Novel therapies for sepsis: antiendotoxin therapies
Bibliographic record
Abstract
Severe sepsis and septic shock is a common problem encountered in the critical care unit with an estimated incidence in the US of 750,000 cases/year and a mortality rate of 30-50%. Sepsis involves a complex interaction between bacterial factors and the host immune system producing a systemic inflammatory state that may progress to multiple organ failure and death. Endotoxin (a lipopolysaccharide) released from Gram-negative bacteria has been implicated as a potent, prototypical stimulus of the immune response to bacterial infection. Current antiendotoxin strategies utilise various approaches ranging from the prevention of binding to endotoxin receptors with antibodies (monoclonal or polyclonal) against endotoxin or endotoxin receptor/carrier molecules (antiCD14 or antilipopolysaccharide-binding protein antibodies), enhancing clearance or neutralisation (haemoperfusion, lipoproteins, lipopolysaccharide-neutralising proteins) or impairing cellular signalling (lipid A analogues, tyrosine kinase inhibitors). In the future, innovative therapies involving Toll-like receptors and their downstream signalling elements will be developed. This review discusses current knowledge regarding endotoxin signalling, antiendotoxin therapies currently under development, and future areas for research.
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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 | 0.000 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".