Monitoring of heparin and its low-molecular-weight analogs by silicon field effect
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it