Background: Carbapenem-resistant Klebsiella pneumoniae (CRKP) is a clinically critical pathogen that causes severe infection. Due to improper antibiotic administration, the prevalence of CRKP infection has been increasing considerably. In recent years, the utilization of matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) has enabled the identification of bacterial isolates at the families and species level. Moreover, machine learning (ML) classifiers based on MALDI-TOF MS have been recently considered a novel method to detect clinical antimicrobial-resistant pathogens. Methods: A total of 2683 isolates (369 CRKP cases and 2314 carbapenem-susceptible Klebsiella pneumoniae [CSKP]) collected in the clinical laboratories of Taipei Medical University Hospital (TMUH) were included in this study, and 80% of data was split into the training data set that were submitted for the ML model. The remaining 20% of data was used as the independent data set for external validation. In this study, we established an artificial neural network (ANN) model to analyze all potential peaks on mass spectrum simultaneously. Results: Our artificial neural network model for detecting CRKP isolates showed the best performance of area under the receiver operating characteristic curve (AUROC = 0.91) and of area under precision–recall curve (AUPRC = 0.90). Furthermore, we proposed the top 15 potential biomarkers in probable CRKP isolates at 2480, 4967, 12,362, 12,506, 12,855, 14,790, 15,730, 16,176, 16,218, 16,758, 16,919, 17,091, 18,142, 18,998, and 19,095 Da. Conclusions: Compared with the prior MALDI-TOF and machine learning studies of CRKP, the amount of data in our study was more sufficient and allowing us to conduct external validation. With better generalization abilities, our artificial neural network model can serve as a reliable screening tool for CRKP isolates in clinical practice. Integrating our model into the current workflow of clinical laboratories can assist the rapid identification of CRKP before the completion of traditional antimicrobial susceptibility testing. The combination of MADLI-TOF MS and machine learning techniques can support physicians in selecting suitable antibiotics, which has the potential to enhance the patients’ outcomes and lower the prevalence of antimicrobial resistance.

Background: Our objective was to evaluate the clinical effects on walking function and symmetry of combined knee-powered exoskeleton-assisted gait training (EAGT) and conventional physiotherapy versus conventional therapy alone in patients with subacute stroke. Methods: This randomized pilot trial enrolled patients 1–3 months post-stroke with Functional Ambulation Classification scores of 3 or 4. Participants were randomly assigned in a 1:1 ratio to receive EAGT plus conventional therapy or conventional therapy alone for 4 weeks. Outcomes included gait speed (10MWT), endurance (6MWT), and motor function (FMA-LE), assessed at baseline, 4 weeks, and 16 weeks. Accelerometer-based spatiotemporal gait parameters were analyzed in the EAGT group. Results: Twenty-four participants (EAGT: 17, control: 7) completed the study. No significant group × time interaction was observed for gait speed, walking endurance, or lower-extremity motor function, indicating that changes over time did not differ significantly between the EAGT and control groups. In the EAGT group, increased gait speed was accompanied by augmented cadence (p < 0.001) without significant changes in stride length or gait symmetry. Conclusions: From this preliminary pilot study, knee-powered EAGT improved walking performance in subacute stroke but did not demonstrate superiority over conventional rehabilitation. Gains were accompanied by cadence augmentation without restoration of gait symmetry. Larger randomized trials are needed to validate these preliminary findings.