Jade N. Young

Seropositive rheumatoid arthritis (RA) is an autoimmune disease whose course typically begins before joint involvement, during a period termed pre-RA, which is initially characterized by the presence of autoantibodies, including anti-citrullinated protein antibodies and rheumatoid factors. The ability to identify autoantibody-positive individuals who are at an increased risk of developing clinical RA has catalysed efforts to define the immune mechanisms underlying the initial break in self-tolerance and the subsequent transition to clinically apparent disease. Although not mutually exclusive, these investigations have broadly followed two complementary approaches: hypothesis-testing studies and agnostic multi-omics analysis. Hypothesis-testing studies have identified site-specific and isotype-specific patterns of mucosal anti-citrullinated protein antibodies and rheumatoid factor production, together with inflammation and/or dysbiosis associated with systemic autoimmunity, supporting a ‘mucosal origins hypothesis’. Cross-sectional and longitudinal multi-omics studies have demonstrated epitope spreading, pro-inflammatory biomarkers and dysregulated peripheral blood immune-cell populations with autoimmune characteristics. Epidemiological studies have further implicated certain environmental exposures in RA development. Collectively, these observations suggest that integrating molecular, cellular and environmental data through emerging data science and artificial intelligence approaches could help to identify pathogenic mechanisms underlying RA development and inform future prevention strategies.

Single‐cell technologies have transformed the understanding of disease heterogeneity, but linking cell‐level phenotypic alterations to clinical outcomes becomes increasingly challenging as single‐cell datasets continue to expand. This is further complicated by the lack of interpretability in existing methods and the difficulty of detecting interaction effects—nonlinear dependencies between factors like sex, age, and disease. To address this, a novel explainable machine learning method, CellPhenoX, is developed to identify cell‐specific phenotypes and interaction effects linked to clinical outcomes. CellPhenoX integrates classification models, explainable artificial intelligence (AI) techniques, and a statistical framework to generate interpretable, cell‐specific scores to uncover condition‐associated cell populations. Extensive benchmarking and applications demonstrate the efficacy of CellPhenoX across diverse single‐cell study designs, including the dedicated and disease‐motivated simulations, binary disease‐control comparisons, and severity‐stratified patient cohorts. Notably, CellPhenoX identifies an activated monocyte phenotype in COVID‐19, with expansion correlated with disease severity after adjusting for covariates and interactive effects. It also uncovers a fibroblast‐specific state transition gradient predicting tissue inflammation in chronic diseases, and identifies therapy‐induced T cell changes and biomarkers linked to the tumor microenvironment. By integrating interpretability into clinical classification, CellPhenoX offers a powerful framework for translating single‐cell findings into clinical impact.

SUMMARY: Single-cell technologies have enhanced our knowledge of molecular and cellular heterogeneity underlying disease. As the scale of single-cell datasets expands, linking cell-level phenotypic alterations with clinical outcomes becomes increasingly challenging. To address this, we introduce CellPhenoX, an eXplainable machine learning method to identify cell-specific phenotypes that influence clinical outcomes. CellPhenoX integrates classification models, explainable AI techniques, and a statistical framework to generate interpretable, cell-specific scores that uncover cell populations associated with relevant clinical phenotypes and interaction effects. We demonstrated the performance of CellPhenoX across diverse single-cell designs, including simulations, binary disease-control comparisons, and multi-class studies. Notably, CellPhenoX identified an activated monocyte phenotype in COVID-19, with expansion correlated with disease severity after adjusting for covariates and interactive effects. It also uncovered an inflammation-associated gradient in fibroblasts from ulcerative colitis. We anticipate that CellPhenoX holds the potential to detect clinically relevant phenotypic changes in single-cell data with multiple sources of variation, paving the way for translating single-cell findings into clinical impact.

This study investigates the application of ChatGPT, an artificial intelligence tool, in providing information on skin toxicities associated with cancer treatments, highlighting that while ChatGPT can serve as a valuable resource for clinicians, its use for patient education requires careful consideration due to the complex nature of the information provided.

Background: Licensed nonmedical, skin‐aware professionals (e.g., hairdressers, massage therapists, etc.) have the potential to identify skin cancer, but baseline knowledge may not be sufficient to accomplish this goal. Following educational intervention, self‐efficacy is one of the best surrogate metrics for behavior change. Curricula that increase knowledge and confidence levels can improve screening behaviors, but few have been tested for efficacy in this populationAims: We assessed whether an online curriculum could reliably improve skin screening knowledge, attitudes, and behaviors of nonmedical professionalsPatients/Methods: Skin‐aware professionals were recruited through the Oregon Health Authority and IMPACT Melanoma TM. Participants completed a pre‐survey, online training module, post‐survey, and one‐year follow‐up survey. We evaluated participants' indicated levels of concern for suspicious and nonsuspicious lesions relative to “gold standard” physician ratings. We also assessed confidence and self‐reported behavior change regarding talking to clients about skin cancer and recommending they see a provider to evaluate suspicious lesionsResults: The pre‐survey was completed by 9872 skin‐aware professionals; 5434 completed the post‐survey, and 162 completed the one‐year follow‐up survey. Participants showed a significant improvement in ability to indicate the correct level of concern for all lesion types in concordance with “gold standard” physician ratings (p < 0.001). Participants reported increased comfort levels in discussing health‐related topics with their clients posttrainingConclusions: Our training module effectively increased skin‐aware professionals' knowledge, confidence, and concern for malignant lesions. Skin‐aware professionals may serve as a valuable extension of the skin self‐exam, but additional studies are needed to evaluate the impact of these curricula long‐term, including potential downstream consequences

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Twelve biologics targeting TNF-α, IL-12/23, IL-17, and IL-23 are FDA-approved for treating moderate-to-severe psoriasis. We analyzed Google Trends search volumes for these medications to examine public interest and awareness over time and geography in the US. We included all 12 FDA-approved psoriasis biologics in our analysis of Relative Search Volume Index from Google Trends, scaling from 0 (no searches) to 100 (peak volume). We analyzed trends since each medication's approval using Mann-Kendall tests and compared search volumes geographically over the past year. Most biologics showed rising search volumes over time, except etanercept and brodalumab. Search interest was higher along the coasts versus the Midwest. Despite no proven superiority, searches rose rapidly for risankizumab since 2021 (P<0.001). Our findings reveal disparities in patient awareness of psoriasis treatments, with consistently low search volumes for many standard biologics across US regions. This study highlights the need to improve public knowledge on the range of available psoriasis medications through comprehensive patient education.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Twelve biologics targeting TNF-α, IL-12/23, IL-17, and IL-23 are FDA-approved for treating moderate-to-severe psoriasis. We analyzed Google Trends search volumes for these medications to examine public interest and awareness over time and geography in the US. We included all 12 FDA-approved psoriasis biologics in our analysis of Relative Search Volume Index from Google Trends, scaling from 0 (no searches) to 100 (peak volume). We analyzed trends since each medication's approval using Mann-Kendall tests and compared search volumes geographically over the past year. Most biologics showed rising search volumes over time, except etanercept and brodalumab. Search interest was higher along the coasts versus the Midwest. Despite no proven superiority, searches rose rapidly for risankizumab since 2021 (P<0.001). Our findings reveal disparities in patient awareness of psoriasis treatments, with consistently low search volumes for many standard biologics across US regions. This study highlights the need to improve public knowledge on the range of available psoriasis medications through comprehensive patient education.