Novel injectable biomimetic hydrogels with carbon nanofibers and self assembled rosette nanotubes for myocardial applications
Why this work is in the frame
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Bibliographic record
Abstract
The objective of the present in vitro study was to investigate cardiomyocyte functions, specifically their adhesion and proliferation, on injectable scaffolds containing RNT (rosette nanotubes) and CNF (carbon nanofibers) in a pHEMA (poly(2-hydroxyethyl methacrylate)) hydrogel to determine their potential for myocardial tissue engineering applications. RNTs are novel biocompatible nanomaterials assembled from synthetic analogs of DNA bases guanine and cytosine that self-assemble within minutes when placed in aqueous solutions at body temperatures. These materials could potentially improve cardiomyocyte functions and solidification time of pHEMA and CNF composites. Because heart tissue is conductive, CNFs were added to pHEMA to increase the composite's conductivity. Our results showed that cardiomyocyte density increased after 4 h, 1 day, and 3 days with greater amounts of CNFs and greater amounts of RNTs in pHEMA (up to 10 mg mL(-1) CNFs and 0.05 mg mL(-1) RNTs). Factors that may have increased cardiomyocyte functions include greater wettability, conductivity, and an increase in surface nanoroughness with greater amounts of CNFs and RNTs. In effect, contact angles measured on the surface of the composites decreased while the conductivity and surface roughness increased as CNFs and RNTs content increased. Lastly, the ultimate tensile modulus decreased for composites with greater amounts of CNFs. In summary, the properties of these injectable composites make them promising candidates for myocardial tissue engineering applications and should be further studied.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it