Current understanding of macrophage type 1 cytokine responses during intracellular infections
Bibliographic record
Abstract
Macrophages are important effector cells in \ncell-mediated immunity against intracelllular infection. \nAmong cytokines that macrophages are able to release \nare IL-12 and TNFa. IL-12 is a critical linker between \nthe innate and adaptive cell-mediated immunity, capable \nof Thl differentiation and IFNy release by T and NK \ncells. IFNy is critically required for the activation of \nmacrophage bactericidal activities. Recently emerging \nevidence suggests that macrophages are able to release \nnot only IL-12 and TNFa but also IFNy. However, the \nmechanisms that control the release of each of these type \n1 cytokines in macrophages appear different. While \nmacrophages release TNFa in an indiscriminate and IL- \n12-independent way, the release of IL-12, particularly \nbioactive IL-12 p70, and IFNy is under tight control. We \nare just beginning to understand what controls the \nrelease of IL-12 p70, a question of fundamental \nimportance to understanding the mechanisms underlying \nthe initiation of cell-mediated immunity. Our recent \nfindings have shed more insights into the regulatory \nmechanisms of macrophage IFNy responses. It has \nbecome evident that IL-12 is required not only for Thl \ndifferentiation but also for IFNy responses by both T \ncells and macrophages during intracellular infection. In \nthis overview, we have discussed about the current \nunderstanding of the regulation of macrophage type 1 \ncytokine responses during intracellular infection, based \nupon the recent findings from us and others.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".