Tumor necrosis factor drives increased splenic monopoiesis in old mice
Bibliographic record
Abstract
Aging is accompanied by changes in hematopoiesis and consequently in leukocyte phenotype and function. Although age-related changes in bone marrow hematopoiesis are fairly well documented, changes in extramedullary hematopoiesis are less well described. We observed that 18-22-mo-old mice had larger spleens than young controls and found that the enlargement was caused by increased monopoiesis. Because extramedullary hematopoiesis is often driven by inflammation, we hypothesized that the chronic, low-level inflammation that occurs with age is a causal agent in splenomegaly. To test this theory, we compared the number of monocytes in 18-mo-old tumor necrosis factor-knockout mice, which are protected from age-associated inflammation, and found that they did not have increased extramedullary monopoiesis. To determine whether increased splenic monopoiesis is caused by intrinsic changes in the myeloid precursors that occur with age or by the aging microenvironment, we created heterochronic bone marrow chimeras. Increased splenic monopoiesis occurred in old recipient mice, regardless of the age of the donor mouse, but not in young recipient mice, demonstrating that these cells respond to signals from the microenvironment. These data suggest that decreasing the inflammatory microenvironment with age would be an effective strategy for reducing inflammatory diseases propagated by cells of myeloid lineage, which increase in number with age.
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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 | 0.001 | 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 | 0.001 |
| 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".