Performance of machine-learning approach for prediction of pre-eclampsia in a middle-income country.

OBJECTIVE: Pre-eclampsia (PE) is a serious complication of pregnancy associated with maternal and fetal morbidity and mortality. As current prediction models have limitations and may not be applicable in resource-limited settings, we aimed to develop a machine-learning (ML) algorithm that offers a potential solution for developing accurate and efficient first-trimester prediction of PE. METHODS: We conducted a prospective cohort study in Mexico City, Mexico to develop a first-trimester prediction model for preterm PE (pPE) using ML. Maternal characteristics and locally derived multiples of the median (MoM) values for mean arterial pressure, uterine artery pulsatility index and serum placental growth factor were used for variable selection. The dataset was split into training, validation and test sets. An elastic-net method was employed for predictor selection, and model performance was evaluated using area under the receiver-operating-characteristics curve (AUC) and detection rates (DR) at 10% false-positive rates (FPR). RESULTS: The final analysis included 3050 pregnant women, of whom 124 (4.07%) developed PE. The ML model showed good performance, with AUCs of 0.897, 0.963 and 0.778 for pPE, early-onset PE (ePE) and any type of PE (all-PE), respectively. The DRs at 10% FPR were 76.5%, 88.2% and 50.1% for pPE, ePE and all-PE, respectively. CONCLUSIONS: Our ML model demonstrated high accuracy in predicting pPE and ePE using first-trimester maternal characteristics and locally derived MoM. The model may provide an efficient and accessible tool for early prediction of PE, facilitating timely intervention and improved maternal and fetal outcome. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Eficiencia de un enfoque de aprendizaje automático para la predicción de la preeclampsia en un país de ingresos medios OBJETIVO: La preeclampsia (PE) es una complicación grave del embarazo asociada a morbilidad y mortalidad materna y del feto. Dado que los modelos de predicción actuales tienen limitaciones y pueden no ser aplicables en situaciones con recursos limitados, se propuso desarrollar un algoritmo de aprendizaje automático (AA) que ofrezca una solución con potencial para desarrollar una predicción precisa y eficiente de la PE en el primer trimestre. MÉTODOS: Se realizó un estudio de cohorte prospectivo en Ciudad de México para desarrollar un modelo de predicción de la PE pretérmino (PEp) en el primer trimestre utilizando AA. Para la selección de variables se utilizaron las características maternas y los múltiplos de la mediana (MdM) obtenidos localmente para la presión arterial media, el índice de pulsatilidad de la arteria uterina y el factor de crecimiento placentario sérico. El conjunto de datos se dividió en subconjuntos de datos de entrenamiento, de validación y de test estadístico. Se empleó un método de red elástica para la selección de predictores, y el rendimiento del modelo se evaluó mediante el área bajo la curva de características operativas del receptor (ABC) y las tasas de detección (TD) con tasas de falsos positivos (TFP) del 10%. RESULTADOS: El análisis final incluyó a 3050 mujeres embarazadas, de las cuales 124 (4,07%) desarrollaron PE. El modelo de AA mostró una buena eficiencia, con un ABC de 0,897, 0,963 y 0,778 para la PEp, la PE de aparición temprana (PEat) y cualquier tipo de PE (todas las PE), respectivamente. Las TD con TFP del 10% fueron del 76,5%, 88,2% y 50,1% para la PEp, PEat y todas las PE, respectivamente. CONCLUSIONES: Nuestro modelo de AA demostró una alta precisión en la predicción de la PEp y la PEat utilizando características maternas del primer trimestre y MdM calculados localmente. El modelo puede proporcionar una herramienta eficiente y accesible para la predicción temprana de la PE, facilitando la intervención oportuna y la mejora de los resultados maternos y del feto.

Dopamine D5 receptor modulates male and female sexual behavior in mice.

RATIONALE: Dopamine exerts its actions through at least five receptor (DAR) isoforms. In female rats, D5 DAR may be involved in expression of sexual behavior. We used a D5 knockout (D5KO) mouse to assess the role of D5 DAR in mouse sexual behavior. Both sexes of D5KO mice are fertile and exhibit only minor disruptions in exploratory locomotion, startle, and prepulse inhibition responses. OBJECTIVE: This study was conducted to characterize the sexual behavior of male and female D5KO mice relative to their WT littermates. METHODS: Female WT and D5KO littermates were ovariectomized and given a series of sexual behavior tests after treatment with estradiol benzoate (EB) and progesterone (P). Once sexual performance was optimal the dopamine agonist, apomorphine (APO), was substituted for P. Male mice were observed in pair- and trio- sexual behavior tests. To assess whether the D5 DAR is involved in rewarding aspects of sexual behavior, WT and D5KO male mice were tested for conditioned place preference. RESULTS: Both WT and D5KO females can display receptivity after treatment with EB and P, but APO was only able to facilitate receptivity in EB-primed WT, not in D5KO, mice. Male D5KO mice display normal masculine sexual behavior in mating tests. In conditioned preference tests, WT males formed a conditioned preference for context associated with either intromissions alone or ejaculation as the unconditioned stimulus. In contrast, D5KO males only showed a place preference when ejaculation was paired with the context. CONCLUSIONS: In females, the D5 DAR is essential for the actions of dopamine on receptivity. In males, D5 DAR influences rewarding aspects of intromissions. Taken together, the work suggests that the D5 receptor mediates dopamine's action on sexual behavior in both sexes, perhaps via a reward pathway.

Development of a tuberculosis education booklet for Latino immigrant patients.

The incidence and prevalence of tuberculosis are far more common among third world populations immigrating to the United States than among US-born citizens. Immigrants' failure to comply with an anti-tuberculosis treatment can impede completion of care and further confound this public health problem. Barriers to patient--provider communication can negatively influence adherence to a medical regimen. Patients who are unable to comprehend medical advice and do not see it as personally salient are less likely to follow their provider's medical advice. In this paper, the authors focus on efforts to develop a patient education tool targeting Spanish-speaking Latino immigrant patients to facilitate communication with tuberculosis clinicians. A description of the multi-stage developmental processes is presented including conducting a needs assessment, development of visual and written messages, review/critique by tuberculosis experts, field-testing, revisions, and distribution. Formative evaluation and field testing indicates promise for improving communication using this tool.