Blunting age-associated chronic inflammation preserves hematopoiesis and immunity and reduces leukemogenesis
Bibliographic record
Abstract
Abstract Aging is the single most important prognostic factor for the development of many cancers. While mutational accumulation may increase the risk of cancer in aged individuals, declining immunity due to chronic inflammation is also a likely contributing factor. We have demonstrated that reducing aging-associated chronic inflammation abrogates fitness declines in B-progenitor cells, and significantly reduces leukemogenesis in aged mice (Henry et al., JCI, 2015). In addition to preserving the function of B-progenitor cells, subsequent studies have revealed that reducing chronic inflammation in aged mice augments immune responses. Specifically, reducing inflammation in aged mice (≥22 months) results in a two-fold reduction in the number of splenic M2 macrophage and T-regulatory cell populations, as well as, a three-fold reduction in the surface expression of the inhibitory protein PD-L1 on innate immune cells. Reducing chronic inflammation in aged mice also results in a three-fold increase in the percentage of interferon-gamma producing CD4+ and CD8+ T-cell lymphocytes when stimulated. These phenotypes were observed in aged, anti-inflammatory transgenic mice (alpha-1-anti-trypsin and interleukin-37) and in geriatric mice (≥27 months) treated with recombinant interleukin-37. Overall, these findings suggest that reducing chronic inflammation in aged populations can rejuvenate hematopoiesis and immunity, while also creating a less permissive environment for leukemogenesis.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".