Devising a novel numerical tool to predict the physical occlusion site of circulating tumor cells traveling in an arbitrary microvasculature
Bibliographic record
Abstract
The hematogenous spread of metastasis, one of the main ways of metastasis progression, occurs when circulating tumor cells (CTCs) are transported in the bloodstream to various organs. Tracking CTCs trajectories in the microvasculature and predicting their entrapment locations can help diagnose metastasis at an early stage and offer new medical interventions. Advanced numerical methods capable of accounting for the high deformability attribute of CTCs and fluid flow in delicate microcapillaries put forward a promising technique to decipher CTCs' fate in microvasculatures. Therefore, in this study, we developed a novel numerical tool that has the potential to predict the CTCs' physical occlusion sites after being intravasated to an arbitrary microvasculature. This numerical tool communicates with a numerical database that is generated in this study and includes the information of cell fate in both confined microcapillaries and bifurcations. For generating the database, first, using previously validated cell models, we obtained an occlusion criterion by numerically investigating the relationship between the plasma flow rate, cell size, and capillary size for CTCs traveling in a microcapillary narrower than the CTC diameter. Second, we numerically studied CTCs' fate in bifurcated microcapillaries in various bifurcated geometries, fluid flow rates, and initial cell positions for both diverging and converging bifurcations. The predictive tool presented in this study is the first of its kind and it can potentially be further expanded to incorporate more sophisticated numerical CTC fate models considering other hematogenous mechanisms such as interactions between CTCs and blood cells.
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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".