Optimum Sterilization Methods of Biocompatible Hybrid Material for Artificial Organs
Bibliographic record
Abstract
We previously reported the development of a new hybrid medical material comprising bio-based materials with high biocompatibility and artificial materials with characteristics of excellent strength and processability.This material shows sufficient biocompatibility and excellent stability in vivo.Moreover, when applied to the surface of an implantable sensor, the biological reaction on the sensor function surface can be well controlled.For commercialization and widespread use of hybrid materials with such superior properties, sterilization and storage are critical considerations, given that hybrid materials must be processed outside the body prior to application as medical materials in vivo, thus posing a risk of contamination despite best efforts.Therefore, the aim of the present study was to establish an optimal sterilization method that will not impair the biocompatibility of the hybrid material.Toward this end, we tested six sterilization methods for the hybrid material: autoclave (121 C, 20 min), dry heat (160 C, 120 min), ethylene oxide gas (37 C, 120 min), hydrogen peroxide plasma (45 C, 45 min), and gamma ray (25 kGy) with and without lyophilization.After sterilization, the material was cultured with vascular endothelial cells to evaluate the engraftment rate, and was observed with light and scanning electron microscopy to determine shape and structure changes.The results demonstrated that gamma sterilization without lyophilization was the best sterilization method for this material, which preserved the collagen network and showed no change in number of adhered vascular endothelial cells compared to the pre-sterilized material.These findings are useful to promote the commercialization of this hybrid material with combined advantages of synthetic and bio-based materials for widespread clinical application in the engineering of artificial organs.
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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 | 0.001 | 0.001 |
| 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".