Non-radioactive 2-deoxy-2-fluoro-D-glucose inhibits glucose uptake in xenograft tumours and sensitizes HeLa cells to doxorubicin in vitro
Bibliographic record
Abstract
A glucose analog called 2-deoxy-D-glucose (2DG) has been successfully used to sensitize cancer cells to ROS-inducing cancer treatments such as ionizing radiation, through the inhibition of glycolysis. However, the use of 2DG can be limited by several factors such as availability, non-specific cytotoxicity, and chemoresistance under hypoxic conditions. The purpose of this study was to investigate the use of non-radioactive 2-deoxy-2-fluoro-D-glucose (19FDG), a drug that potentially addresses current limitations of 2DG. The effectiveness of using either 2DG or 19FDG in combination with doxorubicin (Dox) in HeLa cells was determined in both normoxia and hypoxia. We have also shown that under both oxygen conditions, 19FDG-treated cells produce less lactate than 2DG-treated cells, an important finding that suggests improved inhibition of glycolysis, the preferential pathway for cancerous cells. When used in combination with Dox, we have demonstrated a significant decrease in the number of viable cells, with the effect of 19FDG remaining stable across both normoxic and hypoxic conditions. Moreover, the assessment of apoptosis and necrosis revealed that 19FDG maintained its ability to sensitize HeLa cells to Dox in hypoxia, but 2DG was only effective under normoxic conditions. The retained effectiveness of 19FDG in combination with Dox under hypoxic conditions, suggests that 19FDG may be efficacious for sensitizing hypoxic regions of solid tumour masses. Importantly, the ability of 19FDG to inhibit glucose uptake in vivo was also confirmed using positron emission tomography (PET) of xenograft tumours. The results displayed here suggest 19FDG is a promising combination therapy, which may lead to decreased ROS scavenging via glycolysis, and enhanced treatment success.
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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".