The effects of cetuximab and cisplatin anti-cancer drugs on the mechanical properties of the lung cancerous cells using atomic force microscope
Bibliographic record
Abstract
Each anti-cancer drug has special effects on the target cells. One of the most important reasons to recommend an anti-cancer drug is related to the influences of it on the mechanical properties of the target cells. In this study, the effects of cetuximab and cisplatin anti-cancer drugs on the mechanical properties of A-549 and Calu-6 cells as the cancerous lung cells have been investigated. For both of the cells and anti-cancer drugs, MTT assessment has been used to define the convenient dosages for 24 and 48 h incubations based on IC50 concentration for the cell line viability. The mechanical specifications of the cells before and after treatment were obtained using nanoindentation by the JPK Instruments' NanoWizard3 atomic force microscope. The results show that cetuximab increases the stiffness of A-549 cell from 1225 to 3403 and 12 690 Pa for 24 and 48 h incubations. The influence of cetuximab on the Calu-6 shows that the elastic modulus after 24 and 48 h culture times increases about cisplatin anti-cancer drug, for A-549 cell indicates that the elastic modulus rises from 1225 to 1506 and 2375 Pa for 24 and 48 h, respectively. For Calu-6 cell, cisplatin has an important role to increase the stiffness of the cell. Applying cisplatin increases the elastic modulus from 33 to 682.8 Pa for 24 h and 1105 Pa after 48 h incubations.
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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) | 0.002 | 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".