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PubMed2026

Fungal drivers of mycotoxin contamination in wheat: Early warning and plasma-based control.

Mycotoxin contamination in wheat is a major food safety concern; however, quantitative evidence linking fungal community signals, mycotoxin exceedance risk, and wheat quality traits in naturally contaminated wheat remains limited. In this study, wheat samples were collected from mycotoxin-prone monitoring sites under unusually rainy conditions in 2022 to explore early-warning indicators and post-harvest mitigation strategies. According to the National Food Safety Standard of China GB 2761-2017, aflatoxin B1 (AFB1), deoxynivalenol (DON), and zearalenone (ZEN) exceeded the maximum limits in 52.24, 47.76, and 23.88% of samples, respectively; 38.81% exceeded the reference EU threshold for T-2 toxin, and 46.27% showed co-contamination with at least two mycotoxins above their respective thresholds. Although Alternaria, Cladosporium, and Epicoccum dominated the fungal community, Fusarium abundance was significantly associated with DON contamination and Fusarium-damaged kernels (FDKs). Mediation analysis identified DON as a significant mediator linking Fusarium abundance to FDKs, accounting for 68.41% of the total effect. In addition, Fusarium abundance above 3.70% showed strong predictive performance for DON exceedance, with an area under the curve of 0.906, indicating its potential as an early-warning indicator. Culture-based assays confirmed the toxigenic potential of Aspergillus and Fusarium isolates under simulated temperature and moisture conditions. After optimization using a toxin-spiked wheat flour model, dielectric barrier discharge cold plasma degraded AFB1, DON, and ZEN by 29.30-35.68%, disrupted the morphology of toxigenic fungi, and did not significantly affect wheat quality. This study provides practical insights into mycotoxin risk warning and post-harvest mitigation in wheat.

Triticum↗

PubMed2026

Effects of exercise snacking on neuromuscular performance in insufficiently active adults: A systematic review and meta-analysis.

OBJECTIVE: To examine the effects of exercise snacking (ES) on neuromuscular performance in insufficiently active adults. METHODS: Six databases were searched from inception to July 17, 2026. Randomized controlled trials and non-randomized studies of interventions involving insufficiently active adults undertaking ES interventions were included. Outcomes were functional performance, muscular strength, and muscular power. A three-level random-effects meta-analysis was performed. Risk of bias was assessed with RoB 2 or ROBINS-I, and certainty of evidence was evaluated using GRADE. RESULTS: Nine studies (13 reports) involving 313 participants were included. In the primary three-level model, ES showed a small positive overall effect on neuromuscular performance (g = 0.37, 95% CI 0.16-0.58, p = 0.001), which remained supported under trial-clustered robust inference with small-sample adjustment. Domain-specific estimates for muscular power/velocity, functional performance, and muscular strength were positive but imprecise and were not statistically supported after small-sample robust adjustment. Robust moderator tests provided no evidence of between-subgroup differences, and the certainty of evidence was low for all outcomes. CONCLUSION: Current low-certainty evidence suggests that ES may produce a small improvement in overall neuromuscular performance. However, domain-specific effects remain uncertain because of the limited number of independent trials, and the overall prediction interval crossed zero, indicating uncertainty across future populations and settings. Larger, preregistered randomized controlled trials are needed to confirm these preliminary findings.

Adult↗

PubMed2025

An integrated multiscale air quality modelling framework for industrial park pollution: Linking local emissions to regional transport.

Capturing the spatiotemporal distribution of pollutants in industrial parks remains challenging for regional air quality models because of their coarse resolution (3 km), resulting in uncertainties in local emission quantification. To address this, we developed the Integrated Multiscale Air Quality Modelling System for Industry (IAQMS-Industry), coupling the regional Nested Air Quality Prediction Modelling System (NAQPMS) with a city-scale chemical transport model. This framework integrates point-source locations and Gaussian plume dispersion to simulate particulate matter with a diameter smaller than 2.5 micrometres (PM2.5) at 100 m resolution. Applied to the Beijing Yi Zhuang and Tangshan industrial parks and evaluated against observations. The coupled model achieved a normalized mean bias (NMB) ranging from 3.1 % to 6.2 %, improving upon NAQPMS (-16.9 % to -7.7 %). Spatial analysis revealed that coarse regional grids underestimated the PM2.5 concentrations at industrial sites by smoothing gradients, whereas IAQMS-Industry successfully resolved spatial patterns. Industrial point emissions accounted for 22.9 %-26.4 % of PM2.5 in the coupled model, which was significantly greater than the regional model estimates of 1.6 %-13.7 %. These findings indicate that regional models overestimate pollutant dispersion processes in industrial parks while underestimating local industrial impacts. By explicitly resolving point-source dynamics and linking them to regional transport, IAQMS-Industry provides a robust tool for designing targeted emission controls in industrial cities and balancing local air quality improvements with minimized regional pollution outflow. This study underscores the necessity of multiscale modelling for accurate source apportionment and informed environmental governance in industrial zones.

Air Pollution↗

PubMed2026

Artificial intelligence-supported double reading in European population breast cancer screening: A systematic review and meta-analysis of prospective programs.

BACKGROUND: Most European population mammography screening programs rely on double reading with arbitration, a model that delivers mortality benefit but is increasingly challenged by radiologist workload, variable specificity, and interval cancers. Artificial intelligence (AI) is being evaluated to support or optimize these established European screening pathways. PURPOSE: To synthesize prospective or program-embedded evaluations of AI conducted within European-style population screening programs and to estimate exploratory program-level absolute risk differences (RDs) per 1000 examinations for cancer detection rate (CDR) and recall. MATERIALS AND METHODS: We performed a prespecified, focused evidence synthesis of three large studies embedded within routine population screening programs operating under European-relevant workflows: MASAI (randomized AI-supported risk triage within a national program), ScreenTrustCAD (prospective paired-reader evaluation with AI as an independent reader in a double-reading framework), and PRAIM (nationwide decision-referral implementation). Outcomes were harmonized as AI-control RDs per 1000 examinations. Random-effects pooling used Hartung-Knapp-Sidik-Jonkman models. For the paired-reader design, sensitivity analyses applied a Kish effective sample-size approach across plausible within-examination correlations (ρ = 0.3-0.8). Positive predictive value (PPV) and workflow/time outcomes were summarized descriptively. RESULTS: Across 597,419 examinations, the pooled CDR RD was +0.9 per 1000 (95% CI -0.0 to +1.8; I2 ≈ 12%), consistent with a modest directional increase with borderline statistical uncertainty. The pooled recall RD was -0.6 per 1000 (95% CI -3.1 to +2.1; I2 ≈ 41-43%), indicating no consistent recall increase across screening programs. Where reported, PPV was higher with AI-supported screening. Efficiency signals included 44.3% fewer total readings in MASAI and shorter reading times for AI-normal examinations in PRAIM; in PRAIM, a program-level safety-net mechanism recovered 204 cancers that would otherwise have been missed. CONCLUSION: In European population screening programs characterized by double reading and arbitration, prospective program-embedded evidence suggests that AI integration may yield a small absolute increase in cancer detection (≈1/1000) without a consistent increase in recall, alongside improved PPV and efficiency signals. These findings suggestAI primarily as a complementary reader within European screening workflows, with implementation requiring explicit quality assurance and monitoring of interval cancers and stage distribution.

Humans↗

PubMed2026

Multi-omic biomarkers in cardiovascular disease: Discovery to clinical translation.

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, necessitating improved risk stratification and early detection strategies. Multiomics approaches that integrate genomics, transcriptomics, proteomics, metabolomics, and epigenomics offer unprecedented opportunities for biomarker discovery and precision medicine in cardiovascular care. This narrative review examines the current landscape of multiomics biomarkers for CVD, tracing their evolution from discovery to clinical translation. We synthesize evidence from recent studies evaluating the clinical utility of integrated omics approaches across diverse cardiovascular conditions, including atherosclerotic cardiovascular disease, heart failure, and atrial fibrillation. High-throughput proteomics has identified novel protein signatures that enhance cardiovascular risk prediction beyond traditional risk factors. Metabolomics has revealed pathway-specific biomarkers, including trimethylamine N-oxide and lipid species, associated with atherogenesis. Polygenic risk scores derived from genomic data demonstrate incremental value when combined with clinical risk scores. Multiomics biomarkers represent a transformative approach to cardiovascular risk assessment and disease management.

Humans↗

PubMed2026

Genetic overlap between estimated glomerular filtration rate and cardiovascular disease identifies potential targets for cardiorenal syndrome.

Heart and kidney diseases frequently coexist, but the genetic basis of this relationship remains unclear. We analyzed genetic data from large-scale studies to investigate how kidney function (estimated glomerular filtration rate, eGFR) and six common cardiovascular diseases share genetic risk factors. Using MiXeR method, and conjunctional false discovery rate (conjFDR) to identify overlapping genetic regions, we found 478 shared genomic loci between eGFR and cardiovascular diseases. These shared genes are involved in tissue development and structure. We also identified 29 genes that could be targeted by existing medications approved by the US Food and Drug Administration, such as PRKAG2, PDE1A, and IGF1R. Among these, genetically predicted higher level of IGF1R expression is associated with a higher eGFR, which reflects good kidney function and is protective against cardiorenal diseases, such as atrial fibrillation, and myocardial infarction. These findings reveal genetic overlap between kidney function and cardiovascular diseases, highlighting potential targets for understanding and treating cardiorenal syndrome.

Humans↗

PubMed2026

Artificial intelligence-derived myocardial fibrosis on cardiac magnetic resonance for prognosis in cardiomyopathy: A systematic review of a sparse evidence base.

BACKGROUND: Myocardial fibrosis on cardiovascular magnetic resonance (CMR), assessed by late gadolinium enhancement (LGE) and parametric mapping, is an established predictor of adverse events in cardiomyopathy. We assessed whether artificial intelligence (AI) quantification of fibrosis adds independent prognostic value. METHODS: We searched six databases, a clinical-trials register, and a preprint server from inception to 13 June 2026. Eligible studies used AI to generate a fibrosis marker in adults with ischemic or nonischemic cardiomyopathy, with covariate-adjusted outcomes over ≥12 months. Risk of bias was assessed using PROBAST, PROBAST+AI, and QUIPS. Fewer than three comparable studies precluded meta-analysis; certainty was rated using GRADE. RESULTS: Of 448 records (381 after de-duplication), 18 full texts were reviewed and two included, one peer-reviewed and one preprint. In an ischemic-cardiomyopathy registry (Ghanbari et al.; n = 216 analytic, 26 events), AI-derived dense LGE scar predicted arrhythmic events (univariable hazard ratio [HR] 2.35, 95% CI 1.33-4.15), and AI-derived but not manual scar improved discrimination beyond guideline criteria (area under the curve 0.63 to 0.68; p = 0.02). In a nonischemic dilated-cardiomyopathy preprint (Kim et al.; n = 347, 119 events), automated extracellular volume ≥30% predicted cardiovascular death or heart-failure hospitalization (adjusted HR 2.00, 95% CI 1.32-3.03). Both were at high risk of bias, with data-derived thresholds and no external validation. CONCLUSIONS: Across only two studies, AI-derived fibrosis was independently associated with adverse cardiovascular events, but its added value over manual quantification remains unproven. Certainty was very low. The evidence base is sparse and not yet ready for clinical use.

Humans↗

PubMed2026

Efficacy of the NMIC-150 system in identifying extended-spectrum beta-lactamases in clinical isolates.

Extended-spectrum beta-lactamases (ESBLs) are significant contributors to the growing global crisis of antimicrobial resistance. This study evaluated the performance of the NMIC-150 System for susceptibility testing of third-generation cephalosporins (3GCs) and assessed whether ceftazidime-avibactam and aztreonam-avibactam could identify ESBL-producing carbapenem-resistant Enterobacterales (CREs). A total of 278 non-duplicate clinical isolates (Klebsiella pneumoniae, E. coli, and Proteus mirabilis) were analyzed. Antimicrobial susceptibility was determined using reference broth microdilution (BMD) and the NMIC-150 System. ESBL production was defined as an ≥eight-fold reduction in the minimum inhibitory concentration (MIC) of 3GCs in the presence of clavulanic acid, according to CLSI criteria. Whole-genome sequencing was performed to characterize ESBL and carbapenemase genes among 3GC-resistant isolates. A Random Forest model was used to predict ESBL-producing isolates based on MIC values. The NMIC-150 System demonstrated over 90% categorical and essential agreement with BMD for ceftazidime and ceftriaxone, along with robust predictive performance via Random Forest analysis. These findings suggest that the NMIC-150 System is a reliable platform for 3GC susceptibility testing and that an ≥eight-fold MIC reduction with ceftazidime-avibactam or aztreonam-avibactam may serve as a phenotypic indicator of ESBL production in CRE isolates. In conclusion, the NMIC-150 System shows potential for routine antimicrobial resistance surveillance and may facilitate the rapid identification of ESBL-producing CREs in clinical settings.

Microbial Sensitivity Tests↗

PubMed2026

A versatile reversed-phase liquid chromatography charged aerosol detection method for streamlined monitoring of QS-21 content and stability in liposomal adjuvant formulations.

Identifying and quantifying an active adjuvant along with its degradants in drug formulations is essential for ensuring the safety and efficacy of the drug product. QS-21 is a potent adjuvant that is being evaluated in several clinical trials and is currently formulated in licensed vaccines that protect against shingles, malaria, and RSV. In aqueous environments, QS-21 is subject to hydrolytic degradation that is influenced by pH and temperature, resulting in the formation of a degradant known as QS-21 Hydrolyzed Product, QS-21 HP, which can occur during manufacturing and/or prolonged storage. The intact QS-21 and QS-21 HP induce distinct immune response profiles, making it critical to monitor the degradation of QS-21 in vaccine adjuvant formulations. To date, there has been a paucity of reliable assays for QS-21, its isomers, and degradant QS-21 HP in liposomal adjuvant formulations available that can be transferred seamlessly in quality control (QC) environments. Herein, we introduce a simple and QC-friendly liquid chromatography coupled to a charged aerosol detector (LC-CAD) enabled by stationary phase screening combined with in silico method development optimization. The method exploits 2.7 μm fused-core phenyl hexyl particles, ensuring its versatility in standard and ultra-high pressure LC systems. This approach demonstrates a high correlation between predicted retention time (RT) and experimental outcomes with overall ∆RT < 4%. In addition, this assay shows great linearity, precision, specificity, and accuracy to advance process development characterization of new vaccine formulations.

Liposomes↗

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

HPV circulating tumor DNA as a potential prognostic and predictive biomarker in head and neck squamous cell carcinoma: a systematic review.

PURPOSE: Human papillomavirus circulating tumor DNA (HPVctDNA) has emerged as a promising prognostic biomarker in HPV-related head and neck squamous cell carcinoma (HNSCC). This systematic review aimed to synthesize current evidence on the diagnostic accuracy and prognostic value of HPVctDNA in HNSCC management. MATERIAL/METHODS: We systematically reviewed a PubMed-indexed database of studies published between January 2012 and September 2025. Eligible studies were assessed for design, primary tumor site and stage, treatment modality, HPVctDNA detection method, diagnostic accuracy (sensitivity and specificity), and reported clinical endpoints. Descriptive syntheses were performed; sensitivity and specificity were standardized to proportions and summarized as median values per group. RESULTS: A total of 60 studies, including 8,234 patients were analyzed, of which 41 (68.3%) focused exclusively on oropharyngeal squamous cell carcinoma (OPSCC) and 17 (28.3%) included mixed HPV-related HNSCC subsites and HPV-positive cancers of unknown primary. The median follow-up across the included studies was 23 months. Among the included studies, 19 were retrospective (31.7%) and 33 were prospective (55.0%), with a small proportion of cross-sectional and randomized clinical trials. Overall, 40 (66.7%) evaluated the role of HPVctDNA in a curative setting. Plasma was the most common sample type, analyzed in 55 studies (91.7%), while 5 studies also included saliva. Detection methods varied: 40 employed droplet digital PCR (ddPCR), 16 used quantitative PCR (qPCR) and 4 applied NGS-based assays. Most of these studies (38, 63.3%) evaluated the prognostic utility of HPVctDNA, while only 4 (6.7%) assessed HPVctDNA in a screening or diagnostic setting. Regarding diagnostic accuracy, the median sensitivity across evaluable studies was 91.1%, while the median specificity was 99.4%. In OPSCC-only cohorts, the median sensitivity and specificity were 89.4% and 99.4%, respectively. Dynamic changes in HPVctDNA levels during or after treatment were consistently associated with outcomes: clearance or sustained negativity correlated with higher response rates, improved progression-free survival and overall survival, while persistent positivity or increasing levels predicted disease progression and recurrence. CONCLUSIONS: HPVctDNA demonstrates high diagnostic and prognostic accuracy in HPV-related HNSCC, especially OPSCC, supporting its use for prognosis, treatment monitoring and early detection of recurrence. However, prospective interventional studies are still required to demonstrate that HPVctDNA-guided treatment decisions improve clinical outcomes before routine implementation.

Humans↗

PubMed2026

Prognostic effect of serum glial fibrillary acidic protein and neurofilament light chain for predicting progression independent of relapse activity in multiple sclerosis: A systematic review.

BACKGROUND: Progression independent of relapse activity (PIRA) is increasingly appreciated as one of the important factors contributing to disability accumulation in MS. sGFAP and sNfL could represent markers reflecting two separate biological processes related to relapse-independent progression in MS. OBJECTIVE: To perform a systematic review of the literature on blood GFAP and/or NfL measured in relation to PIRA or other similar relapse-independent progression endpoints in people with MS. METHODS: PubMed, Scopus, and Web of Science databases were searched from inception to 1 June 2026. The eligible studies were original human studies measuring blood GFAP and/or NfL concentrations in serum, plasma, or any other type of blood-derived material and assessing PIRA, PIRMA, CDP/CDW without relapses, relapse-free EDSS progression, non-inflammatory progression, or comparable relapse-independent disability worsening outcomes. Methodological quality was assessed according to the Newcastle-Ottawa scale and the QUIPS instrument for bias detection in the body of evidence on prognostic factors. Due to heterogeneity of outcomes, biomarker measurements and effect estimates, results were synthesized qualitatively rather than quantitatively. RESULTS: After removing duplicates, 1206 records were screened, followed by full-text review of 120 reports. A total of 18 reports were included. Overall, sGFAP was associated more frequently with PIRA or PIRA-like disability progression, particularly in cohorts with suppressed or limited overt inflammatory activity. Evidence for sNfL was more variable and context-dependent: several studies reported associations with PIRA-like or relapse-independent disability worsening when acute inflammatory activity was absent, suppressed, or analytically separated, whereas other studies reported negative or inconclusive findings. Negative or inconclusive results were reported by several articles, particularly when broad outcomes were evaluated or the study population was small. CONCLUSION: Blood GFAP and NfL give complementary but non-interchangeable information concerning PIRA in MS patients. The existing evidence base does not allow us to perform meta-analysis because of heterogeneity in terms of outcomes, standardization of biomarkers, and treatment context. Further prospective investigations with uniform criteria will be necessary for their use as biomarkers of PIRA in clinical settings.

Humans↗

PubMed2026

Integrated photoelectrocatalytic reduction and oxidation processes to achieve efficient degradation of fluoxetine in pharmaceutical wastewater.

Fluorinated organic compounds have been frequently detected in aquatic environments, with the widespread use of fluorinated drugs. The existing processes of urban sewage treatment plants are difficult to completely remove these pollutants containing the persistent C-F bonds. In this work, an integrated system of UV-activated sulfite and UV-assisted electrochemical oxidation was innovatively constructed for efficient degradation of fluoxetine. For the UV-activated sulfite unit system, when the sulfite dosage was 0.5 mmol/L and the initial pH was about 10, the defluorination efficiency of 5 mg/L fluoxetine wastewater under nitrogen atmosphere was about 98 %. Subsequently, the UV-assisted electrochemical oxidation unit system was employed to treat the reduced wastewater mentioned above. When the sodium chloride dosage was 25 mmol/L, the initial pH was about 5, and the current density was 30 mA/cm2, the total organic carbon (TOC) removal of the wastewater arrived at 65 %. Active species capture experiments and ESR tests confirmed that hydrated electrons, hydroxyl, and chlorine radicals were the main components for the efficient degradation of fluoxetine. According to the analysis of Fukui function and HPLC-MS, the degradation pathway of pollutants was proposed including defluorination and mineralization. Meanwhile, the toxicity of intermediates was predicted using the ECOSAR program. In addition, the verification test of actual wastewater treatment indicated that the defluorination and TOC removal efficiency of fluorouracil by the integrated system were similar to those for fluoxetine. This work provided a new approach for the efficient degradation of fluorinated organic pollutants in pharmaceutical wastewater.

Fluoxetine↗

PubMed2026

Alcohol use disorder and childhood adversity in the association between polygenic risk and suicidality.

OBJECTIVE: Suicidal ideation (SI) and suicide attempt (SA) are both influenced by genetic, behavioral, and environmental factors. Alcohol use disorder (AUD) and adverse childhood experiences (ACEs) may mediate or moderate the effects of genetic liability for suicidality. METHODS: Using data from 10,275 participants (43.8% female; 47.2% African-like genetic ancestry [AFR], 52.8% European-like genetic ancestry [EUR]), we tested whether polygenic scores (PGS) for SI and SA predicted lifetime suicidality outcomes. We evaluated whether AUD partially accounted for these associations and ACEs moderated the direct and indirect associations. RESULTS: The SA PGS was significantly associated with SA (AFR: b = 0.36, SE = 0.01; EUR: b = 0.17, SE = 0.01; both ps < 2e-16), but the SI PGS was not associated with SI (p > 0.55). AUD statistically mediated the association between the SA PGS and SA, accounting for approximately 2% of the total association in AFR individuals and 10% in EUR individuals (both ps < 2e-16). Notably, the proportion of the association that was accounted for by AUD decreased as ACEs exposure increased, from 4.30% to 0.54% in AFR individuals and from 13.31% to 3.44% in EUR individuals. In contrast, there was only very modest mediation and no moderated mediation for SI. CONCLUSIONS: Particularly among individuals with lower ACEs exposure, AUD accounted for a meaningful proportion of the association between genetic liability to SA and lifetime SA. These findings highlight different correlates across suicidality phenotypes and suggest potential clinical relevance for AUD in the association between genetic liability and SA.

Adult↗

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

Mobile health apps improve Health-Related Quality of Life in Type 2 Diabetes Mellitus by enhancing medication adherence: A multicentre randomised controlled trial with mediation analysis.

AIMS: This study evaluated whether a gamified mHealth application (CareAide®) improves Health-Related Quality of Life (HRQoL) in Type 2 Diabetes Mellitus (T2DM) and whether this effect is mediated by medication adherence. METHODS: Prespecified secondary analysis of the T2DM cohort from a 6-month multicentre RCT (NCT06068309; N = 663; three Malaysian hospitals). Participants were randomised 1:1 to standard care or CareAide®. Adherence (MMAS-8), EQ-5D-5L utility (Malaysian value set), and AQoL-6D were assessed at baseline and 6 months. Simple mediation analysis (PROCESS Model 4; 5000 bootstraps) adjusted for baseline HRQoL. RESULTS: CareAide® significantly predicted higher MMAS-8 scores (mean difference +1.756; d = 1.638; p < 0.001). Higher MMAS-8 scores significantly predicted improved AQoL-6D utility (b = 0.024; p < 0.001). The direct effect on AQoL-6D was non-significant (p = 0.248). Bootstrapped indirect effect confirmed full mediation via AQoL-6D (0.042; 95% CI [0.024, 0.060]). A sensitivity analysis adjusting for baseline HbA1c confirmed full mediation (indirect = 0.034; 95% CI [0.015, 0.052]; n = 563). EQ-5D-5L utility showed a significant direct between-group difference at 6 months (p = 0.012) but did not operate as a mediation outcome. CONCLUSIONS: Medication adherence fully mediates the AQoL-6D HRQoL benefit of a gamified mHealth intervention in T2DM, as confirmed by both the primary and HbA1c-adjusted sensitivity analyses. These findings support integration of behaviourally informed digital adjuncts into routine primary diabetes care.

Humans↗

PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗

PubMed2026

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↗