A preliminary comparison of two different polyacrylamide hydrogel fabrication methods demonstrate differences in stiffness measurements and adhesion abilities of osteosarcoma cells
Bibliographic record
Abstract
The purpose of this research was to compare two different polyacrylamide hydrogel fabrication methods described in the literature and assess their use in mechanotransduction studies in osteosarcoma. Both methods employ succinimide chemistry to functionalize the hydrogel surface for cell response studies, one in the form of NHS, while the other in the form of Sulfo-SANPAH. Six hydrogels of two different stiffness were created for each method and were evaluated on their receptiveness to cell seeding and Young’s moduli with atomic force microscopy. Both hydrogel fabrication methods lack reproducibility as significant differences were observed in stiffness measurements between six hydrogels at both 0.5 kPa and 50 kPa stiffnesses. Despite the Sulfo- SANPAH method of preparation being more receptive to cell seeding and generating the expected effects on known mechanotransducers, it appeared to have larger variabilities in stiffness measurements for both 0.5 kPa and 50 kPa prepared hydrogels. Researchers may employ the Sulfo-SANPAH method to study the impact of ECM of varying stiffness on osteosarcoma cell mechanotransduction, but should remain cautious when interpreting results as a function of the expected stiffness. Studies should be accompanied by measurements of Young's modulus whenever possible.
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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 | 0.001 |
| 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".