<i>In vivo</i>measurement of the hypoxia marker EF5 in Shionogi tumours using<sup>19</sup>F magnetic resonance spectroscopy
Bibliographic record
Abstract
PURPOSE: (19)F magnetic resonance spectroscopy (MRS) was used to non-invasively detect EF5 [2-(2-nitro-1H-imidazol-1-yl)-N-(2,2,3,3,3-pentafluoropropyl) acetamide] adducts in the Shionogi tumour model of prostate cancer to evaluate hypoxia. MATERIAL AND METHODS: (19)F MRS signal of EF5 in Shionogi mouse tumours was acquired using a 2 cm diameter solenoid volume coil with a 7.05 T Bruker scanner. MRS signal was observed in mouse tumours longitudinally following intraperitoneal (IP) injection of EF5. Another mouse group was injected intravenously (IV) with EF5, and in vivo MRS signal was obtained two hours after injection. This data was compared with the ex vivo percentage of hypoxic cells present in the corresponding excised tumours, determined by flow cytometry of bound EF5. RESULTS: Longitudinal (19)F MRS signal attributable to EF5 began to decline within five hours of EF5 administration. Flow cytometry comparisons yielded an inverse correlation (p-value < 0.006) between the MRS signal and tumour hypoxic cell percentage. The tumours exhibited an average cell viability of 34 +/- 26%. CONCLUSIONS: The results confirmed that MRS of EF5 in mice is an unsuitable technique for the determination of EF5 binding as a measure of tumour hypoxia.
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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) | 0.001 | 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".