RESUMEN
If maternal milk is unavailable, the World Health Organization recommends that the first alternative should be pasteurised donor human milk (DHM). Human milk banks (HMBs) screen and recruit milk donors, and DHM principally feeds very low birth weight babies, reducing the risk of complications and supporting maternal breastfeeding where used alongside optimal lactation support. The COVID-19 pandemic has presented a range of challenges to HMBs worldwide. This study aimed to understand the impacts of the pandemic on HMB services and develop initial guidance regarding risk limitation. A Virtual Collaborative Network (VCN) comprising over 80 HMB leaders from 36 countries was formed in March 2020 and included academics and nongovernmental organisations. Individual milk banks, national networks and regional associations submitted data regarding the number of HMBs, volume of DHM produced and number of recipients in each global region. Estimates were calculated in the context of missing or incomplete data. Through open-ended questioning, the experiences of milk banks from each country in the first 2 months of the pandemic were collected and major themes identified. According to data collected from 446 individual HMBs, more than 800,000 infants receive DHM worldwide each year. Seven pandemic-related specific vulnerabilities to service provision were identified, including sufficient donors, prescreening disruption, DHM availability, logistics, communication, safe handling and contingency planning, which were highly context-dependent. The VCN now plans a formal consensus approach to the optimal response of HMBs to new pathogens using crowdsourced data, enabling the benchmarking of future strategies to support DHM access and neonatal health in future emergencies.
Asunto(s)
Lactancia Materna , COVID-19 , Bancos de Leche Humana , Femenino , Humanos , Lactante , Recién Nacido , Leche Humana , Pandemias/prevención & control , SARS-CoV-2RESUMEN
Multifactorial approaches can quickly and efficiently model complex, interacting natural or engineered biological systems in a way that traditional one-factor-at-a-time experimentation can fail to do. We applied a Design of Experiments (DOE) approach to model ethanol biosynthesis in yeast, which is well-understood and genetically tractable, yet complex. Six alcohol dehydrogenase (ADH) isozymes catalyze ethanol synthesis, differing in their transcriptional and post-translational regulation, subcellular localization, and enzyme kinetics. We generated a combinatorial library of all ADH gene deletions and measured the impact of gene deletion(s) and environmental context on ethanol production of a subset of this library. The data were used to build a statistical model that described known behaviors of ADH isozymes and identified novel interactions. Importantly, the model described features of ADH metabolic behavior without explicit a priori knowledge. The method is therefore highly suited to understanding and optimizing metabolic pathways in less well-understood systems.