The Research Recap: 22- MS, Uterine Involution, Machine Learning, Medical Students, and Feeding Decisions

In this episode of Behind the Latch, Margaret Salty dives into the latest research articles from her Google Scholar alerts, exploring how recent findings can enhance the practice of IBCLCs. Margaret breaks down complex research into actionable strategies for lactation consultants, focusing on how these insights can improve breastfeeding outcomes. Whether you're a seasoned IBCLC or just starting your journey, these episodes will keep you at the forefront of evidence-based lactation care.Key Points Covered:1. Breastfeeding and Multiple Sclerosis ProgressionMargaret discusses a study by Bilge et al. (2025) that investigates the correlation between breastfeeding and disease progression in patients with multiple sclerosis. The findings highlight how breastfeeding can influence health outcomes for mothers with this condition.Reference:Bilge, N., Dagci, Y., Demirdogen, F., & Simsek, F. (2025). Correlation of breastfeeding with disease development and progression in patients with multiple sclerosis. Journal of Multiple Sclerosis Research, 4(3), 89–94. https://doi.org/10.4274/jmsr.galenos.2025.2024-12-32. Early Breastfeeding Initiation and Uterine InvolutionMargaret reviews a study by Rukmawati & Fatimah (2025) examining the effect of early breastfeeding initiation on uterine involution in first-day postpartum mothers. She discusses how early breastfeeding can benefit maternal postpartum recovery.Reference:Rukmawati, S., & Fatimah, N. A. (2025). The effect of early breastfeeding initiation (IMD) on uterine involution in first-day postpartum mothers. Journal for Research in Public Health, 6(2), 65–67. https://jrph.org/3. Predicting Low Milk Supply Through Milk CompositionMargaret shares insights from Jin et al. (2025), which utilized machine learning to analyze milk composition as a predictor of low milk supply. She explores how this technology could enhance lactation assessment tools.Reference:Jin, X., Lai, C. T., Perrella, S. L., Zhou, X., Hassan, G. M., McEachran, J. L., ... & Geddes, D. T. (2025). Milk composition is predictive of low milk supply using machine learning approaches. Diagnostics, 15(2), 191. https://doi.org/10.3390/diagnostics150201914. Breastfeeding Knowledge Among Medical StudentsThis study by Salih et al. (2025) investigates knowledge and attitudes toward breastfeeding among female medical students. Margaret discusses the implications for breastfeeding advocacy and education among healthcare professionals.Reference:Salih, R., Fathallah, S., Mohammed, Z., Mustafa, H., Nouri, E., & Elnaje, T. (2025). Knowledge and attitude toward breastfeeding among female medical students. Alqalam Journal of Medical and Applied Sciences, 8(1), 85–90. https://doi.org/10.54361/ajmas.25810135. Feeding Decisions in EmergenciesMargaret examines the study by Mensah et al. (2024) that explores how the formula shortage during COVID-19 impacted infant feeding decisions. She highlights the importance of supporting breastfeeding during emergencies.Reference:Mensah, D., Agyemang, E. F., & Gewa, C. (2024). Understanding women, infant, and children feeding decisions in emergencies: The case of COVID-19 and the formula shortage. Journal of Health, Medicine, and Nursing, 118, 49-64.

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