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PubMed2026

Effect of Global Postural Re-Education in Individuals With Text Neck Syndrome: A Randomized Controlled Trial.

BACKGROUND AND PURPOSE: This study investigated the effect of Global Postural Re-Education (GPR) versus conventional physical therapy in text neck syndrome (TNS). A prospective, single-blinded, parallel-group randomized controlled trial design was used. METHODS: Sixty participants with TNS (aged 18-40 years) were randomly assigned to either conventional treatment or GPR plus conventional treatment. Both groups received supervised therapy for three sessions per week over 4 weeks. Outcome measures included craniovertebral and shoulder angles assessed by photogrammetry, pain intensity via Visual Analog Scale, and Cervical Range of Motion via a smartphone application (Clinometer). Measured before and after the intervention. RESULTS: Within-group analyses showed significant improvements in pain and CROM in both groups (p < 0.001). However, the between-group analysis revealed no superiority of GPR for pain or CROM (p > 0.05). In contrast, GPR demonstrated statistically significant superiority in postural correction, with greater improvements in craniovertebral angle (MD: 2.14°; 95% CI: 0.69-3.59; p = 0.005) and shoulder angle (MD: 3.2°; 95% CI: 0.33-6.07; p = 0.03), exceeding MCID thresholds and indicating clinically meaningful benefits. However, these findings should be interpreted with caution because of the longer session duration in the GPR group. DISCUSSION: Incorporating Global Postural Reeducation (GPR) into conventional treatment provided significant additional benefits for postural parameters (craniovertebral and shoulder angles) in individuals with text neck syndrome. However, GPR demonstrated no added superiority over conventional treatment alone regarding pain intensity and cervical range of motion outcomes.

Humans↗

PubMed2026

Quantitative susceptibility mapping in neurodegenerative diseases: An umbrella review of iron-related biomarkers and mechanisms.

Pathological iron accumulation is a common pathophysiological hallmark across multiple neurodegenerative diseases (NDDs), motivating the need for accurate, non-invasive quantification methods. Quantitative susceptibility mapping (QSM) is an advanced magnetic resonance imaging (MRI) technique that enables in vivo measurement of tissue magnetic susceptibility (χ), providing a sensitive proxy for iron content. This umbrella review systematically evaluates the diagnostic accuracy, clinical correlations, and distinct iron distribution patterns of QSM in major NDDs, such as Parkinson's disease (PD), Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), and atypical Parkinsonism. We included 15 (13/15 were rated Low or Critically Low on AMSTAR 2) systematic reviews and meta-analyses (through July 15, 2026); however, the findings should be interpreted cautiously because of heterogeneity and the low methodological quality. A Corrected Covered Area (CCA) analysis demonstrated only slight overlap of primary studies across the included reviews (CCA = 5.42%). Collectively, the evidence indicates that QSM provides comparable or higher diagnostic sensitivity and reliability than conventional R2* and SWI techniques, particularly for deep gray matter structures. The findings support significant iron overload in the substantia nigra, particularly in the pars compacta, as a robust biomarker for PD that correlates with motor severity and disease duration. Furthermore, regional iron profiling in the basal ganglia is critical for differential diagnosis; specifically, elevated χ in the putamen and globus pallidus effectively distinguishes multiple system atrophy and progressive supranuclear palsy from idiopathic PD. Distinctively, AD and ALS exhibit specific χ alterations in the thalamus, motor cortex, and hippocampus, reflecting divergent iron-related pathophysiological mechanisms, which correlate with cognitive impairment and upper motor neuron signs. Overall, QSM shows diagnostic promise and offers mechanistic insights into iron-related neurodegenerative processes.

Humans↗

PubMed2026

Impact of education protocols and physiotherapeutic management in improving pain, symptoms, activities of daily living, and quality of life in patients with knee osteoarthritis: A systematic review and meta-analysis.

BACKGROUND: Knee osteoarthritis is a debilitating condition of the knee joint and the major cause of disability globally, with an increased economic burden on healthcare. Patient education (PE) has emerged as a primary care treatment approach in chronic conditions. This review aims to evaluate the effectiveness of PE along with exercise in reducing pain, alleviating symptoms, improving activities of daily living, and promoting quality of life among individuals with knee osteoarthritis. METHOD: A systematic search was conducted across three electronic databases, PubMed, Cochrane Library, and PEDro. The search was limited to between 2020 and 2025, and included only randomized controlled trials. The Cochrane Risk of Bias Tool and the PEDro Scale were used to evaluate the methodological evidence. Statistical synthesis was analysed using mean differences (MDs) with 95% confidence intervals (CIs) under a random-effects model. RESULTS: Eight trials were included, of which four studies evaluated the four Knee Injury and Osteoarthritis Outcome Score (KOOS) domains, and three studies evaluated the Visual Analogue Scale (VAS) and KOOS activities of daily living domain in patients with knee osteoarthritis. For the meta-analysis, we assessed all five domains of the KOOS scale and VAS. Statistical analysis of the studies showed a significant reduction in pain (VAS) for the PE along with exercise group (MD = -3.28, 95% CI (-4.85; -1.70), and I2 = 69.6%), but there was no significant improvement in the domains of the KOOS scale. CONCLUSION: The study highlights the importance of PE along with exercise in reducing pain. It might not be more effective when compared with exercise therapy as a standalone intervention in improving ADLs, QoL, and symptoms, but it has demonstrated some degree of effectiveness. It additionally promotes self-management and self-efficacy as a physiotherapeutic rehabilitation treatment intervention.

Humans↗

PubMed2026

Toward personalized interventions for preventing depression in primary care: Qualitative and quantitative findings from the e-predictD pilot study.

BACKGROUND: The predictD intervention, delivered by family physicians (FPs), has demonstrated effectiveness and cost-efficiency in preventing depression and anxiety. The e-predictD study aims to design, develop, and evaluate a novel personalized intervention for depression prevention by integrating information and communication technologies (ICTs), risk prediction algorithms, and decision support systems (DSS) for both patients and FPs. OBJECTIVE: To evaluate the satisfaction, usability, and acceptability, of a beta version of the e-predictD intervention in primary care settings. METHODS: The e-predictD intervention follows a biopsychosocial approach, including an initial patient-FP interview, specific FP training, and an app. A β-version was tested in a pilot study without a control group over three months. The app integrates a validated depression risk prediction algorithm, decision algorithms, and a monitoring system supporting the DSS. The DSS generates a personalized prevention plan (PPP) from eight intervention modules: physical exercise, social relationships, problem-solving, communication skills, decision-making, assertiveness, sleep improvement, and cognitive restructuring. Patients and FPs discussed the PPP in a 15-minute baseline interview, selecting modules for implementation over three months. Semi-structured interviews gathered feedback. Assessments included depression (PHQ-9), anxiety (GAD-7), quality of life (SF-12), and major depression risk (predictD algorithm). RESULTS: Six FPs from six Spanish cities enrolled 56 non-depressed patients at moderate-to-high risk of depression; 47 (84%) completed follow-up. The app was used for a median of six days (interquartile range: 1-30). Both FPs and patients expressed satisfaction, leading to incorporated improvements. After three months, significant reductions in major depression risk and anxiety symptoms were observed, alongside improved mental quality of life. However, no significant changes were found in depressive symptoms or physical quality of life. CONCLUSION: This pilot study supports the feasibility and acceptability of the e-predictD β-version, despite lower-than-expected app usability. Health improvements were observed, warranting confirmation in a randomized controlled trial. TRIAL REGISTRATION: ClinicalTrials.gov NCT03990792.

Adult↗

PubMed2026

Identifying and Prioritizing Core Components of Relationship Education Programs: a Case Study of an Artificial Intelligence (AI) Assisted Systematic Review.

The field of prevention science seeks to identify and implement effective strategies to address social, emotional, and health challenges. A critical aspect of this endeavor is determining the core components of prevention programs that drive positive outcomes. This article presents a case study utilizing artificial intelligence (AI)-assisted systematic review methods to identify key components of healthy marriage and relationship education programs. Given the growing body of research in this domain, AI tools offer a promising means to enhance the efficiency and accuracy of literature reviews. This study employed AI to screen, code, and validate research articles, demonstrating its effectiveness in expediting systematic reviews while maintaining high accuracy in inclusion screening. This case study involved a systematic review of 22,028 resources (identified from PsycINFO, Academic Search Ultimate, and Google) and a final data set of 268 relevant studies. AI screening was integral in effectively conducting multiple rounds of screening. However, findings also highlight challenges in AI-assisted qualitative data abstraction, underscoring the continued need for human expertise in complex coding tasks. The study contributes to the ongoing discourse on integrating AI into prevention science methodologies and offers insights for optimizing AI applications in systematic reviews.

Artificial Intelligence↗

PubMed2026

Role of Polygenic Risk Scores in Predicting Cognitive Functioning after Mild Traumatic Brain Injury: A TRACK-TBI Study.

Patients with traumatic brain injury (TBI) and Glasgow Coma Scale scores of 13-15 (historically called mild TBI [mTBI]) commonly experience changes in cognitive functioning, including processing speed, memory, and executive functioning. In a prospective sample (N = 523) of individuals of European descent who had been treated in a U.S. level 1 trauma center for mTBI, we examined the prognostic value of four polygenic risk scores (PRS) for cognitive outcomes at 6-months postinjury. To estimate the impact of mTBI on cognition, primary cognitive outcomes were scaled as z-scores reflecting changes in performance relative to predicted preinjury performance. The PRS examined were previously developed and validated to predict cognition-related outcomes of educational attainment (Education-PRS), intelligence (Intelligence-PRS), and Alzheimer's disease (AD-mild traumatic brain injury (APOE)-PRS and AD + APOE-PRS). Both the Education-PRS and Intelligence-PRS displayed bivariate associations with all four cognitive outcomes (β = 0.19-0.32), whereas neither Alzheimer's disease PRS was significantly associated with any outcome. After controlling for other factors known to predict cognitive outcomes of TBI (e.g., sex, education, mTBI severity defined by a combination of Glasgow Coma Scale scores and the presence/absence of acute intracranial findings on clinical neuroimaging), the Education-PRS and Intelligence-PRS remained independently predictive of verbal episodic memory (β = 0.10-0.16), whereas their associations with processing speed and executive functioning were mostly nonsignificant and were mediated through educational attainment. Looking across primary z-score and secondary raw score outcomes, cognitive outcomes 6 months post-mTBI were good on average, and PRS made small independent contributions to outcome prediction. The mediation model findings may support theories of cognitive reserve, which propose that individuals with stronger preinjury cognitive processing abilities (often estimated by educational history) can better compensate for TBI. Moreover, findings indicate that PRS may contribute modestly to multivariable models predicting cognitive function after TBI.

Humans↗

PubMed2026

A STORM-based protocol for nanoscale imaging and quantitative analysis of protein-associated and phospholipid-associated structures in natural rubber.

Stochastic Optical Reconstruction Microscopy (STORM) enables nanoscale mapping of molecular components beyond the diffraction limit; however, its reproducible implementation in hydrophobic polymer matrices remains challenging because fluorescence-labeling specificity, fluorophore photoswitching, three-dimensional localization, chromatic registration, and quantitative image analysis must be carefully controlled. This protocol presents a standardized experimental workflow for dual-color labeling, astigmatism-based three-dimensional STORM acquisition, and quantitative analysis of protein-associated and phospholipid-associated structures in natural rubber (NR). The workflow covers sample pretreatment, Cy5 NHS ester labeling of protein-associated primary amines, DiI labeling of phospholipid-rich domains, STORM imaging-buffer preparation, three-dimensional single-molecule localization, dual-channel registration, generation of standardized xy projections, aggregate-size analysis, and projected lateral spatial correlation assessment. Reproducibility is supported by defined acquisition and localization criteria, three independent sample preparations with at least five fields of view analyzed per condition, and unlabeled, single-color, dye-only matrix, and processing-associated Cy5 controls. Mean lateral localization precisions of 11.8 ± 2.3 nm for Cy5 and 13.5 ± 2.9 nm for DiI were obtained, while two-dimensional Fourier ring correlation analysis of the xy projections yielded effective lateral image resolutions of approximately 25 and 28 nm, respectively. Image-based particle segmentation and localization-coordinate-based density-based spatial clustering of applications with noise (DBSCAN) were applied to standardized xy projections as complementary quantitative approaches. Application of the protocol to untreated, centrifuged, and protease-treated NR samples demonstrated treatment-associated changes in the detected abundance and projected size distributions of protein- and phospholipid-associated aggregates, together with a non-monotonic change in their projected lateral spatial correlation. These observations describe alterations in nanoscale organization but do not, by themselves, establish stable protein-phospholipid complex formation. Unlike previous studies that primarily demonstrated the feasibility of STORM imaging in rubber materials, the principal contribution of this work is an end-to-end, step-by-step protocol incorporating defined controls, three-dimensional localization, image-quality metrics, chromatic-registration procedures, and complementary quantitative-analysis pipelines for non-expert users. The workflow may be adaptable to other hydrophobic polymers and soft-material systems after appropriate optimization and validation.

Rubber↗

PubMed2025

Mindfulness and Sex Education for Sexual Dysfunction in Breast Cancer Survivors: Mediators and Moderators of Treatment Outcome.

Mindfulness-based cognitive therapy (MBCT) and supportive-expressive sex education therapy (STEP) are effective group treatments for sexual dysfunction after breast cancer (BrCa). We explored mediators and moderators of outcomes following the 8-week groups. BrCa survivors (n = 116, mean age = 49.9 ± 9.5) were randomized to group and completed measures before, immediately after, and 6 months after treatment. Mediators assessed were changes in depression, chronic pain acceptance, pain catastrophizing, and trait mindfulness. Potential moderators included age, treatment expectations, baseline mental health, cancer treatment duration, use of chemotherapy, and adjuvant endocrine therapy. Longitudinal mediation and moderation were assessed using linear mixed models. Increases in pain acceptance mediated improvements in sexual desire and reductions in both sexual distress and vaginal pain. Decreases in pain catastrophizing mediated improvements in sexual distress. Higher expectations for treatment led to greater reductions in sexual distress. Those with low baseline anxiety showed greater improvements in desire and distress. Low baseline depression predicted greater improvements in desire, but only in the STEP arm. Older STEP participants improved significantly more than younger STEP participants. Cancer-related treatment variables, and the impact of adjuvant endocrine therapy, had differential effects on outcomes based on the treatment arm of the study. In conclusion, treatments aimed at improving pain acceptance and pain catastrophizing are likely to promote improvements in sexual health among BrCa survivors, and factoring in patients' expectations about treatment improvements, depression and anxiety, age, duration of cancer treatment, chemotherapy, and adjuvant hormonal therapy may help to guide treatment recommendations for sexual dysfunction.

Humans↗

PubMed2026

Challenges and future directions in AI-driven biomaterials for microbiome-associated oral infectious diseases: A systematic review.

Oral biofilm-induced antimicrobial resistance is the core pathogenic mechanism of microbiome-associated oral infectious diseases (dental caries, periodontitis, peri-implantitis, and endodontic infection). Traditional therapies and biomaterials are limited by poor biofilm penetration, drug resistance induction, single functionality, and inadequate adaptation to dynamic oral microenvironmental changes (e.g., pH fluctuations, salivary rinsing, masticatory stimulation). Artificial intelligence (AI) has transformed the field by integrating materials science, microbiology, and stomatology data. Via machine learning, deep learning, and multi-physics simulation, AI optimizes biomaterial physicochemical properties, decodes microenvironmental signals, constructs precise sensing-response loops, and supports the full chain of material design, performance prediction, and action simulation, advancing treatment from empirical intervention to precision regulation. This systematic review retrieved literature from PubMed, Embase, and Web of Science (January 2016-January 2026) using keywords across three dimensions: AI, biomaterials, and oral microbiome. Following inclusion/exclusion criteria, 99 articles were included. It elaborates on five core mechanisms of AI-driven oral biomaterials (precise oral microbiome analysis, targeted material design/optimization, performance prediction/simulation, targeted delivery/intervention, effect evaluation/dynamic regulation), analyzes their applications in microbiome-targeted biomaterial research and development (R&D) and clinical practice for the four major oral infectious diseases, addresses technical bottlenecks (insufficient targeting specificity and precision of biomaterials, poor stability and durability in complex oral microenvironments, inadequate biofilm disruption capacity, and clinical translation obstacles), and proposes future directions (multimodal design to enhance targeting specificity, structural and component optimization to improve stability/durability, development of multi-mechanism synergistic biofilm disruption strategies, strengthening translational research for clinical application, and deep integration of AI in the full chain of biomaterial R&D). This work provides comprehensive theoretical and practical support for the R&D, optimization, and clinical translation of AI-driven microbiome-targeted oral biomaterials.

Humans↗

PubMed2026

Ultrasound-guided high-voltage vs conventional pulsed radiofrequency in elderly cervical radiculopathy: A randomized controlled trial.

BACKGROUND: Elderly patients with cervical radiculopathy present therapeutic challenges owing to comorbidities and medication-related risks. Long-term pharmacotherapy and surgical interventions are often suboptimal, necessitating evaluation of optimized pulsed radiofrequency strategies under image guidance. OBJECTIVES: This superiority trial compared the efficacy and safety of ultrasound-guided cervical nerve root high-voltage pulsed radiofrequency (HVP-PRF) versus conventional pulsed radiofrequency (C-PRF) for pain management in elderly patients with cervical radiculopathy. METHODS: This single-center, parallel-group, assessor-blinded randomized controlled trial enrolled patients aged 60-85 years with cervical radiculopathy, randomly assigned (1:1) to HVP-PRF (70 V) or C-PRF (45 V). Procedures were performed under ultrasound guidance with sensory/motor stimulation confirmation and temperature ≤42°C. The primary outcome was change in upper-limb radiating pain on the Numeric Rating Scale (ΔNRS) from baseline to 3 months. Secondary outcomes included Neck Disability Index (NDI), neck pain NRS, Patient Global Impression of Change, responder rates, rescue analgesia use, and adverse events. Follow-up occurred at 1 week, 1, and 3 months. RESULTS: A total of 104 patients were randomized and 101 received treatment. At 3 months, HVP-PRF demonstrated significantly greater radiating pain improvement versus C-PRF (adjusted mean difference 1.24, 95% CI 0.46-2.02, P=0.002). Functional improvement (NDI) was superior in the HVP-PRF group at 3 months (AMD 6.47, 95% CI 2.11-10.83, P=0.004). Responder rates (≥50% pain reduction) were higher with HVP-PRF at 3 months (68.75% vs. 42.22%, OR 3.01, P=0.011) and 6 months (65.22% vs. 43.18%, OR 2.52, P=0.035). Rescue analgesic use was lower in the HVP-PRF group during 1-3 months intervals (both P<0.05). Adverse event rates were comparable (27.45% vs. 32.00%). CONCLUSION: Under ultrasound visualization and electrical stimulation-based target confirmation with temperature control ≤42°C, HVP-PRF provided greater and more durable relief of upper limb radiating pain compared with C-PRF in elderly patients with cervical radiculopathy, with a comparable safety profile.

Humans↗

PubMed2026

Implementation outcomes of a dementia-focused intervention for family care partners and clinicians in home hospice care.

OBJECTIVES: End-of-life care for persons living with dementia in home hospice relies heavily on coordination between family care partners (FCPs) and clinicians (e.g., hospice social workers and nurses). FCPs and clinicians have reported support and knowledge gaps in end-of-life dementia care. Interventions are needed to improve FCPs' support and clinicians' educational gaps. METHODS: A pilot randomized controlled trial was designed to examine implementation outcomes for a dementia-focused end-of-life intervention for FCPs (n = 37) and clinicians (n = 15). Data on survey completion and acceptability were collected at baseline, during 4 follow-up visits, and at the conclusion of the study. RESULTS: Twenty-eight (75%) caregivers completed the post-study survey, and 10 (27%) reported using the structured worksheet. Thirteen (87%) clinicians completed the post-training survey, 8 (53%) completed the post-study survey, and 8 (100%) used the worksheet. Clinicians (n = 8) were satisfied or highly satisfied with the instructional videos, and half (50%) used the information frequently with patients. Both groups reported the worksheet helpful, easy to use, and satisfactory, though clinicians rated helpfulness slightly higher (mean = 3.88 vs. 3.70 for FCPs). Clinicians liked the worksheet's structured guidance and the enhanced collaboration. SIGNIFICANCE OF RESULTS: This study provides preliminary evidence for implementation outcomes of a dementia-focused end-of-life intervention in home hospice. Findings suggest the intervention can be implemented in a hospice setting, with moderate worksheet uptake and perceived value among FCPs and clinicians.

Humans↗

PubMed2026

Recovering membrane interaction kinetics of single molecules from 3D tracking data.

Interactions between cytosolic biomolecules and the bacterial inner membrane are fundamental to many cellular processes, yet directly measuring their binding kinetics in living cells remains challenging. Conventional 2D single-molecule tracking analyses can be insufficient, particularly when membrane association does not markedly alter the diffusion rate. Here, we present a method to recover membrane interaction kinetics from 3D single-molecule trajectories in rod-shaped bacteria. Using simulated 3D tracking data, we identify membrane-associated motion by quantifying how well short trajectory segments follow the circular curvature of the cell membrane. The resulting measure is further analyzed using a hidden Markov modeling framework, enabling robust discrimination between cytosolic and membrane-bound states and capturing the dynamics of state transitions without requiring diffusion-rate changes or direct colocalization with membrane markers. This work establishes a general framework for extracting membrane interaction kinetics from 3D single-molecule tracking data in live bacteria and highlights the value of realistic microscopy simulations for quantitative interpretation and systematic bias assessment.

Kinetics↗

PubMed2026

Effects of Digital Mental Health Screening Alone and With the Online MINDBODYSTRONG CBT-Based Program on Burnout, Depression, Anxiety, Healthy Behaviors, and Suicidal Ideation in at-Risk Nurses at 3- and 6-Months Post-Intervention: An RCT.

BACKGROUND: Burnout and mental distress among nurses are global public health epidemics that adversely affect nurse well-being and healthcare quality. Evidence-based, scalable mental health interventions are urgently needed. AIMS: To evaluate the 3- and 6-month outcomes of a randomized controlled trial (RCT) comparing a psychologically safe, digital mental health screening and referral program alone versus the same screening and referral program combined with the video-based online MINDBODYSTRONG (MBS) cognitive behavioral therapy (CBT)-based skills-building program among nurses at risk for mental distress. METHODS: 501 nurses were recruited from professional organizations and healthcare systems across the United States by email and randomized to either mental health screening and referral (standard care) or standard care plus the MBS cognitive behavioral skills-building intervention (the intervention). All study activities were conducted remotely. Follow-up surveys administered at 3- and 6-months assessed anxiety, depression, suicidal ideation, burnout, healthy lifestyle beliefs, and healthy lifestyle behaviors using valid and reliable scales. RESULTS: Compared with the screening and referral only group, participants in the intervention group had greater reductions in anxiety and depression and significantly greater increases in healthy lifestyle beliefs and behaviors at 3 and 6 months post-intervention. After controlling baseline risk, the intervention group had a lower risk of suicidal ideation than the screening and referral group at 3 months (relative risk ratio [RRR] = 0.717; 95% CI: 0.320-1.606) and 6 months (RRR = 0.329; 95% CI: 0.101-1.072). The intervention group also had a significantly lower risk of burnout at 6 months (RRR: 0.698, 95% CI: 0.528, 0.929, p = 0.012). Nurses who completed more MBS sessions had less suicidal ideation at 6 months and those who completed more MBS skills-building activities had less burnout at 3 and 6 months. LINKING ACTION TO EVIDENCE: Integrating psychologically safe mental health screening combined with the scalable online CBT-based intervention, MBS, can produce sustained improvements in burnout, mental health symptoms, including suicidality, and healthy lifestyle beliefs and behaviors among nurses experiencing mental distress.

Humans↗

PubMed2026

Effects of Cognitive Behavioral Couple Therapy With Integrated Mindfulness on Mindful Attention, Depressive Symptoms, and Dyadic Adjustment in Low-Income Couples: A Pilot Randomized Clinical Trial.

Psychosocial distress can exacerbate marital conflict, maladjustment, and mental health vulnerability. This pilot randomized clinical trial examined cognitive-behavioral couple therapy (CBCT) integrated with mindfulness in low-income Brazilian couples (per-capita household income up to one minimum wage). Thirty-four participants (17 heterosexual couples) were randomized (independent computer-generated sequence) to an experimental (n = 16) or waitlist control group (n = 18). We assessed dyadic adjustment, mindful attention, marital social skills, and depressive symptoms (R-DAS, MAAS, IHSC, BDI-II) at baseline, post-treatment, and 3-month follow-up. The intervention was eight 80-min conjoint sessions plus daily home exercises. Time × group effects favored the experimental group for dyadic adjustment, mindful attention, and depressive symptoms (all p < 0.001, = 0.20-0.37), but not marital social skills (p = 0.14). Because two outcomes differed at baseline, effects were confirmed with baseline- and dependence-adjusted sensitivity analyses. These findings provide preliminary evidence that CBCT with mindfulness may benefit disadvantaged couples.

Adult↗

PubMed2026

Community-driven advances in computational mass spectrometry: The perspective of EuBIC-MS members.

Advances in data acquisition, artificial intelligence, and integrative bioinformatics are driving the rapid evolution of computational mass spectrometry, and in turn, transforming modern proteomics, metabolomics, and lipidomics. These developments have greatly increased the scale and complexity of mass spectrometry data, underscoring the importance of evolving accurate, transparent, efficient and reproducible data processing workflows. Addressing these challenges requires collaborative innovation that brings together expertise in software engineering, statistics, and biology. The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS), an initiative of the European Proteomics Association (EuPA), fosters a culture of open, community-driven development through its biennial Developers Meetings and Winter Schools. This commentary summarizes the scientific background and outcomes of the EuBIC-MS Developers Meeting 2025, which took place in Novacella, Italy. Three keynote presentations highlighted major frontiers in the field: deep proteome and phosphoproteome profiling, text mining for protein-protein interaction extraction, and scalable proteomics for AI-driven drug discovery. Seven community-selected hackathons addressed emerging challenges such as single-cell proteomics data analysis, FAIR metadata extraction, deep learning frameworks, R-Python interoperability, and DIA validation. Together, these efforts demonstrate the potential for scientific and technical innovation to arise from open collaboration, and highlight how community-driven initiatives can accelerate progress in computational mass spectrometry. SIGNIFICANCE: Modern proteomics increasingly depends on computational advances to translate complex, high-dimensional data into biological knowledge. The EuBIC-MS Developers Meeting 2025 exemplifies how community-driven collaboration can directly accelerate this process by bringing together experts from bioinformatics, statistics, and experimental proteomics to co-develop open, interoperable, and reproducible analytical tools. By fostering shared software frameworks, transparent benchmarking, and collaborative problem solving, the EuBIC-MS community helps ensure that technological innovation translates into reliable biological insights. This collaborative model strengthens the foundation for quantitative, system-level understanding of proteomes and establishes a sustainable path for integrating artificial intelligence and next-generation data acquisition into routine biological discovery. This commentary shows some current highlights in the field of computational mass spectrometry and community-based approaches undertaken during the most recent Developers Meeting to solve these challenges. The approaches discussed and initiated during the meeting - ranging from deep proteome profiling and phosphosite mapping to text mining, single-cell data analysis, and FAIR metadata extraction - address key bottlenecks that currently limit the biological interpretability and comparability of proteomics data.

Mass Spectrometry↗

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

Associations Between Short Video Exposure, Empathy and Attitudes Toward End-Of-Life Care Among Nursing Students: A Cross-Sectional Study.

AIM: This cross-sectional study examined the associations between short video exposure, nursing students' empathy, and attitudes toward end-of-life (EOL) care, and tested whether perceived impact is statistically consistent with an indirect pathway in these relationships. DESIGN: A descriptive cross-sectional study. METHODS: In total, 534 undergraduate nursing students were included. Data were collected using a self-designed questionnaire, including the Attitudes Toward Care of the Dying Scale and the Jefferson Scale of Empathy-Health Professions Student version for empathy assessment. Statistical analysis for correlation and mediation analysis (PROCESS macro) was performed. RESULTS: 85.96% of students watch short videos for more than 30 min daily, with more than 60% of them viewing EOL-related content. Students with prior caregiving experience or formal palliative care education showed significantly higher empathy and more positive attitudes (p < 0.05). Exposure to medical and EOL-related short videos was positively correlated with perceived impact, empathy, and positive EOL attitudes, with effect sizes ranging from very weak to modest (r = 0.10 to 0.27). The data were consistent with an indirect pathway between short video exposure and empathy via perceived impact (indirect effect = 0.04; 95% bootstrap CI [0.01, 0.08]). However, for EOL attitudes, short video exposure showed a direct association rather than an indirect pathway via perceived impact (direct effect = 0.09, p < 0.01). CONCLUSION: In this cross-sectional study, short video exposure was modestly associated with nursing students' empathy, with data consistent with an indirect pathway via perceived impact; the observed associations explained only approximately 1% to 7% of the variance in the outcome variables. However, reshaping EOL attitudes may require more systematic education beyond brief video exposure. These findings are hypothesis-generating and await validation through longitudinal and experimental research using standardized video content. IMPLICATIONS FOR NURSING PRACTICE: Nursing educators should consider integrating curated short video content into palliative care curricula to enhance students' empathy and perceived impact of end-of-life education. However, brief video exposure alone may be insufficient to reshape deeper end-of-life attitudes, suggesting the need for comprehensive, multi-modal educational strategies.

Humans↗

PubMed2026

Circular RNAs in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by the progressive loss of motor neurons, with most cases lacking a clear genetic basis. Emerging evidence highlights the involvement of non-coding RNAs, particularly circular RNAs (circRNAs), in disease onset and progression. Here, we investigated circRNAs implicated in ALS and related motor neuron diseases (MNDs). Here, we provide a general overview of circular RNA metabolism and cellular functions. We then present our systematic literature review that identified ALS-associated circRNAs, followed by in silico analyses of 15 circular RNA candidates that were selected based on the most compelling data regarding ALS. Our results revealed that several circular RNAs regulate ALS-related genes, such as unfolded protein response, oxidative stress, cell cycle regulation, and apoptosis. Protein-RNA interaction analysis further showed that ALS-related circRNAs can sponge 20 RNA-binding proteins. Additionally, molecular docking analysis demonstrated that ALS-associated FUS variants significantly alter its binding affinity to circular RNAs. RNA-seq data from ALS patients confirmed significant alterations in the expression of host genes of ALS-related circRNAs and hub proteins in ALS-affected CNS tissues. Collectively, our findings identify circRNAs as potential key contributors to ALS pathogenesis.

Amyotrophic Lateral Sclerosis↗