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PubMed2026

Association of ERBB4 and SHBG gene polymorphisms with polycystic ovarian syndrome in South Indian women: a case-control genetic analysis.

INTRODUCTION: Polycystic ovary syndrome (PCOS) is a multifactorial endocrinological disorder with a substantial genetic component. However, the role of genes involved in follicular development and androgen regulation remains incompletely understood, particularly in South Indian populations. This study aimed to evaluate how variations in the ERBB4 and SHBG genes affect PCOS risk. METHODOLOGY: A hospital-based case-control study was conducted among 400 South Indian women, comprising 200 women with PCOS and 200 age-matched healthy controls. Genomic DNA was extracted to study SNPs at ERBB4 (rs2178575 and rs1351592) and SHBG (rs1799941 and rs727428) using ARMS-PCR genotyping. The study compared genotype and allele frequencies between cases and controls while assessing their associations with allelic, homozygous, heterozygous, dominant, recessive, and over-dominant genetic models. Genotyping accuracy was confirmed by re-genotyping and Sanger sequencing of a subset of samples. RESULTS: The ERBB4 rs2178575 polymorphism demonstrated a significant association with PCOS, as the AA genotype and A allele combination increased risk across all three genetic models, including homozygous, recessive, and allelic models. The ERBB4 rs1351592 variant was associated with 3-fold higher risk of PCOS in heterozygous and GC carriers. The SHBG rs1799941 polymorphism showed a significant link to PCOS through its effects on heterozygous and allelic states, whereas rs727428 displayed no significant connection due to its monomorphic distribution. CONCLUSION: These findings suggest that polymorphisms in ERBB4 and SHBG may contribute to PCOS susceptibility in South Indian women in a locus- and model-specific manner, revealing the intricate genetic structure that defines this medical condition.

Humans↗

PubMed2026

Simultaneous determination of imiquimod and terbinafine in skin permeation studies: Validation of a liquid chromatography method with fluorescence detection.

Chromoblastomycosis is a chronic, neglected subcutaneous mycosis posing significant therapeutic challenges. A topical strategy combining terbinafine (TBF), an antifungal, with imiquimod (IMQ), a TLR-7/8 agonist immunomodulator, has emerged a promising alternative. However, no validated analytical method is currently available to simultaneously quantify both drugs in skin, which is crucial for novel formulation development. This study reports the development and validation of a simple HPLC method with fluorescence detection (excitation 236 nm, emission 340 nm) for the simultaneous determination of TBF and IMQ extracted from porcine skin. Separation was achieved on a C8 reversed-phase column (125 × 4.0 mm, 5 μm) using a mobile phase of methanol and water (60,40, v/v), both containing 0.1% formic acid at a flow rate of 0.8 mL/min. The method showed excellent linearity (r > 0.999) over 0.01-1.0 μg/mL for IMQ and 0.1-2.0 μg/mL for TBF. Intra- and inter-day precision demonstrated coefficients of variation below 5%, and recovery rates from skin (79-105%) confirmed accuracy. Limits of detection were 0.001 μg/mL for IMQ and 0.004 μg/mL for TBF, with quantification limits of 0.02 μg/mL and 0.16 μg/mL, respectively. This selective, sensitive, and reproducible method represents a valuable analytical tool for supporting the development and quality control of topical formulations for chromoblastomycosis and other fungal skin diseases.

Animals↗

PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗

PubMed2026

Artificial Intelligence for Diagnosing Meibomian Gland Dysfunction: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy Studies.

PURPOSE: To identify, appraise, and synthesize the performance of artificial intelligence-based meibography reading as compared with human graders in diagnosing meibomian gland dysfunction. METHODS: We followed Cochrane methodology and reporting guidelines for diagnostic test accuracy reviews. To assess potential risk of bias and applicability, we used a modified Quality Assessment of Diagnostic Accuracy Studies-2 checklist. We applied bivariate logistic models to estimate summary sensitivity and specificity when appropriate and used the GRADE framework to rate the certainty of the evidence. RESULTS: We identified 14 eligible studies involving 5511 predominantly middle-aged participants (average age: 27-55 years) who were primarily female (≥54.5%). A total of 18,926 meibography images were obtained through noncontact infrared (11 studies) or in vivo confocal microscopy (three studies). Two studies reported external validation of deep learning models, 12 reported internally validated models, and one reported both. All but one study had high risk of bias in at least one domain; 12 studies raised high or intermediate concern about applicability. Based on three external evaluations, the summary sensitivity and specificity for diagnosing meibomian gland dysfunction from normal glands were 97.5% (95% confidence interval: 77.5%-99.8%) and 85.5% (95% confidence interval: 47.3%-97.5%). Sources of heterogeneity in internally validated models included study population, case mix, and others. The overall evidence was very low to low certainty because of imprecision, high risk of bias, and concerns about applicability. CONCLUSIONS: Artificial intelligence-based meibography grading appears less accurate than human graders. Future studies should adopt rigorous designs, including a more diverse participant pool (or image set), and external validation.

Humans↗

PubMed2026

Weight-bearing locomotion mitigates the progression of post-traumatic knee osteoarthritis in male rodents: A systematic review and meta-analysis.

OBJECTIVE: Although knee post-traumatic osteoarthritis (PTOA) has been associated with altered loading, how weight-bearing (WB) activities should be modulated post-injury to preserve knee health remains unknown. This systematic review and meta-analysis examined the effects of WB locomotion on knee PTOA in rodents to provide insight into potential clinical implications. DESIGN: Studies published in PubMed, Cochrane Library, Embase, and CINAHL through 02/2025 and meeting the following criteria were included: 1) rodents with knee PTOA, 2) compared locomotor exercises (treadmill/wheel) to no exercise, 3) outcome measures of PTOA (OARSI/Mankin OA scores, bone quality, osteophyte condition, cartilage quality/morphology). If significant heterogeneity across studies was observed, locomotion speed, time/number of intervention sessions, frequency and duration of intervention, exercise initiation, follow-up time, animal species, PTOA model were analyzed as potential moderator variables that may influence findings. RESULTS: Twenty-two studies met the criteria, resulting in a total of 156 effect sizes (ESs) for meta-analysis (all males). A positive ES denotes less PTOA. Based on the CAMARADES checklist, 5 studies were ranked low risk of bias and 14 studies were ranked moderate risk of bias. Locomotion significantly reduced PTOA severity evaluated using OARSI/Mankin scores (ES=0.927, 95% CI: [0.590, 1.264]) and improved bone quality (ES=0.379, 95% CI: [-0.023, 0.780]) with substantial heterogeneity across studies. Follow-up analyses indicated that greater ESs for reducing OA scores were associated with a shorter duration of each training session, more training sessions, longer intervention duration, and longer follow-up time (all P≤0.004). Greater ESs for improving bone quality were associated with a longer follow-up time and later exercise initiation post-injury (both P≤0.014). Locomotion resulted in small to moderate but non-significant positive effects for isolated measures assessing osteophyte condition (ES=0.379, 95% CI: [-0.023, 0.780]), cartilage quality (ES=0.430, 95% CI: [-0.034, 0.893]), and cartilage morphology (ES=0.464, 95% CI: [-0.006, 0.934]). CONCLUSION: WB locomotion reduced the overall degree of knee PTOA in male rodents. Composite knee OA scores may be more responsive to exercise interventions than isolated cartilage/osteophyte measures. The benefits of locomotion may be more prominent with a longer follow-up time or with exercise protocols consisting of shorter but more individual sessions and longer overall intervention durations.

Animals↗

PubMed2026

ORBIT: Oncogenic Representation Learning via Bi-Prototype Contrastive Learning in Hyperbolic Space for cancer driver gene identification.

Accurate identification of cancer driver genes is crucial for precision oncology but remains challenging due to the complexity of integrating heterogeneous data and modeling dynamic biological systems. To address these limitations, we propose ORBIT (Oncogenic Representation Learning via Bi-Prototype Contrastive Learning in Hyperbolic Space). Our framework synergistically fuses multi-omics profiles with functional network data using a context-adaptive graph reweighting mechanism to capture cancer-specific dynamics. The model employs a bi-prototype contrastive learning strategy within hyperbolic space, which aligns gene representations around distinct driver and non-driver semantic anchors while preserving the intrinsic hierarchy of biological networks. Comprehensive evaluations demonstrate that ORBIT achieves highly competitive stability in pan-cancer analysis while consistently outperforming state-of-the-art methods in cancer-specific predictions. Furthermore, functional enrichment analysis confirms that the model effectively segregates core cancer pathways, and drug sensitivity profiling validates the clinical relevance of the identified drivers. By integrating hyperbolic geometry with context-adaptive learning, ORBIT offers a robust and interpretable paradigm for precision medicine. The source codes and datasets are publicly accessible at https://github.com/spcho-dev/ORBIT.

Humans↗

PubMed2026

Mealtime satisfaction in public nursing homes: Associations with sensory, foodservice, and dining-room environment factors.

Satisfaction with meals is commonly used to assess how meals are experienced in nursing homes (NH), although limited evidence compares breakfast, lunch, and dinner within a unified analytical framework. This study examined the sensory and contextual factors associated with satisfaction across meals in public NH. A cross-sectional observational study was conducted using structured interviews with 290 residents aged ≥60 years (median 85 years; Q1-Q3: 81-88; 63.1% women) from 19 facilities in Galicia, Spain. Overall satisfaction and 12 factors related to sensory attributes of the food, foodservice characteristics, and dining-room environment were assessed using a 5-point Likert scale. Descriptive analyses used medians and quartiles, and group comparisons were performed using nonparametric tests. Three multivariable linear regression models, one per meal, were estimated including all factors simultaneously. In adjusted models, the largest standardized coefficients were observed for taste (lunch: β = 0.366; P < 0.001), food temperature at serving (dinner: β = 0.319; P < 0.001), and menu variety (breakfast: β = 0.301; P < 0.001). Taste, food temperature at serving, menu variety, and meal schedule showed statistically significant coefficients in all models. Overall satisfaction was lower at dinner (29.0% ≥ 4) than at breakfast (34.8%) and lunch (34.5%) (P = 0.003). Selected dining-room environment factors showed significant coefficients in meal-specific models. Mealtime satisfaction was mainly associated with sensory and contextual factors related to how meals are perceived. Lower satisfaction at dinner suggests this mealtime as a relevant context for understanding variations in meal perception in NH residents.

Humans↗

PubMed2026

Peripheral immune markers and choroid plexus volumes as predictors of change in depressive symptoms: Insights from the EMBARC study.

Changes in choroid plexus (ChP) volume and peripheral inflammation have been associated with Major Depressive Disorder (MDD), yet their individual and combined impact on depressive symptoms is unclear. This study investigated whether baseline immune markers and ChP volumes predict changes in depressive symptoms during the 8-week treatment period among Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study participants who received either sertraline or placebo. Adults (n = 222) with MDD with peripheral blood samples were included. Circulating chemokines and cytokines were examined using a 40-plex assay. Depressive symptoms were assessed over 8 weeks using the Hamilton Depression Rating Scale (HAMD-17). Principal component analysis (PCA) was used for dimension reduction. Mixed-effects models were used to examine whether immune profiles and ChP volumes, and their interaction predicted HAMD-17, adjusting for demographic/clinical covariates and baseline depression severity. PCA identified three immune profiles. One profile, characterized by higher levels of cytokines and chemokines including IL-6, TNF-α, and IL-1β, was associated with greater depression severity, higher BMI, age, and CRP at baseline. Higher levels of these immune markers were associated with less improvement in depressive symptoms at 8 weeks (estimate = 1.211, p = 0.018) in models adjusting for right and left ChP volume (right ChP model: estimate = 1.034, p = 0.005; left ChP model: estimate = 0.993, p = 0.007). Interactions between immune markers and ChP volumes were not significant. Future investigations are warranted to examine the relationships between immune markers and ChP volume beyond structural changes in the context of depression symptoms.

Adult↗

PubMed2026

Risk factors for loss of skeletal muscle mass in patients with chronic kidney disease on a low-protein diet.

OBJECTIVES: A low-protein diet (LPD) is recommended for patients with chronic kidney disease (CKD) to prevent a further decline in renal function. However, its impact on muscle mass in these patients remains unclear. This study investigated the risk factors for loss of muscle mass in patients with CKD on an LPD. METHODS: Eighty-four patients with predialysis CKD (59 men, mean age 61.9 ± 11.5 y) who participated in a multicenter randomized controlled trial initiated in 2014 were retrospectively reviewed. We collected data on baseline blood and urine tests, body composition, and dietary records at the start and end of the observation period. We evaluated muscle mass using the skeletal muscle index (SMI) and analyzed risk factors for a decrease in SMI during the 24-wk observation period, using logistic regression analysis. Variables with an association (P < 0.1) in univariate analysis, as well as age, sex, use of low-protein rice, and changes in protein intake, were subjected to multivariate analysis. RESULTS: SMI decreased in 50 patients (59.5%) during the observation period. Multivariate analysis identified significant associations of the SMI with serum albumin at baseline (odds ratio 0.11, 95% confidence interval 0.02-0.52, P = 0.004) and changes in energy intake while on the LPD (odds ratio 3.39, 95% confidence interval 1.00-11.43, P = 0.049). CONCLUSIONS: Risk factors for reduced SMI in patients with CKD on an LPD were malnutrition when initiating the LPD and reduced energy intake during its implementation. Clinicians should optimize nutritional status before initiation of an LPD and ensure adequate energy intake throughout treatment.

Humans↗

PubMed2026

Uce-based phylogeny and classification of Megachilini.

The generic-level classification of the bee tribe Megachilini (Megachilidae) has remained controversial due to poor phylogenetic resolution at the base of the group, particularly among the brood parasitic genera and the numerous dauber ("Chalicodoma s. l.") lineages. We present a phylogenomic analysis of Megachilini based on ultraconserved elements (UCEs), sampling 52 ingroup taxa with emphasis on the dauber lineages. We also present a combined UCE + six-gene analysis to improve taxon coverage, resulting in a dataset with 127 ingroup taxa. Maximum likelihood, coalescent, and Bayesian analyses of multiple UCE matrices recover largely congruent topologies with substantially improved support relative to previous studies. Our results strongly support the monophyly of Megachilini, the early divergence of Noteriades and Gronoceras, and a single origin of brood parasitism. All remaining non-parasitic Megachilini form a moderately supported clade sister to the brood parasitic lineage. The leafcutter bees are monophyletic and nested within dauber lineages. Several major dauber clades are consistently recovered, including an exclusively Australian clade corresponding to the Hackeriapis group of subgenera, while several recognized subgenera are paraphyletic. The lineage known as Morphella, previously placed in synonymy with the subgenus Callomegachile, was not closely related to that subgenus and is here treated as a valid subgenus. Divergence-time analyses place the crown age of Megachilini in the late Eocene to early Oligocene, with major extant lineages diversifying during the Miocene. Limited morphological diagnosability of several clades indicates that splitting non-parasitic lineages into numerous genera would result in an impractical classification that would widen the gap between taxonomists and non-specialists and exacerbate the taxonomic impediment in bees. We therefore advocate retaining a single genus Megachile for non-parasitic Megachilini (excluding Noteriades and Gronoceras), as the classification best supported by phylogenomic evidence and most robust to future taxon sampling.

Animals↗

PubMed2026

Identification and formation pathways of oxidation products of chlorinated paraffins during ozonation in municipal wastewater.

Chlorinated paraffins (CPs) cannot be efficiently removed by conventional water treatment processes and are continually discharged into the aqueous environment. Ozonation can effectively remove lipophilic and persistent pollutants. However, the degradation behaviors of short-chain CPs (SCCPs), medium-chain CPs (MCCPs), and long-chain CPs (LCCPs) in wastewater during the ozonation process remained unknown. In this study, ozonation treatment achieved removal efficiencies of 61 % for SCCPs, 66 % for MCCPs, and 51 % for LCCPs from wastewater within 30 min. Approximately 147 oxidative products of SCCPs, MCCPs, and LCCPs were non-targeted identified through Ph4PCl-enhanced ionization with ultra-high performance liquid chromatography-Orbitrap mass spectrometry. These oxidation products were structurally classified into three categories: carbon chain breakage (53 products), HCl-elimination (27 products), and hydroxylation (67 products). Twenty-three di-hydroxylated CPs were newly identified among the products. Hydroxylation was the predominant pathway for SCCPs, producing di-hydroxylated SCCPs ((OH)₂-SCCPs) with a higher generation rate constant (KG = 22.28 × 10⁻² min⁻¹) compared to other products. MCCPs and LCCPs mainly underwent carbon chain breakage and hydroxylation, generating shorter carbon chain congeners, (OH)2-SCCPs, and di-hydroxylated MCCPs ((OH)2-MCCPs). The KG values of (OH)2-SCCPs (10.56 × 10-2 min-1) and (OH)2-MCCPs (12.05 × 10-2 min-1) generated from the MCCPs were the highest, and the KG values of MCCPs (6.49 × 10-2 min-1), SCCPs (6.27 × 10-2 min-1), and (OH)2-SCCPs (4.74 × 10-2 min-1) generated from the LCCPs were higher than those of other products. These results comprehensively clarify the oxidation efficiencies and pathways of CPs during ozonation. Future studies must explore the potential risks associated with the oxidation products.

Water Pollutants, Chemical↗

PubMed2026

Determination of 13 per- and polyfluoroalkyl substances in human plasma samples using LC-MS/MS: application to capillary microsamples.

Per- and polyfluoroalkyl substances (PFAS) are chemicals widely applied in industrial processes and highly persistent in the environment, whose extensive use has been linked to adverse health effects. Venous plasma is the conventional matrix for PFAS assessment in blood, and LC-MS/MS is the most used quantification technique. Despite the relevance of this topic, biomonitoring data on human exposure to PFAS in Brazil remain limited. This study validated an LC-MS/MS method for determination of 13 PFAS in human plasma. Blood samples were collected from volunteers by phlebotomy, followed by protein precipitation with acetonitrile containing 1% formic acid (v/v) and solid-phase extraction. Chromatographic separation was achieved on an Acquity UPLC HSS T3 column. The assay was linear over a calibration range of 0.2-20 ng/mL. Intra- and inter-assay precision (CV%) were within the ranges of 2.06-12.0% and 0.25-10.7%, respectively. As for accuracy, results were 89.0-112.9%. Matrix effect ranged from -1.31 to 0.05%. Stability after four freeze/thaw cycles and under autosampler conditions were also confirmed for all analytes. The method was applied to 40 paired venous and capillary plasma samples. Both measures exhibited high correlation (r = 0.926). PFOS was the only compound detected at concentrations ≥0.2 ng/mL (LLOQ) in all samples, with capillary plasma concentrations of 0.85-13.50 ng/mL. In summary, the method showed good validation performance and demonstrated the suitability of capillary plasma samples as an alternative matrix for PFAS quantification.

Humans↗

PubMed2026

Pretreatment EBV-DNA/TLG-Based Risk Stratification Is Associated With Survival Outcomes in Nonmetastatic Nasopharyngeal Carcinoma: An Exploratory Study.

Whether combining pretreatment plasma Epstein-Barr virus DNA (EBV-DNA) with 18F-FDG PET/CT-derived total lesion glycolysis (TLG) improves prognostic stratification in nonmetastatic nasopharyngeal carcinoma (NPC) is unclear, particularly in nonendemic populations. We retrospectively analyzed 86 eligible nonmetastatic NPC patients treated with definitive radiotherapy (2010-2024) at a single nonendemic-region institution. EBV-DNA (prespecified cutoff 3500 copies/mL) and TLG (cutoff 200, ROC-derived within this cohort) were dichotomized. Both were available in 59/86 patients (68.6%), who differed from the rest in nodal and overall stage and in RT technique. Baseline PET/CT was in-house in 57 of 86 patients, and a robustness analysis in that subgroup is reported. Given limited events (13 PFS, 9 OS), Cox analyses are exploratory and were supplemented with penalized regression and bootstrap validation. At a median follow-up of 75.5 months, 5-year PFS and OS for the whole cohort (n = 86) were 81.1% and 85.9%. The EBV-DNAhigh/TLGhigh subgroup remained associated with inferior PFS after adjustment in an exploratory model (adjusted HR = 3.97, 95% CI: 1.32-11.93) and, in a single-variable model, with inferior OS (HR = 4.13, 95% CI: 1.10-15.52). Discrimination was comparable to the individual-biomarker model for PFS and lower for OS. Five-year PFS fell monotonically across the four risk groups in the complete-case cohort (n = 59; 89.7%-58.3%). OS differed across groups (log-rank p = 0.044) but was not strictly monotonic, with wide, overlapping confidence intervals. This two-biomarker model is hypothesis-generating and needs prospective, multicenter validation before any consideration of risk-adapted treatment.

Epstein–Barr virus DNA↗

PubMed2026

No association between alcohol consumption and hip osteoarthritis: a diverse national analysis of 87,585 adults from the "All of Us" research program.

INTRODUCTION: Hip osteoarthritis (OA) is estimated to affect 62.6 million individuals by 2050. A probable link exists between alcohol use and hip OA. However, the results are inconsistent, and the relationship between alcohol and hip OA remains speculative. To address these gaps, this study aimed to utilize the diverse, nationally representative All of Us Research Program dataset to explore the association between alcohol consumption and hip OA. METHODS: This retrospective case-control study utilized data from the All of Us Research Program Controlled Tier Dataset v8. 17,517 hip OA cases and 70,068 controls were identified. A 1:4 case-to-control matching ratio was applied based on age and sex. Alcohol use frequency was categorized into five levels: Never, Monthly or Less, Two to Four Times per Month, Two to Three Times per Week, and Four or More Times per Week. Multivariable logistic regression models evaluated the association between alcohol use frequency and hip OA after adjusting for demographic and clinical variables. RESULTS: Multivariable analysis found that alcohol use frequency was not significantly associated with hip OA. Compared to never users, participants with low (OR 0.98, 95% CI 0.93-1.04, P = 0.583), moderate (OR 0.99-1.01, all P > 0.05), and high (OR 1.02, 95% CI 0.95-1.09, P = 0.599) levels of alcohol consumption had no statistically significant differences in odds of hip OA. Female sex, Asian race, diabetes, hypertension, hyperlipidemia, and nicotine dependence increased the odds of hip OA. CONCLUSION: Any level of alcohol consumption was not significantly associated with the odds of hip OA. This study adds valuable insight to the current body of conflicting evidence. Further prospective studies appear warranted to shed light on the long-term effects of different alcoholic beverages on different joints. Key Points • This study found no significant association between any degree of alcohol consumption and the odds of developing hip osteoarthritis. • Utilizing data from 87,585 adults in the NIH "All of Us" Research Program, this is the first study to analyze this relationship in a large, nationally representative population. • The research provides clarity to previously conflicting literature by demonstrating that alcohol lacks a clear harmful or protective effect on the clinical course of the disease. • The analysis highlights that independent risk factors such as Asian race, nicotine dependence, and components of metabolic syndrome increase the odds of hip osteoarthritis.

Humans↗

PubMed2026

In silico identification of DNMT1 inhibitors from the PlantCyc database through computational approach to assess the anti-cancer potential of nutraceutical compounds in breast cancer.

Breast cancer accounts for a disproportionate share of global cancer-related deaths, with 670,000 fatalities and 2.3 million new diagnoses recorded in women during 2022 alone. Existing treatment modalities carry considerable toxicity burdens, and resistance to available agents remains an unresolved clinical problem. DNA methyltransferase 1 (DNMT1), the enzyme chiefly responsible for maintaining genome-wide methylation patterns during DNA replication, has been mapped out as a high-value target in breast cancer because its dysregulation silences tumour suppressor genes through promoter hypermethylation. The present work involves hierarchical in silico workflow to screen 4549 plant-derived compounds from the PlantCyc database (v16.0.3) against the human DNMT1 catalytic domain (PDB ID: 4WXX). Ten top-scoring compounds were taken forward for molecular docking via AutoDock Vina; Quercetin and Kaempferol both recorded the highest binding affinities at -9.5 kcal/mol, Wogonin (-9.3 kcal/mol) and Xanthohumol (-8.1 kcal/mol) also emerged as strong binders. Pharmacokinetic evaluation using ADMET-AI confirmed that all 10 compounds met Lipinski's rule of five, with human intestinal absorption values at or above 0.98. Wogonin and Xanthohumol were selected for a 100 ns all-atom molecular dynamics (MD) simulation in GROMACS due to their well-rounded ADMET profiles and limited existing data on their specific interactions with DNMT1 in breast cancer. Across all measured trajectory metrics, backbone RMSD, residue fluctuation, radius of gyration, solvent-accessible surface area, and intermolecular hydrogen bond count, Wogonin formed a more stable, compact complex. These findings suggest that Wogonin and Xanthohumol are non-toxic nutraceutical candidates suitable for DNMT1 targeted epigenetic therapy, with computational foundation strong enough to facilitate future in vitro and in vivo validation work.

Humans↗

PubMed2026

Can't see the forest for the trees: The influence of marker type on inferred phylogenetic relationships in a cosmopolitan bat genus.

Fine-resolution information on species relationships and biological diversity is critically needed to guide conservation efforts amidst rapid environmental changes. Systematics, which forms the foundation of this knowledge, has been revolutionized by phylogenomics, utilizing genome-scale datasets. However, the use of diverse marker types, non-comparable taxon sampling, and outgroup selection can lead to conflicting phylogenetic hypotheses. These inconsistencies complicate study comparisons and hinder our ability to assess marker-specific impacts on phylogenetic resolution. The phylogenetic reconstruction of the bat genus Myotis, encompassing over 140 species and characterized by a rapid radiation in the last 20 million years, has been particularly influenced by these challenges. Achieving phylogenetic resolution in Myotis is particularly complex due to subtle interspecific differences in both morphological and molecular traits. Mitochondrial and nuclear markers often produce discordant trees, influenced by hybridization, introgression, and methodological variations. In this study, we employed a consistent taxonomic sample set of 44 Myotis taxa to evaluate the impact of five different genetic marker types on phylogenetic reconstruction. We observed significant discordance between topologies derived from conserved nuclear and mitochondrial markers and found that transposable elements were inadequate for resolving relationships across the entire genus. Our results also clarify the placement of previously problematic taxa within the genus. These findings emphasize the importance of aligning genetic marker choice with specific phylogenetic questions and highlight the influence of taxonomic and methodological variation on phylogenomic outcomes. This work provides a framework for improving phylogenetic inference in rapidly radiating groups and enhances our understanding of evolutionary history in Myotis.

Animals↗

PubMed2026

CT-Derived pelvic morphometry for preoperative risk assessment of recurrent unilateral inguinal hernia.

BACKGROUND: Recurrent inguinal hernia remains a significant challenge in abdominal wall surgery despite advances in mesh-based repair techniques and minimally invasive approaches. Although pelvic skeletal morphology has been implicated in inguinal hernia development, its association with recurrent disease remains incompletely understood. This study aimed to evaluate computed tomography (CT)-derived pelvic morphometric parameters and investigate their potential value in preoperative recurrence risk assessment. METHODS: This retrospective study included 251 male patients with preoperative abdominal CT examinations and complete clinical records who underwent elective inguinal hernia repair at a tertiary referral center. After applying the predefined eligibility criteria, 188 patients with unilateral inguinal hernias constituted the primary study cohort, including 162 primary and 26 recurrent unilateral hernias. The Radoievitch angle and Ami's line were measured independently by two blinded radiology residents using a standardized CT-based pelvic morphometric measurement protocol, and the mean values were used for analysis. Multivariable logistic regression and receiver operating characteristic (ROC) curve analyses were performed to evaluate the association between pelvic morphometric parameters and recurrent inguinal hernia. RESULTS: Patients with recurrent unilateral inguinal hernias demonstrated significantly greater affected-side Ami's line measurements (8.27 ± 0.63 vs. 7.90 ± 0.71 cm, p = 0.014) and larger Radoievitch angles (40.68 ± 4.02° vs. 38.80 ± 3.68°, p = 0.018) than patients with primary unilateral hernias. Both the Radoievitch angle (OR 1.14, 95% CI 1.01-1.28, p = 0.033) and Ami's line (OR 2.26, 95% CI 1.14-4.49, p = 0.020) remained independently associated with recurrent inguinal hernia after adjustment for age and body mass index. ROC analysis demonstrated modest discriminatory performance (AUC 0.634 for the Radoievitch angle and 0.633 for Ami's line), while the multivariable model incorporating age, body mass index, and Ami's line showed slightly improved discrimination (AUC 0.655). CONCLUSION: CT-derived pelvic morphometric parameters were independently associated with recurrent unilateral inguinal hernia. Although their individual discriminatory performance was modest, standardized CT-based pelvimetry may serve as an objective adjunctive tool for individualized preoperative recurrence risk assessment in patients who already undergo CT imaging for unrelated clinical indications. Prospective multicenter studies are warranted to validate these findings and determine their clinical applicability.

Humans↗

PubMed2026

Future promise, current clinical ambiguity: a systematic review of machine learning algorithm outputs predicting risk of cardiovascular disease.

OBJECTIVE: To examine whether the outputs of machine learning algorithms designed to predict risk of cardiovascular disease (CVD) address known deficiencies of the Framingham Risk Score (FRS) and improve risk estimates. METHODS: For this critical review, Medline, Embase and IEEE were searched from inception to 1 January 2025. Included were studies describing machine learning algorithms designed to specifically compare output of cardiovascular risk assessment with the FRS. Commentaries, letters, unpublished work or non-peer-reviewed papers were excluded.Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, two reviewers screened titles and abstracts independently, then populated a purpose-built data extraction form. A subsequent qualitative thematic analysis focused on algorithms' strengths, added value, potential harms, unintended consequences and equity implications.The main outcome assessed was whether, among healthy adults, the algorithm improved CVD risk prediction relative to the FRS. RESULTS: Of 707 studies retrieved, 29 met inclusion criteria. 23 reported improved predictive ability relative to the FRS. Most datasets and/or medical records used included sociodemographic predictors of CVD not included among FRS inputs. Some added costly diagnostic tests like CT angiography to FRS screening indicators. When they were defined, inputs and outcomes such as hypertension or myocardial infarction did not always adhere to FRS values. Statistical significance was generally taken as a proxy for clinical significance. Some algorithms overestimated the number at risk compared with the FRS without discussing whether that larger proportion might be at risk of overdiagnosis rather than CVD, while a few decreased the proportion found to be at risk. CONCLUSIONS: Use of artificial intelligence to improve accuracy of risk assessment for CVD demonstrates the technological capacity to merge known sociodemographic predictors with biologic variables and examine non-linear interactions among these. Still needed to achieve patient benefit is clinical insight, adherence to screening principles and cost-benefit assessment of inputs selected.

Humans↗