Hydrogel Strategies for Female Reproduction Dysfunction
Bibliographic record
Abstract
Infertility is an important issue for human reproductive health, with over half of all cases of infertility associated with female factors. Dysfunction of the complex female reproductive system may cause infertility. In clinical practice, female infertility is often treated with oral medications and/or surgical procedures, and ultimately with assisted reproductive technologies. Owing to their excellent biocompatibility, low immunogenicity, and adjustable mechanical properties, hydrogels are emerging as valuable tools in the reconstruction of organ function, supplemented by tissue engineering techniques to increase their structure and functionality. Hydrogel-based female reproductive reconstruction strategies targeting the pathological mechanisms of female infertility may provide alternatives for the treatment of ovarian, endometrium/uterine, and fallopian tube dysfunction. In this review, we provide a general introduction to the basic physiology and pathology of the female reproductive system, the limitations of current infertility treatments, and the lack of translation from animal models to human reproductive physiology. We further provide an overview of the current and future potential applications of hydrogels in the treatment of female reproductive system dysfunction, highlighting the great prospects of hydrogel-based strategies in the field of translational medicine, along with the significant challenges to be overcome.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".