Probing arsenic trioxide (ATO) treated leukemia cell elasticities using atomic force microscopy
Bibliographic record
Abstract
Conventional analytical techniques allow for the diagnosis of leukemia, blood and bone marrow cancers, as well as their classification into the different subtypes. However, a better understanding of the cancer treatment through cell apoptosis staging is still required. Evaluation of the timeline and responses of acute promyelocytic leukemia (APL) cells to the arsenic trioxide (ATO) treatment is essential for determining the oral dosage in leukemia prognosis. Here, an Atomic Force Microscopy (AFM) indentation approach has been used to evaluate the mechanical responses of cellular responses of APL cells to ATO treatment, alongside well-established cell viability assays, as a novel method to determine the impact of drugs. In addition, cell morphology was quantified to monitor cellular apoptosis. Viability, morphology and elasticity changes of NB4 cells (derived from Human APL patients) were correlated to different time courses of the ATO treatments. Unveiling the relationships among structural, morphological and nanomechanical properties in response to ATO drug treatment promises to pave the way for novel diagnostic tools for drug screening and for a better understanding of the specific physical and biological effects of drugs on diseased cells.
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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".