Biomaterial engineering for cell transplantation
Bibliographic record
Abstract
The current paradigm of medicine is mostly designed to block or prevent pathological events. Once the disease-led tissue damage occurs, the limited endogenous regeneration may lead to depletion or loss of function for cells in the tissues. Cell therapy is rapidly evolving and influencing the field of medicine, where in some instances attempts to address cell loss in the body. Due to their biological function, engineerability, and their responsiveness to stimuli, cells are ideal candidates for therapeutic applications in many cases. Such promise is yet to be fully obtained as delivery of cells that functionally integrate with the desired tissues upon transplantation is still a topic of scientific research and development. Main known impediments for cell therapy include mechanical insults, cell viability, host's immune response, and lack of required nutrients for the transplanted cells. These challenges could be divided into three different steps: 1) Prior to, 2) during the and 3) after the transplantation procedure. In this review, we attempt to briefly summarize published approaches employing biomaterials to mitigate the above technical challenges. Biomaterials are offering an engineerable platform that could be tuned for different classes of cell transplantation to potentially enhance and lengthen the pharmacodynamics of cell therapies.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".