Citations

Outpatient parenteral antimicrobial therapy (OPAT) has been widely used in clinical practice for many decades because of its associated cost savings, reductions in inpatient hospital days, and decreases in hospital-associated infections. Despite this long history, evolving practice patterns and new drug delivery devices continue to present challenges as well as opportunities for clinicians when designing appropriate outpatient antimicrobial regimens. One such change is the increasing use of extended and continuous infusion (CI) of antimicrobials to optimize the achievement of pharmacokinetic and pharmacodynamic targets. Elastomeric devices are also becoming increasingly popular in OPAT, including for the delivery of CI. In this article, we review the clinical evidence for CI in OPAT, as well as practical considerations of patient preferences, cost, and antimicrobial stability.

Patients with obstructive sleep apnea (OSA) have a heightened risk of developing cardiovascular diseases, namely hypertension. While seminal evidence indicates a causal role for sympathetic nerve activity in the hypertensive phenotype commonly observed in patients with OSA, no studies have investigated potential sex differences in the sympathetic regulation of blood pressure in this population. Supporting this exploration are large-scale observational data, as well as controlled interventional studies in healthy adults, indicating that sleep disruption increases blood pressure to a greater extent in females relative to males. Furthermore, females with severe OSA demonstrate a more pronounced hypoxic burden (i.e., disease severity) during rapid eye movement sleep when sympathetic nerve activity is greatest. These findings would suggest that females are at greater risk for the hemodynamic consequences of OSA and related sleep disruption. Accordingly, the purpose of this review is three-fold: (1) to review the literature linking sympathetic nerve activity to hypertension in OSA, (2) to highlight recent experimental data supporting the hypothesis of sex differences in the regulation of sympathetic nerve activity in OSA, and (3) to discuss the potential sex differences in peripheral adrenergic signaling that may contribute to, or offset, cardiovascular risk in patients with OSA.

Abstract Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector‐based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well‐described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.

AbstractThe clinical significance of the tumor‐immune interaction in breast cancer is now established, and tumor‐infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple‐negative (estrogen receptor, progesterone receptor, and HER2‐negative) breast cancer and HER2‐positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state‐of‐the‐art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false‐positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in‐depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple‐negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

In this article, we present a flexible model for microbiome count data. We consider a quasi‐likelihood framework, in which we do not make any assumptions on the distribution of the microbiome count except that its variance is an unknown but smooth function of the mean. By comparing our model to the negative binomial generalized linear model (GLM) and Poisson GLM in simulation studies, we show that our flexible quasi‐likelihood method yields valid inferential results. Using a real microbiome study, we demonstrate the utility of our method by examining the relationship between adenomas and microbiota. We also provide an R package “fql” for the application of our method.

Abstract Objective Transformer-based language models are prevailing in the clinical domain due to their excellent performance on clinical NLP tasks. The generalizability of those models is usually ignored during the model development process. This study evaluated the generalizability of CancerBERT, a Transformer-based clinical NLP model, along with classic machine learning models, i.e., conditional random field (CRF), bi-directional long short-term memory CRF (BiLSTM-CRF), across different clinical institutes through a breast cancer phenotype extraction task. Materials and methods Two clinical corpora of breast cancer patients were collected from the electronic health records from the University of Minnesota (UMN) and Mayo Clinic (MC), and annotated following the same guideline. We developed three types of NLP models (i.e., CRF, BiLSTM-CRF and CancerBERT) to extract cancer phenotypes from clinical texts. We evaluated the generalizability of models on different test sets with different learning strategies (model transfer vs locally trained). The entity coverage score was assessed with their association with the model performances. Results We manually annotated 200 and 161 clinical documents at UMN and MC. The corpora of the two institutes were found to have higher similarity between the target entities than the overall corpora. The CancerBERT models obtained the best performances among the independent test sets from two clinical institutes and the permutation test set. The CancerBERT model developed in one institute and further fine-tuned in another institute achieved reasonable performance compared to the model developed on local data (micro-F1: 0.925 vs 0.932). Conclusions The results indicate the CancerBERT model has superior learning ability and generalizability among the three types of clinical NLP models for our named entity recognition task. It has the advantage to recognize complex entities, e.g., entities with different labels.

Background and Aims: Cyanobacteria are commonly found in water bodies and their production of hepatotoxins can contribute to liver damage. However, the population health effects of cyanobacteria exposure (CE) are unknown. Our objectives were to determine the effect of chronic exposure to cyanobacteria through proximity to water bodies with high cyanobacteria counts on the incidence and mortality of liver cancers, as well as to identify location-based risk factors. Approach and Results: Across the contiguous United States, regions with high cyanobacteria counts in water bodies were identified using satellite remote sensing data. The data were geospatially mapped to county boundaries, and disease mortality and incidence rates were analyzed. Distinctive spatial clusters of CE and mortality related to liver diseases or cancer were identified. There was a highly significant spatial association between CE, liver disease, and liver cancer but not between CE and all cancers. Hot spots of CE and mortality were identified along the Gulf of Mexico, eastern Texas, Louisiana, and Florida, and cold spots across the Appalachians. The social vulnerability index was identified as a major location-based determinant by logistic regression, with counties in the fourth or fifth quintiles having the highest prevalence of hot spots of CE and mortality from liver cancer. Conclusions: These findings emphasize the importance of environmental exposure to cyanobacteria as a location-based determinant of mortality from liver cancer. Public health initiatives addressing CE may be considered to reduce mortality, particularly in areas of high social vulnerability.