Effect of prenatal micronutrient-fortified balanced energy-protein supplementation on maternal and newborn body composition: A sub-study from the MISAME-III randomized controlled efficacy trial in rural Burkina Faso.

BACKGROUND: Micronutrient-fortified balanced energy-protein (BEP) supplements are promising interventions to prevent intrauterine growth retardation in low- and middle-income countries. On the other hand, one concern with blanket prenatal supplementation programs using energy-dense supplements is that they could lead to more maternal and/or infant overweight. However, evidence is lacking on the potential effect of BEP on maternal and offspring body composition. This study evaluates the effects of micronutrient-fortified BEP supplementation during pregnancy on body composition of mothers and their newborns in rural Burkina Faso. METHODS AND FINDINGS: The MISAME-III study is an open label individually randomized controlled trial where pregnant women (n = 1,897) of gestational age <21 weeks received either a combination of micronutrient-fortified BEP and iron-folic acid (IFA) tablets (i.e., intervention) or IFA alone (i.e., control). The prenatal phase of the MISAME-III study was conducted between the first enrollment in October 2019 and the last delivery in August 2021. In a sub-study nested under the MISAME-III trial, we evaluated anthropometry and body composition in newborns who were born starting from 17 November 2020 (n: control = 368 and intervention = 352) and their mothers (n: control = 185 and intervention = 186). Primary study outcomes were newborn and maternal fat-free mass (FFMI) and fat-mass (FMI) indices. We used the deuterium dilution method to determine FFMI and FMI and %FFM and %FM of total body weight within 1 month postpartum. Our main analysis followed a modified intention-to-treat approach by analyzing all subjects with body composition data available. Univariable and multivariable linear regression models were fitted to compare the intervention and control arms, with adjusted models included baseline maternal age, height, arm fat index, hemoglobin concentration and primiparity, household size, wealth and food security indices, and newborn age (days). At study enrollment, the mean ± SD maternal age was 24.8 ± 6.13 years and body mass index (BMI) was 22.1 ± 3.02 kg/m2 with 7.05% of the mothers were underweight and 11.5% were overweight. Prenatal micronutrient-fortified BEP supplementation resulted in a significantly higher FFMI in mothers (MD (mean difference): 0.45; 95% CI (confidence interval): 0.05, 0.84; P = 0.026) and newborns (MD: 0.28; 95% CI: 0.06, 0.50; P = 0.012), whereas no statistically significant effects were found on FMI. The effect of micronutrient-fortified BEP on maternal FFMI was greater among mothers from food secure households and among those with a better nutritional status (BMI ≥21.0 kg/m2 or mid-upper arm circumference (MUAC) ≥23 cm). Key limitations of the study are the relatively high degree of missing data (approximately 18%), the lack of baseline maternal body composition values, and the lack of follow-up body composition measurements to evaluate any long-term effects. CONCLUSIONS: Micronutrient-fortified BEP supplementation during pregnancy can increase maternal and newborn FFMI, without significant effects on FMI. TRIAL REGISTRATION: ClinicalTrials.gov with identifier NCT03533712.

Advancement of Sensor Integrated Organ-on-Chip Devices.

Organ-on-chip devices have provided the pharmaceutical and tissue engineering worlds much hope since they arrived and began to grow in sophistication. However, limitations for their applicability were soon realized as they lacked real-time monitoring and sensing capabilities. The users of these devices relied solely on endpoint analysis for the results of their tests, which created a chasm in the understanding of life between the lab the natural world. However, this gap is being bridged with sensors that are integrated into organ-on-chip devices. This review goes in-depth on different sensing methods, giving examples for various research on mechanical, electrical resistance, and bead-based sensors, and the prospects of each. Furthermore, the review covers works conducted that use specific sensors for oxygen, and various metabolites to characterize cellular behavior and response in real-time. Together, the outline of these works gives a thorough analysis of the design methodology and sophistication of the current sensor integrated organ-on-chips.

Hydrodynamic Assembly of Astrocyte Cells in Conductive Hollow Microfibers.

The manufacturing of 3D cell scaffoldings provides advantages for modeling diseases and injuries as it enables the creation of physiologically relevant platforms. A triple-flow microfluidic device is developed to rapidly fabricate alginate/graphene hollow microfibers based on the gelation of alginate induced with CaCl2 . This five-channel microdevice actualizes continuous mild fabrication of hollow fibers under an optimized flow rate ratio of 300:200:100 µL min-1 . The polymer solution is 2.5% alginate in 0.1% graphene and a 30% polyethylene glycol solution is used as the sheath and core solutions. The biocompatibility of these conductive microfibers by encapsulating mouse astrocyte cells (C8D1A) within the scaffolds is investigated. The cells can successfully survive both the manufacturing process and prolonged encapsulation for up to 8 days, where there is between 18-53% of live cells on both the alginate microfibers and alginate/graphene microfibers. These unique 3D hollow scaffolds can significantly enhance the available surface area for nutrient transport to the cells. In addition, these conductive hollow scaffolds illustrate unique advantages such as 0.728 cm3  gr-1 porosity and two times more electrical conductivity in comparison to alginate scaffolds. The results confirm the potential of these scaffolds as a microenvironment that supports cell growth.

A Multi-Omics and Human Biomonitoring Approach to Assessing the Effectiveness of Fortified Balanced Energy-Protein Supplementation on Maternal and Newborn Health in Burkina Faso: A Study Protocol.

Fortified balanced energy-protein (BEP) supplementation is a promising intervention for improving maternal health, birth outcomes and infant growth in low- and middle-income countries. This nested biospecimen sub-study aimed to evaluate the physiological effect of multi-micronutrient-fortified BEP supplementation on pregnant and lactating women and their infants. Pregnant women (15-40 years) received either fortified BEP and iron-folic acid (IFA) (intervention) or IFA only (control) throughout pregnancy. The same women were concurrently randomized to receive either a fortified BEP supplement during the first 6 months postpartum in combination with IFA for the first 6 weeks (i.e., intervention) or the postnatal standard of care, which comprised IFA alone for 6 weeks postpartum (i.e., control). Biological specimens were collected at different timepoints. Multi-omics profiles will be characterized to assess the mediating effect of BEP supplementation on the different trial arms and its effect on maternal health, as well as birth and infant growth outcomes. The mediating effect of the exposome in the relationship between BEP supplementation and maternal health, birth outcomes and infant growth were characterized via biomonitoring markers of air pollution, mycotoxins and environmental contaminants. The results will provide holistic insight into the granular physiological effects of prenatal and postnatal BEP supplementation.

Machine learning-assisted E-jet printing for manufacturing of organic flexible electronics.

Electrohydrodynamic-jet (E-jet) printing technique enables the high-resolution printing of complex soft electronic devices. As such, it has an unmatched potential for becoming the conventional technique for printing soft electronic devices. In this study, the electrical conductivity of the E-jet printed circuits was studied as a function of key printing parameters (nozzle speed, ink flow rate, and voltage). The collected experimental dataset was then used to train a machine learning algorithm to establish models capable of predicting the characteristics of the printed circuits in real-time. A decision tree was applied to the data set and resulted in an accuracy of 0.72, and further evaluations showed that pruning the tree increased the accuracy while sensitivity decreased in the highly pruned trees. The k-fold cross-validation (CV) method was used in model selection to test the ability of the model to get trained on data. The accuracy of CV method was the highest for random forest at 0.83 and K-NN model (k = 10) at 0.82. Precision parameters were compared to evaluate the supervised classification models. According to F-measure values, the K-NN model (k = 10) and random forest are the best methods to classify the conductivity of electrodes.