Tuberculosis Medication Nonadherence—A Qualitative Case Study

India is grappling with the problem of controlling tuberculosis nearly for the past 50 years. The problem of nonadherence to treatment regimen has also worsened the situation of multidrug resistant tuberculosis (MDR-TB) in India. This article explores the factors behind nonadherence among erstwhile TB defaulters in a rural district in India. In-depth interviews with seven chronic defaulters and with healthcare professionals were conducted at a government-run Chest Clinic. In addition to in-depth interviews with defaulters and healthcare professionals, medical records and government orders related to TB control were examined extensively. Participants were also observed to understand their interaction with healthcare professionals and fellow patients, especially during drug delivery time. Qualitative content analysis is the most appropriate method to analyze the transcribed text and archival records. Qualitative content analysis brought out five major themes responsible for their past nonadherence behavior, namely, (a) Awareness about tuberculosis and treatment, (b) Symptom recognition and self-medication, (c) Family support, (d) Accessibility, and (e) Stigma. Findings are documented according to the major themes and documenting direct quotes from participants and with healthcare professionals wherever appropriate. This case study also provided context-specific recommendations to the healthcare professionals as regards the nonadherence behavior among TB patients. It is hoped these focused recommendations, albeit known to the healthcare professionals, would be extremely useful in making modifications to the existing program to tackle the nonadherence behavior

Alkylation of cyclic 1,3-diketones with Mannich bases from indoles

The alkylations of some cyclic 1, 3-diketones with gramine and substituted Gramines and the alkylations of 2-carbethoxycyclohexanone and 2-carbethoxycyclopentanone with gramine are described

Deep Semantic Segmentation of Cell Painted Nuclei Images using UNet++

Fluorescence microscopy based cell painting technique profiles the morphological characteristics of specific cell organelles with high resolution. However, photo toxicity, photo bleaching and advanced instrumentation limits its utility for comprehensively annotating the cell structure. Generating cell painted organelles from simple and least invasive transmitted light microscopy provides a surrogate for clinical applications. In this study, the employability of semantic segmentation model for delineating the nuclei from composite image using UNet++ is investigated. For this, a public dataset consisting of 3456 composite images are considered from Broad Bioimage Benchmark collection. The binary masks of endoplasmic reticulum (ER), nuclei and cytoplasm are generated for pixel wise labelling. The composite images and their labelled dataset are fed to the UNet++ model for segmenting the cell painted nuclei. The performance of the deep semantic network is analysed for 50 epochs and the segmentation results are validated with mean intersection-over-union (IoU). The UNet++model provides an accuracy of 95.8% with a minimum loss of 0.1. Mean IoU of 0.91 is achieved for the prediction of nuclei from the composite images. The obtained results suggest that this study could be employed for predicting the subcellular components from transmitted light microscopy without the use of fluorescent labelling

Diagnostics of Multi Drug Resistant Tuberculosis in Chest Radiographs using Local Textures & Extreme Gradient Boosting

This study attempts to detect and differentiate Multi Drug Resistant (MDR) - Tuberculosis (TB) and Drug Sensitive (DS)-TB Chest Radiographs (CXR) using local texture descriptors and Ensemble Learning method. Studies report that CXR images contain likelihood information of the drug resistance which can be utilized computationally. Initially, CXR images are subjected to lung fields segmentation using Reaction Diffusion Level Set method. Further, Local Directional Texture Pattern (LDTP) features are extracted from the segmented lungs to characterize the localized textural variations. Extreme Gradient Boosting (XGBoost) classifier is employed to differentiate DS-TB and MDR-TB images. The obtained results demonstrate the ability of extracted LDTP features to characterize nonspecific textural inhomogeneities in images by operating on its principal directions. XGBoost algorithm provides maximum accuracy of 93% and true positive rate of 94.6% in detecting MDR-TB. As the proposed study differentiates the MDR-TB condition using CXR images, its computerized diagnostics could be used in the early screening and followup of TB ridden patients for public health infection control in any setting