Motivation Reverse vaccinology (RV) is a milestone in rational vaccine design, and machine learning (ML) has been applied to enhance the accuracy of RV prediction. However, ML-based RV still faces challenges in prediction accuracy and program accessibility. Results This study presents Vaxign-ML, a supervised ML classification to predict bacterial protective antigens (BPAgs). To identify the best ML method with optimized conditions, five ML methods were tested with biological and physiochemical features extracted from well-defined training data. Nested 5-fold cross-validation and leave-one-pathogen-out validation were used to ensure unbiased performance assessment and the capability to predict vaccine candidates against a new emerging pathogen. The best performing model (eXtreme Gradient Boosting) was compared to three publicly available programs (Vaxign, VaxiJen, and Antigenic), one SVM-based method, and one epitope-based method using a high-quality benchmark dataset. Vaxign-ML showed superior performance in predicting BPAgs. Vaxign-ML is hosted in a publicly accessible web server and a standalone version is also available. Availability and implementation Vaxign-ML website at http://www.violinet.org/vaxign/vaxign-ml, Docker standalone Vaxign-ML available at https://hub.docker.com/r/e4ong1031/vaxign-ml and source code is available at https://github.com/VIOLINet/Vaxign-ML-docker. Supplementary information Supplementary data are available at Bioinformatics online.

Vaccination is one of the most significant inventions in medicine. Reverse vaccinology (RV) is a state-of-the-art technique to predict vaccine candidates from pathogen's genome(s). To promote vaccine development, we updated Vaxign2, the first web-based vaccine design program using reverse vaccinology with machine learning. Vaxign2 is a comprehensive web server for rational vaccine design, consisting of predictive and computational workflow components. The predictive part includes the original Vaxign filtering-based method and a new machine learning-based method, Vaxign-ML. The benchmarking results using a validation dataset showed that Vaxign-ML had superior prediction performance compared to other RV tools. Besides the prediction component, Vaxign2 implemented various post-prediction analyses to significantly enhance users’ capability to refine the prediction results based on different vaccine design rationales and considerably reduce user time to analyze the Vaxign/Vaxign-ML prediction results. Users provide proteome sequences as input data, select candidates based on Vaxign outputs and Vaxign-ML scores, and perform post-prediction analysis. Vaxign2 also includes precomputed results from approximately 1 million proteins in 398 proteomes of 36 pathogens. As a demonstration, Vaxign2 was used to effectively analyse SARS-CoV-2, the coronavirus causing COVID-19. The comprehensive framework of Vaxign2 can support better and more rational vaccine design. Vaxign2 is publicly accessible at http://www.violinet.org/vaxign2.

During the start of the COVID-19 pandemic, shortages of personal protective equipment (PPE) necessitated unprecedented and non-validated approaches to conserve PPE at healthcare facilities, especially in high income countries where single-use disposable PPE was ubiquitous. Our team conducted a systematic literature review to evaluate historic approaches for conserving single-use PPE, expecting that lower-income countries or developing contexts may already be uniquely conserving PPE. However, of the 50 included studies, only 3 originated from middle-income countries and none originated from low-income countries. Data from the included studies suggest PPE remained effective with extended use and with multiple or repeated use in clinical settings, as long as donning and doffing were performed in a standard manner. Multiple decontamination techniques were effective in disinfecting single use PPE for repeated use. These findings can inform healthcare facilities and providers in establishing protocols for safe conservation of PPE supplies and updating existing protocols to improve sustainability and overall resilience. Future studies should evaluate conservation practices in low-resource settings during non-pandemic times to develop strategies for more sustainable and resilient healthcare worldwide.

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Background: Existing literature has not yet evaluated the impact of postoperative length of stay (LOS) on patientreported outcome measures (PROMs) and minimum clinically important difference (MCID) in patients undergoing anterior lumbar interbody fusion (ALIF). The authors investigates the influence of postoperative LOS following ALIF on PROMs and MCID achievement rates.Methods: A single-surgeon database was retrospectively reviewed for patients undergoing single-level ALIF. The following 2 cohorts were studied: patients with LOS <45 hours and patients with LOS ≥45 hours. The following PROMs were recorded at preoperative and 6-week, 12-week, 6-month, 1-year, and 2-year postoperative timepoints: visual analog scale (VAS) back and leg, Oswestry Disability Index (ODI), 12-item short form (SF-12) physical composite score (PCS), and patientreported outcome measurement information system physical function. MCID achievement was compared by LOS grouping using χ 2 analysis. The rates of complications by LOS grouping and the relative risk among demographic and perioperative characteristics for a longer hospital stay of ≥45 hours were calculated.Results: A total of 52 subjects were included in each cohort. LOS ≥45 hours demonstrated worse ODI at 6 weeks and SF-12 PCS preoperative and at 12 weeks (P ≤ 0.026, all). LOS <45 hours demonstrated greater MCID rates for all PROMs except VAS back (P ≤ 0.004, all). Postoperative urinary retention (POUR), fever, and total complications (P ≤ 0.003, all) were associated with increased LOS. Diabetes (P = 0.037), preoperative VAS neck ≥7 (P = 0.012), and American Society of Anesthesiologists classification ≥2 (P = 0.003) served as preoperative risk factors for postoperative stay ≥45 hours.Conclusion: Following single-level ALIF, patients with shorter LOS demonstrated significantly greater overall MCID achievement for most PROMs. POUR, fever, and total complications were associated with longer LOS and greater blood loss. Diabetes and higher preoperative leg pain were identified as risk factors for longer LOS.Clinical Relevance: Patients undergoing ALIF with shorter LOS had greater MCID achievement for disability, physical function, and leg pain outcomes. Patients with greater preoperative leg pain and diabetes may be at risk for longer LOS.