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PubMed2026

Probiotic supplementation improves body composition, lipid profiles, and fatty acid metabolism in combat sports athletes during the weight reduction phase.

PURPOSE: Pre-competition weight control for combat sports athletes may alter body composition and fatty acid metabolism. Probiotics have shown potential to regulate overall metabolism; however, their specific effects on fatty acid metabolism during weight control in athletes remain unclear. METHODS: Thirty-two combat sports athletes participating in the 4-week weight reduction period were assigned to either the probiotic group (Group E) or the placebo group (Group C). Body composition, lipid profiles, and fatty acid metabolism were measured before and after the 4-week weight reduction phase. RESULTS: All the athletes experienced a decrease in body weight, body mass index (BMI), body fat percentage, and muscle mass following the 4-week weight loss intervention. Notably, a more significant reduction in body fat percentage (p < 0.05) was observed, along with lower levels of triglycerides (TG) and low-density lipoprotein cholesterol (LDL-C), specifically in Group E. Weight loss intervention resulted in increased levels of short-chain fatty acids (SCFAs), specifically acetic acid, propionic acid, and butyric acid. Notably, Group E exhibited higher mean values for SCFAs compared to Group C (p < 0.05). Additionally, the serum levels of γ-linolenic acid and 8,11,14-eicosatrienoic acid were significantly reduced in Group E (p < 0.05). In contrast, the majority of free fatty acids (FFAs) showed significant increases, with greater magnitudes of change observed in Group C (p < 0.05). CONCLUSION: Probiotic supplementation can improve body composition, enhance SCFAs levels, and decrease circulating FFAs in combat sports athletes, suggesting that probiotics may have a beneficial impact on pre-competition weight management. TRIAL REGISTRATION NUMBER: chiCTR2400079908 (Reg Date:2024-01-16).

Humans↗

PubMed2026

Novel non-contrast computed tomography parameters for predicting spontaneous stone passage and surgical requirement in ureteral stones: The role of ureteral wall thickness and dilatation ratio.

We investigated the predictive value of standard non-contrast computed tomography (NCCT) measurements, the ureteral dilatation ratio (DDR) and intraluminal urine stasis markers, for spontaneous stone passage (SSP) versus surgical intervention in patients with ureteral stones. We also evaluated ureteral wall thickness (UWT) as a practical clinical marker. This retrospective study included 461 patients diagnosed with ureteral stones via NCCT. Patients were categorised into two groups based on clinical outcomes: the spontaneous passage group (MET; n = 229) and the endoscopic surgery group (URS; n = 232). Stone volume, stone density (HU), UWT, DDR and intraluminal urine attenuation values were measured for all patients. Independent risk factors were identified using a multivariate logistic regression model and clinical cut-off values were determined via ROC curve analysis. Stone volume, density, UWT and hydronephrosis grade were all significantly higher in the URS group. Multivariate regression analysis revealed that increased UWT (OR: 5.03, 95% CI: 3.66-6.90; p < 0.001) was the strongest independent predictor of surgery. Higher DDR (OR: 1.88; p = 0.003), advanced hydronephrosis, stone volume, and density also increased surgical risk. A UWT cut-off ≥ 2.97 mm predicted surgery with 84.8% sensitivity and 84.3% specificity (AUC: 0.872). A DDR cut-off > 1.79 yielded 81.7% specificity and 40.4% sensitivity. UWT weakly correlated with stone volume (r = 0.145), indicating wall thickening reflects an inflammatory response rather than a mere mechanical consequence. UWT is a superior predictor of SSP failure, supported by increased DDR as a highly specific complementary risk factor. These parameters could help clinicians to identify patients who would benefit from early surgical counselling and intervention rather than prolonged conservative management.

Humans↗

PubMed2026

Metal-organic frameworks nanozyme-integrated portable microneedle patch for visual bacterial monitoring in meat.

Foodborne microbial contamination is a major global health concern, with conventional methods often being time-consuming and complex. Herein, we developed a novel portable biosensor by integrating microneedle patch technology and a metal-organic framework (Fe/Cu-NBDC MOF) nanozyme, enabling rapid, on-site, visual detection of bacteria in meat. The sensing system works by encapsulating aptamer-functionalized MOF nanozymes within a hydrogel patch, where their catalytic sites are initially blocked by the aptamer. In the presence of Staphylococcus aureus (S. aureus) as the target, the specific aptamer's binding to bacteria exposes numerous catalytic sites, further activating the chromogenic reaction of the tetramethylbenzidine‑hydrogen peroxide (TMB-H₂O₂) system, enabling visual detection of S. aureus. The biosensor demonstrates a detection limit of 82 CFU/mL with excellent specificity to successfully apply to commercial mutton. By integrating sampling, enrichment, and visual detection into a single compact device, this platform offers a practical, efficient solution for rapid on-site screening of foodborne pathogens.

Biosensing Techniques↗

PubMed2026

Risk of mortality and complications in people with depressive disorder and co-occurring diabetes mellitus: a systematic review and meta-analysis.

AIMS: People with depressive disorder have increased premature mortality and higher rates of diabetes mellitus than general population. Evidence shows that diabetes may further increase their risk of premature death from diabetes-related complications, especially cardiovascular diseases (CVDs). Earlier studies examining depression-associated outcomes in diabetes patients have shown mixed results and were hindered by important limitations, especially the use of self-reported questionnaires to ascertain depression, causing misclassification bias by identifying subclinical symptoms or diabetes distress. Associations of depression with specific diabetes complications have not been systematically evaluated. This meta-analysis aimed to investigate the risk of mortality and complications among patients with depression and co-occurring diabetes (depression-diabetes group) relative to patients with diabetes-only (diabetes-only group), on their all-cause mortality rates, and if applicable cause-specific mortality rates, and occurrence of specific diabetes complications. METHODS: We systematically reviewed and quantitatively synthesized diabetes-related outcomes in patients with depression by searching Embase, MEDLINE, PsycInfo and Web-of-Science from inception to 20 December 2024, and included studies that examined mortality and complication outcomes in depression-diabetes group relative to diabetes-only group. Results were synthesized by random-effects meta-analytic models, with stratified-analyses (subgroup analyses and meta-regression) by study-level characteristics, including age, gender, study period, geographic region, follow-up duration and nature of diabetes sample. The study was registered with PROSPERO (CRD42024595145). RESULTS: Twenty-six studies were identified from nine geographic regions. Regarding mortality risk, depression-diabetes group exhibited increased risks of all-cause mortality (RR = 1.30 [95% CI: 1.21-1.39]) and CVD-specific mortality (1.15 [1.02-1.29]) relative to diabetes-only group. Regarding complication risk, depression-diabetes group showed increased risk of complications (1.28 [1.18-1.40]) relative to diabetes-only group, especially in incident-diabetes sample signifying advanced disease stage upon presentation, with stratified-analyses showing higher risk of metabolic complications (1.63 [1.33-1.99]) and cardiovascular complications (1.20 [1.11-1.29]), and lower likelihood of retinopathy (0.84 [0.76-0.94]), albeit comparable rates of cerebrovascular complications (1.36 [0.99-1.87]), nephropathy (1.09 [0.93-1.27]) and peripheral-vascular complications (0.97 [0.79-1.18]). Both overall mortality and complication risks were present in various regions and persisted over time. Heterogeneities were noted and could not be entirely explained by stratified analyses. CONCLUSIONS: Our study demonstrated that patients with depression and co-occurring diabetes were associated with elevated overall mortality risk and complication risk (particularly metabolic and cardiovascular-complications) than non-depressed counterparts, suggesting an overall poorer glycemic control that might eventually drive their earlier death. Comprehensive and multipronged interventions are needed for individualized risk estimation of diabetes-related outcomes, with consequent early interventions to minimize the avoidable physical morbidity and premature mortality in this vulnerable population.

Humans↗

PubMed2026

The present and future of nonviral delivery-based genome editing for hereditary hearing loss.

PURPOSE OF REVIEW: This review summarizes nonviral genome-editing delivery platforms for hereditary hearing loss, focusing on lipid nanoparticles (LNPs) and engineered virus-like particles (eVLPs), and discusses their advantages over adeno-associated virus-based delivery, as well as the barriers to clinical translation. RECENT FINDINGS: Recent advances have established LNPs as a clinically advanced nonviral platform, although challenges related to inner ear biodistribution, cell type specificity, endosomal escape, and immunogenicity remain to be addressed. In parallel, eVLPs have undergone substantial technical evolution, progressing from early low efficiency systems to advanced base editor- and prime editor-eVLP architectures that enhance cargo loading and editing efficiency. Extracellular vesicle-based genome editing has also emerged as an additional platform, although issues related to reproducibility, loading efficiency, and scalability remain major hurdles. SUMMARY: Nonviral genome editing platforms expand the therapeutic toolkit for hereditary hearing loss by enabling transient delivery of genome editors with potential safety advantages. Future efforts should focus on characterizing biodistribution and immunogenicity, refining cell type-specific tropism, and establishing scalable manufacturing processes to enable successful clinical translation.

Humans↗

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

Meta-analysis of source identification and apportionment in soil: A systematic review of analytical procedures, receptor modeling, and environmental applications.

Soil pollution poses significant risks to ecosystems and human health, necessitating accurate source identification and apportionment to guide mitigation strategies. This systematic review evaluates the application of Positive Matrix Factorization (PMF) and other receptor models in soil pollution studies, focusing on analytical procedures, tracer indicators, and environmental applications. This review aims to provide a comprehensive framework for conducting soil source apportionment studies, aiding policymakers in designing effective, region-specific environmental management strategies by compiling global trends and methodological insights. The study addresses sampling protocols, emphasizing representativeness and quality control. Data from 500 peer-reviewed publications highlight the dominance of research in China, Eastern Europe, and South Asia, with agricultural soils being the most frequently studied. Key findings reveal that traffic emissions (20.8 %) and industrial activities (19.4 %) are the primary global contributors to soil contamination, with regional variations such as coal combustion in cold climates and agricultural inputs in developing regions. Policy recommendations include stricter industrial regulations, sustainable agricultural practices, and targeted remediation efforts based on source-specific risks.

Soil Pollutants↗

PubMed2026

Psychotherapy vs antidepressants in heart failure: Impact of adherence.

BACKGROUND: Depression affects nearly half of patients with heart failure (HF), which is associated with increased morbidity and reduced health-related quality of life (HRQoL). This secondary per-protocol analysis of a previously published randomized trial evaluated the behavioral activation (BA) versus antidepressant medication (MEDS) among patients with depression and HF who adhered to study interventions. METHODS: This analysis is based on a pragmatic randomized comparative-effectiveness trial conducted from 2018 to 2022, with a 1-year follow-up. 416 participants diagnosed with HF and a DSM-5 depressive disorder were randomized to BA or MEDS, and the current analysis examined outcomes according to treatment adherence. OUTCOMES: The primary outcome was depressive symptom severity (PHQ-9) at 6 months, and the secondary outcomes were physical and mental HRQoL (SF-12v2-PC and SF-12v2-MC, respectively) and HF-specific HRQoL (Kansas City Cardiomyopathy Questionnaire; KCCQ) overall and clinical scores (KCCQ-OS and KCCQ-CS), at 3, 6, and 12 months. High adherence was defined as completion of >75-100% of intervention components. Overall adherence rates were similar between BA and MEDS. At 6 months, among patients with >75-100% adherence who followed treatment as directed in both arms, BA was associated with higher Mental HRQoL (SF-12v2-MC: 5.22; 95% CI: 0.72 to 9.72; p = 0.023) and higher HF-specific HRQoL (KCCQ-OS: 16.08; 95% CI: 7.20 to 24.97; p < 0.001); (KCCQ-CS: 16.04; 95%CI: 7.05 to 25.02; p < 0.001) compared to MEDS. There were no significant differences in depressive symptoms or physical HRQoL at 6 months. INTERPRETATION: Both BA and MEDS were equally effective treatments for depression in HF. However, among highly adherent patients, BA was associated with greater improvements in mental and HF-specific HRQoL than MEDS. FUNDING/SUPPORT: The study was funded by PCORI Award Number: 2017C2-7716.

Humans↗

PubMed2026

The role of the external genitalia score (EGS) in evaluation of disorders of sex development.

OBJECTIVE: To investigate the utility of the External Genitalia Score (EGS) in the diagnosis of disorders of sex development (DSD) and decision-making regarding gender assignment in affected patients. METHODS: A retrospective cohort study was conducted, enrolling 114 DSD patients aged <2 years (88 reared as males, 26 reared as females) treated at our hospital between April 2005 and June 2023, alongside 40 hypospadias patients aged <2 years who underwent surgery at our institution from January to July 2023. Demographic data (age) and EGS assessments of external genitalia were collected for all participants. Statistical analyses included independent samples t-tests, Mann-Whitney U tests and Receiver Operating Characteristic (ROC) curve analysis. Specifically, EGS scores were compared between the hypospadias group and the male-reared subgroup of the DSD cohort; additionally, EGS scores were contrasted between male-reared and female-reared DSD subgroups. RESULTS: The mean age was 20.3 months in the hypospadias group, 17.9 months in the male-reared DSD group, and 18.8 months in the female-reared DSD group. EGS ranged from 5.5 to 11.5 (median 10.5) in the hypospadias group and from 1 to 12 (median 4.75) in the DSD group. ROC curve analysis was performed to compare EGS scores between the hypospadias group and the male-reared DSD subgroup. The optimal diagnostic threshold was determined by maximizing the Youden index (sensitivity + specificity - 1), which balances sensitivity and specificity. A cut-off value of ≤8.50 was identified as indicative of DSD; clinically, patients with an EGS score <9 should be prioritized for DSD screening. Further comparison between male-reared and female-reared DSD subgroups yielded a threshold of 4.00. Clinically, an EGS score ≤4 may suggest a preference for female gender assignment. DISCUSSION: The EGS scale is a reliable, valid, and clinically feasible tool for characterizing external genitalia in DSD patients. An EGS score of 9 can serve as an indicator for initiating detailed sex development evaluation in hypospadias patients. While gender assignment in DSD is a complex, multifactorial process, EGS scores showed a significant association with the sex of rearing in our cohort. In settings where major determinants are balanced, EGS may serve as an adjunctive descriptive parameter rather than a standalone decision-making tool.

Humans↗

PubMed2026

Assessing time to symptomatic progression, a patient-relevant efficacy endpoint, in the MARIPOSA study in non-small cell lung cancer.

INTRODUCTION: In the phase 3 randomized MARIPOSA study, amivantamab and lazertinib combination therapy demonstrated improved progression-free survival (PFS) and overall survival (OS) versus osimertinib in participants with previously untreated, epidermal growth factor receptor-mutated advanced non-small cell lung cancer. Time to symptomatic progression (TTSP) was introduced to assess clinical worsening and complement endpoints that investigate radiographic disease progression and patient-reported outcomes. TTSP provides an easily interpretable measure of disease-specific symptom worsening to further support patient experience. METHODS: In MARIPOSA, TTSP was quantitatively assessed as a secondary efficacy endpoint and defined as the time from randomization until participants experience disease-specific symptom worsening requiring a clinical intervention or treatment change, or death. To evaluate the impact of amivantamab and lazertinib on TTSP considering its established OS benefit against osimertinib, an exploratory analysis censoring death events was performed. RESULTS: At the final protocol-specified OS analysis (median follow up: 37.8 months), median TTSP was 43.6 months with amivantamab and lazertinib versus 29.3 months with osimertinib (hazard ratio [HR]: 0.69; 95% confidence interval [CI]: 0.57-0.83; p < 0.0001). Amivantamab and lazertinib reduced deaths following a TTSP event compared to osimertinib. A strong correlation between TTSP and PFS or OS was observed. CONCLUSIONS: Amivantamab and lazertinib significantly delayed TTSP versus osimertinib. TTSP offers a clinician-validated measurement of disease-specific symptom worsening, capturing symptoms perceived by patients that prompt clinical action. TTSP is highly correlated with PFS and OS, providing complementary insights alongside traditional endpoints. TTSP enhances understanding of treatment benefit and supports informed clinical decision-making by integrating patient experience.

Humans↗

PubMed2026

Sodium-glucose cotransporter-2 inhibitors and gastrointestinal neoplasm risk in type 2 diabetes: a systematic review and meta-analysis of randomized controlled trials.

The potential carcinogenic effects of sodium-glucose cotransporter 2 (SGLT2) inhibitors in patients with type 2 diabetes mellitus (T2DM) remain controversial, particularly regarding site-specific gastrointestinal (GI) neoplasms. This systematic review and meta-analysis aimed to determine the relationship between SGLT2 inhibitors and the risk of GI neoplasms in patients with T2DM. We searched PubMed, EMBASE, Cochrane CENTRAL, Scopus, and Web of Science through March 17, 2025, for RCTs in T2DM comparing SGLT2 inhibitors with placebo or active comparators. Two reviewers independently screened studies, extracted data, and assessed the risk of bias. The primary outcome was GI neoplasms reported in publications, supplementary materials, or trial registries, usually as adverse events rather than centrally adjudicated cancer endpoints. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated in Stata 17.0. In 48 RCTs (n = 48,765), SGLT2 inhibitor therapy was not associated with overall GI neoplasm risk (OR = 1.10, 95% CI: 0.84-1.44; p = 0.46; I² = 0%). Site-specific analyses showed no statistically significant association for esophageal (OR = 1.12, 95% CI 0.37-3.45), gastric (1.20, 0.65-2.23), hepatic (0.62, 0.31-1.22), pancreatic (0.91, 0.51-1.64), colonic (1.28, 0.78-2.08), colorectal (0.76, 0.27-2.17), and rectal neoplasms (0.98, 0.49-1.97), with all p-values > 0.05. Subgroup analyses by agents (e.g., canagliflozin, dapagliflozin, empagliflozin), baseline age, body mass index (BMI), HbA1c, treatment duration, and dose were also non-significant (all p > 0.05). Approximately half of the trials had follow-up of one year or less, limiting our ability to evaluate long-term risk. Available RCT evidence does not show a clear increase in GI neoplasm risk with SGLT2 inhibitors in T2DM. However, limited follow-up, low event counts, and non-cancer-specific outcome ascertainment, the findings should be interpreted as reassuring but not definitive evidence of long-term oncologic safety.Systematic review registration: PROSPERO No. CRD42024619019.

Humans↗

PubMed2026

Thermal analysis techniques for microplastic mass quantification: Methodological challenges and standardization needs.

Microplastics (MPs, 1 μm-5 mm) and nanoplastics (NPs, <1 μm) are ubiquitous contaminants requiring standardized quantification methods. This systematic review evaluates thermal analysis techniques for mass-based MP detection, including pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS), thermogravimetry-MS (TGA-MS), thermal extraction desorption-GC-MS (TED-GC-MS), and differential scanning calorimetry (DSC). Database searches (Web of Science, from inception to December 1, 2025) following PRISMA guidelines identified studies across seven environmental matrices (water, soil/sediment, atmosphere, biota, human tissues). We identify critical standardization gaps: inconsistent marker ion selection, unvalidated conversion factors for tire and road wear particles (TRWPs), and the absence of certified reference materials for complex matrices. Py-GC-MS demonstrates versatility but suffers from lipid interference in biological samples; TED-GC-MS offers superior sensitivity (sample capacity ∼200× Py-GC-MS) but lacks real-time chromatographic monitoring. To advance data comparability, we propose: (i) harmonized ion selection hierarchies based on specificity-sensitivity balance, (ii) matrix-specific TRWP quantification protocols, and (iii) inter-laboratory validation using environmental reference materials. This review provides a methodological roadmap for standardizing thermal analysis in MP research.

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

Translational reprogramming of TGF-β signaling via TRMT61A-mediated tRNA m1A drives prostatic fibrosis and hyperplasia.

Dysregulation of the epitranscriptomic landscape is closely linked to pathological proliferation, but its specific role in benign prostatic hyperplasia (BPH) remains unclear. Here, we identify the tRNA methyltransferase TRMT61A as a critical driver of BPH progression. We found that TRMT61A and global N1-methyladenosine (m1A) levels are aberrantly upregulated in human BPH tissues. Functionally, TRMT61A knockdown potently suppresses prostate cell proliferation and reduces stromal fibrosis, inducing G1 cell cycle arrest and reversing pathological remodeling both in vitro and in vivo. By integrating ribosome profiling (Ribo-seq) and tRNA-seq, we observed that TRMT61A drives translational reprogramming. TRMT61A preserves the stability of specific tRNA isoacceptors (e.g., tRNA-Leu-CAA), which is required for the efficient decoding of mRNAs containing m1A-dependent codons. Consequently, TRMT61A selectively promotes the translational elongation of the key receptor TGFβR1. This amplifies downstream TGF-β/SMAD signaling and drives epithelial-mesenchymal transition (EMT) without affecting mRNA transcription. In summary, our study reveals how TRMT61A drives BPH progression through TGFβR1 translation, highlighting the therapeutic potential of targeting epitranscriptomic pathways to reverse prostatic hyperplasia and fibrosis.

Male↗

PubMed2026

Plant cis-regulatory grammar: Decoding the multidimensional code of transcriptional regulation for programmable crop engineering.

Cis-regulatory elements (CREs) orchestrate the spatiotemporal precision of gene expression that underlies plant development, adaptation, and domestication. Decoding the cis-regulatory grammar of plant genomes remains a central challenge in modern biology, with profound implications for programmable crop engineering. Here, recent conceptual and technological advances are synthesized to reshape our understanding of plant CREs. This review first argues that CRE function is not only an intrinsic property of DNA sequence alone but also emerges from a multidimensional context, including chromatin accessibility, histone modifications, three-dimensional genome topology, and cell type-specific regulatory landscapes. Furthermore, the convergence of single-cell epigenomics, high-throughput functional assays, and CRISPR-based dissection has begun to unravel this contextual grammar, revealing the computational principles governing transcriptional regulation. Critically, we propose that artificial intelligence (AI) platforms are catalyzing an ongoing transition from descriptive discovery to predictive engineering, wherein these platforms outperform natural evolution in designing synthetic CREs. Finally, a roadmap is outlined toward a plant regulatory grammar foundation model, which will enable truly predictive engineering of gene expression when fine-tuned for specific tasks. Collectively, the integration of single-cell resolution maps, precise genome editing, AI-driven design, and regulatory-compliant delivery systems promises to transform our ability to reprogram plant gene regulation for next-generation agriculture, bridging the gap between foundational regulatory biology and tangible crop improvement.

artificial intelligence↗

PubMed2026

Engineering bubble structures as Cas12a activators for highly sensitive monitoring of WRN helicase function.

The Werner syndrome helicase (WRN) is a critical synthetic lethal target in microsatellite instability cancers, essential for resolving complex genomic structures like replication bubbles and R-loops. However, strategies to simultaneously discriminate WRN activity on DNA versus DNA-RNA substrates in living cells are lacking. Here, we developed a structure-specific CRISPR/Cas12a biosensing strategy to visualize WRN functional activity by engineering bubble-structure probes. These probes were rationally designed to structurally mimic DNA replication bubbles and R-loop associated DNA-RNA hybrids. Upon specific unwinding by WRN, the probes release a sequestered activator strand that triggers Cas12a trans-cleavage, effectively converting the unwinding event into an amplified fluorescent signal. This assay achieves low picomolar sensitivity (LODs: 5.6-6.0 pM) and exceptional selectivity against homologous RecQ helicases. Uniquely, this strategy enables the parallel quantification of WRN activity on both substrate types, providing insights into distinct WRN-mediated pathways for resolving genomic stress. We further demonstrated the strategy's utility by visualizing endogenous WRN dynamics in living cells and profiling the efficacy of small-molecule inhibitors. This work offers a powerful molecular toolkit for dissecting WRN biology and facilitating high-throughput drug screening in targeted cancer therapy.

Werner Syndrome Helicase↗

PubMed2026

The Thyroid-Brain Network: Exploring Inflammation, Immune Mechanisms and Common Triggers in Thyroid-Related Neurological Dysfunction.

Autoimmune thyroid diseases (AITD), including Hashimoto's thyroiditis and Graves' disease, represent the most prevalent endocrine disorders worldwide, affecting hundreds of millions with profound but often under recognized neurological consequences. There are emerging lines of evidence establishing inflammation and immunity as the critical missing link connecting peripheral thyroid dysfunction to central nervous system manifestations. Thyroid hormones function as essential neuromodulators governing neurodevelopment, synaptic plasticity, and cognitive processing through integrated genomic and non-genomic mechanisms, with region-specific cerebral metabolic disturbances correlating with distinct neuropsychiatric symptoms. The immunological perspective reveals that AITD propagates neuroinflammation through convergent pathways: molecular mimicry enabling cross-reactivity between thyroid and neural antigens, cytokine-mediated disruption of neurotransmitter metabolism, HMGB1-driven glial activation, and blood-brain barrier compromise facilitating immune cell infiltration. The thyroid-gut-microbiota axis emerges as a critical mediator wherein dysbiosis perpetuates both thyroid autoimmunity and neuroinflammation through impaired serotonin precursor availability and increased intestinal permeability. Mitochondrial dysfunction represents an energetic common denominator, as thyroid hormone dysregulation directly impairs oxidative phosphorylation, producing region-specific cerebral metabolic disturbances. Simultaneous compromise of monoamine systems, cholinergic signaling abnormalities, and glutamate excitotoxicity creates a particularly toxic neurochemical state in untreated thyroid dysfunction. Common triggers such as psychological stress, gut dysbiosis, and mitochondrial impairment may activate interconnected pathways that simultaneously compromise thyroid and brain function, revealing that these disorders share fundamental mechanistic origins. These insights have been discussed in the current review to enhance the understanding of thyroid-brain function, the core mechanisms and consequences of functional deficits.

Journal Article↗

PubMed2026

Endocrine-disrupting chemical-induced gene networks confer coronary heart disease risk revealed by causal inference and single-cell analyses.

BACKGROUND: Endocrine-disrupting chemicals (EDCs) are linked to coronary heart disease (CHD), but underlying mechanisms remain unclear. We aimed to identify EDC-related genes and evaluate their causal roles in CHD. METHODS: We curated EDC-related genes from a compound-gene interaction database and integrated them with CHD genome-wide association study (GWAS) summary statistics and tissue-specific expression quantitative trait loci (eQTL) data. Two-sample Mendelian randomization (MR) and Bayesian colocalization were applied to infer causality. Functional enrichment, single-cell RNA sequencing of human coronary arteries, and EDC-gene networks were further analyzed. RESULTS: After FDR correction, 39 genes were significantly associated with CHD risk via MR. Four genes-ZNF827, FCHO1, IPO9 (protective), and RPL13 (risk-increasing)-showed strong colocalization (PPH4 > 0.9). Pathway and single-cell analyses of coronary artery tissue indicated that vascular and immune pathways mediate these effects. An interaction network highlighted associations between specific EDCs and candidate genes implicated in CHD susceptibility. CONCLUSION: This integrative genomic study provides evidence that EDCs influence CHD susceptibility through distinct gene networks, revealing potential mechanisms and molecular targets for prevention and therapy.

Humans↗