A Novel 3D Co-culture Platform for Integrating Tissue Interfaces for Tumor Growth, Migration and Therapeutic Sensitivity: “PP-3D-S”
Bibliographic record
Abstract
ABSTRACT Metastatic cancers can be highly heterogeneous, show large patient variability and are typically hard to treat due to chemoresistance. Personalized therapies are therefore needed to suppress tumor growth and enhance patient’s quality of life. Identifying appropriate patient-specific therapies remains a challenge though, due mainly to non-physiological in vitro culture systems. Therefore, more complex and physiological in vitro human cancer microenvironment tools could drastically aid in development of new therapies. We developed a plasma-modified, electro-spun 3D scaffold (PP-3D-S) that can mimic the human cancer microenvironment for customized-cancer therapeutic screening. The PP-3D-S were characterized for optimal plasma-modifying treatment and scaffolds morphology including fiber diameter and pore size. PP-3D-S was then seeded with human fibroblasts to mimic a stromal tissue layer; cell adhesion on plasma-modified poly (lactic acid), PLA, electrospun mats vastly exceeded that on untreated controls. The cell-seeded scaffolds were then overlaid with alginate/gelatin-based hydrogel embedded with MDA-MB231 human breast cancer cells, representing a tumor-tissue interface. Among three different plasma treatments, we found that NH 3 plasma promoted the most tumor cell migration to the scaffold surfaces after 7 days of culture. For all treated and non-treated mats, we observed a significant difference in tumor cell migration between small-sized and either medium- or large-sized scaffolds. In addition, we found that the PP-3D-S was highly comparable to the standard Matrigel® migration assays in two different sets of doxorubicin screening experiments, where 75% reduction in migration was achieved with 0.5μM doxorubicin for both systems. Taken together, our data indicate that PP-3D-S is an effective, low-cost, and easy-to-use alternate 3D tumor migration model which may be suitable as a physiological drug screening tool for personalized medicine against metastatic cancers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".