Multidrug-resistant (MDR) bacteria are important public health problems. Antibiotic susceptibility testing (AST) currently uses time-consuming culture-based procedures, which cause treatment delays and increased mortality. We developed a machine learning model using Acinetobacter baumannii as an example to explore a fast AST approach using metagenomic next-generation sequencing (mNGS) data.

Carbapenem resistance is a major concern in the management of antibiotic-resistant Pseudomonas aeruginosa infections. The direct prediction of carbapenem-resistant phenotype from genotype in P. aeruginosa isolates and clinical samples would promote timely antibiotic therapy. The complex carbapenem resistance mechanism and the high prevalence of variant-driven carbapenem resistance in P. aeruginosa make it challenging to predict the carbapenem-resistant phenotype through the genotype. In this study, using whole genome sequencing (WGS) data of 1,622 P . aeruginosa isolates followed by machine learning, we screened 16 and 31 key gene features associated with imipenem (IPM) and meropenem (MEM) resistance in P. aeruginosa , including oprD(HIGH), and constructed the resistance prediction models. The areas under the curves of the IPM and MEM resistance prediction models were 0.906 and 0.925, respectively. For the direct prediction of carbapenem resistance in P. aeruginosa from clinical samples by the key gene features selected and prediction models constructed, 72 P . aeruginosa -positive sputum samples were collected and sequenced by metagenomic sequencing (MGS) based on next-generation sequencing (NGS) or Oxford Nanopore Technology (ONT). The prediction applicability of MGS based on NGS outperformed that of MGS based on ONT. In 72 P . aeruginosa -positive sputum samples, 65.0% (26/40) of IPM-insensitive and 63.2% (24/38) of MEM-insensitive P. aeruginosa were directly predicted by NGS-based MGS with positive predictive values of 0.897 and 0.889, respectively. By the direct detection of the key gene features associated with carbapenem resistance of P. aeruginosa , the carbapenem resistance of P. aeruginosa could be directly predicted from cultured isolates by WGS or from clinical samples by NGS-based MGS, which could assist the timely treatment and surveillance of carbapenem-resistant P. aeruginosa .

Evaluation of digital PCR for detecting low-level EGFR mutations in advanced lung adenocarcinoma patients: a cross-platform comparison study Evaluation of digital PCR for detecting low-level EGFR mutations in advanced lung adenocarcinoma patients: a cross-platform comparison study

For patients with pneumonia, the rapid detection of pathogens is still a major global problem in clinical practice because traditional diagnostic techniques for infection are time-consuming and insensitive. Metagenomic next-generation sequencing (mNGS) is a novel technique that has the potential to improve pathogen diagnosis. This study aimed to investigate the microbiological diagnostic ability of mNGS compared with conventional culture and to determine the optimal time to test patients for pneumonia. A prospective study using data from June 2020 to June 2021 was performed at a tertiary teaching hospital in China. We included 56 patients from all adult patients with a clinical diagnosis of pneumonia. Blood and bronchoalveolar lavage fluid (BALF) samples were taken for simultaneous mNGS and conventional culture testing. All 56 patients underwent both conventional culture and mNGS. Of these patients, 37 were diagnosed with severe pneumonia and 17 were diagnosed with non-severe pneumonia. The top three pathogenic bacteria detected by mNGS were Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa. Enterococcus faecium was detected more frequently in the non-severe pneumonia group (4 vs. 0, p < 0.05). The findings revealed that the detection rate of mNGS (84%) was superior to that of conventional culture methods (48%). Notably, the percentage of mNGS-positive BALF samples (46/56, 82.14%) was significantly greater than that of blood samples (27/56, 48.21%). The etiological comparison demonstrated that mNGS-positive samples, which received clinical approval, tended to be associated with a more normalized temperature, lower PCO2 levels, and a higher SOFA score than mNGS-negative samples (p = 0.022, p = 0.0.028, and p = 0.038, respectively). In this study, we discovered that the etiology of lung infections frequently involves multiple pathogens. The use of mNGS in BALF is instrumental for detecting nonviral pathogens associated with lung infections. Although the rate of positive blood NGS results is significantly influenced by various clinical factors, for patients suspected of having viral, Legionella, or tsutsugamushi infections, plasma mNGS could serve as a complementary diagnostic tool.

The study aimed to develop a genotypic antimicrobial resistance testing method for Klebsiella pneumoniae using metagenomic sequencing data. We utilized Lasso regression on assembled genomes to identify genetic resistance determinants for six antibiotics (Gentamicin, Tobramycin, Imipenem, Meropenem, Ceftazidime, Trimethoprim/Sulfamethoxazole). The genetic features were weighted, grouped into clusters to establish classifier models. Origin species of detected antibiotic resistant gene (ARG) was determined by novel strategy integrating "possible species", "gene copy number calculation" and "species-specific kmers". The performance of the method was evaluated on retrospective case studies. Our study employed machine learning on 3928 K. pneumoniae isolates, yielding stable models with AUCs > 0.9 for various antibiotics. GenseqAMR, a read-based software, exhibited high accuracy (AUC 0.926-0.956) for short-read datasets. The integration of a species-specific kmer strategy significantly improved ARG-species attribution to an average accuracy of 96.67%. In a retrospective study of 191 K. pneumoniae-positive clinical specimens (0.68%-93.39% genome coverage), GenseqAMR predicted 84.23% of AST results on average. It demonstrated 88.76%-96.26% accuracy for resistance prediction, offering genotypic AST results with a shorter turnaround time (mean ± SD: 18.34 ± 0.87 hours) than traditional culture-based AST (60.15 ± 21.58 hours). Furthermore, a retrospective clinical case study involving 63 cases showed that GenseqAMR could lead to changes in clinical treatment for 24 (38.10%) cases, with 95.83% (23/24) of these changes deemed beneficial. In conclusion, GenseqAMR is a promising tool for quick and accurate AMR prediction in Klebsiella pneumoniae, with the potential to improve patient outcomes through timely adjustments in antibiotic treatment.