Citations

Background While rural health guidelines have often cautioned against digital interventions due to infrastructure and literacy barriers, this study introduces an SMS-based conversational artificial intelligence (CAI) that overcomes these limitations to promote HPV vaccination. Objective To evaluate an SMS-based CAI using the Centers for Disease Control (CDC)’s presumptive and CASE (Corroborate, About Me, Science and Explain) frameworks to promote HPV vaccination among rural parent-adolescent dyads by: (1) testing its usability; (2) assessing the feasibility of co-design with clinicians and parent-adolescents; (3) identifying adoption barriers and (4) examining communication preferences across behavioural profiles. Methods We developed a CAI simulator using the presumptive approach and the structured CASE method to address 11 common concerns about the HPV vaccine. A multimethod usability study with 13 rural parent-adolescent dyads (n=26) included in-depth interviews and the System Usability Scale. Results The CAI achieved excellent usability (mean SUS 85.2, ~96th percentile). A previously undocumented behavioural profile—vaccine-unaware—was identified, distinct from hesitancy, and best addressed through empathetic, exploratory dialogue. Key adoption barriers included integration with existing preventive care routines and endorsement by trusted local providers. Conclusions Contrary to the assumption that rural residents resist digital tools, participants engaged readily when content was credible, locally endorsed and accessible via SMS. Findings highlight the conditional effectiveness of the CDC’s presumptive style in CAIs—well-suited for vaccine-compliant users but less effective for sceptical or unaware groups, who, we argue, would respond better to conversational, empathetic tones.

Abstract Medulloblastoma (MB) is one of the most prevalent malignant brain tumors in children, with tremendous cognitive and neuroendocrine disability among survivors. Group 3 (G3) MBs have poor overall survival at < 50%, higher frequency of metastasis, and no targeted therapies. Amplification of MYC and activation of TGFβ signaling are frequent in G3MB. Remarkably, some tumors have no reported mutations, suggesting roles for epigenetic mechanisms in driving disease. We have generated novel humanized models for G3MB by transducing neuroepithelial stem cells (NESC) derived from human induced pluripotent stem (iPSC) cells with TGFβ effectors alone and/or in combination with MYC, prioritizing combinations observed in patients. We determined that both MYC and TGFβ effectors drove tumor formation in vivo with the combination driving more aggressive tumors and higher frequency of metastasis. NESC-derived tumors clustered with human G3/G4MB, indicating our models recapitulated human disease. To decipher mechanisms of resistance, we integrated CUT&RUN for MYC genomic localization and histone PTMs with RNA-sequencing and discovered MYC-bound neural developmental genes repressed in MYC and TGFβR1-driven lines. Further interrogation identified JARID1B, a MYC binding partner, as a regulator of many of these repressed neural genes. We postulate that deregulation of MYC and context-specific targeting to the chromatin template culminates in transcriptional alterations that promote aggressive G3MB. To further investigating therapeutic vulnerabilities, we have performed a in vivo whole genome CRISPRi screen to reveal important drivers of malignancy, one of the first of it’s kind in brain tumors. We are functionally validating key candidate sgRNAs that were differentially significant in primary tumors and may also have key roles in driving metastasis. Excitingly, many sgRNAs identified from the screen were chromatin modifiers and may be important co-factors of MYC-mediated regulation on the epigenome. Collectively, our studies provide a critical framework for understanding mechanisms of resistance and the epigenetic landscape of MYC- and TGFβ-driven MB.

Discoidin Domain Receptor 2 (DDR2) is a collagen-activated tyrosine kinase required for bone and tooth development as well as regeneration of calvarial and long bones. Although DDR2 is expressed in alveolar bone, its functions in this site have not been previously examined. The present study used global and conditional knockout approaches and lineage tracing to examine the functions of DDR2 in alveolar bone formation during tooth socket healing. First molars were extracted from the maxillae of wild-type and globally Ddr2 -deficient ( Ddr2 LacZ/LacZ ) mice, and socket healing was measured after 1, 2, or 4 wk. At all times, Ddr2 inactivation significantly reduced socket healing. These changes were associated with decreased cell proliferation 3 d after extraction without any detectable changes in apoptosis. In addition, collagen fibril orientation, measured using Picrosirius Red staining under polarized light, was disrupted in tooth sockets from Ddr2 -deficient mice. For lineage tracing, 4-wk-old Ddr2 mer-cre-mer ; R26R tdTomato and Gli1 CreERT2 ; R26R tdTomato mice were induced with tamoxifen, first molars were extracted, and socket healing was examined after 1, 3, or 7 d. In both cases, tdTomato+ cells migrated into sockets, where they sequentially co-localized with preosteoblast and osteoblast markers. Thus, both DDR2 + and GLI1 + cells contribute to socket healing. The considerable overlap observed between DDR2 + and GLI1+ cells suggested that DDR2 functioned in GLI1 + cells, which are known to have skeletal progenitor properties. Consistent with localization data, conditional knockout ( Gli1 CreERT2 ; Ddr2 fl/fl ) mice exhibited reductions in socket healing 1 wk postextraction, although the inhibition of socket repair was less than that seen in global Ddr2 knockouts. In summary, this work demonstrates a role for DDR2 in alveolar bone repair, a finding that may have therapeutic implications for the treatment of alveolar bone loss associated with periodontal disease and other disorders.

Artificial intelligence (AI) and machine learning have emerged as transformative analytic methods capable of identifying patterns and proposing treatment strategies from complex, heterogeneous data generated in patient care settings. However, translating reports of AI tools into clinical decision-making requires careful interpretation and contextualization. Here, we present a structured framework (ABCDEFG framework) for clinicians to critically appraise studies reporting AI tools using a high-profile reinforcement learning analysis of vasopressin initiation in septic shock as a case example. We highlight key methodological strengths including adherence to reporting standards, alignment with clinical outcomes, and external validation. We also identify limitations related to causal inference, reward structure design, assumptions about state representation, and implementation readiness. Our framework emphasizes the importance of rigorous, clinically grounded critique before integrating AI tools into practice, and provides a roadmap for clinicians to assess the reliability and relevance of emerging AI-based decision support.

Abstract Introduction and Objective This study aims to evaluate the learning curve and perioperative outcomes of single‐port extraperitoneal robotic radical prostatectomy (SP‐EP‐RARP) performed by a single surgeon at a high‐volume academic institution. Methods A retrospective review of a prospectively maintained database was conducted for patients who underwent SP‐EP‐RARP from September 2023 to August 2025. Key metrics included operative time, estimated blood loss, conversion rate, nerve‐sparing status, pathological margin status and continence (0–1 pad for safety), and 30‐day complication rate. Descriptive statistics were used, and outcomes were analysed chronologically to assess for trends suggesting a learning curve. Results The cohort included 53 patients who underwent extraperitoneal SP‐EP‐RARP. Median operative time was 213 min (IQR: 145–281). Median estimated blood loss was 100 mL (IQR: 75–125). Nerve‐sparing was attempted in 75.5% of cases. No assist or plus one ports were utilized. Final pathology revealed pT2 disease in 64.1% (34 cases), pT3a in 32.1% (17 cases) and pT3b in 3.7% (2 cases). Overall positive margin rate was 26.4%. Thirty‐day Clavien–Dindo Grades I–II complication rates were 11.3%, and no major complications or 90‐day complications were reported. Continence rates at 6 weeks, 3 months and 6 months were 72%, 72% and 75%, respectively. Stabilization of operative times and intraoperative outcomes occurred after approximately 25–30 cases. Conclusion SP‐EP‐RARP can be safely implemented by a newly graduated fellowship‐trained robotic surgeon with acceptable oncologic and functional outcomes. Initial learning can be estimated at approximately 25–30 cases.

Background Suicidal behaviour shows notable sex differences, and understanding whether genetic factors contribute to these differences is critical for identifying at-risk individuals and prevention. Objective We aim to investigate the genetic contribution to suicide attempts and examine whether genetics account for sex differences in incidence. Methods This population-based cohort study includes 3.1 million individuals born 1963–1998 and followed through Swedish National Registers, including hospitals and specialist outpatient diagnoses and cause of death data. Suicide attempts were identified using ICD codes, indicating intentional self-harm, self-harm using lethal methods or leading to hospitalisation, or resulting in death. Familial aggregation, coaggregation, pedigree heritability and genetic correlations were estimated using genealogical data. For sex-specific analyses, we examined mother–daughter, female sibling, father–son and male sibling pairs, separately. Findings Suicide attempts were more common among females than males (3.3% vs 2.6%). In both sexes, risk aggregated within families (ORs ranged 1.6–3.4 across relative types) and was higher in first-degree than second-degree relatives. Familial aggregation was stronger in females than in males, and in same-sex first degree relatives compared with cross-sex pairs. Pedigree heritability was 41.9% (95% CI 36.0 to 48.4%) and did not differ significantly by sex (female 51.4% (95% CI 40.1% to 58.6%), male 45.1% (95% CI 32.3% to 52.5%), Bootstrap p value 0.40). Suicide attempt showed moderate to high pedigree genetic correlations with psychiatric disorders, strongest with substance use disorders (SUD, r g =0.85 (95% CI 0.83 to 0.96)), with no significant sex differences. The genetic correlation between female and male suicide attempts was high (0.85 (95% CI 0.80 to 0.99)), suggesting a substantial genetic overlap. Conclusions Suicide attempt has a moderate heritable component that largely overlaps between females and males and with other psychiatric disorders, particularly SUD. Stronger familial aggregation in females and in same-sex pairs highlights the potential role of sex-specific environmental or social factors. Future research should focus on non-genetic contributors and their potential interaction with genetic factors to better understand and address sex disparities in suicidal behaviour Clinical implications Genetic risk for suicide attempt is substantial but does not fully explain sex differences in incidence. Clinicians should, therefore, consider non-genetic, including sex-specific environmental and social factors, alongside family history and psychiatric comorbidity when assessing suicidal risk.

Summary Family history of haematological malignancy (FHHM) reflects inherited susceptibility to non‐Hodgkin lymphoma (NHL), but its prognostic significance remains poorly understood. We used a cohort study of 1994 NHL patients to evaluate associations between FHHM and clinical outcomes across lymphoma subtypes. We estimated associations, adjusted for sex and lymphoma‐specific prognostic index, of self‐reported FHHM in a first‐degree relative with overall survival (OS), event‐free survival (EFS) and lymphoma‐specific survival (LSS) using multivariable Cox regression models and with failure to achieve EFS at 24 months (EFS24) using logistic regression. FHHM prevalence was 12.8%, and the median follow‐up among survivors was 12.0 years. FHHM was not associated with OS. In subtype‐specific analyses, FHHM was associated with inferior LSS (hazard ratio [HR] = 1.98, 95% confidence interval [CI] 1.11–3.53) in follicular lymphoma (FL); this association and the association with EFS24 failure strengthened among those treated with front‐line immunochemotherapy (EFS24 failure odds ratio = 2.57, 95% CI 1.08–5.99; LSS HR = 2.68, 95% CI 1.09–6.58). In mantle cell lymphoma, FHHM was associated with inferior EFS (HR = 1.70, 95% CI 1.02–2.85). Interaction testing did not demonstrate significant heterogeneity of FHHM effects by lymphoma subtype. FHHM may be associated with adverse outcomes in select NHL groups, particularly FL. These findings warrant further investigation of germline and environmental factors potentially contributing to more aggressive FLs.

Abstract INTRODUCTION Determining apolipoprotein E ( APOE ) ε4 allele status, a key genetic risk factor for Alzheimer's disease (AD), requires molecular genotyping infrastructure not widely accessible beyond specialized centers. METHODS A fully automated high‐throughput apoE E4 proteotyping immunoassay was evaluated for clinical performance (460 participants across three cohorts) and analytical validity. Concordance with polymerase chain reaction (PCR)‐based genotyping and measures of analytical validity were reported. RESULTS The apoE E4 immunoassay demonstrated 99.6% (95% confidence interval [CI]: 98.4% to 99.9%) concordance with PCR‐based APOE ε4 genotype results across the pooled clinical cohort; 100.0% (95% CI: 97.1% to 100.0%) in those with AD ( N = 127) and 99.4% (95% CI: 97.8% to 99.8%) in those without AD (333). The assay met analytical validity criteria for E4 isoform specificity, interference, precision, and stability. DISCUSSION The apoE E4 immunoassay demonstrated high concordance with PCR‐based genotyping and robust analytical validity, offering an accessible alternative for APOE ε4 zygosity assessment. Highlights A novel high‐throughput plasma‐based proteotyping immunoassay for APOE ε4 zygosity classification was developed and evaluated for clinical performance and analytical validity. The apoE E4 immunoassay demonstrated high concordance (99.6%) with PCR‐based APOE ε4 genotyping across a diverse international cohort, and a robust analytical profile. An apoE E4 immunoassay may offer a more cost‐effective and accessible alternative to DNA genotyping approaches currently used for AD risk evaluation and anti‐amyloid treatment decisions.

Abstract Objectives Traditional laboratory regulatory frameworks provide robust guidance for conventional clinical testing but lack requirements for data documentation for artificial intelligence and machine learning (AI/ML) solutions. This gap creates significant risks since AI solutions directly inherit patterns, biases, and limitations from their training data. Here, we describe the development and implementation of a comprehensive data documentation checklist with accompanying data lineage for use within an AI lifecycle framework for clinical laboratories. Methods Building on the previously established Clinical AI Readiness Evaluator (CARE) framework, we developed a comprehensive data checklist and data lineage methodology through a multiphase process, including (1) comprehensive review of existing AI/ML data documentation frameworks, (2) focused meetings with 3 institutional AI operations teams, and (3) 3 rounds of iterative refinement by our multidisciplinary team. The checklist’s effectiveness was then assessed using 3 diverse AI/ML projects moving through the CARE framework. Results The CARE Data Checklist and Data Lineage provide a structured approach to documenting critical aspects of datasets used in AI/ML projects, including data composition, demographics, collection methods, labeling processes, usage constraints, maintenance requirements, and a data readiness assessment. The checklist addresses unique data-centric challenges of AI/ML applications, facilitating transparency, reproducibility, and regulatory compliance. Conclusions The CARE Data Checklist and Data Lineage serve as both a technical guide and a communication tool bridging gaps between technical and clinical stakeholders. By working on these documents early in the AI lifecycle, laboratories can anticipate and address data-related challenges, ultimately saving time, optimizing resources, and improving the reliability of AI-augmented laboratory solutions.