The One Health resistome: Integrating environmental, microbial, and human antimicrobial resistance surveillance and risk analysis in the digital age.

The contribution of late-life vascular risk factors to dementia risk remains controversial. Because low blood pressure (BP) has been associated with worse clinical outcomes in frail individuals, we hypothesized hypertension, but not diabetes or smoking, is associated with higher dementia risk in robust than in frail older adults. We performed a prospective cohort analysis of the Atherosclerosis Risk in Communities Neurocognitive Study (ARIC-NCS) over 11 years (2011-2022). We included all community-living White and Black participants aged 67-89 years without dementia at baseline visit 5 (2011-2013) from ARIC-NCS field centers (Jackson, Mississippi; Forsyth County, North Carolina; Washington County, Maryland; Minneapolis suburbs, Minnesota). The primary vascular risk factors measured at baseline included elevated BP (systolic BP 120-129 mm Hg and diastolic BP < 80 mm Hg), hypertension (systolic BP ≥ 130 mm Hg, diastolic BP ≥ 80 mm Hg, or use of medication for BP), diabetes (fasting glucose ≥126 mg/dL, nonfasting glucose ≥200 mg/dL, self-reported physician's diagnosis, or use of diabetes medication), and former and current smoking (self-reported). We defined frailty status using the Fried criteria (5 components that include low energy, low physical activity, slowness, weakness, and weight loss). We estimated cause-specific hazard ratios (HRs) of incident dementia (ascertained from in-person neuropsychological assessments, semiannual participant or informant report, or surveillance of claims from hospitalizations and death certificates) with Cox proportional hazards models including a multiplicative interaction between vascular factors and frailty. HRs of dementia were then stratified by frailty (robust [no frailty components present] vs prefrail/frail [at least 1 frailty component present]). There were 377 (15.8%) dementia cases in robust participants (n = 2,383; mean age, 74.2 years; 55.4% female) and 812 (30.0%) in prefrail/frail participants (n = 2,710; mean age 76.4 years; 61.3% female). There was a significant interaction between BP and frailty on dementia risk (p = 0.026). For robust participants, HRs were 1.03 (95% CI 0.65-1.64) for elevated BP and 1.39 (95% CI 1.00-1.94) for hypertension relative to normal BP. For prefrail/frail participants, HRs were 0.68 (95% CI 0.49-0.95) and 0.82 (95% CI 0.66-1.01), respectively. Diabetes and current smoking were associated with higher dementia risk in both robust and prefrail/frail individuals. Late-life hypertension was associated with a lower relative risk of dementia in prefrail/frail participants, but the association was positive in robust participants. BP interpretation and management to support brain health in older adults could consider age-related functional status.

ABSTRACT Background Chronic fatigue (CF) is increasingly prevalent, yet its role as an independent risk factor for cerebrovascular and neurodegenerative diseases remains underexplored. Objective This study aimed to prospectively assess the association between CF and the risks of Parkinson's disease (PD), Alzheimer's disease (AD), cerebral infarction (CI), and all-cause mortality (ACM). Methods We utilized data from 167,098 UK Biobank participants (aged 40–69) with baseline information on CF (≥6 months). Cox proportional hazards models with three tiers of covariate adjustment (demographics, lifestyle, comorbidities) were used to examine associations with incident PD, AD, CI, and ACM. Fine-Gray competing risk models accounting for death were also employed for PD, AD, and CI. Results CF was independently associated with significantly increased risks of PD (fully-adjusted HR = 3.02, 95% CI = 2.42–3.76, p < 0.0001), AD (HR = 1.68, 1.21–2.34, p = 0.002), CI (HR = 1.19, 1.02–1.39, p = 0.027), and ACM (HR = 1.86, 1.73–2.00, p < 0.0001). Competing risk analyses confirmed robustness (e.g. for PD: sHR = 2.97, p < 0.0001). Conclusions Chronic fatigue is an independent risk factor for developing PD, AD, CI, and for ACM. Assessment of CF may help identify high-risk populations for targeted prevention.

Risk analysis on the impact of floating ice at the water intake of power plants during cold wave weather

Coupled Analysis and Simulation of Safety Risks in Deep Foundation Pit Construction for Subway Stations Based on the N-K Model and System Dynamics

Dialysis patients face high ischemic stroke risk, only partly explained by cardiometabolic and genetic factors. Homelessness, race, and other social determinants may further elevate risk, but the pathways affecting outcomes in dialysis patients remain uncharacterized. We conducted a retrospective cohort study using the TriNetX electronic health record network, identifying 8,821 adults with at least one homelessness code and 320,432 housed adults who initiated dialysis between 2014-2024 with ≥5 years of prior data. The primary outcome was time to first ischemic stroke within 5years. A multivariable Cox model adjusted for age, sex, race/ethnicity, and dialysis modality estimated the total homelessness effect (HR0). We then ran 3 hazard-attenuation models, each adding 1 composite mediator block, clinical (hypertension, diabetes, hyperlipidemia, obesity), psychosocial (nicotine dependence, depression), and social-adversity (ICD-10 Z56-Z65 codes), to calculate the percent reduction in the homelessness log-hazard. Sensitivity analyses used a composite stroke-or-death endpoint and a landmark survivor cohort excluding early deaths. Homelessness conferred a 4.61-fold higher 5-year stroke hazard. The clinical block attenuated this hazard by 12.1%, and the psychosocial block by 15.2%. In contrast, the social-adversity block amplified the effect by 27.4%, reflecting a suppressor phenomenon from under-documented social determinants of health codes. Sensitivity checks confirmed robustness, with a stroke-or-death HR 1.35 and death-free cohort HR 4.50. Pre-dialysis homelessness and Black race independently predict stroke risk in dialysis patients. This reaffirms the need for standardized social-needs screening, area-level deprivation measures, and advanced data-extraction methods to accurately capture social adversity and guide targeted interventions.

Analysis of Donor-Derived Cell-Free DNA Value and Risk of Infection After Heart Transplant

AI Conflict Observer: Conflict severity and scenario identification for intersection based on Ensemble Transformer.

Priority control list of PFASs' effects on green algae photosynthesis through an in silico approach: Non-negligible ecological risks of emerging alternatives.

The Multiethnic Cohort Study (MEC) is a US prospective cohort of more than 215,000 participants, designed to investigate variation in risk factors and disease across diverse racial and ethnic groups. More than 74,000 participants contributed biospecimens for genetic studies. We describe this subcohort and demonstrate the types of analyses it enables. The MEC recruited adults aged 45 to 75 in California and Hawaii between 1993 and 1996. Cancer diagnoses were identified via state tumor registries. The MEC Genetics Database includes 73,139 participants with germline genotype data. We evaluated genetic similarity, its relationship with self-reported race/ethnicity, and baseline characteristics, including neighborhood socioeconomic status (nSES). Using breast, colorectal, and prostate cancer as examples, we conducted genome-wide association studies (GWAS), assessed nongenetic risk factors, and performed time-to-event analyses. Participants included 10,962 African Americans, 24,234 Japanese Americans, 17,242 Latinos, 5,488 Native Hawaiians, 14,649 Whites, and 564 others. Principal component analysis showed substantial diversity. Multiethnic GWAS replicated known variants with effective control of population stratification. Polygenic risk score (PRS) effects varied across groups. Time-to-event models revealed associations between cancer incidence and nSES, population descriptors, and genetic similarity. The MEC Genetics Database enables multiancestry analyses of genetic and nongenetic cancer risk, supporting research on disparities, polygenic traits, and integrated risk prediction. Example analyses using these resources show the relationship between population descriptors, PRSs, and common cancer risk factors that require special consideration in genetic analyses.

The impact of coronal tibiofemoral subluxation (CTFS) on 15-year outcomes after unicompartmental knee arthroplasty (UKA) remains unclear. This study evaluated whether preoperative CTFS influences implant survivorship. A total of 175 patients (219 knees) underwent medial UKA between 2007 and 2017 and were prospectively followed; the mean age was 64 years (range, 20 to 92), and CTFS was 4.1 ± 2.8 mm. Patients were stratified by preoperative CTFS: ≥ five mm (subluxation group, n = 87) and less than five mm (non-subluxation group, n = 132). Outcomes were assessed with Kaplan-Meier survival, competing risk analyses, and multivariate Cox regressions. The 10- and 15-year survivorships for the entire cohort were 94.7 and 92.2%, respectively, after a minimum of eight years of follow-up (mean, 11.8 ± 3.8). Kaplan-Meier analyses demonstrated lower survivorship in the subluxation group, with 95.4, 90.5, and 88.3%, respectively, at five, 10, and 15 years compared with 99.2, 97.6, and 95.0% in the nonsubluxation group (P = 0.020). Competing risk analyses confirmed this difference (P = 0.020). Preoperative CTFS ≥ five mm was independently associated with an increased conversion risk (hazard ratio 3.9, P = 0.029), whereas the hip-knee-ankle angle was not. Neither preoperative correctability on postero-anterior flexion radiographs nor residual postoperative subluxation significantly altered the risk of conversion. The most common indications for conversion were progression of osteoarthritis (n = seven) and aseptic loosening (n = six). All cases of aseptic loosening occurred exclusively in the subluxation group (P = 0.002). Both groups exhibited survivorship higher than 90% at 15 years; however, preoperative CTFS ≥ five mm was associated with a higher risk of conversion to TKA due to aseptic loosening after mobile-bearing UKA. CTFS should be incorporated into preoperative evaluation and counseling before UKA.

Data-driven risk analysis and management framework for rail hazmat transportation in Canada: Machine learning approach

Adaptive risk analysis framework for network-Level moving target defense under adversarial intelligence uncertainty

To investigate the association between skeletal muscle mass loss and long-term outcomes in patients with intraductal papillary mucinous neoplasm (IPMN). This retrospective, single-center cohort study included 700 patients diagnosed with IPMN at Hokkaido University Hospital between April 2011 and April 2023. Skeletal muscle mass was assessed using the psoas muscle index (PMI) measured on a computed tomography scan at the initial visit. The primary outcome was the incidence of pancreatic cancer, and the secondary outcome was overall mortality. Cox proportional hazard models and competing risk analyses were used to identify independent risk factors. During a median follow-up of 71 months, 27 patients developed pancreatic cancer with an annual incidence rate of 0.63% (95% CI: 0.55%-1.86%). Patients with a low PMI had a significantly higher risk of pancreatic cancer than those with a high PMI (adjusted HR: 3.44, 95% CI: 1.62-7.32, P <0.01). Multivariate analysis identified a low PMI and a main pancreatic duct diameter ≥5mm as independent risk factors for the development of pancreatic cancer. Among the 69 deaths, 61 were comorbidity-related and 8 were pancreatic cancer-related. Low PMI (adjusted HR: 2.57, 95% CI: 1.60-4.12, P <0.01) and a high age-adjusted Charlson comorbidity index (aCCI) (adjusted HR: 9.06, 95% CI: 4.63-17.72, P <0.01) were independently associated with all-cause mortality. Competing risk analysis revealed that skeletal muscle mass loss was significantly associated with the incidence of pancreatic cancer in patients with a low aCCI score but not in those with a high aCCI score. Skeletal muscle mass loss was an independent risk factor for all-cause mortality, and it might be associated with the risk factor for the incidence of pancreatic cancer, particularly IPMN-derived carcinoma in patients with IPMN. Patients with a low PMI and minimal comorbidities might be better to undergo long-term surveillance due to their increased risk of pancreatic cancer.

Holidays in the United States can be quite deadly, with more than 500 traffic fatalities on a given holiday. Another uniquely dangerous period is the annual spring break for college students, which starts at the end of February and continues through the first week of April. Although spring break is not a phenomenon across the entire United States, it is especially popular in certain hotspots. In this study, we focus on Florida, the state with the highest number of spring break hotspots in the country. We analyze weekly county-level crash data from the Florida Department of Highway Safety and Motor Vehicles that provides information on all motor vehicle crashes (whether fatal or not) reported to law enforcement. We compare the estimates for fatal and nonfatal traffic injuries during spring break to those during the holiday period between Thanksgiving and New Year's Day. While both periods are similar in terms of fatalities, the spikes in nonfatal injuries are more pronounced during spring break. Subgroup analyses provide additional insight. Numerous robustness checks and sensitivity analyses confirm the core findings.

BackgroundCivil aviation cargo operations have expanded rapidly, but the occupational health and safety risks faced by cargo workers are still rarely examined through an integrated causal framework that captures chemical, ergonomic, psychosocial, and operational exposures together.ObjectiveThis study aims to identify, prioritize, and interpret the causal relationships among occupational health and safety risks in civil aviation cargo operations from a worker-centered perspective.MethodsThe study employs a comprehensive dataset drawn from industry professionals and applies the Fermatean Fuzzy Decision-Making Trial and Evaluation Laboratory (FF-DEMATEL) method. This approach enables the analysis of complex interrelationships among risk factors, offering a systematic framework for understanding the dynamics of aviation cargo hazards. FF-DEMATEL was applied to 16 cargo-related risk factors evaluated by three occupational safety experts. Expert weights were derived through a machine-learning-based dimensionality reduction procedure using age, occupational safety experience, and firm tenure, enabling the model to reflect both interdependence and expert heterogeneity.ResultsThe analysis reveals a network of critical risks, including improper cargo loading, closed storage conditions, hazardous substances, and unpredictable customer demands. The FF-DEMATEL method identifies both cause and effect relationships among these factors, highlighting which risks exert the greatest influence on overall safety outcomes. The model provides a clear hierarchy of risk sources that require targeted intervention. The leading weighted risks were sabotage, time pressure, incorrect loading of cargo, customer-related uncertainty, and third stakeholder effects. Prominence values showed that sabotage and time pressure were the dominant drivers of the system, while incorrect loading of cargo emerged mainly as an effect factor. A robustness check based on row sums of the normalized and total relation matrices preserved the same upper-tier risk set, supporting the consistency of the prioritization.ConclusionsThe findings indicate that security management, workload and schedule control, loading discipline, and stakeholder coordination should be prioritized together rather than addressed separately. By translating causal risk interactions into concrete priorities, the study offers practical guidance for improving worker protection and operational resilience in civil aviation cargo systems. The findings underscore the necessity of implementing proactive risk management strategies in air cargo operations. Emphasizing the role of advanced analytical methods and a strong safety culture, the study offers actionable recommendations to industry stakeholders.

Systemic lupus erythematosus (SLE) has a significant female bias; however, it remains unclear whether X-chromosomal dysregulation increases the risk in females. Furthermore, while the role of X-linked genes in SLE pathogenesis is recognised, the analysis of genetic risks in SLE has largely focussed on autosomal genes. Here, we analysed a public single-cell RNA sequencing (scRNA-seq) dataset of peripheral blood mononuclear cells using machine learning to test whether X-linked gene expression classifies female SLE in a cell type-specific manner. Using pseudobulked expression profiles from 1.1 million cells across 228 female donors, we trained an ensemble of classifiers on all X-chromosomal genes or a literature-derived set of SLE-associated genes. In CD4+ and CD8+ T cells, models based on X-chromosomal genes achieved classification performance comparable to SLE gene models and were significantly enriched for XCI escape genes, with IL2RG, CD40LG, and TKTL1 emerging as key features. When applied to an independent paediatric-adult SLE cohort, CD8+ T-cell models retained a consistent performance, indicating a stable and reproducible X-linked signature in this subset. Flow cytometry revealed a modest increase in IL2RG protein expression in SLE T cells, consistent with partial escape from XCI in these cells. These findings demonstrate that X-linked transcriptional programs, enriched for known XCI escape genes and concentrated in activated T cells, encode a reproducible transcriptional signal of female SLE and provide a framework for dissecting the contributions of sex chromosomes to autoimmunity.

To characterise the current clinical adoption of AI-driven automation and adaptive radiotherapy (ART) in Italy. A structured, modular questionnaire was distributed to 133 Italian radiotherapy and medical physics departments. The survey captured: centre characteristics, clinical use of automation tools (synthetic-imaging, autosegmentation, autoplanning, motion management, and QA automation), ART implementation (offline/online approaches, triggers, dose summation, and patient-specific QA) and perceived benefits and barriers. Responses were analysed including the maturity index (MI), defined as a weighted sum of 14 binary implementation items spanning key workflow domains. Sixty-one centres responded (46.0%). Routine autosegmentation was reported by 45/61 centres (73.8%) and autoplanning by 27/61 (44.2%). Only 5 centres reported having performed a dedicated FMEA for automation-related processes; heterogeneous QA approaches were reported by 21/61 (34%). A broad definition of ART (including offline re-planning prompted by interfractional changes) was reported by 56/61 centres (91.8%); however, online inter-fraction and intra-fraction adaptation remained limited and largely confined to centres with dedicated ART-ready platforms. Mean (median) MI was 4.99 (5.00) on a theoretical maximum of 9.334, with a weak positive correlation with the number of installed LINACs (r=0.25, p=0.049). Among responding centres, automation tools are widely available and ART is frequently practised, predominantly through offline approaches. The results suggest that broad penetration of adaptive practices does not yet correspond to homogeneous end-to-end maturity. Standardisation of QA, clearer operational definitions of ART triggers, formal risk analysis, and validated dose accumulation workflows appear as key priorities to support broader and safer scaling of online adaptive strategies.

Stereotactic arrhythmia radioablation (STAR) is an emerging treatment for refractory or recurrent arrhythmias. Compared with conventional stereotactic body radiotherapy (SBRT), STAR involves greater complexity in target delineation, motion management, and organs at risk (OARs) protection, yet it lacks established consensus clinical guidelines, and workflow-specific risk analyses remain limited. To develop a C-arm linear accelerator (LINAC)-based STAR workflow and perform a failure modes and effects analysis (FMEA) with fault tree analysis (FTA), supplemented by an exploratory segment-based anatomical proximity analysis. A multidisciplinary team constructed a process map for C-arm LINAC-based STAR and identified potential failure modes across the workflow. Risks were scored using occurrence, severity, and detectability to calculate risk priority numbers (RPNs), and FTA was used to analyze causal pathways. In a predefined exploratory imaging subgroup of eight patients with ventricular tachycardia and simulation-acquired coronary computed tomography angiography (CCTA), we divided the left ventricle according to the 17-segment model and measured the minimum distances from each segment to adjacent OARs as an anatomical surrogate of exposure likelihood. Seventy-nine failure modes were identified, of which 17 were classified as high risk. The highest-risk failure modes were concentrated in target delineation and motion-related steps, including inaccurate multimodal image registration (RPN 432), improper motion evaluation/management (RPN 336), and diagnostic error of arrhythmia substrate definition (RPN 320). In the exploratory imaging subgroup (n = 8), segmental spatial patterns were moderately consistent across patients, with most standard deviations of minimum distance below 2.0cm, although variability remained for several segment-OAR relationships. Segments 4, 5, and 10 were each located within 2cm of both the stomach and esophagus, indicating relatively higher anatomical proximity-related risk. In this FMEA of C-arm LINAC-based STAR, the principal high-risk workflow steps were concentrated in substrate definition, image registration, and motion management. The segment-based analysis provides an exploratory anatomical risk-mapping framework for OAR awareness. Further multicenter studies are needed to refine workflow risk prioritization and evaluate the clinical relevance of the segment-based anatomical findings. Not applicable.

The Association Between the Risk Analysis Index of Frailty and Patient Safety Indicators: A Single-System Cohort Study.