Genitourinary Tissue Engineering: Reconstruction and Research Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Tissue engineering is an emerging field of research that initially aimed to produce 3D tissues to bypass the lack of adequate tissues for the repair or replacement of deficient organs. The basis of tissue engineering protocols is to create scaffolds, which can have a synthetic or natural origin, seeded or not with cells. At the same time, more and more studies have indicated the low clinic translation rate of research realised using standard cell culture conditions, i.e., cells on plastic surfaces or using animal models that are too different from humans. New models are needed to mimic the 3D organisation of tissue and the cells themselves and the interaction between cells and the extracellular matrix. In this regard, urology and gynaecology fields are of particular interest. The urethra and vagina can be sites suffering from many pathologies without currently adequate treatment options. Due to the specific organisation of the human urethral/bladder and vaginal epithelium, current research models remain poorly representative. In this review, the anatomy, the current pathologies, and the treatments will be described before focusing on producing tissues and research models using tissue engineering. An emphasis is made on the self-assembly approach, which allows tissue production without the need for biomaterials.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it