The fraction of recombinant factor <scp>VIII</scp>:Ag unable to bind von Willebrand factor has no <scp>FVIII</scp> coagulant activity: studies <i>in vitro</i>
Bibliographic record
Abstract
A fraction of FVIII:Ag in commercial recombinant FVIII (rFVIII) cannot bind VWF whereas all the FVIII:Ag in plasma-derived FVIII (pd-FVIII) concentrates does. To compare the FVIII:C activities of the fractions of rFVIII:Ag that can and cannot bind VWF. The FVIII:Ag contents of the rFVIII Kogenate, and Advate and a pd-FVIII-pd-VWF (Fanhdi) were measured by ELISA. The FX activation was initiated by adding 1.0 IU of FVIII:C of each FVIII-containing product to a coagulant phospholipids suspension containing 1.0 nm FIXa, 100 nm FX, 1 μm hirudin and 2 mm calcium chloride and measured after 1, 5 and 10 min. The same approach was followed after adding 2.0 IU of pd-VWF to 1.0 IU of FVIII:C of Kogenate or Advate. The FVIII:Ag content/IU of FVIII:C of Kogenate, Advate and Fanhdi were 1.80 ± 0.05, 1.31 ± 0.9 and 0.84 ± 1.5 IU respectively. Only Kogenate and Advate effectively enhanced FX activation 1 min after adding each FVIII:C to the coagulant suspension containing FIXa and FX. Thus, the FXa initially generated by FIXa readily activated FVIII:C in control Kogenate and Advate to thereby effectively enhance FX activation while the VWF in Fanhdi continued to suppress FX activation for up to 10 min. Addition of pd-VWF to Kogenate or Advate effectively decreased their enhancements of FX activation to the same level as Fanhdi over 10 min. The FVIII:Ag fraction in Kogenate and Advate that cannot bind VWF appears to be inactive as it has no measureable FVIII:C activity in the presence of added VWF in vitro.
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 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) | 0.002 | 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".