Eluting characteristics of a platelet glycoprotein receptor antibody using a PLLA-coated stent
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Bibliographic record
Abstract
Drug-eluting stents (DES) have emerged as a recognized alternative to treat stent restenosis but many questions remain regarding the optimal type and eluting characteristics of both drug and stent. The first component of the study examines the extent of surface coating of PLLA (poly(L-lactic acid)) on a Nitinol stent. The second characterizes the adsorption and elution rates of monoclonal mouse anti-human platelet glycoprotein (GP) IIIa antibody SZ-21 from a PLLA-coated surface. The PLLA coating was examined by fluorescence staining and image analysis using the Image Processing Box of MATLAB. Stents exposed to the monoclonal mouse anti-human platelet GP IIIa antibody were tested for their adsorption characteristics by radioisotope technique with (125)I-labelled SZ-21. The elution rates were then measured in looped circuits at different velocities (10 or 20 ml/min) and durations (30 min up to 312 h). Results showed that the fluorescence staining and image analysis showed a striking difference in the extent of coating between PLLA-coated stents and SZ-21 eluting stents on the gray-scale distribution of Nitinol surfaces. The amount of SZ-21 adsorbed onto the PLLA-coated stents was dependent on the concentration and duration of immersion in the solution. The method of preparation the mAb eluting stent significantly influenced the elution characteristics for a continuous perfusion of more than 300 h. The eluting curve was biphasic with initial rapid elution for the first 24 h followed by a gradual slow elution. These results indicate that the Image Processing Box of MATLAB appears to be a useful method for semi-quantitative analysis of fluorescence images. Furthermore, SZ-21 can be passively adsorbed onto PLLA-coated stents and predictably influenced by the concentration and duration of immersion. These studies may pave the way to developing stent-based delivery of a potent anti-platelet agent.
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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 | 0.001 | 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