Extracellular Matrix-Based and Electrospun Scaffolding Systems for Vaginal Reconstruction
Bibliographic record
Abstract
Congenital vaginal anomalies and pelvic organ prolapse affect different age groups of women and both have significant negative impacts on patients' psychological well-being and quality of life. While surgical and non-surgical treatments are available for vaginal defects, their efficacy is limited, and they often result in long-term complications. Therefore, alternative treatment options are urgently needed. Fortunately, tissue-engineered scaffolds are promising new treatment modalities that provide an extracellular matrix (ECM)-like environment for vaginal cells to adhere, secrete ECM, and be remodeled by host cells. To this end, ECM-based scaffolds or the constructs that resemble ECM, generated by self-assembly, decellularization, or electrospinning techniques, have gained attention from both clinicians and researchers. These biomimetic scaffolds are highly similar to the native vaginal ECM and have great potential for clinical translation. This review article aims to discuss recent applications, challenges, and future perspectives of these scaffolds in vaginal reconstruction or repair strategies.
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) | 0.001 | 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) | 0.003 | 0.001 |
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".