Bibliographic record
Abstract
Nature has had millions of years to perfect the structural components of the human body, but has also produced the dysfunctions that result in the cancers and diseases, which ruin that perfection.Congenital heart defects, and myocardial infarction lead to scarring that remodels heart muscle, decreasing the contractility of the heart, with profound consequences for the host.Regenerative medicine is the study of strategies to return diseased body parts to their evolutionarily optimum structure.Cells alone cannot develop into functional tissue, as they require mechanical support and chemical signals from the extracellular matrix in order to play the correct role in the body.In order to imitate the process of tissue formation optimized by nature, scaffolds are developed as the architectural support for tissue regeneration.To mimic the elasticity and strength seen in the heart muscle is one of the major scientific conundrums of our time.The development of new multifunctional materials for scaffolds is an accepted solution for repairing failing heart muscle.In this thesis I accept the notion that endogenous cardiac cells can play a major role in addressing this problem, if we can attract them to the site of defect or injury and make them proliferate.I then proceed to show how improving on a commonly used synthetic polymer was used to develop two new biomaterials.Polycaprolactone (PCL) fibers and sheets were studied for their ability to adsorb proteins based on their surface energies.We found that although the wettability of the PCL might be similar to positive controls for cell attachment, the large differences in surface energies may account for the increased serum protein adsorption and limit cell adhesion.The effect of fiber morphology was then investigated with respect to proliferation of mesenchymal stem cells and cardiac progenitor cells.PCL was also mechanically enhanced with thiophene conjugated single walled carbon nanotubes (T-CNT); where small concentrations of the T-CNT allowed for a 2.5 fold increase in the percentage of elongation, while retaining the proliferation profile of the cardiac progenitor cells.Although PCL is a well-known implant material, the ability to attract and adhere cardiac cells was limited.Therefore we sought to develop new biomaterials with fiber morphologies similar to the muscle fiber of the heart, but with surface energies similar to positive controls for cell attachment.Poly[2,3-bis-(3iii octyloxyphenyl)quinoxaline-5,8-diyl-alt-thiophene-2,5-diyl] (TQ1) was then explored as a ribbon fiber and compared to collagen with embryonic cardiac cells, in vitro, and then implanted into rats for in vivo long term evaluations.The cardiac cells had a preferential adhesion to the TQ1 fibers, and in vivo the fibers attracted more blood vessels and regrew functional tissue compared to the collagen controls.TQ1 fibers had the added ability to emit light in the near infrared region, which would allow for consistent tracking of the material.Although this material offered the morphological preference for the cardiac cells, it does not degrade nor did it offer electrical conductivity.The heart muscle is an electrically active muscle.The dead tissue that is formed in the ischemic area loses its ability to transfer the electrical signals.Hence, I have then developed collagen fibrous materials with silver nanowires to help store and inject charges that would be generated during the contraction of the heart muscle.The silver nanowires served to help carry charges whilst providing resistance to bacterial growth on the material.The collagen/silver nanowires composites were mechanically apt for the culture of embryonic cardiac cells.This thesis promotes the idea that morphology and favorable surface energies can help to attract and retain cardiac cells.It also shows the ability to manipulate materials to provide different functionalities, without losing their biocompatibility.I have added one new highly functional biomaterial to the list of materials capable of mimicking the extracellular matrix.I have also shown that small additions of metallic nanowires to a collagen matrix can increase its ability to conduct electrical signals, whilst retaining the mechanical properties that cardiac cells prefer.With the help of doctors and biomedical scientists, the materials of this thesis will hopefully translate to help patients have a better quality of life after heart injury.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".