Passive and active components of neonatal innate immune defenses
Bibliographic record
Abstract
Innate immune defenses are crucial for survival in the first days and weeks of life. At birth, newborns are confronted with a vast array of potentially pathogenic microorganisms that were not encountered in utero. At this age, cellular components of the adaptive immune system are in a naive state and are slow to respond. Antibodies received from the dam are essential for defense, but represent a finite and dwindling resource. Innate components of the immune system detect pathogen-associated molecular patterns (PAMPs) on microorganisms (and their products) by means of pattern-recognition receptors (PRRs). Soluble mediators of the innate system such as complement proteins, pentraxins, collectins, ficolins, defensins, lactoferrin, lysozyme etc. can bind to structures on pathogens, leading to agglutination, interference with receptor binding, opsonization, neutralization, direct membrane damage and recruitment of additional soluble and cellular elements through inflammation. Cell-associated receptors such as the Toll-like receptors (TLRs) can activate cells and coordinate responses (both innate and adaptive). In this paper, accumulated knowledge of the receptors, soluble and cellular elements that contribute to innate defenses of young animals is reviewed. Research interest in this area has been intermittent, and the literature varies in quantity and quality. It is hoped that documentation of the limitations of our knowledge base will lead to more extensive and enlightening studies.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".