Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Non-speech motor learning”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

865 records · Page 7Linked to original sources
PubMed2026

Dynamic evolution of chaperone-mediated autophagy is associated with tumor microenvironment remodeling and prognostic stratification in lung adenocarcinoma: insights from single-cell transcriptomics, ensemble machine learning, and experimental validation.

BACKGROUND: Lung adenocarcinoma (LUAD) shows prognostic heterogeneity, and tumor-node-metastasis (TNM) staging is limited for individualized management. Chaperone-mediated autophagy (CMA) maintains proteostasis, but its role during adenocarcinoma in situ (AIS)-minimally invasive adenocarcinoma (MIA)-invasive adenocarcinoma (IAC) progression remains unclear. METHODS: Single-cell RNA sequencing (scRNA-seq) data from GSE189357 and bulk transcriptomes from The Cancer Genome Atlas (TCGA)-LUAD and Gene Expression Omnibus (GEO) cohorts were integrated. CMA activity, cell-cell communication, weighted gene co-expression network analysis (WGCNA), tumor-normal differential expression, machine-learning survival modeling, tumor microenvironment (TME) features, drug sensitivity, and EPC1 function were analyzed. RESULTS: CMA-high tumor epithelial cells increased from AIS (58.1%) to MIA (65.7%) but declined in IAC (44.4%; p < 0.001). CMA-low cells preferentially received fibroblast-derived extracellular matrix cues. A CMA-negatively correlated module identified 69 core genes. Random survival forest (RSF) performed best among 117 machine-learning combinations (mean concordance index > 0.873). High-risk patients had worse survival across cohorts, and the risk score was independently associated with overall survival (hazard ratio = 16.013, 95% confidence interval: 9.579-26.768, p < 0.001). High-risk tumors showed proliferative activation and M0 macrophage enrichment, whereas low-risk tumors showed stronger immune-related signaling. EPC1 overexpression suppressed malignant phenotypes in A549 cells. CONCLUSION: CMA dynamics are associated with stromal and immune remodeling during LUAD progression. A CMA-based model provides robust prognostic stratification and may offer a basis for future TME-guided studies.

Chaperone-mediated autophagy↗
PubMed2026

Diagnostic Performance of Machine Learning for Systemic Lupus Erythematosus: Systematic Review and Meta-Analysis.

BACKGROUND: Early and accurate diagnosis of systemic lupus erythematosus (SLE) and its organ involvement is essential. Previous reviews of machine learning (ML) in SLE combined heterogeneous tasks and validation strategies and may have overinterpreted model performance. OBJECTIVE: This study evaluated the diagnostic performance of ML and deep learning (DL) models for 3 clinically distinct SLE-related tasks: SLE classification or diagnosis, lupus nephritis (LN) diagnosis, and neuropsychiatric systemic lupus erythematosus (NPSLE) discrimination. We also assessed methodological quality and certainty of evidence. METHODS: PubMed, Embase, Cochrane Library, Web of Science, and IEEE Xplore were searched from January 2014 to April 2026. Eligible peer-reviewed diagnostic accuracy studies developed or validated ML or DL models for 1 of the 3 prespecified tasks, used an accepted reference standard, and provided data for a 2&#xd7;2 contingency table. Bivariate random-effects meta-analyses with the Hartung-Knapp-Sidik-Jonkman adjustment were used to pool sensitivity and specificity. We reported 95% prediction intervals (PIs), assessed risk of bias using the Quality Assessment of Diagnostic Accuracy Studies for Artificial Intelligence tool (QUADAS-AI; Viknesh Sounderajah [Imperial College London]), and evaluated certainty of evidence using the Grading of Recommendations Assessment, Development, and Evaluation framework for diagnostic test accuracy. RESULTS: Twenty-nine studies were included: 17 for SLE classification, 5 for LN diagnosis, and 7 for NPSLE discrimination. In the primary task-stratified analysis, pooled sensitivity was 0.91 (95% CI 0.86-0.94; 95% PI 0.56-0.99), and pooled specificity was 0.94 (95% CI 0.91-0.96; 95% PI 0.69-0.99), with low heterogeneity (I&#xb2;=23.9% and 22.9%, respectively). DL models showed a sensitivity of 0.93 and specificity of 0.95, compared with 0.88 and 0.94 for traditional ML models. Certainty of evidence was high for most analyses but low for LN diagnosis because of inconsistency and imprecision. All studies were retrospective, and only 9 of 29 (31%) performed independent external validation. Overall risk of bias was high or unclear in 22 of 29 (75.9%) studies. No study reported model calibration, decision-curve analysis, or net clinical benefit. CONCLUSIONS: ML models showed promising diagnostic accuracy across 3 distinct SLE-related tasks, but wide PIs, limited external validation, and pervasive risk of bias restrict conclusions about real-world generalizability. Prospective multicenter studies with standardized tasks and reference standards, independent external validation, and formal assessment of calibration and clinical utility are required before clinical implementation.

Humans↗
PubMed2026

From fear to empowerment: the&#xa0;impact of employees AI awareness on workplace well-being - a new insight from the JD-R model.

PURPOSE: The primary purpose of the study was to explore the impact of health workers' awareness of artificial intelligence (AI) on their workplace well-being, addressing a critical gap in the literature. By examining this relationship through the lens of the Job demands-resources (JD-R) model, the study aimed to provide insights into how health workers' perceptions of AI integration in their jobs and careers could influence their informal learning behaviour and, consequently, their overall well-being in the workplace. The study's findings could inform strategies for supporting healthcare workers during technological transformations. DESIGN/METHODOLOGY/APPROACH: The study employed a quantitative research design using a survey methodology to collect data from 420 health workers across 10 hospitals in Ghana that have adopted AI technologies. The study was analysed using OLS and structural equation modelling. FINDINGS: The study findings revealed that health workers' AI awareness positively impacts their informal learning behaviour at the workplace. Again, informal learning behaviour positively impacts health workers' workplace well-being. Moreover, informal learning behaviour mediates the relationship between health workers' AI awareness and workplace wellbeing. Furthermore, employee learning orientation was found to strengthen the effect of AI awareness on informal learning behaviour. RESEARCH LIMITATIONS/IMPLICATIONS: While the study provides valuable insights, it is important to acknowledge its limitations. The study was conducted in a specific context (Ghanaian hospitals adopting AI), which may limit the generalizability of the findings to other healthcare settings or industries. Self-reported data from the questionnaires may be subject to response biases, and the study did not account for potential confounding factors that could influence the relationships between the variables. PRACTICAL IMPLICATIONS: The study offers practical implications for healthcare organizations navigating the digital transformation era. By understanding the positive impact of health workers' AI awareness on their informal learning behaviour and well-being, organizations can prioritize initiatives that foster a learning-oriented culture and provide opportunities for informal learning. This could include implementing mentorship programs, encouraging knowledge-sharing among employees and offering training and development resources to help workers adapt to AI-driven changes. Additionally, the findings highlight the importance of promoting employee learning orientation, which can enhance the effectiveness of such initiatives. ORIGINALITY/VALUE: The study contributes to the existing literature by addressing a relatively unexplored area - the impact of AI awareness on healthcare workers' well-being. While previous research has focused on the potential job displacement effects of AI, this study takes a unique perspective by examining how health workers' perceptions of AI integration can shape their informal learning behaviour and, subsequently, their workplace well-being. By drawing on the JD-R model and incorporating employee learning orientation as a moderator, the study offers a novel theoretical framework for understanding the implications of AI adoption in healthcare organizations.

Humans↗
PubMed2026

Distal versus proximal radial access for diagnostic cerebral angiography: comparative outcomes and learning curve analysis.

BACKGROUND AND PURPOSE: Distal transradial access (dTRA) is an alternative to proximal transradial access (pTRA) for neuroangiography, but comparative real-world data and evidence on its early learning curve remain limited. We compared procedural performance and access-site complications between dTRA and pTRA and evaluated the early learning curve of dTRA. METHODS: We retrospectively analyzed 470 diagnostic cerebral angiography procedures, representing 421 unique patients, performed via radial access at a single center between January 2025 and February 2026, including 237 dTRA and 233 pTRA procedures. Baseline characteristics, including age, sex, body mass index (BMI) category, aortic arch type, and antiplatelet/anticoagulant use, procedural performance, and clinically assessed access-site events were compared between groups. Radial artery occlusion (RAO) was assessed by postoperative bedside pulse examination and confirmed with Doppler ultrasound when clinical findings were uncertain. Multivariable logistic regression was used to evaluate predictors of RAO, persistent bleeding or repeated compression, hand edema, and a composite access-site event endpoint. Because repeated procedures occurred in a subset of patients and event counts were limited, first-procedure sensitivity analysis and analyses of infrequent outcomes were interpreted cautiously. The dTRA learning process was assessed in the first 100 dTRA cases performed by a single operator using multivariable regression, cumulative sum (CUSUM) analysis, segmented trend analysis, and phase-based comparisons. RESULTS: Baseline characteristics were comparable between groups, including age, male sex, BMI category, aortic arch type, and antiplatelet/anticoagulant use. Compared with pTRA, dTRA was associated with more puncture attempts (3.0 [2.0-4.0] vs 2.0 [1.0-3.0], P&#xa0;<&#xa0;0.001), longer puncture time (2.0 [1.0-5.0] vs 2.0 [1.0-3.0] min, P&#xa0;=&#xa0;0.003), lower first-pass success (19.4% vs 35.2%, P&#xa0;<&#xa0;0.001), and a higher crossover rate (11.4% vs 6.0%, P&#xa0;=&#xa0;0.037). However, dTRA was associated with a lower clinically assessed RAO rate (2.5% vs 7.7%, P&#xa0;=&#xa0;0.011). On multivariable analysis, pTRA was independently associated with higher odds of RAO (OR 3.27, 95% CI 1.26-8.49, P&#xa0;=&#xa0;0.015) and the composite access-site event endpoint (OR 3.12, 95% CI 1.55-6.28, P&#xa0;=&#xa0;0.001). Similar findings were observed in a sensitivity analysis restricted to the first procedure per patient. In the first 100 dTRA cases, cumulative dTRA experience was independently associated with shorter total procedure time (beta&#xa0;=&#xa0;-0.074&#xa0;min/case, P&#xa0;=&#xa0;0.009), while CUSUM and moving-average analyses suggested that the major learning effect occurred within approximately the first 10-15 cases. CONCLUSIONS: In this retrospective single-operator cohort, dTRA was associated with lower clinically assessed RAO than pTRA despite greater access difficulty. The early learning effect was mainly reflected in shorter total procedure time. These findings support the feasibility of dTRA but should be interpreted cautiously given the study's observational design and limited anatomical data.

Humans↗
PubMed2026

Integrative machine learning and transcriptomic analysis reveals molecular mechanisms underlying low survival rate in larval Chinese Bahaba (Bahaba taipingensis).

Chinese Bahaba (Bahaba taipingensis) is a Class I protected marine fish endemic to China. Low larvae survival during artificial breeding severely hinder population recovery. To investigate the molecular mechanism of high mortality in larval fish, this study performed RNA-seq on liver from naturally deceased (ND) and mass-dead (MD) individuals, combined with least absolute shrinkage and selection operator (LASSO) regression and random forest (RF) algorithms to screen for core signature genes. A total of 873 differentially expressed genes (DEGs) were identified, including 112 upregulated and 761 downregulated genes. GO and KEGG enrichment analyses revealed significant enrichment in amino acid metabolism disorders, one&#x2011;carbon folate pool impairment, PPAR signaling abnormalities, ECM-receptor interaction, focal adhesion pathway, indicating widespread metabolic suppression accompanied by extracellular matrix remodeling and signaling disturbances in the livers of MD fish. MAD pre-filtering combined with dual machine learning algorithms yielded 18 robust core signature genes, among which SLC38A4, MMP1, FADD, FKBP5, and APOB were consistently identified as high-frequency core genes by both algorithms. SLC38A4 exhibited the highest importance score in the RF model and was significantly downregulated, making it the primary molecule distinguishing ND from MD phenotypes. ROC curve analysis showed that both models achieved an AUC of 1.000 (95% CI lower bound: 0.610), confirming the precise discriminatory ability of the core genes. GSEA further demonstrated significant enrichment of this core gene set in ND samples. This study provides the first systematic elucidation of the molecular mechanisms underlying liver dysfunction in low survival rate B. taipingensis, characterized by amino acid transport impairment, metabolic reprogramming, and structural remodeling, offering theoretical foundations for health assessment, early mortality risk warning, and artificial breeding conservation of this species.

Animals↗
PubMed2026

A flipped classroom approach compared with low-interactive online learning for pediatric pain management knowledge and instructional motivation in nursing students: A randomized controlled study.

AIM: This study aimed to compare a flipped classroom approach with low-interactive online learning in terms of nursing students' questionnaire-assessed pediatric pain management knowledge and instructional motivation. BACKGROUND: Pain management in children is a critical and multidimensional nursing responsibility. However, limited curricular time and opportunities for applied learning may restrict nursing students' preparedness in this area. Structured and interactive instructional formats, such as the flipped classroom, may support knowledge acquisition and motivation in pediatric nursing education. METHODS: This study employed a parallel-group randomized controlled trial design with a 1:1 allocation ratio. Eighty-eight third-year prelicensure nursing students were randomized to either the flipped classroom group (n&#xa0;=&#xa0;44) or the low-interactive online learning group (n&#xa0;=&#xa0;44). Due to attrition (2 intervention, 2 control), analyses included 42 participants per group (n&#xa0;=&#xa0;84 in total). Data were collected between February and July 2022 using the Pediatric Pain Management Knowledge Scale for Nursing Students and the Instructional Materials Motivation Survey. This study was prospectively registered at ClinicalTrials.gov (Identifier: NCT07129044). RESULTS: At baseline, the groups were comparable in terms of knowledge and learning motivation. Following the intervention, the flipped classroom group demonstrated greater improvements in questionnaire-assessed pediatric pain management knowledge and instructional motivation than the low-interactive online learning group. Although scores declined from post-test to the three-month follow-up, they remained above baseline in the flipped classroom group. CONCLUSIONS: Within the context of this course, the flipped classroom approach was associated with greater improvement in questionnaire-assessed pediatric pain management knowledge and instructional motivation than low-interactive online learning. The findings should be interpreted as proximal educational outcomes rather than evidence of improved clinical competence or durable long-term effectiveness. Further studies using objective performance-based outcomes and longer follow-up periods are needed.

Humans↗
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&#xa0;=&#xa0;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&#xa0;=&#xa0;35), colorectal cancer (n&#xa0;=&#xa0;21), and pancreatic cancer (n&#xa0;=&#xa0;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↗
PubMed2026

Effects of an actuated ankle exoskeleton on walking stability in healthy adults: a controlled laboratory study.

BACKGROUND: Ankle exoskeletons are widely used to reduce the metabolic cost of walking, yet their effects on walking stability during unperturbed gait remain insufficiently understood. Walking stability can be characterized using complementary measures that capture stride-to-stride variability, global temporal organization, and local dynamic stability. Understanding how walking with an actuated ankle exoskeleton system influences these different aspects of gait stability is essential for the safe design and control of wearable robotic devices. METHODS: Eighteen healthy adults walked on a treadmill at a constant speed (1.1&#xa0;m/s) with and without an actuated bilateral ankle exoskeleton in a randomized crossover design. Spatiotemporal variability was quantified using coefficients of variation (CoV) of stride length, step width, and stance ratio. Global gait stability was assessed using detrended fluctuation analysis of stride time. Local dynamic stability was evaluated using maximum Lyapunov exponent calculated for the trunk, hip, upper leg, lower leg, and foot. Paired-samples two-sided t-tests were used to compare conditions. RESULTS: Walking with the ankle exoskeleton resulted in increased stride-to-stride spatiotemporal variability, reflected by higher CoV values for stride length (p&#x2009;<&#x2009;0.001) and stance ratio (p&#x2009;=&#x2009;0.005), while mean stride length and step width remained unchanged. Mean stance ratio was reduced in the exoskeleton condition (p&#x2009;<&#x2009;0.001). Global gait stability did not differ between conditions, indicating preserved long-range temporal gait organization. Local dynamic stability increased at the lower leg (p&#x2009;<&#x2009;0.001) and foot (p&#x2009;=&#x2009;0.019) when walking with the exoskeleton. CONCLUSIONS: Walking with the actuated ankle exoskeleton alters gait control across multiple levels during steady walking. While stride-to-stride variability in stride length and stance ratio increased, global gait stability remained unchanged. Local dynamic stability was increased at the lower leg and foot, suggesting segment-specific effects of ankle-level assistance close to the assisted joint. However, these findings should be interpreted as the combined effect of wearing the exoskeleton and receiving active assistance, rather than the isolated effect of plantarflexion assistance. These&#xa0;results provide insight for the design and control of ankle exoskeletons with respect to stability-related effects during walking.

Humans↗
PubMed2026

The Role of Artificial Intelligence Combined With Digital Cholangioscopy for Indeterminant and Malignant Biliary Strictures: A Systematic Review and Meta-analysis.

BACKGROUND: Current endoscopic retrograde cholangiopancreatography (ERCP) and cholangioscopic-based diagnostic sampling for indeterminant biliary strictures remain suboptimal. Artificial intelligence (AI)-based algorithms by means of computer vision in machine learning have been applied to cholangioscopy in an effort to improve diagnostic yield. The aim of this study was to perform a systematic review and meta-analysis to evaluate the diagnostic performance of AI-based diagnostic performance of AI-associated cholangioscopic diagnosis of indeterminant or malignant biliary strictures. METHODS: Individualized searches were developed in accordance with PRISMA and MOOSE guidelines, and meta-analysis according to Cochrane Diagnostic Test Accuracy working group methodology. A bivariate model was used to compute pooled sensitivity and specificity, likelihood ratio, diagnostic odds ratio, and summary receiver operating characteristics curve (SROC). RESULTS: Five studies (n=675 lesions; a total of 2,685,674 cholangioscopic images) were included. All but one study analyzed a deep learning AI-based system using a convoluted neural network (CNN) with an average image processing speed of 30 to 60 frames per second. The pooled sensitivity and specificity were 95% (95% CI: 85-98) and 88% (95% CI: 76-94), with a diagnostic accuracy (SROC) of 97% (95% CI: 95-98). Sensitivity analysis of CNN studies (4 studies, 538 patients) demonstrated a pooled sensitivity, specificity, and accuracy (SROC) of 95% (95% CI: 82-99), 88% (95% CI: 72-95), and 97% (95% CI: 95-98), respectively. CONCLUSIONS: Artificial intelligence-based machine learning of cholangioscopy images appears to be a promising modality for the diagnosis of indeterminant and malignant biliary strictures.

Humans↗
PubMed2026

Exploring the mechanism of aroma production in fermented cherry juice by L. brevis LD1.0600 using flavomics and whole genome analysis.

This study focused on L.brevis LD1.0600 with excellent fermentation traits: it analyzed genome-wide key regulatory genes for micro-metabolites, combined with fermented cherry juice flavor metabolomics data, and used machine learning to explore correlations between gene regulation, metabolite production, and flavor formation. The SVM model screened and verified fermented cherry juice VOCs; through OAV and flavor wheel analysis, LD1.0600 emerged as the top-performing strain, with a sweet, fruity dominant aroma. Key aroma-active components (OAV&#xa0;>&#xa0;100) included 2-methoxy-4-vinylphenol, benzaldehyde, 2-methyl-butanoic acid and hexanoic acid, and 2-methoxy-4-vinylphenol and hexanoic acid elevated by LD1.0600-regulated genes (Chrom1-001884, Chrom1-000925, fabF and Chrom1-000199). At the same time, through research, a "strain screening-SVM screening of DVCs-OAV screening of key aroma components-whole genome sequencing of flavor regulatory genes" system was established. This system can not only be applied to the screen fermentation strains, but also can be extended to the application of other fermentation products.

Fermentation↗
PubMed2026

Teaching Acute Coronary Syndrome High-Risk ECG Interpretation and Clinical Decision-Making Through FOAMed Videos and Podcast Versus Print-Based Materials Among Emergency Care Providers: Randomized Controlled Mixed Methods Trial.

BACKGROUND: Accurate interpretation of high-risk acute coronary syndrome (ACS) electrocardiograms (ECGs) is essential for early diagnosis and timely reperfusion, yet substantial deficits persist across health care professions. Digital self-learning formats such as FOAMed (Free Open Access Medical Education) are widely used, but their effectiveness has rarely been evaluated for complex, high-risk ACS ECG patterns. Existing ECG education studies often focus on students or single professional groups and established ST-segment elevation myocardial infarction (STEMI) criteria, leaving newer guideline-recognized STEMI equivalents, selected emerging occlusion myocardial infarction (OMI)-related patterns, and interprofessional emergency care underrepresented. OBJECTIVE: This study aimed to compare the effectiveness of FOAMed podcast and videos versus traditional print-based materials for teaching high-risk ACS ECG patterns and related clinical decision-making in emergency providers. METHODS: We conducted a prospective, interprofessional, controlled mixed methods trial across 5 training sites in Germany. Paramedics, prehospital emergency physicians, and emergency department clinicians received either a FOAMed multimedia module or print-based materials through concealed allocation; deviations from the intended 1:1 ratio resulted from participant no-shows. The intervention consisted of a 30-minute supervised self-learning session. In total, 103 participants were allocated to FOAMed (n=45) or print-based materials (n=58). Two coprimary outcomes were assessed: ECG interpretation accuracy and text-based ACS clinical decision-making. Secondary outcomes included subjective confidence, learning experience, and exploratory qualitative free-text responses. Outcome assessment was automated and blinded; mixed ANOVA was the primary analysis. The study was not prospectively registered because it assessed educational outcomes in health care professionals rather than patient health outcomes. RESULTS: All 103 participants completed the study. Both groups improved, with greater gains in the FOAMed group: ECG interpretation increased from 55% to 65.5% and text-based ACS clinical decision-making from 45% to 68%, versus 57% to 60% and from 47% to 63%, respectively, in the print-based group. Effect sizes were &#x3b7;&#xb2;=0.055 for ECG interpretation and &#x3b7;&#xb2;=0.044 for clinical decision-making. Exploratory subgroup analyses provided no evidence of differential effects across age, gender, or professional background and were likely underpowered. Qualitative responses (46 and 37 entries) provided contextual insights into perceived clarity, engagement, and practical relevance supporting the quantitative findings. CONCLUSIONS: This study is innovative in directly comparing a curated FOAMed multimedia module with selected print-based materials in an interprofessional emergency care population. It differs from existing research by focusing on subtle, emerging ischemic patterns and evaluating realistic, time-limited self-learning formats. The findings provide evidence that curated FOAMed resources can produce greater short-term improvements in ECG interpretation and text-based ACS clinical decision-making than traditional print-based materials in this setting. Although implications for clinical performance remain hypothetical, concise, high-quality digital modules may represent a practical supplement to structured continuing education in emergency care.

Humans↗
PubMed2026

A systematic review and meta-analysis of visuospatial attentional deficits in Parkinson's patients.

Parkinson's disease (PD) is a neurodegenerative condition primarily characterized by motor deficits, yet cognitive impairments are increasingly recognized. While deficits in executive functioning are well documented even in the absence of cognitive decline, evidence of attentional deficits in PD remains inconsistent, and the role of motor symptom lateralization is unclear. In this systematic review and meta-analysis, we examined visual attention in right-handed, cognitively unimpaired idiopathic PD patients, focusing on the canonical attentional domains (sustained, selective, divided) and processes (alerting, endogenous and exogenous orienting, reorienting), as well as visuospatial bias. Four databases were searched for studies comparing PD patients with healthy controls. Meta-analytic estimates were derived using Hedges' g within random-effects models, and studies that could not be quantitatively integrated were summarized narratively. In addition, studies directly comparing patients with left- and right-predominant motor symptoms (LPD vs. RPD) were reviewed qualitatively. Across 51 studies, PD patients exhibited deficits in sustained, selective, and divided attention. Among attentional processes, only exogenous orienting was impaired, whereas alerting, endogenous orienting, and reorienting were preserved. Findings from the few studies examining visuospatial bias indicated small, context-dependent shifts in spatial attention rather than a consistent directional bias. These findings indicate that PD patients show visual-attentional impairments, particularly under high-demand conditions, while basic alertness and voluntary orienting appear preserved. Exogenous orienting deficits and subtle rightward spatial tendencies in LPD suggest disruption of right-hemisphere attentional networks. These results have implications for early cognitive assessment, rehabilitation strategies, and understanding the neural bases of attentional dysfunction in PD.

Humans↗
PubMed2026

A mechanism-guided framework for prioritizing membrane-interaction anti-Vibrio peptides from peptidomics data.

A mechanism-guided framework for prioritizing membrane-interaction antimicrobial peptide candidates from proteomics-derived peptide mixtures is presented. The framework integrates conservative machine-learning-based antimicrobial peptide (AMP) screening with a literature-derived membrane-interaction plausibility (MAP) assessment and a data-driven membrane-interaction ranking function (AIPx), followed by structural visualization for interpretability. MAP encodes physicochemical characteristics commonly associated with peptide-membrane interaction and provides a graded plausibility assessment. Building upon this physicochemically interpretable framework, AIPx ranks peptides using feature weights calibrated from experimentally characterized anti-Vibrio peptides, where minimum inhibitory concentration (MIC) values are used as a coarse-grained ranking reference rather than a direct prediction target. In a peptidomics-based peptide fractionation study targeting Vibrio spp., AIPx exhibited a consistent relationship with experimentally observed antibacterial activity. Distributional analysis revealed that peptide fractions exhibiting high anti-Vibrio activity are characterized by enrichment of high-ranking peptides rather than by AMP abundance alone. By structuring AMP identification and prioritization as sequential stages, the MAP&#xa0;+&#xa0;AIPx framework enables interpretable and experimentally actionable candidate selection by reducing biologically implausible candidates. The framework facilitates species-oriented prioritization of AMP candidates, addressing a key challenge in antimicrobial peptide discovery where activity may depend on target-specific membrane characteristics. Moreover, the approach is extensible through species-specific calibration and supports interpretable, mechanism-informed prioritization in antimicrobial peptide discovery.

Proteomics↗
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

Comparing trajectories of cognitive functioning in treatment-resistant and non-resistant depression: a multicentre linear mixed-effects analysis.

BACKGROUND: Impaired cognitive functioning is a severe symptom in major depressive disorder (MDD). Recent evidence suggests it may be a central characteristic in its treatment resistant form (TRD), potentially constituting a clinical marker for treatment resistance and a target amenable to intervention. To date, cognitive functioning in TRD remains poorly understood and longitudinal investigations are scarce. METHODS: This observational prospective cohort study, including 320 patients diagnosed with MDD from the multicentre PROMPT study, examined differences in cognitive functioning between 118 TRD and 202 non-TRD patients over a period of twelve weeks in a real-world setting, using linear mixed modelling. Patients that failed to respond to at least two prior antidepressants trials at baseline were classified as TRD. RESULTS: TRD patients showed significantly poorer baseline performances than non-TRD patients in attention/processing speed (&#x3b2;&#xa0;=&#xa0;-0.45; 95%CI[-0.70, -0.19]; FDR-p&#xa0;=&#xa0;0.003) and verbal memory (&#x3b2;&#xa0;=&#xa0;-0.45; 95%CI[-0.72, -0.18]; FDR-p&#xa0;=&#xa0;0.003). Significant time &#xd7; group interactions were observed in motor speed and verbal fluency tasks. Post-hoc-analyses revealed stagnation in TRD patients and significant improvement in non-TRD patients. Across all other tasks improvement was observed in both groups, and random effects showed large heterogeneity between patients, indicating notable individual differences in cognitive performances. CONCLUSIONS: The results suggest distinct recovery patters between non-TRD and TRD patients, and diminished functioning in TRD patients at the domain level. However, intact and diminished performances likely occur in both groups, warranting further investigation of cognitive heterogeneity. These short-term findings highlight the need for more comprehensive longitudinal research on cognition in TRD.

Humans↗
PubMed2026

Effect of different liposomal bupivacaine concentrations in ultrasound-guided superior trunk block on postoperative analgesia and mobility: a randomized double-blind controlled trial protocol for shoulder arthroscopy.

BACKGROUND: Shoulder arthroscopy frequently causes severe postoperative pain that may impede recovery. Liposomal bupivacaine provides prolonged analgesia, and ultrasound-guided superior trunk block (STB) offers comparable analgesia to interscalene block with a lower risk of hemidiaphragmatic paralysis. However, the optimal concentration of liposomal bupivacaine for STB remains unknown. METHODS: This randomized, double-blind, controlled trial will enrol 282 adult patients scheduled for elective arthroscopic rotator cuff repair. Patients will be randomly allocated (1:1:1) to receive ultrasound-guided STB with liposomal bupivacaine 66&#x2009;mg (Group A), 44&#x2009;mg (Group B) or 33&#x2009;mg (Group C), each diluted to 10&#x2009;mL. The co-primary outcomes are (1) rest pain Numeric Rating Scale (NRS) score at 48&#x2009;h post-surgery and (2) cumulative oral morphine milligram equivalents (MME) consumption within 0-48&#x2009;h after surgery. Secondary outcomes include rest pain NRS scores at 6, 24 and 72&#x2009;h; motor function assessed by Muscle Balance Scale, Bromage score and American Shoulder and Elbow Surgeons (ASES) score at 6, 24, 48 and 72&#x2009;h; and Quality of Recovery-15 (QoR-15) score at 24 and 48&#x2009;h. DISCUSSION: This study will provide evidence on the optimal concentration of liposomal bupivacaine for STB in arthroscopic shoulder surgery, aiming to achieve effective and prolonged analgesia without compromising shoulder mobility.

Humans↗
PubMed2026

A machine learning-derived and functionally validated circadian rhythm signature predicts clinical outcomes and in silico drug sensitivity in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) displays considerable heterogeneity in clinical outcomes, highlighting the need for reliable prognostic biomarkers. While the aberrant expression of circadian rhythm-related genes has been implicated in cancer pathogenesis, its comprehensive role in CRC progression and predicted therapeutic vulnerabilities remains inadequately characterized. METHODS: Bulk and single-cell RNA-sequencing data were integrated from multiple CRC cohorts. A circadian rhythm signature (CRS) was developed through machine learning algorithms and validated for prognostic value. Comprehensive analyses of tumor microenvironment, genomic alterations, and drug sensitivity were performed. Furthermore, the biological function of the core gene, BHLHE40, was validated in CRC cell lines through CCK-8, EdU, and wound healing assays. RESULTS: Single-cell analysis demonstrated an elevated expression signature of circadian rhythm-related genes in dendritic cells. The optimized CRS, comprising 14 circadian rhythm-related genes, successfully categorized patients into high- and low-risk groups. Patients with a high CRS showed markedly poorer overall survival and computationally inferred immunosuppressive features, including reduced CD8+ T cell infiltration and increased M2 macrophage polarization. Genomic analysis revealed enhanced mutation burden in TP53 and alterations in RTK-RAS/WNT pathways. Notably, in vitro assays confirmed that BHLHE40 is significantly overexpressed in CRC cells. Knockdown of BHLHE40 markedly inhibited tumor cell proliferation and migration. Drug sensitivity profiling identified bexarotene and SMER-3 as potential therapeutic options for high-CRS patients. A nomogram integrating CRS with clinical parameters demonstrated superior predictive accuracy for 1-, 3-, and 5-year survival. CONCLUSIONS: The CRS represents a promising prognostic biomarker that reflects tumor immune status and genomic features, providing valuable insights for personalized treatment strategies in CRC.

Circadian rhythm↗
PubMed2026

Applications of quantum AI in brain disorder diagnosis: A systematic review.

BACKGROUND AND OBJECTIVE: Brain disorder diagnosis and prediction remain challenging because neuroimaging, electrophysiological, behavioral, and multimodal data are high-dimensional, noisy, heterogeneous, and limited by small clinical cohorts. This systematic review synthesised applications of quantum artificial intelligence (QAI) for brain disorder diagnosis, prediction, detection, and monitoring. METHODS: Following PRISMA guidelines, studies published from 2016 to 13 January 2026 were retrieved from Scopus, Web of Science, and IEEE Xplore. After screening, 36 studies met the eligibility criteria and were qualitatively analysed according to disorder category, data modality, QAI method, implementation setting, validation strategy, and performance. RESULTS: At the broader disease-group level, neurodegenerative disorders were the most frequently investigated, followed by mental health and psychiatric disorders. At the individual level, Parkinson's disease and schizophrenia were the leading applications, followed by depression, anxiety, Alzheimer's disease, and stress-related tasks. MRI-based modalities were the most frequently used data source, followed by multimodal data and EEG. Methodologically, primary QAI approaches were dominated by quantum neural and QDL architectures, followed by quantum-inspired optimization or feature-selection methods and quantum-kernel/conventional QML classifiers. Qiskit/IBM Quantum and PennyLane were the most frequently reported quantum software frameworks. However, most studies relied on simulators, classical quantum-inspired implementations, or unclear implementation settings, with limited real-hardware evaluation. CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.

Humans↗