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. METHODS: The study sample was composed of women in the United States who responded to the Pregnancy Risk Assessment Monitoring Systems survey in 2016, for the following questions-when they first suspected pregnancy, when they attended their first prenatal visit, were they able to receive prenatal care as early as they wished, and perceived barriers to receiving prenatal care. RESULTS: On average, participants became certain they were pregnant at 6.0 (SE = 0.1) weeks gestation, while participants reported having their first prenatal care visit at 9.3 (SE = 0.1) weeks, with clear health disparities by race, age, WIC participation, education level, and marital status. About 15% of women reported not receiving prenatal care as early as they wished. Structural or financial barriers in the health care system were common: 38.1% reported that no appointments available, 28.2% reported not having money or insurance to pay for the visit, 27.3% reported that the doctor or health plan would not start care, and 22.5% reported not having a Medicaid card. CONCLUSIONS FOR PRACTICE: This study illustrates a window for opportunity to provide earlier prenatal care, which would facilitate earlier implementation of prenatal counseling. Strategies to address barriers to care on the patient, provider and systemic levels, particularly among vulnerable population groups, are warranted. WHAT IS ALREADY KNOWN ON THIS SUBJECT?: Seeking prenatal care early is associated with better health outcomes for women and infants. A window of opportunity exists between suspecting pregnancy and attending a first prenatal visit. WHAT THIS STUDY ADDS?: Clear health disparities were apparent in both recognizing their pregnancies, and receiving early prenatal care by race, age, WIC participation, education level, and marital status. About 15% of women reported not receiving prenatal care as early as they wished, and many attributed this later care to structural or financial barriers in the health care system.

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. Using the Stanford EchoNet-Dynamic database of 10,036 A4C videos with calculated exact EF, researchers tested 3 ML training permeations. First, training on unaltered Stanford A4C videos, then unaltered and 90° clockwise (CW) rotated videos and finally unaltered, 90° rotated and horizontally flipped videos. As a real-world test, we obtained 615 SX videos from Harbor-UCLA (HUCLA) with EF calculations in 5% ranges. Researchers performed 1000 randomizations of EF point estimation within HUCLA EF ranges to compensate for ML and HUCLA EF mismatch, obtaining a mean value for absolute error (MAE) comparison and performed Bland-Altman analyses. RESULTS: The ML algorithm EF mean MAE was estimated at 23.0, with a range of 22.8-23.3 using unaltered A4C video, mean MAE was 16.7, with a range of 16.5-16.9 using unaltered and 90° CW rotated video, mean MAE was 16.6, with a range of 16.3-16.8 using unaltered, 90° CW rotated and horizontally flipped video training. Bland-Altman showed weakest agreement at 40-45% EF. CONCLUSIONS: Researchers successfully adapted unrelated ultrasound window data to train a POCUS ML algorithm with fair MAE using data manipulation to simulate a different ultrasound examination. This may be important for future POCUS algorithm design to help overcome a paucity of POCUS databases.

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. The resulting modified CNCs were characterized for their chemical and morphological features using a combination of analytical, spectroscopic, and microscopic techniques. Zeta potential measurements indicated a positive surface charge, and further proof of the cationic nature was confirmed by gold deposition as evidenced by electron microscopy. The cytotoxicity of these cationic modified CNCs was evaluated utilizing a 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay in two different cell lines, J774A1 (mouse monocyte cells) and MCF-7 (human breast adenocarcinoma cells). The results indicated that none of the cationic modified CNCs decreased cell viability at low concentrations, which could be suitable for biomedical applications.

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. RESULTS: Participants indicated the LBMI-A intervention modules were helpful in highlighting alcohol use patterns. Tools related to managing alcohol craving, monitoring consumption, and identifying triggers to drink were rated by participants as particularly helpful. Participants also demonstrated significant reductions in hazardous alcohol use while using the system (56% of days spent hazardously drinking at baseline vs. 25% while using the LBMI-A) and drinks per day diminished by 52%. CONCLUSIONS: Implications for system improvement as well as suggestions for designing ecological momentary assessment and intervention systems for substance use disorders are discussed.