Toto-Cell: A new software to analyze cellular events during video-microscopy
Bibliographic record
Abstract
Video-microscopy is a technology widely used to follow, in a single cell manner, cell behavior. A number of new studies are searching a way to track these behaviors by artificial intelligence; unfortunately some real-time events still have to be track manually. For that reason, we developed a software that helps the experimenter to analyze collected data. Toto-cell is very simple to use and it can be adapted at different type of analyses or treatments. It allows a wide new range of parameters that were nearly impossible to calculate only by hand. We thus developed this new software using HEC-1-A endometrial cell line to track different cellular parameters such as: the number of normal/abnormal mitosis, the ratio per day of death, mitosis, cell fusions or finally the length between two mitosis cycles. We treated our cells with cisplatin, doxorubicin or AZD5363 (an Akt inhibitor) to obtain different cellular events. What emerged is a huge heterogeneity for these analyzed parameters between the cells in a single treatment which is clearly demonstrated by the results provided by Toto-Cell. In conclusion, our software is an important tool to facilitate the analysis of video-microscopy, in a quantifying and qualifying manner. It enables a higher accuracy when compared to manual calculations.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".