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PubMed2026

AI echo INSIGHT study: A prospective blinded randomized trial of artificial intelligence echocardiogram interpretation.

BACKGROUND: Transthoracic echocardiography (TTE) is the most commonly performed cardiac imaging modality with over 30 million studies annually. Demand for timely expert interpretation continues to outpace capacity, creating diagnostic delays and inter-observer variability that impact patient care. Recent research has suggested computer vision artificial intelligence (AI) models can generate accurate preliminary comprehensive TTE reports, however, prospective evaluation is needed to determine whether AI-assisted TTE interpretation can improve clinician efficiency while preserving diagnostic accuracy. METHODS: AI ECHO INSIGHT is a prospective randomized blinded clinical trial conducted at Kaiser Permanente Northern California that will evaluate 1200 historical TTE studies (1000 consecutive unselected studies plus 200 with moderate or greater valvular disease) interpreted using three workflows: (1) AI-generated preliminary report finalized by a blinded cardiologist (AI-assisted); (2) cardiologist-generated preliminary report finalized by a blinded cardiologist (cardiologist-assisted); and (3) sonographer-generated preliminary report finalized by a blinded cardiologist (sonographer-assisted). The primary outcome is the rate of substantial change between preliminary and final reports, comparing the AI-assisted workflow to the pooled cardiologist-assisted and sonographer-assisted workflows. Secondary outcomes include cardiologist interpretation time for report finalization, superiority testing for diagnostic accuracy, and reporting consistency. CONCLUSION: AI ECHO INSIGHT is a prospective randomized blinded clinical trial evaluating the clinical impact of AI-assisted TTE interpretation on diagnostic accuracy, cardiologist efficiency, and reporting consistency in real-world echocardiography workflows. TRIAL REGISTRATION: ClinicalTrials.gov registration number NCT07229300.

Humans↗
PubMed2026

A systematic approach to standardizing the visual appearance of endometriotic lesions for artificial intelligence recognition.

INTRODUCTION: Numerous studies have shown that the diagnostic performance and reproducibility of visual recognition of endometriosis during laparoscopy are poor. The use of artificial intelligence (AI) seems relevant for exhaustive lesion recognition. Standardization of the visual classification of lesions, in the form of an ontology, is an essential prerequisite to enable medical experts to annotate surgical data consistently and subsequently allow engineers to train and build an artificial intelligence tool for endometriosis recognition. MATERIAL AND METHODS: A systematic search was conducted in the MEDLINE (via PubMed), EMBASE, and the Cochrane Library databases up to May 2022, aiming to identify studies describing the laparoscopic visual appearance of superficial endometriosis, endometriomas, and deep infiltrating endometriosis. The accumulated data in the literature concerning the visual appearance of the different forms of endometriosis were used to create an ontology that could be used for artificial intelligence applications. RESULTS: Out of 932 articles screened, 35 studies were selected based on the inclusion criteria of human subjects with histologically confirmed endometriosis lesions visualized via laparoscopy. The selected studies were reviewed to develop a visual ontology of endometriosis lesions observed via laparoscopy. The lesions were categorized into 4 classes and further subdivided into 11 subclasses: superficial (black, red, white, or subtle), adhesions (dense or filmy), deep (obliteration, retraction, or deformation), and ovarian (endometrioma or chocolate fluid). The positive predictive value (PPV) varied across lesion types: black lesions (PPV 47%-97%), red lesions (PPV 33%-100%), white lesions (PPV 20%-81%), and ovarian endometriosis (PPV 42%-98%). Nonspecific lesions such as adhesions (PPV 16%-50%) and subtle superficial lesions (PPV 0%-67%) presented lower PPVs. Deep endometriosis lesions, often buried within organs, required indirect signs (obliteration, retraction, deformation) for identification. CONCLUSIONS: The visual ontology proposed in this systematic search could facilitate the detection and classification of endometriosis lesions using artificial intelligence. This study highlights the challenges of reaching a consensus on lesion recognition and classification in AI projects due to the diverse visual presentations of endometriosis.

Humans↗
PubMed2026

Noncombustible Nicotine or Tobacco Product Use After Smoking Cessation and Major Vision-Impairing Diseases: A Nationwide Cohort Study.

OBJECTIVE: We assessed the risk of major vision-impairing eye diseases among smokers who quit combustible cigarettes (CC) and switch to noncombustible nicotine or tobacco products (NNTPs) compared with those who completely quit using tobacco. DESIGN: Retrospective cohort study. PARTICIPANTS: About 179 273 adults from the Korean National Health Insurance Service who smoked CC in 2011 to 2012 and reported cessation in 2018 to 2019, classified into complete quitters and NNTP switchers. METHODS: This nationwide longitudinal cohort study followed participants for up to 6 years and identified incident major vision-impairing eye diseases (cataract, glaucoma, age-related macular degeneration, diabetic retinopathy, and refractive and accommodation disorders) using standardized diagnostic codes. Propensity score matching was applied to emulate a pseudo-randomized comparison, balanced on key demographic, clinical, comorbidity, and lifestyle characteristics. Subdistribution hazard ratios (SHRs) were evaluated using the Fine-Gray subdistribution hazards model accounting for all-cause mortality as a competing risk. MAIN OUTCOME MEASURES: Adjusted SHRs for incident major vision-impairing eye diseases. RESULTS: Among 32 316 matched participants followed for a mean of 4.6 years, 6328 incident major vision-impairing eye disease events occurred. The incidence was 41.1 and 44.0 per 1000 person-years for complete quitters and NNTP switchers, respectively. Switching to NNTPs was associated with an increased risk of major vision-impairing eye disease (SHR, 1.07; 95% CI, 1.02-1.13). The risk elevation was most pronounced for diabetic retinopathy (SHR, 1.24; 95% CI, 1.00-1.53) and refractive and accommodation disorders (SHR, 1.07; 95% CI, 1.01-1.12). These findings were robust across inverse probability-weighted and Cox proportional hazards models. The association remained consistent in sensitivity analyses and across clinical subgroups. CONCLUSIONS: Transitioning from CC to NNTPs is associated with a modest but consistent increase in the risk of major vision-impairing eye diseases compared with complete nicotine abstinence. These findings challenge the assumption that substituting NNTPs for CCs is visually harmless and indicate that, from an ophthalmic perspective, complete cessation of all nicotine products should remain the preferred cessation goal.

Humans↗
PubMed2026

Repeated low-level red-light therapy for improving asthenopic symptoms and accommodation in presbyopia.

BACKGROUND: To assess the short-term effectiveness of repeated low-level red light (RLRL) therapy in relieving asthenopia and enhancing accommodation in presbyopia. METHODS: This randomized, parallel-group, double-masked clinical trial enrolled adults with presbyopia and self-reported asthenopia. Participants were allocated using computer-generated randomization and randomly assigned at a 1:1 ratio to RLRL or sham groups. Blinding included participants, examiners, assessors, and statisticians. The primary outcome was the change from baseline in the Computer Vision Syndrome Questionnaire (CVS-Q) score at day 31. Secondary outcomes were the change in accommodative amplitude (AA), Near Activity Visual Questionnaire (NAVQ) score, habitual near visual acuity, near-addition power, accommodative facility, positive and negative relative accommodation, binocular cross-cylinder response, and accommodative convergence-to-accommodation ratio. Continuous outcomes were analyzed using linear mixed-effects models. RESULTS: Sixty-four of 66 randomized participants (aged 41-62 years) completed the 1-month trial. At day 31, RLRL showed greater improvement than sham in CVS-Q score (adjusted mean difference, -1.75 points; 95% CI, -3.10 to -0.39), binocular AA (1.09 D; 95% CI, 0.37 to 1.82), and NAVQ score (-8.07 points; 95% CI, -14.17 to -1.97). The effect on AA was most pronounced in a subgroup of eyes with baseline amplitude >2.0 D (adjusted mean difference 1.33 D; 95% CI 0.32-2.34). Other measures did not differ between groups at each visit. No treatment-related adverse events were reported. Adherence was similar between groups (mean compliance: 98.2% vs 97.5%). CONCLUSIONS: Short-term treatment with RLRL significantly reduced asthenopic symptoms and improved accommodative amplitude in individuals with presbyopia.Trial registration: NCT06745661 (registered December 8, 2024).

Humans↗
PubMed2026

Orbital involvement in sickle cell disease: A systematic review.

Orbital involvement in sickle cell disease (SCD) is rare but potentially vision-threatening and is often misdiagnosed due to overlap with infectious orbital disease. We conducted a systematic review of case reports and series describing orbital complications in patients with confirmed SCD, following PRISMA and MOOSE guidelines. Across 53 studies, 76 cases were identified. Patients were predominantly male (77.6%), with an average age of 13.2 years. Orbital disease was the initial SCD manifestation in 6.6%. Presentations included periorbital edema in all, proptosis in 64.1%, restricted ocular motility in 56.5%, reduced visual acuity in 28.1%, and bilateral involvement in 38.2%. Laboratory findings commonly included leukocytosis (73%) and raised inflammatory markers (86.7%). Radiologically, orbital subperiosteal hematoma were observed in 70%, combined orbital bone infarction and hematoma in 38.2%, and orbital bone infarction alone in 19.7%. Magnetic resonance imaging is critical for accurate diagnosis. Intracranial hemorrhage was present in 9.2%. Less frequent manifestations included orbital apex syndrome, lacrimal gland disease, and nonspecific soft tissue swelling. Management was primarily conservative (82.9%), and surgery was reserved for vision-threatening or intracranial complications. Complete recovery was achieved in 93.1% of cases. While severe vision-threatening complications are uncommon, early recognition remains critical to optimising outcomes in sickle cell orbitopathy.

Humans↗
PubMed2026

Effectiveness of an AI-based home exercise app for rehabilitation of rotator cuff-related shoulder pain: A randomized controlled trial.

BACKGROUND: Rotator cuff-related shoulder pain contributes to disability and healthcare use. Although therapeutic exercise is first-line treatment, limited supervision and adherence may reduce its effectiveness; digital rehabilitation with real-time feedback may address these limitations. OBJECTIVES: To evaluate the effectiveness of adding a digital rehabilitation program to standard physiotherapy on pain, function, fear-avoidance beliefs, and healthcare utilization. DESIGN: Single-center, assessor-blinded, randomized controlled trial with two parallel groups. METHOD: Forty-six adults (mean age 59 years) with rotator cuff-related shoulder pain were randomized to 12 weeks of conventional physiotherapy or physiotherapy plus an AI-based digital rehabilitation program using computer vision for real-time feedback and performance monitoring. Outcomes were assessed at baseline and at 2, 4, and 12 weeks. Pain intensity (NPRS) was primary outcome; secondary outcomes included upper limb function (QuickDASH), fear-avoidance beliefs (FABQ), and post-intervention healthcare utilization. Analyses followed an intention-to-treat approach. RESULTS: Pain reduction exceeded the MCID (1.3) at 4 and 12 weeks. Between-group differences favoured the intervention at Weeks 2 and 4 (MD -0.7; 95% CI -1.13 to -0.14 and MD -1.01; 95% CI -1.8 to -0.2, respectively). Upper limb function improved more at Week 4 (MD -7.3; 95% CI -12.3 to -2.2). FABQ scores decreased more at Week 12 (MD -7.6; 95% CI -14 to -0.5). Fewer participants in the experimental group required post-intervention healthcare (3 vs 10; p = 0.02). CONCLUSION: Adding AI-based home exercise app to conventional treatment improve pain and may improve function and reduce healthcare utilization in rotator cuff-related shoulder pain.

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

The utility of 18F-fluorodeoxyglucose PET/computed tomography in relapsing polychondritis: a systematic review and meta-analysis.

Relapsing polychondritis is a rare chronic autoimmune inflammation of the cartilage associated with life-threatening respiratory complications. Currently, no clear role of imaging modalities such as 18F-fluorodeoxyglucose (FDG) PET/computed tomography (CT) is defined in the literature. This systematic review and meta-analysis provide current evidence on the PET-positivity rate and utility in relapsing polychondritis. Prospective or retrospective studies with more than five patients of suspected relapsing polychondritis who underwent 18F-FDG PET/CT during their management and reported a PET-positivity rate were included. Low-sample-size studies describing chondritis due to other aetiologies or utilizing PET-based radiopharmaceuticals other than FDG were excluded. A systematic search using relevant keywords was conducted across four databases (PubMed, Embase, Scopus and Web of Science) to include studies up to 25 April 2025. The Joanna Briggs Institute critical appraisal tools were used for risk-of-bias analysis. Data were analysed using the R software package (v4.3.1; 2023). Out of 962 articles, three with a total of 97 patients were included. With a pooled PET-positivity rate of 94% [95% confidence interval (CI): 73-99%, I2 = 0%, P = 0.76] and a pooled baseline SUVmax of 4.0 (95% CI: 3.5-4.6, I2 = 32%, P = 0.23), 18F-FDG PET identified asymptomatic cartilage involvement in more than 25% patients and PET parameters correlated well with inflammatory markers. It had a higher positivity rate for inaccessible sites, such as peripheral airways, and was crucial in treatment monitoring. The pooled PET-positivity rate of 18F-FDG PET in relapsing polychondritis is high but requires prospective large-sample-size studies to explore the diagnostic accuracy and prognostic implications of 18F-FDG PET in relapsing polychondritis.

Polychondritis, Relapsing↗
PubMed2026

Baseline Computed Tomography Coronary Angiography and Polygenic Risk Profiles in Adults With Type 2 Diabetes: A Cross-Sectional Analysis From the VOLTAIRE Study.

AIMS: To characterise baseline clinical, anatomical, and genetic cardiovascular risk profiles in participants enrolled in the VOLTAIRE (Evaluation of Polygenic Scores and CT Imaging in Risk Factor Modification in Patients with Type 2 Diabetes) study and examine concordance across these domains. METHODS: This analysis included adults with T2D who completed baseline computed tomography coronary angiography (CTCA) and polygenic risk score (PRS) assessment prior to randomisation in the VOLTAIRE study. Coronary atherosclerosis was evaluated using coronary artery calcium (CAC) score and CTCA-derived stenosis severity. Clinical risk was assessed using the New Zealand Society for the Study of Diabetes 5-year cardiovascular risk calculator. Polygenic risk for coronary artery disease was assessed using a genome-wide PRS and categorised into tertiles. RESULTS: Among 126 participants with T2D (mean age 57.5 ± 8.7 years; 62.7% male), coronary atherosclerotic burden was highly heterogeneous: 34.9% had CAC = 0, whereas 19.8% had CAC ≥ 400. Moderate-to-severe coronary stenosis (≥ 50%) was present in 40.5% of participants overall, including 20.4% of those classified as low clinical risk. PRS distribution was variable (low 37.3%, intermediate 35.7%, high 27.0%). Overlap between anatomical, genetic, and clinical domains was limited, with only 8.7% of participants classified as high risk across all three. CONCLUSIONS: Substantial heterogeneity and limited overlap exist between anatomical, genetic, and clinical cardiovascular risk measures in T2D. These findings support a multimodal approach to risk assessment integrating imaging and genetic profiling. TRIAL REGISTRATION: https://www. CLINICALTRIALS: gov; ID: NCT07091162.

Aged↗
PubMed2026

In silico identification of DNMT1 inhibitors from the PlantCyc database through computational approach to assess the anti-cancer potential of nutraceutical compounds in breast cancer.

Breast cancer accounts for a disproportionate share of global cancer-related deaths, with 670,000 fatalities and 2.3 million new diagnoses recorded in women during 2022 alone. Existing treatment modalities carry considerable toxicity burdens, and resistance to available agents remains an unresolved clinical problem. DNA methyltransferase 1 (DNMT1), the enzyme chiefly responsible for maintaining genome-wide methylation patterns during DNA replication, has been mapped out as a high-value target in breast cancer because its dysregulation silences tumour suppressor genes through promoter hypermethylation. The present work involves hierarchical in silico workflow to screen 4549 plant-derived compounds from the PlantCyc database (v16.0.3) against the human DNMT1 catalytic domain (PDB ID: 4WXX). Ten top-scoring compounds were taken forward for molecular docking via AutoDock Vina; Quercetin and Kaempferol both recorded the highest binding affinities at -9.5 kcal/mol, Wogonin (-9.3 kcal/mol) and Xanthohumol (-8.1 kcal/mol) also emerged as strong binders. Pharmacokinetic evaluation using ADMET-AI confirmed that all 10 compounds met Lipinski's rule of five, with human intestinal absorption values at or above 0.98. Wogonin and Xanthohumol were selected for a 100 ns all-atom molecular dynamics (MD) simulation in GROMACS due to their well-rounded ADMET profiles and limited existing data on their specific interactions with DNMT1 in breast cancer. Across all measured trajectory metrics, backbone RMSD, residue fluctuation, radius of gyration, solvent-accessible surface area, and intermolecular hydrogen bond count, Wogonin formed a more stable, compact complex. These findings suggest that Wogonin and Xanthohumol are non-toxic nutraceutical candidates suitable for DNMT1 targeted epigenetic therapy, with computational foundation strong enough to facilitate future in vitro and in vivo validation work.

Humans↗
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

Bioactive peptides for meat quality and preservation: Integrating peptidomics and computational screening.

Bioactive peptides generated from meat proteins, fermented meat products, and slaughter by-products have attracted increasing attention as functional molecules for improving meat quality and preservation. In meat systems, peptides can be produced through endogenous postmortem proteolysis, microbial fermentation, gastrointestinal digestion, or controlled enzymatic hydrolysis of underutilized animal by-products. These peptides are closely associated with key meat science endpoints, including postmortem tenderization, oxidative stability, color retention, flavor development, microbial inhibition, and the valorization of processing by-products. However, although high-resolution peptidomics has greatly expanded the identification of meat-derived peptide sequences, their translation into practical meat applications remains limited by matrix interactions, processing stability, sensory constraints, safety concerns, and insufficient validation in real meat systems. This review synthesizes recent advances in meat-related peptidomics and computational screening, including sequence-based prediction, machine learning, molecular docking, molecular dynamics, stability assessment, and safety-oriented filtering. Particular attention is given to how these approaches can prioritize peptides with antioxidant, antimicrobial, flavor-modulating, and preservation-related functions under meat-specific technological constraints. By integrating peptide generation pathways, mass spectrometry-based identification, in silico prioritization, and meat quality endpoints, this review proposes a stage-gated framework for translating meat-derived bioactive peptides from discovery to application. Future research should strengthen matrix-specific validation, standardized peptidomic reporting, and safety assessment to support the use of bioactive peptides in meat quality improvement, clean-label preservation, and circular utilization of meat industry by-products.

Animals↗
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

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

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↗
PubMed2026

Affective reactivity to a remote computer-based Trier Social Stress Test during a planned quit attempt: associations with short-term cigarette smoking lapse risk.

BACKGROUND: The Trier Social Stress Test (TSST) elicits affective responses and has been linked to smoking behavior. However, its remote use during a planned quit attempt-when stress reactivity may influence early lapse-remains understudied. OBJECTIVE: To quantify affective reactivity to a remotely administered TSST on a planned quit date following overnight abstinence and evaluate associations with cigarette use and lapse within 48 h. METHODS: This secondary analysis used data from a randomized controlled trial of adult smokers completing a remotely administered TSST following overnight nicotine abstinence. Urge, anxiety, and stress were assessed using visual analog scales and summarized using area under the curve (AUC) metrics. Smoking outcomes included cigarette count and lapse within 48 h. Associations were estimated using generalized estimating equations. RESULTS: In adjusted models, anxiety reactivity-but not urge or stress-was associated with cigarette count and lapse. Greater anxiety exposure (AUCtot) and change above baseline (AUCab) were associated with higher cigarette count (IRR=1.0004, 95%CI:1.0002-1.001, p=.002; IRR=1.01, 95%CI: 1.002-1.01, p=.002) and increased odds of lapse (OR=1.001, 95%CI: 1.0001-1.002, p=.03; OR=1.02, 95%CI: 1.001-1.03, p=.03). Effect sizes were small. CONCLUSIONS: Anxiety reactivity under nicotine deprivation was associated with increased cigarette use and lapse 48 h post quit attempt, suggesting individual differences in stress-evoked anxiety may serve as a behavioral marker for early lapse. Remote TSST administration appears feasible for eliciting affective responses on a quit date.

Humans↗
PubMed2026

Virtual surgical planning-assisted versus free-hand head and neck reconstruction: Systematic review, meta-analysis, and a novel classification.

Virtual surgical planning (VSP)-assisted reconstruction is increasingly used as an alternative to conventional free-hand (FH) techniques in mandibular and maxillary free-flap reconstruction. This systematic review and meta-analysis compared clinical outcomes and proposed a Reconstruction Complexity-Completeness classification. PubMed/MEDLINE, Scopus, Web of Science, Google Scholar, and reference lists were searched from inception to 20 June 2026. Comparative studies were eligible. Risk of bias was assessed using RoB 2 or the Newcastle-Ottawa Scale. Random-effects meta-analyses used restricted maximum likelihood estimation and Hartung-Knapp adjustment. Forty-two studies included 2763 patients (1204 VSP; 1559 FH). VSP significantly reduced operative time (33 studies; MD -64.75 min, 95% CI -83.51 to -46.00), ischemia time (15 studies; MD -37.40 min, 95% CI -48.97 to -25.82), and hospital stay (16 studies; MD -1.75 days, 95% CI -3.43 to -0.08). VSP was associated with significantly lower odds of bony non-union (OR 0.31, 95% CI 0.16-0.59) and malocclusion (OR 0.14, 95% CI 0.03-0.64), whereas flap loss, surgical site infection, and plate exposure did not differ significantly. VSP-assisted reconstruction was associated with improved operative efficiency, shorter hospitalization, and lower odds of bony non-union and malocclusion, while no statistically significant differences were detected in flap loss, surgical site infection, or plate exposure. The proposed classification may support complexity-adjusted reporting and comparison.

Humans↗
PubMed2026

Non-destructive prediction of lead content in oilseed rape leaves by fluorescence hyperspectral technology based on neural network.

Based on fluorescence hyperspectral imaging (FHSI), this study targeted rapid, non-destructive quantification of lead (Pb) content in oilseed rape leaves treated with varying silicon (Si) concentrations, acquiring fluorescence spectra over the 484.43-1001.61 nm wavelength range. To optimize spectral data quality, preprocessing methods (Savitzky-Golay smoothing, first derivative, detrending) were comprehensively compared. Characteristic wavelengths were then selected via interval variable iterative shrinkage, which effectively compressed data dimensionality and reduced computational load. A hybrid SE-CL1DA model, fusing a 1D convolutional neural network, a long short-term memory network and SE attention mechanism was constructed, with Bayesian optimization tuning hyperparameters to boost stability. The BO-SE-CL1DA outperformed both traditional machine learning and insufficiently optimized deep learning model (Rp2=0.9609, RMSE = 0.0377 mg/kg, RPD = 5.1736), thus enabling accurate Pb estimation, supporting Si-regulated heavy metal stress management and facilitating agricultural contamination monitoring.

Plant Leaves↗