Development of a Delivery Platform for Protein-Based Regenerative and Reparative Cardiac Therapies
Bibliographic record
Abstract
Cell-based therapies for heart failure have been investigated in the field of regenerative medicine, however recent discoveries have shown that cardiac progenitor-cell derived paracrine effects (the secretome) may be inducing endogenous repair, instead of an intrinsic benefit from the stem cells themselves. We hypothesize that polyurethane-based nanoparticles will enable the delivery of therapeutic components of neonatal cardiac progenitor cell secretomes (BMP-4 and Ang1-7) and will induce characteristics representative of repair in damaged heart tissue. Polyurethane-based nanoparticles with a diameter of 190 ± 2 nm (polydispersity index < 0.2) and a zeta potential of -40 ± 1 mV were generated through an emulsion inversion technique. Through optimization of the concentration, coating duration, and coating efficiency, BMP-4 and Ang1-7 were coated onto the surface of the nanoparticles. Treatment with the coated nanoparticles significantly reduced expression of senescence associated b-galactosidase, a marker for senescence which is an important contributor to heart failure. On-going future work will further investigate the biological function of the nanoparticles with their biological payload, characterize both the particle and the biomolecule stabilities, and investigate co-delivery of multiple biomolecules simultaneously, in both in vitro cardiac cell and in vivo heart tissue models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".