Molecular Mechanisms of Sepsis
Bibliographic record
Abstract
In cancer, therapies are targeted at 6 important pathways. In sepsis, there is ongoing controversy regarding the number and relative roles of pathways that are activated or repressed and which are important in the progression from health to death. Adding to complexity, there is interaction of pathways, there are differences in temporal pattern of up and down-regulation of pathways and there are different responses of pathways to therapies of sepsis. In this review, we define four key pathways of sepsis: (1) inflammation and immunity, (2) coagulation and fibrinolysis, (3) apoptosis, and (4) endocrine. Each of these pathways can impair endothelial function, a unifying aspect of the pathophysiology of sepsis. There are few studies of interactions of pathways except for the interacttion of inflammation/immunity with coagulation/fibrinolysis. Successful treatment of cancer requires that cancer therapies interrupt several key pathways of cancer. Accordingly, we suggest that successful treatment of sepsis will require therapies that interrupt several key pathways of sepsis. Perhaps the paucity of approved therapies for sepsis is related in part to the underevaluation of novel pathways, to lack of understanding of interactions of pathways and to lack of interruption of key pathways of sepsis.
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 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) | 0.000 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".