High-affinity antibodies in a new immunoassay for plasma tissue factor: reduction in apparent intra-individual variation
Bibliographic record
Abstract
Tissue factor, the main initiator of blood coagulation, is shed into plasma by blood cells and endothelium. While studying such circulating plasma tissue factor with a commercially available immunoassay, we found unsatisfactory results and therefore developed a new and highly sensitive enzyme-linked immunosorbent assay (ELISA). High-affinity monoclonal antibodies raised against recombinant soluble tissue factor were used and the new assay had a detection limit of 40 fmol/L, approximately six-fold lower than existing assays. Normal ranges in 20 healthy donors were established in serum and in citrated EDTA and heparinized plasma. Tissue factor was also measured in three successive plasma samples from 43 patients with type 2 diabetes mellitus. In citrated plasma from healthy donors, tissue factor concentrations were 2.5 (1.0-9.3) pmol/L (median with range) and were not significantly different in diabetics. With a commercially available immunoassay, seven plasma samples were below the detection limit. Use of the new assay reduced intra-individual variation in diabetics from 49% to 14% and we conclude that high-affinity antibodies may markedly improve immunoassay performance.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".