Improvement in hemocompatibility of chitosan/soy protein composite membranes by heparinization
Bibliographic record
Abstract
OBJECTIVE: To improve the hemocompatibility of chitosan/soy protein isolate composite membranes by heparinization. METHODS: Chitosan/soy protein isolate membranes (ChS-n, n=0, 10 and 30, corresponding to the soy protein isolate content in the membranes) and heparinized ChS-n membranes (HChS-n) were prepared by blending in dilute HAc/NaAc solution. The hemocompatibility of ChS-n and HChS-n membranes were comparatively evaluated by measuring surface heparin density, blood platelet adhesion, plasma recalcification time (PRT), thrombus formation and hemolysis assay. RESULTS: The surface heparin density analysis showed that heparinized chitosan/SPI soy protein isolate membranes have been successfully prepared by blending. The density of heparin on the surface of HChS-n membranes was in the range of 0.67-1.29 μg/cm2. The results of platelet adhesion measurement showed that the platelet adhesion numbers of HChS-n membranes were lower than those of the corresponding ChS-n membranes. The PRT of the HChS-0, HChS-10 and HChS-30 membranes were around 292, 306 and 295 s, respectively, which were longer than the corresponding ChS-0 (152 s), ChS-10 (204 s) and ChS-30 (273 s) membranes. The hemolysis rate of HChS-n membranes was lower than 1%. CONCLUSION: The hemocompatibility of ChS membranes could be improved by blending with heparin. Compared with ChS membranes, HChS membranes showed lower platelet adhesion, longer PRT, higher BCI, significant thromboresistivity and a lower hemolysis rate due to the heparinization. This widens the application of chitosan and soy protein-based biomaterials that may come in contact with blood.
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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) | 0.001 | 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".