Host Fecal mRNAs Predicted Environmental Enteric Dysfunction among Children with Moderate Acute Malnutrition in Sierra Leone
Bibliographic record
Abstract
Examining the role of environmental enteric dysfunction (EED) in child growth requires noninvasive, field-appropriate biomarkers. Alternatives to the traditionally used lactulose:mannitol (L:M) test have been explored, but few studies have compared the L:M test to host fecal mRNA transcripts. The objectives of this study were to examine whether 1) host fecal mRNA transcripts could predict presence and severity of EED, measured using the L:M test, and 2) EED modifies the effect of specialized nutritious foods (SNFs) on recovery from moderate acute malnutrition (MAM). This substudy was nested within a cluster randomized trial comparing four SNFs in the treatment of MAM among children 6 to 59 months in Sierra Leone. EED was assessed at enrollment using the L:M test and 15 host fecal mRNA transcripts on 522 children. Recovery from MAM was defined as achieving mid-upper arm circumference ≥ 12.5 cm within 12 weeks of supplementation. Random forest classification models were used to examine prediction of presence and severity of EED by host fecal mRNA transcripts. Logistic regression was used to test for effect modification by L:M test variables including % lactulose excreted (%L). Eight host fecal mRNA transcripts (AQP9, REG3A, IFI30, DECR1, BIRC3, SELL, PIK3AP1, DEFA6) identified EED (%L ≥ 0.2) and severe EED (%L ≥ 0.45) with high sensitivity and specificity. The L:M test variables did not modify the effect of SNFs on recovery from MAM. In this study, we found host fecal mRNA transcripts that could be biomarkers of EED but did not find EED to modify the effect of SNFs on MAM treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".