Host–microbiome intestinal interactions during early life: considerations for atopy and asthma development
Bibliographic record
Abstract
PURPOSE OF REVIEW: The body's largest microbial community, the gut microbiome, is in contact with mucosal surfaces populated with epithelial, immune, endocrine and nerve cells, all of which sense and respond to microbial signals. These mutual interactions have led to a functional coevolution between the microbes and human physiology. Examples of coadaptation are anaerobes Bifidobacteria and Bacteroides, which have adjusted their metabolism to dietary components of human milk, and infant immune development, which has evolved to become reliant on the presence of beneficial microbes. Current research suggests that specific composition of the early-life gut microbiome aligns with the maturation of host immunity. Disruptions of natural microbial succession patterns during gut colonization are a consistent feature of immune-mediated diseases, including atopy and asthma. RECENT FINDINGS: Here, we catalog recent birth cohorts documenting associations between immune dysregulation and microbial alterations, and summarize the evidence supporting the role of the gut microbiome as an etiological determinant of immune-mediated allergic diseases. SUMMARY: Ecological concepts that describe microbial dynamics in the context of the host environment, and a portray of immune and neuroendocrine signaling induced by host-microbiome interactions, have become indispensable in describing the molecular role of early-life microbiome in atopy and asthma susceptibility.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".