Cancer Epidemiology, Biomarkers & Prevention

Purpose: Tumor genomic testing (TGT) is standard-of-care for most patients with advanced/metastatic cancer. Despite established guidelines, patient education prior to TGT is frequently omitted. The purpose of this study was to evaluate the impact and durability of a concise 3-4 minute video for patient education prior to TGT in community versus academic sites and across cancer types. Patients and Methods: Patients undergoing standard-of-care TGT were enrolled at a tertiary academic institution in three cohorts: Cohort 1-breast cancer; Cohort 2-lung cancer; Cohort 3-other cancers. Cohort 4 consisted of patients with any cancer type similarly undergoing SOC TGT at one of three community cancer centers. Participants completed survey measures prior to video viewing (T1), immediately post-viewing (T2), and after return of TGT results (T3). Outcome measures included: 1) 10-question objective genomic knowledge/understanding (GKU); 2) 10-question video message-specific knowledge (VMSK); 3) 11-question Trust in Physician/Provider (TIPP); 4) perceptions regarding TGT. Results: A total of 203 participants completed all survey timepoints. Higher baseline GKU and VMSK scores were significantly associated with higher income and greater years of education. For the primary objective, there was a significant and sustained improvement in VMSK from T1:T2:T3 (Poverall p<0.0001), with no significant change in GKU (p=0.41) or TIPP (p=0.73). This trend was consistent within each cohort (all p[&le;]0.0001). Results for four VMSK questions significantly improved, including impact on treatment decisions, incidental germline findings, and insurance coverage of testing. Conclusions: A concise, 3-4 minute, broadly applicable educational video administered prior to TGT significantly and sustainably improved video message-specific knowledge in diverse cancer types and in academic and community settings. This resource is publicly available at http://www.tumor-testing.com, with a goal to efficiently educate and empower patients regarding TGT while addressing guidelines within the flow of clinical practice.

Accurate classification of BRCA1 and BRCA2 variants is essential for cancer risk assessment and therapy selection, yet over one-third remain variants of uncertain significance (VUS). Here, using 120,660 real-world cancer genomic profiles with BRCA1 or BRCA2 variants from a >800,000-sample cohort, we develop machine learning models that predict pathogenicity using clinical and tumor-derived features, including a pan-cancer homologous recombination deficiency signature, co-mutated genes, zygosity, and cancer type. Trained on classified variants from ClinVar, our models achieved near-perfect performance, with validation ROC-AUC of 1.000 for BRCA1 and 0.989 for BRCA2 variants with [&ge;]5 observations, translating to strong benign or pathogenic evidence for VCEP classification. Applying these models to 1,073 BRCA1 and 1,639 BRCA2 VUS, we strengthened or enabled classification of 39.48% BRCA1 and 50.52% BRCA2 assessable variants. This approach transforms underutilized tumor profiling data into evidence that can be directly integrated into variant classification, providing a scalable framework for other tumor profiling datasets and cancer genes associated with defined tumor genomic features.

Background The microbial aetiology of early childhood caries (ECC) in sub-Saharan African populations remains poorly characterised, with most studies focusing on conventional cariogenic pathogens like Streptococcus mutans. This study aimed to characterise the salivary microbial profile of children with ECC in urban Kano, northern Nigeria. Methods In this cross-sectional study of 162 children aged 3-5 years in urban Kano, unstimulated saliva samples were collected and analysed using standard bacteriological culture methods. Caries status was assessed using decayed, missing, and filled teeth (dmft) index and International Caries Detection and Assessment System (ICDAS). Microbial isolates were identified through Gram staining, colony morphology, and biochemical tests (catalase, coagulase, oxidase). Results Of 32 microbial isolates obtained, Staphylococcus aureus was the most prevalent (43.8%, n=14), followed by Streptococcus species (28.1%, n=9), Klebsiella species (12.5%, n=4), non-aureus staphylococci (6.3%, n=2), yeast (6.3%, n=2), and Pseudomonas species (3.1%, n=1). Only one isolate demonstrated direct association with dmft-detectable caries. Polymicrobial colonisation occurred in four cases (12.5%), predominantly featuring S. aureus-yeast combinations (n=2). White spot lesions (ICDAS 1-2) were associated with S. aureus and Klebsiella species in two separate cases. Conclusion This study reveals an unexpected predominance of S. aureus in the salivary microbiome of children in northern Nigeria, challenging conventional paradigms of ECC microbiology. The low correlation between microbial isolates and clinical caries suggests complex, multifactorial aetiology. These findings highlight the need for molecular characterisation of oral microbiomes in African populations and reconsideration of caries pathogenesis models in this unique epidemiological context.

Importance: Lung cancer mortality in the United States has fallen substantially in recent decades, yet the relative influence of behavioral, environmental, socioeconomic, and therapeutic factors and their sex specific contributions remains unclear. Understanding these drivers is essential to sustain progress and reduce persistent disparities. Objective: To quantify how behavioral, environmental, socioeconomic, and therapeutic determinants collectively shaped US lung cancer mortality from 1994 to 2020, assess sex specific differences, and forecast mortality trajectories through 2030 using an integrated machine learning framework. Design, Setting, and Participants: Ecological time series study using publicly available national data from 1994 to 2020. Sex stratified analyses were conducted integrating lung cancer mortality, smoking prevalence, fine particulate matter PM2.5 exposure, Human Development Index HDI, per capita healthcare expenditure, healthcare inflation, insurance coverage, income inequality, and annual drug approvals. Exposures: Behavioral smoking, environmental PM2.5, socioeconomic HDI health expenditure inflation, uninsurance inequality, and therapeutic drug approval indicators. Main Outcomes and Measures: Age-standardized lung cancer mortality per 100000 population. Temporal changes were modeled using Joinpoint regression. Concurrent associations were assessed using multivariable and elastic net regression, and forecasts were estimated with AutoRegressive Integrated Moving Average models with exogenous variables ARIMAX. Results: From 1994 to 2020, mortality declined by 59 percent in men, from 52.9 to 21.7 per 100000, and by 40 percent in women, from 26.7 to 15.9 per 100000, with faster declines after 2015. Smoking and PM2.5 decreased by more than 45 percent but remained strongly correlated with mortality. In elastic net models, PM2.5 was the strongest predictor for men, while smoking was the strongest predictor for women. Per capita expenditure and HDI ranked higher for men, while uninsurance and income inequality were strong predictors for women. Mortality declines occurred during periods of major approvals of lung cancer drugs. Forecasts suggest continued but slower declines through 2030, with projected rates of 20.2 and 14.9 deaths per 100000 in men and women, respectively. Conclusions and Relevance: Sex specific declines in lung cancer mortality reflect different dominant correlates, with air pollution more important in men and smoking more important in women, while socioeconomic conditions and therapeutic advances also influence trends. Continued tobacco control, improved air quality, and equitable access to screening and modern treatment are essential to sustain further reductions in mortality. Keywords: Lung Neoplasms, Sex Factors, Air Pollution, Smoking, Socioeconomic Factors, Machine Learning

A recent randomized clinical trial in non-small cell lung cancer1 confirms what numerous observational studies have reported time of day (ToD) may dramatically influence treatment outcomes in cancer patients. In this recent trial median overall survival (OS) decreased from 28 months in the early ToD arm to 16.8 months in the late ToD arm. We raise the concern that clinical trial outcomes may be influenced by seemingly minor biases in treatment time across arms. We also suggest that by measuring or randomizing treatment-time in clinical trials, we may identify beneficial ToD dependent treatments that would otherwise be overlooked.

Introduction: Manual data extraction from unstructured clinical notes is labor-intensive and impractical for large-scale clinical and research operations. Existing automated approaches typically require large language models, dedicated computational infrastructure, and/or task-specific fine-tuning that depends on curated data. The objective of this study is to enable accurate extraction with smaller locally deployed models using a disease-site specific pipeline and prompt configuration that are optimized and reusable. Materials/Methods: We developed OncoRAG, a four-phase pipeline that (1) generates feature-specific search terms via ontology enrichment, (2) constructs a clinical knowledge graph from notes using biomedical named entity recognition, (3) retrieves relevant context using graph-diffusion reranking, and (4) extracts features via structured prompts. We ran OncoRAG using Microsoft Phi-3-medium-instruct (14B parameters), a midsize language model deployed locally via Ollama. The pipeline was applied to three cohorts: triple-negative breast cancer (TNBC; npatients=104, nfeatures=42; primary development), recurrent high-grade glioma (RiCi; npatients=191, nfeatures=19; cross-lingual validation in German), and MIMIC-IV (npatients=100, nfeatures=10; external testing). Downstream task utility was assessed by comparing survival models for 3-year progression-free survival built from automatically extracted versus manually curated features. Results: The pipeline achieved mean F1 scores of 0.80 +/- 0.07 (TNBC; npatients=44, nfeatures=42), 0.79 +/- 0.12 (RiCi; npatients=61, nfeatures=19), and 0.84 +/- 0.06 (MIMIC-IV; npatients=100, nfeatures=10) on test sets under the automatic configuration. Compared to direct LLM prompting and naive RAG baselines, OncoRAG improved the mean F1-score by 0.19 to 0.22 and 0.17 to 0.19, respectively. Manual configuration refinement further improved the F1-score to 0.83 (TNBC) and 0.81 (RiCi), with no change in MIMIC-IV. Extraction time averaged 1.7-1.9 seconds per feature with the 14B model. Substituting a smaller 3.8B model reduced extraction time by 57%, with a decrease in F1-score (0.03-0.10). For TNBC, the extraction time was reduced from approximately two weeks of manual abstraction to under 2.5 hours. In an exploratory survival analysis, models using automatically extracted features showed a comparable C-index to those with manual curation (0.77 vs 0.76; 12 events). Conclusions: OncoRAG, deployed locally using a mid-size language model, achieved accurate feature extraction from multilingual oncology notes without fine-tuning. It was validated against manual extraction for both retrieval accuracy and survival model development. This locally deployable approach, which requires no external data sharing, addresses a critical bottleneck in scalable oncology research.

Molecular subtyping of cancer is traditionally defined in transcriptomic space, yet routine clinical deployment is limited by the availability and cost of sequencing. Meanwhile, histopathology captures rich morphological information that is known to correlate with molecular state but lacks a principled, mechanistic bridge to gene-level representations. We propose a graph-constrained learning framework that aligns morphology-derived signals with a fixed, data-driven gene network discovered via hierarchical Monte Carlo screening. We can derive new gene sets for classification using random sampling, and use the coexpression network of that graph to enforce the learning of a pure morphology model without using gene expression. The resulting model performs subtype prediction using morphology alone, while being explicitly forced to operate through a gene-structured latent space. Structural alignment is enforced during training. For Moffitt classification in pancreatic cancer using PANCAN and TCGA datasets, the model has a reported 85% AUC using an alternative gene set network structure, while the alternate gene set itself has an 84% AUC in all patients that were classified with subtyping with pancreatic cancer in the dataset. This demonstrates that virtual transcriptomics can provide biologically grounded molecular insights using only routine histopathology slides, potentially expanding access to precision oncology in resource-limited settings.

Objective: Minimum unit pricing (MUP) aims to reduce use of cheap, high strength alcoholic beverages that drive harm, yet concerns remain about inequitable effects for structurally vulnerable groups. As part of the Costs, Harms, Expenditures and Alcohol Prices (CHEAP) study, we linked individual-level, product-specific alcohol consumption data from a customized survey with provincial retail price data to estimate prices per standard drink (PPSD) and examine their association with alcohol-related outcomes across sociodemographic groups. Method: A cross-sectional survey of past-week drinkers in British Columbia, Canada, was linked to provincial product-level alcohol sales data. The population weighted sample included 1,217 adults aged [&ge;] 19 years (716 men; mean age 49.34, SD 16.98). Participants reported product-specific consumption, which was matched to retail prices to calculate individual-level PPSD. Survey weighted quasibinomial models then examined associations between PPSD and three outcomes: (1) causing harm to self or others in the past year, (2) scoring [&ge;] 8 on the Alcohol Use Disorder Identification Test, and (3) consuming [&ge;] 15 standard drinks per week. Analyses were stratified by income, education, subjective social status, and race/ethnicity. Results: Lower price per standard drink was associated with higher odds of harm (OR 3.05, 95% CI 1.25-7.40) and scoring [&ge;] 8 on the AUDIT (OR 2.34, 95% CI 1.37-3.99). Associations were stronger among structurally disadvantaged groups, including low-income respondents and Indigenous participants. Conclusions: Lower alcohol affordability is linked to risky alcohol use, with the strongest effects among structurally disadvantaged groups. MUP would reduce this risk and promote health equity.

Abstract Background: Enhanced Recovery After Surgery (ERAS) optimizes perioperative management for colorectal cancer (CRC), improving short-term outcomes, but its impact on long-term outcomes remains inconclusive, supporting the need for this meta-analysis. This study evaluates the effect of perioperative ERAS (therapy-focused) on 1-, 2-, 3-, and 5-year postoperative survival in patients with CRC. Methods: We conducted a systematic review and meta-analysis following a pre-registered protocol in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed, Web of Science, Embase, Medline Ovid, and Cochrane Library Wiley were searched up to December 31, 2025, for clinical studies reporting long-term postoperative survival outcomes of patients with CRC undergoing ERAS implementation. Of 1,063 retrieved reports, 10 studies (5,876 patients) were included in Kaplan-Meier-based meta-analyses and eight studies (5,556 patients) in aggregated data meta-analyses. Data extraction was performed independently by two reviewers, with study quality and risk of bias assessed using the Newcastle-Ottawa Scale (NOS) and RevMan software. Effect sizes were pooled using fixed-or random-effects models according to heterogeneity, with cross-validation and subgroup analyses examining the influence of tumor stage and ERAS adherence. The pre-specified primary outcome was postoperative overall survival (OS) [&ge;]12 months, and the secondary outcome was disease-free survival (DFS). Results: ERAS significantly improved OS at 1 year (93.2%, 95% CI: 92.3-94.2 vs. 90.2%, 95% CI: 89.1-91.2), 2 years (86.7% vs. 81.3%), 3 years (81.1% vs. 72.4%), 5 years (70.9% vs. 60.6%) (all P<0.01). The pooled HR for mortality was 0.72 (95% CI: 0.63-0.83, P<0.01), indicating a 28% reduction in long-term mortality. Stage I-II tumors and ERAS adherence [&ge;]70% conferred the greatest benefits. DFS did not show a statistically significant improvement (HR=0.90, 95% CI: 0.68-1.19, P=0.45). Included studies were of moderate to high quality (NOS score 6-9). Conclusions: Perioperative ERAS significantly improves 1- to 5-year OS and reduces long-term mortality in patients with CRC, with the greatest benefits in early-stage disease and high adherence. These findings support ERAS as a critical component of comprehensive CRC care.

Rare Mendelian disorders affect 300-400 million people globally. Although genetic testing has become widely adopted, gene-specific evidence for tailored variant interpretation remains scattered across resources. We present Gene Portals, a framework for gene-centered multimodal knowledge bases that co-localize expert-harmonized clinical data, functional assays, population variation, structural annotations and gene-specific ACMG/AMP specifications within a single resource. A modular interface integrates this unified evidence with VCEP-refined ACMG specifications to enable automated gene-specific variant classification, infer molecular mechanisms, and support cross-gene analyses. We demonstrate the framework's utility across five Gene portals spanning eleven neurodevelopmental disorder-associated genes, integrating data from 4,423 individuals with 2,838 unique variants, 36,149 ClinVar submissions, and 1,044 expert-curated molecular readouts. By organizing evidence that is otherwise dispersed across multiple sources into a unified, queryable framework, the SCN, GRIN, CACNA1A, SATB2 and SLC6A1 Gene Portals became widely used community resources and provide an extensible template for standardized rare-disease variant interpretation and mechanism-aware discovery.

Study question: How is glycodelin, a glycoprotein secreted by reproductive tissues, causally related to reproductive diseases and traits? Summary answer: We present evidence for a causal role of sex hormones in determining glycodelin levels, but limited evidence that glycodelin subsequently causally impacts reproductive traits. What is known already: Glycodelin is expressed in female and male reproductive tissues and has four glycoforms (-A, -C, -F and -S), with the glycosylation pattern determining its function. Differences in the levels of glycodelin are associated with reproductive traits, including fertility, endometriosis, preeclampsia, and female-specific malignancies. Study design, size, duration: We used cross-sectional data from the UK Biobank to investigate relationships between glycodelin and reproductive-related traits in men and women by performing genome-wide association studies (GWAS) and Mendelian randomization (MR) analyses. Participants/materials, setting, methods: We included individuals of European genetic ancestry aged 40-69 in 2006-2010, with genetic data in the UK Biobank v3 release. We performed GWAS of glycodelin levels in 46,468 people, stratified by sex (21,368 men and 25,100 women) and menopause status (6,409 pre- and 18,691 post-menopausal women). We tested bidirectional casual associations between glycodelin levels and 19 reproductive-related traits using one- and two-sample MR analyses. Main results and the role of chance: Nine genetic signals reached genome-wide significance (P<5x10-8) across the glycodelin phenotypes. A known genetic signal (rs9409964) near the PAEP gene, which encodes glycodelin, was most strongly associated (P<3x10-80 across all phenotypes), and had heterogeneous effects (effect (SD) per A allele of 1.31 in men vs 0.60 in women, and 0.4 in pre- vs 0.9 in post-menopausal women). Higher serum concentrations of bioavailable testosterone raised glycodelin in men (effect = 0.14 SD, IVW P=4.1x10-13), while effects in women depended on menopause status (pre-menopausal effect = -0.16 SD, IVW P=3.6x10-3; post-menopausal effect = 0.10 SD, IVW P=5.9x10-4). There was no strong evidence that differences in glycodelin levels were caused by, or were the cause of, other reproductive-related traits. Limitations, reasons for caution: Proteomic measurements of glycodelin did not differentiate between glycoforms and were derived from blood and might not reflect levels in reproductive tissues. The sample size for the pre-menopausal GWAS was modest, reducing our power to detect relationships with reproductive conditions. Genetic instruments are assumed to be proxies for average lifelong exposure, which does not reflect variation in hormones and biomarkers over lifetime. Wider implications of the findings: We suggest that reported associations of glycodelin with reproductive conditions are likely to result from the effects of sex hormones rather than being directly causal. These findings may help reconcile previously conflicting associations between glycodelin and reproductive traits.

Hybrid controlled trials (HCTs) incorporate real-world data into randomized controlled trials (RCTs) by augmenting the internal control arm with patients receiving the same treatment in routine care. Beyond increasing power, HCTs may improve recruitment by supporting unequal randomization ratios that increase patient access to experimental treatments. However, HCT validity is threatened by bias from unmeasured confounding due to lack of randomization of external controls, leading to outcome non-exchangeability between internal and external control patients. To address this challenge, we developed a sensitivity analysis framework to assess the robustness of HCT results to potential unmeasured confounding. We propose a tipping point analysis that adapts the E-value framework to the HCT setting where trial participation rather than treatment assignment is subject to confounding. To aid interpretation, we also introduce a data-driven benchmark representing the strength of unmeasured confounding reflected by the observed outcome non-exchangeability. We then propose an operational decision rule and evaluate its performance through simulation studies. Finally, we illustrate the approach using an asthma trial augmented by data from electronic health records. Simulation results demonstrate that our decision rule safeguards against Type I error inflation while preserving the power gains achieved by incorporating external data. In settings where moderate unmeasured confounding led to poorer outcomes for external controls, Type I error was controlled near the nominal 5% level, and power increased by 10-20% compared with analyses using RCT data alone. Our approach provides a practical, interpretable method to assess HCT robustness, supporting rigorous inference when integrating external real-world data.

Background Persistent inequities in immunisation coverage, particularly among zero-dose and under-immunised children, continue to challenge Pakistan's Expanded Programme on Immunization. Weak feedback loop, inconsistent data quality, and limited real-time monitoring impede effective decision-making. This Implementation Research was conducted under the MAINSTREAM Initiative funded by Alliance for Health Policy and Systems Research (AHPSR) and supported by the Aga Khan Community Health Services Department and National Institutes of Health Pakistan to design, implement, and evaluate a digital monitoring and action planning tool to strengthen data-driven decision-making within routine immunisation systems. Methodology/Principal Findings A co-creation approach was employed to design a digital monitoring solution through inclusive consultations, key informant interviews, and focus group discussions with EPI Punjab at provincial and district levels. The solution included a customised mobile application for data collection and a Power BI visualisation dashboard to map low-coverage areas, identify drivers of dropouts and zero-dose children, and capture caregivers' information sources to inform targeted communication. The intervention was piloted in 60 households across six clusters of a Union Council of District Lahore. Advanced analytics identified reasons for non-vaccination and missed opportunities, generating tailored recommendations and practical plans for program managers. The analysis assessed acceptability, adoption, fidelity, and perceived scalability through field observations, system use, and stakeholder feedback. The co-developed digital tool enhanced visibility of coverage gaps through UC-level mapping, real-time dashboards, and structured action planning. Pilot testing in Lahore showed strong acceptability, ease of use, fidelity, and adaptability among managers, supervisors, and vaccinators. Scalability and sustainability potential were demonstrated, though barriers included leadership turnover, system fragmentation, workload pressures, and resource constraints. Conclusion The tool demonstrated feasibility to strengthen immunisation equity, accountability, and responsiveness. Co-creation with stakeholders enhanced ownership, operational relevance, and adoption, while complementing existing platforms. Sustainability will depend on effective integration, local ownership, capacity building, and accountability, while scalability requires interoperability, resource commitment, policy support, and alignment with existing workflows.

The use of semaglutide (SE), a glucagon-like peptide-1 receptor agonist (GLP-1RA) with glucose-lowering and weight-loss effects, has risen rapidly, particularly among women of reproductive age. While preclinical studies suggest benefits for ovarian function via the hypothalamic-pituitary-ovarian axis, its impact on the endometrial-embryo interface remains unclear. Here, we show that GLP-1R is dynamically expressed in fertile human endometrium, restricted to epithelial cells and markedly upregulated during the mid-secretory phase of the menstrual cycle. In a preclinical model of endometrial epithelial organoids, SE at physiological concentrations activates intracellular cAMP signaling, enhances epithelial metabolism, and upregulates receptivity markers without steroid hormone priming, whereas higher concentrations modestly reduce expression of a key receptivity marker PAEP/glycodelin and shift metabolism towards oxidative phosphorylation. By contrast, in stromal cells lacking detectable GLP-1R, SE disrupts decidualization, induces endoplasmic reticulum stress and suppresses cell-cycle at G2/M phase. Human embryo models, blastoids, expressed GLP-1R and underwent concordant SE-mediated transcriptional remodeling in epiblast and trophectoderm lineages, encompassing changes in metabolism and epigenetic regulation, but without shifts in lineage proportions. Notably, SE increased blastoid attachment to the endometrial epithelium in the absence of exogenous steroid hormones, suggesting enhanced epithelial-embryo interaction. Together, these findings reveal a compartment-specific mismatch, as SE augments epithelial and embryonic metabolic activity but compromises stromal support for implantation, with potential consequences for implantation due to stromal dysfunction.

Background: Nursing interns are at high risk of psychological distress due to academic and clinical stressors. While poor sleep quality is linked to anxiety and depression, the buffering role of social support remains underexplored in this population. Aims: To explore the role of social support in regulating the relationship between sleep and mental health among nursing interns. Methods: A total of 396 nursing interns completed self-administered questionnaires including the Pittsburgh Sleep Quality Index (PSQI), Social Support Rate Scale (SSRS), Generalized Anxiety Disorder-7 (GAD-7), and Patient Health Questionnaire-9 (PHQ-9). Hierarchical regression and simple slope analyses were used to test moderation effects. Results: Poor sleep quality was significantly associated with higher anxiety ({beta}=0.449, P<0.001) and depression ({beta}=0.535, P<0.001). Social support significantly moderated these relationships. Under low social support, the effects of sleep quality on anxiety ({beta} = 0.602) and depression ({beta} = 0.779) were stronger than under high support (anxiety: {beta} = 0.396; depression: {beta} = 0.515). Conclusions: Social support buffers the adverse psychological effects of poor sleep among nursing interns. Interventions should integrate sleep hygiene education with strategies to enhance social support.

Background Artificial intelligence (AI) is increasingly being integrated into healthcare systems, with growing applications in clinical decision support, workflow optimization, and population health management. While substantial investments have been made in digital infrastructure, the successful adoption of AI in primary care depends critically on the readiness, awareness, and educational preparedness of healthcare professionals. Global health authorities emphasize the need for ethically grounded and workforce-focused approaches to AI integration; however, evidence on clinicians readiness for AI, particularly in primary care settings and in the Middle East region, remains limited. Objectives This study aims to assess the level of awareness, perceptions, attitudes, and educational needs related to AI among healthcare professionals working within Qatars Primary Health Care Corporation (PHCC). In addition, it seeks to examine organizational factors influencing the integration of AI-focused education in primary care and to develop an AI readiness framework that can inform targeted training strategies and policy planning. Methods This study will adopt a mixed-methods design guided by the Organizational Readiness for Change (ORC) framework, adapted for AI integration in primary care. The quantitative component will consist of an anonymous, census-style online survey distributed to all healthcare professionals across PHCC health centers and headquarters, assessing AI awareness, attitudes, training needs, and perceived infrastructure readiness. Composite AI awareness and attitude scores will be calculated, and regression analyses will be used to explore factors associated with AI readiness. The qualitative component will include semi-structured interviews and focus group discussions using maximum variation sampling to capture diverse professional perspectives. Qualitative data will be analyzed thematically, following COREQ and SRQR reporting standards. Quantitative and qualitative findings will be integrated to generate an AI readiness profile and an actionable education roadmap aligned with national digital health priorities. Discussion This study will provide the first comprehensive assessment of AI readiness among primary care healthcare professionals in Qatar. By identifying knowledge gaps, training priorities, and organizational enablers and barriers, the findings are expected to inform the development of evidence-based AI education strategies within continuing professional development frameworks. The proposed AI readiness framework may also offer a transferable model for other health systems seeking to align workforce development with responsible AI implementation in primary care.

Abstract Background: Urinary tract infections (UTIs) are common health issue during pregnancy, often lead to adverse maternal and neonatal outcomes if left untreated, low knowledge contribute to high UTI rates, particularly in resource-limited settings like Jordan. To assess the knowledge levels about UTIs among pregnant women in Jordan and its association with socio-demographic characteristics. Methods: A descriptive cross-sectional study was conducted among 500 pregnant women attending antenatal clinics in four major governmental hospitals across Jordan. Data were collected using a validated questionnaire based on the Theory of Planned Behavior (TPB) comprising 25 questions, including 5 socio-demographic questions and 20 knowledge questions, scores were categorized as "adequate" or "inadequate" based on the median score. Results: Among participants, 51.4% had inadequate knowledge, while 48.6% demonstrated adequate knowledge. Higher knowledge levels were significantly associated with younger age (21-30 years), urban residence, higher education (university and postgraduate), and employment status. Conclusion: The findings highlight a knowledge gap among pregnant women regarding UTIs. Integrating targeted health education and addressing socio-demographic disparities into antenatal care, especially for women with low education and rural residence, may improve maternal outcomes. Keywords: Urinary tract infection, Knowledge, Pregnancy, Antenatal care, Jordan, Maternal health.

Understanding host susceptibility to Mycobacterium tuberculosis (Mtb) is critical for the development of new vaccines. Certain individuals "resist" becoming infected with Mtb despite intensive exposure; however, it is unknown whether there is a genetic basis for "resistance" to Mtb infection across populations. Here we conducted a genome-wide association study (GWAS) of resistance to Mtb infection by carefully characterizing exposure to TB patients among 4,058 close contacts in India, Brazil, and South Africa. 476 (12%) "resisters" remained free of Mtb infection despite substantial exposure to highly infectious TB patients. GWAS identified a novel chromosome 13 locus (rs1295104126) associated with resistance across the multi-ancestry meta-analysis. Comparing Mtb-infection to all uninfected contacts, irrespective of exposure, yielded a different locus on chromosome 6 (rs28752534), near the HLA-II region. These findings demonstrate a common genetic basis for resistance to Mtb infection across multi-ancestral cohorts with potential to elucidate novel mechanisms of protection from Mtb infection.

Background: Quadriceps weakness may reduce sagittal plane shock absorption during landing, shifting load toward the frontal plane and increasing knee abduction moment (KAM), a biomechanical risk factor for anterior cruciate ligament (ACL) injuries. Purpose: The purpose of this study was to evaluate the association between isokinetic quadriceps strength and peak KAM during drop vertical jump landing in adolescent athletes. Study Design: Secondary analysis of previously collected data. Methods: Healthy adolescent athletes completed quadriceps strength testing using an isokinetic dynamometer and a biomechanical assessment during a drop vertical jump task. Quadriceps strength was quantified as peak concentric torque and the peak external KAM was calculated during the landing phase on the dominant limb. Both strength and KAM were normalized to body mass. Linear regression was used to examine the association between normalized quadriceps strength and peak external KAM on the dominant limb. Results: The association between quadriceps strength and peak normalized KAM on the dominant limb was not statistically significant ({beta} = -0.053 (95% CI [-0.137 to 0.030]), F(1,119) = 1.62, R2 = 0.013, p = 0.206). Quadriceps strength explained only 1.3% of the variance in peak KAM, indicating a negligible association between these variables in this cohort. Discussion: Quadriceps strength was not associated with peak normalized KAM during landing, suggesting that frontal-plane knee loading during a drop vertical jump is not meaningfully explained by maximal concentric quadriceps strength alone. KAM appears to be driven more by multi-joint movement strategy and neuromuscular coordination than by the capacity of a single muscle group.

Objectives: Compare Anterior Cruciate Ligament (ACL) Return to Sport after Injury (ACL-RSI) scores over time following ACL reconstruction (ACLR) between male and female patients aged 15 to 25 years with primary ACL injuries and ACL reinjuries. Design: Retrospective cohort design. Setting: Sports physical therapy clinics. Participants: 332 patients aged 15-25 years who underwent ACLR following either primary ACL injury or ACL reinjury, either contralateral or ipsilateral graft reinjury, and had at least one observation of the ACL-RSI. Main Outcome Measures: ACL-RSI score. Results: ACL-RSI scores significantly increased over time post- ACLR (p < .001), males reported significantly higher scores compared to females (p < .001), and patients with contralateral ACL reinjury demonstrated higher scores than those with ipsilateral ACL graft reinjury (p = .006), though there was no difference in scores between patients with primary ACL injury and ACL reinjury. A significant interaction effect of sex and injury status was also observed (p = .009), generally demonstrating that females had lower psychological readiness compared to males across injury statuses. Conclusions: ACL-RSI following ACLR varies based on biological sex and time post-ACLR, though ACL reinjury, independent of the reinjured leg, does not appear to effect scores compared to primary ACL injury.