Assessment of the sensitivity of <sup>2</sup>H MR spectroscopy measurements of [2,3‐<sup>2</sup>H<sub>2</sub>]fumarate metabolism for detecting tumor cell death
Bibliographic record
Abstract
Abstract Imaging the metabolism of [2,3‐2H2]fumarate to produce malate can be used to detect tumor cell death post‐treatment. Here, we assess the sensitivity of the technique for detecting cell death by lowering the concentration of injected [2,3‐2H2]fumarate and by varying the extent of tumor cell death through changes in drug concentration. Mice were implanted subcutaneously with human triple negative breast cancer cells (MDA‐MB‐231) and injected with 0.1, 0.3, and 0.5 g/kg [2,3‐2H2]fumarate before and after treatment with a multivalent TRAlL‐R2 agonist (MEDI3039) at 0.1, 0.4, and 0.8 mg/kg. Tumor conversion of [2,3‐2H2]fumarate to [2,3‐2H2]malate was assessed from a series of 13 spatially localized 2H MR spectra acquired over 65 min using a pulse‐acquire sequence with a 2‐ms BIR4 adiabatic excitation pulse. Tumors were then excised and stained for histopathological markers of cell death: cleaved caspase 3 (CC3) and DNA damage (terminal deoxynucleotidyl transferase dUTP nick end labeling [TUNEL]). The rate of malate production and the malate/fumarate ratio plateaued at tumor fumarate concentrations of 2 mM, which were obtained with injected [2,3‐2H2]fumarate concentrations of 0.3 g/kg and above. Tumor malate concentration and the malate/fumarate ratio increased linearly with the extent of cell death determined histologically. At an injected [2,3‐2H2]fumarate concentration of 0.3 g/kg, 20% CC3 staining corresponded to a malate concentration of 0.62 mM and a malate/fumarate ratio of 0.21. Extrapolation indicated that there would be no detectable malate at 0% CC3 staining. The use of low and nontoxic fumarate concentrations and the production of [2,3‐2H2]malate at concentrations that are within the range that can be detected clinically suggest this technique could translate to the clinic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".