Robust formation of optimal single spheroids towards cost‐effective <i>in vitro</i> three‐dimensional tumor models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While useful for fundamental in vitro studies, monolayer cell cultures are not physiologically relevant. Spheroids, a complex three‐dimensional (3D) structure, more closely resemble in vivo tumor growth. Spheroids allow the results obtained relating to proliferation, cell death, differentiation, metabolism, and various antitumor therapies to be more predictive of in vivo outcomes. In the protocol herein, a rapid and high‐throughput method is discussed for the generation of single spheroids using various cancer cell lines, including brain cancer cells (U87 MG, SEBTA‐027, SF188), prostate cancer cells (DU‐145, TRAMP‐C1), and breast cancer cells (BT‐549, Py230) in 96‐round bottom‐well plates. The proposed method is associated with significantly low costs per plate without requiring refining or transferring. Homogeneous compact spheroid morphology was evidenced as early as 1 day after following this protocol. Proliferating cells were traced in the rim, while dead cells were found to be located inside the core region of the spheroid using confocal microscopy and the Incucyte ® live imaging system. H&E staining of spheroid sections was utilized to investigate the tightness of the cell packaging. Through western blotting analyses, it was revealed that a stem cell‐like phenotype was adopted by these spheroids. This method was also used to obtain the EC50 of the anticancer dipeptide carnosine on U87 MG 3D culture. This affordable, easy‐to‐follow five‐step protocol allows for the robust generation of various uniform spheroids with 3D morphology characteristics.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it