Bibliographic record
Abstract
Abstract Today's targeted therapeutics exert their anti-cancer effects mainly by blocking molecular signals that promote tumor cell proliferation, obstruct cell death, hamper cellular differentiation, or facilitate angiogenesis. However, the molecular pathways that underlie these cellular processes are multifaceted and often redundant, allowing tumor cells to escape these drugs. Another approach may be to target tumor cell metabolism. In the 1920s, Warburg noted that cancers exhibit increased rates of glycolysis even in the presence of oxygen, a phenomenon known as aerobic glycolysis or the ‘Warburg effect’. The switch from oxidative phosphorylation to glycolysis that underlies the Warburg effeect represents an alteration to tumor cell metabolism that permits cancer cells to adapt and thrive in conditions that kill normal cells Such adaptations may include upregulated or altered molecular pathways to increase energy production, expansion of the cell's biosynthetic capacity, and/or adoption of an enhanced redox state to cope with elevated levels of deleterious reactive oxygen species. Understanding the energy sources and metabolic pathways utilized by cancer cells to survive should allow the exploitation of this knowledge to design more effective anti-cancer drugs. Citation Format: Tak W. Mak. Metabolic regulation of tumor cells survival [abstract]. In: Proceedings of the AACR 101st Annual Meeting 2010; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr SY31-04
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".