Molecular characteristics and evaluation of the phenotypic detection of carbapenemases among Enterobacterales and Pseudomonas via whole genome sequencing.

Background/purposes: The continuously increasing carbapenem resistance within Enterobacterales and Pseudomonas poses a threat to public health, nevertheless, the molecular characteristics of which in southern China still remain limited. And carbapenemase identification is a key factor in effective early therapy of carbapenem-resistant bacteria infections. We aimed to determine the molecular characteristics of these pathogens and compare commercial combined disc tests (CDTs) with the modified carbapenem inactivation method (mCIM) and EDTA-CIM (eCIM) in detecting and distinguishing carbapenemases using whole genome sequencing (WGS). Methods: A total of 78 Enterobacterales, 30 Pseudomonas were obtained from two tertiary hospitals in southern China. Susceptibility tests were conducted using an automated VITEK2 compact system with confirmation via the Kirby-Bauer method. The WGS was conducted on all clinical isolates and the molecular characteristics were analyzed by screening the whole genome sequences. CDTs with or without cloxacillin, mCIM, and eCIM, were performed and compared by taking WGS results as the benchmark. Results: A total of 103 carbapenem non-susceptible and 5 carbapenem susceptible bacteria were determined, with Klebsiella pneumoniae (42.7%), Pseudomonas aeruginosa (23.3%) and Escherichia coli (18.4%) being most prevalent. Carbapenemase genes were detected in 58 (56.3%) of the 103 carbapenem-non-susceptible clinical isolates, including 46 NDM, 6 KPC, 3 IMP, 1 IPM+VIM,1NDM+KPC, and 1 OXA-181. Carbapenemase-producing isolates were detected more frequently in Enterobacterales (76.3%). Among K. pneumoniae, the major sequence types were st307 and st11, while among E. coli and P. aeruginosa, the most prevalent ones were st410 and st242 respectively. For carbapenemase detection in Enterobacterales, the mCIM method achieved 100.00% (95% CI, 92.13-100.00%) sensitivity and 94.44% (70.63-99.71%) specificity (kappa, 0.96); for Pseudomonas, detection sensitivity was 100% (5.46-100.00%), and 100% (84.50-100.00%) specificity (kappa, 0.65). Commercial CDT carbapenemase detection sensitivity for Enterobacterales was 96.49% (86.84-99.39%), and 95.24% (74.13-99.75%) specificity (kappa, 0.90); for Pseudomonas, carbapenemase detection sensitivity was 100.00% (5.46-100.00%) and 37.93% (21.30-57.64%) specificity (kappa, 0.04). When cloxacillin testing was added, CDT specificity reached 84.61% (64.27-94.95%). Conclusion: The molecular epidemiology of carbapenem-non-susceptible isolates from pediatric patients in Southern China exhibited distinctive characteristics. Both the mCIM-eCIM combination and CDT methods effectively detected and differentiated carbapenemases among Enterobacterales isolates, and the former performed better than CDT among Pseudomonas.

Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning.

Unsorted retired batteries with varied cathode materials hinder the adoption of direct recycling due to their cathode-specific nature. The surge in retired batteries necessitates precise sorting for effective direct recycling, but challenges arise from varying operational histories, diverse manufacturers, and data privacy concerns of recycling collaborators (data owners). Here we show, from a unique dataset of 130 lithium-ion batteries spanning 5 cathode materials and 7 manufacturers, a federated machine learning approach can classify these retired batteries without relying on past operational data, safeguarding the data privacy of recycling collaborators. By utilizing the features extracted from the end-of-life charge-discharge cycle, our model exhibits 1% and 3% cathode sorting errors under homogeneous and heterogeneous battery recycling settings respectively, attributed to our innovative Wasserstein-distance voting strategy. Economically, the proposed method underscores the value of precise battery sorting for a prosperous and sustainable recycling industry. This study heralds a new paradigm of using privacy-sensitive data from diverse sources, facilitating collaborative and privacy-respecting decision-making for distributed systems.