Modulation of Tumor Necrosis Factor by Microbial Pathogens
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it