SEARCH · Search PubMed

Results for “technology- or image-based abuse”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

2,206 records · Page 4Linked to original sources

PubMed2026

Decoding the spatiotemporal patterns of food spoilage microbial communities: Integrating multi-omics and artificial intelligence to enable precision preservation.

In the global food supply chain, food wastage caused by spoilage has resulted in significant economic losses, food shortages, and environmental pressure. This process is fundamentally driven by the spatiotemporal dynamics of microbial communities. However, traditional research methods struggle to elucidate the complex mechanisms of spatial heterogeneity, interspecies interactions, and functional succession. This limits the development of effective preservation strategies. This review systematically reviews the cutting-edge progress of integrating multi-omics technologies and artificial intelligence (AI) to study food spoilage microbial communities, breaking through this bottleneck. We propose an intelligent theoretical framework that could potentially analyze microbial metabolic activities and predict dynamic shelf life if implemented. The conceptual framework integrates multidimensional data, including spatial metabolomics, temporal metatranscriptomics, single-cell transcriptomics, and longitudinal metagenomics. It can also be combined with AI models, such as graph neural networks. The article elaborates on the principles and applications of spatio-temporal monitoring technologies, such as nano secondary ion mass spectrometry, hyperspectral imaging, and the Internet of Things sensing. Through illustrative cases of typical perishable foods, it also explores how such a multi-omics - AI system might be applied to spoilage warning and precise intervention. Additionally, the article addresses the current challenges in data coverage, model generalization, and federated learning implementation. Then the research further explores emerging areas such as engineered probiotics, edge AI, and microfluidic sensing. These areas are targeted at transforming food preservation from an empirical control approach to a data-driven, precise regulatory framework. This transformation provides theoretical support and technical approaches for developing a smart, sustainable food preservation system.

Multiomics↗

PubMed2026

Prenatal exome sequencing of fetuses with central nervous system anomalies based on prenatal ultrasound and magnetic resonance imaging diagnosis: A retrospective cohort study with a systematic review and meta-analysis.

INTRODUCTION: Fetal central nervous system (CNS) abnormalities have diverse etiologies, with genetic factors as a major contributor. Prenatal exome sequencing (ES) is a powerful tool for precise molecular diagnosis of CNS anomalies, but its diagnostic yield varies among studies. This study aimed to evaluate the additional diagnostic yield of prenatal ES compared with chromosomal microarray analysis (CMA) in fetuses with CNS anomalies detected by prenatal imaging. MATERIAL AND METHODS: We collected ES results from fetuses diagnosed with CNS anomalies by prenatal imaging (2019-2024) who had negative results. Subgroup analyses assessed phenotype-specific ES diagnostic yield for associated genes and variants. A systematic review and meta-analysis incorporating our data and published studies further explored the association between phenotype and diagnostic yield. RESULTS: In the cohort study of 219 cases, ES identified pathogenic/likely pathogenic single nucleotide variations in 36 cases (16%). The highest diagnostic yield of ES was in cases with multisystem malformations (25%, 14/55), followed by multiple CNS anomalies (15%, 2/13) and isolated CNS anomalies (13%, 20/151). The most commonly identified isolated CNS anomaly was agenesis of the corpus callosum (31%, 5/16). Neural tube defects with urogenital anomalies were associated with a positive ES finding in 57% (4/7) of cases. The meta-analysis of 989 cases from 22 studies showed a pooled diagnostic yield of ES of 27% (95% CI, 21%-34%). The highest diagnostic yield of ES was in cases of corpus callosum anomalies with facial abnormalities (75%, 8/11) and neural tube defects with urogenital malformations (80%, 12/15). The diagnostic yield of ES for three or more CNS abnormalities was 43% (95% CI, 31%-58%), significantly higher than that for only two abnormalities (10%, 95% CI, 4%-18%). No significant difference in diagnostic yield was found between cases identified by prenatal MRI combined with ultrasound (27%, 95% CI, 20%-36%) and those identified by ultrasound alone (25%, 95% CI, 17%-35%). CONCLUSIONS: ES provided a significantly higher diagnostic yield than CMA for fetal CNS abnormalities, with diagnostic yields varying by phenotype. The systematic review and meta-analysis confirmed that the complexity and combination of malformations are key factors associated with differences in ES diagnostic yield.

Humans↗

PubMed2026

A Web-Based, Pedometer-Mediated Intervention Increases Amount and Intensity of Physical Activity in COPD: A Randomized Controlled Trial.

INTRODUCTION: Ground-based walking training is an aerobic exercise used in supervised pulmonary rehabilitation (PR). Physical activity (PA) interventions typically promote step counts, but it is unclear whether community-based walking intensity can be targeted as aerobic exercise. This randomized controlled trial evaluated a web-based, pedometer-mediated PA intervention designed to increase walking amount and intensity. MATERIAL AND METHODS: Participants with COPD who had never enrolled in PR were randomized 1:1 to control or intervention. The intervention included individualized step-count goals, iterative feedback, educational content, and an online community forum. The Fitbit Inspire Heart Rate objectively monitored daily step counts. Participants were instructed to achieve step-count goals with as many steps of moderate-intensity as possible guided by a modified Borg rating of 4-5 for dyspnea. The primary outcome was change in PA measured as average daily step count at 12 weeks. Aerobic intensity was assessed by the Rapid Assessment of PA Questionnaire which uses self-reported moderate or vigorous PA to categorize responders as underactive or active. Linear mixed-effects models (PROC MIXED, SAS v9.4), adjusting for group, time, group*time, FEV1%predicted, enrollment season, and study modality (eg, in-person, virtual, hybrid), assessed between-group change. RESULTS: Participants (57 intervention, 52 control) were 97% male, mean age 73±7 years, and baseline FEV1 73±23% predicted. Baseline daily steps were 4,222±1,929 (intervention) and 4,851±2,637 (control). Intervention participants increased average daily steps by 1,410 steps/day more than controls (p=0.005). The intervention group showed greater transitions from underactive to active intensity (between-group: p=0.025), with 20 (41%) moving to active status (within-group: p=0.001). CONCLUSION: Technology-mediated community-based walking increased PA amount and intensity. These findings support further evaluation of this intervention as a potential option for ground-based walking training with objective measurement of exercise intensity.

Humans↗

PubMed2026

Comparative evaluation of molecular technologies for the identification of prevalent non-tuberculous mycobacteria in pulmonary infections: a systematic review and meta-analysis.

BACKGROUND: The increasing prevalence of non-tuberculous mycobacteria pulmonary disease (NTM PD) is a burden to public health. Successful management of NTM PD critically depends on accurate species identification and reliable drug susceptibility testing to guide appropriate antibiotic therapy. Emerging molecular technologies offer rapid diagnostic solutions compared to conventional methods, but their performance varies. This study aims to provide a comprehensive evaluation of current molecular techniques for NTM identification and to present a global antibiotic resistance profile. METHODS: A systematic literature search was conducted in PubMed and Web of Science for studies published between 2005 and 2024. Studies applying molecular methods for NTM identification and resistance detection in humans were included. Data on study characteristics, diagnostic methods, sample types, sample sizes, identification sensitivity, and drug susceptibility results were extracted. Meta-analysis was performed using R with the meta4diag package. The quality of included studies was assessed using the QUADAS-2 tool. RESULTS: The analysis included 49 studies on NTM identification and 33 studies on antibiotic resistance. For species identification, all evaluated molecular technologies (MALDI-TOF MS, PCR-based methods, Sequencing, DNA chip, and DNA strip) demonstrated high pooled sensitivities (>0.92). Subgroup analysis revealed that sample type significantly affected performance for MALDI-TOF MS. Preliminary analysis of antibiotic resistance rates revealed varying patterns. For slowly growing mycobacteria, a significantly high Ethambutol resistance rate was observed in M. avium (69.20%). Among rapidly growing mycobacteria, resistance to Imipenem was notable (54.22%), and Clarithromycin resistance varied significantly within the Mycobacterium abscessus complex. CONCLUSION: Emerging molecular technologies have revolutionized the methodology for NTM identification with excellent performance. However, their performance can be influenced by sample type, particularly for MALDI-TOF MS. The alarming and heterogeneous antibiotic resistance patterns also highlight the critical need for rapid and accurate species identification and drug susceptibility testing to inform effective therapeutic strategies. Key messagesMolecular technologies demonstrate high accuracy for NTM identification.Antibiotic resistance is a serious concern with variations among NTM species and subspecies.Rapid and accurate species identification and drug susceptibility testing are crucial for guiding effective clinical management of NTM PD.

Humans↗

PubMed2026

Comparison of Iodinated Contrast Doses Based on Total Body Weight and Lean Body Weight in Pediatric Patients: Impact on Image Quality and Contrast Exposure.

INTRODUCTION: Iodinated contrast dosing in pediatric computed tomography (CT) traditionally relies on total body weight (TBW), which may result in excessive contrast administration, particularly in patients with higher adiposity. Lean body weight (LBW)-based protocols have shown promise in adults but remain underexplored in children. Therefore, the aim of this study was to compare contrast volume requirements and hepatic enhancement quality among three dosing protocols: LBW-based, TBW-based, and the Control Group (CG), based on the institutional standard for pediatric abdominal CT. METHODS: This prospective study enrolled 66 patients (age 0-16 years) undergoing contrast-enhanced abdominal CT between September 2023 and August 2024. Patients were randomly assigned to receive iodinated contrast (iobitridol 350mg I/mL) dosed by: (1) LBW (0.63 g iodine/kg x LBW, calculated using Peters formula; n = 23), (2) TBW (0.46 g iodine/kg x TBW; n = 20), or (3) institutional control protocol (2 mL/kg x TBW, equivalent to 0.7 g iodine/kg; n = 23). Kruskal-Wallis, ANOVA, Two-way ANOVA, ANCOVA, Scheirer-Ray-Hare, and Cohen's Kappa tests with Likert scale were used. RESULTS: The LBW group received lower median contrast volumes (27 mL; IQR, 10-80 mL) compared to the TBW group (34.5 mL; IQR, 18-78 mL) and the CG group (40 mL; IQR, 13-80 mL), although the differences did not reach statistical significance (P > 0.05). Notably, this reduction did not compromise hepatic enhancement, which remained comparable to the CG (552 ± 139 HU; P = 0.107). CONCLUSION: Lean body weight may be a useful parameter for estimating contrast dose in pediatric abdominal CT, potentially reducing administered volumes without compromising diagnostic image quality. IMPLICATIONS FOR PRACTICE: These results provide early evidence that LBW-based dosing may support more individualized contrast administration in pediatric CT, potentially reducing exposure-related risks.

Humans↗

PubMed2026

The effects of fitspiration TikTok content on body image and mood among young adult women in the U.S.

Fitspiration is an appearance-based form of media that promotes physical fitness and dieting. While not true of all fitspiration media, some forms promote these ideals through visuals of toned, athletic bodies that have become increasingly prevalent on the short-form video platform, TikTok. Although often framed as health-promoting, fitspiration exposure has been associated with upward appearance comparison (i.e., comparison with people perceived as more attractive) and negative effects on body image and mood. The present study investigated the effects of short-form video-based fitspiration on social comparison, appearance importance, appearance anxiety, body dissatisfaction, and negative affect. Using an experimental design, 150 undergraduate women (Mage = 19.28) in the United States were randomly assigned to view a five-minute TikTok compilation of either animal (n = 75) or fitspiration videos (n = 75). Participants completed baseline measures prior to viewing and state-level measures after completing their video set. Results indicated that, relative to control, viewing fitspiration content led to greater social comparison, appearance concerns, feelings of being fat, and sadness. Baseline appearance concerns and depressive symptoms significantly moderated group differences in responses, such that negative fitspiration effects on state-level appearance concerns were found among individuals high but not low in baseline appearance concerns and among individuals low but not high in baseline depressive symptoms. These findings contribute to the growing literature on fitspiration by demonstrating the immediate psychological effects of this content in short-form videos and highlighting the importance of considering individual differences in 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

Shielding performance and clinical applicability of lead-free materials in computed tomography.

Owing to the high radiation exposure associated with computed tomography (CT) examinations and the image quality degradation caused by conventional radiation shielding materials, this study evaluated the dose reduction performance and image quality maintenance potential of a newly developed lead-free composite shielding material. This material was composed of bismuth, tungsten, tungsten carbide, aluminium, and polyurethane. Phantom-based dose measurements demonstrated that the shielding material achieved dose reduction rates ranging from 17.6% to 37.6%, depending on tube voltage. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and changes in tube current-time product (mAs) under a scout-based automatic exposure control (AEC) protocol were analysed according to the presence or absence of the shielding material across regions. For the clinical evaluation, CT scans were performed on four patients. Furthermore, the images were reviewed to evaluate whether this material affected image quality. The shielding material exhibited radiation reduction levels comparable to those reported in previous studies. SNR and CNR analyses showed minor statistical variations in certain regions; however, most differences were not statistically significant, and even significant differences remained within a range that did not compromise diagnostic image quality. Under the scout-based AEC protocol, the use of the shielding material resulted in less than 1% variation in mAs values. No visually perceptible artefacts or clinically significant image quality degradation were observed. The proposed composite shielding material demonstrated the potential to mitigate some limitations of conventional shielding materials and showed preliminary clinical feasibility as an adjunctive strategy for radiation dose reduction in CT examinations.

Radiation Protection↗

PubMed2026

Advanced mitigation strategies for acrylamide formation in foods: Mechanistic insights, emerging innovations, and future perspectives.

Acrylamide is a heat-induced contaminant formed predominantly in carbohydrate-rich foods during high-temperature processing, posing significant concerns due to its potential carcinogenic, neurotoxic, and genotoxic effects. This review critically examines the mechanisms of acrylamide formation, emphasizing the role of the Maillard reaction and key precursors such as asparagine and reducing sugars, along with the influence of processing conditions including temperature, time, pH, and moisture. Various mitigation strategies are comprehensively discussed, ranging from raw material selection and genetic approaches to enzymatic treatments such as asparaginase and the application of natural and chemical inhibitors. Advances in processing technologies, including optimization of conventional thermal methods and emerging non-thermal techniques such as cold plasma and ultrasound, are evaluated for their effectiveness. The review also highlights the role of food additives, functional ingredients, and fermentation in reducing acrylamide formation. Furthermore, recent developments in analytical techniques, including chromatographic methods, biosensors, and artificial intelligence-based predictive models, are explored for improved detection and control. Risk assessment, toxicological implications, and global regulatory frameworks are also examined. Finally, future perspectives focusing on genetic engineering, personalized nutrition, and digital technologies such as AI and blockchain are discussed to support sustainable and industry-applicable mitigation strategies.

Acrylamide↗

PubMed2026

Imaging techniques for assessing the hand in systemic sclerosis: a systematic review.

BACKGROUND: Systemic sclerosis (SSc) is a rare autoimmune connective tissue disease frequently associated with hand involvement, leading to significant functional impairment. Imaging techniques provide unique opportunities to visualize and quantify structural and functional abnormalities of the hand, supporting diagnosis, monitoring, and treatment evaluation. This systematic review summarizes the imaging techniques used in SSc. METHODS: A systematic search of PubMed and Embase was conducted. Eligible studies included original research articles in English that applied or evaluated imaging techniques of the hands in SSc, published after 2000. Ultrasound and nailfold capillaroscopy were excluded, given their established use. Screening was performed independently by two authors. Findings were synthesized by clinical manifestations, study quality was assessed using the QUADAS-2 tool. RESULTS: Sixty-one studies met the inclusion criteria. In total, 25 distinct imaging techniques were identified, enabling assessment of various hand structures, including vascular involvement, inflammation, fibrosis, calcifications, erosions, and bone marrow edema. Vascular imaging was most extensively studied, particularly in the context of Raynaud's phenomenon and digital ischemia, with multiple techniques demonstrating impaired perfusion and altered thermoregulatory responses. MRI consistently detected subclinical inflammatory and erosive changes of joints and soft tissues,. CT-based techniques provided detailed assessment of calcinosis cutis, while optical and photoacoustic methods showed promise for quantifying skin fibrosis. CONCLUSION: Imaging techniques provide valuable, complementary insights into hand involvement in SSc, often revealing subclinical disease. Despite promising results, limited standardization and longitudinal validation currently restrict clinical implementation. Future studies should focus on harmonizing protocols and validating against clinically meaningful outcomes.

Humans↗

PubMed2026

Diffusion MRI radiomics in meningiomas: imaging correlates of tumor grade and intraoperative consistency.

OBJECTIVE: Despite advancements in imaging studies, the preoperative prediction of the biological behavior and intraoperative consistency of intracranial meningiomas remains limited. This study evaluated the association of volumetric diffusion-based and texture-derived radiomic features extracted from routine MRI with histopathological aggressiveness and intraoperative tumor consistency. METHODS: Ninety-seven intracranial meningiomas resected at two tertiary centers were retrospectively analyzed. Volumetric segmentation was performed on contrast-enhanced T1-weighted MRI and coregistered to apparent diffusion coefficient (ADC) maps. Data on first-order diffusion metrics and selected texture features were collected. The associations between World Health Organization (WHO) grade and Ki-67 index were assessed using nonparametric tests and Spearman correlation analysis. Independent factors associated with intraoperative tumor consistency (Zada grades 1-5) were evaluated via multivariate ordinal logistic regression analysis that adjusted for tumor volume, skull base location, calcification status, and WHO grade. Secondary receiver operating characteristic (ROC) curve analyses were performed to differentiate solid (Zada grades 4-5) from soft (Zada grades 1-2) tumors. ROC analyses were performed within the study cohort and were intended as exploratory assessments of discriminative performance. RESULTS: The mean ADC (ADCmean) and the 10th percentile of the ADC decreased significantly with increasing WHO grade (p < 0.001). ADCmean had a moderate inverse correlation with the Ki-67 index (r = -0.42, p < 0.001) and intraoperative tumor consistency (r = -0.45, p < 0.001). In the multivariate analysis, the ADCmean remained independently associated with increasing tumor firmness. Each 0.1 × 10-3 mm2/sec increase corresponded to a 38% reduction in the odds of belonging to a higher consistency category (OR 0.62, 95% CI 0.51-0.74, p < 0.001). The ROC analysis showed good discrimination for solid tumors (area under the curve 0.847, 95% CI 0.742-0.953) and soft tumors (area under the curve 0.824, 95% CI 0.714-0.935). Texture features had weaker associations with intraoperative tumor consistency. CONCLUSIONS: Volumetric diffusion-derived metrics, particularly ADCmean, are associated with both histopathological aggressiveness and intraoperative tumor firmness in meningiomas. Diffusion imaging may reflect a graded microstructural continuum rather than a purely dichotomous property, providing complementary preoperative insights into surgical complexity.

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

Predicting ACL injury risk in athletes: A systematic review of machine learning-based models.

BACKGROUND: Early ACL injury risk identification in athletes is essential. This systematic review examines machine learning (ML) models for predicting ACL injuries, evaluating their methodological quality, performance, and reliability. METHOD: A comprehensive electronic search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore databases, supplemented by Google Scholar for grey literature, covering articles published between January 1, 2015, and August 30, 2025. Eligible studies were appraised using the Prediction Model Study Risk of Bias Assessment Tool (PROBAST) for methodological quality and risk of bias, and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines for quality of evidence. RESULTS: Ten studies were included. PROBAST showed eight studies had moderate risk of bias and two low risk. TRIPOD found only two studies met quality criteria. ML models included logistic regression (n = 5), support vector machines (n = 4), k-nearest neighbor (n = 3), decision trees (n = 3), random forests (n = 5), neural networks (n = 2), linear discriminant analysis (n = 1), and pre-trained CNNs (n = 1). AUC ranged from 0.63 to 0.98. Accuracy (reported in six studies) ranged from 26% to 95%; however, these values should be interpreted with caution due to the absence of confidence intervals, lack of class imbalance handling, and limited external validation across studies. Tree-based ensemble methods such as random forest achieved competitive accuracy (74-86%), while SVM, a non-ensemble classifier, reported accuracy ranging from 71% to 95%; however, the highest values were obtained in studies with notably small sample sizes (n = 12 to n = 39), raising concerns about overfitting and generalizability. CONCLUSION: Current ML algorithms show promise for identifying athletes at high ACL injury risk and detecting relevant risk factors. Although study quality was generally satisfactory, future research should prioritize external validation and model interpretability to support clinical translation.

Humans↗

PubMed2026

Assessing AI literacy and attitudes among medical students: implications for integration into healthcare practice.

PURPOSE: This study aims to assess AI literacy and attitudes among medical students and explore their implications for integrating AI into healthcare practice. DESIGN/METHODOLOGY/APPROACH: A quantitative research design was employed to comprehensively evaluate AI literacy and attitudes among 374 Lusaka Apex Medical University medical students. Data were collected from April 3, 2024, to April 30, 2024, using a closed-ended questionnaire. The questionnaire covered various aspects of AI literacy, perceived benefits of AI in healthcare, strategies for staying informed about AI, relevant AI applications for future practice, concerns related to AI algorithm training and AI-based chatbots in healthcare. FINDINGS: The study revealed varying levels of AI literacy among medical students with a basic understanding of AI principles. Perceptions regarding AI's role in healthcare varied, with recognition of key benefits such as improved diagnosis accuracy and enhanced treatment planning. Students relied predominantly on online resources to stay informed about AI. Concerns included bias reinforcement, data privacy and over-reliance on technology. ORIGINALITY/VALUE: This study contributes original insights into medical students' AI literacy and attitudes, highlighting the need for targeted educational interventions and ethical considerations in AI integration within medical education and practice.

Students, Medical↗

PubMed2026

Engineered MXene-based nanozyme platform: NIR-II photothermal and dual enzyme-mimetic potentiated chemodynamic synergy for precision tumor eradication.

The antioxidant defense barrier in the tumor microenvironment, particularly glutathione (GSH), considerably restricts the therapeutic efficacy of chemodynamic therapy (CDT). Moreover, CDT generally exhibits relatively mild therapeutic efficacy owing to its intrinsic reaction kinetics, making it difficult to achieve complete tumor eradication within a short time. To address these issues, we construct a functionalized nanotherapeutic platform, Nb2CTx@Ru-PEG2000-FA (NCRPF), for tumor photothermal ablation and enhanced CDT resulting from GSH depletion. NCRPF possesses three key advantages: 1. Efficient near-infrared II photothermal conversion (η = 42.08%), raising the tumor temperature above 45 °C within 90 s for rapid ablation; 2. Dual peroxidase-like and glutathione peroxidase-like activities, simultaneously depleting GSH and generating a burst of ·OH to eliminate residual tumors; 3. Targeted tumor accumulation with 2.9-fold higher efficiency than passive diffusion. Both in vitro and in vivo results confirm that this combined strategy achieves complete tumor eradication with favorable biosafety. Collectively, the NCRPF nanotherapeutic system provides a powerful new paradigm with high translational potential for the complete eradication of breast cancer.

Animals↗

PubMed2026

Probiotic-derived extracellular vesicles as food-based nanocarriers: Mechanisms, functional applications, and future perspectives in food systems.

Probiotic-derived extracellular vesicles (PDEVs) are a promising type of postbiotic nanoparticle derived by fermentation of probiotics, and have gained growing interest as a potential application in food science and nutrition. These are lipid bilayer vesicles of nanoscale, which are naturally released by probiotic cells and contain a wide variety of bioactive molecules, such as proteins, nucleic acids, and metabolites. Moreover, PDEVs are highly stable, biocompatible, and can be easily engineered to have surfaces with high functionality, which makes them good candidates in functional engineering. In contrast to traditional live probiotics, PDEVs overcome the difficulties of preserving microbial viability during processing and storage, thus providing superior safety, stability, and predictable biological performance. This is a systematic review of the various functions of PDEVs in food systems. We conclude on the processes through which PDEVs control intestinal barrier integrity, alter gut microbiota composition, and alter host immune responses, and their potential to enhance gut health when added to functional foods. In addition to their health-promoting effects, PDEVs have shown significant potential as natural antimicrobial agents to preserve food and as effective nanocarriers of hydrophobic bioactive compounds, including fucoxanthin, to improve their stability, bioavailability, and targeted delivery. Moreover, PDEVs can be used as new regulators of microbial fermentation. However, it should be noted that a lot of the evidence that is available is still preliminary and the effectiveness of these applications in real food-processing and storage conditions has not been fully proven. Although they have potential, there are a number of challenges that still hinder the widespread use of PDEVs in the food industry. These involve the creation of scalable and cost-effective production processes, batch-to-batch consistency, vesicle stability in a variety of food matrices, and regulatory and safety considerations. Other emerging engineering approaches, such as surface functionalization and cargo loading, are also discussed in this review and could further increase the specificity, functionality, and application versatility of PDEVs in food systems. Moving forward, the incorporation of PDEVs into the next generation functional foods, novel food preservation methods, and customized nutrition plans should be prioritized in future studies. Further developments in these fields can make PDEVs useful platforms at the interface of food microbiology, nanotechnology, and human health.

Probiotics↗

PubMed2026

Brain network alterations underlying cue reactivity and craving in abstinent methamphetamine users: a systematic review of functional MRI findings.

BACKGROUND: Methamphetamine use disorder (MUD) is marked by intense craving and high relapse risk, often triggered by drug-related cues. Functional magnetic resonance imaging (fMRI) provides key insight into the neural basis of this cue reactivity, implicating large-scale brain networks for reward, motivation, and control. Yet, findings remain inconsistent across studies due to differences in task design, abstinence duration, and participant characteristics. OBJECTIVE: This systematic review synthesises evidence on how abstinence influences brain network alterations underlying cue reactivity and craving in methamphetamine users, integrating task-based and resting-state fMRI findings within leading neurobiological models of addiction. METHODS: A systematic search of PubMed, Scopus, Web of Science, and Ovid was conducted up to August 10, 2025, following PRISMA 2020 guidelines. Eligible fMRI studies examined cue reactivity or craving in abstinent methamphetamine users. Data were extracted on activation, connectivity, and brain-behaviour associations, and synthesised narratively. RESULTS: Task-based studies revealed heightened activation across reward, salience, and control networks during cue exposure, which diminished as parietal and executive control systems re-engaged with longer abstinence. Resting-state findings showed disrupted intrinsic connectivity among default mode, salience, and frontoparietal networks, reflecting persistent imbalances linked to craving and use severity. CONCLUSION: fMRI evidence shows that MUD is marked by network-level disruption linking reward, salience, and control systems. Task-based findings reveal strong cue reactivity in reward circuits, while resting-state data show persistent imbalance among default mode and control networks. With abstinence, partial restoration of network integrity emerges, highlighting both vulnerability and opportunities for targeted, recovery-based interventions.

Humans↗

PubMed2026

Educational Effects of Electronic Documents and Videos on Parents' Responses to Acute Illness in Young Children: A Randomized Controlled Trial.

AIM: This study compared changes associated with electronic document-based and video-based education for parents responding to acute illness in young children, focusing on self-reported knowledge, anxiety, and satisfaction. METHODS: A randomized controlled trial with pre- and post-intervention measurements was conducted among 140 adults in Japan who self-reported raising a child under 3 years of age and having experienced their child's acute illness. Participants were assigned to an electronic document group or a video group (n = 70 each). Self-reported knowledge was assessed using a researcher-developed questionnaire, and anxiety was measured using the State-Trait Anxiety Inventory. Pre-post changes and between-group differences in change scores were examined. RESULTS: Total self-reported knowledge scores increased significantly in both groups (p < 0.01), with no significant between-group difference. The video group showed significant improvements in items related to symptoms requiring attention at home and information sources, whereas the electronic document group improved in items related to symptoms requiring medical consultation and emergency calls. State and trait anxiety did not change significantly in either group. Satisfaction was high in both groups. CONCLUSIONS: Both educational formats may support parents' learning about responses to acute illness in young children, although appropriate formats may differ according to the educational content. Information provision alone may have limited effects on anxiety; therefore, future parent education should incorporate interactive and reassurance-focused approaches. TRIAL REGISTRATION: UMIN-CTR: UMIN000056457.

Humans↗