Hydrogels For Peptide Hormones Delivery: Therapeutic And Tissue Engineering Applications
Bibliographic record
Abstract
Mohsen Doostmohammadi,1 Atefeh Ameri,1 Reza Mohammadinejad,1 Negar Dehghannoudeh,2 Ibrahim M Banat,3 Mandana Ohadi,1 Gholamreza Dehghannoudeh1,4 1Pharmaceutics Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran; 2Faculty of Arts and Science, University of Toronto, Toronto M5S3G3, Ontario, Canada; 3School of Biomedical Sciences, Faculty of Life & Health Sciences, University of Ulster, Coleraine BT52 1SA, Northern Ireland, UK; 4Department of Pharmaceutics, Faculty of Pharmacy, Kerman University of Medical Sciences, Kerman, IranCorrespondence: Mandana Ohadi; Gholamreza DehghannoudehPharmaceutics Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, IranTel +98 34 31325243; +98 34 31325014Fax +98 34 31325003Email mandanaohady@gmail.com; ghr_dehghan@kmu.ac.irAbstract: Peptides are the most abundant biological compounds in the cells that act as enzymes, hormones, structural element, and antibodies. Mostly, peptides have problems to move across the cells because of their size and poor cellular penetration. Therefore, a carrier that could transfer peptides into cells is ideal and would be effective for disease treatment. Until now, plenty of polymers, e.g., polysaccharides, polypeptides, and lipids were used in drug delivery. Hydrogels made from polysaccharides showed significant development in targeted delivery of peptide hormones because of their natural characteristics such as networks, pore sizes, sustainability, and response to external stimuli. The main aim of the present review was therefore, to gather the important usages of the hydrogels as a carrier in peptide hormone delivery and their application in tissue engineering and regenerative medicine.Keywords: hydrogels, peptides hormones, tissue engineering, drug delivery
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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 | 0.001 |
| 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".