanced cytoskeletal dynamics, mitochondrial metabolism, and organelle reorganization. Mechanistically, elamipretide alleviated age-associated oocyte maturation defects through synergistic activation of the vitamin B6-VEGF axis. Furthermore, elamipretide significantly promoted maturation, fertilization, and cleavage of aged human oocytes. Elamipretide treatment also rescued the developmental competence of porcine oocytes under oxidative damage. Collectively, this study identifies a novel peptide therapeutic candidate for aging-related infertility, showing that elamipretide restores the quality of aged oocytes for fertility by coordinately boosting nuclear and cytoplasmic maturation via activation of the VEGF signaling pathway.

This study evaluated whether a structured nickel-titanium file-group framework improves large language model recommendations for endodontic instrumentation. Five models were tested across 48 standardised scenarios under label-only and structured-framework conditions. Two independent endodontists, blinded to model identity, explicit stage labels and each other's ratings, scored 480 responses. The Clinical Appropriateness Score, a study-specific expert-rated measure on a 0-100 scale, increased from 75.0 to 80.6 after framework provision. Strict concordance with the expert-preferred group rose from 24.6% to 37.9%, but this gain was concentrated among responses containing prohibited proprietary names, which increased from 14.2% to 40.8%. Overall, 5.2% of responses were non-acceptable and the reduction from 7.1% to 3.3% was not statistically significant. Structured prompting improved recommendation quality, but did not produce a detectable concordance gain among terminology-compliant responses. Expert oversight remains essential.

Accurate forecasting of operational expenditure (OPEX) in wastewater treatment plants is critical for financial planning and utility management, yet it remains challenging due to complex, nonlinear interactions between plant capacity, influent characteristics, and fluctuating market prices. This research develops an interpretable predictive framework for OPEX estimation by systematically evaluating multiple advanced machine learning algorithms on a dataset of 2847 operational records (1139 empirical and 1708 synthetic). We compared the predictive performance of decision tree, AdaBoost, random forest, 1D-CNN (serving as an architectural negative control), support vector regression (SVR), multilayer perceptron artificial neural networks (MLP-ANN), and ensemble approaches using a multidimensional dataset comprising influent parameters, operational factors, and economic variables. The predictive models were strictly evaluated using a 90% training and 10% testing data split, prioritizing robust metrics including R[2], root mean square error (RMSE), and mean absolute error (MAE). Results demonstrated that the MLP-ANN significantly outperformed all other models, achieving an outstanding testing R[2] of 0.993. In contrast, traditional tree-based ensembles exhibited varying degrees of overfitting, and the 1D-CNN performed poorly on this tabular dataset (R[2] = 0.568). To resolve the black-box nature of the winning neural network, SHapley Additive exPlanations (SHAP) was applied, revealing that volumetric capacity, energy unit cost, and sludge disposal cost are the dominant drivers of OPEX, reflecting known physical and economic principles. Importantly, this model is evaluated exclusively on historical data and currently lacks validation on external datasets. While the integration of SHAP ensures mathematical interpretability and validates economies of scale, future research must incorporate independent external validation to confirm the framework's broad regional transferability.

The integration of generative artificial intelligence (GenAI) into pediatric health care offers exciting opportunities alongside critical challenges. GenAI tools, like large language models (LLMs), show promise in several health care applications, including but not limited to clinical decision support, documentation, and medical education across a wide range of pediatric subspecialties. However, real-world validation remains limited, and concerns persist around accuracy, bias, reliability, sustainability, and durability. Studies indicate that LLMs, when applied to pediatrics, often underperform compared with adult medical specialties, raising questions about their readiness for use in the care of children and adolescents. Moreover, the risk of exacerbating health disparities among patients of various races, ethnicities, genders, languages, abilities, and socioeconomic statuses because of biased or nonrepresentative training data underscores the need for rigorous oversight and accountability. This policy outlines recommendations for the safe, equitable, and effective use of GenAI in pediatric settings. Developers should prioritize diverse pediatric data sets, proactively address bias, and implement strong data privacy and security safeguards. Health care institutions must establish rigorous validation protocols, ensure compliance with privacy regulations, and maintain clear human oversight. Relevant regulatory and oversight bodies should enforce pediatric-specific evaluations and require postmarket surveillance. Governance frameworks must support interdisciplinary collaboration, transparency, and education initiatives to equip pediatricians with GenAI literacy. Reasonable disclosure of GenAI involvement in patient care is essential to establish and strengthen trust. GenAI policies pertaining to the care of children and adolescents must remain adaptive to ensure ethical, equitable, and evidence-based implementation. Despite these numerous challenges, GenAI holds transformative potential to improve pediatric health outcomes, and this policy seeks to empower developers, researchers, and clinicians to responsibly build and harness this technology.