Persistent glycolysis defines pathological foreign body–associated inflammation to polymeric implants
Bibliographic record
Abstract
The clinical deployment of nondegradable polymeric medical devices is hindered by unresolving inflammation that drives localized fibrosis. This inflammatory niche is distinct; phagocytic macrophages remain persistently activated by biomolecular signals adsorbed to the implant surface, exhibiting profibrotic behavior. Increasingly, alterations to metabolic regulation offers insight into chronically inflamed macrophage function, which remains unexplored in peri-implant inflammation. Here, ex vivo profiling of metabolic dependence and capacity in peri-implant tissues revealed persistent glycolytic reliance by macrophages up to 6 weeks postimplantation. Expression of glucose transporter 1 (GLUT1) increased temporally and with proximity to the implant interface in glycolytically dependent cells, paired with increased capacity for biosynthetic pathways. Glycolytic dependence was notable in multinucleated macrophages, hallmark to the phagocytic behavior of implant pathology. Transcriptomic assessment correlated the up-regulation of pathological wound healing to cells where the capacity for glucose import was highest, highlighting glycolysis as the definitive metabolic system in persistent peri-implant inflammation.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".