Bibliographic record
Abstract
The fabrication of the Guided Tissue Regeneration (GTR) membrane materials have become the key technique of the tissue engineering scaffold study. The cells adhere well on the fibers whose dimension is below their own so that the porous three dimension scaffold material can mimic the strueture of the natural extracellular matrix better and have the potential to be an ideal GTR membrane material. Gelatin, a kind of protein obtained from hydrolyzed and denatured animal skin, is a condensation polymer of a variety of amino acids and so it is a kind of bio-polymer with good water-solubility. Gelatin fiber mats with submicro and nanometer scale can simulate extracellular matrix structure of the human tissues and organs and can be used widely in the tissue engineering field because of their excellent bio-affinity. Electrospinning is a very attractive method for preparing polymer or composite nanofibers and so electrospinning technique was developed to prepare nanofibrous gelatin matrix. The electrospun of gelatin to fabricate the scaffold material has obtained more attention recently because of its biocompatibility, high surface area-to-volume ratio, degradability and less immunogenic property. The structure and performance of the electrospinning gelatin fiber mats which were manufactured by different solvents, electrospinning process, cross-linking process were reviewed. The properties and application of the two-component and multicomponent gelatin fiber mats were analyzed.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".