Cytokine modulation in sepsis and septic shock
Bibliographic record
Abstract
Sepsis and septic shock are a major cause of morbidity and mortality in patients admitted to the intensive care unit. Since the introduction of antibiotic therapy, the mortality associated with sepsis has remained within the 30- 50% range. Sepsis constitutes the systemic response to infection. This response encompasses both pro-inflammatory and anti-inflammatory phases that are marked by the sequential generation of pro- and anti-inflammatory cytokines. Among the most important pro-inflammatory cytokines are TNF-alpha and IL-1beta. The pro-inflammatory effects of such cytokines are inhibited by soluble receptors/receptor antagonists and anti-inflammatory cytokines including IL-10 and transforming growth factor-beta. Modulation of the activity of both pro- and anti-inflammatory cytokines to improve outcome in patients with sepsis has been subject of multiple clinical studies. This review will examine clinical trials evaluating several strategies for blocking or attenuating TNF-alpha and IL-1beta activity. This review will also survey the current state of experimental therapies involving IL-10, transforming growth factor-beta, granulocyte colony-stimulating factor and IFN-phi. Finally, newer developments related to less known cytokines such as macrophage migration inhibitory factor and high mobility group 1 protein will be evaluated.
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.
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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".