Advanced Strategies for Modulation of the Material–Macrophage Interface
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
Abstract Biomaterials are becoming increasingly crucial for healthcare solutions, with extensive use in the field of tissue engineering and drug delivery. After implantation, biomaterials trigger an immune response characterized by the recruitment of bone‐marrow‐derived proinflammatory macrophages that develop as the most abundant cell type surrounding the biomaterial. Chronic activation of this recruited macrophage population induces a foreign body reaction response and consequent biomaterial rejection. However, transition toward a proreparative phenotype is associated with biomaterial integration and tissue homeostasis restoration. In this review, the most relevant strategies that modulate biomaterial immune response are discussed, including mechanical properties, surface coatings, release of anti‐inflammatory molecules and cytokines, antibacterial features, origin and inner moieties of biomaterials, and cell crosstalk. Moreover, the role of tissue resident macrophages, an embryo‐derived macrophage population with a strong reparative potential, in promoting biomaterial tolerance will be reviewed. This provides new insights to better tune the reaction of the host immune system to implanted biomaterials in order to favor integration and increase the knowledge of macrophages as key players in tissue homeostasis.
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) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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