Biological and Technical Considerations Regarding the Removal of Bacteriotoxins in Sepsis With Emphasis on Toxic Shock Syndrome Toxin 1
Bibliographic record
Abstract
Severe sepsis is characterized by rapid development of multiple organ failure associated with high mortality. Bacterial toxin release triggers a sequence of events that activates intracellular pathways to produce inflammatory mediators and nitric oxide. There have been numerous attempts to interrupt this devastating cascade by removing toxins, removing or inhibiting mediators, and by blocking receptors of mediators. This review considers toxin properties with a strong focus on toxic shock syndrome toxin 1 and the potential of various removal technologies in relation to these properties. The distribution of toxins in vivo forms a key issue but is nevertheless poorly defined. For toxic shock syndrome toxin 1, either a high clearance or a high degree of compartmentalization to a space not accessible by pheresis or immunoabsorption technologies seems likely. Attempts to remove toxins to treat sepsis may appear futile if we cannot access this space or when the level of induced clearance is too low compared with natural clearance. The impact of these considerations is highly dependent on the exact toxin biology in vivo. Extrapolated to other toxins, we indicate a set of general requirements to be met to facilitate successful toxin removal by a pheresis technique.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 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".