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Objectives: Myelin oligodendrocyte glycoprotein (MOG) antibody-associated disease (MOGAD) is frequently preceded by infections. The underlying pathomechanism, however, remains poorly understood. Here, we present the clinical data of two MOGAD patients with concurrent syphilis infection and investigate the reactivity of patient-derived antibodies to MOG and Treponema pallidum (T. pallidum). Methods: Longitudinal serumsamples and soluble immunoglobulins in single B cell supernatants were measured for MOG reactivity by a live cell-based assay. Reactivity against T. pallidum was assessed by enzyme-linked immunosorbent assay. Results: The two patients presented MOGAD and concurrent latent syphilis infection, manifesting as cervical myelitis and unilateral optic neuritis, respectively. The first patient had been living with HIV on antiretroviral therapy, and the second was concomitantly diagnosed with chronic hepatitis B infection. Upon screening of B cell supernatants, we identified reactivity to MOG or T. pallidum. Notably, one B cell showed reactivity to both antigens. Discussion: The coexistence of MOGAD diagnoses and latent syphilis, alongside the identification of antibody reactivity to MOG and T. pallidum, underscores the potential pathomechanistic link between syphilis infection and subsequent autoimmune neuroinflammation. Cross-reactivity between MOG and T. pallidum antibodies remains to be validated on a molecular level, and further characterization of infectious triggers associated with MOGAD is needed. [ABSTRACT FROM AUTHOR]

Aims/hypothesis: The prevalence of type 2 diabetes is increasing worldwide, and previous studies have suggested that it is higher in individuals who are seropositive for herpesviruses. This study examines the prospective association of herpesviruses with (pre)diabetes to evaluate their potential role in diabetes aetiology. Methods: Two follow-up examinations of the German population-based KORA cohort (F4 and FF4) were used to identify participants with normal glucose tolerance at baseline, thus being at risk for (pre)diabetes (n = 1257). All participants had repeated OGTTs and antibody measurements for herpes simplex virus (HSV) 1 and 2, varicella-zoster virus, Epstein–Barr virus, cytomegalovirus (CMV) and human herpesvirus 6 and 7. Regression models were used to evaluate the association between serostatus with (pre)diabetes incidence after a 7 year follow-up and HbA1c. Results: HSV2 and CMV were associated with (pre)diabetes incidence after adjustment for sex, age, BMI, education, smoking, physical activity, parental diabetes, hypertension, lipid levels, insulin resistance and fasting glucose. Seropositivity of both viruses was also cross-sectionally associated with higher HbA1c at baseline, with the association of HSV2 being independent of confounders, including the prevalence of (pre)diabetes itself. While seropositivity for multiple herpesviruses was associated with a higher incidence of (pre)diabetes, this association was not independent of confounders. Conclusions/interpretation: The associations of HSV2 and CMV serostatus with (pre)diabetes incidence indicate that these herpesviruses may contribute to the development of impaired glucose metabolism. Our results highlight the link between viral infection and (pre)diabetes, and the need for more research evaluating viral prevention strategies. [ABSTRACT FROM AUTHOR]

Background: It is unknown whether large language models (LLMs) may facilitate time- and resource-intensive text-related processes in evidence appraisal., Objectives: To quantify the agreement of LLMs with human consensus in appraisal of scientific reporting (PRISMA) and methodological rigor (AMSTAR) of systematic reviews and design of clinical trials (PRECIS-2). To identify areas, where human-AI collaboration would outperform the traditional consensus process of human raters in efficiency., Design: Five LLMs (Claude-3-Opus, Claude-2, GPT-4, GPT-3.5, Mixtral-8x22B) assessed 112 systematic reviews applying the PRISMA and AMSTAR criteria, and 56 randomized controlled trials applying PRECIS-2. We quantified agreement between human consensus and (1) individual human raters; (2) individual LLMs; (3) combined LLMs approach; (4) human-AI collaboration. Ratings were marked as deferred (undecided) in case of inconsistency between combined LLMs or between the human rater and the LLM., Results: Individual human rater accuracy was 89% for PRISMA and AMSTAR, and 75% for PRECIS-2. Individual LLM accuracy was ranging from 63% (GPT-3.5) to 70% (Claude-3-Opus) for PRISMA, 53% (GPT-3.5) to 74% (Claude-3-Opus) for AMSTAR, and 38% (GPT-4) to 55% (GPT-3.5) for PRECIS-2. Combined LLM ratings led to accuracies of 75-88% for PRISMA (4-74% deferred), 74-89% for AMSTAR (6-84% deferred), and 64-79% for PRECIS-2 (29-88% deferred). Human-AI collaboration resulted in the best accuracies from 89-96% for PRISMA (25/35% deferred), 91-95% for AMSTAR (27/30% deferred), and 80-86% for PRECIS-2 (76/71% deferred)., Conclusions: Current LLMs alone appraised evidence worse than humans. Human-AI collaboration may reduce workload for the second human rater for the assessment of reporting (PRISMA) and methodological rigor (AMSTAR) but not for complex tasks such as PRECIS-2., (Copyright © 2024. Published by Elsevier Inc.)

Background: Technological devices such as smartphones, wearables and virtual assistants enable health data collection, serving as digital alternatives to conventional biomarkers. We aimed to provide a systematic overview of emerging literature on 'digital biomarkers,' covering definitions, features and citations in biomedical research., Methods: We analysed all articles in PubMed that used 'digital biomarker(s)' in title or abstract, considering any study involving humans and any review, editorial, perspective or opinion-based articles up to 8 March 2023. We systematically extracted characteristics of publications and research studies, and any definitions and features of 'digital biomarkers' mentioned. We described the most influential literature on digital biomarkers and their definitions using thematic categorisations of definitions considering the Food and Drug Administration Biomarkers, EndpointS and other Tools framework (ie, data type, data collection method, purpose of biomarker), analysing structural similarity of definitions by performing text and citation analyses., Results: We identified 415 articles using 'digital biomarker' between 2014 and 2023 (median 2021). The majority (283 articles; 68%) were primary research. Notably, 287 articles (69%) did not provide a definition of digital biomarkers. Among the 128 articles with definitions, there were 127 different ones. Of these, 78 considered data collection, 56 data type, 50 purpose and 23 included all three components. Those 128 articles with a definition had a median of 6 citations, with the top 10 each presenting distinct definitions., Conclusions: The definitions of digital biomarkers vary significantly, indicating a lack of consensus in this emerging field. Our overview highlights key defining characteristics, which could guide the development of a more harmonised accepted definition., Competing Interests: Competing interests: RC2NB (Research Center for Clinical Neuroimmunology and Neuroscience Basel) is supported by Foundation Clinical Neuroimmunology and Neuroscience Basel. One of the main projects of RC2NB is the development and evaluation of a digital biomarkers which is supported by grants from Novartis, Roche and Innosuisse (Swiss Innovation Agency). All authors declare no competing interests., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)