Association Between Prepregnancy Body Mass Index and Newborn Breastfeeding Initiation.

Background: The benefits of breastfeeding a newborn are well documented. Identification of mothers who do not initiate breastfeeding is essential for developing initiatives to improve breastfeeding initiation. Methods: The study used data from the National Center for Health Statistics (NCHS) National Vital Statistics System (NVSS) birth certificate data (2014-2021) to identifying 15,599,930 in-hospital deliveries. We used multivariable logistic regression to assess the association between seven body mass index (BMI) categories and initiation of breastfeeding before hospital discharge. Prepregnancy BMI (weight in kilograms/height in meters2) included underweight (<18.5), healthy weight (18.5-24.9), overweight (25.0-29.9), Obesity Class I (30-34.9), Obesity Class II (35-39.9), and Obesity Class III (40-49.9) classes, in addition to a class newly identified in the literature as super obese (≥50), hereafter "Obesity Class IV." "This project was deemed non-human subjects research." Results: Approximately, 83% of mothers initiated breastfeeding before hospital discharge. Compared to mothers with a healthy prepregnancy BMI, the likelihood of breastfeeding initiation before hospital discharge decreased with increasing prepregnancy BMI. Specifically, we found reduced likelihood of initiation for mothers who were overweight (adjusted odds ratio [aOR]: 0.952, 95% confidence interval [CI]: [0.948-0.955]), Obesity Class I (aOR: 0.884, 95% CI: [0.880-0.888]), Obesity Class II (aOR: 0.816, 95% CI: [0.811-0.820]), Obesity Class III (aOR: 0.750, 95% CI: [0.745-0.755]), and Obesity Class IV (aOR 0.672: 95% CI: [0.662-0.683]). Conclusions: Mothers with prepregnancy BMI above the healthy range had reduced likelihood of initiating breastfeeding prior hospital discharge. This information should be used to develop and initiate interventions for mothers who wish to breastfeed but may need additional lactation assistance support.

Predicting pediatric severe asthma exacerbations: an administrative claims-based predictive model.

OBJECTIVE: Previous machine learning approaches fail to consider race and ethnicity and social determinants of health (SDOH) to predict childhood asthma exacerbations. A predictive model for asthma exacerbations in children is developed to explore the importance of race and ethnicity, rural-urban commuting area (RUCA) codes, the Child Opportunity Index (COI), and other ICD-10 SDOH in predicting asthma outcomes. METHODS: Insurance and coverage claims data from the Arkansas All-Payer Claims Database were used to capture risk factors. We identified a cohort of 22,631 children with asthma aged 5-18 years with 2 years of continuous Medicaid enrollment and at least one asthma diagnosis in 2018. The goal was to predict asthma-related hospitalizations and asthma-related emergency department (ED) visits in 2019. The analytic sample was 59% age 5-11 years, 39% White, 33% Black, and 6% Hispanic. Conditional random forest models were used to train the model. RESULTS: The model yielded an area under the curve (AUC) of 72%, sensitivity of 55% and specificity of 78% in the OOB samples and AUC of 73%, sensitivity of 58% and specificity of 77% in the training samples. Consistent with previous literature, asthma-related hospitalization or ED visits in the previous year (2018) were the two most important variables in predicting hospital or ED use in the following year (2019), followed by the total number of reliever and controller medications. CONCLUSIONS: Predictive models for asthma-related exacerbation achieved moderate accuracy, but race and ethnicity, ICD-10 SDOH, RUCA codes, and COI measures were not important in improving model accuracy.

Targeting the Non-Canonical NF-κB Pathway in Chronic Lymphocytic Leukemia and Multiple Myeloma.

In this study, we evaluated an NF-κB inducing kinase (NIK) inhibitor, CW15337, in primary chronic lymphocytic leukemia (CLL) cells, CLL and multiple myeloma (MM) cell lines and normal B- and T-lymphocytes. Basal NF-κB subunit activity was characterized using an enzyme linked immunosorbent assay (ELISA), and the effects of NIK inhibition were then assessed in terms of cytotoxicity and the expression of nuclear NF-κB subunits following monoculture and co-culture with CD40L-expressing fibroblasts, as a model of the lymphoid niche. CW15337 induced a dose-dependent increase in apoptosis, and nuclear expression of the non-canonical NF-κB subunit, p52, was correlated with sensitivity to CW15337 (p = 0.01; r2 = 0.39). Co-culture on CD40L-expressing cells induced both canonical and non-canonical subunit expression in nuclear extracts, which promoted in vitro resistance against fludarabine and ABT-199 (venetoclax) but not CW15337. Furthermore, the combination of CW15337 with fludarabine or ABT-199 showed cytotoxic synergy. Mechanistically, CW15337 caused the selective inhibition of non-canonical NF-κB subunits and the transcriptional repression of BCL2L1, BCL2A1 and MCL1 gene transcription. Taken together, these data suggest that the NIK inhibitor, CW15337, exerts its effects via suppression of the non-canonical NF-κB signaling pathway, which reverses BCL2 family-mediated resistance in the context of CD40L stimulation.