Local Delivery of Nicotine does not Mitigate Fibrosis but may Lead to Angiogenesis
Bibliographic record
Abstract
As with most implanted biomaterials, the wound healing response following implantation of a silicone breast implant leads to the formation of a fibrotic capsule. This can result in capsular contracture, a painful complication that often necessitates the removal of implant. It is well established that nicotine and nicotinic agonists inhibit inflammatory signaling. Based on the link between the inflammatory response and capsule formation, we hypothesized that local delivery of nicotine from the implant may lead to the reduction in inflammation and capsule thickness, which may ultimately reduce the incidence of capsular contracture. Nicotine was loaded into PDMS membranes using a previously established method. The loaded materials were implanted into the submammary pockets between the third and fourth mammary glands of rats. To confirm that the nicotine was acting locally and not systemically, serum cotinine, the primary metabolite of nicotine, was measured by ELISA at 3 days. Thirty days post implantation, the animals were euthanized and the tissue samples were fixed for histological analysis. Blood vessel density was measured immunohistochemically, while the capsule thickness was evaluated microscopically. While the presence of the nicotine metabolite, cotinine, in the serum at the early time points demonstrated that the nicotine was released locally from the devices, there were no significant differences in the capsule thickness between the control and experimental implants. However, the results indicated that there were differences in angiogenesis with the local delivery of nicotine, which may have other implications for the development of biomaterials.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".