Deciphering the Influence of the Immune Environment on Trained Immunity Induction
Bibliographic record
Abstract
Trained immunity is a novel vaccination concept that enhances innate immune responses through epigenetic reprogramming. Targeting trained immunity provides tremendous opportunities for vaccine generation against infections and conditions in which classical, adaptive immunity failed to provide protection (e.g., Tuberculosis, sepsis). For clinical application, the robustness of vaccination strategies in heterogenous populations is crucial. Diverse baseline immune statuses originate from genetics as well as persistent conditions, e.g., chronic infections and allergies. To date, the extent to which the host’s baseline immune status impacts trained immunity generation is unknown. To evaluate vaccine robustness across different immune environments, we have tested trained immunity-inducing vaccination in mouse models of both pro-inflammatory and pro-allergic skewed immune systems. To that end, we administered β-glucan, a potent inducer of trained immunity, to mice of different genetic backgrounds with or without established allergic sensitization and challenged these mice in sepsis experiments. Immune responses were measured by flow cytometry. While trained immunity vaccination conferred host protection independent of the immunological background of the mice, protection was mediated through different pathways. Characterizing the impact of immune environments on trained immunity induction will strengthen clinical translation and enable host-directed adaptation of these next generation vaccines.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".