The early life gut microbiota and atopic disease
Bibliographic record
Abstract
Asthma is the most prevalent of all childhood diseases and accounts for the majority of hospitalizations and school absences in children [ 1 ]. Current mouse model research has identified the early life gut microbiota as a potential therapeutic target for the prevention of asthma and atopic diseases [ 2 – 4 ]. We hypothesize that the early life gut microbiota could play a similar preventative role against atopic disease development in humans. 1262 children enrolled in the Canadian Healthy Infant Longitudinal Development (CHILD) Study with complete skin prick test and wheeze data at one year were grouped into four clinically relevant phenotypes: atopy + wheeze, atopy only, wheeze only, and control. Bacterial 16S rDNA from 3-month and 1-year stool samples of 319 children in these four phenotypes was extracted, amplified, and subjected to high throughput Illumina sequencing. Quantitative polymerase chain reaction (qPCR) and short chain fatty acid (SCFA) analysis were also conducted on 44 children in the two extreme phenotypes (atopy + wheeze vs. control). 16S sequence analysis of our sample cohort (319 subjects) identified bacterial populations that differed in abundance in the atopy + wheeze group at 3-months of age but not at 1-year of age. Additionally, significant changes in the abundance of certain bacterial genera were found in the atopy + wheeze group when compared to controls by qPCR at 3-months of age only. Changes in stool short chain fatty acid production between the atopy + wheeze group and the control group were also observed at 3-months of age only. Shifts in the relative abundance of certain gut bacterial populations and differences in the levels of stool SCFAs before 3-months of age are associated with atopy and wheeze at one year of age.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".