IMPLICATION OF TOLL-LIKE RECEPTOR AND TUMOR NECROSIS FACTOR α SIGNALING IN SEPTIC SHOCK
Bibliographic record
Abstract
Septic shock is initiated by a systemic inflammatory response to microbial infection that frequently leads to impaired perfusion and multiple organ failure. Because of its high risk of death, septic shock is a major problem particularly for patients in the intensive care unit. In general, bacterial lipopolysaccharide (LPS) is a strong activator of various immune responses and stimulates monocytes/macrophages to release a variety of inflammatory cytokines. However, overproduction of inflammatory factors in response to bacterial infections is known to cause septic shock, similar to that induced by LPS. Studies of LPS-signaling pathways and downstream inflammatory cytokines may have critical implications in the treatment of sepsis. In recent years, there has been significant progress in understanding the signaling pathways activated by LPS and its receptor Toll-like receptor 4 (TLR4), as well as by tumor necrosis factor alpha (TNFalpha), a potent inflammatory cytokine induced by LPS stimulation. This review briefly summarizes our current knowledge of these signaling pathways and critical signal transducers. Characterization of key signal transducers may allow us to identify tractable, novel targets for the therapeutic interventions of sepsis.
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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".