LLAMA HEAVY-CHAIN ANTIBODY FRAGMENTS EFFICIENTLY REMOVE TOXIC SHOCK SYNDROME TOXIN 1 FROM PLASMA IN VITRO BUT NOT IN EXPERIMENTAL PORCINE SEPTIC SHOCK
Bibliographic record
Abstract
Staphylococcus aureus produces the superantigen toxic shock syndrome toxin 1 (TSST-1). When the bacterium invades the human circulation, this toxin can induce life-threatening gram-positive sepsis. Current sepsis treatment does not remove bacterial toxins. Variable domains of llama heavy-chain antibodies (VHH) against toxic shock syndrome toxin 1 ([alpha]-TSST-1 VHH) were previously found to be effective in vitro. We hypothesized that removing TSST-1 with [alpha]-TSST-1 VHH hemofiltration filters would ameliorate experimental sepsis in pigs. After assessing in vitro whether timely removing TSST-1 interrupted TSST-1-induced mononuclear cell TNF-[alpha] production, VHH-coated filters were applied in a porcine sepsis model. Clinical course, survival, plasma interferon [gamma], and TSST-1 levels were similar with and without VHH-coated filters as were TSST-1 concentrations before and after the VHH filter. Plasma TSST-1 levels were much lower than anticipated from the distribution of the amount of infused TSST-1, suggesting compartmentalization to space or adhesion to surface not accessible to hemofiltration or pheresis techniques. Removing TSST-1 from plasma was feasible in vitro. However, the [alpha]-TSST-1 VHH adsorption filter-based technique was ineffective in vivo, indicating that improvement of VHH-based hemofiltration is required. Sequestration likely prevented the adequate removal of TSST-1. The latter warrants further investigation of TSST-1 distribution and clearance in vivo.
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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) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".