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Testing How Mindfulness Skills Change for Novice Meditators Using Headspace: Examining Trait Mindfulness and Perceived Stress as Moderators.

Mindfulness-based interventions are found to effectively reduce stress and improve mental health outcomes. Yet, it is not always clear how the mindfulness skills of attention and acceptance develop throughout the intervention. This knowledge gap is especially pertinent for novice meditators learning these skills for the first time, including whether some individuals are more prone to learning them. Using a randomized waitlist-controlled trial, we tested the effect of the app Headspace on changes in attention and acceptance over 8 weeks among participants new to mindfulness meditation. Further, we tested the moderating effects of trait mindfulness and perceived stress. Non-faculty university employees were randomized to a Headspace or waitlist control condition. Trait mindfulness and perceived stress were measured at baseline. Ecological momentary assessment survey data for attention and acceptance were collected five times a day in 4-day bursts at baseline and 2, 5, and 8 weeks post-randomisation. Attention and acceptance were significantly higher at Week 8 compared to baseline for the Headspace group, but not the control group. For the Headspace group, both skills showed significant change by Week 2. Trait mindfulness moderated this effect with those who were lower in trait mindfulness displaying greater increases in attention, but not acceptance. Perceived stress also moderated this effect with those who were lower in perceived stress displaying greater increases in attention and acceptance. Our discussion draws attention to implications for matching intervention content to individual needs to ensure participants reporting different levels of characteristics benefit from mindfulness training.

Humans

Toward real-time quantification of driving risks: a systematic review and research agenda of risk field theory.

In complex traffic systems, driving risk often evolves in a continuous and progressive manner prior to crash occurrence. How to effectively represent and analyze such latent risk states remains a central challenge in traffic safety research. In recent years, risk field-based approaches have introduced spatial and spatiotemporal continuous modeling paradigms, providing new perspectives for characterizing the distribution of traffic risk and its dynamic evolution. Motivated by the rapid growth of this research area and the lack of a systematic synthesis, this paper presents a comprehensive review of studies applying risk field theory to driving safety and traffic risk analysis. Following the PRISMA guidelines, relevant literature was collected through multi-database searches and analyzed using a combination of bibliometric analysis and qualitative review. The review systematically summarizes the theoretical foundations, modeling elements, data sources, analytical methods, and application domains of risk field-related research. Particular attention is given to studies that conceptualize traffic risk as a continuous field, complemented by a broader review of traffic risk factor literature to identify key elements and analytical dimensions involved in risk field modeling. On this basis, the paper synthesizes research progress in major application areas, including traffic safety state representation, driving behavior analysis, traffic conflict assessment, and autonomous driving and human-machine cooperative systems. Differences and commonalities among existing studies are compared in terms of modeling strategies, data support, and application scenarios. Through this systematic review, the paper clarifies the main research themes and methodological trends of risk field-based studies, providing a structured framework for understanding the evolution and application of this approach and offering methodological insights for risk perception modeling and safety-oriented decision support in intelligent transportation systems (ITS).

Humans

Effectiveness and usability of artificial intelligence-powered assistive technologies in Supporting daily activities of children with cerebral palsy: a systematic review.

BACKGROUND: Cerebral Palsy (CP) is the main cause of motor disabilities in childhood, necessitating innovative approaches to rehabilitation and assistive technology (AT). Simultaneously, artificial intelligence (AI) is increasingly being integrated into devices to create more adaptive, personalized, and effective AT. This systematic review aimed to evaluate the effectiveness and usability of AI-powered assistive technologies designed to support daily activities and rehabilitation in children with CP. MATERIALS AND METHODS: Five databases, including Scopus, Web of Science, PubMed, Embase, and IEEE Xplore, were systematically searched, and 23 articles were included in the final analysis. Articles were identified, selected, and categorized into emerging thematic areas based on the primary function and application of the technology. RESULTS: Five key thematic topics were identified: 1) AI-driven motor rehabilitation and gait training for functional mobility; 2) intelligent assessment and monitoring systems for clinical decision support; 3) AI-supported communication, social interaction, and intention recognition tools; 4) gamified and virtual reality-based interventions to enhance engagement and usability; and 5) smart assistive systems supporting daily living and independent mobility. The findings demonstrate a strong trend toward the application of AI technologies in personalized, engaging, and data-driven interventions for children with CP. However, the field is predominantly in the proof-of-concept stage, with limitations including small sample sizes, lack of long-term clinical validation, challenges in user-centered design, and usability for children with CP. CONCLUSION: AI-powered assistive technologies hold significant potential for transforming the care of children with CP by enabling highly personalized and engaging interventions. To actualize this potential, future work must realize that practical application remains challenging owing to limited clinical validation, technological integration, and usability barriers for children with CP. Future research must prioritize user-centered design and multidisciplinary collaboration to ensure that AI and robotic advancements improve the usability and quality of life for children with CP.

Humans

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

Proteomics in environmental pollution research: Advances, challenges, and future directions.

Environmental proteomics has emerged as a powerful approach for elucidating the molecular mechanisms underlying pollutant-induced biological effects. Although this field has developed rapidly, the systematic review of recent proteomics applications in environmental pollution research remains limited. This review explored the emerging roles of toxicoproteomics in biomarker discovery and mechanistic elucidation, as well as ecotoxicoproteomics in ecological risk assessment and bioremediation strategies. Here, we review the field, highlighting recent trends such as the integration of proteomics with genomics, transcriptomics, and metabolomics to provide a comprehensive view of biological responses to environmental stressors. We further discuss the growing application of artificial intelligence in improving proteomics data interpretation and accelerating biomarker discovery. In addition, recent technological advances in environmental proteomics are highlighted, including next-generation tissue microarray proteomics, nanoscale proteomics, single-cell proteomics, and spatial proteomics. Despite its potential, proteomics faces challenges, such as high operational costs, computational complexity in analysis, and technical limitations in low-abundance protein detection. We propose that the convergence of proteomics with artificial intelligence and multi-omics approaches offers promising solutions to these challenges, enhancing the practical application of proteomics in environmental monitoring and risk assessment.

Proteomics

Cryostick pre-cooling reduces pain during intra-articular knee injections: a randomized, contralateral-controlled trial.

BACKGROUND: Intra-articular knee injections are essential for osteoarthritis management but often limited by "needle phobia" and procedural pain. The cryostick, a high-thermal-conductivity device, is a potential analgesic; however, evidence regarding its efficacy in reducing pain and bleeding is limited. The purpose of the study was to evaluate whether cryostick application reduces procedural pain, reduces bleeding, and improves patient satisfaction during intra-articular knee injections. METHODS: This randomized, contralateral-controlled trial included 50 patients (100 knees) with bilateral knee osteoarthritis. One knee received a 20-s cryostick protocol (-20&#xb0;C) immediately before injection; the contralateral knee received a standard injection. Primary outcome was pain intensity (100-mm VAS) during needle penetration and at 5-min post-injection. Secondary outcomes included bleeding area (mm2) and satisfaction (1-5 Likert scale). RESULTS: Cryostick application significantly reduced pain during needle penetration (Mean Difference [MD] -24.8&#xa0;mm; 95% CI -29.9 to -19.7; P&#xa0;<&#xa0;0.001) and at 5-min post-injection (MD -20.8&#xa0;mm; 95% CI -26.8 to -14.8; P&#xa0;<&#xa0;0.001). The Number Needed to Treat (NNT) to achieve the Minimal Clinically Important Difference (13&#xa0;mm) was 1.28 (95% CI 1.15-1.54) during penetration and 1.79 (95% CI 1.45-2.36) at 5-min post-injection. The bleeding area was significantly smaller with cryostick (MD -4.82&#xa0;mm2; P&#xa0;=&#xa0;0.014). Patients reported significantly higher satisfaction scores with the cryostick (4.10 vs 2.96; P&#xa0;<&#xa0;0.001). CONCLUSIONS: A 20-s cryostick application is a safe, well-tolerated, and effective adjunct for attenuating pain and bleeding during knee injections. This technically simple approach requires minimal complexity, offering an efficient, non-pharmacological tool to enhance patient comfort.

Humans

Clinical performance of a giomer-based pit and fissure sealant with and without air-abrasion pretreatment: a 12-month randomized clinical trial.

BACKGROUND: Pit and fissure sealants are widely used for caries prevention; however, their long-term success depends largely on retention. Giomer-based sealants containing surface pre-reacted glass ionomer fillers offer bioactive properties, yet concerns remain regarding their bonding durability when applied with mild self-etch primers. This randomized clinical trial evaluated the effect of bioactive glass air-abrasion pretreatment on the retention and caries preventive efficacy of a giomer-based sealant in young adults over 12 months. METHODS: This parallel-arm randomized clinical trial included 96 participants, each contributing one eligible sound permanent molar (n&#x2009;=&#x2009;48 per group). Participants were randomly allocated to either bioactive glass air-abrasion pretreatment followed by application of a giomer-based sealant (intervention group) or application of the same sealant without pretreatment (comparator group). Sealant retention and secondary caries incidence were evaluated at baseline, 6 months, and 12 months using Simonsen's criteria, and modified United States Public Health Service (USPHS) criteria, respectively. The primary outcome was sealant retention at 12 months, whereas secondary caries incidence was assessed as a secondary outcome. Intergroup comparisons were analyzed using the Chi-square test. Intragroup comparisons were analyzed using Cochran's Q test followed by multiple comparisons. Relative risk with 95% confidence intervals was calculated. Statistical significance was set at p&#x2009;&#x2264;&#x2009;0.05. RESULTS: At 6 months, complete sealant retention was observed in 91.7% of teeth in the intervention group and 75.0% in the comparator group, with no statistically significant difference between groups (p&#x2009;=&#x2009;0.068). At 12 months, complete sealant retention was significantly higher in the intervention group (87.5%) than in the comparator group (33.3%) (p&#x2009;<&#x2009;0.0001). Teeth in the intervention group exhibited an 81.25% lower risk of sealant retention failure compared with the comparator group (RR&#x2009;=&#x2009;0.1875; 95% CI: 0.0864-0.4069; p&#x2009;<&#x2009;0.0001). No differences in secondary caries incidence were detected between groups during the 12-month follow-up period (p&#x2009;=&#x2009;1.0000). CONCLUSIONS: Bioactive glass air-abrasion pretreatment significantly improved the retention of a giomer-based fissure sealant compared with sealant application without pretreatment. No differences in secondary caries incidence were detected between groups during the 12-month follow-up period. Incorporating mechanical surface conditioning prior to sealant placement may enhance sealant retention without compromising preventive efficacy. TRIAL REGISTRATION: https://clinicaltrials.gov/ , (NCT06003452), 15-08-2023.

Humans

A Multimethod Evaluation to Assess Feasibility, Acceptability, and Preliminary Efficacy of HPVVaxFacts, a Tailored Mobile Web App, for Parents With Unvaccinated Children: Pilot 2-Arm Randomized Controlled Trial.

BACKGROUND: Mobile health (mHealth) interventions may improve provider-parent communication on human papillomavirus (HPV) vaccination to reduce concerns, and increase intention and uptake. HPVVaxFacts (233 Analytics) is a novel, mobile web app delivering tailored education based on the Health Belief Model and Theory of Reasoned Action, addressing parental concerns preclinic visit. OBJECTIVE: This study aimed to assess the feasibility, acceptability, and preliminary efficacy of HPVVaxFacts among parents of adolescents aged 9-17 years. METHODS: We conducted a pilot, randomized controlled trial in 2 urban Tennessee clinics from June to September 2023 comparing 2 groups: tailored education via HPVVaxFacts mobile web app (intervention, n=27), and nutrition education (attention control, n=30). Eligible parents had or were caregivers to a child aged 9 to 17 years unvaccinated against HPV, had a mobile phone, had an upcoming clinic visit, and spoke English. The recruitment strategy was patient intake software-Phreesia (Phreesia, Inc) and eClinicalWorks (eClinicalWorks). Although unblinded, parents could deduce their study arm assignment. Providers were blinded. Feasibility, acceptability, and preliminary efficacy (HPV vaccine knowledge, concerns, intentions, and vaccination rates) were assessed using multimethod evaluation. Parents were assessed at baseline and immediately post intervention via surveys. Vaccination rates were assessed at 12 months post intervention via electronic health records. Nineteen parent interviews were conducted up to 9 months post intervention. A clinic staff consultation (n=6) was 1 month post intervention. RESULTS: Of 57 enrolled parents, most were female (52/57, 91%), non-Hispanic White (44/57, 77%), had &#x2264;US $80,000 household income (32/57, 56%), and had some college or less (27/57, 47%). In total, 81% (29/36) of parents viewed HPVVaxFacts. Post intervention, HPV vaccine initiation was higher in the intervention group compared to the attention control group (48% vs 17%; difference 0.24; 95% CI 0.03-0.46; P=.01). Parents in the HPVVaxFacts arm demonstrated a greater reduction in knowledge (ie, knowledge increase; mean change: -0.6 vs 0.1) and concern scores (mean change: -3.4 vs -1.4) than those in the nutrition education arm. However, between-arm differences were not statistically significant (P=.13 and P=.14, respectively). The majority found the study protocol and HPVVaxFacts acceptable. Benefits of HPVVaxFacts include confirming their decision to vaccinate, supporting parent-child discussion on the vaccine, and answering questions preclinic visit or offering questions for the provider. Study protocol delivery and mobile web app instructions were suggested areas for improvement. Barriers for HPVVaxFacts use include content in English only and digital format. CONCLUSIONS: Our study suggests HPVVaxFacts was feasible and acceptable among parents to provide previsit, tailored information on HPV vaccination. Outcomes offer a positive trajectory but need more exploration. Next steps include a well-powered efficacy trial to determine the impact of HPVVaxFacts on initiation vaccine rates and parental hesitancy factors, as well as to explore an interaction, effect modification, and mediation among different variables.

Humans

Targeted Nanoparticle Delivery CRISPR/Cas9: overcoming biological barriers, enhancing stability, and improving therapeutic precision.

Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) has emerged as a promising gene-editing platform for genetic disorders; however, its in vivo application remains limited by low delivery efficiency and biological barriers. Many CRISPR payloads fail to reach target sites due to extracellular degradation, immune clearance, and intracellular trafficking limitations. This review examines the interplay between biological barriers and nanoparticle engineering strategies for CRISPR/Cas9 delivery. A barrier-oriented engineering approach is proposed as a central framework, encompassing ligand-based surface modification for enhanced targeting and uptake, improved circulation stability via PEGylation and biomimetic coatings, and optimized payload release through endosomal escape strategies. Stimulus-responsive nanoparticle systems further enable spatiotemporal control over payload release. Nuclear targeting strategies, including optimization of nuclear localization signals (NLS) and exploitation of endogenous trafficking pathways, are highlighted as key factors for improving genome-level editing efficiency. Despite these advances, major challenges-including limited intracellular delivery efficiency, insufficient targeting precision, and safety concerns-continue to hinder clinical translation. Future directions highlight artificial intelligence-driven nanoparticle design, personalized delivery systems, and next-generation CRISPR platforms. Overall, an integrated, barrier-oriented engineering strategy is essential for advancing CRISPR/Cas9 delivery toward clinical applications, ultimately advancing global good health and well-being.

CRISPR/Cas9

Smartphone Apps for Preventing Adolescent Health Problems Among Health Care Professionals: Systematic Search and Quality Assessment.

BACKGROUND: Health care professionals must consider multiple dimensions of prevention when consulting with adolescents. Identifying risky behaviors early in adolescence is crucial for reducing both morbidity and mortality. General practitioners are increasingly eager to incorporate digital tools for prevention into their consultations with adolescents; however, the relevance and clinical validity of these digital tools are not always established or well-known. Consequently, primary care professionals require guidance and support in selecting relevant mobile health (mHealth) tools. OBJECTIVE: The aim of this study is to identify relevant and useful digital apps to help primary care professionals detect at-risk adolescents across all recommended areas of prevention: orthopedics, mental health, substance abuse, risk behaviors, sexual health, vaccinations, social relationships, and nutrition. METHODS: A systematic review of smartphone apps, with an analysis of content quality, was carried out by 4 researchers using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist. The App Store and Google Play Store platforms were surveyed. The inclusion criteria were as follows: free of charge, date of last update, availability in French or English, relevance of the preventive approach to adolescents, and scientific validation. Four health care professionals assessed the apps: 2 selected the apps relevant to health care professionals, then 3 analyzed these apps using the French version of the Mobile App Rating Scale (MARS-F). Intraclass correlation coefficient, model (2,1) (2-way random effects, absolute agreement, single measures); standard error of measurement; and mean absolute error were also calculated. RESULTS: A total of 976 apps were identified, 49 of which had disappeared from the platforms prior to analysis. Nine apps were retained. Seven (0.72%) were included after evaluation using the MARS-F: 2 on mental health and 5 on sexual health (including 3 on contraception only). The mean MARS-F interrater score ranged from 2.5/5 to 3.8/5. The global MARS-F score demonstrated a pooled SD of 0.60 and an intraclass correlation coefficient (2,1) of 0.0003, resulting in a calculated standard error of measurement of 0.60. The average discrepancy between raters was a mean absolute error of 0.53. CONCLUSIONS: No similar studies have been identified in the literature that specifically focus on mobile apps designed to support health care professionals in delivering preventive care to adolescents. Of the 8 areas of prevention identified as relevant for adolescents, only 3 are addressed by the apps validated through our methodology (5 focus on sexual health). Consequently, current apps are insufficient to support health care professionals in their overall preventive work with adolescents. Such a review should be conducted systematically prior to the development of any new tool to prevent duplication and channel creative efforts toward truly innovative digital solutions. Furthermore, a thorough analysis of relevant, recommended websites is essential, as these resources complement the use of mobile apps designed for health care professionals.

Humans

A streamlined workflow for high throughput metaproteomic analysis of the rumen microbiome.

Metaproteomics can provide direct functional insights into complex microbial communities, yet its application in rumen research remains limited due to labor-intensive and low-throughput sample preparation workflows before the MS analysis. This work aimed to develop and characterize a streamlined, high throughput metaproteomic workflow optimized for rumen samples. Key steps, including microbial cell extraction, cell lysis, protein digestion, and LC-MS/MS acquisition, were systematically assessed and optimized to reduce hands-on time while maintaining deep proteome coverage. The optimized workflow integrates a minimized cell extraction protocol using 0.5&#xa0;g starting material and in-solution tryptic digestion. Application of the final workflow to 72 samples from in vitro fermentation revealed that biological variability between inocula dominated technical variability, which remained moderate (median CV of 21-24% across batches). Overall, the optimized workflow supports robust taxonomic and functional characterization of the rumen microbiome with improved scalability. These advances provide a foundation for applying metaproteomics to larger experimental designs, including nutritional trials and cohort studies, thereby enabling broader functional interrogation of rumen microbial ecosystems. SIGNIFICANCE: This study addresses current limitations in the application of metaproteomics to rumen microbiome research by developing a streamlined and scalable sample preparation workflow. By optimizing key steps and reducing sample input while maintaining reproducibility and proteome coverage, this work enables more efficient processing of larger sample sets. These advances support the broader use of metaproteomics in rumen studies and facilitate functional investigations relevant to animal nutrition and sustainable livestock production.

Animals

Dynamics of soil fungal communities restored with biochar from a quarry site.

Quarrying activities have intensified due to population expansion, leading to landscape degradation and ecological destruction. Quarry restoration is usually mandatory in Hong Kong, China. Although biochar is used for sustainable soil amendment, its effectiveness in restoring quarry soil with poor properties has rarely been investigated. A 24-month field study was conducted to evaluate the ecological feasibility of restoring a quarry site by using native species (that is, Castanopsis fissa and Cyclobalanopsis edithiae) and biochar amendment. The results revealed that after 24 months, the application of biochar increased the organic carbon, phosphorus and potassium of the vegetated soil by at least 120 %, 31 % and 12 %, respectively, due to higher cation exchange capacity and better plant growth. The relative abundance of Ascomycota and Basidiomycota increased by 24 % and 47 % with biochar application when C. fissa was planted, which was likely associated with the improved nutrient cycling and soil fertility. Even though adding biochar to bare soil was found to increase the complexity of fungal co-occurrence networks, biochar application only increased fungal diversity in vegetated quarry soil but usually reduced its fungal richness. Moreover, fungal co-occurrence networks in vegetated soil became less complex, suggesting that biochar potentially helped plants to assemble specific, beneficial fungal communities. This effect is most pronounced in the soil planted with C. edithiae, where the structure of fungal communities after 24 months was significantly different from that at other restoration times. This study identifies key fungal phyla enhanced by biochar in quarry soil and provides an effective strategy for facilitating the restoration and management of degraded lands, especially quarry sites.

Charcoal

Artificial Intelligence for Diagnosing Meibomian Gland Dysfunction: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy Studies.

PURPOSE: To identify, appraise, and synthesize the performance of artificial intelligence-based meibography reading as compared with human graders in diagnosing meibomian gland dysfunction. METHODS: We followed Cochrane methodology and reporting guidelines for diagnostic test accuracy reviews. To assess potential risk of bias and applicability, we used a modified Quality Assessment of Diagnostic Accuracy Studies-2 checklist. We applied bivariate logistic models to estimate summary sensitivity and specificity when appropriate and used the GRADE framework to rate the certainty of the evidence. RESULTS: We identified 14 eligible studies involving 5511 predominantly middle-aged participants (average age: 27-55 years) who were primarily female (&#x2265;54.5%). A total of 18,926 meibography images were obtained through noncontact infrared (11 studies) or in vivo confocal microscopy (three studies). Two studies reported external validation of deep learning models, 12 reported internally validated models, and one reported both. All but one study had high risk of bias in at least one domain; 12 studies raised high or intermediate concern about applicability. Based on three external evaluations, the summary sensitivity and specificity for diagnosing meibomian gland dysfunction from normal glands were 97.5% (95% confidence interval: 77.5%-99.8%) and 85.5% (95% confidence interval: 47.3%-97.5%). Sources of heterogeneity in internally validated models included study population, case mix, and others. The overall evidence was very low to low certainty because of imprecision, high risk of bias, and concerns about applicability. CONCLUSIONS: Artificial intelligence-based meibography grading appears less accurate than human graders. Future studies should adopt rigorous designs, including a more diverse participant pool (or image set), and external validation.

Humans

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

Overcoming Immunological Barriers in MSC-Derived Insulin-Producing Cells through CRISPR-Based Hypoimmunogenic Engineering and Translational Perspectives for Type 1 Diabetes.

Mesenchymal stromal cell (MSC)-derived insulin-producing cells (IPCs) represent an emerging strategy for &#x3b2;-cell replacement in type 1 diabetes mellitus (T1DM) owing to their differentiation potential, intrinsic immunomodulatory properties, and lower tumorigenic risk compared with pluripotent stem cell-derived platforms. However, accumulating evidence indicates that differentiation-associated immunogenicity, context-dependent immune recognition, and recurrent autoimmune responses may substantially limit long-term graft survival and therapeutic durability following transplantation. This review critically examines the immunological barriers associated with MSC-derived IPCs, including altered MHC expression, susceptibility to alloimmune and autoimmune-mediated rejection, and potential reactivation of autoreactive immune memory. We discuss the application of CRISPR-based hypoimmunogenic engineering strategies targeting antigen presentation pathways, NK-cell activation, and immune checkpoint modulation to generate more immune-evasive MSC-derived IPCs while preserving &#x3b2;-cell functionality. By integrating insights from T1DM immunopathogenesis, MSC biology, genome editing, and translational immunology, we propose a framework linking immune engineering with controlled differentiation, functional maturation, and long-term safety evaluation. In parallel, we comparatively position MSC-derived IPCs alongside clinically advancing iPSC-derived &#x3b2;-cell platforms to highlight their distinct translational niche, including potential advantages related to safety, immunomodulatory capacity, manufacturing accessibility, and scalability, while acknowledging the superior functional maturity and clinical progression currently demonstrated by iPSC-derived systems. Finally, we discuss key translational challenges, including genomic stability, immune-evasion durability, GMP-compliant manufacturing, and the need for rigorous functional and immunological benchmarking prior to clinical application of hypoimmunogenic MSC-derived IPC therapies in T1DM.

Humans

The Role of Artificial Intelligence for Intimate Partner Violence Prevention: A Systematic Review.

INTRODUCTION: Intimate partner violence (IPV), encompassing physical, sexual, emotional and economic abuse, remains a pervasive global health concern. Traditional prevention efforts face obstacles such as underreporting, delayed detection and limited personalised support. Emerging artificial intelligence (AI) approaches offer new opportunities to enhance IPV prevention. AIM: This systematic review maps and synthesises evidence on AI-driven tools in IPV prevention based on studies published between 2004 and 2024. METHODS: Following PRISMA 2020 guidelines and PROSPERO registration, we searched PubMed, Embase, CINAHL, PsycINFO, IEEE Xplore and Web of Science. Eligible studies explicitly evaluated AI technologies targeting IPV prediction, screening, intervention or support delivery. Study quality was appraised using the Mixed Methods Appraisal Tool (MMAT). RESULTS: Of 1304 records initially identified, 41 studies met eligibility criteria. AI applications ranged from machine learning (ML) for risk prediction and natural language processing (NLP) for IPV detection in clinical and social media data, to image analysis for forensic evaluation and chatbot-based support. Predictive modelling demonstrated strong discriminative performance, while NLP-based screening detected IPV with notable sensitivity. Chatbots showed feasibility and user acceptability, but evidence of their direct impact on reducing IPV incidence was limited, with one randomised controlled trial showing a modest reduction. Key challenges identified included algorithmic bias, data privacy risks and barriers to integration across health and social care systems. DISCUSSION: AI-informed interventions show promise for improving IPV detection, risk assessment, and scalable support, but questions remain about long-term effectiveness, ethical fairness, transparency and equitable implementation. Future interdisciplinary research should address these concerns to responsibly deploy AI in IPV prevention. RELEVANCE TO CLINICAL PRACTICE: The findings highlight the importance of trauma-informed, culturally responsive care and provider training in AI applications. Nurse-led innovation and policy advocacy will be crucial for safe, equitable integration of AI in IPV prevention.

Artificial Intelligence

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

The journey of fluxapyroxad, mandipropamid and mefentrifluconazole residues in two morphologically distinct chilli peppers: A comprehensive risk assessment from field to processing.

Understanding the residue fate of novel pesticides in crops is crucial for ensuring their safe application and safeguarding public health. This study examined the dissipation, processing factors (PFs), and risk assessment of fluxapyroxad, mandipropamid, and mefentrifluconazole in two morphologically distinct varieties of chilli peppers from field to processing. The half-lives of the three pesticides ranged from 5.42 to 10.05&#xa0;days, following first-order kinetics. The initial residues were higher in Chaotian chilli peppers (CCP) than in long green chilli peppers (GCP). However, dissipation occurred more rapidly in CCP. Washing notably reduced the residues (PF: 0.60-0.89), whereas sun drying and oven drying concentrated them (PF: 1.92-3.74), with oven drying leading to greater concentrations. Both chronic and acute dietary risk assessments suggested acceptable risk levels for the general population. This study offers reliable guidance for the rational application of these three pesticides in chilli pepper cultivation.

Capsicum