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PubMed2026

Efficacy of a multimodal conservative rehabilitation program for bladder control in individuals with incomplete spinal cord injury: A randomized controlled trial.

ObjectiveThe study aimed to evaluate the efficacy of a multimodal intervention on urodynamic outcomes, urinary incontinence severity, and pelvic floor muscle strength in individuals with overactive bladder after incomplete spinal cord injury.MethodsA single-blind randomized controlled trial was conducted on 74 male participants diagnosed with overactive bladder and incomplete spinal cord injury. Participants were randomly assigned to an experimental group or a control group. Treatment was conducted for 8 weeks, three sessions per week. Outcomes were assessed at baseline, post-intervention, and the 8-week follow-up.ResultsThe experimental group showed significantly greater improvements in the measured outcomes compared with the control group (P&#x2009;<&#x2009;0.001).ConclusionIncorporating a multimodal regimen demonstrated significant efficacy in enhancing bladder capacity, continence, and muscle function in individuals with overactive bladder due to incomplete spinal cord injury.Clinical trial registry (ID: NCT07008157).https://clinicaltrials.gov/study/NCT07008157?cond=Spinal%20Cord%20Injuries&intr=Multimodal%20Rehabilitation%20Program&viewType=Card&rank=1.

Humans↗
PubMed2026

What Constitutes Effective Support and Provision Within Day Service Centres for People With Intellectual Disabilities? A Systematic Review of Qualitative Research.

BACKGROUND: This review aimed to investigate the effectiveness and quality of support and provision within day service centres for people with intellectual disabilities. METHOD: The International Bibliography of the Social Sciences, Scopus and PsycInfo databases were searched in August 2024, and the results were reported according to the PRISMA guidelines. Peer-reviewed, English-language, qualitative studies that investigated the effectiveness of day service provision for people with intellectual disabilities in non-residential settings were considered for review. Methodological quality of the included studies was assessed using the JBI Critical Appraisal Tool for qualitative research. Qualitative themes were identified through thematic analysis and synthesised using the ConQual approach. RESULTS: Fourteen studies were included and four key themes emerged: 'perceptions of service quality'; 'community-orientation, integration, and empowerment'; 'challenging behaviours and safety'; and 'staff-centred factors and job satisfaction'. Confidence in the evidence was 'very low' for 3/4 themes, while there was 'moderate' confidence in the evidence related to the theme 'perceptions of service quality'. CONCLUSIONS: Day service centres for people with intellectual disabilities may enhance their effectiveness and quality of provision by concentrating on promoting communication, engagement, relationships, social networks and community integration. Addressing the methodological shortcomings and incomplete reporting of related research in future would contribute to improvements in overall confidence in the evidence base. This can then be better used to inform and further enhance day service provision for people with intellectual disabilities.

Humans↗
PubMed2026

Lower urinary tract evaluation in children with cerebral palsy: A crossectional study.

INTRODUCTION: Cerebral palsy (CP) is a chronic, non-progressive motor disorder affecting voluntary movement and posture. Lower urinary tract (LUT) dysfunction is highly prevalent in children with CP. This study aims to evaluate LUT function in children with CP. MATERIAL AND METHODS: This cross-sectional study was conducted at a tertiary care hospital. Patients aged 5-18 years with established CP diagnosis were included. Evaluation included clinical history, physical examination, urinary ultrasonography with post-void residual (PVR) measurement, and urodynamic studies when indicated. Patients were categorized into three groups; group-1 (LUT dysfunction), group-2 (symptomatic), and group-3 (asymptomatic) for analysis. RESULTS: The study included 97 children with CP (41 girls, 56 boys; median age 8 years). Of the patients, 61.8% were ambulatory (GMFCS I-III) and 38.2% were non-ambulatory (GMFCS IV-V). At least one LUT symptom was detected in 75.3% of patients. Incontinence was the most common symptom at 69.1%. Incontinence prevalence was significantly higher in non-ambulatory patients (81.1% vs 61.7%, p = 0.044). Invasive urodynamics was performed in 19 patients, and LUT dysfunction was diagnosed in 89.5% of them (19.3% of the entire population). Prematurity rate was significantly higher in patients with LUT dysfunction (94.1% vs 64.8%, p = 0.017). Binary logistic regression analysis identified elevated PVR as the strongest independent risk factor for LUT dysfunction (OR = 108, p < 0.001). Abnormal urinary frequency (OR = 14.9, p = 0.022) and quadriplegia (OR = 10.3, p = 0.016) were other independent risk factors. ROC analysis determined the optimal cut-off value for PVR as 19 mL (sensitivity 58.82%, specificity 94.92%). In the intergroup analysis, multinomial logistic regression identified elevated PVR as the strongest predictor (Group-1 vs Group-2: OR = 101; Group-1 vs Group-3: OR = 24, both p < 0.003). Lower gestational age was also an independent risk factor in both comparisons (OR = 1.25-1.26, p < 0.020). DISCUSSION: This study demonstrates LUT dysfunction affects 19.3% of children with CP, strongly correlating with motor impairment severity. Elevated PVR emerged as the strongest independent predictor (OR = 108), offering a practical non-invasive screening tool. Our proposed urodynamic criteria achieved 94.7% diagnostic yield, enabling selective evaluation. Limitations include single-center design and cross-sectional methodology without longitudinal follow-up. These findings support integrating systematic urological assessment into standard CP care for early intervention. CONCLUSION: LUT dysfunction prevalence is high in children with CP, and symptom frequency increases with higher GMFCS levels. Elevated PVR is the strongest predictor, with a clinically applicable cut-off value of 19 mL. Particularly in quadriplegic, non-ambulatory, and premature patients, close follow-up and urodynamic evaluation when necessary should be performed with a multidisciplinary approach.

Humans↗
PubMed2026

Efficacy and Safety of Psychedelic Microdosing on Psychological Outcomes in Healthy Adults: A Systematic Review and Meta-analysis.

BACKGROUND: Psychedelic microdosing has gained increasing popularity for enhancing mood and cognition, yet its effects on psychological outcomes in healthy adults remain unclear. We aim to evaluate the efficacy and safety of psychedelic microdosing on psychological outcomes in healthy/non-clinical adult&#xa0;population. METHODS: This review was registered in the International Prospective Register of Systematic Reviews (PROSPERO; CRD420251035294). We searched Embase, MEDLINE, and PsycINFO from inception to February 2026 for original studies in healthy adults using sub-hallucinogenic psychedelic doses on separate days. We included randomized studies, nonrandomized prospective studies, cross-sectional studies, and observational longitudinal designs and stratified meta-analyses by design. Random-effects models were used; safety in randomized controlled trials (RCTs) was pooled as risk differences (RDs). Risk of bias was assessed using the Joanna Briggs Institute (JBI) critical appraisal tools appropriate for each study design. RESULTS: 24 studies (3,681 participants) met inclusion criteria, 6 contributed to meta-analyses. RCTs of psilocybin and LSD microdosing showed subjective and neurophysiological effects but minimal impact on cognition, creativity, or sustained mood. Psilocybin altered EEG and speech with little behavioral change, while LSD caused transient mood and minor physiological effects without lasting cognitive or personality benefits. Observational studies indicated mood and personality improvements, likely influenced by expectancy or lifestyle factors. Meta-analyses included two parallel RCTs (3 comparisons; n = 117) and three non-RCT studies (n = 1,013). Adverse event analyses included two RCTs (4 comparisons; n = 109). RCTs showed no clear evidence of immediate symptom reduction: depressive symptoms (SMD = -0.19; 95% CI -0.56, 0.19; I2 = 0%), anxiety (SMD = -0.20; 95% CI -1.11, 0.71; I2 = 82.3%),and stress (SMD = 0.02; 95% CI -0.39, 0.43; I2 = 0%). Non-RCT within-arm estimates were imprecise and heterogeneous for depressive symptoms (SMCC -0.33; 95% CI -0.75, 0.08) and anxiety (SMCC -0.29; 95% CI-0.84, 0.26). Exploratory pooling across all designs suggested decreases in depressive symptoms and stress, while anxiety remained uncertain. Adverse event risks were similar between treatment and control. CONCLUSIONS: Microdosing does not show consistent immediate benefits for depressive, anxiety, or stress symptoms in healthy adults, and evidence from RCTs remains inconclusive. Although improvements were observed in some studies, these effects were not significantly different from placebo. Larger, well-designed RCTs are needed to clarify the efficacy and safety of psychedelic microdosing beyond placebo.

Humans↗
PubMed2026

Advancing nursing education through social and emotional learning: A systematic review guided by the Collaborative for Academic, Social, and Emotional Learning framework.

BACKGROUND: With Generation Z entering the nursing workforce in growing numbers, strengthening social and emotional learning is critical for academic success, professional adaptation, and safe practice. However, the existing evidence remains fragmented because of varied interventions and inconsistent approaches. OBJECTIVES: This systematic review examined (1) the social and emotional learning essential for nursing students and nurses within the Collaborative for Academic, Social, and Emotional Learning framework, (2) their impact on educational and clinical outcomes, and (3) implications for advancing nursing education and practice. METHODS: Following Joanna Briggs Institute methodology and Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, five international (PubMed, EMBASE, CINAHL, PsycINFO, Cochrane) and three Korean (RISS, KoreaMed, KMBASE) databases were searched up to June 2025. Eighteen studies involving 2,952 participants met the inclusion criteria, including quasi-experimental quantitative studies, descriptive quantitative studies, qualitative studies, and mixed-methods studies. The methodological quality of the included studies was appraised using the Mixed Methods Appraisal Tool. RESULTS: Within the Collaborative for Academic, Social, and Emotional Learning framework, relationship skills and self-management were the most frequently studied competencies, emphasizing teamwork, communication, and stress regulation. Self-awareness and social awareness were underexplored, despite their importance in empathy, resilience, and reflective practice. Responsible decision-making was the least studied competency, despite its importance in ethical reasoning. Social and emotional learning was consistently associated with enhanced adaptation, communication, leadership, relationships, and clinical performance. Effective strategies included blended learning, simulation, reflective activities, and mentorship, which are aligned with Generation Z's learning preferences. CONCLUSION: Although social and emotional learning integration is associated with improvements in educational and clinical outcomes in nursing, current research has largely centered on relational and stress-related competencies while underrepresenting responsible decision-making. To cultivate reflective, empathetic, and ethically grounded nurses, curricula should integrate social and emotional learning through a balanced and structured approach. REGISTRATION: This study was registered on PROSPERO (ID: CRD420251005683).

Humans↗
PubMed2026

Implicit and explicit statistical learning in reading: Evidence from a randomized controlled-learning study and computational modeling.

A key challenge in reading acquisition is understanding how learners extract the complex probabilistic mappings between print, meaning, and sound. Statistical learning (SL) theory offers a mechanistic account of how such mappings are acquired, whether implicitly through exposure or explicitly through instruction. We conducted a randomized controlled-learning study in Chinese, a writing system characterized by multiple sub-lexical regularities linking orthography, semantics, and phonology. Ninety-five 2nd-3rd graders with or at risk for dyslexia were randomly assigned to one of three groups: an implicit-SL training group exposed to repeated lexical and sublexical orthography-semantics-phonology associations, an explicit-SL training group receiving the same input plus explicit instruction on the sublexical print-sound mapping, and a no-SL control group. Both SL groups outperformed controls on the characters they were trained on, as well as on untrained characters that required generalization. However, only the explicit group demonstrated abstraction of print-sound mapping to novel items. Neural network simulations further revealed distinct mechanisms supporting implicit and explicit SL, consistent with a dual-system account of reading acquisition. Together, these findings (1) clarify how implicit and explicit learning distinctly support the discovery of statistical structure in written language and (2) underscore the implicit-explicit dual learning mechanism underlying reading acquisition.

Humans↗
PubMed2026

Effectiveness of artificial intelligence in nursing simulation education: A systematic review, meta-analysis and bibliometric visualization analysis.

OBJECTIVES: To synthesize the roles and core functions of AI in nursing simulation education for nursing students via systematic review, quantitatively evaluate its effects on students' knowledge and skill outcomes through meta-analysis, and map the research landscape and development trends of this field through bibliometric visualization analysis. DESIGN: Systematic review, meta-analysis and bibliometric visualization analysis. DATA SOURCES: Eight electronic databases: PubMed, Web of Science, MEDLINE, ERIC, Academic Search Complete, China National Knowledge Infrastructure (CNKI), Wanfang Database, VIP Chinese Science and Technology Journal Database (VIP) were employed to search studies from the time of construction to 16 December 2025. REVIEW METHODS: Studies meeting the inclusion criteria were screened. The revised Cochrane Risk of Bias tool (ROB 2) and Joanna Briggs Institute (JBI) critical appraisal checklists were used for quality assessment. Meta-analysis was performed with Review Manager 5.4, and bibliometric visualization analysis was conducted using VOSviewer 1.6.20 and Bibliometrix (based on R4.4.3). RESULTS: A total of 61 studies were included. AI primarily played two roles in nursing simulation education: peer-type new subject (n&#xa0;=&#xa0;24) and direct mediator (n&#xa0;=&#xa0;22). Meta-analysis showed that AI interventions significantly improved nursing students' knowledge (SMD&#xa0;=&#xa0;1.49, 95% CI [0.55,2.43], p&#xa0;=&#xa0;0.002) and skills (SMD&#xa0;=&#xa0;0.66, 95% CI [0.02,1.31], p&#xa0;=&#xa0;0.04). Bibliometric analysis identified that the United States of America and China were the two main contributing countries in this field, and the key motor themes included generative artificial intelligence, virtual patients, and geriatric care. CONCLUSIONS: AI exerts positive effects on nursing students' knowledge acquisition and skill enhancement in simulation education, with peer-type new subject and direct mediator as the dominant roles. Future research should focus on expanding AI applications in multi-specialty simulation scenarios, activating the data-driven value of machine learning, and strengthening international collaboration and standardization construction, so as to promote the sustainable development of AI-integrated nursing simulation education.

Humans↗
PubMed2026

Reinforcement learning-based dynamic ensemble for missense variant effect prediction and tiered prioritization of VUS.

BACKGROUND: Accurate classification of missense variants remains a challenging task despite major advances in genomics. Numerous computational models have been developed to assist in variant classification, but often require repeated integration and benchmarking efforts. Ensemble methods have been proposed to overcome the limitations of single predictors, but mostly rely on fixed, predefined weights that constrain their ability to capture interactions among predictive signals. METHODS: We present GenixRL, a dynamic ensemble framework that reformulates model fusion as a reinforcement learning optimization problem. GenixRL uses a Q-learning agent to learn a policy that dynamically weights the probabilistic outputs of complementary predictors, including BayesDel (addAF and noAF), ClinPred, and MetaRNN. Replacing static weighting with policy learning allows GenixRL to adaptively identify optimal weightings and substantially improve classification accuracy. RESULTS: In benchmark evaluation against 25 state-of-the-art predictors, GenixRL achieved an AUROC of 0.9644 on an independent ClinVar dataset. On saturation genome editing assays for BRCA1 and BRCA2, GenixRL achieved the best performance and ranked highest on 14 of 17 clinically significant genes in a zero-shot evaluation. Applied to uncertain and conflicting ClinVar variants, GenixRL enabled tiered, evidence-based prioritization of hundreds of thousands of variants as likely pathogenic or pathogenic with high confidence, supported by orthogonal population evidence from gnomAD. CONCLUSION: GenixRL advances pathogenicity prediction for missense variants and provides an adaptive ensemble that sorts variants of uncertain significance into tiered candidates for expert curation and functional validation.

Mutation, Missense↗
PubMed2026

A Meta-learning-driven strategy for adulteration detection in sweet potato starch and vermicelli using Raman spectroscopy.

To address the widespread adulteration of sweet potato starch and its vermicelli with cheaper starches and overcome conventional supervised learning's dependency on large labeled datasets, this study developed a few-shot discrimination method integrating Raman spectroscopy with meta-learning. We constructed a meta-learning framework using cassava- and wheat-adulterated sweet potato starch as the source domain for training, with potato-adulterated sweet potato starch and cassava-adulterated sweet potato vermicelli as two target domains for testing. Raman spectra showed high consistency between sweet potato vermicelli and its raw starch, laying the foundation for cross-domain detection. Testing yielded comprehensive classification accuracies of 95.33% and 98.00% for the two target domains, significantly outperforming SVM, RF, and CNN (max. 85.24%). This approach effectively identifies subtle starch variety differences in complex adulteration, providing novel food quality inspection solutions and verifying the feasibility of raw material-to-finished product cross-domain detection.

Ipomoea batatas↗
PubMed2026

Clinical efficacy and brain mechanism characteristics of guide chi and regulate spirit tuina therapy in the treatment of post-stroke walking dysfunction: A randomized controlled trial based on fNIRS.

BACKGROUND: This study aims to preliminarily evaluate the role of Guide Chi and Regulate Spirit(GCRS) Tuina in enhancing walking function in post-stroke patients with walking dysfunction; secondly, by using functional near-infrared spectroscopy (fNIRS), it investigates the effect of GCRS Tuina on the restoration of brain function in this patient population. METHODS: Participants in the control group received 4-week rehabilitation treatment, while those in the Combined Tuina Group (CTG) additionally received GCRS Tuina therapy for another 4 weeks on this basis. Functional Ambulation Category (FAC), Fugl - Meyer Assessment Scale for Lower Extremity Motor Function (FMA - LE), and Modified Barthel Index (MBI) were evaluated at the baseline and after 4 treatment weeks. A gait and motion analysis system was used to measure step length, stride, walking speed, and step frequency. FNIRS was used to measure the resting-state functional connectivity(FC) strength, as well as the &#x3b2; - value and HbO2 concentration mean during the walking task. RESULTS: A total of 60 participants completed the randomized, and 53 completed the trial and entered the statistical analysis. Compared with the Single Rehabilitation Group(SRG), the CTG group had higher FAC, FMA-LE, and MBI scores after 4 weeks. After the treatment course, the standardized step length, stride, walking speed, and step frequency of the CTG were higher than SRG. At the Region of Interest(ROI) level, the CTG exhibited 13 inter-ROI FC strengths that were higher than SRG, and the differences could survive the FDR correction (PFDR<0.05). Under the walking task, the CTG group had higher &#x3b2; values in 18 channels and higher Oxyhemoglobin(HbO2) concentrations in 19 channels than the SRG (PFDR <0.05). CONCLUSION: GCRS Tuina therapy can significantly improve patients' walking function, enhance lower limb motor ability and daily living ability. It can improve walking efficiency. Tuina can significantly increase the FC and enhance the activation levels and HbO2 concentrations. The stimulation of Tuina may help reconstruct the brain's motor control network, restore impaired motor function, promote the occurrence of neural plasticity, strengthen the neural circuits in the cognitive-motor-sensory cortex to improve walking function. TRIAL REGISTRATION: This study has been registered with the International Traditional Medicine Clinical Trial Registry (ITMCTR2024000654).

Humans↗
PubMed2026

Artificial intelligence in treatment prediction for skeletal Class III malocclusion: A systematic review.

In skeletal Class III patients, treatment options range from orthodontics to orthognathic surgery. Choosing the optimal approach requires a comprehensive clinical evaluation, which may be supported by AI tools. The aim of this study was to assess the performance of AI models in predicting the need for orthognathic surgery and in identifying predictors influencing treatment decisions. A PRISMA-guided electronic database search (PubMed, Web of Science; 2009-2024; English/French) was performed to identify studies using machine learning (ML) or deep learning (DL) on cephalometric and clinical data. After screening and assessment for eligibility, 15 studies were critically appraised. Model performance was summarized using accuracy, sensitivity, specificity, and the area under the curve (AUC). ML algorithms (particularly Random Forest and XGBoost) and DL models (ResNet-based convolutional neural networks (CNNs)) achieved high accuracy for predicting surgical need. Frequently selected predictors included Wits appraisal, ANB angle, the maxillomandibular ratio (Mx/Md), overjet, and the divergence of the lower gonial angle. AI methods show promise for assisting treatment decisions in Class III malocclusion, with Random Forest and XGBoost performing well on tabular cephalometric data and CNNs on imaging. Larger, multicentre datasets and external validation are needed to improve reliability, address bias, and support clinical implementation.

Humans↗
PubMed2026

Analysis of deep learning techniques in computer-aided diagnosis for meniscus injuries: a systematic literature review.

Meniscus informatics is a growing subject of study in the healthcare industry. One of the major hindrances to the healthcare system's transformation is obtaining knowledge and meaningful information from complicated, high-dimensional and diverse sources. Modern biomedical research, for instance, has seen an increase in the use of complex, dissimilar, poorly documented, and generally unstructured electronic health records, imaging, sensor data and text, even after many current techniques have been used to extract more robust and useful elements from the data for analysis. New efficient standards for building end-to-end learning models from complex data are therefore needed. Therefore, the current study aims to examine the most recent research on the use of deep learning techniques for diagnosing meniscus tears and recommend creating comprehensive and meaningful interpretable structures that might benefit the healthcare industry. We also draw attention to shortcomings and the need for better technique development, and we provide new perspectives about this exciting new development in the field.

Humans↗
PubMed2026

Systematic review of machine learning approaches for predicting sickle cell crisis and mortality risk at the climate-health nexus.

BACKGROUND: Sickle cell anemia (SCA) is a severe genetic blood disorder characterized by recurrent vaso-occlusive crises and increased mortality, with the greatest burden occurring in low- and middle-income countries. Climatic and environmental conditions, including temperature variability, humidity, rainfall, air pollution, and seasonal changes, have been associated with disease exacerbation. However, the extent to which these factors have been incorporated into predictive models remains unclear. This study systematically reviews the application of machine learning (ML) models for predicting SCA crises and mortality in relation to climate and environmental factors. METHODOLOGY: The PRISMA guidelines were used, and 34 peer-reviewed studies published between 2005 and 2026 were analyzed to identify the climate variables, ML approaches employed, and predictive performance. The reviewed studies applied a range of ML techniques, including artificial neural networks, random forests, support vector machines, decision trees, logistic regression, and deep learning models. Temperature, humidity, rainfall, wind speed, air quality indicators, and seasonal patterns were the most frequently examined environmental variables. RESULTS: The findings indicate that most existing models rely predominantly on clinical and demographic data, with limited integration of climate information and inadequate representation of high-burden regions, especially Sub-Saharan Africa. Studies incorporating environmental variables reported improved predictive performance and highlighted the potential of climate-informed early warning systems for SCA management. CONCLUSION: The review recommends development of interdisciplinary, climate-aware ML frameworks, expansion of longitudinal environmental datasets, and increased research in underrepresented regions to support climate-resilient and patient-centered SCA care.

Humans↗
PubMed2026

A systematic literature review of cultural concepts taught by pharmacist preceptors during pharmacy student experiential placements.

BACKGROUND: Cultural concepts such as cultural intelligence, awareness, competency and safety are essential in guiding culturally responsive care in health professional practice. Pharmacist preceptors play a pivotal role in sharing both clinical and cultural safe practice with pharmacy students. Culturally responsive care can contribute to achieving health equity, which is especially important for Indigenous communities. AIM: To review literature on cultural concepts in pharmacist preceptorship practices, and how these concepts are taught and communicated to pharmacy students during experiential learning. METHOD: The systematic review followed the PRISMA 2020 guideline. Scopus, PubMed, and Google Scholar were used to identify articles specific to pharmacist preceptors and pharmacy students published between 2015 and 2025, and available in English. RESULTS: Three full-text articles met the inclusion criteria. Major themes and subthemes were identified; pharmacist preceptors lacked preparedness to teach cultural concepts, resulting in variability in preceptors' understanding of cultural concepts and confidence in fulfilling preceptor responsibilities, underutilised structured frameworks to guide students' learning, challenges with preceptorship due to limited resources and support, and the influence of preceptorship on student learning, which impacted students' learning and competency. CONCLUSION: Pharmacy students had minimal exposure to culturally informed pharmacist preceptorship. It is likely that pharmacist preceptors require country-specific educational resources to support culturally safe preceptorship. Future research is required to substantiate these findings, and to guide culturally responsive practice and promote equitable health outcomes in diverse populations.

Humans↗
PubMed2026

Systematic evaluation of one-dimensional-to-two-dimensional near-infrared spectroscopy transformations with deep learning for quantifying coconut sap adulteration.

Near-infrared (NIR) spectroscopy have limitations when combined with deep learning (DL) algorithms because they rely on low-dimensional datasets. Therefore, we investigated the potential of transforming one-dimensional (1D) NIR spectra into two-dimensional (2D) spectrograms using synchronous and asynchronous techniques and the continuous wavelet transform (CWT) and their effectiveness by integrating with DL for detecting adulteration in coconut sap. NIR spectra (12,500-4000&#xa0;cm-1) were collected from binary mixtures (0%-100%;w/w). The performance of all DL (convolutional neural networks-CNN, AlexNet and ResNet) models was compared with that of partial least squares (PLS). The models were ranked in the mentioned order based on their performances: 2D-CWT&#xa0;>&#xa0;2D-asynchronous > 2D-synchronous > 1D/2D-PLS. The important features of the best model can be explained and visualized using gradient-weighted-class-activation-mapping. The findings highlight that the 1D-to-2D NIR data transformation combined with DL is a highly robust approach because it addresses the feature representation gap in NIR data and effectively captures the spatial-spectral correlations.

Spectroscopy, Near-Infrared↗
PubMed2026

A systematic review of human avoidance learning: Cognition, computation, and methods.

Avoidance behaviour is fundamental for survival but can become maladaptive in clinical conditions. A large body of literature has accumulated on the dynamics of human avoidance learning. However, current theories and overviews do not provide an exhaustive account of this evidence. In this systematic review, we identify N = 116 studies on human avoidance learning. We analyse these studies with the goal of distilling robust empirical phenomena as a basis for theory-building, and examine their diagnostic value in differentiating between competing theories. We find that the evidence is difficult to reconcile with foundational two-factor and classical safety-signal accounts, and most strongly supports expectancy- and inference-based views, in which avoidance responses are selected with respect to represented consequences. At the same time, no current framework provides a complete account of the evidence: several findings point to an additional role for operant valuation, Pavlovian influences, and contextual or latent-state control over the expression of avoidance. Methodologically, we observe that the problem setting in the most common experimental paradigms is radically simpler than real-world avoidance and therefore unlikely to expose the limits of inferential or reflective mechanisms. Consequently, we argue that paradigms with greater computational demands and more realistic action affordances are required to identify the mechanisms underlying avoidance learning. Collectively, these insights provide a foundation for theoretical refinement, computational modelling, and methodological innovation, with implications for advancing interventions targeting maladaptive avoidance.

Humans↗
PubMed2026

Construction of precision clinical-proteomics risk model based on machine learning for predicting heart failure in type II diabetes mellitus.

BACKGROUND AND AIMS: Heart failure (HF) is a severe complication in type 2 diabetes mellitus (T2DM), but current risk stratification scores have limited predictive accuracy. We aimed to develop novel prediction tools integrating clinical variables with proteomics to improve risk stratification of hospitalization for HF in T2DM. METHODS AND RESULTS: In this study, we included 2111 UK Biobank participants with T2DM but no prior HF, and profiled 2920 proteins to predict 10-year incident HF hospitalization. Participants were randomly divided into training (70%), tuning (10%), and validation (20%) sets.Three prediction models were developed: a Clinical model based on demographic characteristics, comorbidities, medication use, and laboratory indices; a Protein model based on 40 proteins selected by the Light Gradient Boosting Machine (LGBM); and the Clinical OMics and Protein ASSessment for Heart Failure (COMPASS-HF) model, which integrated both clinical variables and the LGBM-selected proteins. Models were evaluated for area under the curve (AUC), sensitivity, and specificity. During follow-up, 168 participants (7.96%) developed incident HF. The COMPASS-HF model showed better discrimination than the Clinical model, with an AUC of 0.897 (95% CI: 0.850-0.945) versus 0.790 (95% CI: 0.723-0.856). It also demonstrated higher sensitivity (0.882; 95% CI: 0.725-0.967) and consistent performance in subgroups. COMPASS-HF effectively stratified risk of hospitalization for HF, with cumulative incidence rates of 31.9% in the high-risk group and 1.2% in the low-risk group. CONCLUSIONS: By combining clinical and proteomic variables, we developed a high-performance HF prediction model for T2DM, enabling precise risk stratification and informing early intervention strategies.

Humans↗