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PubMed2026

From host response to genomic targets: electrochemical biosensing of tuberculosis biomarkers.

Tuberculosis (TB) remains one of the leading causes of death from a single infectious agent worldwide, with timely diagnosis continuing to be a major challenge, particularly in resource-limited settings. Conventional TB diagnostic methods are limited by low sensitivity, long turnaround times, and an inability to reliably differentiate latent from active disease. Biomarker-based diagnostic strategies have therefore gained increasing attention as they offer the potential to improve early detection, disease differentiation, and treatment monitoring. Herein, we examine electrochemical biosensing strategies for TB diagnostics using a biomarker-class-driven framework, covering host-response biomarkers (IFN-γ and TNF-α), pathogen-derived antigens (ESAT6, CFP10, CFP10-ESAT6, MPT64, Ag85, HspX and LpqH), cell-wall signatures and whole-cell markers (LAM and whole cell Mtb), and genomic markers (Mtb DNA and IS6110). Through structured comparison of recognition elements, biointerface designs, signal amplification strategies, electrochemical techniques, matrices, and validation levels, this review identifies the most promising technical approaches for different TB biomarker classes. It further highlights key translational bottlenecks, including limited clinical validation, buffer-based testing, complex multistep amplification, redox-probe dependence, matrix fouling, and insufficient evidence of manufacturability. This review therefore provides practical guidance for developing electrochemical TB biosensors that are analytically sensitive, clinically relevant, and suitable for decentralized diagnostic applications.

Biosensing Techniques↗

PubMed2026

AI-driven snapshot hyperspectral imaging for on-line sorting systems in food industry: From real-time sensing to intelligent decision-making.

High-throughput food sorting requires rapid, non-destructive detection of external defects, foreign materials, and internal quality attributes in heterogeneous food matrices. Conventional scanning hyperspectral imaging may suffer from motion-induced spatial-spectral mismatches, whereas snapshot hyperspectral imaging (S-HSI) captures spectral images within a single integration time. However, its advantage is limited by trade-offs in resolution, signal-to-noise ratio (SNR), reconstruction uncertainty, and calibration stability, which are further amplified by variable tissue structure, surface reflection, moisture, and fat distribution in foods. This review critically examines artificial intelligence (AI)-driven S-HSI for on-line food sorting within a sensing-representation-decision-execution framework. Compact architectures are compared according to their physical constraints, food-sorting suitability, and ability to support mapping between spectral responses and physicochemical quality attributes. AI strategies are reviewed for spectral reconstruction, image restoration, spatial-spectral representation, band selection, uncertainty-aware decision-making, and edge implementation. AI can partially compensate for snapshot-specific limitations, but current evidence remains largely limited to laboratory or prototype studies. Future work should link system performance to food safety and quality outcomes by reporting throughput, decision latency, calibration drift, missed-detection risk, false-rejection cost, and closed-loop sorting success.

Hyperspectral Imaging↗

PubMed2026

Effectiveness and implementation of task-sharing cognitive-behavioral interventions for perinatal mental health: A systematic review and meta-analysis.

OBJECTIVE: To evaluate the effectiveness of cognitive-behavioral interventions (CBIs) delivered by nonspecialist providers (NSPs) on perinatal depressive (PND) and anxiety symptoms, and to narratively synthesize their implementation processes and reported implementation outcomes, including acceptability, feasibility, fidelity, cost, and sustainability. METHODS: We systematically searched eight databases from inception to April 8, 2025. Eligible studies were randomised controlled trials (RCTs) assessing CBIs delivered by NSPs for PND and/or anxiety. Two reviewers independently screened, extracted, and assessed trials. Meta-analyses employed random-effects models, with subgroup, sensitivity, meta-regression, and publication bias analyses conducted in Stata 18.0. Implementation processes and outcomes were reported as frequencies or percentages across trials. RESULTS: A total of 47 trials (11, 357 participants) were included in the systematic review, of which 37 trials (8,709 participants) were included for meta-analyses. CBIs were conducted in 12 countries. Nurses and midwives delivered 45% of CBIs. CBIs were associated with reduced PND post-intervention compared with control conditions (standardized mean difference [SMD] -0.49, 95% CI -0.63 to -0.35; I² = 86.8%). Limited evidence from four trials suggested a small sustained effect at 12 months (SMD -0.14, 95% CI -0.27 to -0.02; I² = 26.4%). Reductions in anxiety symptoms were observed immediately post-intervention (SMD, -0.45, 95% CI -0.65 to -0.25; I²=81%), but evidence for longer-term effects was limited. Subgroup analyses confirmed consistent effects across diverse settings, populations, and intervention characteristics. Reporting of implementation processes (e.g., training, supervision, fidelity) was limited, with only 23.4% of trials assessing fidelity and 10.6% evaluating costs. CONCLUSIONS: NSP-delivered CBIs showed beneficial effects on PND and anxiety, with generally encouraging evidence for acceptability and feasibility. However, evidence for sustained effects beyond the immediate post-intervention period remains limited. Future studies should strengthen long-term follow-up and improve reporting of implementation processes and outcomes, particularly in rural and adolescent perinatal populations, to inform scalable and equitable task-sharing models.

Humans↗

PubMed2026

Clinical applications of digital twin technology in In Vitro Fertilisation.

BACKGROUND: Digital twin technology, originating from aerospace and manufacturing industries, has emerged as a transformative tool in healthcare. In vitro fertilisation (IVF) faces persistent challenges including suboptimal embryo selection, unpredictable treatment outcomes, and limited personalisation of protocols. Despite advances in assisted reproductive technology, existing literature exhibits fragmentation: artificial intelligence applications in embryo selection, ovarian stimulation, and endometrial assessment have been developed independently without systematic integration into comprehensive treatment frameworks. Digital twin technology offers unprecedented opportunities to create virtual replicas of biological systems, enabling real-time monitoring, predictive modelling, and personalised treatment strategies. AIM: This narrative review aims to critically examine the current applications of digital twin technology in IVF, evaluate its potential benefits and limitations, synthesize existing evidence into an integrative conceptual model, and identify future directions for implementation in reproductive medicine. METHOD: A comprehensive narrative review was conducted using PubMed, Scopus, Web of Science, and IEEE Xplore databases. A narrative review approach was selected over systematic review to accommodate the heterogeneity of evidence types in this emerging field, including theoretical frameworks, simulation studies, and proof-of-concept implementations that would be excluded from systematic reviews. Search terms included "digital twin," "IVF," "in vitro fertilisation," "assisted reproductive technology," "embryo selection," and "predictive modelling." Studies published between 2015 and 2025 were included, focusing on original research articles, systematic reviews, and proof-of-concept studies describing digital twin applications in reproductive medicine. RESULTS: Digital twin technology in IVF demonstrates significant potential across multiple domains including embryo development simulation, ovarian response prediction, endometrial receptivity modelling, and personalised stimulation protocols. Current applications integrate artificial intelligence, machine learning algorithms, time-lapse imaging, and omics data to create comprehensive virtual models. Early evidence suggests improvements in embryo selection accuracy, ovarian response prediction, and treatment protocol optimization, though large-scale randomized controlled trials remain limited. Implementation challenges include data integration complexity, computational requirements, regulatory considerations, and validation requirements. CONCLUSION: Digital twin technology represents a paradigm shift in IVF practice, offering personalised, predictive, and precision medicine approaches. This review synthesizes existing evidence to propose an integrative conceptual model for digital twin implementation across the IVF treatment spectrum, identifies critical knowledge gaps, and establishes research priorities to advance clinical translation. Despite current limitations, continued advancement promises improved success rates and patient outcomes.

Humans↗

PubMed2026

Artificial intelligence for dental caries detection: An umbrella review.

Artificial intelligence (AI) has been proposed as a tool to improve dental caries detection across imaging modalities; however, its clinical value remains uncertain. This umbrella review aimed to synthesize and critically appraise systematic reviews evaluating AI for caries detection and diagnosis. An umbrella review was conducted following PRIOR guidance (PROSPERO CRD420261340728). Searches were performed in MEDLINE, Embase, Scopus, Web of Science, and Google Scholar up to 15 March 2026. Methodological quality was assessed using AMSTAR 2, and overlap of primary studies was quantified using the corrected covered area (CCA). Seventeen systematic reviews were included, of which five reported diagnostic test accuracy meta-analyses using bivariate or HSROC models. Across these meta-analyses, pooled sensitivity ranged from 0.76 to 0.94 and specificity from 0.85 to 0.91. Most systems were based on deep learning models applied to bitewing radiographs and intraoral photographs. However, substantial heterogeneity was observed in imaging modalities, lesion thresholds, analytical tasks, and evaluation metrics. In addition, a high degree of overlap across reviews and recurrent methodological limitations, including reliance on retrospective datasets, limited external validation, and inconsistent reporting, substantially weaken the reliability of the evidence. Although AI models demonstrate high diagnostic performance under experimental conditions, current evidence does not support their use as stand-alone diagnostic tools. Their clinical applicability remains limited, and implementation should be restricted to decision-support contexts until robust prospective validation demonstrates meaningful impact on clinical decision-making and patient outcomes.

Dental Caries↗

PubMed2026

Clinical outcomes of Epstein-Barr virus infection/reactivation following CAR-T cell therapy: A systematic review.

BACKGROUND: Epstein-Barr virus (EBV) infection or reactivation is an emerging but underrecognized complication following chimeric antigen receptor T-cell (CAR-T) therapy and is likely associated with treatment-induced immune dysregulation. Data regarding its clinical impact remain limited. OBJECTIVE: To evaluate the reported occurrence, clinical manifestations, and outcomes of EBV infection or reactivation in adults undergoing CAR-T therapy. METHODS: A systematic review was conducted in accordance with the PRISMA 2020 guidelines. PubMed, Embase, and Cochrane CENTRAL were searched from inception to March 2025 for studies reporting EBV infection or reactivation after CAR-T therapy in adults. Due to limited and heterogeneous data, results were synthesized descriptively. RESULTS: Five studies comprising 80 patients were included (median age, 55 years; 52.6% male among patients with reported sex data [10/19]). Across the included studies, 11 EBV infection/reactivation events were identified among 80 described CAR-T recipients, representing 13.8% of the reported sample rather than a true incidence estimate. Among events with usable individualized timing data, the median interval from CAR-T infusion to EBV detection/reactivation was 9.8 months (approximate range, 1-44 months). Because EBV surveillance strategies and definitions were inconsistently reported across studies, this proportion should not be interpreted as a true incidence estimate. Four patients (36.4%) developed EBV-associated disease, including three cases of EBV-related lymphoproliferative disorder and one case of EBV-associated diffuse large B-cell lymphoma. Among seven patients with reported post-CAR-T treatment response, four achieved Complete Remission/ Continuous Complete Remission; treatment response should be interpreted separately from final survival status. Confirmed EBV-related mortality occurred in 2/11 patients with reported EBV infection/reactivation and in 2/4 patients with EBV-associated disease; all-cause mortality could not be reliably estimated because patient-level vital status could not be fully attributed to the EBV-reactivated subgroup. Reported toxicities predominantly consisted of low-grade cytokine-release syndrome; however, toxicity data were limited. CONCLUSION: Although infrequently reported, EBV infection or reactivation after CAR-T therapy may be associated with substantial morbidity and mortality among affected patients. However, the available evidence is limited by the small sample size, heterogeneous study designs, and inconsistent EBV surveillance practices.

Humans↗

PubMed2026

Emerging hantavirus risks in mass gatherings: epidemiology, diagnostic challenges, and outbreak preparedness.

Hantaviruses are emerging rodent borne zoonotic pathogens of increasing global public health concern because of their high mortality, expanding ecological distribution, and potential for international dissemination. Although traditionally associated with sporadic rural outbreaks, recent ecological disruption, climate variability, urbanization, and increased global mobility have heightened concerns regarding hantavirus risks in mass gathering settings. This review critically examines the epidemiology, transmission uncertainty, diagnostic and surveillance challenges, and preparedness strategies related to hantavirus infections in the context of mass gatherings, including religious events, refugee settlements, cruise tourism, sporting events, and temporary accommodations. Particular emphasis is placed on the 2026 multinational cruise ship associated outbreak linked to the MV Hondius, which highlighted vulnerabilities related to delayed diagnosis, international passenger dispersal, and uncertainties surrounding possible human to human transmission of Andes virus. Current evidence indicates that hantavirus transmission occurs primarily through inhalation of aerosolized rodent excreta; however, controversies regarding limited interpersonal transmission, environmental persistence, and asymptomatic infections continue to complicate risk assessment and outbreak preparedness. Diagnostic limitations, underreporting, insufficient environmental surveillance, and lack of mass gathering specific preparedness frameworks remain major public health challenges, especially in resource limited settings. Strengthening proactive preparedness through integrated One Health approaches, ecological surveillance, genomic monitoring, AI driven epidemic intelligence, and coordinated international response systems is essential for mitigating future risks. The review emphasizes the urgent need for multidisciplinary research and evidence based policy development to improve global preparedness against emerging hantavirus associated threats in increasingly interconnected mass gathering environments.

Humans↗

PubMed2026

Effectiveness of peer recovery support services for substance use disorders: A systematic review of healthcare utilization, behavioral health, and engagement outcomes.

BACKGROUND: Peer recovery support services (PRS) delivered by individuals with lived experience of substance use, are increasingly incorporated into substance use disorder (SUD) care systems to improve care engagement, reduce acute care use, and support recovery. However, existing systematic reviews have focused on substance use outcomes, with limited attention to healthcare utilization, psychosocial functioning, and outcomes across settings, and populations. METHODS: This systematic review, registered in PROSPERO (CRD42023469279), synthesized peer-reviewed studies from 2003 to 2026 evaluating PRS for individuals with alcohol or drug-related SUD. Using MEDLINE, Embase, PsycINFO, and CINAHL, the review included 53 studies primarily conducted in high-income countries that reported quantitative outcomes across substance use, healthcare utilization, behavioral health, and treatment engagement. Risk of bias was assessed using Cochrane RoB 2, ROBINS-I, and ROBINS-E tools. RESULTS: Overall, evidence was most favorable for selected treatment-linkage and engagement outcomes, whereas findings for substance use, emergency department use, hospitalization, overdose, and mortality were inconsistent. Uncontrolled longitudinal studies frequently reported improvements in depression and anxiety, but no randomized trials evaluated these outcomes, limiting causal inference. Exploratory cross-study patterns suggested that sustained navigation, practical assistance, and repeated peer contact were more often present in programs reporting favorable outcomes; however, these components were not independently evaluated. Substantial heterogeneity, frequent multicomponent interventions, high risk of bias in many nonrandomized studies, and limited long-term and economic data constrain conclusions. CONCLUSIONS: Findings support the promise of PRS while underscoring the need for more rigorous comparative studies, cost-effectiveness data, and further research in low- and middle-income countries.

Humans↗

PubMed2026

Care Experience Disparities in Individuals With Lower Urinary Tract Symptoms: Systematic Review and Content Analysis.

OBJECTIVES: In this study, we aimed to characterize the landscape of the literature and describe lower urinary tract symptom (LUTS) care experiences using the Agency for Healthcare Research and Quality's (AHRQ's) patient experience framework, describe the characteristics of the studies, and identify critical knowledge gaps. METHODS: We performed a systematic search of MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and Scopus of peer-reviewed publications from 1995 to 2024. The search terms were related to LUTSs, drivers of healthcare inequities, and the domains of the AHRQ. We then performed a content analysis of the included studies. RESULTS: Of the 4597 articles reviewed, we included 11 studies in the analysis. The most studied LUTS was urinary incontinence (10/11, 91%). Of the included studies, six were comparative, and most (4/6, 66.7%) found worse care experience in patients with limited English proficiency and low socioeconomic status. When examining the studies using the care experience framework of the AHRQ, the most frequently evaluated domains of care experience were communication with clinicians (8/11, 73%) and access to care (8/11, 73%). For communication with clinicians, language barriers (3/11, 27%) and symptom minimization by clinicians (3/11, 27%) were common, especially among patients with limited English proficiency and of older age, respectively. In regard to access to care, concerns about healthcare costs (5/11, 45%) and patients' fear or embarrassment about accessing LUTS care (4/11, 36%) were commonly occurring themes, especially among racially minoritized groups. CONCLUSIONS: The findings of this systematic review demonstrated that patients with limited English proficiency, older age, low socioeconomic status, and racially minoritized backgrounds have poor LUTS care experiences.

Humans↗

PubMed2026

How AI-supported intelligent systems support infection prevention and control training in healthcare: A systematic review of educational functions and outcomes.

AIMS: Artificial intelligence (AI)-supported intelligent systems have been increasingly incorporated into infection prevention and control (IPC) education and training, primarily to support the monitoring of observable behaviors and the provision of feedback. However, existing evidence has focused largely on short-term compliance outcomes, with limited synthesis of the educational role of AI-supported intelligent systems in supporting sustained IPC competence. This systematic review examined how AI-supported intelligent systems have been designed and used to support IPC education and training, with a focus on system characteristics, educational functions, and reported outcomes. DESIGN: A systematic literature search was conducted across the PubMed/MEDLINE, Embase, Cochrane, and CINAHL databases. DATA SOURCES: A total of 18 studies met the inclusion criteria. Findings were qualitatively synthesized according to system design characteristics, educational functions, and outcome domains. REVIEW METHODS: Methodological quality was appraised using the Mixed Methods Appraisal Tool. RESULTS: Most AI-supported intelligent systems focused on hand hygiene and relied on fully automated monitoring systems to capture behaviors and provide performance feedback. Educational functions were predominantly limited to performance assessment, automated feedback, and reminders. Outcomes were mainly measured using compliance or performance metrics, whereas sustained behavioral change and decision quality were rarely assessed. CONCLUSIONS: AI-supported intelligent systems have been used primarily to reinforce short-term IPC performance and compliance. However, their current applications for supporting sustained competence over time remain limited. The findings of this review suggest that AI-supported intelligent systems may serve as maintenance-oriented educational support by extending learning beyond initial instruction through repeated practice and feedback. Future research should prioritize outcome measures that capture the durability of performance and decision-making processes to better align AI-supported intelligent systems used in IPC education and training with the educational demands of clinical practice.

Humans↗

PubMed2026

Hierarchical modeling of tumor subtypes in cell lines using large-scale genomic datasets.

Cancer cell lines (CLs) are widely used to study tumor biology and drug response, yet their translational relevance is often limited by inaccurate subtype annotations. Existing CL-tumor matching approaches are frequently constrained by flat classification schemes, weak subtype definitions, and the exclusion of normal tissue references, leading to potential confounding of tumor-specific and tissue-of-origin signals. To address these limitations, a hierarchical classification (HC) framework is presented in which CLs are aligned with patient tumors across biological resolutions, from organ to molecular subtype. Gene expression profiles from 802 CLs, 5,612 tumors from The Cancer Genome Atlas (TCGA) , and 8,939 non-cancerous tissues were integrated to separate oncogenic signals from tissue-specific signals. Node-specific features were selected using maximum relevance minimum redundancy, and balanced accuracies of 89% in cross-validation and 75%, and 80% on external datasets were achieved. Through the framework, 43 CLs were reassigned, and clinically relevant underrepresented subtypes were identified.

cancer cell lines↗

PubMed2026

Cannabis and cannabinoids for the treatment of mental and substance use disorders and symptoms: A systematic review and meta-analysis of experimental and observational studies.

BACKGROUND: Interest in cannabinoids for mental and substance use disorders is increasing. We examined experimental and observational evidence for treating these disorders and their symptoms. METHODS: Systematic review and meta-analysis (PROSPERO CRD42023467536). We searched CENTRAL, MEDLINE, Embase and PsycINFO to May 2025 for studies of cannabinoids in adults (≥18 years) with ADHD, anxiety, depression, PTSD, psychosis or Tourette syndrome, or alcohol, cannabis, opioid or tobacco use disorders. Two reviewers screened, extracted and assessed quality using a risk-of-bias tool and GRADE. RESULTS: We included 82 experimental and 118 observational studies. In RCTs, cannabinoids reduced anxiety symptoms (SMD=-0.40; 95% CI: -0.57, -0.23; I²=90%) and, in one small trial, PTSD symptoms (SMD=-2.60; 95% CI: -4.58, -0.62; n=20), with trivial-to-no effect on depression (SMD=-0.20; 95% CI: -0.43, 0.04; I²=91.6%), ADHD, psychosis and Tourette syndrome. Much anxiety and depression evidence came from symptoms measured as secondary outcomes in other primary conditions. Cannabinoids worsened cannabis use disorder severity in one RCT (SMD=2.35; 95% CI: 1.49, 3.21), with no effect on craving or withdrawal; evidence for alcohol, opioid and tobacco use disorders was very limited. Observational studies suggested improvements but had high risk of bias. The only significant safety finding was increased withdrawals due to adverse events with THC (OR=2.78; 95% CI: 1.66, 4.65). Certainty was predominantly very low. DISCUSSION: The evidence base shows very low certainty, high heterogeneity and methodological limitations, and is insufficient to support cannabinoids as first-line treatment. Signals for anxiety and PTSD are limited by indirectness and low certainty; no benefit was evident for depression; THC-related safety signals warrant careful consideration.

Humans↗

PubMed2026

Associations between smart infusion pump-electronic health record interoperability and healthcare outcomes: A systematic review.

OBJECTIVE: This study synthesized available evidence on the associations between smart infusion pump-electronic health record (EHR) interoperability and healthcare outcomes. METHODS: A systematic review of PubMed, CINAHL, Embase, and Scopus databases identified 901 records, which were imported into Rayyan® for duplicate removal, independent screening by three reviewers, and resolution of discrepancies. Eligible studies were peer-reviewed, data-driven, and reported associations between smart infusion pump-EHR interoperability and healthcare outcomes. Studies focused solely on technical validation or interoperability prototypes were excluded. A backward citation search identified additional studies. Two reviewers independently extracted and cross-validated study characteristics using standardized templates. Methodological quality was assessed with the Joanna Briggs Institute Critical Appraisal Tools. RESULTS: Twenty records of 14 full-text studies and 6 conference proceedings were included. Most records reported positive associations between smart infusion pump-EHR interoperability and outcomes related to safety (e.g., medication administration errors, safety-reported events, pump alerts, and compliance with interoperability and drug library), operational efficiency (e.g., programming and documentation time and technical issues), financial performance (e.g., charges captured, and cost avoided), and user experience domains. Most studies used observational designs, reflecting real-world interoperability implementations, where controlling confounding factors is challenging. Limited reporting of baseline characteristics, pump type, and sample sizes limited comparability across studies. CONCLUSIONS: Smart infusion pump-EHR interoperability was associated with improvements in patient safety, efficiency, charge capture, and user experience, with variable findings across studies. Future research should use rigorous methodologies and standardized measures, examine relationships across outcome domains, assess limitations of pump-EHR interoperability, and evaluate underexplored outcomes, including team communication, cognitive workload, and AI-enabled pumps. IMPLICATIONS FOR CLINICAL PRACTICE: Interoperability should be viewed as a component of a broader sociotechnical system, in which technology, user, workflow, clinical content, and organizational practices collectively determine overall effectiveness.

Humans↗

PubMed2026

Association between pulse pressure and markers of cognitive function: a systematic review and meta-analysis.

Our aim was to systematically review and meta-analyse evidence on the association between pulse pressure (PP) and cognitive function using PubMed, PsycInfo, Embase and Scopus (inception-July 2025) publication databases. Studies were included if they reported an association between PP and cognitive function and summarized narratively and by performing fixed-effects meta-analysis. The search identified 4171 publications with 43 studies meeting inclusion criteria. Domains assessed included global cognition, memory, language, attention, executive function, processing speed and visuospatial ability. Meta-analysis suggests a positive association between PP and global cognition, and a negative association with memory in both cross-sectional and longitudinal studies with inconsistent findings from narratively summarized studies. Processing speed, executive function and language negatively associated with PP in cross-sectional studies with limited evidence provided by longitudinal studies or narratively summarized studies. There was limited evidence of an association with attention and visuospatial ability.

Humans↗

PubMed2026

Meta-PseU: A meta-classifier for robust prediction of RNA pseudouridine modification sites from long sequences.

BACKGROUND AND OBJECTIVES: Pseudouridine (Ψ) represents one of the most abundant and conserved RNA modifications. Ψ provides an additional hydrogen-bond donor that enhances RNA structural stability and modulates translation. It participates in diverse biological processes, including RNA-protein interactions, splicing, translational control, and stress responses. Aberrant pseudouridylation is implicated in cancer, neurodegenerative disorders, and autoimmune diseases. Despite its biological importance, experimental identification of Ψ sites remains time-consuming and costly, limiting the feasibility of transcriptome-wide profiling. Computational approaches have therefore become essential complements to experimental techniques. However, state-of-the-art machine-learning and deep-learning predictors often suffer from limited generalizability due to small training datasets. To overcome these issues, we aim at constructing new long-sequence datasets and developing a novel Ψ site predictor. METHODS: New long-sequence datasets were constructed as benchmarks for RNA Ψ-site prediction. The Ψ modification sites in RMBase 3.0 were mapped to the reference genomes across three species of human, mouse, and yeast, and the RNA sequences with a length of 201 were generated by extending the upstream and downstream from the mapped, central sites. To eliminate sequence redundancy, the sequences were clustered using CD-HIT with a 70% sequence identity threshold. We developed Meta-PseU, a logistic regression-based meta-classifier that considered 118 machine learning and deep learning classifiers. The datasets and programs are freely accessible at https://github.com/kuratahiroyuki/MetaPseU. RESULTS: By optimizing model configuration, we proposed the Meta-PseU model stacking 32 machine learning and deep learning classifiers out of 118 classifiers. Meta-PseU substantially improved model generalizability, overcoming a key limitation of existing approaches. It greatly outperformed state-of-the-art predictors and achieved increasing accuracy with increasing sequence length. CONCLUSIONS: Long-sequence datasets were newly constructed as benchmarks for RNA Ψ-site prediction. Meta-PseU offers a new framework for robust Ψ-site identification by using long sequences.

Pseudouridine↗

PubMed2026

Virtual reality physical education and adolescents' exercise interest and physical fitness: An explanatory sequential mixed-methods randomized trial with exploratory pathway analysis.

Traditional physical education (PE) faces declining student interest and limited fitness gains. Virtual reality (VR) offers immersive, gamified experiences, but evidence regarding its effectiveness and explanatory pathways remains limited. This explanatory sequential mixed-methods randomized trial assigned 360 adolescents (aged 13-16) from three middle schools to either VR-supported PE (n = 180) or conventional PE (n = 180) for 12 weeks, with a 4-week follow-up. Outcomes included exercise interest (validated scale), physical fitness (coordination via MABC-2, cardiorespiratory endurance via the 20-m shuttle run, explosive power via the standing long jump, and speed via the 10-m sprint), and accelerometer-measured physical activity. The qualitative component involved 38 unique students: 32 completed individual semi-structured interviews, and six additional students participated only in focus groups. Three-level linear mixed-effects models and exploratory structural equation modeling were used. The VR group showed significantly greater improvements in exercise interest (d = 0.78), coordination (d = 0.62), cardiorespiratory endurance (d = 0.55), and speed (d = 0.48) than the control group (all p < 0.001), but not in explosive power (d = 0.12, p = 0.148). Effects were partially retained at follow-up (interest d = 0.65, coordination d = 0.48, endurance d = 0.42, and speed d = 0.30), a pattern not fully consistent with a purely novelty-driven explanation. Exploratory mediation identified exercise interest as a statistically compatible explanatory pathway (indirect effect = 0.34, 95% CI [0.22, 0.46]), although the timing of measurement precludes causal interpretation. Qualitative findings contextualized these results by highlighting immersion, feedback, self-efficacy, and perceived transfer. VR-supported PE may enhance adolescents' exercise interest and selected fitness dimensions, but its limited effect on explosive power and possible novelty contribution indicate that it should complement, rather than replace, conventional PE. Longer-term studies are needed.

Humans↗

PubMed2026

Recent advances in supramolecular macrocycle-based artificial light-harvesting systems.

Artificial light-harvesting systems (ALHSs) inspired by the antenna function of natural photosynthesis provide molecular platforms for collecting excitation energy and directing it to emissive or reactive acceptors. In many supramolecular ALHSs, however, practical performance is limited by poorly defined donor-acceptor orientation, aggregation-caused quenching (ACQ), interfacial defects, and limited stability in aqueous or complex media. Supramolecular macrocycles-particularly pillar[n]arenes (PAs), cucurbit[n]urils (CBs), calixarenes (CAs), cyclodextrins (CDs), and supramolecular coordination complexes (SCCs)-offer a useful design space because their cavities, pre-organized scaffolds, and reversible non-covalent interactions can confine chromophores, tune local donor/acceptor ratios, and modulate Förster resonance energy transfer (FRET). This Review systematically examines the unique structural advantages and assembly mechanisms of the five macrocyclic families, with an emphasis on their use in constructing ALHSs-from single-step to cascaded FRET-and in advancing aqueous photocatalysis, near-infrared bioimaging, panchromatic fluorescence modulation, and singlet oxygen generation. The resulting structure-property-application framework is intended to guide the rational design of macrocycle-assisted photofunctional materials while avoiding overextension of the photosynthesis analogy.

Journal Article↗

PubMed2026

Beyond predictive performance: A systematic review and critical methodological appraisal of AI/ML and conventional modelling strategies in breast, colorectal, and pancreatic Cancer.

BACKGROUND: Predictive modelling for cancer risk, treatment-related complications, and survival is central to precision oncology. Conventional logistic regression (LR) and Cox proportional hazards (CoxPH) regression remain widely used but are limited when modelling nonlinear interactions, high-dimensional imaging features, and multimodal clinical-metabolic predictors. Artificial intelligence (AI) and machine learning (ML) methods offer expanded capability through automated feature extraction, ensemble learning, and flexible survival modelling, but the evidence on when AI/ML adds value over conventional models across cancer sites and predictive tasks remains fragmented. OBJECTIVE: To systematically evaluate the methodological performance, validation strategies, and translational limitations of AI/ML models compared with conventional statistical models in published predictive-modelling studies for breast, colorectal, or pancreatic cancer. METHODS: PubMed, Scopus, and Web of Science were searched for studies published between January 2019 and March 2025. Two reviewers independently conducted title-and-abstract screening, full-text eligibility assessment, and PROBAST risk-of-bias assessment. Sixty-five studies (n = 907,567 participants) were narratively synthesised by cancer site, predictive task, model family, comparator, validation strategy, predictor modality, and calibration or explainability reporting. RESULTS: The 65 studies comprised breast cancer (n = 35), colorectal cancer (n = 21), and pancreatic cancer (n = 9). AI/ML superiority over LR and CoxPH was task- and data-dependent. CNN- and U-Net-based models predominated in imaging and body-composition tasks, tree-based ensembles consistently outperformed LR for tabular perioperative complication prediction, and CoxPH remained competitive, and in the largest pancreatic risk study, superior to XGBoost (C-index 0.802 vs 0.723) in well-structured datasets. PROBAST analysis-domain risk was moderate in 54 of 65 studies (83%), driven by limited external validation, sparse calibration reporting (11/65), and few decision-curve analyses (7/65). CONCLUSION: AI/ML adds the most methodological value in imaging-derived feature extraction and nonlinear perioperative prediction, while conventional regression remains preferable in large, structured datasets with linear predictors. Clinical translation requires standardised body-composition definitions, external validation, calibration assessment, decision-curve analysis, and explainability, in line with TRIPOD+AI and CLAIM standards.

Humans↗