A bioengineering method for modeling alveolar Rhabdomyosarcoma and assessing chemotherapy responses
Bibliographic record
Abstract
Rhabdomyosarcoma (RMS) is the most common pediatric soft-tissue malignant tumor. Treatment of RMS usually includes primary tumor resection along with systemic chemotherapy. Two-dimensional (2D) cell culture systems and animal models have been extensively used for investigating the potential efficacy of new RMS treatments. However, RMS cells behave differently in 2D culture than in vivo, which has recently inspired the adoption of three-dimensional (3D) culture environments. In the current paper, we will describe the detailed methodology we have developed for fabricating a 3D engineered model to study alveolar RMS (ARMS) in vitro. This model consists of a thermally cross-linked collagen disk laden with RMS cells that mimics the structural and bio-chemical aspects of the tumor extracellular matrix (ECM). This process is highly reproducible and produces a 3D engineered model that can be used to analyze the cytotoxicity and autophagy induction of drugs on ARMS cells. The most improtant bullet points are as following:•We fabricated 3D model of ARMS.•The current ARMS 3D model can be used for screening of chemotherapy drugs.•We developed methods to detect apoptosis and autophagy in ARMS 3D model to detect the mechansims of chemotherapy agents.
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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 | 0.001 | 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".