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Systemic lupus erythematosus (SLE) is a systemic autoimmune disease, that mainly affects skin, joints and kidneys but can affect any organ in the body. It is characterized by presence of multiple autoantibodies like ANA, antibodies to dsDNA and RNA associated proteins. The major mechanism leading to tissue damage includes immune complex mediated complement activation, interferon alpha release by plasmacytoid dendritic cells, NETosis by neutrophils as well as defects in monocytes leading to poor clearance of cellular debris and direct cellular dysfunction mediated by antibodies. A child can present with pyrexia of unknown origin, immune mediated cytopenias, malar rash, oral ulcers, serositis, glomerulonephritis or nervous system dysfunction. As renal disease has a bearing on the long term impact, all children should have urine exam and blood pressure measurement done to rule out renal disease. The treatment varies depending on the severity and organs involved. In life or organ threatening situations, pulse methylprednisolone is used. Hydroxychloroquine, Mycophenolate mofetil, Azathioprine and Cyclophosphamide are the commonly used drugs in SLE. Over the years the prognosis of SLE has improved probably due to early diagnosis and better use of immunosuppressive treatment, regular follow up and treatment of co-morbidities. The 10-year survival now approaches 90% and with advent of new and targeted therapy it is hoped that the morbidity and organ damage can also be minimized., (© 2023. The Author(s), under exclusive licence to Dr. K C Chaudhuri Foundation.)

A subset of Takayasu arteritis (TAK) has onset in the pediatric age group (≤18 years). The differences in mortality between pediatric-onset and adult-onset TAK are unclear. Therefore, we undertook a systematic review with meta-analysis to compare mortality risk in pediatric-onset with adult-onset TAK. Scopus, Pubmed (MEDLINE and Pubmed Central), recent conference abstracts, clinicaltrials.gov, and the Cochrane database were searched up to August 2023 for relevant studies. Five studies (all of moderate or high quality on the Newcastle Ottawa scale) were identified which had compared mortality between 151 pediatric-onset and 499 adult-onset TAK. Pediatric-onset TAK was associated with a significantly higher risk of death than adult-onset TAK (pooled risk ratio 2.27, 95% confidence interval 1.05 - 4.85, I 2 =0%). Cardiovascular disease and infections were the major causes of death in both pediatric-onset and adult-onset TAK. Sub-group analyses identified a greater mortality risk with pediatric-onset TAK in retrospective (but not prospective) studies and in studies of high quality (but not in those of moderate quality). Meta-regression did not reveal a significant influence of differences in sex distribution or age or the proportions of patients with pediatric-onset or adult-onset TAK on the pooled mortality risk. An increased mortality risk with pediatric-onset TAK on meta-analysis is consistent with more frequent severe organ manifestations of pediatric-onset TAK (heart failure, renal failure) when compared with adult-onset TAK. Future studies should systematically evaluate differences in the pathogenesis between pediatric-onset and adult-onset to understand the reasons for such observed differences in the mortality risk., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Intelligence is the ability of humans to learn from experiences to ascribe conscious weights and unconscious biases to modulate their outputs from given inputs. Transferring this ability to computers is artificial intelligence (AI). The ability of computers to understand data in an intelligent manner is machine learning. When such learning is with images and videos, which involves deeper layers of artificial neural networks, it is described as deep learning. Large language models are the latest development in AI which incorporate self-learning into deep learning through transformers. AI in Rheumatology has immense potential to revolutionize healthcare and research. Machine learning could aid clinical diagnosis and decision-making, and deep learning could extend this to analyze images of radiology or positron emission tomography scans or histopathology images to aid a clinician's diagnosis. Analysis of routinely obtained patient data or continuously collected information from wearables could predict disease flares. Analysis of high-volume genomics, transcriptomics, proteomics, or metabolomics data from patients could help identify novel markers of disease prognosis. AI might identify newer therapeutic targets based on in-silico modelling of omics data. AI could help automate medical administrative work such as inputting information into electronic health records or transcribing clinic notes. AI could help automate patient education and counselling. Beyond the clinic, AI has the potential to aid medical education. The ever-expanding capabilities of AI models bring along with them considerable ethical challenges, particularly related to risks of misuse. Nevertheless, the widespread use of AI in Rheumatology is inevitable and a progress with great potential., Competing Interests: Conflict of Interest: The authors declared no conflicts of interest with respect to the authorship and/or publication of this article., (Copyright © 2024, Turkish League Against Rheumatism.)

To assess the impact of the COVID-19 pandemic on the training of rheumatology trainees. We conducted an observational cross-sectional study using an online survey-based questionnaire sent to rheumatology trainees in India. Rheumatology trainees from India, including DM/DNB residents and fellows, were included. A total of 78 trainees from 24 institutes in 12 states participated in the study. An overwhelming majority of residents (84%) felt COVID-19 Pandemic Negatively impacted their residency and their Physical (65%), Mental (74%) and Social well-being (80%); 79% of trainees felt burnt out. Majority of trainees felt the pandemic negatively impacted their training with clinical teaching (91%), Clinical examination skills (74%), current (80%) and future (70%) research opportunities suffering during the pandemic. Most had significant reduction in the overall footfall (72%) of patients in rheumatology including OPD (77%) and indoor (67%) admissions along with academics (35%), procedures (66%) and exposure to musculoskeletal ultrasound (71%). Almost 60% and 40% of trainees had OPDs, and indoor admissions stopped during COVID-19 pandemic of these 20% had OPDs, and Admissions closed for more than 6 months. 85% of participants had one or the other psychological symptoms with almost half experiencing anxiety (44%), low mood (47%) or lack of sleep (41%). We found The COVID-19 Pandemic has significantly affected the physical, social and mental well-being of Rheumatology trainees. Academic and clinical training reduced, current and future Research became difficult, disruptions in OPDs and Admissions, recurrent COVID postings and reduction in patient footfall, procedures and MSK-US have been detrimental to trainees., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Competing Interests: Competing interests: None declared.

Competing Interests: Competing interests: None declared.