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58% and max 5.0% of total fats within the same mother). The average total fat content of home collected EBM was similar to the cot-side collected samples, at 27.37 ± 8.23 mg/mL, and the FFA concentration was slightly higher at 2.49% of total fats (IQR 1.74-3.29%). Overall, the FFA concentration of breast milk in the neonatal unit before and even after a short period of cold storage and handling is universally low.

Tenofovir disoproxil fumarate (TDF)-based regimens have been associated with impaired kidney function and loss of bone mineral density among patients living with HIV (PLWH). We assess the association between TDF exposure and the odds of chronic kidney disease (CKD) and osteoporotic fracture in HIV patients.

Demographics, administrative claims, and pharmacy dispensation were extracted from the Veterans Affairs Informatics and Computing Infrastructure (VINCI). Patients were categorized based on TDF utilization. Incidence rates for patients exposed and unexposed to TDF were calculated per 1000 patient-years (PYs). Logistic regression was used to calculate the odds of outcome after adjusting for baseline and clinical characteristics.

The sample included 4,630 PLWH who were currently exposed to TDF and 1,181 who were never exposed to TDF for the CKD analyses. For fracture analyses, the sample included 6,883 PLWH who were currently exposed to TDF and 1,951 who were never exposed to TDF. In adjusted models, current TDF exposure was associated with increased odds of CKD compared to never having been exposed (OR 1.48, 95% CI 1.18-1.85). Odds of fracture were 2.32 times higher for patients who were currently on a TDF regimen (OR 2.32, 95% CI 1.58-3.42) compared to those who had never been exposed to TDF in adjusted models.

In a large cohort of US veterans with HIV, current exposure to TDF was associated with a 48% higher odds of CKD and a greater than two-fold increase in the odds of osteoporotic fracture.

In a large cohort of US veterans with HIV, current exposure to TDF was associated with a 48% higher odds of CKD and a greater than two-fold increase in the odds of osteoporotic fracture.

This study sought to compare outcomes of trauma patients taken directly from the field to a Level I trauma center (direct) versus patients that were first brought to a Level III trauma center prior to being transferred to a Level I (transfer) within our inclusive Delaware trauma system.

A retrospective review of the Level I center's trauma registry was performed using data from 2013 to 2017 for patients brought to a single Level I trauma center from 2 surrounding counties. The direct cohort consisted of 362 patients, while the transfer cohort contained 204 patients. Linear regression analysis was performed to investigate hospital length of stay (LOS), while logistic regression was used for mortality, complications, and craniotomy. Covariates included age, gender, county, and injury severity score (ISS). Propensity score weighting was also performed between the direct and transfer cohorts.

When adjusting for age, gender, ISS, and county, transferred patients demonstrated worse outcomes compared with direct patients in both the regression and propensity score analyses. Transferred patients were at increased risk of mortality (odds ratio [OR] 2.17, CI 1.10-4.37,

= .027) and craniotomy (OR 3.92, CI 1.87-8.72,

< .001). Age was predictive of mortality (

< .001). ISS was predictive of increased risk of mortality (

< .001), increased LOS (

< .001), and craniotomy (

< .001). Older age, Sussex County, and higher ISS were predictive of patients being transferred (

< .001).

Delays in the presentation to our Level I trauma center resulted in worse outcomes. Patients that meet criteria should be considered for transport directly to the highest level trauma center in the system to avoid delays in care.

Delays in the presentation to our Level I trauma center resulted in worse outcomes. Patients that meet criteria should be considered for transport directly to the highest level trauma center in the system to avoid delays in care.Background Liquid biopsies offer a minimally invasive approach to patient disease diagnosis and monitoring. However, these are highly affected by preprocessing variables with many protocols designed for downstream analysis of a single molecular biomarker. Here we investigate whether specialized blood tubes could be repurposed for the analysis of an increasingly valuable biomarker, extracellular vesicles (EVs). Methods Blood was collected from three donors into K3-EDTA, Roche, or Streck cell-free DNA (cfDNA) collection tubes and processed using sequential centrifugation either immediately or after storage for 3 days. MicroEV were collected from platelet-poor plasma by 10,000 g centrifugation and NanoEVs isolated using size exclusion chromatography. Particle size and counts were assessed by Nanoparticle Tracking Analysis, protein quantitation by bicinchoninic acid assay (BCA) assay, and dot blotting for blood cell surface proteins. VT104 TEAD inhibitor Results MicroEVs and NanoEVs could be isolated from plasma collected using all ts if the molecular target of interest is not present in blood cells.There is a growing interest in using machine learning (ML) methods for causal inference due to their (nearly) automatic and flexible ability to model key quantities such as the propensity score or the outcome model. Unfortunately, most ML methods for causal inference have been studied under single-level settings where all individuals are independent of each other and there is little work in using these methods with clustered or nested data, a common setting in education studies. This paper investigates using one particular ML method based on random forests known as Causal Forests to estimate treatment effects in multilevel observational data. We conduct simulation studies under different types of multilevel data, including two-level, three-level, and cross-classified data. Our simulation study shows that when the ML method is supplemented with estimated propensity scores from multilevel models that account for clustered/hierarchical structure, the modified ML method outperforms preexisting methods in a wide variety of settings.