Environment impacts innate immune ontogeny
Bibliographic record
Abstract
Susceptibility to infection and response to vaccination differ between populations and as a function of age. The underlying mechanisms for this age- and population-dependent variation are not known. Specifically, it is unclear if these variations are due to differences in genetically encoded host programs or driven by environmental influences or a combination of both. To address the relationship between gene and environment regarding immune ontogeny, we determined the innate cytokine responses following PRR stimulation of blood mononuclear cells at birth, 1, and 2 yr of age in infants from Caucasian vs . Asian parents and were raised in the same city. At birth, we found that innate cytokine responses were significantly elevated in Asian compared with Caucasian infants. However, these differences waned and responses became more similar over the course of 1-2 yr of living in a similar environment. Our observations that innate response differences present at birth subsequently equalized rather than diverged suggest a key role for environmental effects common to both racial groups in shaping the innate immune responses early in life. Delineating the underlying environmental factors that modulate innate immune responses early in life could provide avenues for targeted beneficial immune modulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".