Implant Fibrosis and the Underappreciated Role of Myofibroblasts in the Foreign Body Reaction
Bibliographic record
Abstract
Body implants and implantable medical devices have dramatically improved and prolonged the life of countless patients. However, our body repair mechanisms have evolved to isolate, reject, or destroy any object that is recognized as foreign to the organism and inevitably mounts a foreign body reaction (FBR). Depending on its severity and chronicity, the FBR can impair implant performance or create severe clinical complications that will require surgical removal and/or replacement of the faulty device. The number of review articles discussing the FBR seems to be proportional to the number of different implant materials and clinical applications and one wonders, what else is there to tell? We will here take the position of a fibrosis researcher (which, coincidentally, we are) to elaborate similarities and differences between the FBR, normal wound healing, and chronic healing conditions that result in the development of peri-implant fibrosis. After giving credit to macrophages in the inflammatory phase of the FBR, we will mainly focus on the activation of fibroblastic cells into matrix-producing and highly contractile myofibroblasts. While fibrosis has been discussed to be a consequence of the disturbed and chronic inflammatory milieu in the FBR, direct activation of myofibroblasts at the implant surface is less commonly considered. Thus, we will provide a perspective how physical properties of the implant surface control myofibroblast actions and accumulation of stiff scar tissue. Because formation of scar tissue at the surface and around implant materials is a major reason for device failure and extraction surgeries, providing implant surfaces with myofibroblast-suppressing features is a first step to enhance implant acceptance and functional lifetime. Alternative therapeutic targets are elements of the myofibroblast mechanotransduction and contractile machinery and we will end with a brief overview on such targets that are considered for the treatment of other organ fibroses.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".