Genetic mutations underlying isoniazid-resistant Mycobacterium tuberculosis in Khyber Pakhtunkhwa, Pakistan.

Tuberculosis, caused by Mycobacterium tuberculosis, is a major public health issue in Pakistan. Isoniazid is a first-line pro-drug that requires activation through an enzyme called catalase peroxidase, but is subject to widespread resistance, driven by mutations in katG and inhA genes and other loci with compensatory effects (e.g., ahpC). Here, we used whole genome sequencing data from 51 M. tuberculosis isolates collected from Khyber Pakhtunkhwa province (years 2016-2019; all isoniazid phenotypically resistant) to investigate the genetic diversity of mutations in isoniazid candidate genes. The most common mutations underlying resistance were katG S315T (37/51), fabG1 -15C>T (13/51; inhA promoter), and inhA -154G>A (7/51). Other less common mutations (n < 5) were also identified in katG (R128Q, V1A, W505*, A109T, D311G) and candidate compensatory genes ahpC (-54C>T, -51G>A) and oxyS (M249T). Using DynaMut2 software, the mutants exhibited various degrees of stability and flexibility on protein structures, with some katG mutations leading to a decrease in KatG protein flexibility. Overall, the characterisation of circulating isoniazid resistant-linked mutations will assist in drug resistant TB management and control activities in a highly endemic area of Pakistan.

A Rare Case of Asymptomatic Massive Colonic Lithobezoar in a Young Child.

Colonic bezoar is a rare condition of accumulation of foreign bodies or non-nutritious material in the large intestine, usually presenting with symptoms of obstruction. Colonic lithobezoar is an even more rare type of condition with only 12 cases reported in the literature to date. We present a case of a young, intellectually disabled kid, who was diagnosed incidentally with lithobezoar after a road traffic accident. The first-line treatment for uncomplicated non-obstructed bezoar is a medical treatment with laxatives and fluids. For acutely obstructed bezoars, the treatment of choice is evacuation under general anesthesia. Surgical evacuation may be considered a last resort in complicated or refractory cases. Moreover, regardless of obstruction, all cases must be treated as inpatients and must receive a psychiatric and hematologic evaluation.

COVID-19 detection in chest X-ray images using deep boosted hybrid learning.

The new emerging COVID-19, declared a pandemic disease, has affected millions of human lives and caused a massive burden on healthcare centers. Therefore, a quick, accurate, and low-cost computer-based tool is required to timely detect and treat COVID-19 patients. In this work, two new deep learning frameworks: Deep Hybrid Learning (DHL) and Deep Boosted Hybrid Learning (DBHL), is proposed for effective COVID-19 detection in X-ray dataset. In the proposed DHL framework, the representation learning ability of the two developed COVID-RENet-1 & 2 models is exploited individually through a machine learning (ML) classifier. In COVID-RENet models, Region and Edge-based operations are carefully applied to learn region homogeneity and extract boundaries features. While in the case of the proposed DBHL framework, COVID-RENet-1 & 2 are fine-tuned using transfer learning on the chest X-rays. Furthermore, deep feature spaces are generated from the penultimate layers of the two models and then concatenated to get a single enriched boosted feature space. A conventional ML classifier exploits the enriched feature space to achieve better COVID-19 detection performance. The proposed COVID-19 detection frameworks are evaluated on radiologist's authenticated chest X-ray data, and their performance is compared with the well-established CNNs. It is observed through experiments that the proposed DBHL framework, which merges the two-deep CNN feature spaces, yields good performance (accuracy: 98.53%, sensitivity: 0.99, F-score: 0.98, and precision: 0.98). Furthermore, a web-based interface is developed, which takes only 5-10s to detect COVID-19 in each unseen chest X-ray image. This web-predictor is expected to help early diagnosis, save precious lives, and thus positively impact society.

Defining exacerbations in chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease is a very common disease often punctuated by intermittent episodes of exacerbation. These exacerbations affect the natural history of the disease, accelerating a decline in lung function. They affect the individual in many ways and affect the health service caring for these patients. The definition of exacerbation varies and lacks clarity. The definitions used most are either symptom based, for example, breathlessness, sputum production and sputum purulence, or event driven, for example, an event causing a patient to seek healthcare input or change to medications. In this article, we discuss the importance of exacerbations, the clinical definitions, clinical trial definitions, physiological and biomarker evidence of exacerbations and the challenges associated with each of these. Application of a practical definition would aid in our clinical management of patients with chronic obstructive pulmonary disease and facilitate developments in future therapeutic advances through clinical trials.