Ultrasonic atomization and subsequent desolvation for monoclonal antibody (mAb) to the glycoprotein (GP) IIIa receptor into drug eluting stent
Bibliographic record
Abstract
An eluting-stent system with mAb dispersed in the PLLA (poly (L-lactic acid)) was validated in vitro. Specifically designed spray equipment based on the principle of ultrasonic atomization was used to produce a thin continuous PLLA (poly (L-lactic acid)) polymer coating incorporating monoclonal antibody (mAb). This PLLA coating was observed in light microscopy (LM) and scanning electron microscopy (SEM). The concentration of the monoclonal antibody (mAb) to the platelet glycoprotein (GP) IIIa receptor and the eluting rate were then measured by a radioisotope technique with 125I-labelled GP IIIa mAb. An in vitro perfusion circuit was designed to evaluate the release rates at different velocities (10 or 20 ml min−1). The PLLA coating was thin and transparent, uniformly distributed on the surface of the stent. Three factors influenced its thickness: PLLA concentration, duration and gas pressure. The concentration of mAb was influenced by the duration of absorption and the concentration of the mAb solution; the maximum was 1662.23 ± 38.83 ng. The eluting rate was fast for the first 2 h, then decreased slowly and attained 80% after 2 weeks. This ultrasonic atomization spray equipment and technological process to prepare protein eluting-stents were proved to be effective and reliable.
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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".