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Objectives: To study whether a trained convolutional neural network (CNN) can be of assistance to radiologists in differentiating Coronavirus disease (COVID)–positive from COVID-negative patients using chest X-ray (CXR) through an ambispective clinical study. To identify subgroups of patients where artificial intelligence (AI) can be of particular value and analyse what imaging features may have contributed to the performance of AI by means of visualisation techniques. Methods: CXR of 487 patients were classified into [4] categories—normal, classical COVID, indeterminate, and non-COVID by consensus opinion of 2 radiologists. CXR which were classified as "normal" and "indeterminate" were then subjected to analysis by AI, and final categorisation provided as guided by prediction of the network. Precision and recall of the radiologist alone and radiologist assisted by AI were calculated in comparison to reverse transcriptase-polymerase chain reaction (RT-PCR) as the gold standard. Attention maps of the CNN were analysed to understand regions in the CXR important to the AI algorithm in making a prediction. Results: The precision of radiologists improved from 65.9 to 81.9% and recall improved from 17.5 to 71.75 when assistance with AI was provided. AI showed 92% accuracy in classifying "normal" CXR into COVID or non-COVID. Analysis of attention maps revealed attention on the cardiac shadow in these "normal" radiographs. Conclusion: This study shows how deployment of an AI algorithm can complement a human expert in the determination of COVID status. Analysis of the detected features suggests possible subtle cardiac changes, laying ground for further investigative studies into possible cardiac changes. Key Points: • Through an ambispective clinical study, we show how assistance with an AI algorithm can improve recall (sensitivity) and precision (positive predictive value) of radiologists in assessing CXR for possible COVID in comparison to RT-PCR. • We show that AI achieves the best results in images classified as "normal" by radiologists. We conjecture that possible subtle cardiac in the CXR, imperceptible to the human eye, may have contributed to this prediction. • The reported results may pave the way for a human computer collaboration whereby the expert with some help from the AI algorithm achieves higher accuracy in predicting COVID status on CXR than previously thought possible when considering either alone. [ABSTRACT FROM AUTHOR]

Cryptococcosis is a life-threatening infection caused by Cryptococcus neoformans and C. gattii species complex. In the present study, to understand the molecular epidemiology of 208 clinical isolates of Cryptococcus from different parts of India, multilocus sequence typing (MLST) using ISHAM MLST consensus scheme for C. neoformans/C. gattii species complex was used. MLST analysis yielded a total of 10 Sequence Types (STs)—7 STs for C. neoformans and 3 for C. gattii species complex. The majority of isolates identified as C. neoformans belonged to molecular type VNI with predominant STs 31 and 93. Only 3 isolates of C. gattii species complex were obtained, belonging to ST58 and ST215 of VGI and ST69 of VGIV. Phylogenetic analysis revealed less diversity among the clinical Indian isolates compared to the global MLST database. No association between prevalent STs and HIV status, geographical origin or minimum inhibitory concentration (MIC) could be established. [ABSTRACT FROM AUTHOR]

Background and Aim: There is a paucity of data on the clinical presentations and outcomes of Corona Virus Disease-19 (COVID-19) in patients with underlying liver disease. We aimed to summarize the presentations and outcomes of COVID-19-positive patients and compare with historical controls. Methods: Patients with known chronic liver disease who presented with superimposed COVID-19 (n = 28) between 22 April 2020 and 22 June 2020 were studied. Seventy-eight cirrhotic patients without COVID-19 were included as historical controls for comparison. Results: A total of 28 COVID-19 patients (two without cirrhosis, one with compensated cirrhosis, sixteen with acute decompensation [AD], and nine with acute-on-chronic liver failure [ACLF]) were included. The etiology of cirrhosis was alcohol (n = 9), non-alcoholic fatty liver disease (n = 2), viral (n = 5), autoimmune hepatitis (n = 4), and cryptogenic cirrhosis (n = 6). The clinical presentations included complications of cirrhosis in 12 (46.2%), respiratory symptoms in 3 (11.5%), and combined complications of cirrhosis and respiratory symptoms in 11 (42.3%) patients. The median hospital stay was 8 (7–12) days. The mortality rate in COVID-19 patients was 42.3% (11/26), as compared with 23.1% (18/78) in the historical controls (p = 0.077). All COVID-19 patients with ACLF (9/9) died compared with 53.3% (16/30) in ACLF of historical controls (p = 0.015). Mortality rate was higher in COVID-19 patients with compensated cirrhosis and AD as compared with historical controls 2/17 (11.8%) vs. 2/48 (4.2%), though not statistically significant (p = 0.278). Requirement of mechanical ventilation independently predicted mortality (hazard ratio 13.68). Both non-cirrhotic patients presented with respiratory symptoms and recovered uneventfully. Conclusion: COVID-19 is associated with poor outcomes in patients with cirrhosis, with worst survival rates in ACLF. Mechanical ventilation is associated with a poor outcome. [ABSTRACT FROM AUTHOR]

We assessed the anti-V3 antibody content and viral neutralization potential of the plasma of 63 HIV-1-infected patients (antiretroviral naïve=39, treated=24) against four primary isolates (PIs) of clade C and a tier 1 clade B isolate SF162. Depletion and inhibition of anti-V3 antibodies in the plasma of five patients with high titers of anti-V3 antibodies led to modest change in the neutralization percentage against two PIs (range 0-21%). The plasma of antiretroviral-treated patients exhibited higher neutralization potential than that of the drug-naïve plasmas against the four PIs tested which was further evidenced by a follow-up study. [ABSTRACT FROM AUTHOR]

The viral reverse transcriptase gene was amplified from the plasma of 27 drug-naïve and five drug-experienced HIV patients in Northern India. Follow-up samples of naïve patients after antiretroviral therapy initiation were collected in six patients at 3 months and three patients at 6 months. Phylogenetic analysis revealed two new recombinant forms, CRF_CH and CRF_CK, and an accessory non-nucleoside reverse transcriptase inhibitor mutation E138A, not previously reported from India. The major DRMs found in two 6-month follow-up samples were absent in their baseline blood samples. This is the first follow-up study to determine anti-retroviral drug resistance mutations in Indian HIV patients. [ABSTRACT FROM AUTHOR]