Characterizing Antibiotic Allergy Labels in a Large UK Hospital Population to Inform Antimicrobial Stewardship and Delabeling Assessment Strategy.

BACKGROUND: Antibiotic allergy labels are important barriers to treatment and antimicrobial stewardship, but their prevalence in UK hospitals is poorly described. OBJECTIVE: To ascertain the prevalence and characteristics of antibiotic allergy labels in a large UK hospital setting and estimate the proportion of penicillin allergy labels for which point-of-care (POC) delabeling assessment would be appropriate. METHODS: Electronic health records data were analyzed from all patients treated at Cambridge University Hospitals NHS Foundation Trust in 2019. Validated POC delabeling risk stratification criteria were retrospectively applied to penicillin allergy labels. RESULTS: Recorded reactions to antibiotics were present in 11.8% of all patients (32,148 of 273,216), 16.3% of inpatients (13,874 of 85,230), and 9.7% of outpatients (18,274 of 187,986). Penicillins were the commonest reaction precipitant described (9.0% of patients; 24,646 of 273,216), followed by sulfonamides/trimethoprim (1.4%; 3869 of 273,216) and macrolides/lincosamides (1.3%; 3644 of 273,216). A total of 3.9% of inpatients had recorded reactions to >1 antibiotic class (3348 of 85,230). Cutaneous manifestations were the most commonly described reaction features (40.7% of labels; 15,821 of 38,902). Of 15,949 labels describing probable or possible penicillin "allergy" with sufficient detail to allow for the retrospective assessment of POC delabeling suitability, 1702 were deemed suitable for removal or downgrading of the label to "intolerance" without further investigation (10.7%), 11,887 were appropriate for POC assessment using an oral penicillin challenge (OPC) or OPC with prior bedside skin testing (74.5%), and 2360 were identified as unsuitable for any form of POC assessment (14.8%). CONCLUSIONS: Antibiotic allergy labels are highly prevalent in a UK hospital setting. A large proportion of penicillin allergy labels may be suitable for POC delabeling assessment.

Identifying the spatial and temporal dynamics of molecularly-distinct glioblastoma sub-populations.

Glioblastomas (GBMs) are the most aggressive primary brain tumours and have no known cure. Each individual tumour comprises multiple sub-populations of genetically-distinct cells that may respond differently to targeted therapies and may contribute to disappointing clinical trial results. Image-localized biopsy techniques allow multiple biopsies to be taken during surgery and provide information that identifies regions where particular sub-populations occur within an individual GBM, thus providing insight into their regional genetic variability. These sub-populations may also interact with one another in a competitive or cooperative manner; it is important to ascertain the nature of these interactions, as they may have implications for responses to targeted therapies. We combine genetic information from biopsies with a mechanistic model of interacting GBM sub-populations to characterise the nature of interactions between two commonly occurring GBM sub-populations, those with EGFR and PDGFRA genes amplified. We study population levels found across image-localized biopsy data from a cohort of 25 patients and compare this to model outputs under competitive, cooperative and neutral interaction assumptions. We explore other factors affecting the observed simulated sub-populations, such as selection advantages and phylogenetic ordering of mutations, which may also contribute to the levels of EGFR and PDGFRA amplified populations observed in biopsy data.

Learning Equations from Biological Data with Limited Time Samples.

Equation learning methods present a promising tool to aid scientists in the modeling process for biological data. Previous equation learning studies have demonstrated that these methods can infer models from rich datasets; however, the performance of these methods in the presence of common challenges from biological data has not been thoroughly explored. We present an equation learning methodology comprised of data denoising, equation learning, model selection and post-processing steps that infers a dynamical systems model from noisy spatiotemporal data. The performance of this methodology is thoroughly investigated in the face of several common challenges presented by biological data, namely, sparse data sampling, large noise levels, and heterogeneity between datasets. We find that this methodology can accurately infer the correct underlying equation and predict unobserved system dynamics from a small number of time samples when the data are sampled over a time interval exhibiting both linear and nonlinear dynamics. Our findings suggest that equation learning methods can be used for model discovery and selection in many areas of biology when an informative dataset is used. We focus on glioblastoma multiforme modeling as a case study in this work to highlight how these results are informative for data-driven modeling-based tumor invasion predictions.

Future Cardiovascular Disease Risk for Women With Gestational Hypertension: A Systematic Review and Meta-Analysis.

Background Inconsistent findings have been found among studies evaluating the risk of cardiovascular disease for women who have had pregnancies complicated by gestational hypertension (the new onset of high blood pressure without proteinuria during pregnancy). We provide a comprehensive review of studies to quantify the association between gestational hypertension and cardiovascular events in women. Methods and Results We conducted a systematic search of PubMed, Embase, and Web of Science in March 2019 for studies examining the association between gestational hypertension and any cardiovascular event. Two reviewers independently assessed the abstracts and full-text articles. Study characteristics and the relative risk (RR) of cardiovascular events associated with gestational hypertension were extracted from the eligible studies. Where appropriate, the estimates were pooled with inverse variance weighted random-effects meta-analysis. A total of 21 studies involving 3 60 1192 women (127 913 with gestational hypertension) were identified. Gestational hypertension in the first pregnancy was associated with a greater risk of overall cardiovascular disease (RR, 1.45; 95% CI, 1.17-1.80) and coronary heart disease (RR, 1.46; 95% CI, 1.23-1.73), but not stroke (RR, 1.26; 95% CI, 0.96-1.65) or thromboembolic events (RR, 0.88; 95% CI, 0.73-1.07). Women with 1 or more pregnancies affected by gestational hypertension were at greater risk of cardiovascular disease (RR, 1.81; 95% CI, 1.42-2.31), coronary heart disease (RR, 1.83; 95% CI, 1.33-2.51), and heart failure (RR, 1.77; 95% CI, 1.47-2.13), but not stroke (RR, 1.50; 95% CI, 0.75-2.99). Conclusions Gestational hypertension is associated with a greater risk of overall cardiovascular disease, coronary heart disease, and heart failure. More research is needed to assess the presence of a dose-response relationship between gestational hypertension and subsequent cardiovascular disease. Registration URL: https://www.crd.york.ac.uk/prosp​ero/; Unique identifier: CRD42018119031.

H+ Transport by K+ EXCHANGE ANTIPORTER3 Promotes Photosynthesis and Growth in Chloroplast ATP Synthase Mutants.

The composition of the thylakoid proton motive force (pmf) is regulated by thylakoid ion transport. Passive ion channels in the thylakoid membrane dissipate the membrane potential (Δψ) component to allow for a higher fraction of pmf stored as a proton concentration gradient (ΔpH). K+/H+ antiport across the thylakoid membrane via K+ EXCHANGE ANTIPORTER3 (KEA3) instead reduces the ΔpH fraction of the pmf. Thereby, KEA3 decreases nonphotochemical quenching (NPQ), thus allowing for higher light use efficiency, which is particularly important during transitions from high to low light. Here, we show that in the background of the Arabidopsis (Arabidopsis thaliana) chloroplast (cp)ATP synthase assembly mutant cgl160, with decreased cpATP synthase activity and increased pmf amplitude, KEA3 plays an important role for photosynthesis and plant growth under steady-state conditions. By comparing cgl160 single with cgl160 kea3 double mutants, we demonstrate that in the cgl160 background loss of KEA3 causes a strong growth penalty. This is due to a reduced photosynthetic capacity of cgl160 kea3 mutants, as these plants have a lower lumenal pH than cgl160 mutants, and thus show substantially increased pH-dependent NPQ and decreased electron transport through the cytochrome b 6 f complex. Overexpression of KEA3 in the cgl160 background reduces pH-dependent NPQ and increases photosystem II efficiency. Taken together, our data provide evidence that under conditions where cpATP synthase activity is low, a KEA3-dependent reduction of ΔpH benefits photosynthesis and growth.