Photocrosslinkable chitosan modified with angiopoietin‐1 peptide, QHREDGS, promotes survival of neonatal rat heart cells
Bibliographic record
Abstract
Myocardial infarction (MI) results in the death of cardiomyocytes (CM), which causes scar formation and pathological remodeling of the heart. The delivery of healthy myocytes or bone marrow cells reduces pathological remodeling after MI, however, current cell injection methods have low cell survival rates and high cell loss. The main objective of this work was to develop a novel hydrogel that can promote survival of CMs. Photocrosslinkable azidobenzoic acid modified chitosan (Az-chitosan) was conjugated with the angiopoietin-1-derived peptide, QHREDGS. This novel peptide is thought to mediate attachment and survival responses of CM to angiopoietin-1 via integrin binding. Thin layers of Az-chitosan, Az-chitosan-QHREDGS, and Az-chitosan-DGQESHR (scrambled peptide control) were spin coated on glass slides and photocrosslinked with application of UV light (365 nm). Neonatal rat heart cells cultured up to 5 days, demonstrated significantly higher attachment and viability on Az-chitosan-QHREDGS compared to cells on other hydrogel controls. Surfaces were also stained for the CM-specific marker troponin I, demonstrating significantly higher percentage of CMs on Az-chitosan-QHREDGS compared to Az-chitosan. The cells cultivated on Az-chitosan-QHREDGS demonstrated significantly lower levels of caspase 3/7 activation after taxol treatment in comparison to cells cultivated on the control hydrogels, glass substrate, or Az-chitosan linked to RGD, an established integrin binding peptide that did not protect against apoptosis. Thus, Az-chitosan-QHREDGS supports attachment and survival of neonatal rat heart cells.
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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 | 0.000 |
| 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".