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PubMed2026

Variants in the interferon regulatory factor 5 gene confer genetic risk for systemic lupus erythematosus in a Han Chinese population.

BACKGROUND: Interferon regulatory factor 5 (IRF5), integral to interferon signaling pathways, has been identified as a susceptibility locus for systemic lupus erythematosus (SLE). Nevertheless, the relationship between IRF5 variants and SLE risk within the Han Chinese demographic remains inadequately characterized. MATERIALS AND METHODS: Genotyping of two functional single nucleotide variants (SNVs) in IRF5 was conducted in 167 individuals with SLE and 246 healthy controls utilizing sequence-specific primer polymerase chain reaction (PCR-SSP). Chi-square and Fisher's exact tests were employed to assess associations. RESULTS: The rs10954213 variant demonstrated a significant association with SLE susceptibility under the recessive model (GG vs. AG+AA, OR = 2.20, 95% CI: 1.30-3.75, p = 0.003, adjusted p [pc] = 0.030) and homozygous model (GG vs. AA, OR = 2.43, 95% CI: 1.36-4.42, p = 0.003, pc = 0.032). Similarly, the rs2004640 variant was associated with an increased risk of SLE across allelic (T vs. G, OR = 1.66, 95% CI: 1.22-2.26, p = 0.001, pc = 0.011), dominant (TG+TT vs. GG, OR = 1.77, 95% CI: 1.19-2.63, p = 0.005, pc = 0.047), and homozygous models (TT vs. GG, OR = 3.72, 95% CI: 1.58-8.78, p = 0.002, pc = 0.016). Haplotype analysis identified protective haplotype HT1 (A/G, OR = 0.54, 95% CI: 0.41-0.73, p < 0.001) and risk haplotype HT4 (G/T, OR = 2.51, 95% CI: 1.42-4.42, p = 0.001). CONCLUSIONS: These findings indicate that IRF5 gene variants substantially modulate susceptibility to SLE in the Han Chinese population. They hold potential as biomarkers for evaluating SLE risk and offer valuable perspectives into disease pathogenesis.

Adult↗

PubMed2026

Whole-Genome Deep Learning Predicts Chemotherapy Response in Colorectal Cancer.

Chemotherapy response in colorectal cancer (CRC) exhibits significant heterogeneity, with current clinical predictors failing to capture complex genomic determinants of resistance. We developed a hybrid deep learning framework integrating convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) networks to analyze whole-genome somatic mutations, evolutionary conservation, chromatin accessibility, and 3D genome architecture in 2,546 TCGA patients. An attention mechanism identified predictive genomic regions. The model achieved an AUC of 0.92 (95% CI: 0.89-0.94) in cross-validation and 0.88 (95% CI: 0.85-0.91) in independent validation, outperforming clinical models (ΔAUC = +0.18, p < 0.001). Key predictors included non-coding variants in TP53, KRAS, and PIK3CA regulatory regions. Triple-positive patients (mutations in all 3 regions) had significantly worse progression-free survival (HR = 4.7, p < 0.001). Our framework enables accurate chemotherapy response prediction and reveals novel non-coding resistance mechanisms, advancing precision oncology in CRC.

Humans↗

PubMed2026

Non-destructive prediction of lead content in oilseed rape leaves by fluorescence hyperspectral technology based on neural network.

Based on fluorescence hyperspectral imaging (FHSI), this study targeted rapid, non-destructive quantification of lead (Pb) content in oilseed rape leaves treated with varying silicon (Si) concentrations, acquiring fluorescence spectra over the 484.43-1001.61 nm wavelength range. To optimize spectral data quality, preprocessing methods (Savitzky-Golay smoothing, first derivative, detrending) were comprehensively compared. Characteristic wavelengths were then selected via interval variable iterative shrinkage, which effectively compressed data dimensionality and reduced computational load. A hybrid SE-CL1DA model, fusing a 1D convolutional neural network, a long short-term memory network and SE attention mechanism was constructed, with Bayesian optimization tuning hyperparameters to boost stability. The BO-SE-CL1DA outperformed both traditional machine learning and insufficiently optimized deep learning model (Rp2=0.9609, RMSE = 0.0377 mg/kg, RPD = 5.1736), thus enabling accurate Pb estimation, supporting Si-regulated heavy metal stress management and facilitating agricultural contamination monitoring.

Plant Leaves↗

PubMed2026

Are Adverse Childhood Experiences Associated with Metabolic Syndrome in Patients with Severe Mental Illness?

BACKGROUND: Patients with severe mental disorders (SMD) are at substantially elevated risk for metabolic syndrome (MetS), contributing to excess cardiovascular morbidity and premature mortality. Adverse childhood experiences (ACEs) have been associated with dysregulation of metabolic pathways, yet their contribution to MetS risk in SMD remains poorly understood. OBJECTIVE: This study aimed to investigate the association between ACEs and MetS in outpatients with bipolar disorder (BD) and schizophrenia (SZ) in clinical remission and to identify independent and incremental predictors of MetS using a hierarchical analytical framework. METHODS: This cross-sectional study included 140 outpatients with SMD (96 with BD and 44 with SZ) in clinical remission, recruited from a university hospital in Eastern Turkey. MetS was defined according to NCEP-ATP III criteria, and ACEs were assessed using the Turkish version of the Adverse Childhood Experiences Scale (ACE-TR). Hierarchical and multivariable logistic regression analyses were performed to examine factors associated with MetS. RESULTS: MetS was highly prevalent in this sample (46.4%). ACE-TR total score was independently and consistently associated with MetS across all hierarchical models (odds ratio [OR] range: 1.68-1.77), with each one-unit increase conferring approximately 71% higher odds in the fully adjusted model (OR = 1.71; 95% confidence interval [CI] 1.26-2.32; P = 0.001). The number of hospitalizations was the only other independently associated variable (OR = 1.19; 95% CI 1.02-1.39). Sexual abuse (16.9% vs. 2.7%; P = 0.004), emotional neglect (63.1% vs. 30.7%; P < 0.001), and physical neglect (30.8% vs. 14.7%; P = 0.022) were significantly more prevalent in the MetS group. ACE-TR total score was positively correlated with waist circumference and triglyceride levels. CONCLUSION: The strong and consistent association between ACEs and MetS underscores the importance of trauma-informed care models in psychiatric practice, where metabolic comorbidity remains a leading cause of premature mortality.

Humans↗

PubMed2026

Pilot Distractions and Interruptions in Airlines: Ranking of Sources by Analytic Hierarchy Process.

ObjectiveThis work establishes a methodological framework for sources of pilot distraction and interruptions in a structured model that can be used as a tool for cockpit design/procedure assessment.BackgroundPilots must complete complex tasks, and distractions can impair performance and lead to errors that can cause aircraft accidents. Although various cockpit distractors are examined individually, there is no integrated approach.MethodDistraction and interruption sources were identified through a literature review and confirmed/extended by interviews with airline pilots. Associated weights were determined through pairwise comparisons, yielding a hierarchical model using the Analytic Hierarchy Process.Results26 sources of pilot distraction and interruptions were quantified and categorized into four categories: communication, head-down time, responding to abnormal conditions & unexpected situations, and searching for traffic.ConclusionA taxonomic structure for assessment is achieved with the top 5 sources identified as communications, technical interruptions, experience in type, environmental factors, operational irregularities, and airspace high terrain, accounting for 63.07%.ApplicationThe structured system is a flexible assessment scale that provides a taxonomic framework for airline risk management, supports future research, and cockpit design efforts.

Humans↗

PubMed2026

AI-enabled viral genomics: from virus discovery to host prediction and emerging variant forecasting.

The rapid expansion of metagenomic sequencing has generated vast repositories of viral sequence data that far outpace our capacity to interpret them using conventional approaches. Highly divergent sequences, sparse functional annotation, and taxonomically uneven sampling present fundamental challenges for reference-dependent methods, which lose sensitivity precisely for novel and understudied viruses with high public health relevance. Artificial intelligence (AI) provides a new avenue to address these challenges by enabling predictive inference from viral genomes and proteins while reducing dependence on sequence similarity. In this Review, we discuss representative advances in AI for virus discovery, taxonomic classification and functional annotation, prediction of host range and zoonotic potential, and efforts toward forecasting emerging variants. These advances are transforming viral genomics from a largely descriptive discipline into one with increasing predictive capability. We also critically assess the major challenges that constrain current approaches, including the availability of high-quality and representative datasets, rigorous model evaluation, biological interpretability and responsible governance for increasingly capable AI models.

Artificial Intelligence↗

PubMed2026

Unravelling Ovarian Cancer: an analysis of the Influence of LRP1 and PAI1 Genetic Variations.

To assess the potential association between LRP1 (rs715948) and PAI1 (rs2227631, rs1799889) gene variation and ovarian cancer (OC) susceptibility. This study evaluated the genotypic and allelic distributions of LRP1 gene and PAI1 gene variants using Restriction Fragment Length Polymorphism (RFLP) analysis in 134 °C patients and 134 healthy controls. LRP1 (rs715948) showed a significant association with OC risk. The TC genotype was (OR = 3.7823, 95% CI: 2.1732-6.5825, p < 0.0001), and the CC genotype has (OR = 2.1613, 95% CI: 1.0054-4.6459, p = 0.0484). The C allele was significantly more frequent in cases (46%) than controls (32%) (OR = 1.7684, 95% CI: 1.2443-2.5133, p = 0.0015). For PAI1 (rs2227631), AG and GG genotypes showed no significant association (p = 0.3519 and p = 0.1165, respectively). PAI1 (rs1799889) AG genotype was (OR = 5.855, 95% CI: 2.4663-13.9027, p < 0.0001), while GG genotype showed no significance (p = 0.1025). The dominant model of LRP1, (TC + CC) and C alleles, were significantly more frequent in OC cases, indicating a potential risk factor. In contrast, the dominant models (AG + GG) and G alleles of PAI1 (rs2227631, rs1799889) showed no significance with OC susceptibility. Genetic variation in LRP1 (rs715948) significantly associated with increased OC risk, particularly the TC and CC genotypes and C allele. The C allele of this gene is key markers linked to higher OC susceptibility. Whereas in PAI1 (rs2227631, rs1799889), dominant models (AG + GG) show no significance, association suggesting a less prominent role in OC susceptibility. These findings highlight LRP1 as a potential genetic biomarker for OC risk assessment, while the role of PAI1 variants warrants further investigation in larger sample size.

Humans↗

PubMed2026

A systematic review and meta-analysis of OCT-based ophthalmic changes in amyotrophic lateral sclerosis.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease marked by motor decline and respiratory failure. Optical coherence tomography (OCT), a non-invasive imaging technique, has been explored for detecting retinal structural changes that may reflect neurodegeneration in ALS. While some studies report thinning of retinal layers, findings remain inconsistent. Therefore, a meta-analysis is needed to clarify the extent of retinal involvement and the potential of OCT as a biomarker in ALS. METHODS: A systematic literature search was conducted across PubMed, EMBASE, and Cochrane databases for studies published between 2010 and May 2025. Study quality was assessed using the Newcastle-Ottawa Scale (NOS), and publication bias was evaluated through funnel plot asymmetry and Egger's test. Pooled effect sizes were calculated using random-effects models to account for between-study heterogeneity, and differences in OCT parameters between ALS patients and healthy controls were expressed as standardized mean differences (SMD) with 95% confidence intervals (CI). Statistical heterogeneity was quantified using the I2 statistic. RESULTS: A total of 17 studies were included in the present meta-analysis. The primary unadjusted global model demonstrated significant reduction of retinal nerve fibre layer (RNFL) thickness in ALS patients compared to controls (unadjusted SMD = -0.295, 95% CI: -0.522, -0.068). Upon applying a Design Effect variance inflation model to address fellow-eye non-independence, the pooled estimate remained robustly significant across a conservative range of intraclass correlations (SMD ranged from -0.256 to -0.249). Subgroup analyses revealed that RNFL thinning was particularly pronounced in spinal-onset ALS (SMD = -0.54, 95% CI: (-0.98, -0.10). When studies were stratified by the region of conduct, RNFL and macular thinning reached statistical significance only within the non-Asian subgroup, though the formal test for subgroup differences was not significant. CONCLUSION: This meta-analysis demonstrates significant bilateral RNFL thinning in ALS, with relative preservation of the Inner Nuclear Layer and Ganglion Cell Layer - Inner Plexiform Layer, supporting retinal neurodegeneration as a feature of this multisystem disorder. SYSTEMATIC REVIEW REGISTRATION: PROSPERO identifier CRD420251076035.

Humans↗

PubMed2026

Precision targeting of teacher burnout using network-informed ecological momentary interventions.

Teacher well-being affects classroom functioning and workforce stability, yet generic digital programs rarely use person-specific affect dynamics to select support. This cluster-randomised trial evaluated whether micro-interventions selected from high expected influence (EI) nodes in teachers' contemporaneous affect networks produced larger changes in burnout-related EI and everyday happiness than content-matched random allocation. The objectives were to estimate allocation effects on changes in estimated network summaries and happiness, evaluate network change as a statistical mediator, examine personality moderation, and benchmark simpler allocation rules. A two-arm cluster randomised platform trial was conducted in 84 public schools across four urban districts in H Province. After a 14 day baseline of ecological momentary assessment (EMA), person specific partial correlation networks were estimated for happiness, exhaustion, detachment, efficacy and rumination. An optimisation engine prioritised three brief micro-intervention types per teacher according to baseline EI, while the active control received the same library without network information. EMA continued for 8 weeks; Bayesian multilevel models, permutation-based mediation, and benchmarking analyses were applied. EI-based targeting produced larger reductions in the composite EI-change index than active control (mean difference 0.11, 95% credible interval 0.08 to 0.14) and higher week 7 EMA happiness (4.4 points on a 0 to 100 scale, 95% credible interval 2.7 to 6.0), with a positive arm by week slope difference of 0.62 points per week (95% credible interval 0.39 to 0.85). Model-based mediation estimates were consistent with approximately one half of the happiness difference being statistically associated with change in the composite EI-change index (average conditional mediation estimate 3.5 points, 95% credible interval 2.0 to 5.2). Benchmarking showed smaller gains under severity, threshold, or group-level centrality rules. Effects were stronger among teachers higher in conscientiousness. The findings indicate that integrating EMA, network modelling, and EI-driven optimisation yields measurable gains beyond content-matched exposure, providing a proof of concept for district-scale precision mental health that requires prospective implementation testing. Replication in additional regions, expanded node sets, and longer follow up are warranted to assess durability and generalisability.

Female↗

PubMed2026

Physical Appearance Anxiety and Eating Disorders Symptomatology: A Systematic Review and Meta-Analysis.

The present study aimed to assess the link between physical appearance anxiety (PAA) and eating disorder (ED) symptomatology by a meta-analysis of existing literature. Eligible studies were searched across six electronic databases up until November 20, 2025. Pooled effect sizes (r) were calculated using random-effects models. Potential variables that influence effect heterogeneity were analyzed by univariable and multivariable meta-regressions. Influence analyses and a three-parameter selection model (3PSM) were used to assess robustness of the results and publication bias. Twenty-seven effect sizes from 21 studies (N = 5261) were obtained. The results indicated a strong association (i.e., r = 0.559) between the two variables under consideration, which was notably stronger (i) among females compared to males; and (ii) for overall eating disorder symptoms rather than bulimic symptoms. The results of this study advocate for further investigation into the effectiveness of addressing anxiety responses related to personal body traits, particularly among females, within the context of preventing and treating eating disorders.

Humans↗

PubMed2026

Substance use and co-occurring mental health conditions among adolescents and young adults in North America: a systematic review.

BACKGROUND: Substance use and co-occurring mental health conditions are common among adolescents and young adults and represent an important clinical and public health concern in North America. Trauma exposure, psychiatric symptoms, and social adversity frequently co-occur with substance-related problems in this population. This systematic review aimed to synthesize evidence on prevalence patterns, associated clinical and psychosocial factors, and care-related implications described in the literature on adolescents and young adults with substance use and co-occurring mental health conditions. METHODS: This systematic review followed PRISMA guidelines and was preregistered in PROSPERO (CRD42024581685). A systematic search was conducted in MEDLINE (Ovid), Embase, PsycINFO, and Google Scholar. Eligible studies examined adolescents and young adults (≤ 25 years) in North America. A combination of subject headings, keywords, and synonyms for the concepts "adolescents," "young adults," "substance use disorders," and "co-occurring mental health conditions" was used. Of 1,882 records identified, 29 studies met the inclusion criteria. Given the heterogeneity of the literature, findings were synthesized narratively. RESULTS: The included studies showed heterogeneity in population, setting, denominator, and ascertainment method. Across the literature, depressive, anxiety-related, trauma-related, and externalizing mental health presentations were frequently reported alongside substance-related problems in adolescents and young adults. Trauma exposure, adverse childhood experiences, and broader social adversity were also commonly reported in association with substance-related problems across studies. Care-related implications were more limited and were largely derived as future directions mentioned across studies rather than direct evaluations of youth-specific service models. CONCLUSION: Co-occurring mental health and substance-related problems were frequently reported among adolescents and young adults in high-risk and service-engaged populations in North America. Trauma and social adversity were prominent across the reviewed literature, while direct evidence for evaluated, integrated, youth-specific care models remains limited. These findings support the relevance of developmentally appropriate, integrated, and trauma-informed approaches to care and highlight the need for further research directly evaluating improved service delivery models for this population. CLINICAL TRIAL NUMBER: Not applicable.

Humans↗

PubMed2026

Machine learning-ready genomic biomarkers: ATF3 polymorphisms predict postoperative analgesic demand through AI-compatible phenotyping.

PURPOSE: To determine whether ATF3 polymorphisms can serve as genetic biomarkers for machine learning-based precision analgesia by establishing a genotype-phenotype association suitable for predictive modeling of postoperative opioid requirements. METHODS: In a prospective cohort of 167 adults undergoing abdominal surgery, ATF3 SNPs rs3122721 and rs3125293 were genotyped. A structured dataset architecture was developed to represent genetic profiles as input features for supervised learning models, enabling translational analysis of genotype‑dependent opioid consumption over 72 h. RESULTS: Patients with homozygous genotypes of the ATF3 SNPs had significantly higher opioid requirements than non‑carriers, despite reporting similar subjective pain scores. This consistent genotype‑dependent pattern provided a clinically relevant phenotype suitable for integration into predictive algorithms. CONCLUSION: ATF3 genotyping offers a promising biomarker for computationally informed precision analgesia. By linking genomic variability to clinically meaningful outcomes within a structured clinical and genomic framework, this approach supports the future development of risk-stratified clinical decision-support systems to optimize postoperative pain management.Trial registration ChiCTR1900021991, registered 30 April 2019. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s13755-026-00480-9.

ATF3↗

PubMed2026

Association of time-averaged systemic immune-inflammation indices with in-hospital mortality after intracerebral hemorrhage: a retrospective study.

BACKGROUND: Systemic inflammation plays a central role in secondary brain injury following intracerebral hemorrhage (ICH). Although inflammatory indices such as the neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI) are linked to poor outcomes, their associations with mortality are commonly assumed to be linear, potentially overlooking nonlinear patterns where mortality risk rises steeply at higher levels. METHODS: We conducted a retrospective study using the MIMIC-IV database, including 440 patients with non-traumatic ICH who were alive and remained in the ICU for at least 72 h after admission. Mean NLR, SII, and SIRI were calculated from measurements obtained during this period. Multivariable logistic regression and restricted cubic spline (RCS) analyses were applied to assess their independent and nonlinear associations with in-hospital mortality. Model discrimination and calibration were internally validated using 1,000 bootstrap resamples. RESULTS: The in-hospital mortality rate was 26.1%. After multivariable adjustment, NLR and SIRI remained independently associated with mortality. Patients in the highest SIRI quartile had the highest risk of death (aOR = 5.12; 95% CI: 2.57-12.24; p < 0.001). RCS analysis revealed a significant nonlinear association between SIRI and mortality (p-nonlinearity < 0.05), showing a steep risk increase at higher SIRI levels. Adding SIRI to the base model provided a modest improvement in discrimination (AUC 0.762 to 0.785, p = 0.045) and significantly improved risk reclassification (cNRI = 0.4778, p < 0.001; IDI = 0.0240, p = 0.0151). CONCLUSIONS: Among patients with ICH who met the 72-hour eligibility criterion, higher 72-hour average SIRI was independently associated with in-hospital mortality. As a time-averaged measure, SIRI should be interpreted as a dynamic marker integrating the initial inflammatory state and the early clinical course rather than as a purely baseline prognostic factor. Although adding SIRI to the base model modestly improved discrimination and risk reclassification, it should be considered a candidate prognostic marker requiring external validation before clinical application.

Humans↗

PubMed2026

Reliability, Device Agreement and Validity of Load-Velocity Profiles: A Systematic Review with Meta-analysis.

BACKGROUND: For a valid one-repetition maximum (1RM) prediction via load-velocity (LV) relationships, high reliability and accuracy must be assumed. OBJECTIVE: Since individual study results indicate ambivalent prediction, this systematic review and meta-analysis was designed to provide a updated and comprehensive overview, extending knowledge about the validity and reliability of commercially available velocity sensors in Part I and the validity and reliability of velocity-based 1RM prediction models in Part II. METHODS: A systematic literature search was conducted in PubMed/MEDLINE, Web of Science, and Scopus. Validity and/or reliability studies or velocity-based 1RM prediction evaluations were included. Methodological quality was assessed using adapted COSMIN. The analysis was performed for intraclass correlation coefficient (ICC), Lin's concordance correlation coefficient (CCC), and Pearson's correlation coefficient (r). The review was preregistered in PROSPERO (CRD42025634595). RESULTS: Sixty-three studies were included for sensor validity and reliability and 38 for 1RM prediction models. Part I: Velocity sensors demonstrated good-to-excellent pooled validity and device agreement (ICC = 0.91-0.92 [0.83-0.97]; k = 55 and 439, respectively); intra- and inter-day reliability were classified as good to excellent with ICC = 0.90-0.91 [0.85-0.95] (k = 228 and 608, respectively), with sensor technology moderating the results. However, substantial heterogeneity and wide ranges of study-level estimates indicated considerable variability across moderators, linear position transducer (LPT) generally showing more consistent performance than inertial measurement units (IMU). Part II: Velocity-based 1RM prediction showed ICCs = 0.90 [0.83-0.94] (k = 124) and ICC = 0.91 [0.72-0.98] (k = 9); for reliability and validity, respectively. DISCUSSION: Commercial velocity sensors generally provide high relative validity and reliability. Results varied depending on exercise complexity, intensity, sensor technology, and modeling approach. While velocity-based 1RM prediction demonstrated high average validity, large heterogeneity in lower body exercises significantly biased the results. Furthermore, the dearth of measurement error and agreement analyses prohibits final conclusions. CONCLUSION: Therefore, velocity-based monitoring and 1RM prediction require cautious interpretation, as sensor- and exercise-specific evidence remains limited.

Load–velocity relationship↗

PubMed2026

Association between hepatic steatosis index and female infertility: a cross-sectional study using NHANES 2013-2018.

Female infertility is a major reproductive health concern. Hepatic steatosis index (HSI), a non-invasive marker of liver-related metabolic burden, has not been well studied in relation to infertility across its distribution. We analyzed women aged 20-49 years from three NHANES cycles (2013-2018). HSI was examined as both a continuous variable and in quartiles, and restricted cubic spline models were used to assess potential nonlinearity. Sensitivity analyses using alternative infertility definitions and exploratory interaction analyses were also performed. Among 3,007 women, 416 (13.8%) were classified as having infertility. In the fully adjusted model, women in the highest HSI quartile had higher odds of infertility than those in the lowest quartile (OR 1.66, 95% CI 1.08-2.57; P = 0.031), with a significant trend across quartiles (P for trend = 0.010). Restricted cubic spline analysis showed a nonlinear association between HSI and infertility (P for overall association = 0.005), which was more evident at higher HSI levels. Higher HSI was associated with greater odds of infertility among reproductive-aged women.

Humans↗

PubMed2026

Proteomic profiling reveals that DPP4 overexpression increases cell adhesion, inhibits cell migration, and restores androgen sensitivity in prostate cancer.

Dipeptidyl peptidase-4 (DPP4), a serine protease with both enzymatic and non-enzymatic roles, has emerged as a context-dependent modulator of tumor progression. In the present study, we investigated the expression and function of DPP4 in androgen-sensitive and castration-resistant prostate cancer (CRPC) models. Proteomic analysis of androgen-resistant prostate cells overexpressing DPP4 identified the involvement of the cellular adhesion molecules pathway. In prostate cells, lentiviral-mediated DPP4 overexpression restored androgen receptor signaling, inhibited epithelial-to-mesenchymal transition, and reduced cell migration, whereas DPP4 silencing produced the opposite effects. We demonstrate that DPP4 expression is down-regulated in CRPC cells and that treatment with capsaicin (CAP), a bioactive compound derived from red peppers, restores DPP4 expression. Moreover, DPP4 restoration by CAP suppresses prostate tumorigenesis in the TRAMP mice in vivo model of prostate cancer. Our results suggest that DPP4 could be a new target for CRPC.

Male↗

PubMed2026

The effect of tDCS on emotion-related risk-taking behavior and delay discounting in adults with ADHD.

INTRODUCTION: Adults with Attention Deficit Hyperactivity Disorder (ADHD) often engage in risky behaviors due to impaired decision-making processes. This study aims to investigate the effects of transcranial direct current stimulation (tDCS) over the dorsolateral prefrontal cortex (dlPFC) and ventromedial prefrontal cortex (vmPFC) on emotion-related risk-taking behavior and delay discounting in adults with ADHD. METHODS: Thirty adults with ADHD underwent three tDCS conditions, administered in a randomized order with at least one week between sessions: (1) left dlPFC anode/right vmPFC cathode, (2) left dlPFC cathode/right vmPFC anode, and (3) sham stimulation. In each session, participants completed the Delay Discounting Task (DDT) and the Modified Balloon Analogue Risk Task (mBART) under three emotional conditions (neutral, positive, and negative) which were induced using emotionally congruent photographs and sounds. Galvanic skin responses (GSR) were also recorded. In the DDT, both area under the curve (AUC) values and log-transformed discounting rates (log k) were calculated for small, medium, and large reward magnitudes (RM). Exploratory electric field modeling was also performed to characterize current distribution. RESULTS: The findings demonstrated task-specific effects of tDCS on decision-making. Although no overall tDCS effect was observed on DDT performance, significant tDCS × RM interactions emerged, particularly for smaller rewards. In contrast, exploratory analyses suggested that tDCS affected all mBART scores. Emotional condition did not influence consistently behavioral performance in either task, whereas both emotional stimulation and tDCS significantly affected GSR responses. However, exploratory electric field modeling indicated a broad prefrontal current distribution extending beyond the intended cortical targets. CONCLUSIONS: These findings suggest preliminary evidence that prefrontal tDCS can influence risk-related decision-making and autonomic responses in adults with ADHD. However, its effects on delay discounting appear to be context-dependent and limited to specific RMs. Future studies combining neuroimaging with individualized electric field modeling are needed to clarify the neural mechanisms underlying the observed effects of tDCS and to optimize stimulation protocols in adults with ADHD.

Humans↗

PubMed2026

Predictive evolutionary genomics: principles, validation, and practice.

Climate change and habitat loss are driving rapid evolutionary responses in populations world-wide, which creates an urgent need for evolutionary forecasting in conservation and agriculture. Such forecasting can be categorized into three time scales: trait-based models that use multivariate quantitative genetic equations to project correlated phenotypic responses up to c. 20 generations, allele-based analyses that model allele frequency dynamics up to 100 generations, and composite adaptation scores that aggregate many small effects to yield predictions across longer horizons. However, these approaches have remained largely disconnected. Here, we present a Bayesian framework that integrates these three complementary approaches for evolutionary prediction. Our framework combines genomic, phenotypic, and environmental data to yield probabilistic predictions with explicit uncertainty. We show how predictive evolutionary forecasts can be validated with experimental evolution, field experimentation, historical specimens, and reciprocal transplants. These validated forecasts can help advance conservation and agricultural programmes by helping predict which populations are at risk of future extinction, optimizing breeding programmes for future climates, and planning ecosystem management under environmental change. By supporting a shift towards more predictive approaches in evolutionary biology, this framework may help improve our ability to manage biodiversity and food security in a changing world.

Genomics↗