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Aim/hypothesisFive subgroups were described in European diabetes patients using a data driven machine learning approach on commonly measured variables. We aimed to test the applicability of this phenotyping in Indian individuals with young-onset type 2 diabetes.MethodsWe applied the European-derived centroids to Indian individuals with type 2 diabetes diagnosed before 45 years of age from the WellGen cohort (n = 1612). We also applied de novo k-means clustering to the WellGen cohort to validate the subgroups. We then compared clinical and metabolic-endocrine characteristics and the complication rates between the subgroups. We also compared characteristics of the WellGen subgroups with those of two young European cohorts, ANDIS (n = 962) and DIREVA (n = 420). Subgroups were also assessed in two other Indian cohorts, Ahmedabad (n = 187) and PHENOEINDY-2 (n = 205).ResultsBoth Indian and European young-onset type 2 diabetes patients were predominantly classified into severe insulin-deficient (SIDD) and mild obesity-related (MOD) subgroups, while the severe insulin-resistant (SIRD) and mild age-related (MARD) subgroups were rare. In WellGen, SIDD (53%) was more common than MOD (38%), contrary to findings in Europeans (Swedish 26% vs 68%, Finnish 24% vs 71%, respectively). A higher proportion of SIDD compared with MOD was also seen in Ahmedabad (57% vs 33%) and in PHENOEINDY-2 (67% vs 23%). Both in Indians and Europeans, the SIDD subgroup was characterised by insulin deficiency and hyperglycaemia, MOD by obesity, SIRD by severe insulin resistance and MARD by mild metabolic-endocrine disturbances. In WellGen, nephropathy and retinopathy were more prevalent in SIDD compared with MOD while the latter had higher prevalence of neuropathy.Conclusions /interpretationOur data identified insulin deficiency as the major driver of type 2 diabetes in young Indians, unlike in young European individuals in whom obesity and insulin resistance predominate. Our results provide useful clues to pathophysiological mechanisms and susceptibility to complications in type 2 diabetes in the young Indian population and suggest a need to review management strategies.

IntroductionComputer vision extracts meaning from pixelated images and holds promise in automating various clinical tasks. Convolutional neural networks (CNNs), a deep learning network used therein, have shown promise in analyzing X-ray images and joint photographs. We studied the performance of a CNN on standardized smartphone photographs in detecting inflammation in three hand joints and compared it to a rheumatologist’s diagnosis.MethodsWe enrolled 100 consecutive patients with inflammatory arthritis with an onset period of less than 2 years, excluding those with deformities. Each patient was examined by a rheumatologist, and the presence of synovitis in each joint was recorded. Hand photographs were taken in a standardized manner, anonymized, and cropped to include joints of interest. A ResNet-101 backbone modified for two class outputs (inflamed or not) was used for training. We also tested a hue-augmented dataset. We reported accuracy, sensitivity, and specificity for three joints: wrist, index finger proximal interphalangeal (IFPIP), and middle finger proximal interphalangeal (MFPIP), taking the rheumatologist’s opinion as the gold standard.ResultsThe cohort consisted of 100 individuals, of which 22 of them were men, with a mean age of 49.7 (SD 12.9) years. The majority of the cohort ( n  = 68, 68%) had rheumatoid arthritis. The wrist (125/200, 62.5%), MFPIP (94/200, 47%), and IFPIP (83/200, 41.5%) were the three most commonly inflamed joints. The CNN achieved the highest accuracy, sensitivity, and specificity in detecting synovitis in the MFPIP (83, 77, and 88%, respectively), followed by the IFPIP (74, 74, and 75%, respectively) and the wrist (62, 90, and 21%, respectively).DiscussionWe have demonstrated that computer vision was able to detect inflammation in three joints of the hand with reasonable accuracy on standardized photographs despite a small dataset. Feature engineering was not required, and the CNN worked despite a diversity in clinical diagnosis. Larger datasets are likely to improve accuracy and help explain the basis of classification. These data suggest a potential use of computer vision in screening and follow-up of inflammatory arthritis.

Several studies report that ashwagandha, a traditional Ayurvedic supplement, has anti-inflammatory properties. Type 2 (T2) asthma is characterized by eosinophilic airway inflammation. We hypothesized that allergen-induced eosinophilic airway inflammation in mice would be reduced following administration of Withaferin A (WFA), the primary active phytochemical in Ashwagandha. C57BL/6J mice were given 10 total intra-peritoneal injections of 2 mg/kg WFA or vehicle control, concurrent with 6 total intranasal administrations of 50 μg house dust mite extract (HDM) or saline control over 2 weeks. We observed that treatment with WFA reduced allergen-induced peribronchial inflammation and airway eosinophil counts compared to mice treated with controls. In addition, we observed that treatment with WFA reduced lung levels of interleukin-25 (IL-25) but increased lung gene expression levels of its co-receptor, Il17ra, in HDM-challenged mice compared to HDM-challenged mice that received the vehicle control. This study pinpoints a potential mechanism by which WFA modulates allergen-induced airway eosinophilia via the IL-25 signaling pathway. Future studies will investigate the effects of WFA administration on lung eosinophilia and IL-25 signaling in the context of chronic allergen-challenge.

Type 2 diabetes is a common disease worldwide, but its prevalence varies widely by geographical region and by race/ethnicity. This review summarises differences in the frequencies of type 2 diabetes according to race, ethnicity, socioeconomic position, area of residence and environmental toxins. Type 2 diabetes susceptibility often begins early in life, starting with genetic susceptibility at conception and continuing in later life, via in utero, childhood and adult exposures. Early-life factors may lead to overt type 2 diabetes in childhood or in later life, supporting the concept of developmental origins of health and disease. The causes of the racial/ethnic differences in incidence of type 2 diabetes are not well understood. Specifically, the relative contributions of genetic and environmental factors to such differences are largely unknown. With a few exceptions in isolated populations, there is little evidence that differences in frequencies of known type 2 diabetes susceptibility genetic alleles account for racial/ethnic differences, although the search for genetic susceptibility has not been uniform among the world’s racial/ethnic groups. In the USA, race/ethnicity is associated with many other risk factors for type 2 diabetes, including being overweight/obese, diet and socioeconomic status. Some studies suggest that some of these factors may account for the race/ethnic differences in prevalence of type 2 diabetes, although there is inadequate research in this area. A better understanding of the impact of these factors on type 2 diabetes risk should lead to more effective prevention and treatment of this disease. This has not yet been achieved but should be a goal for future research.

Generic tofacitinib has been available in India for more than a year and is widely used in rheumatoid arthritis (RA) therapy. There is scarce real-world data on its effectiveness and safety from India, especially given infection endemicity. We retrospectively analysed records (demographic and clinical information, haematology and biochemistry, adverse events) of patients prescribed generic tofacitinib from a single centre in Mumbai, India. Disease activity was calculated using the disease activity score-28 and erythrocyte sedimentation rate (DAS28-ESR) and other tools, and we used paired T -tests for significant response. We defined clinical tofacitinib failure as a composite outcome, including clinician’s decision to change to an alternative disease-modifying anti-rheumatic drug (DMARD) or flare after self-withdrawal. We performed logistic regression and survival analysis for determinants of clinical failure. We reviewed records of 102 patients (92 female; median age: 53 years) with mean RA duration of 146 months. Thirteen had prior treatment with innovator tofacitinib. There was significant improvement in disease activity parameters at a mean duration of 186 days. No serious adverse events were reported; 4 patients had tuberculosis and 19 patients had mild COVID-19 while on treatment. Clinical failure was seen in 25 patients, and mean time to failure on survival analysis was 357 days. No baseline characteristic predicted clinical failure. Generic tofacitinib showed good effectiveness and a tolerable adverse effect profile, despite tuberculosis endemicity and COVID-19. Setting up registries would be valuable in gaining more data on generic tofacitinib. Key Points • There is scarce data from India regarding the use of tofacitinib in rheumatoid arthritis, despite widespread use. • In this retrospective analysis of 102 patients at a single centre, we found tofacitinib monotherapy was efficacious and tolerable. • Tuberculosis was detected in four and nineteen patients had mild covid.

A young woman presented with recurrent pericardial effusion, she had previously been treated with antitubercular medications. She had clinical features of systemic sclerosis (SSc) which was subsequently confirmed on further workup. She was also found to be profoundly hypothyroid. Cardiac tamponade is uncommon in both SSc as well as hypothyroidism, unlike in our patient who was found to have both of these disorders. In her case, the pericardial involvement probably ante-dated the other features of SSc.

Post-cricoid web or Plummer-Vinson syndrome has been identified as a risk factor for the development of upper gastrointestinal tract malignancy. 2 Learning points Plummer-Vinson syndrome classically presents as a triad of iron-deficiency anaemia, postcricoid dysphagia and upper esophageal webs.

Management of patient with Lupus Nephritis (LN) continues to remain a challenge for the treating physicians because of considerable morbidity and even mortality. The search of biomarkers in serum and urine is a focus of researchers to unravel new targets for therapy. In the present study, the utility of NMR-based serum metabolomics has been evaluated for the first time in discriminating LN patients from non-nephritis lupus patients (SLE) and further to get new insights into the underlying disease processes for better clinical management. Metabolic profiling of sera obtained from 22 SLE patients, 40 LN patients and 30 healthy controls (HC) were performed using high resolution 1D 1 H-CPMG and diffusion edited NMR spectra to identify the potential molecular biomarkers. Using multivariate analysis, we could distinguish SLE and LN patients from HC and LN from SLE patients. Compared to SLE patients, the LN patients had increased serum levels of lipid metabolites (including LDL/VLDL lipoproteins), creatinine and decreased levels of acetate. Our results revealed that metabolic markers especially lipids and acetate derived from NMR spectroscopy has high sensitivity and specificity to distinguish LN among SLE patients and has the potential to be a useful adjunctive tool in diagnosis and clinical management of LN.