Describing Biological Vulnerability in Small, Vulnerable Newborns in Urban Burkina Faso (DenBalo): Gut Microbiota, Immune System, and Breastmilk Assembly
Bibliographic record
Abstract
Background: Small vulnerable newborns (SVNs), including those born preterm, small for gestational age, or with low birth weight, are at higher risk of neonatal mortality and long-term health complications. Early exposure to maternal vaginal microbiota and breastfeeding plays a critical role in the development of the neonatal microbiota and immune system, especially in low-resource settings like Burkina Faso, where neonatal mortality rates remain high. Objectives: The DenBalo study aims to investigate the role of maternal and neonatal factors, such as vaginal and gut microbiota, immune development, and early nutrition, in shaping health outcomes in SVNs and healthy infants. Methods: This prospective cohort observational study will recruit 141 mother-infant pairs (70 SVNs and 71 healthy controls) from four health centers in Bobo-Dioulasso, Burkina Faso. The mother-infant pairs will be followed for six months with anthropometric measurements and biospecimen collections, including blood, breast milk, saliva, stool, vaginal swabs, and placental biopsies. Multi-omics approaches, encompassing metagenomics, metabolomics, proteomics, and immune profiling, will be used to assess vaginal and gut microbiota composition and functionality, immune cell maturation, and cytokine levels at critical developmental stages. Conclusions: This study will generate comprehensive data on how microbiota, metabolomic, and proteomic profiles, along with immune system development, differ between SVNs and healthy infants. These findings will guide targeted interventions to improve neonatal health outcomes and reduce mortality, particularly in vulnerable populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".