RESUMEN
Meropenem is a broad-spectrum antibiotic used for the treatment of multi-drug-resistant infections. Due to its pharmacokinetic profile, meropenem's activity is optimized by maintaining a specific time the serum concentration remains above the minimum inhibitory concentration (MIC) via extended infusion (EI), continuous infusion, or intermittent infusion dosing strategies. The available literature varies regarding the superiority of these dosing strategies. This study's primary objective was to determine the difference in time to clinical stabilization between intravenous push (IVP) and EI administration. We performed a retrospective pilot cohort study of 100 critically ill patients who received meropenem by IVP (n = 50) or EI (n = 50) during their intensive care unit (ICU) admission. There was no statistically significant difference in the overall achievement of clinical stabilization between IVP and EI (48% vs. 44%, p = 0.17). However, the median time to clinical stability was shorter for the EI group (20.4 vs. 66.2 h, p = 0.01). EI administration was associated with shorter hospital (13 vs. 17 days; p = 0.05) and ICU (6 vs. 9 days; p = 0.02) lengths of stay. Although we did not find a statistically significant difference in the overall time to clinical stabilization, the results of this pilot study suggest that EI administration may produce quicker clinical resolutions than IVP.
RESUMEN
CD4+ T cell responses are exquisitely antigen specific and directed toward peptide epitopes displayed by human leukocyte antigen class II (HLA-II) on antigen-presenting cells. Underrepresentation of diverse alleles in ligand databases and an incomplete understanding of factors affecting antigen presentation in vivo have limited progress in defining principles of peptide immunogenicity. Here, we employed monoallelic immunopeptidomics to identify 358,024 HLA-II binders, with a particular focus on HLA-DQ and HLA-DP. We uncovered peptide-binding patterns across a spectrum of binding affinities and enrichment of structural antigen features. These aspects underpinned the development of context-aware predictor of T cell antigens (CAPTAn), a deep learning model that predicts peptide antigens based on their affinity to HLA-II and full sequence of their source proteins. CAPTAn was instrumental in discovering prevalent T cell epitopes from bacteria in the human microbiome and a pan-variant epitope from SARS-CoV-2. Together CAPTAn and associated datasets present a resource for antigen discovery and the unraveling genetic associations of HLA alleles with immunopathologies.