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Few studies have compared provider and patient perceptions of barriers, motivators and facilitators of prenatal care (PNC) initiation. The current study compared these perceptions in providers and patients in Washington, DC, a city characterized by infant mortality and low birth weight rates that are among the highest in the nation, and poor utilization of PNC, particularly among minority groups. The results reported here were part of a larger study of barriers, motivators and facilitators influencing PNC utilization in Washington, DC. A convenience sample of 331 African American and Latino patients and 61 providers were interviewed to identify which of 63 motivators, facilitators, and barriers significantly influenced PNC initiation. Both sample groups were recruited at 14 PNC facilities, selected to represent all sites in DC known to serve high-risk, low-income minority women, including hospital-based clinics, community-based clinics, and private practices. Data were analyzed using Fisher exact tests and Kendall’s concordance tests. Results indicated that there was good agreement between patients and providers about the relative importance of the various barriers (especially psychosocial), motivators, and facilitators. However, differences were found between patients and providers in the response frequencies. Providers were more likely to report barriers while patients were more likely to report certain motivators (especially learning better health habits and how to protect health). These results indicate that despite widespread agreement on most issues, especially psychosocial barriers, patients rated health education higher than providers.

The FDA’s Center for Drug Evaluation and Research has recommended withdrawing the approval of Makena for the prevention of recurrent preterm birth after a trial failed both to verify clinical benefit to neonates and to substantiate a reduction in preterm birth.

Pregnancy is a period of significant change that impacts physiological and metabolic status leading to alterations in the disposition of drugs. Uncertainty in drug dosing in pregnancy can lead to suboptimal therapy, which can contribute to disease exacerbation. A few studies show there are increased dosing requirements for antidepressants in late pregnancy; however, the quantitative data to guide dose adjustments are sparse. We aimed to develop a physiologically based pharmacokinetic (PBPK) model that allows gestational-age dependent prediction of sertraline dosing in pregnancy. A minimal physiological model with defined gut, liver, plasma, and lumped placental-fetal compartments was constructed using the ordinary differential equation solver package, ‘ mrgsolve ’, in R. We extracted data from the literature to parameterize the model, including sertraline physicochemical properties, in vitro metabolism studies, disposition in nonpregnant women, and physiological changes during pregnancy. The model predicted the pharmacokinetic parameters from a clinical study with eight subjects for the second trimester and six subjects for the third trimester. Based on the model, gestational-dependent changes in physiology and metabolism account for increased clearance of sertraline (up to 143% at 40 weeks gestational age), potentially leading to under-dosing of pregnant women when nonpregnancy doses are used. The PBPK model was converted to a prototype web-based interactive dosing tool to demonstrate how the output of a PBPK model may translate into optimal sertraline dosing in pregnancy. Quantitative prediction of drug exposure using PBPK modeling in pregnancy will support clinically appropriate dosing and increase the therapeutic benefit for pregnant women.

Various models of community research have been employed to assess community health needs and address community health problems. Infant mortality remains a problem in urban minority communities throughout the United States. This article compares various community research models with a three-tiered community involvement model used in Washington, D.C., an urban, predominantly African American community with one of the worst rates of infant mortality in the nation. The research design incorporated community input throughout the research design process, resulting in a better understanding of issues and problems associated with infant mortality within the community setting. The use of the model improved community participation in the research process, facilitating improved health initiatives.

The purpose of this study was to develop a model to guide families in documenting complete health information. Design issues addressed include format, size, arrangement, content, and other elements to improve effectiveness. This model will help patients, parents, and other consumers to maintain their medical records. The review of literature indicated medical outcomes improve when patients become partners in medical decision-making. Empowering patients with information and assigning responsibility for documentation to them enhances this participation. Members of the Grand Council for the International Federation of Health Record Organizations and past-presidents of the American Health Information Management Association formed a panel of experts. Three Delphi rounds were facilitated by questionnaires asking the panel to evaluate the importance of choices for each design area. Respondents indicated “not important”, “somewhat important”, or “very important” on a Likert-type scale. Responses were tallied for each round resulting in a ranking of the choices based on total score for round 2 and mean, score for round 3. Items with less than a 2.0 mean score at the conclusion of round 2 were eliminated from consideration for round 3. Participants added design items and made many comments. These items were categorized and used to design questionnaires for rounds two and three. At the conclusion of the third round, consensus was reached. The best design for a family medical record was defined in terms of format, size, arrangement, and content. The best format is both electronic and paper. The paper format should be a 3-ring binder and include a computer disk with screens that match the paper forms. A separate record should be developed for each family member. The record should be divided into sections and include entries from providers as well as patients. Record content should include family and personal health history, information on the current health status and records of all healthcare services. Reminders for preventive and routine services should be included. Forms in the record should be designed so users can easily understand how to make entries and locate information.

Russian forces in Ukraine seized Europe's largest nuclear plantafter their shelling set part of the complex on fire, sparking fearsacross the continent of a nuclear disaster. Management at theZaporizhzhia nuclear power plant in Enerhodar is now working atgunpoint. The Defense Department set up the deconfliction line onTuesday for the purposes of preventing miscalculation, militaryincidents and escalation. A school in Zhytomyr, northern Ukraine, washit by a Russian rocket. Two of Russia's landmark liberal mediaoutlets were dissolved or suspended operations amid an unprecedentedKremlin crackdown over its war with Ukraine. GUESTS: Joseph Cirincione, Karine Orlova

The use of deep learning models to identify lesions on cotton leaves on the basis of images of the crop in the field is proposed in this article. Cultivated in most of the world, cotton is one of the economically most important agricultural crops. Its cultivation in tropical regions has made it the target of a wide spectrum of agricultural pests and diseases, and efficient solutions are required. Moreover, the symptoms of the main pests and diseases cannot be differentiated in the initial stages, and the correct identification of a lesion can be difficult for the producer. To help resolve the problem, the present research provides a solution based on deep learning in the screening of cotton leaves which makes it possible to monitor the health of the cotton crop and make better decisions for its management. With the learning models GoogleNet and Resnet50 using convolutional neural networks, a precision of 86.6% and 89.2%, respectively, was obtained. Compared with traditional approaches for the processing of images such as support vector machines (SVM), Closest k-neighbors (KNN), artificial neural networks (ANN) and neuro-fuzzy (NFC), the convolutional neural networks proved to be up to 25% more precise, suggesting that this method can contribute to a more rapid and reliable inspection of the plants growing in the field.