Making Artificial Intelligence Lemonade Out of Data Lemons: Adaptation of a Public Apical Echo Database for Creation of a Subxiphoid Visual Estimation Automatic Ejection Fraction Machine Learning Algorithm.

Objectives: A paucity of point-of-care ultrasound (POCUS) databases limits machine learning (ML). Assess feasibility of training ML algorithms to visually estimate left ventricular ejection fraction (EF) from a subxiphoid (SX) window using only apical 4-chamber (A4C) images.

Methods: Researchers used a long-short-term-memory algorithm for image analysis.

Correlates of Early Prenatal Care Access among U.S. Women: Data from the Pregnancy Risk Assessment Monitoring System (PRAMS).

Objectives: Early first trimester prenatal counseling could reduce adverse maternal and child health outcomes. Existing literature does not identify the length of time between suspecting pregnancy and attending their first prenatal visit. Identifying this potential window for change is critical for clinical practice, intervention programming and policy change.

Cationic poly(2-aminoethylmethacrylate) and poly(N-(2-aminoethylmethacrylamide) modified cellulose nanocrystals: synthesis, characterization, and cytotoxicity.

Cellulose nanocrystals (CNCs) continue to gain increasing attention in the materials community as sustainable nanoparticles with unique chemical and mechanical properties. Their nanoscale dimensions, biocompatibility, biodegradability, large surface area, and low toxicity make them promising materials for biomedical applications. Here, we disclose a facile synthesis of poly(2-aminoethylmethacrylate) (poly(AEM)) and poly(N-(2-aminoethylmethacrylamide) (poly(AEMA)) CNC brushes via the surface-initiated single-electron-transfer living radical polymerization technique.

Results of a pilot test of a self-administered smartphone-based treatment system for alcohol use disorders: usability and early outcomes.

Background: This paper provides results from a pilot study focused on assessing early-stage effectiveness and usability of a smartphone-based intervention system that provides a stand-alone, self-administered intervention option, the Location-Based Monitoring and Intervention for Alcohol Use Disorders (LBMI-A). The LBMI-A provided numerous features for intervening with ongoing drinking, craving, connection with supportive others, managing life problems, high-risk location alerting, and activity scheduling.

Methods: Twenty-eight participants, ranging in age from 22 to 45, who met criteria for an alcohol use disorder used an LBMI-A-enabled smartphone for 6 weeks.