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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

Applications of artificial intelligence in robot-assisted surgery: a systematic review.

To characterize applications of artificial intelligence (AI) in robot-assisted surgery, summarize technical and clinical performance, and assess the quality of the available evidence. PubMed, Web of Science Core Collection, and Scopus were searched for English-language journal articles published from 1 January 2020 through 31 October 2025. Randomized, observational, model-development, validation, and feasibility studies evaluating AI in robot-assisted surgery or closely related image-guided minimally invasive workflows were eligible. Two reviewers independently performed study selection, data extraction, and risk-of-bias assessment. Owing to heterogeneity in surgical procedures, AI tasks, analytical units, validation strategies, and outcomes, findings were synthesized descriptively without statistical pooling. The review was registered in the International Prospective Register of Systematic Reviews (CRD420251175699). Seventeen studies were included: seven clinical prediction or decision-support studies, eight intraoperative recognition, segmentation, or image-guided studies, and two training or workflow studies. Five prediction studies reported area-under-the-curve values of 0.74-0.95. Technical studies reported F1 or Dice scores of 0.525-0.995 and task-specific accuracies of 0.840-0.998. Two randomized studies suggested benefits for personalized suturing feedback and automated camera control, but neither established improved patient outcomes. Only one study had low overall risk of bias; the remaining studies were at high or unclear risk or raised some concerns. AI applications in robot-assisted surgery show promise for prediction, intraoperative perception, training, and workflow support. Evidence primarily demonstrates technical feasibility rather than established clinical effectiveness. Independent multicenter validation and prospective evaluation of patient, educational, and workflow outcomes are required before widespread implementation.

Robotic Surgical Procedures↗
PubMed2026

Dynamic lysine acetylation and succinylation of platelet proteins regulates platelet storage lesion: mechanistic insights from multi-omics.

OBJECTIVES: Platelet storage lesion (PSL) severely impairs platelet function during storage, presenting a major hurdle in transfusion medicine; however, the dynamic interplay between global proteomic changes and post-translational modifications (PTMs) underlying these functional deteriorations remains insufficiently characterized. Here, we report the first comprehensive multi-omics analysis integrating global proteomics, acetylomics, and succinylomics to dissect the molecular dynamics during platelet storage. METHODS: We performed quantification of global proteomics, acetylome and succinylome based on TMT-labeled LC-MS/MS analysis, combined with antibody-affinity enrichment and purification. Dynamic molecular changes and functional transformation of platelet were also characterized under proper conditions stored for 1, 3, 5, 7 days, respectively. RESULTS: We systematically characterized 3,609 proteins, 1,308 acetylation sites, and 1,947 succinylation sites across multiple storage time points (D1, D3, D5, D7). We distinct temporal patterns of post-translational modifications, with succinylation showing more extensive coverage than acetylation in platelets. Pathway enrichment analysis revealed extensive metabolic reprogramming involving complement activation, energy metabolism, and cellular detoxification processes. The identification of specific motif patterns provided mechanistic insights into the functional specificity of these modifications. Random forest machine learning identified 20 core regulatory proteins representing critical nodes in PSL development. Furthermore, we employed real - time quantitative polymerase chain reaction (RT - QPCR) to measure the expression levels of key genes related to platelet function and PTM - associated pathways. CONCLUSION: By mapping the interplay between proteomic abundance shifts and PTM dynamics, this study provides a multidimensional understanding of PSL, establishing a foundational framework for optimizing storage protocols and enhancing transfusion safety.

Blood Platelets↗
PubMed2026

The application of artificial intelligence in healthcare practice: A mapping review of systematic reviews.

Artificial intelligence (AI) is rapidly transforming healthcare practice, with growing evidence supporting its use in diagnosis, prognosis, treatment planning, and operational decision-making. The proliferation of systematic reviews in recent years underscores the need for an updated synthesis of the literature to inform research, policy, and practice. We searched PubMed, Web of Science, Scopus, IEEE Xplore, and CINAHL for systematic reviews and meta-analyses published between 2019 and February 2026. Eligible reviews focused on AI applications in healthcare practice, were peer-reviewed, and written in English. A total of 368 reviews met the inclusion criteria. Publication volume increased steadily, peaking in 2025. AI research was concentrated in high-density domains, such as radiology, oncology, and critical care. Across reviews, diagnostic imaging, electronic health record (EHR) data, and biomarkers/laboratory results accounted for 68% of training data sources, though newer data types, such as wearable device and sensor data, emerged from 2022 onward. Diagnosis, prognosis, and treatment comprised over 80% of AI applications, with novel uses emerging in recent years, such as AI-assisted clinical documentation (e.g., ambient documentation tools) and patient education. Ethical concerns were reported in 78.5% of reviews, with privacy, model accuracy, data and algorithmic bias, and explainability as recurrent themes. The proportion of reviews reporting ethical concerns increased from 2021 to 2025. AI applications in healthcare are expanding in scope, diversifying in data sources, and evolving toward novel clinical and operational uses. The human-centered AI or augmented intelligence paradigm, integrating computational precision with clinical expertise, holds significant promise but will require parallel advances in governance, regulatory frameworks, and ethical oversight to ensure safe adoption.

Artificial Intelligence↗
PubMed2026

Dual-tasking reveals severity-dependent reorganization of cortical beta energy landscapes in Parkinson's disease.

Dual-task impairment is a hallmark of Parkinson's disease (PD), yet the large-scale neural mechanisms underlying postural-motor interference remain poorly understood. In particular, it is unclear how cortical network dynamics reorganize across disease severity when postural control competes with concurrent task demands. This study investigated EEG-derived beta-band cortical energy landscapes in healthy older adults, early-stage PD, and mid-stage PD during single- and dual-task conditions. Dual-task behavioral cost increased with disease severity for concurrent manual performance (p&#xa0;<&#xa0;0.001), whereas a quadratic pattern was observed for postural performance. Energy landscape analysis revealed severity-dependent reconfiguration of cortical beta dynamics. Dual-task-related landscape changes in effective network flexibility (&#x394;Neff), landscape geometry (&#x394;Evar and &#x394;Gmag), and dominant low-energy attractor organization (&#x394;Low mass and &#x394;Low area) showed significant monotonic trends (p&#xa0;<&#xa0;0.05), reflecting progressive constrained cortical network dynamics with advancing PD severity. In addition, dual-task-related landscape alterations were associated with clinical severity, as indexed by Hoehn and Yahr stage (|r|&#xa0;=&#xa0;0.353-0.423, p&#xa0;=&#xa0;0.016-0.048), and showed associations with motor impairment, as measured by MDS-UPDRS part III scores (|r|&#xa0;=&#xa0;0.333-0.455, p&#xa0;=&#xa0;0.009-0.063). These findings demonstrate that dual-task demands induce severity-dependent reconfiguration of cortical beta energy landscapes in PD. Energy landscape geometry may capture systems-level neural constraints associated with dual-task susceptibility in PD, providing a physiologically grounded framework to characterize disease-related functional vulnerability.

Humans↗
PubMed2026

The future of pediatric vesicoureteral reflux management.

BACKGROUND AND OBJECTIVE: Vesicoureteral reflux (VUR) is a common condition in pediatric urology, yet important uncertainties persist regarding risk stratification, imaging strategies, and prevention of long-term renal damage. Emerging technologies may help address these challenges. This review provides a forward-looking overview of recent advances in artificial intelligence (AI) and immunomodulation that may influence future management of pediatric VUR. METHODS: A forward-looking literature review was performed using the PubMed database (January 2000-March 2025), focusing on studies addressing AI, immunomodulation, or vaccination in the context of VUR and urinary tract infections. Criteria of inclusion were the relevance to pediatric VUR, the novelty of the proposed concept, the potential clinical implications and, for the AI literature, the existence of a clinical evaluation of the algorithm on a dataset from patients. KEY FINDINGS AND LIMITATIONS: AI-based models show promising performance in supporting clinical decision-making, including prediction of the need for voiding cystourethrography, automated grading of VUR, estimation of recurrent urinary tract infection risk and prediction of chemoprophylaxis. These tools may facilitate more individualized diagnostic and therapeutic strategies, although current evidence is largely retrospective and requires prospective validation. Immunization and immunomodulatory approaches aim to reduce infection burden and modulate inflammatory pathways associated with renal scarring. While early experimental and adult clinical data are encouraging, pediatric-specific evidence remains limited, and clinical applicability in children with VUR is not yet established. CONCLUSION: Artificial intelligence and immunologically targeted strategies represent complementary, emerging approaches that may contribute to more personalized management of pediatric VUR. At present, both should be regarded as exploratory tools whose clinical impact will depend on further validation and appropriately designed pediatric studies.

Humans↗
PubMed2026

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of management. However, several diagnostic challenges persist in the SRS pathway, particularly the differentiation of radiation necrosis (RN) from true tumor progression, which conventional MRI and even advanced imaging techniques often cannot reliably resolve. Recent advances in artificial intelligence (AI) offer the potential to address these diagnostic limitations. This systematic review synthesizes current literature on AI applications for MRI-based diagnostic decision support in BM patients undergoing SRS, with a focus on radiomics and deep learning tools for distinguishing RN from progression, classifying molecular and histologic subtypes, and predicting treatment response. METHODS: A systematic review was performed in accordance with PRISMA guidelines. PubMed, Web of Science, and Scopus were searched using a targeted query combining terms related to AI, brain metastasis, diagnosis or imaging, and SRS. After screening 483 records and applying strict inclusion and exclusion criteria, 18 studies published between 2015 and 2025 were included. Data were extracted on study design, cohort characteristics, imaging modality, AI methodology, validation strategy, and reported diagnostic performance. RESULTS: Among the 18 included studies, AI models demonstrated strong performance across diagnostic tasks in the BM-SRS pathway. The differentiation of RN from true tumor progression was the most extensively studied application, addressed by 14 of 18 studies, with reported AUCs ranging from 0.71 to 0.94. Support vector machines, random-forest ensembles, convolutional neural networks, and transformer-based multimodal architectures were widely used. The literature evolved from single-sequence radiomic classifiers in 2018 to multimodal deep learning frameworks fusing imaging with clinical and genomic data in 2025. Contrast-enhanced T1-weighted MRI was the dominant imaging input, and texture-based radiomic features (GLCM, GLSZM, GLDM, and wavelet-derived features) were the most consistently predictive. The highest-performing models reached AUCs of 0.85-0.91 through multimodal integration of imaging with clinical and genomic features, and consistently outperformed expert neuroradiologist read on matched cases. Remaining studies addressed longitudinal segmentation-based detection of local failure and adverse radiation effects, BRAF mutation status in melanoma BM, early Gamma Knife treatment response, and primary tumor histology classification, with more variable performance. CONCLUSION: AI models, particularly those integrating MRI-derived radiomic features with clinical and genomic data, show high accuracy in supporting diagnostic decisions for BM patients treated with SRS. The post-SRS differentiation of radiation necrosis from true tumor progression has reached the greatest level of maturity and is closest to clinical translation, with potential to reduce unnecessary biopsies, personalize surveillance intervals, and rationalize treatment-pathway decisions. Other diagnostic applications, including molecular subtyping and primary tumor histology classification, remain exploratory and require further multicenter validation. Integration of AI tools into multidisciplinary tumor-board workflows, combined with prospective validation and standardized reporting, will be essential to realize the full clinical benefits of AI in SRS for brain metastases.

Humans↗
PubMed2026

The potential of clustering methods for pre-test triage in sleep medicine: A systematic review.

Sleep disorders exhibit substantial heterogeneity, and traditional classifications may not fully capture clinically relevant subtypes. Clustering techniques can identify patient subgroups that improve phenotypic characterization and may support personalized management. This systematic review evaluated the application of clustering in sleep medicine, with particular focus on its potential use as a pre-test triage tool prior to formal sleep testing. PubMed/MEDLINE, Embase, Web of Science, and Scopus were searched to February 2025. Eligible studies applied clustering to classify sleep disorders in adults. Two reviewers independently conducted screening, data extraction, and risk-of-bias assessment using QUADAS-2. The protocol was registered on PROSPERO. Fifty-one studies (1983-2025) were included, predominantly focused on obstructive sleep apnea (OSA) (n&#x202f;=&#x202f;38, 74%). Hierarchical clustering (n&#x202f;=&#x202f;20) and K-means clustering (n&#x202f;=&#x202f;14) were the most frequently used techniques. Internal validation was reported in only 18% of studies, and external validation was reported in only 1 study. Seven studies relied exclusively on baseline clinical, demographic, or questionnaire data, representing pre-test scenarios, whereas most incorporated polysomnography-derived variables, limiting their applicability to early clinical stratification. Hierarchical clustering was the most commonly applied method; however, the overall lack of validation limits confidence in the robustness and clinical applicability of identified phenotypes. The potential role of clustering as a pre-test triage strategy remains largely unexplored, as most studies focused on post-diagnostic phenotyping and were affected by incorporation bias. Future research should prioritize pre-test clinical variables, rigorously validate internally and externally, and adopt standardized methodological and reporting practices to facilitate clinical translation.

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

A comparative systematic review of pharmacist education systems and pharmacy service quality in ASEAN-5: Indonesia, Malaysia, Thailand, the Philippines, and Singapore.

BACKGROUND: The global transition toward patient-centered pharmaceutical care has exposed structural disparities in ASEAN pharmacy workforce training and deployment. This review examines four research questions: how pharmacy education systems and accreditation standards differ across Indonesia, Malaysia, Thailand, the Philippines, and Singapore (collectively, the ASEAN-5); the extent to which pre-registration education influences clinical service scope and professional confidence; how education reform and regulatory change have shaped pharmacist clinical roles; and what barriers and enablers exist for regional qualification harmonization. METHODS: A systematic literature review following PRISMA 2020 was conducted. Searches of PubMed/MEDLINE and Scopus, supplemented by grey literature, were completed in May 2026. Of 78 unique records screened, 46 studies published between 2005 and 2026 met inclusion criteria. Quality appraisal used an adapted Mixed Methods Appraisal Tool; synthesis employed narrative thematic analysis. RESULTS: The five countries represent four structurally distinct pharmacy education architectures: Thailand's standardized six-year Doctor of Pharmacy with dual specialization tracks; four-year Bachelor of Pharmacy programmes in Malaysia and the Philippines with institutional variation; Indonesia's clinically underdeveloped system despite rapid expansion; and Singapore's four-year Bachelor of Pharmacy followed by a nationally mandated one-year pre-registration pathway. Evidence links deeper clinical training to broader practice scope, higher confidence, and improved patient outcomes. Reform produced uneven results: Thailand's PharmD transition improved clinical recognition but exposed deployment paradoxes; Singapore achieved the strongest training-to-practice alignment; Indonesia's health insurance reforms were not absorbed by an underprepared workforce; the Philippines lacks a national competency framework. No binding mutual recognition arrangement was identified; divergent qualification structures, incompatible accreditation systems, and an asymmetric evidence base remain the primary barriers. DISCUSSION: These findings indicate that clinical service scope is bounded less by national policy ambition than by the depth and clinical orientation of the pre-registration education that precedes it, and that credentialing reforms which outpace a health system's capacity to absorb new clinical roles, or the reverse, do not by themselves translate into expanded practice. CONCLUSIONS: Pharmacy education across the ASEAN-5 remains nationally distinct and clinically uneven. Clinical service scope is directly bounded by pre-registration education quality. No country has fully closed the education-practice gap. Regional harmonization requires national-level educational reform as a prerequisite.

Humans↗
PubMed2026

User Engagement and Feature Preferences in an AI-Powered mHealth Intervention for Diabetes Prevention: Secondary Analysis of a Randomized Controlled Trial.

BACKGROUND: Prediabetes is highly prevalent and increasing globally, yet lifestyle interventions remain underused. AI-driven mobile health (mHealth) tools can help scale diabetes prevention efforts, but the key factors driving their success are not well understood. OBJECTIVE: This post hoc secondary analysis of a randomized controlled trial (RCT) aimed to characterize the most valued features and the role of user engagement in outcomes of a fully automated mHealth intervention for diabetes prevention. METHODS: Data from 151 participants with prediabetes and overweight or obesity who were assigned to an AI-based diabetes prevention program (Sweetch) in a parent RCT (NCT05056376) were analyzed. Engagement (defined as the total number of days the app was used) was categorized into tertiles (low, medium, and high). Baseline characteristics were compared across engagement groups using ANOVA, Kruskal-Wallis, and chi-square tests, and regression models assessed the association between engagement and achievement of diabetes risk reduction outcomes (&#x2265;5% weight loss, &#x2265;4% weight loss with &#x2265;150 min/week of physical activity, or &#x2265;0.2 percentage point reduction in hemoglobin A1c [HbA1c] at 12 months). Perceived usefulness of intervention features was surveyed at 12 months. RESULTS: Median engagement was 98 (IQR 34-232) days. Older age (P<.001) and lower baseline BMI (P=.04) were significantly associated with higher engagement. Compared with low engagement, high engagement was associated with greater odds of achieving the composite diabetes risk reduction outcome (odds ratio [OR] 2.59, 95% CI 1.11-6.01; P=.03), &#x2265;5% weight loss (OR 3.31, 95% CI 1.16-9.42; P=.03), and &#x2265;0.2 percentage point reduction in HbA1c (OR 3.57, 95% CI 1.19-10.75; P=.02). Participants most frequently rated weight tracking, physical activity tracking, and the digital body weight scale as the features that were most helpful for achieving their health goals. CONCLUSIONS: Higher engagement with an AI-driven intervention requiring no human intervention was associated with improved diabetes risk reduction. Contrary to concerns about lower digital literacy, older adults engaged with the intervention more than younger adults. Features related to weight and physical activity tracking were most valued by patients in the program. TRIAL REGISTRATION: ClinicalTrials.gov NCT05056376; https://clinicaltrials.gov/study/NCT05056376.

Humans↗
PubMed2026

Use of wearable technologies for physical activity promotion in older adults: A systematic review.

This systematic review, conducted according to PRISMA guidelines and registered in PROSPERO (CRD420251055299), examined the use of wearable technologies for promoting physical activity (PA) in adults aged 60 years and older. Searches across five databases (PubMed, Scopus, Web of Science, CINAHL, Cochrane) identified 2438 records, of which only six randomized controlled trials published between 2021 and 2025 met inclusion criteria, with sample sizes ranging from 36 to 551 participants and mean ages between 65 and 79 years. Given the small number of included studies, findings should be interpreted as preliminary. The studies ranged from the standalone use of commercial trackers (Fitbit, Polar, ActiGraph) to multicomponent interventions combining wearables with physiotherapist feedback, telephone counseling, web-based platforms, or interactive cognitive-motor training. Wearables used alone, as in the REACT trial, produced small or non-significant PA effects. In contrast, interventions integrating devices with personalized feedback, professional support, or digital platforms, such as PROMOTE and TASMANIA, were associated with more consistent improvements in PA, physical function, and cognitive outcomes. Multicomponent programs, such as PEER and ICMT, reported broader benefits, including cognition, balance, and reductions in sedentary behavior, though these findings derive from individual trials and require replication. Risk of bias, assessed with the Cochrane Risk of Bias tool version 2 (RoB 2.0), was rated as "some concerns" for five studies and low for only one, mainly due to gaps in randomization reporting, missing data, and lack of preregistration. Tentatively, and based on a very limited evidence base, wearables may have greater impact when embedded within broader behavioral systems, incorporating feedback, coaching, or interactive components, rather than when used in isolation as passive monitoring tools. Adherence and psychosocial outcomes appeared related to comfort and perceived usefulness among older adults, though larger and more robust trials are needed to confirm these patterns.

Humans↗
PubMed2026

Integration of single-cell transcriptomics and genomic mutation analysis identifies an immunotherapy-resistant tumor subcluster and validates ARNTL2 as a malignant driver in lung adenocarcinoma.

BACKGROUND: Immunotherapy resistance in lung adenocarcinoma (LUAD) remains a critical clinical challenge, and the mechanisms underlying resistance-associated intratumoral heterogeneity are poorly characterized. METHODS: We performed single-cell RNA sequencing of LUAD patients receiving neoadjuvant immunotherapy (responders vs. non-responders), integrating inferCNV, GSVA, and differential expression analyses. Cluster-specific genes were validated across seven independent cohorts (TCGA-LUAD, GSE13213, GSE26939, GSE29016, GSE30219, GSE31210, GSE42127). A multi-algorithm machine learning framework was used to construct a prognostic model, and the immune microenvironment was characterized using TCIA scoring, seven infiltration algorithms, and ESTIMATE. ARNTL2 function was assessed by CCK-8 and Transwell assays in A549 and H1299 cells. RESULTS: Non-responders showed significant enrichment of epithelial cells, depletion of cytotoxic T/NK cells, and elevated copy number variation burden versus responders (p < 0.0001). A resistance-enriched malignant subcluster (Cluster 2) exhibited hyperproliferative and metabolic reprogramming signatures with upregulated KRT17, S100A2, and CST6, which showed tumor-specific overexpression, adverse prognostic value, and genomic amplification across cohorts. CoxBoost combined with survivalSVM achieved optimal predictive performance (C-index = 0.686), yielding robust risk stratification (HR: 2.54-10.51, all p < 0.05). Low-risk patients showed greater immune infiltration and higher TCIA immunophenoscores. ARNTL2 was an independent prognostic factor (HR: 2.07-4.64) strongly correlated with risk score (r = 0.69), and its knockdown suppressed proliferation and invasion in both LUAD cell lines (all p < 0.05). CONCLUSION: This study identifies a resistance-associated malignant subcluster in LUAD, constructs a validated CoxBoost + survivalSVM prognostic model with robust immune stratification, and establishes ARNTL2 as a core oncogenic driver and therapeutic target.

ARNTL2↗
PubMed2026

Adverse Experiences in Brief Meditation Practices: Randomized Controlled Trial.

BACKGROUND: Meditation has become increasingly popular in recent decades. However, relatively little remains known about the prevalence of and risk factors for adverse experiences related to a single meditation practice. OBJECTIVE: The objective of our study was to examine adverse experiences associated with 3 brief, digitally delivered meditation practices (mindfulness, self-compassion, and gratitude) relative to using the internet as usual, as well as to investigate whether preintervention characteristics could predict such outcomes. METHODS: In a secondary analysis of a randomized controlled trial using samples that were representative of the US and UK adult populations with regard to ethnicity, sex, and age, we examined adverse experiences associated with 3 brief (ie, 5 or 10 minutes) meditation practices (ie, mindfulness, self-compassion, and gratitude) relative to using the internet as usual. We also investigated the potential of using preintervention characteristics to predict such outcomes. RESULTS: A total of 5049 participants completed all preintervention measures and were randomly assigned to meditation or control conditions. Across the sample, 4.1% (204/4925) of participants reported having a distressing experience during the intervention, and 7.1% (348/4908) of participants experienced an increase in negative affect from before to after the intervention. The results showed that participants who were randomized to a brief meditation intervention were no more likely to report a distressing experience than those who were randomized to use the internet as usual (odds ratio [OR] 1.05, 95% CI 0.76-1.47; P=.76). The results also showed that participants who were randomized to a brief meditation intervention were less likely to report clinically relevant increases in negative affect relative to using the internet as usual (OR 0.63, 95% CI 0.50-0.80; P<.001). Notably, participants in the 10-minute condition had a significantly higher likelihood of reporting a distressing experience than those in the 5-minute condition (OR 1.42, 95% CI 1.07-1.89; P=.02). Preintervention characteristics showed acceptable discrimination ability to predict a distressing experience (area under the curve=0.73) and slightly lower ability to predict increased negative affect (area under the curve=0.67). CONCLUSIONS: Taken together, we found that the brief, digitally delivered meditation practices tested in this study carry risks of adverse experiences that are comparable to or lower than those of typical activities on the internet; 10-minute condition was more likely to result in distressing experiences than 5-minute condition; and adverse responses to a brief meditation practice can, at least to a certain degree, be predicted using preintervention characteristics. TRIAL REGISTRATION: Open Science Framework 94HKS; https://osf.io/94hks/overview.

Humans↗
PubMed2026

RNA dysregulation as a determinant of aging and neurodegenerative vulnerability.

In the nervous system, aging causes deterioration of cellular and molecular processes that are associated with declines in cognition, sensory perception, and motor coordination. Aging is also the strongest risk factor for neurodegenerative disease, yet the mechanisms by which aging predisposes neurons to dysfunction remain incompletely understood. While genomic instability, proteostasis decline, mitochondrial dysfunction, and chronic inflammation have dominated prevailing models, recent evidence highlights RNA dysregulation as a central component of age-associated decline. In this review, we summarize recent findings suggesting that aging progressively erodes RNA regulatory fidelity through alterations in RNA-binding protein abundance, localization, biophysical behavior, and RNA interactions. We argue that age-dependent RNA dysregulation represents an important mechanism that converges with genetic risk to drive neuronal vulnerability and neurodegeneration.

RNA dysregulation↗
PubMed2026

Final-year nursing students' clinical practice experiences: a reflection study.

OBJECTIVES: This study aimed to explore the most impactful clinical practice experiences of final-year nursing students and the future-oriented actions developed in response to these experiences. METHODS: A retrospective descriptive qualitative design was used. Following reflection training in the internship practice course, 134&#xa0;final-year nursing students were asked to describe the experience that affected them most during clinical practice. A total of 123 written reflections were analyzed using content analysis. RESULTS: Three themes emerged: near-miss events, incivility behaviors, and positive preceptoring roles. Negative experiences were mainly related to patients, relatives, and nurses and often led students to feel fear and inadequacy. Students reported action plans focused on effective communication, safe patient care, and becoming positive role models. CONCLUSIONS: These findings highlight the importance of supportive clinical learning environments and positive professional socialization during the transition from student to&#xa0;nurse. IMPLICATIONS FOR INTERNATIONAL AUDIENCE: Nursing students worldwide may encounter incivility and near-miss events during clinical practice, potentially adversely affecting their learning experiences and professional development.

Humans↗
PubMed2026

Health-Related quality of life (HRQoL) and health state utility values (HSUV) in patients with head and neck Cancer: A systematic review and Meta-Analysis.

BACKGROUND: Head and neck cancer (HNC) and its treatment can substantially impair speech, swallowing, eating, appearance, and social functioning, resulting in persistent reductions in health-related quality of life (HRQoL). Although the EuroQol 5-Dimensions questionnaire (EQ-5D) is widely used to assess generic HRQoL and derive health state utility values (HSUVs), EQ-5D-based evidence in HNC has not been comprehensively synthesized. This study aimed to summarize EQ-5D-based HRQoL and HSUVs in HNC, estimate pooled utility and EQ-VAS scores, explore subgroup differences, and identify predictors of poorer HRQoL. METHODS: A systematic review and meta-analysis was conducted according to PRISMA guidelines and registered in PROSPERO (CRD420261307907). PubMed, EMBASE, Web of Science, Cochrane Library, and Scopus were searched from inception to February 10, 2026. Studies reporting baseline EQ-5D utility values and/or EQ-VAS scores in patients with HNC were included. Random-effects meta-analyses using the DerSimonian-Laird (DL) estimator with the Hartung-Knapp-Sidik-Jonkman (HKSJ) adjustment were performed to pool mean scores. Between-study variance (&#x3c4;2) and 95&#xa0;% prediction intervals (PI) were calculated to capture parameter dispersion. Subgroup analyses were conducted across clinical and methodological vectors. RESULTS: Twenty studies involving 7,403 patients were included. The pooled mean EQ-5D utility score was 0.79 (95&#xa0;% CI: 0.75-0.83; &#x3c4;2&#xa0;=&#xa0;0.0011; 95&#xa0;% PI: 0.72-0.86). The pooled mean EQ-VAS score was 69.36 (95&#xa0;% CI: 65.71-73.01; &#x3c4;2&#xa0;=&#xa0;38.4586; 95&#xa0;% PI: 55.11-83.61). Extreme heterogeneity was observed (I2&#xa0;=&#xa0;96.4&#xa0;% and 97.1&#xa0;%, respectively). Utility values were significantly higher in studies utilizing the EQ-5D-5&#xa0;L than the EQ-5D-3&#xa0;L version (0.82 vs. 0.76). By tumor subsite, nasopharyngeal cancer showed the highest utility value (0.85, exploratory), whereas oral cancer demonstrated the lowest (0.73). Adjusted multivariable models revealed that advanced stage, high treatment intensity, severe pharyngolaryngeal pain, dysphagia, malnutrition, and older age were robust predictors of poorer HRQoL. CONCLUSIONS: Patients with HNC experience substantial and persistent HRQoL impairment, with meaningful variations driven by tumor subsites and instrument versions. In light of the extreme heterogeneity, these pooled findings establish a macro-level, broad reference estimate rather than a fixed target. These parameters directly inform localized survivorship care planning, health technology evaluations, and cost-utility decision-making modeling in head and neck oncology.

Humans↗
PubMed2026

Robot-assisted versus freehand cannulated-screw fixation for femoral neck fractures: a systematic review of technical, clinical and adoption outcomes.

Robot-assisted guidance may improve the technical precision of percutaneous cannulated-screw fixation for femoral neck fractures. Whether these procedural advantages translate into better clinical outcomes remains uncertain. We compared robot-assisted and conventional freehand fixation in adults with femoral neck fractures. MEDLINE, Embase and CINAHL were searched from inception to 15 July 2026 without language restrictions. Google Scholar was used only as a supplementary search source, together with forward and backward citation searching. Comparative studies of robot-assisted versus freehand fluoroscopy-guided cannulated-screw fixation were included. Risk of bias was assessed using RoB 2 and ROBINS-I, with the Newcastle-Ottawa Scale used as a complementary appraisal of non-randomised studies. Random-effects meta-analyses included prediction intervals and prespecified sensitivity analyses. The protocol was registered prospectively (PROSPERO CRD420261465038). Sixteen comparative studies involving 1,293 participants were included. Of these, 597 underwent robot-assisted fixation and 696 underwent freehand fixation. Two studies reporting random allocation and 14 non-randomised studies were included in the study. Robot-assisted fixation was associated with fewer guide-wire manipulations, greater screw-placement accuracy and 13.9 fewer fluoroscopic acquisitions per procedure (95% confidence interval [CI] -20.3 to -7.5). Earlier radiographic healing and modestly higher final Harris Hip Scores were also observed. Pooled estimates suggested lower risks of union failure, avascular necrosis and composite complications. Fluoroscopy duration, overall operative time and reoperation did not differ significantly. Heterogeneity was substantial for several continuous outcomes, with prediction intervals crossing the null for several estimates, indicating that the magnitude of benefit varied considerably between studies. Some clinical associations were also sensitive to eligibility-restricted analyses. Robot-assisted cannulated-screw fixation improves technical execution compared with freehand fixation. Patient-important clinical superiority and economic value have not been established, and evidence concerning learning curves, operator acceptability and system reliability remains insufficient. Current evidence does not support routine widespread adoption; adequately powered multicentre randomised trials incorporating economic and implementation evaluation are required.

Humans↗