Monitoring of heparin and its low-molecular-weight analogs by silicon field effect
Bibliographic record
Abstract
Heparin is a highly sulfated glycosaminoglycan that is used as an important clinical anticoagulant. Monitoring and control of the heparin level in a patient's blood during and after surgery is essential, but current clinical methods are limited to indirect and off-line assays. We have developed a silicon field-effect sensor for direct detection of heparin by its intrinsic negative charge. The sensor consists of a simple microfabricated electrolyte-insulator-silicon structure encapsulated within microfluidic channels. As heparin-specific surface probes the clinical heparin antagonist protamine or the physiological partner antithrombin III were used. The dose-response curves in 10% PBS revealed a detection limit of 0.001 units/ml, which is orders of magnitude lower than clinically relevant concentrations. We also detected heparin-based drugs such as the low-molecular-weight heparin enoxaparin (Lovenox) and the synthetic pentasaccharide heparin analog fondaparinux (Arixtra), which cannot be monitored by the existing near-patient clinical methods. We demonstrated the specificity of the antithrombin III functionalized sensor for the physiologically active pentasaccharide sequence. As a validation, we showed correlation of our measurements to those from a colorimetric assay for heparin-mediated anti-Xa activity. These results demonstrate that silicon field-effect sensors could be used in the clinic for routine monitoring and maintenance of therapeutic levels of heparin and heparin-based drugs and in the laboratory for quantitation of total amount and specific epitopes of heparin and other glycosaminoglycans.
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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) | 0.000 | 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) | 0.001 | 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".