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PubMed2026

Transcutaneous vagus nerve stimulation influences sleep quality and insomnia: A systematic review and meta-analysis.

Impairments in sleep quality, timing, or duration disrupt normal sleep patterns. This systematic review and meta-analysis investigated the effects of transcutaneous vagus nerve stimulation (tVNS) protocols on sleep outcomes. Thirteen randomized controlled trials with parallel or crossover designs that applied tVNS intervention and assessed sleep quality (Pittsburgh Sleep Quality Index) and insomnia severity (Athens Insomnia Scale and Insomnia Severity Index) were included. Effect sizes were calculated by comparing changes between the active tVNS and control groups. Moderator analyses examined whether stimulation of different targeted regions influences sleep outcomes. Meta-regression analyses examined potential relationships between the effects of tVNS protocols on sleep quality and demographic characteristics and multiple tVNS parameters, respectively. The random-effects meta-analysis indicated that tVNS protocols influenced better sleep quality and lower insomnia severity. Moderator variable analysis revealed that tVNS targeting the concha region induced better sleep quality. Meta-regression analysis revealed that better sleep quality was associated with lower ages of participants. These findings suggest that tVNS protocols, particularly those targeting the concha, were associated with favorable changes in sleep quality and insomnia severity, with age potentially moderating the treatment response.

Humans↗
PubMed2026

Effects of apple phenolics on the human metabolome: modulation of key metabolic pathways.

Apples are widely recognized for their potential health benefits, partly attributed to their phenolic compounds. However, their impact on human metabolism remains incompletely understood. This study investigated metabolic effects of apple-derived phenolic compounds using untargeted metabolomics approach across multiple biofluids. In a crossover intervention study, 30 healthy men consumed a phenolic-rich apple juice or a placebo for two weeks. Blood, urine and saliva samples were collected before and after each intervention and analyzed by direct infusion ultra-high resolution mass spectrometry. Consumption of apple phenolic compounds resulted in significant alterations of the human metabolome, including increased levels of phenolic-derived degradation products and microbial-associated metabolites across all biofluids. Pathway enrichment analysis revealed pronounced effects on phenylalanine and tyrosine metabolism, as well as linoleic and arachidonic acid metabolism, Overall, these findings demonstrate that apple phenolic compounds induce measurable, microbiota-associated and systemic metabolic changes, providing new insights into their metabolic fate and biological relevance.

Humans↗
PubMed2026

Colchicine attenuates cardiac hypertrophy by targeting the macrophage-driven Interleukin-6 suppression.

Hypertrophic cardiomyopathy (HCM), the most prevalent inherited cardiovascular disease, is strongly linked to progressive heart failure and sudden cardiac death (SCD). However, its underlying pathogenic mechanisms remain incompletely understood, and effective therapeutic strategies are still lacking. Here, we established two murine HCM models harboring high SCD risk-associated mutations. Single-cell RNA sequencing revealed immune activation and enhanced fibrotic remodeling in the myocardium of these models. Therefore, we hypothesized that colchicine, a widely used anti-inflammatory drug known to reduce cardiovascular events in multiple cardiac disorders, may also represent a promising therapeutic candidate for HCM. As we expected, colchicine treatment attenuated pathological remodeling in our study, as evidenced by reduced cardiomyocyte hypertrophy, decreased fibrosis, and downregulation of cardiac stress markers (Anp, Bnp) and fibrotic mediators (Ctgf, Col1a1, Col3a1). In addition, colchicine attenuated pro-inflammatory macrophage populations and suppressed IL-6 expression, thereby contributing to the preservation of cardiac function. These findings provide the first preclinical evidence that colchicine alleviates myocardial inflammation and fibrosis in HCM, underscoring its potential as a novel therapeutic strategy to reduce fibrosis, lower SCD risk, and improve patient outcomes.

Animals↗
PubMed2026

Ancient DNA and Human Physiology.

Ancient DNA (aDNA) enables the reconstruction of chronologically sampled genomes from ancient humans, animals, plants, pathogens, and microorganisms, as well as environmental DNA, providing a record of biological changes through time. Improvements in short and degraded DNA extraction methods and low-cost sequencing now enable the generation of broad, cross-regional datasets that expand evolutionary analyses from past population demography to biological mechanisms. By tracking temporal shifts of allele frequencies, integrating functional genomics resources (e.g., gene expression, chromatin structure variation), modeling population demography to separate selection from genetic drift, and aligning genetic changes with archaeological, cultural, and climatic data, aDNA has the potential to link sequence variation to physiological function within their temporal and environmental contexts. In this review, we summarize illustrative case studies from aDNA research spanning complex traits, dietary adaptations, and responses to pathogens and other environmental changes, showing how human biology has evolved under multiple selective pressures through time. These dated signals help triage experimental work and expose mechanisms that are rare or absent in living cohorts. Although some challenges remain, such as geographic and temporal sampling disparities, limitations in data resolution and variant detection, and genotype-phenotype uncertainties, rapid methodological progress and stronger ethical frameworks are expanding what can be inferred, making aDNA a promising tool for refining physiological pathways, their timing, and their drivers.

Humans↗
PubMed2026

Mul-PheG2P: decoupled learning and prediction-space fusion enables robust and interpretable multi-phenotype genomic prediction.

Genomic prediction of multiple phenotypes is crucial in modern plant breeding; however, existing methods struggle with negative transfer and lack interpretability, particularly across high-dimensional small-sample data and diverse species. To address this, we propose Mul-PheG2P, a novel paradigm based on decoupled learning and predictive space fusion. It employs a two-stage design: first training phenotype-specific encoders using genetic data, then decoupling phenotype-specific learning from cross-phenotype aggregation via an interpretable prediction layer. Mul-PheG2P outperforms existing methods across diverse crop datasets, including maize (Zea mays), wheat (Triticum aestivum), and tomato (Solanum lycopersicum). It provides a multi-scale interpretability chain: at the macro level, it quantifies phenotypic contributions via attention-based weighting; at the micro level, Integrated Gradients reveal the genetic basis of predictions. Notably, the model successfully identified the CCT (CONSTANS, CO-like, and TOC) motif regulating photoperiodism and the SQUAMOSA (SQUAMOSA promoter binding protein) promoter for inflorescence development, confirming its ability to capture functional biological mechanisms. These results highlight the high performance and interpretability of Mul-PheG2P, showcasing its value for low-cost, large-scale screening to advance precision breeding.

Phenotype↗
PubMed2026

Non-linear predictive modeling and comprehensive meta-analysis of rectal temperature in Santa Inês sheep: a systematic review of thermal challenges and biometerological trends.

A systematic and bibliometric review, combined with a meta-analysis, was used to adjust an equation for estimating the physiological responses of Santa Inês sheep subjected to different thermal challenges. The systematic review compiled data on physiological responses and the thermal environment, which were then used in the meta-analysis to adjust regression models. The bibliometric analysis mapped the relationships among studies, highlighting their usefulness in interpreting research findings and biases. Addressing prior methodological critiques, the core of this study involves replacing the linear approach with a non-linear segmented regression model to accurately define the Thermal Neutral Zone (TNZ). The Segmented Regression Model was crucial, establishing the upper limit of the Thermal Neutral Zone (TNZ) at an air temperature (tair) of 34.64 °C, where trectal begins to increase abruptly. The model, while identifying a biologically significant breakpoint, exhibited a moderate Multiple R-squared of 0.3529, highlighting the high heterogeneity and methodological variability in the current Santa Inês literature. This non-linear approach offers a biologically superior tool for identifying the onset of thermal distress.

Animals↗
PubMed2026

The impact of sex, age, and genetic ancestry on DNA methylation across tissues.

Understanding the consequences of individual DNA methylation variation is crucial for advancing our knowledge of human biology and disease, yet the collective impact of individual traits on DNA methylation and their downstream effects on gene expression across human tissues remains poorly understood. Here, we quantify the contributions of sex, age, genetic ancestry, and BMI on autosomal DNA methylation variation across nine human tissues and 424 individuals from the Genotype-Tissue Expression project. We show that genetic ancestry and age have a greater impact on DNA methylation compared with sex, with aging effects being more widespread but less pronounced. On average, <10% of the gene expression variation in sex, age, and ancestry is mediated by DNA methylation differences, with ancestry showing the largest proportion of mediation. We further show that ancestry-associated DNA methylation differences accumulate at CpG sites with extreme methylation states and are largely under genetic control. The female autosomal genome exhibits consistent hypermethylation across tissues at Polycomb-repressed regions. Ultimately, we show that age-related Polycomb target hypermethylation is observed across multiple tissues but not in the gonads. Our multi-individual, multitissue approach defines the key drivers of human DNA methylation variation in healthy conditions, establishing a baseline for the interpretation of DNA methylation changes in disease contexts.

Humans↗
PubMed2026

Beyond Photometric Consistency: Addressing Loss Insensitivity to Depth Noise in Endoscopic Estimation via Error Calibration.

Self-supervised monocular depth estimation in endoscopy is fundamentally constrained by the ill-posed nature of photometric supervision. In this work, we identify a critical yet overlooked cause of this ambiguity: the inherent insensitivity of photometric loss to depth noise. To overcome this intrinsic limitation, we propose Depth Error Calibration Learning (DECL), a two-stage framework that suppresses prediction variance and mitigates residual errors in self-supervised depth estimation. In Stage I (Variance Reduction), a cyclic depth generation strategy produces multiple depth hypotheses for the input image. The per-pixel empirical variance is quantified and integrated into a dedicated variance loss term, which penalizes inconsistent predictions and encourages the network to generate more stable and reliable depth estimates. In Stage II (Bias Calibration), an image-conditioned diffusion model refines the Stage-I depth prior and mitigates structured residuals through iterative denoising, thereby improving geometric accuracy and global consistency. Extensive experiments on three public endoscopic datasets demonstrate that DECL achieves consistent improvements over representative self-supervised monocular depth estimation methods under the evaluated protocols. Moreover, ablation studies on two representative backbones indicate that DECL is not restricted to a single network implementation, while broader validation on additional backbone families remains necessary. The source code is publicly available at https://github.com/DavidLuBit/EndoDenoising.

Journal Article↗
PubMed2026

Quantitative assessment of the fingerprint evidential value using machine learning.

Fingerprints as physical evidence have long supported criminal investigation and adjudication. In practice, however, fingerprint identification relies mainly on examiners' experience. Furthermore, expert opinions tend to be categorical, even though the opinions with the same conclusion could differ substantially in evidential strength. To quantitatively assess fingerprint evidential value, this study proposes a machine learning-based framework as an interpretable decision-support tool. A lightweight residual one-dimensional convolutional neural network was constructed, incorporating channel recalibration and a similarity-driven attention mechanism to learn adaptive contribution weights for different matched minutiae (minutiae for short). Controlled experiments revealed that the predicted evidential value increased with the number of minutiae and was significantly influenced by the quality of minutiae. With 10 minutiae, the mean predicted scores were 4.49, 7.00, and 9.09 for blurred, moderately blurred, and clear minutiae, respectively. Multiple regression analysis indicated that replacing a pair of blurred minutiae with a pair of clear minutiae increased the score by 0.492, whereas replacing it with a pair of moderately blurred minutiae increased the score by only 0.216. By mapping predicted scores to graded levels of evidential strength, the framework contributes to a paradigm shift from categorical expert opinions to graded ones, helping courts evaluate fingerprint evidence more scientifically.

Humans↗
PubMed2026

Ensemble DNA methylation clock demonstrates Immune-metabolic aging signatures associated with mortality.

Aging is a multifactorial process that is best described in terms of the progressive acquisition of multiple layers of phenotypic changes, such as epigenetic modifications, inflammation, and metabolic dysregulation. DNA methylation clocks have been extensively used to construct epigenetic clocks based on the DNAm profiles that can be used to estimate biological age and predict age-associated outcomes. Nevertheless, the vast majority of clocks constructed so far have been based on linear models, which are unlikely to fully account for the heterogeneity and non-linearity of survival-related DNAm signatures. In this work, we constructed a heterogeneous stacked ensemble survival model based on DNAm data obtained from the Framingham Heart Study. We first identified 190 CpG loci using elastic net Cox regression and subsequently constructed a survival prediction model based on the fusion of five complementary survival models by means of a neural network meta-learner. The prediction power of the survival model was evaluated in an external validation cohort, where we observed strong performance for predicting all-cause mortality that significantly exceeded PhenoAge and was statistically comparable to GrimAge. These performance estimates were derived in cohorts of European ancestry and externally validated in postmenopausal women aged 50-79 years, and should therefore be interpreted as applicable only to demographically similar populations.

Humans↗
PubMed2026

Efferocytosis regulatory factors in atherosclerosis: A preclinical systematic review.

BACKGROUND: Impaired efferocytosis is a key driver of plaque instability during atherosclerosis progression. Efficient clearance of apoptotic cells through efferocytosis relies on the coordinated action of multiple regulatory factors. METHODS: PubMed, Web of Science, ScienceDirect, OVID MEDLINE, and Scopus were searched for studies published up to February 7, 2026. Eligible preclinical studies were systematically reviewed to identify endogenous factors that regulate efferocytosis in atherosclerosis. Clinical evidence was also incorporated to enable a preliminary translational assessment of these regulatory factors. RESULTS: Thirty-five endogenous regulatory factors were identified from 36 included studies, and their functional roles across distinct stages of efferocytosis were characterized. Notably, metabolic regulators such as PKM2, PFKFB3, GLS1, and Drp1 were involved in distinct efferocytosis stages. This suggests that metabolic reprogramming may provide the metabolic support require for efficient efferocytosis and inflammation resolution. Ten factors were supported by preliminary clinical evidence consistent with preclinical data. PKM2 was the only candidate biomarker with prospective observational data. However, its independent predictive value still requires validation in multicenter prospective studies. CONCLUSIONS: This review provides a systematic synthesis of 35 endogenous efferocytosis regulators and elucidates their regulatory network in atherosclerosis based on a functional stage framework. Metabolic reprogramming is identified as a central hub linking efferocytosis efficiency to inflammation resolution. This review offers a new theoretical basis for efferocytosis-targeted intervention strategies.

Animals↗
PubMed2026

Behind the Curtain of Care. Nurses' Experiences Providing Care to Consumers With Alcohol and Other Drug Issues: A Qualitative Scoping Review.

AIM: To scope and synthesise qualitative literature relating to nurses' experiences of providing care to consumers with alcohol and other drug issues and explore how meaning is constructed in practice. DESIGN: Scoping review. METHODS: A scoping review was conducted following Arksey and O'Malley's framework. Findings were analysed using thematic analysis. DATA SOURCES: Systematic searches were conducted between September and November 2025 across Medline, Emcare, CINAHL and Google Scholar, using controlled vocabulary and keywords relevant to nurses' experiences of providing care to consumers with alcohol and other drug issues. RESULTS: Twenty-four studies from 12 countries were included. Seven themes were identified: emotional aspects of care, education, training and skills in practice, the spectrum of stigma, ethical issues in professional practice, navigating pain management, limited support, and how meaning is constructed in practice. CONCLUSION: Nurses' experiences of providing care to consumers with alcohol and other drug issues are shaped by multiple intersecting factors influencing care delivery and professional practice. Further research is needed to examine how workplace culture, language and interpersonal interactions influence healthcare experiences, and inform education, service development and support needs. REPORTING METHOD: Reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

alcohol and other drugs↗
PubMed2026

Bergamottin, a bioactive component of bergamot: dual inhibition of Japanese encephalitis virus internalization and genome replication.

Japanese encephalitis virus (JEV) is associated with high mortality and severe neurological sequelae, and existing prevention and control strategies remain insufficient. Therefore, the development of novel antiviral agents is of critical public health importance. This study systematically evaluated the antiviral activity and underlying mechanism of bergamottin, a natural product. Bergamottin exhibited significant dose-dependent inhibitory effects against JEV in multiple cell lines, including BHK-21, HuH-7, and Vero cells, demonstrating potent antiviral efficacy. Mechanistic investigations revealed that bergamottin primarily targeted the internalization and replication stages of the JEV life cycle, thereby effectively suppressing viral proliferation. Additionally, adaptive mutation screening indicated that the D389G mutation in envelope protein E confers drug resistance by potentially changing E protein conformation or reducing endocytic efficiency. In vivo experiment, bergamottin significantly reduced viral loads in mouse brain tissue and effectively improved the survival rate of infected mice. Our findings indicated that bergamottin exerted antiviral activity by dual targeting of key steps in the viral life cycle, making it a highly promising candidate for anti-JEV therapy. Further exploration of the antiviral properties of bergamottin is expected to facilitate its clinical development as a treatment for JEV infection.

Animals↗
PubMed2026

Implicit and explicit statistical learning in reading: Evidence from a randomized controlled-learning study and computational modeling.

A key challenge in reading acquisition is understanding how learners extract the complex probabilistic mappings between print, meaning, and sound. Statistical learning (SL) theory offers a mechanistic account of how such mappings are acquired, whether implicitly through exposure or explicitly through instruction. We conducted a randomized controlled-learning study in Chinese, a writing system characterized by multiple sub-lexical regularities linking orthography, semantics, and phonology. Ninety-five 2nd-3rd graders with or at risk for dyslexia were randomly assigned to one of three groups: an implicit-SL training group exposed to repeated lexical and sublexical orthography-semantics-phonology associations, an explicit-SL training group receiving the same input plus explicit instruction on the sublexical print-sound mapping, and a no-SL control group. Both SL groups outperformed controls on the characters they were trained on, as well as on untrained characters that required generalization. However, only the explicit group demonstrated abstraction of print-sound mapping to novel items. Neural network simulations further revealed distinct mechanisms supporting implicit and explicit SL, consistent with a dual-system account of reading acquisition. Together, these findings (1) clarify how implicit and explicit learning distinctly support the discovery of statistical structure in written language and (2) underscore the implicit-explicit dual learning mechanism underlying reading acquisition.

Humans↗
PubMed2026

Domestication-associated reduction of methyl salicylate in tomato root and its significance for resistance to root-knot nematode.

Methyl salicylate (MeSA) plays diverse roles in the aerial parts of plants. By contrast, its biosynthesis and function in roots remain poorly understood. Here, we investigated root MeSA biosynthesis and function in tomato. Genome-wide association studies (GWAS) were performed using root MeSA levels as the phenotype in a diversity panel of 167 accessions to identify associated loci. Candidate genes were biochemically characterized, and the role of MeSA in defense against root-knot nematode (RKN, Meloidogyne incognita) was evaluated using transgenic plants. MeSA was identified as a major root volatile in tomato and showed a domestication-associated reduction. GWAS revealed multiple loci associated with natural variation in root MeSA, including a major locus on Chromosome 9 encoding the salicylic acid methyltransferase (SlSAMT). SlSAMT-overexpressing plants showed reduced resistance to RKNs, whereas SlSAMT-knockdown plants exhibited enhanced resistance. Our results suggest complex roles of MeSA and the salicylic acid (SA) signaling pathway in belowground plant defense. The SA signaling pathway likely plays critical roles in protecting roots against diverse natural enemies, including RKNs. Nevertheless, RKNs appear to have co-opted MeSA as a host-location signal, and the domestication-associated reduction of root MeSA in tomato has likely contributed to enhanced resistance against RKNs.

Solanum lycopersicum↗
PubMed2026

Psychological skills training for sport-related outcomes: An umbrella review of evidence credibility and methodological quality.

Psychological skills training (PST) is widely used in sport, but review-level evidence remains fragmented across interventions, populations, outcomes, and methodological standards. This umbrella review synthesized evidence on PST-related interventions for sport-related outcomes, including athletic performance, psychological outcomes, cognitive performance, and sports injury, and evaluated evidence credibility, certainty, and methodological quality. A systematic literature search was conducted on 21 May 2025 in MEDLINE, PsycInfo, PubMed, Scopus, SportDiscus, Web of Science, and CINAHL Complete. Overall, 61 reviews involving 42,106 participants were included; 35 meta-analyses involving 28,076 participants were eligible for quantitative synthesis, and 26 reviews or meta-analyses involving 14,030 participants were summarized qualitatively. Quantitative findings showed generally positive point estimates for mindfulness-based interventions, imagery practice, music-based interventions, neurofeedback, perceptual-cognitive training, and multiple PST interventions, but most findings were weak or non-significant and certainty was generally very low. Qualitative evidence suggested potential benefits, but conclusions varied according to intervention definition, delivery, outcome measurement, and methodological quality. Evidence was more developed for athletic performance and psychological outcomes than for cognitive performance and sports injury. Overall, PST-related interventions may benefit sport-related outcomes, but conclusions should remain cautious because of low certainty, methodological heterogeneity, and limited evidence credibility.

Humans↗
PubMed2026

Identifying and Prioritizing Core Components of Relationship Education Programs: a Case Study of an Artificial Intelligence (AI) Assisted Systematic Review.

The field of prevention science seeks to identify and implement effective strategies to address social, emotional, and health challenges. A critical aspect of this endeavor is determining the core components of prevention programs that drive positive outcomes. This article presents a case study utilizing artificial intelligence (AI)-assisted systematic review methods to identify key components of healthy marriage and relationship education programs. Given the growing body of research in this domain, AI tools offer a promising means to enhance the efficiency and accuracy of literature reviews. This study employed AI to screen, code, and validate research articles, demonstrating its effectiveness in expediting systematic reviews while maintaining high accuracy in inclusion screening. This case study involved a systematic review of 22,028 resources (identified from PsycINFO, Academic Search Ultimate, and Google) and a final data set of 268 relevant studies. AI screening was integral in effectively conducting multiple rounds of screening. However, findings also highlight challenges in AI-assisted qualitative data abstraction, underscoring the continued need for human expertise in complex coding tasks. The study contributes to the ongoing discourse on integrating AI into prevention science methodologies and offers insights for optimizing AI applications in systematic reviews.

Artificial Intelligence↗
PubMed2026

Retention strategies and participant retention rates among prospective longitudinal pregnancy cohorts: a systematic mapping review.

Prospective longitudinal pregnancy cohorts can answer questions about fetal and early life exposures and later health outcomes; however, there are challenges to retaining participants in longitudinal studies, particularly over life transitions like the birth of a child. Optimal methods for retaining participants in longitudinal research are unclear. A systematic mapping review was conducted to identify prospective cohort studies and randomized controlled trials that enrolled pregnant participants and their infants. Data on retention rates and 17 retention strategies was extracted. A random effects meta-analysis generated pooled annual retention rates inversely weighted to the number of baseline participants. Spearman rank coefficients were used to assess correlation between strategy use and retention. A random-effects meta-regression was used to determine if select retention strategies were associated with participant retention. We identified 130 studies, involving 472 022 pregnancies. A downward trend in pooled mean retention rates were observed. Studies utilized an average of 6.8 (SD 3.9) retention strategies. Statistically significant associations were not observed between strategy use and retention rates at follow-up (p&#x202f;>&#x202f;0.05). Prospective studies of pregnant people and their infants used multiple retention strategies. Participant retention rates declined over time, suggesting that additional factors may influence study participation in the postpartum period.

Humans↗