Biomaterials, Fibrosis, and the Use of Drug Delivery Systems in Future Antifibrotic Strategies
Bibliographic record
Abstract
All biomaterials, when implanted into the body, elicit an inflammatory response that evolves into fibrovascular tissue formation on and around the material. As a result, material scientists and tissue engineers should be concerned about host response to tissue-engineered constructs that have a biomaterial component, because the host response to this component will interfere with device function and reduce the lifespan of tissue engineering devices in vivo. The fibrotic response to biomaterials is not unlike pathological fibrosis of the liver, lung, kidney, and peritoneum in many ways: i) the presence of mononuclear leukocytes are common in the local environment of both pathological fibrosis and biomaterial-induced fibrosis even though cells of mesenchymal origin are responsible for laying the majority of the extracellular matrix; ii) paracrine-signaling molecules, such as transforming growth factor beta;1, are essential mediators of fibrosis, whether it is pathological or biomaterial induced; and iii) injury and/or the presence of foreign materials (including bacterial components, toxins, or man-made objects) are essential initiators for the development of the fibrotic response. This review discusses mechanisms and research methodology related to pathological fibrosis that is of interest to researchers focused on biomaterials. Potential research models for the study of fibrosis from the fields of biomaterials and drug delivery are also discussed, which may be of interest to scientists working on the pathology of fibrotic disease.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".