Intracellular galectins in cancer cells: Potential new targets for therapy
Bibliographic record
Abstract
Dysregulation of galectin expression is frequently observed in cancer tissues. Such an abnormal expression pattern often correlates with aggressiveness and relapse in many types of cancer. Because galectins have the ability to modulate functions that are important for cell survival, migration and metastasis, they also represent attractive targets for cancer therapy. This has been well-exploited for extracellular galectins, which bind glycoconjugates expressed on the surface of cancer cells. Although the existence of intracellular functions of galectins has been known for many years, an increasing number of studies indicate that these proteins can also alter tumor progression through their interaction with intracellular ligands. In fact, in some instances, the interactions of galectins with their intracellular ligands seem to occur independently of their carbohydrate recognition domain. Such findings call for a change in the basic assumptions, or paradigms, concerning the activity of galectins in cancer and may force us to revisit our strategies to develop galectin antagonists for the treatment of cancer.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".