Gut Microbiome Pilot Study of Patients With <scp>CHARGE</scp> Syndrome and Sibling Controls
Bibliographic record
Abstract
ABSTRACT Difficulties with feeding and digestion are common in individuals with CHARGE syndrome. Animal models with CHD7 gene variants demonstrate abnormal gut innovation and dysmotility. Our pilot study evaluated whether individuals with CHARGE syndrome have differences in their gut microbiome compared to unaffected siblings. Participants between the ages of 2–18 were recruited from Atlantic Canada with a confirmed genetic diagnosis of CHARGE syndrome. Gut Microbiome DNA analysis was performed on stool samples using 16S ribosomal RNA (rRNA) gene sequences. The PASSFP and PEDSQL served as GI symptom questionnaires. Eleven participants completed this study with one twin pair (CHARGE syndrome = 7, sibling controls = 4). The mean percent abundance for the four most common phyla in individuals with CHARGE versus Controls showed a trend towards increased Bacteroidetes, Proteobacteria, and a decrease in Firmicutes and Actinobacteria but was not significant. Microbiome comparisons based on abnormal (< 77) and normal ( 77) GI scores, found significantly elevated Bacteroidetes (p = 0.042, 59.5% ± 15.1% vs. 33.1% ± 14.6%) and decreased Firmicutes (p = 0.042, 37.5% ± 15.9% vs. 62.4% ± 14.0%) with abnormal scores. Alpha diversity did not differ with either disease or GI symptom scores. Our data showed that, although there was a trend in changes in the gut microbiome in individuals with CHARGE compared to unaffected siblings, this change appears to be related to the severity of GI symptoms and not necessarily CHARGE itself, as differences were more pronounced in individuals with more difficulties with feeding and GI symptoms.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".