Glucose Metabolism in Breast Cancer and its Implication in Cancer Therapy
Bibliographic record
Abstract
It is well known that malignant cells have accelerated glucose uptake and metabolism in order to maintain their fast proliferation rates. With the increased influx of glucose into cancer cells, glycolysis is facilitated through a coordinated regulation of metabolic enzymes and pyruvate consumption. Shiftting from mitochondrial oxidative phosphorylation to glycolysis and other pathways such as pentose phosphate pathway (PPP) and de novo fatty acid synthesis in the breast tumor provides not only energy but also the materials needed for cell proliferation. Glucose augmentation in tumor cells can be due to the elevated level of glucose transporter (GLUT) proteins, such as the over-expression of GLUT1 and expression of GLUT5 in breast cancers. Moreover, other factors such as hypoxia-inducible factor-1 (HIF-1), estrogen and growth factors are important modulators of glucose metabolism in the progression of breast carcinomas. Therapies targeting at the glycolytic pathway, fatty acid synthesis and GLUTs expression are currently being investigated. Restoring tumor cells to its normal glucose metabolic state would endow tumor specific and accessible treatment that targets glucose metabolism.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".