Biomaterial and cellular implants: foreign surfaces where immunity and coagulation meet
Bibliographic record
Abstract
Exposure of blood to a foreign surface in the form of a diagnostic or therapeutic biomaterial device or implanted cells or tissue elicits an immediate, evolutionarily conserved thromboinflammatory response from the host. Primarily designed to protect against invading organisms after an injury, this innate response features instantaneous activation of several blood-borne, highly interactive, well-orchestrated cascades and cellular events that limit bleeding, destroy and eliminate the foreign substance or cells, and promote healing and a return to homeostasis via delicately balanced regenerative processes. In the setting of blood-contacting synthetic or natural biomaterials and implantation of foreign cells or tissues, innate responses are robust, albeit highly context specific. Unfortunately, they tend to be less than adequately regulated by the host's natural anticoagulant or anti-inflammatory pathways, thereby jeopardizing the functional integrity of the device, as well as the health of the host. Strategies to achieve biocompatibility with a sustained return to homeostasis, particularly while the device remains in situ and functional, continue to elude scientists and clinicians. In this review, some of the complex mechanisms by which biomaterials and cellular transplants provide a "hub" for activation and amplification of coagulation and immunity, thromboinflammation, are discussed, with a view toward the development of innovative means of overcoming the innate challenges.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".