Resident memory macrophages and trained innate immunity at barrier tissues
Bibliographic record
Abstract
Innate immune memory, or trained innate immunity (TII), represents a form of immunological adaptation in which innate immune cells, including myeloid and lymphoid cells, retain a trained state following prior exposure to immunological stimuli. This long-lasting modification either enhances or reduces the innate immune response to subsequent heterologous infections or inflammatory insults. While TII often provides protective benefits, including enhanced protection against pathogens and tumors, it can contribute to maladaptive inflammation in certain conditions. Epigenetic changes and metabolic reprogramming are key drivers of innate immune memory, but it is important to distinguish between transient acute changes and persistent modifications that define bona fide innate immune memory. Innate immune memory can be induced centrally, through systemic events that train hematopoietic progenitors in the bone marrow, or locally, via tissue-resident cells such as macrophages. The presence of trained tissue-resident immune cells offers significant advantages, but their responses may not always result in universally enhanced protection. This review explores recent advances in the understanding of tissue-resident memory macrophages and TII at barrier tissue sites, including the lung, skin, gut, and peritoneum, highlighting the implications for vaccine and immunotherapeutic strategies. Ongoing research promises to accelerate progress in this field and inform new clinical and vaccinology approaches.
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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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".