The effect of glycosaminoglycans with acetaldehyde on the activation of prothrombin
Bibliographic record
Abstract
Heparin17-19k, (25, 50, and 100 ng), heparin6k (50 and 100 ng), heparin3k (50, 100, and 200 microg), chondroitin sulfates B (dermatan sulfate) (0.25, 0.5, and 1.0 microg), C (1 and 10 microg), and A (1 and 10 microg) each prolong the activated partial thromboplastin time (APTT) when preincubated with prothrombin to a greater extent than when preincubated with Factor II-deficient plasma prior to their mixing and subsequent additions of APTT reagent and Ca2+. In all cases statistical significance (p < or = 0.05) was observed except with the 2 lower levels of heparin3k. These results suggest that the glycosaminoglycans (GAGs) may exert a direct effect upon prothrombin (FII) in their anticoagulant activity. Pre mix tures of [(FII/25 ng H17-19k) + 447 mmol acetaldehyde (AcH)/L] as well as [(AcH/H) + FII] and [(FII/AcH) + H] each exert a synergistic anticoagulant effect upon APTT. At low AcH concentrations (44.7 mmol/L), neither a synergistic nor an additive effect is seen. H6k and H3k, on premixing with 447 mmol AcH/L, exhibit an additive effect on APTT prolongation but no synergism. Similarly, premixtures of CSB/447 mmol AcH/L/FII show a greater anticoagulant effect than do [(CSB/AcH) + FII] or [(FII/AcH) + CSB] premixtures. CSC-AcH and CSA-AcH patterns are analogous to those of CSB (DS). These data suggest the possibility that AcH, the primary product of ethanol metabolism, may serve as a crosslinking adduct with proteins, in this case, prothrombin, as well as GAGs. Thus ternary complexes between the zymogen form of coagulation factors, GAGs, and AcH are possible, further influencing coagulopathy.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".