TB-DROP: deep learning-based drug resistance prediction of Mycobacterium tuberculosis utilizing whole genome mutations.

The most widely practiced strategy for constructing the deep learning (DL) prediction model for drug resistance of Mycobacterium tuberculosis (MTB) involves the adoption of ready-made and state-of-the-art architectures usually proposed for non-biological problems. However, the ultimate goal is to construct a customized model for predicting the drug resistance of MTB and eventually for the biological phenotypes based on genotypes. Here, we constructed a DL training framework to standardize and modularize each step during the training process using the latest tensorflow 2 API.

An integrated multi-omics analysis of identifies distinct molecular characteristics in pulmonary infections of Pseudomonas aeruginosa.

Pseudomonas aeruginosa (P. aeruginosa) can cause severe acute infections, including pneumonia and sepsis, and cause chronic infections, commonly in patients with structural respiratory diseases. However, the molecular and pathophysiological mechanisms of P.

Identification of region of difference and H37Rv-related deletion in by structural variant detection and genome assembly.

complex (MTBC), the main cause of TB in humans and animals, is an extreme example of genetic homogeneity, whereas it is still nevertheless separated into various lineages by numerous typing methods, which differ in phenotype, virulence, geographic distribution, and host preference. The large sequence polymorphism (LSP), incorporating region of difference (RD) and H37Rv-related deletion (RvD), is considered to be a powerful means of constructing phylogenetic relationships within MTBC. Although there have been many studies on LSP already, focusing on the distribution of RDs in MTBC and their impact on MTB phenotypes, a crumb of new lineages or sub-lineages have been excluded and RvDs have received less attention.

Identification of Mobile Colistin Resistance Gene in Disinfectant and Antibiotic Resistant from Disinfected Tableware.

The widespread escalation of bacterial resistance threatens the safety of the food chain. To investigate the resistance characteristics of strains isolated from disinfected tableware against both disinfectants and antibiotics, 311 disinfected tableware samples, including 54 chopsticks, 32 dinner plates, 61 bowls, 11 cups, and three spoons were collected in Chengdu, Sichuan Province, China to screen for disinfectant- (benzalkonium chloride and cetylpyridinium chloride) and tigecycline-resistant isolates, which were then subjected to antimicrobial susceptibility testing and whole genome sequencing (WGS). The coliform-positive detection rate was 51.

Drug resistance prediction and resistance genes identification in Mycobacterium tuberculosis based on a hierarchical attentive neural network utilizing genome-wide variants.

Prediction of antimicrobial resistance based on whole-genome sequencing data has attracted greater attention due to its rapidity and convenience. Numerous machine learning-based studies have used genetic variants to predict drug resistance in Mycobacterium tuberculosis (MTB), assuming that variants are homogeneous, and most of these studies, however, have ignored the essential correlation between variants and corresponding genes when encoding variants, and used a limited number of variants as prediction input. In this study, taking advantage of genome-wide variants for drug-resistance prediction and inspired by natural language processing, we summarize drug resistance prediction into document classification, in which variants are considered as words, mutated genes in an isolate as sentences, and an isolate as a document.