Biomimetic implants for pelvic floor repair
Bibliographic record
Abstract
BACKGROUND: Polypropylene implants are used for the reconstructive surgery of urogynaecological disorders like pelvic organ prolapse, but severe complications associated with their use have been reported. There is evidence that surface properties and a difference in mechanical stiffness between the implant and the host tissue contribute to these adverse events. Electrospinning is an innovative engineering alternative that provides a biomimetic microstructure for implants, resulting in a different mechano-biological performance. AIM: The main objective of this review is to inform about the potential of electrospun matrices as an alternative modality for pelvic floor repair. METHODS: Publications with the following studies of electrospun matrices were reviewed: (i) the technique; (ii) in vitro use for soft tissue engineering; (iii) in vivo use for reconstruction of soft tissues in animals; and (iv) clinical use in humans. RESULTS: Electrospun matrices provide a synthetic mimic of natural extracellular matrix (ECM), favoring cellular attachment, proliferation and matrix deposition, through which a proper, low-inflammatory tissue-implant interaction can be established. Electrospun sheets can also be created with sufficient mechanical strength and stiffness for usage in prolapse surgery. CONCLUSION: Electrospun matrices mimic the structural topography of the extracellular matrix and can be functionalized for better biological performance. As such, they have great potential for the next generation of urogynecological implants. However, their long-term safety and efficacy must still be established in vivo.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".