Age-associated inflammation enhances macrophage-pathogen interactions in vitro. (INC7P.400)
Bibliographic record
Abstract
Abstract Increasing age is associated with elevated levels of circulating pro-inflammatory cytokines, termed inflammaging. This inflammatory state can modulate a variety of immune functions and contribute to the increased susceptibility of the elderly to numerous infection diseases. Using Mycobacterium tuberculosis (M.tb) as a model pathogen we evaluated the influence of inflammation on macrophage function in vitro using the aged mouse model. We detected elevated basal levels of inflammatory cytokines in the lungs of naïve old mice, and pulmonary macrophages were shown to be in a pre-activated state(elevated mRNA for IRF-1, IRGM1 and CIITA). Furthermore, lung macrophages from old mice had altered trafficking marked by enhanced phago-lysosome (P-L) fusion upon infection with M.tb in vitro, which was refractory to enhancement by IFN-gamma activation. When old mice were placed on a diet of the non-steroidal anti-inflammatory drug (NSAID) ibuprofen for 2 weeks prior to isolation of lung macrophages, this pre-activated phenotype was reversed in old mice. These data demonstrate that the hyper-inflammatory state of the lung in old mice can influence the early interactions of immune cells with M.tb and that this phenotype can be reversed by NSAIDs. These data suggest that inflammaging in the lung could have a beneficial impact on innate immune function during infection, but the long term consequences of this phenotype in the context of M.tb infection are currently unknown.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".