Gut Microbiome in Children with Congenital Heart Disease After Cardiopulmonary Bypass Surgery (GuMiBear Study)
Bibliographic record
Abstract
The gut microbiome of infants with congenital heart disease (CHD) undergoing cardiopulmonary bypass surgery (CPB) is at risk of profound alteration. The aim of this study was to examine the gut microbiome pre- and post-bypass surgery to explore potential implications of altered gut biodiversity. A prospective cohort study involving infants with CHD who underwent CPB was performed. Faecal samples were collected from infants alongside the collection of demographic and clinical data in order to examine gut microbiome changes before and after surgery. 16S rRNA sequencing analysis was performed on DNA isolated from stool samples to determine changes in gut microbiome composition. Thirty-three patients were recruited, with samples from thirteen of these available for final analysis. Compared with healthy, matched controls, at a genus level, pre-operative samples for infants with CHD demonstrated a higher relative abundance of Escherichia-Shigella (31% vs 2-6%) and a lower relative abundance of Bifidobacterium (13% vs 40-60%). In post-operative samples, the relative abundance of Escherichia-Shigella (35%), Enterococcus (11%), Akkermansia (6%), and Staphylococcus (5%) were higher than pre-op samples. One infant developed post-operative necrotising-enterocolitis (NEC). They displayed a marked abundance of the Enterococcus (93%) genus pre-operatively. This study demonstrates that infants with CHD have an altered gut microbiome when compared with healthy controls and there might be a possible link between an abundance of virulent species and NEC.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".