Deuterium MRS of early treatment‐induced changes in tumour lactate in vitro
Bibliographic record
Abstract
Elevated production of lactate is a key characteristic of aberrant tumour cell metabolism and can be non‐invasively measured as an early marker of tumour response using deuterium (2H) MRS. Following treatment, changes in the 2H‐labelled lactate signal could identify tumour cell death or impaired metabolic function, which precede morphological changes conventionally used to assess tumour response. In this work, the association between apoptotic cell death, extracellular lactate concentration, and early treatment‐induced changes in the 2H‐labelled lactate signal was established in an in vitro tumour model. Experiments were conducted at 7 T on acute myeloid leukaemia (AML) cells, which had been treated with 10 μg/mL of the chemotherapeutic agent cisplatin. At 24 and 48 h after cisplatin treatment the cells were supplied with 20 mM of [6,6′‐2H2]glucose and scanned over 2 h using a two‐dimensional 2H MR spectroscopic imaging sequence. The resulting signals from 2H‐labelled glucose, lactate, and water were quantified using a spectral fitting algorithm implemented on the Oxford Spectroscopy Analysis MATLAB toolbox. After scanning, the cells were processed for histological stains (terminal deoxynucleotidyl transferase UTP nick end labelling and haematoxylin and eosin) to assess apoptotic area fraction and cell morphology respectively, while a colorimetric assay was used to measure extracellular lactate concentrations in the supernatant. Significantly lower levels of 2H‐labelled lactate were observed in the 48 h treated cells compared with the untreated and 24 h treated cells, and these changes were significantly correlated with an increase in apoptotic fraction and a decrease in extracellular lactate. By establishing the biological processes associated with treatment‐induced changes in the 2H‐labelled lactate signal, these findings suggest that 2H MRS of lactate may be valuable in evaluating early tumour response.
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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".