<i>In vitro</i> detection of apoptosis using oscillating and pulsed gradient diffusion magnetic resonance imaging
Bibliographic record
Abstract
Cellular apoptosis, a common pathway towards tumor regression, is induced by many radiotherapy and chemotherapy regimens. Imaging methods that can detect apoptosis may be able to assess treatment response earlier than typical tumor volume measurements. In this paper, a wide range of diffusion experiments and a simple model of diffusion in tissues were used to probe the microstructural effects of apoptosis. Experiments were conducted on acute myeloid leukemia cell pellets, where apoptosis was induced by treatment with the chemotherapeutic agent cisplatin. Seventy-two hours following cisplatin treatment, pulsed and oscillating gradient diffusion measurements were utilized to assess effects across a broad range of structural scales. The presence of apoptosis, which was histologically confirmed by TUNEL (terminal deoxynucleotidyl transferase UTP nick end labelling) staining, significantly changed diffusion properties. To describe these changes, the data were fit to the parallel plane model, which characterizes the effects of restricted diffusion in terms of three parameters: d, the restricted size, Dfree , the intrinsic, free diffusion coefficient of the solvent, and Drest , the long time or "restricted" diffusion coefficient. Apoptotic samples exhibited significant decreases in parameters d and Dfree and a significant increase in Drest . These changes appear consistent with the established morphological effects of apoptosis. In particular, the decrease in d may be a result of the combined effects of cell shrinkage, nuclear fragmentation and membrane blebbing, the decrease in Dfree may relate to cytosolic condensation, while the increase in Drest can be attributed to increased membrane permeability and extracellular volume fraction. By non-invasively detecting apoptosis, the methods reported in this study have the potential to improve upon current MRI methods for monitoring therapeutic response. Furthermore, these methods may offer sufficient specificity to differentiate between apoptosis and other modes of cell death, such as oncosis or necrosis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".