Evaluating a Cellular Microstructure Model Within Apoptotic Cell Death via Diffusion Magnetic Resonance and Long Diffusion Times
Bibliographic record
Abstract
ABSTRACT The growing trend for personalized cancer treatment needs a complementary system to detect treatment response more rapidly and robustly. Inducing apoptosis is still a common treatment target as the immune system can clear the apoptotic cancer cells. Many studies have demonstrated the potential of diffusion‐based MRI techniques for detecting apoptotic changes in tumors by correlating the changes of cellularity. Here we used diffusion data with long diffusion times to elucidate water exchange rates in acute myeloid leukemia cells (AML) which were acquired using stimulated echo acquisition mode (STEAM) in diffusion imaging. The two‐pool exchange model was fitted where the key parameters of interest were the intracellular fraction, , intracellular exchange rate, , and cell radius, r. Apoptosis was induced with cisplatin and significant differences were found for each of the following three parameters: decreased by ~53%, increased by ~61%, and r decreased by ~15%. These results highlight the potential of longer diffusion times to monitor cancer treatments that induce apoptosis.
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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) | 0.000 | 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 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".