Films, Gels and Electrospun Fibers from Serum Albumin Globular Protein for Medical Device Coating, Biomolecule Delivery and Regenerative Engineering
Bibliographic record
Abstract
Albumin is a natural biomaterial that is abundantly available in blood and body fluids. It is clinically used as a plasma expander, thereby increasing the plasma thiol concentration due to its cysteine residues. Albumin is a regulator of intervascular oncotic pressure, serves as an anti-inflammatory modulator, and it has a buffering role due to its histidine imidazole residues. Because of its unique biological and physical properties, albumin has also emerged as a suitable biomaterial for coating implantable devices, for cell and drug delivery, and as a scaffold for tissue engineering and regenerative medicine. As a biomaterial, albumin can be used as surface-modifying film or processed either as cross-linked protein gels or as electrospun fibers. Herein we have discussed how albumin protein can be utilized in regenerative medicine as a hydrogel and as a fibrous mat for a diverse role in successfully delivering drugs, genes, and cells to targeted tissues and organs. The review of prior studies indicated that albumin is a tunable biomaterial from which different types of scaffolds with mechanical properties adjustable for various biomedical applications can be fabricated. Based on the progress made to date, we concluded that albumin-based device coatings, delivery of drugs, genes, and cells are promising strategies in regenerative and personalized medicine.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".