Bibliographic record
Abstract
Conventionally, it was thought that innate immunity operated through a simple system of nonspecific responses to an insult. However, this perspective now seems overly simplistic. It has become evident that intricate cooperation and networking among various cells, receptors, signaling pathways, and protein complexes are essential for regulating and defining the overall activation status of the immune response, where the distinction between innate and adaptive immunity becomes ambiguous. Given the evolutionary timeline of vertebrates and the success of plants and invertebrates which depend solely on innate immunity, immune memory cannot be considered an innovation of only the lymphoid lineage. Indeed, the evolutionary innate immune memory program is a conserved mechanism whereby innate immune cells can induce a heightened response to a secondary stimulus due to metabolic and epigenetic reprogramming. Importantly, the longevity of this memory phenotype can be attributed to the reprogramming of self-renewing hematopoietic stem cells (HSCs) in the bone marrow, which is subsequently transmitted to lineage-committed innate immune cells. HSCs reside within a complex regulated network of immune and stromal cells that govern their two primary functions: self-renewal and differentiation. In this review, we delve into the emerging cellular and molecular mechanisms as well as metabolic pathways of innate memory in HSCs, which harbor substantial therapeutic promise.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".