Genome sequence and characterization of Streptomyces phage Pablito, representing a new species within the genus Janusvirus

Streptomycetes are ubiquitous soil bacteria. Here we report the complete and annotated genome sequence and characterization of Streptomyces phage Pablito, isolated from a soil sample in Haarlem, the Netherlands using Streptomyces lividans as host. This phage was able to infect a diverse range of Streptomyces strains, but none belonging to the sister genus Kitasatospora. Phage Pablito has double-stranded DNA with a genome length of 49,581 base pairs encoding 76 putative proteins, of which 26 could be predicted. The presence of a serine integrase protein indicated the lysogenic nature of phage Pablito. The phage remained stable over a wide range of temperatures (25--45 \textdegreeC) and at pH\thinspace\thinspace7.0, but lost infectivity at temperatures above 55 \textdegreeC or when the pH dropped below 6.0. This newly isolated phage is closely related to Streptomyces phage Janus and Hank144 and considered a new species classified in the genus Janusvirus, within the subfamily Arquattrovirinae.This work was funded by a VICI grant from the Dutch Research Council (NWO) to DC (Grant Number VI.C.192.002) and an NWO XS grant to AB (Grant Number OCENW.XS.041). Microscope access was supported by the Netherlands Center for Electron Nanoscopy and partially funded by the Netherlands Electron Microscopy Infrastructure Grant 84.034.014.info:eu-repo/semantics/publishedVersio

Development and evaluation of uncertainty quantifying machine learning models to predict piperacillin plasma concentrations in critically ill patients

Background: Beta-lactam antimicrobial concentrations are frequently suboptimal in critically ill patients. Population pharmacokinetic (PopPK) modeling is the golden standard to predict drug concentrations. However, currently available PopPK models often lack predictive accuracy, making them less suited to guide dosing regimen adaptations. Furthermore, many currently developed models for clinical applications often lack uncertainty quantification. We, therefore, aimed to develop machine learning (ML) models for the prediction of piperacillin plasma concentrations while also providing uncertainty quantification with the aim of clinical practice. Methods: Blood samples for piperacillin analysis were prospectively collected from critically ill patients receiving continuous infusion of piperacillin/tazobactam. Interpretable ML models for the prediction of piperacillin concentrations were designed using CatBoost and Gaussian processes. Distribution-based Uncertainty Quantification was added to the CatBoost model using a proposed Quantile Ensemble method, useable for any model optimizing a quantile function. These models are subsequently evaluated using the distribution coverage error, a proposed interpretable uncertainty quantification calibration metric. Development and internal evaluation of the ML models were performed on the Ghent University Hospital database (752 piperacillin concentrations from 282 patients). Ensuing, ML models were compared with a published PopPK model on a database from the University Medical Centre of Groningen where a different dosing regimen is used (46 piperacillin concentrations from 15 patients.). Results: The best performing model was the Catboost model with an RMSE and R-2 of 31.94-0.64 and 33.53-0.60 for internal evaluation with and without previous concentration. Furthermore, the results prove the added value of the proposed Quantile Ensemble model in providing clinically useful individualized uncertainty predictions and show the limits of homoscedastic methods like Gaussian Processes in clinical applications. Conclusions: Our results show that ML models can consistently estimate piperacillin concentrations with acceptable and high predictive accuracy when identical dosing regimens as in the training data are used while providing highly relevant uncertainty predictions. However, generalization capabilities to other dosing schemes are limited. Notwithstanding, incorporating ML models in therapeutic drug monitoring programs seems definitely promising and the current work provides a basis for validating the model in clinical practice

Extending brat, an online annotation tool

Brat (brat.nlplab.org) is an open source browser-based tool for collaborative text annotation. It supports annotation of character and word spans, relations and events, which are stored as standoff plain text files. We present a number of extensions to brat: - Text level annotations, as free text or according to a structured flowchart - Automatic validation rules - FoLiA support: importing, visualizing and exporting FoLiA information - Visualization separated into selectable layers - Performance improvement

Pathogen-based target attainment of optimized continuous infusion dosing regimens of piperacillin-tazobactam and meropenem in surgical ICU patients: a prospective single center observational study

Abstract Background Several studies have indicated that commonly used piperacillin-tazobactam (TZP) and meropenem (MEM) dosing regimens lead to suboptimal plasma concentrations for a range of pharmacokinetic/pharmacodynamic (PK/PD) targets in intensive care unit (ICU) patients. These targets are often based on a hypothetical worst-case scenario, possibly overestimating the percentage of suboptimal concentrations. We aimed to evaluate the pathogen-based clinically relevant target attainment (CRTA) and therapeutic range attainment (TRA) of optimized continuous infusion dosing regimens of TZP and MEM in surgical ICU patients. Methods A single center prospective observational study was conducted between March 2016 and April 2019. Free plasma concentrations were calculated by correcting total plasma concentrations, determined on remnants of blood gas samples by ultra-performance liquid chromatography with tandem mass spectrometry, for their protein binding. Break points (BP) of identified pathogens were derived from epidemiological cut-off values. CRTA was defined as a corrected measured total serum concentration above the BP and calculated for increasing BP multiplications up to 6 × BP. The upper limit of the therapeutic range was set at 157.2 mg/L for TZP and 45 mg/L for MEM. As a worst-case scenario, a BP of 16 mg/L for TZP and 2 mg/L for MEM was used. Results 781 unique patients were included with 1036 distinctive beta-lactam antimicrobial prescriptions (731 TZP, 305 MEM) for 1003 unique infections/prophylactic regimens (750 TZP, 323 MEM). 2810 samples were available (1892 TZP, 918 MEM). The median corrected plasma concentration for TZP was 86.4 mg/L [IQR 56.2–148] and 16.2 mg/L [10.2–25.5] for MEM. CRTA and TRA was consistently higher for the pathogen-based scenario than for the worst-case scenario, but nonetheless, a substantial proportion of samples did not attain commonly used PK/PD targets. Conclusion Despite these pathogen-based data demonstrating that CRTA and TRA is higher than in the often-used theoretical worst-case scenario, a substantial proportion of samples did not attain commonly used PK/PD targets when using optimised continuous infusion dosing regimens. Therefore, more dosing optimization research seems warranted. At the same time, a ‘pathogen-based analysis’ approach might prove to be more sensible than a worst-case scenario approach when evaluating target attainment and linked clinical outcomes