Innate sensing and cellular metabolism: role in fine tuning antiviral immune responses
Bibliographic record
Abstract
Several studies over the last decade have identified intimate links between cellular metabolism and macrophage function. Metabolism has been shown to both drive and regulate macrophage function by producing bioenergetic and biosynthetic precursors as well as metabolites (and other bioactive molecules) that regulate gene expression and signal transduction. Many studies have focused on lipopolysaccharide-induced reprogramming, assuming that it is representative of most inflammatory responses. However, emerging evidence suggests that diverse pathogen-associated molecular patterns (PAMPs) are associated with unique metabolic profiles, which may drive pathogen specific immune responses. Further, these metabolic pathways and processes may act as a rheostat to regulate the magnitude of an inflammatory response based on the biochemical features of the local microenvironment. In this review, we will discuss recent work examining the relationship between cellular metabolism and macrophage responses to viral PAMPs and describe how these processes differ from lipopolysaccharide-associated responses. We will also discuss how an improved understanding of the specificity of these processes may offer new insights to fine-tune macrophage function during viral infections or when using viral PAMPs as therapeutics.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".