Collagen-based biomaterials in organoid technology for reproductive medicine: composition, characteristics, and applications
Bibliographic record
Abstract
Abstract Collagen-based biomaterials (CBB) are highly esteemed by researchers in materials science and biomedicine due to their extensive applications across various biomedical disciplines. In recent years, owing to advancements in developmental biology techniques, this superior biomaterial has seen increasing utilization in 3D in vitro tissue culture. Three-dimensional cell cultures, often referred to as organoids, have emerged in response to technological advancements in biomaterials and the growing need in the field of medical research. They serve as important models for simulating normal physiological activities in vivo, addressing limitations in experimental material sources, and resolving ethical issues. In this review, we discuss the material characteristics of CBBs commonly used for organoid culture, integrating aspects such as Matrigel and decellularized ECM as culture matrices. We also analyzed the development prospects and directions of various materials in the context of biology, clinical medicine, and particularly reproductive medicine. Currently, despite the FDA approval and clinical research incorporating numerous CBBs, existing challenges in multiple studies indicate a significant unmet need in the development of key tissue models for both medical research and clinical applications. In summary, CBBs are swiftly broadening their applicability in the realms of organoid nature and medical research, serving as a versatile and high-performing material for 3D in vitro tissue culture. Graphical abstract
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".