A novel approach to engineering three-dimensional bladder tumor models for drug testing
Bibliographic record
Abstract
Bladder cancer (BCa) poses a significant health challenge, particularly affecting men with higher incidence and mortality rates. Addressing the need for improved predictive models in BCa treatment, this study introduces an innovative 3D in vitro patient-derived bladder cancer tumor model, utilizing decellularized pig bladders as scaffolds. Traditional 2D cell cultures, insufficient in replicating tumor microenvironments, have driven the development of sophisticated 3D models. The study successfully achieved pig bladder decellularization through multiple cycles of immersion in salt solutions, resulting in notable macroscopic and histological changes. This process confirmed the removal of cellular components while preserving the native extracellular matrix (ECM). Quantitative analysis demonstrated the efficacy of decellularization, with a remarkable reduction in DNA concentration, signifying the removal of over 95% of cellular material. In the development of the in vitro bladder cancer model, muscle invasive bladder cancer patients' cells were cultured within decellularized pig bladders, yielding a three-dimensional cancer model. Optimal results were attained using an air-liquid interface technique, with cells injected directly into the scaffold at three distinct time points. Histological evaluations showcased characteristics resembling in vivo tumors derived from bladder cancer patients' cells. To demonstrate the 3D cancer model's effectiveness as a drug screening platform, the study treated it with Cisplatin (Cis), Gemcitabine (Gem), and a combination of both drugs. Comprehensive cell viability assays and histological analyses illustrated changes in cell survival and proliferation. The model exhibited promising correlations with clinical outcomes, boasting an 83.3% reliability rate in predicting treatment responses. Comparison with traditional 2D cultures and spheroids underscored the 3D model's superiority in reliability, with an 83.3% predictive capacity compared to 50% for spheroids and 33.3% for 2D culture. Acknowledging limitations, such as the absence of immune and stromal components, the study suggests avenues for future improvements. In conclusion, this innovative 3D bladder cancer model, combining decellularization and patient-derived cells, marks a significant advancement in preclinical drug testing. Its potential for predicting treatment outcomes and capturing patient-specific responses opens new avenues for personalized medicine in bladder cancer therapeutics. Future refinements and validations with larger patient cohorts hold promise for revolutionizing BCa research and treatment strategies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".