Advancements in tissue engineering for cardiovascular health: a biomedical engineering perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Myocardial infarction (MI) stands as a prominent contributor to global cardiovascular disease (CVD) mortality rates. Acute MI (AMI) can result in the loss of a large number of cardiomyocytes (CMs), which the adult heart struggles to replenish due to its limited regenerative capacity. Consequently, this deficit in CMs often precipitates severe complications such as heart failure (HF), with whole heart transplantation remaining the sole definitive treatment option, albeit constrained by inherent limitations. In response to these challenges, the integration of bio-functional materials within cardiac tissue engineering has emerged as a groundbreaking approach with significant potential for cardiac tissue replacement. Bioengineering strategies entail fortifying or substituting biological tissues through the orchestrated interplay of cells, engineering methodologies, and innovative materials. Biomaterial scaffolds, crucial in this paradigm, provide the essential microenvironment conducive to the assembly of functional cardiac tissue by encapsulating contracting cells. Indeed, the field of cardiac tissue engineering has witnessed remarkable strides, largely owing to the application of biomaterial scaffolds. However, inherent complexities persist, necessitating further exploration and innovation. This review delves into the pivotal role of biomaterial scaffolds in cardiac tissue engineering, shedding light on their utilization, challenges encountered, and promising avenues for future advancement. By critically examining the current landscape, we aim to catalyze progress toward more effective solutions for cardiac tissue regeneration and ultimately, improved outcomes for patients grappling with cardiovascular ailments.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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