Charcot-Marie-Tooth (CMT) disease is a heterogeneous group of genetic disorders of the peripheral nervous system. Copy-number variants (CNVs) contribute significantly to CMT, as duplication of PMP22 underlies the majority of CMT1 cases. We hypothesized that CNVs and/or single-nucleotide variants (SNVs) might exist in patients with CMT with an unknown molecular genetic etiology. Two hundred patients with CMT, negative for both SNV mutations in several CMT genes and for CNVs involving PMP22, were screened for CNVs by high-resolution oligonucleotide array comparative genomic hybridization. Whole-exome sequencing was conducted on individuals with rare, potentially pathogenic CNVs. Putatively causative CNVs were identified in five subjects (~2.5%); four of the five map to known neuropathy genes. Breakpoint sequencing revealed Alu-Alu-mediated junctions as a predominant contributor. Exome sequencing identified MFN2 SNVs in two of the individuals. Neuropathy-associated CNV outside of the PMP22 locus is rare in CMT. Nevertheless, there is potential clinical utility in testing for CNVs and exome sequencing in CMT cases negative for the CMT1A duplication. These findings suggest that complex phenotypes including neuropathy can potentially be caused by a combination of SNVs and CNVs affecting more than one disease-associated locus and contributing to a mutational burden. Genet Med 18 5, 443–451.

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 Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn.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). Background: Large language model-based chatbots are increasingly used by the public to access medical information. While these tools offer considerable potential in terms of accessibility and scalability, their accuracy, transparency, and clarity remain insufficiently evaluated for rare and diagnostically complex conditions such as myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD). Objective: This study aimed to evaluate the quality, comprehensibility, transparency, and readability of responses generated by widely used AI chatbot platforms in response to a standardized, patient-centered question about MOGAD. Methods: We conducted a cross-sectional content analysis using the query: “What is MOGAD, and how is MOGAD treated?” Ten widely used chatbot platforms were selected to reflect diversity in architecture, access model, and functional design. Responses were collected on the same day, anonymized, and independently evaluated by seven blinded neurologists. Validated instruments were used, including DISCERN (treatment quality), PEMAT-P (understandability), Web Resource Rating (WRR; citation transparency), and two readability metrics: Flesch–Kincaid Grade Level (FKGL) and Coleman–Liau Index (CLI). Chatbots were also compared by access type (free vs paid) and functional focus (conversation-based vs search-based). Inter-rater reliability was assessed using intraclass correlation coefficients (ICCs). Results: Significant differences were observed across platforms in DISCERN, PEMAT-P, and WRR scores (all p < 0.001). Paid chatbots demonstrated higher treatment quality (p = 0.020) and citation transparency (p = 0.001) compared to free versions. Search-based models produced more understandable responses than conversation-based ones (p = 0.035). However, none of the chatbot responses achieved the recommended readability threshold for public-facing health communication (FKGL < 8). Inter-rater agreement was excellent across all expert-rated measures (ICC ≥ 0.838). Conclusions: AI chatbot responses to patient queries about MOGAD vary widely in quality, clarity, and transparency. These findings highlight the need for structured benchmarking, transparent evaluation frameworks, and thoughtful oversight in the use of generative AI tools for digital health communication, particularly in the context of rare and clinically complex diseases.

Aile hekimliği tıp disiplinin özelliklerini, ilkelerini ve temel değerlerini detaylarıyla bulabileceğiniz bölümlerin yanında, klinik uygulamalarda izlenecek yollar ve kılavuzların eşlik ettiği güncel bilgileri sizlerle paylaşmayı hedefledik. Aile hekimliğinin, yaş, cinsiyet, hastalık ayırt etmeksizin sunduğu sağlık bakımına ek olarak koruyucu sağlık hizmetlerinin prekonsepsiyonel dönemden bireyin ölümü sonrasındaki aile bireylerinin durumuna kadar geniş konu yelpazesinin varlığı bu kitabın kapsamlı ve güncel olması yönünde bize yol haritası olmuştur. Aile hekimliğinin, kendi eğitim içeriği, araştırmaları, kanıta dayalı ve klinik uygulamaları olan akademik ve bilimsel bir disiplin ve birinci basamağa odaklı bir klinik uzmanlık modeli ile konuların bilimsel ve pragmatik düzenlemesi yapılarak akışı sağlanmış, bilgilerin sistematik anlatımı kitap içerisinde planlanmıştır.

Introduction: Given the involvement of monocytes in multiple sclerosis (MS) pathogenesis, we aimed to compare the pan-immune-inflammation value (PIV) with the systemic immune-inflammation index (SII) in differentiating MS patients from healthy controls and in detecting relapse (attack) periods of disease activity. Methods: This case–control study included 43 newly diagnosed, treatment-naïve MS patients and 44 healthy controls. Blood neutrophil, lymphocyte, platelet, and monocyte counts were measured, and the SII and PIV values were calculated. Samples from patients with MS were collected during both the relapse and remission phases. SII and PIV were then compared between the patient and control groups and between relapse and remission states in the MS cohort. Results: Neither SII nor PIV differed significantly between patients with MS and healthy controls or between the relapse and remission phases (p>0.05 for all comparisons). In contrast, the median Expanded Disability Status Scale (EDSS) score was significantly higher during relapses than during remission (median 2.5 vs. 1.0, p<0.001), confirming greater disability during attack periods. Notably, although statistically non-significant, PIV yielded lower p-values than SII, which may indicate a weak trend requiring further study in larger samples. in the relapse vs remission comparison (PIV, p=0.261; SII, p=0.819). Conclusions: Hemogram-derived inflammatory indices, such as SII and PIV, appear insufficient for diagnosing MS or distinguishing relapses from remission. However, the consistently lower p-values observed with PIV suggest that it may be a slightly more sensitive marker than SII. Further large-scale studies, including both newly diagnosed and treated MS populations, are warranted to clarify the potential role of PIV in monitoring MS disease activity.