Background/Aim: Women with rheumatic and musculoskeletal disorders often discontinue using their medications prior to conception or during the few early weeks of pregnancy because drug use during pregnancy frequently results in anxiety. Pregnant women have reported seeking out health-related information from a variety of sources, particularly the Internet, in an attempt to ease their concerns about the use of such medications during pregnancy. The objective of this study was to evaluate the accuracy and completeness of health-related information concerning the use of anti-rheumatic medications during pregnancy as provided by Open Artificial Intelligence (AI's) Chat Generative Pre-trained Transformer (ChatGPT) versions 3.5 and 4, which are widely known AI tools. Methods: In this prospective cross-sectional study, the performances of OpenAI's ChatGPT versions 3.5 and 4 were assessed regarding health information concerning anti-rheumatic drugs during pregnancy using the 2016 European Union of Associations for Rheumatology (EULAR) guidelines as a reference. Fourteen queries from the guidelines were entered into both AI models. Responses were evaluated independently and rated by two evaluators using a predefined 6-point Likert-like scale (1 – completely incorrect to 6 – completely correct) and for completeness using a 3-point Likert-like scale (1 – incomplete to 3 – complete). Inter-rater reliability was evaluated using Cohen’s kappa statistic, and the differences in scores across ChatGPT versions were compared using the Mann–Whitney U test. Results: No statistically significant difference between the mean accuracy scores of GPT versions 3.5 and 4 (5 [1.17] versus 5.07 [1.26]; P=0.769), indicating the resulting scores were between nearly all accurate and correct for both models. Additionally, no statistically significant difference in the mean completeness scores of GPT 3.5 and GPT 4 (2.5 [0.51] vs 2.64 [0.49], P=0.541) was found, indicating scores between adequate and comprehensive for both models. Both models had similar total mean accuracy and completeness scores (3.75 [1.55] versus 3.86 [1.57]; P=0.717). In the GPT 3.5 model, hydroxychloroquine and Leflunomide received the highest full scores for both accuracy and completeness, while methotrexate, Sulfasalazine, Cyclophosphamide, Mycophenolate mofetil, and Tofacitinib received the highest total scores in the GPT 4 model. Nevertheless, for both models, one of the 14 drugs was scored as more incorrect than correct. Conclusions: When considering the safety and compatibility of anti-rheumatic medications during pregnancy, both ChatGPT versions 3.5 and 4 demonstrated satisfactory accuracy and completeness. On the other hand, the research revealed that the responses generated by ChatGPT also contained inaccurate information. Despite its good performance, ChatGPT should not be used as a standalone tool to make decisions about taking medications during pregnancy due to this AI tool’s limitations.

Objective A novel inflammation-based score, C-reactive protein (CRP)-to-albumin ratio (CAR), has been shown to have an association with the inflammatory status in several diseases. We aimed to analyse the association between CAR and disease activity in patients with early rheumatoid arthritis (RA) and to determine the cut-off value of CAR in early and established RA. Methods A total of 177 patients with RA and 111 age and gender-matched healthy controls were included in this study. Cases with a disease duration of less than 1 year were classified as early RA. Serum albumin, CRP, erythrocyte sedimentation rate (ESR), Disease Activity Score-28 (DAS-28-ESR), Clinical Disease Activity Index (CDAI) and Health Assessment Questionnaire (HAQ) scores were recorded. Results CAR was 2.44 (0.21–30.83) in the RA group and 0.45 (0.21–10.47) in the control group (p<0.001). Eighty-seven (49.15%) of the RA cases were classified as early RA. The analyses indicated that the ESR, CRP and CAR values were higher in patients with early RA than in those with established RA and controls. CAR was correlated with albumin, CRP, ESH, DAS-28 and HAQ scores in both early RA and established RA groups. The receiver operating characteristic curves revealed a CAR cut-off value of 2.67 (80% sensitivity and 85% specificity) and 1.63 (77% sensitivity and 72% specificity) for the prediction of early and established RA, respectively. Conclusion CAR, a formulated ratio, has been described as a predictor for disease activity in patients with early RA as well as in those with established RA. However, CAR has higher sensitivity and specificity for early RA than for established RA.

Osteoporosis, defined as a systemic skeletal disease, is characterized by increased bone fragility and fracture risk. Studies have shown that dysregulation of the functions of miRNAs or the mechanisms they mediate may be an important pathological factor in bone degeneration. Therefore, the aim of the study was to determine the role of miRNAs, which are thought to play a role in bone metabolism, in osteoporosis. The study included 48 patients who were diagnosed with osteoporosis according to the results of a bone mineral density assessment by quantitative computed tomography and 36 healthy individuals. MiRNAs from plasma samples obtained from blood samples taken into ethylenediaminetetraacetic acid (EDTA) tubes were isolated with the miRNA isolation kit and converted to cDNA. Expression analysis of miR-21-5p, miR-34a-5p, miR-210, miR-122-5p, miR-125b-5p, miR-133a, miR-143-3p, miR-146a, miR-155-5p, and miR-223 was performed on the real-time PCR (RT-PCR) device. When miRNA expression levels in the patient group were compared with the control group, all miRNAs were found to be downregulated in the patients. When fold changes in expression levels in the patient group were examined, significant differences were found in miR-21-5p, miR-133a, mir143-3p, miR-210, and miR-223. In the receiver operating curve analysis, area under the curve=0.882 for the combination of miR-34, miR-125, miR-133, and miR-210. In this study, it was determined that the combined effects of miRNAs, as well as their single effects, were effective in the development of osteoporosis. Therefore, a miRNA panel to be created can make a significant contribution to the development of novel diagnostic and treatment approaches for this disease.