Comparative analysis of porcine-uterine decellularization for bioactive-molecule preservation and DNA removal
Bibliographic record
Abstract
Introduction: Decellularized uterine extracellular matrix has emerged as a pivotal focus in the realm of biomaterials, offering a promising source in uterine tissue regeneration, research on disease diagnosis and treatments, and ultimately uterine transplantation. In this study, we examined various protocols for decellularizing porcine uterine tissues, aimed to unravel the intricate dynamics of DNA removal, bioactive molecules preservation, and microstructural alterations. Methods: X-100, peracetic acid + ethanol, and DNase I. After decellularization, we examined DNA quantification, histological staining (H&E and DAPI), glycosaminoglycans (GAG) assay, scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FT-IR), X-ray photoelectron spectroscopy (XPS), and Thermogravimetric Analysis (TGA). Results: X-100, coupled with agitation, demonstrated the highest efficiency in DNA removal. Also, it was found that DNase I played a key role in enhancing the efficiency of the decellularization process by underscoring its significance in digesting cellular contents and eliminating cell debris by 99.79% (19.63 ± 3.92 ng/mg dry weight). Conclusions: Our findings enhance the nuanced understanding of DNA removal, GAG preservation, microstructural alteration, and protein decomposition in decellularized uterine extracellular matrix, while highlighting the importance of decellularization protocols designed for intended applications. This study along with our findings represents meaningful progress for advancing the field of uterine transplantation and related tissue engineering/regenerative medicine.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".