Can we prevent allergic disease? Understanding the links between the early life microbiome and allergic diseases of childhood
Bibliographic record
Abstract
PURPOSE OF REVIEW: The microbiome and immune system are intrinsically linked, and during infancy these crucial biological systems undergo a concurrent and expansive maturation process. As these maturation processes progress, some children develop a sequence of IgE-mediated immune disorders termed the 'Allergic March', and unfortunately the prevalence of these lifelong and burdensome allergic conditions has increased over the past half century. Our current treatment strategies are unable to prevent or cure components of the Allergic March. However, recent discoveries have enhanced our mechanistic understanding of early-life microbiota-immune interactions with exciting implications for preventing these allergic disorders. RECENT FINDINGS: The current review will detail recent literature regarding perinatal factors (e.g. birth mode, antibiotic exposure, breastmilk seeding of the microbiota, built environment) that shape the infant gut microbiota composition. Furthermore, we will discuss new findings that have highlighted immune cells which are particularly sensitive to microbial influences in utero and during the early-life window of development. SUMMARY: As our understanding of the dynamic relationship between the developing infant microbiota and immune system grows, a priority toward preserving critical early-life interactions may provide life-long protection to these diseases in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".