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South Korea has one of the highest suicide rates among countries in the Organisation for Economic Co-Operation and Development, and the suicide rate among people with disabilities is more than twice that of the general population. This study aimed to develop an artificial intelligence-based suicide ideation prediction model for people with disabilities in order to provide a proactive approach for managing high-risk groups and offer evidence for establishing suicide prevention policies. The support vector machine, adaptive boost (AdaBoost), and bidirectional long short-term memory (Bi-LSTM) models were used in this study. Data from the Disability and Life Dynamics Panel for 2018–2021 were used. The performance of the models was evaluated based on the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). All the prediction models demonstrated excellent performance, with AUC > 0.80 (0.83–0.87). The best-performing models were AdaBoost (0.87) for accuracy, Bi-LSTM (0.90) for sensitivity, and AdaBoost (0.90) for specificity. This study is the first to develop an artificial intelligence-based suicide ideation prediction model for disabled people and is significant in that it suggests ways to pre-emptively manage groups at high risk for suicide, providing evidence for the establishment of suicide prevention policies.

Background and aimsBody size-metabolic phenotype may help predict whether or not individuals with nonalcoholic fatty liver disease (NAFLD) develop advanced liver disease. We studied the association of body size-metabolic phenotype with nonalcoholic steatohepatitis (NASH) and significant fibrosis.MethodsOur cross-sectional study included 559 subjects (mean age of 53 years; women 51%) with biopsy-proven NAFLD. Clinical, genetic, and histological characteristic features of NAFLD were evaluated. The metabolically unhealthy phenotype was defined by the presence of two or more metabolic components, while body size was categorized based on body mass index: obese (≥ 25 kg/m2) or non-obese (< 25 kg/m2). Body size-metabolic phenotypes were divided into four study groups: (1) non-obese metabolic syndrome (MS)−, (2) non-obese MS+ , (3) obese MS−, and (4) obese MS+.ResultsObese MS− and non-obese MS+ groups demonstrated comparable levels of insulin resistance, adipose tissue insulin resistance indexes, and visceral adipose tissue (VAT) areas. The VAT area was significantly higher in the obese MS+ group versus obese MS− group. However, the VAT to subcutaneous adipose tissue (SAT) ratio was highest in the non-obese MS+ group. There was no difference in histology between the non-obese MS+, obese MS−, and obese MS+ groups. Multivariate analyses adjusted for age, sex, smoking status, PNPLA3, TM6SF2, and VAT/SAT areas demonstrated an independent and dose-dependent relationship between the body size-metabolic phenotype and NASH or significant fibrosis.ConclusionThe non-obese MS+ group displayed similar degree of hepatic histological severity compared to their obese MS− counterparts. Metabolic milieu beyond obesity may play a pathogenic role in non-obese MS+ individuals who develop NASH with significant hepatic fibrosis.Clinical trial numberNCT 02206841.

Submegabase-size topologically associating domains (TAD) have been observed in high-throughput chromatin interaction data (Hi-C). However, accurate detection of TADs depends on ultra-deep sequencing and sophisticated normalization procedures. Here we propose a fast and normalization-free method to decode the domains of chromosomes (deDoc) that utilizes structural information theory. By treating Hi-C contact matrix as a representation of a graph, deDoc partitions the graph into segments with minimal structural entropy. We show that structural entropy can also be used to determine the proper bin size of the Hi-C data. By applying deDoc to pooled Hi-C data from 10 single cells, we detect megabase-size TAD-like domains. This result implies that the modular structure of the genome spatial organization may be fundamental to even a small cohort of single cells. Our algorithms may facilitate systematic investigations of chromosomal domains on a larger scale than hitherto have been possible. Accurate detection of TADs requires ultra-deep sequencing and sophisticated normalisation procedures, which limits the analysis of Hi-C data. Here the authors develop a normalisation-free method to decode the domains of chromosomes (deDoc) that utilizes structural entropy to predict TADs with ultra-low sequencing data.

Memristive devices have been explored as electronic synaptic devices to mimic biological synapses for developing hardware-based neuromorphic computing systems. However, typical oxide memristive devices suffered from abrupt switching between high and low resistance states, which limits access to achieve various conductance states for analog synaptic devices. Here, we proposed an oxide/suboxide hafnium oxide bilayer memristive device by altering oxygen stoichiometry to demonstrate analog filamentary switching behavior. The bilayer device with Ti/HfO 2 /HfO 2−x (oxygen-deficient)/Pt structure exhibited analog conductance states under a low voltage operation through controlling filament geometry as well as superior retention and endurance characteristics thanks to the robust nature of filament. A narrow cycle-to-cycle and device-to-device distribution were also demonstrated by the filament confinement in a limited region. The different concentrations of oxygen vacancies at each layer played a significant role in switching phenomena, as confirmed through X-ray photoelectron spectroscopy analysis. The analog weight update characteristics were found to strongly depend on the various conditions of voltage pulse parameters including its amplitude, width, and interval time. In particular, linear and symmetric weight updates for accurate learning and pattern recognition could be achieved by adopting incremental step pulse programming (ISPP) operation scheme which rendered a high-resolution dynamic range with linear and symmetry weight updates as a consequence of precisely controlled filament geometry. A two-layer perceptron neural network simulation with HfO 2 /HfO 2−x synapses provided an 80% recognition accuracy for handwritten digits. The development of oxide/suboxide hafnium oxide memristive devices has the capacity to drive forward the development of efficient neuromorphic computing systems.

Background Endoscopic retrograde cholangiopancreatography (ERCP) in patients with Billroth II anastomosis is challenging due to post-surgical anatomical alterations. This study aims to compare the clinical outcomes of using a duodenoscope and a cap-assisted gastroscope in these patients. Methods Seventy-nine patients with Billroth II anastomosis and a naïve papilla were included in the study. ERCP was performed using either a cap-assisted gastroscope ( n  = 45) or a duodenoscope ( n  = 34). The primary outcome was the cannulation success rates, while secondary outcomes included clinical success rates, cannulation time, procedure duration, and complications. Results Afferent limb intubation was successful in 67.1% of patients. Among these, selective biliary cannulation (SBC) was achieved in 73.6%, with no significant difference between the two groups. However, cannulation time was significantly longer in the cap-assisted gastroscope group (7.6 min vs. 5.8 min, p  = 0.011). Complications occurred only in the cap-assisted gastroscope group, including one perforation (2.2%) and two cases of pancreatitis (4.4%), though the overall complication rate was not significantly different. Among the 40 patients (50.7%) who failed ERCP, percutaneous transhepatic biliary drainage (PTBD) was the most common rescue intervention (55%), followed by other procedures, including percutaneous gallbladder drainage, repeated ERCP, surgery, and conservative treatment. Conclusions Both cap-assisted gastroscopes and duodenoscopes are viable options for ERCP in patients with Billroth II anastomosis. However, cannulation time was significantly shorter in the duodenoscope group.

Esophageal cancer (EC) is a malignancy with a high incidence and poor prognosis, significantly influenced by dysbiosis in the esophageal, oral, and gut microbiota. This review provides an overview of the roles of microbiota dysbiosis in EC pathogenesis, emphasizing their impact on tumor progression, drug efficacy, biomarker discovery, and therapeutic interventions. Lifestyle factors like smoking, alcohol consumption, and betel nut use are major contributors to dysbiosis and EC development. Recent studies utilizing advanced sequencing have revealed complex interactions between microbiota dysbiosis and EC, with oral pathogens such as Porphyromonas gingivalis and Fusobacterium nucleatum promoting inflammation and suppressing immune responses, thereby driving carcinogenesis. Altered esophageal microbiota, characterized by reduced beneficial bacteria and increased pathogenic species, further exacerbate local inflammation and tumor growth. Gut microbiota dysbiosis also affects systemic immunity, influencing chemotherapy and immunotherapy efficacy, with certain bacteria enhancing or inhibiting treatment responses. Microbiota composition shows potential as a non-invasive biomarker for early detection, prognosis, and personalized therapy. Novel therapeutic strategies targeting the microbiota—such as probiotics, dietary modifications, and fecal microbiota transplantation—offer promising avenues to restore balance and improve treatment efficacy, potentially enhancing patient outcomes. Integrating microbiome-focused strategies into current therapeutic frameworks could improve EC management, reduce adverse effects, and enhance patient survival. These findings highlight the need for further research into microbiota-tumor interactions and microbial interventions to transform EC treatment and prevention, particularly in cases of late-stage diagnosis and poor treatment response.

Inflammatory bowel disease (IBD) presents unpredictable therapeutic responses and complex immune dysregulation. Current precision medicine approaches lack robust molecular tools integrating transcriptomic signatures with immune dynamics for personalized treatment guidance. We performed multi-omics analyses of GEO datasets using machine learning algorithms (LASSO/Random Forest) to derive a four-gene signature. Validation employed ten algorithms and nomogram construction. Immune infiltration (CIBERSORT/ssGSEA), single-cell RNA sequencing, and DSS-colitis models characterized immune dynamics, cellular specificity, and therapeutic response modulation. We identified 536 differentially expressed genes significantly enriched in IL-17 signaling, TNF signaling, and cytokine-cytokine receptor interactions. WGCNA revealed six co-expression modules with disease-specific correlations: turquoise module strongly correlated with Crohn's disease (r=0.6, P=4×10 ) and purple module with ulcerative colitis (r=0.55, P=1×10 ). The four-gene signature (CDC14A, PDK2, CHAD, UGT2A3) demonstrated exceptional diagnostic performance across ten validation algorithms (AUC range: 0.86-0.97), with the integrated nomogram achieving superior accuracy (AUC=0.952) compared to individual genes (CDC14A: 0.934, PDK2: 0.913, CHAD: 0.893, UGT2A3: 0.797). Consensus clustering stratified patients into two distinct molecular subtypes: Cluster 1 exhibited elevated M1 macrophages, activated dendritic cells, and neutrophils with enhanced glycolysis and mTORC1 signaling, while Cluster 2 showed higher signature gene expression, enhanced oxidative phosphorylation, and enrichment in regulatory immune populations including Tregs and M2 macrophages. Single-cell RNA sequencing revealed cell-type-specific expression patterns: PDK2 demonstrated widespread expression across epithelial cycling cells and stem cells, UGT2A3 showed preferential epithelial localization, and CDC14A exhibited selective enrichment in innate lymphoid cells. Nomogram-based risk stratification effectively predicted biologic treatment responses across multiple therapeutic classes using four independent treatment datasets (GSE16879, GSE92415, GSE73661, GSE206285): low-risk patients demonstrated superior response rates to golimumab (63.3%), infliximab (54.8%), and vedolizumab (29% vs. 15% in high-risk group). Connectivity Map analysis identified MS.275 as the top therapeutic enhancer, with experimental validation in DSS-induced colitis confirming synergistic anti-inflammatory effects with TNF-α inhibitors, improving disease activity indices and restoring signature gene expression patterns. This mechanistically grounded four-gene signature enables precise IBD patient stratification across distinct immunological subtypes and predicts biologic responses, providing validated molecular tools for precision immunotherapy and personalized treatment optimization.

IntroductionStroke, one of the leading causes of death worldwide, leads to disability in most patients, posing a burden on society and families. Intensive rehabilitation in the early stages is the most effective method of minimising these disabilities. Home-based rehabilitation strategies have emerged as a promising alternative to conventional approaches, as highlighted during the coronavirus disease pandemic. Currently, no systematic reviews are available on the effects of home-based lower extremity rehabilitation systems in patients with stroke, or on the design specifications for their further development. Therefore, we present a protocol for a systematic review and meta-analysis to provide up-to-date evidence on this topic.Methods and analysisThe study search for this review will be conducted in October 2026 across five electronic databases—MEDLINE, CENTRAL, EMBASE, Web of Science and CHINAL—and will be supplemented by Google searches and manual screening of the reference lists of the finally included studies. The search strategy has been developed using relevant keywords such as stroke, lower extremity rehabilitation, robotics and virtual reality, and study selection, data collection and risk of bias assessment will be performed independently by two authors; if these yield different results, final decisions will be made after discussion with the third author. The risk of bias in the existing literature will be evaluated using appropriate tools and methods in accordance with the Cochrane group guidelines (eg, RoB 2, Risk of Bias in Nonrandomised Studies of Interventions). Moreover, the level of evidence presented by this study will be evaluated using the Grading of Recommendations Assessment, Development, and Evaluation approach. If feasible, we will conduct a meta-analysis using the RevMan Web version; otherwise, a narrative review will be presented.Ethics and disseminationSince this study will be conducted without recruiting participants or using personal information, ethical approval and informed consent are not required (PROSPERO registration number: CRD 42024551398). Our proposed study will provide important evidence for policymakers, rehabilitation providers and system developers, and the results will be published in a journal and presented at conferences.

In order to achieve a wider range of ionizing radiations detection, novel fluorescence sensing materials have been developed that utilize the fluorescence enhancement phenomenon caused by the intramolecular photoinduced electron transfer (PET) effect. Two perylene diimide isomers PDI-P and PDI-B were designed and synthesized, and their molecular structures were characterized by high-resolution Fourier transform mass spectrometry (HRMS), nuclear magnetic resonance hydrogen and carbon spectroscopy (1H and 13C NMR). The interaction between ionizing radiation and fluorescent molecules was simulated by HCl titration. The results show that combining PDIs and HCl can improve fluorescence through the retro-PET process. Despite the similarities in chemical structures, the fluorescent enhancement multiple of PDI-B with aromatic amine as electron donor is much higher than that of PDI-P with alkyl amine. In the direct irradiation experiments of ionizing radiation, the emission enhancement multiples of PDI-P and PDI-B are 2.01 and 45.4, respectively. Furthermore, density functional theory (DFT) and time-dependent density functional theory (TDDFT) calculations indicate that the HOMO and HOMO-1 energy ranges of PDI-P and PDI-B are 0.54 eV and 1.13 eV, respectively. A wider energy range has a stronger driving force on electrons, which is conducive to fluorescence quenching. Both femtosecond transient absorption spectroscopy (fs-TAS) and transient fluorescence spectroscopy (TFS) tests show that PDI-B has shorter charge separation lifetime and higher electron transfer rate constant. Although both isomers can significantly reduce LOD during PET process, PDI-B with aromatic amine has a wider detection range of 0.118–240 Gy due to its larger emission enhancement, which is a leap of three orders of magnitude. It breaks through the detection range of gamma radiation reported in existing studies, and provides theoretical support for the further study of sensitive and effective new materials for ionizing radiation detection.

Uveitis encompasses a heterogeneous group of intraocular inflammatory disorders and remains a leading cause of preventable visual loss. Its immunopathogenesis reflects the interplay between a uniquely regulated ocular environment and triggers that breach or bypass that privilege. Much of our mechanistic understanding derives from animal models, which have helped define key features of ocular immune regulation. Layered mechanisms normally restrain inflammation: physical barriers (blood–aqueous and blood–retina), a locally immunosuppressive milieu, and systemic tolerance circuits such as anterior chamber–associated immune deviation. When these controls fail, disease emerges through a number of broad pathways. In autoimmune uveitis, genetic susceptibility (HLA class I/II and peptide-trimming enzymes such as ERAP) shapes antigen display and lowers activation thresholds for autoreactive T-cells. Antigen presentation in draining nodes primes Th1/Th17 responses. Within the eye, effector T-cells are restimulated by resident microglia and recruited macrophages, driving cytokine cascades that disrupt the blood–retina barrier and amplify leukocyte recruitment. B cells may augment tissue injury via antigen presentation, cytokine production, local antibody formation, and, in some entities, ectopic lymphoid structures. These mechanisms are largely defined in experimental autoimmune uveitis and form the basis for extrapolating human pathogenesis. Tissue-resident memory T-cells persist into remission and may influence relapse risk. Autoinflammatory uveitis arises from dysregulated innate pathways independent of antigen specificity. Infectious uveitis reflects direct intraocular infection or reactivation. Post-infectious inflammation may be sustained by antigen persistence or molecular mimicry. Paraneoplastic uveitis (autoimmune retinopathy) arises when anti-tumour immunity cross-reacts with retinal antigens. Therapy should mirror the dominant immunopathology. In infectious uveitis, clinicians first reduce pathogen load with targeted antimicrobials and then add anti-inflammatory therapy under antimicrobial cover; maintenance antivirals curb reactivation when indicated. In autoimmune disease, where Th1/Th17–macrophage circuits dominate, steroid-sparing treatment targets TNF and IL-6 pathways. In autoinflammatory forms, excess inflammasome/IL-1 signalling supports IL-1 blockade. Advances in humanised modelling will be key to defining condition-specific mechanisms and supporting the evolution of tailored interventions.