A high-resolution system for metabolic imaging of cancer
Bibliographic record
Abstract
Alterations in metabolic signaling pathways allow tumors to achieve balance between oxygen supply and consumption via activation of several genes involved in adaptation to low oxygen conditions (hypoxia). Hypoxia stimulates glucose uptake, glycolysis and changes in mitochondrial respiration in tumors as a direct result of increased HIF-1 transcriptional activity. HIF-1α stabilization induces the expression of genes involved in cell survival, proliferation, angiogenesis and glucose transport (GLUTs) and the stimulation of genes involved in glycolytic energy production. Elevated anaerobic metabolism results in increased production of lactate as an alternate cellular energy source. Recent studies suggested that increased levels of lactate in tumors are associated with tumor progression, development of metastases, radioresistance and decrease of patient survival. Therefore, the ability to monitor products of altered metabolism in tumors would provide valuable information to improve cancer treatment. We have developed a novel high-resolution imaging system to assess the distribution of metabolites in tumors such as lactate and ATP. Initial tests demonstrate the capacity of this technique in providing quantitative measurements of metabolites with high spatial resolution. This technology offers several important advantages over other techniques including ease of translation to the clinic, microscopic spatial localization and quantitation of metabolite concentration. Another advantage on the use of this technique is the possibility of spatial co-registration of metabolites with other tumors biomarkers. The main focus of this approach is the development of novel technologies for imaging tumor metabolism. This will lead to a better understanding of the role of tumor metabolism in treatment outcome, leading to more rationale therapeutic protocols.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".