A case report on anti-tubercular agent induced hepatotoxicity

Hepatotoxicity is the serious adverse effect of tuberculosis treatment and it leads to the discontinuation of Anti-tubercular agent (ATT) causing increased drug resistance, morbidity and mortality. We report a 69 years old male patient with ATT induced hepatotoxicity.

RANet: a custom CNN model and quanvolutional neural network for the automated detection of rheumatoid arthritis in hand thermal images

Abstract Rheumatoid arthritis is an autoimmune disease which affects the small joints. Early prediction of RA is necessary for the treatment and management of the disease. The current work presents a deep learning and quantum computing-based automated diagnostic approach for RA in hand thermal imaging. The study’s goals are (i) to develop a custom RANet model and compare its performance with the pretrained models and quanvolutional neural network (QNN) to distinguish between the healthy subjects and RA patients, (ii) To validate the performance of the custom model using feature selection method and classification using machine learning (ML) classifiers. The present study developed a custom RANet model and employed pre-trained models such as ResNet101V2, InceptionResNetV2, and DenseNet201 to classify the RA patients and normal subjects. The deep features extracted from the RA Net model are fed into the ML classifiers after the feature selection process. The RANet model, RA Net+ SVM, and QNN model produced an accuracy of 95%, 97% and 93.33% respectively in the classification of healthy groups and RA patients. The developed RANet and QNN models based on thermal imaging could be employed as an accurate automated diagnostic tool to differentiate between the RA and control groups

Arsenic biosorption using pretreated biomass of psychrotolerant Yersinia sp. strain SOM-12D3 isolated from Svalbard, Arctic

A Gram-negative, arsenite-resistant psychrotolerant bacterial strain, Yersinia sp. strain SOM-12D3, was isolated from a biofilm sample collected from a lake at Svalbard in the Arctic area. To our knowledge, this is the first study on the ability of acid-treated and untreated, non-living biomass of strain SOM-12D3 to absorb arsenic. We conducted batch experiments at pH 7, with an initial As(III) concentration of 6.5 ppm, at 30 °C with 80 min of contact time. The Langmuir isotherm model fitted the equilibrium data better than Freundlich, and the sorption kinetics of As(III) biosorption followed the pseudo-second-order rate equation well for both types of non-living biomass. The highest biosorption capacity of the acid-treated biomass obtained by the Langmuir model was 159 mg/g. Further, a high recovery efficiency of 96% for As(III) was achieved using 0.1 M HCl within four cycles, which indicated high adsorption/desorption. Fourier transformed infrared (FTIR) demonstrated the involvement of hydroxyl, amide, and amine groups in As(III) biosorption. Field emission scanning electron microscopy–energy dispersive analysis (FESEM-EDAX) indicated the different morphological changes occurring in the cell after acid treatment and arsenic biosorption. Our results highlight the potential of using acid-treated non-living biomass of the psychrotolerant bacterium, Yersinia sp. Strain SOM-12D3 as a new biosorbent to remove As(III) from contaminated waters