Uniformity of spheroid-on-chip by surface treatment of PDMS microfluidic platforms
Bibliographic record
Abstract
Abstract Spheroids have emerged as a more reliable model for drug screening when compared with 2D culture models. Microfluidic based biochips have many advantages over other 3D cell culture models for drug testing on spheroids, including precise control of the cellular microenvironment. The control of the cell adhesion to the surface is one of the most important challenges affecting the size and the geometry of the spheroids which could be controlled by appropriate surface engineering methods. We have studied the modification of the PDMS surface properties treated by applying different concentrations of the two anti-fouling coatings (BSA and Pluronic F-68). The desired treatment of PDMS surface effectively inhibits cell adhesion to the surface and promotes cells self-aggregations to form more uniform and healthy spheroids for a longer period of time. The microscopic observations with qualitative and quantitate data revealed that surface properties drastically affect the number of the spheroids formed on-chip and their geometry. We used human breast cancer cell line (MDA-MB-231-GFP) while the concentration of the chemical coatings and incubation time were adjusted. Proper repellent PDMS surfaces were provided with minimum cell attachment and facilitated spheroid formation when compared with non-treated PDMS. The results demonstrate fundamental and helpful patterns for microfluidic based cell culture applications to improve the quantity and quality of spheroid formation on-chip which are strongly manipulated by surface properties (i.e., morphology, roughness, wettability and etc.)
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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 | 0.000 | 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".