Attachment of Primary Vaginal Fibroblasts to Absorbable and Nonabsorbable Implant Materials Coated With Platelet-Rich Plasma
Bibliographic record
Abstract
OBJECTIVES: Pelvic organ prolapse (POP) is a common condition in women. The lifetime risk of undergoing surgery to correct POP is 11%, with 30% recurrence rate. Various types of vaginal implants, absorbable and nonabsorbable, that have been introduced in pelvic floor reconstructive surgeries have numerous serious adverse effects. Platelet-rich plasma (PRP) is an autologous product that accelerates tissue healing and regeneration. We hypothesized that autologous PRP will promote human vaginal fibroblast (HVF) attachment to vaginal implants and increase their healing potential. METHODS: Vaginal tissue biopsies were collected from postmenopausal patients with POP (n = 10) and asymptomatic control subjects (n = 4) during vaginal hysterectomy or repair. Primary cells were isolated and characterized by immunocytochemistry. Cell attachment and proliferation were compared between POP HVFs and control HVFs (n = 4/group). Twelve weeks after the surgery, blood samples were collected from 6 POP patients to obtain autologous PRP. Two meshes, absorbable (Vicryl) and nonabsorbable (Restorelle), were coated in PRP or control media; autologous POP HVFs (n = 6) were seeded on meshes for 2 hours. Cells attached to the meshes were fixed, stained with DAPI (4,6-diamidino-2-phenylindole dihydrochloride), and counted. RESULTS: Pelvic organ prolapse HVFs were similar to control HVFs in attachment to different matrix substrates and in proliferation rate. Attachment of POP HVFs to both meshes was significantly increased after coating with PRP versus Dulbecco modified Eagle medium (Vicryl: 9875 vs. 1006 cells/cm, Restorelle: 3724 vs. 649 cells/cm; P < 0.001 for both). CONCLUSIONS: In vitro, primary POP HVFs show better attachment to implant materials when treated with PRP, which may lead to reduced mesh-related complications in vivo, indicating its great potential for urogynecologic surgeries.
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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) | 0.000 | 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) | 0.002 | 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".