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The evolving research landscape in the time of the Coronavirus disease 2019 (COVID-19) pandemic calls for greater understanding of the perceptions of scholars regarding the current state and future of publishing. An anonymised and validated e-survey featuring 30 questions was circulated among rheumatologists and other specialists over social media to understand preferences while choosing target journals, publishing standards, commercial editing services, preprint archiving, social media and alternative publication activities. Of 108 respondents, a significant proportion were clinicians (68%), researchers (60%) and educators (47%), with median 23 publications and 15 peer-review accomplishments. The respondents were mainly rheumatologists from India, Ukraine and Turkey. While choosing target journals, relevance to their field (69%), PubMed Central archiving (61%) and free publishing (59%) were the major factors. Thirty-nine surveyees (36%) claimed that they often targeted local journals for publishing their research. However, only 18 (17%) perceived their local society journals as trustworthy. Occasional publication in the so-called predatory journals (5, 5%) was reported and obtaining support from commercial editing agencies to improve English and data presentation was not uncommon (23, 21%). The opinion on preprint archiving was disputed; only one-third believed preprints were useful. High-quality peer review (56%), full and immediate open access (46%) and post-publication social media promotion (32%) were identified as key anticipated features of scholarly publishing in the foreseeable future. These perceptions of surveyed scholars call for greater access to free publishing, attention to proper usage of English and editing skills, and a larger role for engagement over social media.

Repurposing of antirheumatic drugs has garnered global attention. The aim of this article is to overview available evidence on the use of widely used antirheumatic drugs hydroxychloroquine, methotrexate and colchicine for additional indications. Hydroxychloroquine has endothelial stabilizing and anti-thrombotic effects. Its use has been explored as an adjunctive therapy in refractory thrombosis in antiphospholipid syndrome. It may also prevent recurrent pregnancy losses in the absence of antiphospholipid antibodies. Hydroxychloroquine favourably modulates atherogenic lipid and glycaemic profiles. Methotrexate has been tried for modulation of cardiovascular events in non-rheumatic clinical conditions, although a large clinical trial failed to demonstrate a benefit. Colchicine has been shown to successfully reduce the risk of recurrent cardiovascular events in a large multicentric trial. Potential antifibrotic effects of colchicine require further exploration. Hydroxychloroquine, methotrexate and colchicine are also being tried at different stages of the ongoing Coronavirus Disease 19 (COVID-19) pandemic for prophylaxis and treatment. While the use of these agents is being diversified, their adverse effects should be timely diagnosed and prevented. Hydroxychloroquine can cause retinopathy and rarely cardiac and auditory toxicity, retinopathy being dose and time dependent. Methotrexate can cause transaminitis, cytopenias and renal failure, particularly in acute overdoses. Colchicine can rarely cause myopathies, cardiomyopathy, cytopenias and transaminitis. Strong evidence is warranted to keep balance between benefits of repurposing these old antirheumatic drugs and risk of their adverse effects.

The ongoing pandemic coronavirus disease 19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a matter of global concern. Environmental factors such as air pollution and smoking and comorbid conditions (hypertension, diabetes mellitus and underlying cardio-respiratory illness) likely increase the severity of COVID-19. Rheumatic manifestations such as arthralgias and arthritis may be prevalent in about a seventh of individuals. COVID-19 can result in acute interstitial pneumonia, myocarditis, leucopenia (with lymphopenia) and thrombocytopenia, also seen in rheumatic diseases like lupus and Sjogren’s syndrome. Severe disease in a subset of patients may be driven by cytokine storm, possibly due to secondary hemophagocytic lymphohistiocytosis (HLH), akin to that in systemic onset juvenile idiopathic arthritis or adult-onset Still’s disease. In the absence of high-quality evidence in this emerging disease, understanding of pathogenesis may help postulate potential therapies. Angiotensin converting enzyme 2 (ACE2) appears important for viral entry into pneumocytes; dysbalance in ACE2 as caused by ACE inhibitors or ibuprofen may predispose to severe disease. Preliminary evidence suggests potential benefit with chloroquine or hydroxychloroquine. Antiviral drugs like lopinavir/ritonavir, favipiravir and remdesivir are also being explored. Cytokine storm and secondary HLH might require heightened immunosuppressive regimens. Current international society recommendations suggest that patients with rheumatic diseases on immunosuppressive therapy should not stop glucocorticoids during COVID-19 infection, although minimum possible doses may be used. Disease-modifying drugs should be continued; cessation may be considered during infection episodes as per standard practices. Development of a vaccine may be the only effective long-term protection against this disease.

Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.

(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020-2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The proposed study compared the COVID Lung Image Analysis System, COVLIAS 1.0 (GBTI, Inc., and AtheroPoint(TM) Roseville, CA, USA, referred to as COVLIAS), against MedSeg, a web-based Artificial Intelligence (AI) segmentation tool, where COVLIAS uses hybrid deep learning (HDL) models for CT lung segmentation. (2) Materials and Methods: The proposed study used 5000 ITALIAN COVID-19 positive CT lung images collected from 72 patients (experimental data) that confirmed the reverse transcription-polymerase chain reaction (RT-PCR) test. Two hybrid AI models from the COVLIAS system, namely, VGG-SegNet (HDL 1) and ResNet-SegNet (HDL 2), were used to segment the CT lungs. As part of the results, we compared both COVLIAS and MedSeg against two manual delineations (MD 1 and MD 2) using (i) Bland-Altman plots, (ii) Correlation coefficient (CC) plots, (iii) Receiver operating characteristic curve, and (iv) Figure of Merit and (v) visual overlays. A cohort of 500 CROATIA COVID-19 positive CT lung images (validation data) was used. A previously trained COVLIAS model was directly applied to the validation data (as part of Unseen-AI) to segment the CT lungs and compare them against MedSeg. (3) Result: For the experimental data, the four CCs between COVLIAS (HDL 1) vs. MD 1, COVLIAS (HDL 1) vs. MD 2, COVLIAS (HDL 2) vs. MD 1, and COVLIAS (HDL 2) vs. MD 2 were 0.96, 0.96, 0.96, and 0.96, respectively. The mean value of the COVLIAS system for the above four readings was 0.96. CC between MedSeg vs. MD 1 and MedSeg vs. MD 2 was 0.98 and 0.98, respectively. Both had a mean value of 0.98. On the validation data, the CC between COVLIAS (HDL 1) vs. MedSeg and COVLIAS (HDL 2) vs. MedSeg was 0.98 and 0.99, respectively. For the experimental data, the difference between the mean values for COVLIAS and MedSeg showed a difference of

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most.

Background: COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint (TM), Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation. Methodology: The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT. Results: Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of similar to 0.96, similar to 0.97, similar to 0.98, and similar to 0.96 (p-value < 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet > VGG-SegNet > NIH > SegNet. The HDL runs in

Deficiency of adenosine deaminase 2 (DADA2) is a potentially fatal monogenic syndrome characterized by variable manifestations of systemic vasculitis, bone marrow failure, and immunodeficiency. Most cases are diagnosed by pediatric care providers, given the typical early age of disease onset. This study was undertaken to describe the clinical phenotypes and treatment response both in adults and in children with DADA2 in India.A retrospective analysis of pediatric and adult patients with DADA2 diagnosed at various rheumatology centers across India was conducted. Clinical characteristics, diagnostic findings, and treatment responses were analyzed in all subjects.In total, 33 cases of DADA2 were confirmed in this cohort between April 2017 and March 2020. Unlike previous studies, nearly one-half of the confirmed cases presented during adulthood. All symptomatic patients exhibited features of vasculitis, whereas constitutional symptoms and anemia were more common in pediatric patients. Cutaneous and neurologic involvement were common, and 18 subjects had experienced at least one stroke. In addition, the clinical spectrum of DADA2 was expanded by recognition of novel features in these patients, including pancreatic infarction, focal myocarditis, and diffuse alveolar hemorrhage. Treatment with tumor necrosis factor inhibitors (TNFi) was initiated in 25 patients. All of the identified disease manifestations showed marked improvement after initiation of TNFi, and disease remission was achieved in 19 patients. Two cases were complicated by tuberculosis infection, and 2 deaths were reported.This report presents the first case series of patients with DADA2 from India, diagnosed by adult and pediatric care providers. The findings raise awareness of this syndrome, particularly with regard to its presentation in adults.

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic “cognitive” functions that we associate with our mind, such as “learning” and “solving problem”. New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors.

In this narrative review, we overview the recent literature on enthesitis-related arthritis (ERA). For the purpose of our review, we searched Scopus for recent articles on this subject from 2013 onward, including some classic older articles for perspective. ERA is a juvenile idiopathic arthritis (JIA) subtype more common in males, associated in a majority with human leucocyte antigen B27. Such children generally present with asymmetric oligoarthritis or polyarthritis, predominantly of lower limb joints, associated with enthesitis or sacroiliitis. While diagnosis remains clinical, ultrasound is being increasingly used to detect subclinical enthesitis and for guiding entheseal site injections. Spine MRI can help detect sacroiliitis, inflammatory spinal changes, and pelvic sites of enthesitis in such patients. The recent juvenile spondyloarthropathy disease activity index recognizes the key clinical features of ERA, viz enthesitis and inflammatory back pain, which other disease activity indices used in JIA did not include. Management includes NSAIDs with physical therapy. Conventional disease-modifying agents like sulfasalazine and methotrexate may be used to minimize duration of NSAID use and in those with high inflammatory burden. In patients refractory to these drugs, biologics such as antitumor necrosis factor alpha agents have proven useful, based on evidences from randomized controlled trials and retrospective registry analyses. Factors predicting a poorer outcome in such children include hip or ankle involvement or restricted spinal mobility. Considering that children with ERA have overall poorer long-term outcomes than other subtypes of JIA, there is a need to further optimize therapeutic strategies for such patients.

Rutviz Rajendra Mistry,1 Pallavi Patro,2 Vikas Agarwal,1 Durga Prasanna Misra1 1Department of Clinical Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, India; 2Department of Pharmacology, Sriram Chandra Bhanja (SCB) Medical College, Cuttack, India Abstract: In this narrative review, we overview the recent literature on enthesitis-related arthritis (ERA). For the purpose of our review, we searched Scopus for recent articles on this subject from 2013 onward, including some classic older articles for perspective. ERA is a juvenile idiopathic arthritis (JIA) subtype more common in males, associated in a majority with human leucocyte antigen B27. Such children generally present with asymmetric oligoarthritis or polyarthritis, predominantly of lower limb joints, associated with enthesitis or sacroiliitis. While diagnosis remains clinical, ultrasound is being increasingly used to detect subclinical enthesitis and for guiding entheseal site injections. Spine MRI can help detect sacroiliitis, inflammatory spinal changes, and pelvic sites of enthesitis in such patients. The recent juvenile spondyloarthropathy disease activity index recognizes the key clinical features of ERA, viz enthesitis and inflammatory back pain, which other disease activity indices used in JIA did not include. Management includes NSAIDs with physical therapy. Conventional disease-modifying agents like sulfasalazine and methotrexate may be used to minimize duration of NSAID use and in those with high inflammatory burden. In patients refractory to these drugs, biologics such as antitumor necrosis factor alpha agents have proven useful, based on evidences from randomized controlled trials and retrospective registry analyses. Factors predicting a poorer outcome in such children include hip or ankle involvement or restricted spinal mobility. Considering that children with ERA have overall poorer long-term outcomes than other subtypes of JIA, there is a need to further optimize therapeutic strategies for such patients. Keywords: enthesitis-related arthritis, juvenile spondyloarthropathy, diagnosis, management, exercise, anti-TNF

A young male was referred to us for evaluation of fever of unknown origin (FUO). He had history of recurrent painful oral ulcers for one year and moderate to high grade fever, pustulopapular rash, and recurrent genital ulcers for 6 months and hemoptysis for 3 days. He was detected to have intracardiac thrombi and pulmonary arterial thrombosis along with underlying Behcet’s disease (BD). Patient responded to high dose prednisolone (1 mg/Kg/day) along with monthly parenteral cyclophosphamide therapy. This case highlights the fact that BD is an important cause for pulmonary artery vasculitis with intracardiac thrombus formation, and such patients can present with FUO.

Leprosy is a disease typically found in the tropics. Patients with leprosy can have varying presentation with constitutional symptoms, joint pains, skin nodules, and rarely a vasculitis-like picture with skin ulcers and neuropathy. We present a young lady who presented with the rare manifestation of skin infarcts mimicking cutaneous vasculitis, diagnosed on histopathology to have Lucio phenomenon on a background of lepromatous leprosy. With increasing migration and widespread use of biologic response modifiers, clinicians all over the world need to be aware of various presentations of leprosy as well as needing to keep an open mind while considering the differential diagnoses of vasculitis.