Bibliographic record
Abstract
PURPOSE OF REVIEW: The phenomenon of tolerance induced during immunologic immaturity has been explored for more than 60 years. Although direct application of neonatal tolerance to organ transplantation in human newborns is limited, exploiting discrete components of neonatal immaturity is proving fruitful. RECENT FINDINGS: Two reviews comprehensively considered features and impact of neonatal tolerance as described in the 1950s. Recent imaging studies in mice demonstrated complex functional interactions especially of donor regulatory T cells with emerging neonatal immune components. The propensity of the developing immune system toward tolerance rather than immunity to non-self carbohydrates in ABO-incompatible transplantation was shown using glyconanotechnology tools to have exquisite specificity, and is associated with age-related changes in the B-cell compartment and complement components. Discarded infant thymus was found to be a source of abundant therapeutic regulatory T cells. Erythroid precursors transiently present in newborn mice and humans were shown to have immunosuppressive properties that may contribute to a tolerogenic environment. SUMMARY: Neonatal tolerance has profound impact on immunology well beyond transplantation. Continued exploration of mechanisms underlying the malleability of the developing immune system and exploitation of particular components are leading to tools for immune manipulation beyond infancy.
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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 | 0.001 |
| 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) | 0.012 | 0.007 |
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".