A Survey of Demographics and Treatments in Melanoma Case Reports: Retrospective Bibliometric Analysis.

Melanoma case reports show variations in treatment by age and sex.

Relationship of Soluble Lectin-Like Low-Density Lipoprotein Receptor-1 (sLOX-1) With Inflammation and Coronary Plaque Progression in Psoriasis.

BACKGROUND: Psoriasis is a chronic inflammatory condition associated with coronary artery disease risk. Uptake of oxidized low-density lipoprotein by the lectin-like low-density lipoprotein receptor-1 triggers release of the soluble extracellular domain of the receptor (sLOX-1). We sought to characterize the relationship between sLOX-1, inflammation, and coronary plaque progression in psoriasis. METHODS AND RESULTS: A total of 327 patients with psoriasis had serum sLOX-1 levels measured at baseline by an ELISA-based assay. Stratification by high-sensitivity C-reactive protein ≥4.0 mg/L (quartile 4), identified 81 participants who had coronary plaque phenotyping at baseline and were followed longitudinally by coronary computed tomography angiography. Subjects within high-sensitivity C-reactive protein quartile 4 were middle-aged (51.47±12.62 years), predominantly men (54.3%) with moderate psoriasis disease severity (6.60 [interquartile range, 3.30-13.40]). In the study cohort, participants with sLOX-1 above the median displayed increased vulnerable coronary plaque features. At baseline, sLOX-1 was associated with total burden (rho=0.296; P=0.01), noncalcified burden (rho=0.286; P=0.02), fibro-fatty burden (rho=0.346; P=0.004), and necrotic burden (rho=0.394; P=0.002). A strong relationship between sLOX-1, noncalcified burden (ß=0.19; P=0.03), and fibro-fatty burden (ß=0.29; P=0.003) was found in fully adjusted models at baseline and 1- and 4-year follow-up. Finally, coronary plaque features progressed over 1 year regardless of biologic or systemic treatment in subjects with high sLOX-1. CONCLUSIONS: Patients with psoriasis with both high sLOX-1 and high-sensitivity C-reactive protein levels have increased coronary plaque burden associated with atherosclerotic plaque progression independent of biologic and systemic treatment. Thus, sLOX-1 might be considered as a promising marker in coronary artery disease risk estimation beyond traditional risk factors. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT01778569.

Longitudinal association of epicardial and thoracic adipose tissues with coronary and cardiac characteristics in psoriasis.

Background: s: Psoriasis is a disease of systemic inflammation associated with increased cardiometabolic risk. Epicardial adipose tissue (EAT) and thoracic adipose tissue (TAT) are contributing factors for atherosclerosis and cardiac dysfunction. We strove to assess the longitudinal impact of the EAT and TAT on coronary and cardiac characteristics in psoriasis. Methods: The study consisted of 301 patients with baseline coronary computed tomography angiography (CTA), of which 139 had four-year follow up scans. EAT and TAT volumes from non-contrast computed tomography scans were quantified by an automated segmentation framework. Coronary plaque characteristics and left ventricular (LV) mass were quantified by CTA. Results: When stratified by baseline EAT and TAT volume quartiles, a stepwise significant increase in cardiometabolic parameters was observed. EAT and TAT volumes associated with fibro-fatty burden (FFB) (TAT: ρ = 0.394, P < 0.001; EAT: ρ = 0.459, P < 0.001) in adjusted models. Only EAT had a significant four-year time-dependent association with FFB in fully adjusted models (ß = 0.307 P = 0.003), whereas only TAT volume associated with myocardial injury in fully adjusted models (TAT: OR = 1.57 95 % CI = (1.00-2.60); EAT: OR = 1.46 95 % CI = (0.91-2.45). Higher quartiles of EAT and TAT had increased LV mass and developed strong correlation (TAT: ρ = 0.370, P < 0.001; EAT: ρ = 0.512, P < 0.001). Conclusions: Our study is the first to explore how both EAT and TAT volumes associate with increased cardiometabolic risk profile in an inflamed psoriasis cohorts and highlight the need for further studies on its use as a potential prognostic tool for high-risk coronary plaques and cardiac dysfunction.

Trends in Accuracy and Appropriateness of Alopecia Areata Information Obtained from a Popular Online Large Language Model, ChatGPT.

BACKGROUND: Patients with alopecia areata (AA) may access a wide range of sources for information about AA, including the recently developed ChatGPT. Assessing the quality of health information provided by these sources is crucial, as patients are utilizing them in increasing numbers. OBJECTIVES: The aim of the study was to evaluate appropriateness and accuracy of responses to common patient questions about AA generated by ChatGPT. METHODS: Responses generated by ChatGPT 3.5 and ChatGPT 4.0 to 25 questions addressing common patient concerns were assessed by multiple attending dermatologists in an academic center for appropriateness and accuracy. Appropriateness of responses by both models for use in two hypothetical contexts as follows: (1) for patient-facing general information websites, and (2) for electronic health record (EHR) message drafts. RESULTS: The accuracy across all responses was 4.41 out of 5. Accuracy scores of responses ChatGPT 3.5 responses had a mean of 4.29, whereas those generated by ChatGPT 4.0 had mean accuracy score of 4.53. Assessments ranged from 100% of responses rated as appropriate for the general question category to 79% questions about management for an EHR message draft. Raters largely preferred responses generated by ChatGPT 4.0 versus ChatGPT 3.5. Reviewer agreement was found to be moderate across all questions, with a 53.7% agreement and Fleiss' κ co-efficient of 0.522 (p value <0.001). CONCLUSIONS: The large language model ChatGPT outputted mostly appropriate information for common patient concerns. While not all responses were accurate, the trend toward improvement with newer iterations suggests potential future utility for patients and dermatologists.

A Survey of Patient Demographics in Inflammatory Skin Disease Case Reports.

Case reports serve many functions in the medical literature. We explore patient demographics in case reports for common inflammatory skin diseases.

Deep learning on electronic medical records identifies distinct subphenotypes of diabetic kidney disease driven by genetic variations in the Rho pathway.

Kidney disease affects 50% of all diabetic patients; however, prediction of disease progression has been challenging due to inherent disease heterogeneity. We use deep learning to identify novel genetic signatures prognostically associated with outcomes. Using autoencoders and unsupervised clustering of electronic health record data on 1,372 diabetic kidney disease patients, we establish two clusters with differential prevalence of end-stage kidney disease. Exome-wide associations identify a novel variant in ARHGEF18, a Rho guanine exchange factor specifically expressed in glomeruli. Overexpression of ARHGEF18 in human podocytes leads to impairments in focal adhesion architecture, cytoskeletal dynamics, cellular motility, and RhoA/Rac1 activation. Mutant GEF18 is resistant to ubiquitin mediated degradation leading to pathologically increased protein levels. Our findings uncover the first known disease-causing genetic variant that affects protein stability of a cytoskeletal regulator through impaired degradation, a potentially novel class of expression quantitative trait loci that can be therapeutically targeted.

Estimated sdLDL-C for predicting high-risk coronary plaque features in psoriasis: a prospective observational study.

BACKGROUND: Psoriasis (PSO) is a skin disorder with systemic inflammation and high coronary artery disease risk. A distinct lipid phenotype occurs in psoriasis, which is characterized by high plasma triglycerides (TGs) with typically normal or even low LDL-C. The extent to which cholesterol on LDL subfractions, such as small dense LDL-C (sdLDL-C), are associated with vulnerable coronary plaque characteristics in PSO remains elusive. METHODS: A recently developed equation for estimating sdLDL-C from the standard lipid panel was utilized in a PSO cohort (n = 200) with 4-year follow-up of 75 subjects. Coronary plaque burden was assessed by quantitative coronary computed tomography angiography (CCTA). Multivariate regression analyses were used for establishing associations and prognostic value of estimated sdLDL-C. RESULTS: Estimated sdLDL-C was positively associated with non-calcified burden (NCB) and fibro-fatty burden (FFB), which remained significant after multivariate adjustment for NCB (ß = 0.37; P = 0.050) and LDL-C adjustment for FFB (ß = 0.29; P < 0.0001). Of note, total LDL-C calculated by the Friedewald equation was not able to capture these associations in the study cohort. Moreover, in the regression modelling estimated sdLDL-C was significantly predicting necrotic burden progression over 4 years follow-up (P = 0.015), whereas LDL-C did not. Finally, small LDL particles (S-LDLP) and small HDL particles (S-HDLP), along with large and medium TG-rich lipoproteins (TRLPs) had the most significant positive correlation with estimated sdLDL-C. CONCLUSIONS: Estimated sdLDL-C has a stronger association than LDL-C with high-risk features of coronary atherosclerotic plaques in psoriasis patients. CLINICAL TRIAL REGISTRATION: URL: https://www. CLINICALTRIALS: gov . Unique identifiers: NCT01778569.