3D Culture Histology Cryosectioned Well Insert Technology Preserves the Structural Relationship between Cells and Biomaterials for Time‐Lapse Analysis of 3D Cultures
Bibliographic record
Abstract
When performing histology of softer biomaterials, aspiration disrupts the cellular and molecular location information. This study aims to develop a cryosectionable well insert able to preserve the biomaterial and cell's original 3D conformation from the well to histology analysis. The well insert is composed of a paraffin-coated gelatine pill. Within the coated capsule, the human epithelial cell line (NS-SV-AC) is cultured in Matrigel, GrowDex, Myogel, Myogel + GrowDex, or cell culture media for 14 days. At 0 and 14 days, the samples are frozen in liquid nitrogen and cryotome is used to create sections. The slides are stained by Sirius Red and immunohistochemistry using antibodies human collagens I-V and human Ki-67. Sirius Red shows pink shades of biomaterials and the best cellular vertical distribution throughout the sagittal section of the well is achieved with Matrigel, GrowDex, and Myogel + GrowDex; in Myogel and media, the cells sink. For collagen protein expression, only Matrigel induces a notable difference while in the other materials, collagen staining is weak or difficult to distinguish from endogenous collagens. Ki-67 expression is maintained over time. The 3D-cryo well insert provides a new time-lapse histology perspective of analysis for liquid or gel cultures that maintains cells and macromolecules in their unaltered in-well configuration.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".