Modulation of Tumor Necrosis Factor by Microbial Pathogens
Bibliographic record
Abstract
In response to invasion by microbial pathogens, host defense mechanisms get activated by both the innate and adaptive arms of the immune responses. TNF (tumor necrosis factor) is a potent proinflammatory cytokine expressed by activated macrophages and lymphocytes that induces diverse cellular responses that can vary from apoptosis to the expression of genes involved in both early inflammatory and acquired immune responses. A wide spectrum of microbes has acquired elegant mechanisms to overcome or deflect the host responses mediated by TNF. For example, modulatory proteins encoded by multiple families of viruses can block TNF and TNF-mediated responses at multiple levels, such as the inhibition of the TNF ligand or its receptors, or by modulating key transduction molecules of the TNF signaling pathway. Bacteria, on the other hand, tend to modify TNF-mediated responses specifically by regulating components of the TNF signaling pathway. Investigation of these diverse strategies employed by viral and bacterial pathogens has significantly advanced our understanding of both host TNF responses and microbial pathogenesis. This review summarizes the diverse microbial strategies to regulate TNF and how such insights into TNF modulation could benefit the treatment of inflammatory or autoimmune diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".