GD2 is a Crucial Ganglioside in the Signal Modulation and Application as a Target of Cancer Therapeutics
Bibliographic record
Abstract
While various glycosphingolipids were identified as cancer-associated carbohydrate antigens to be used as tumor markers, disialylated gangliosides such as GD3 and GD2 have particularly attracted attention from many researchers as promising cancer-associated antigens. Simultaneously, their functions in cancer and normal tissues have also been reported. Although GD3 is expressed at the early neural developmental stage and in various cancers, it is also found in the activated status of some normal cells such as astrocytes and lymphocytes. On the other hand, GD2 is expressed in more restricted cells than GD3, enabling anti-GD2 immune therapy to be more applicable for immunotherapy. Recently, the expression of GD2 has been reported in various epithelial cancers and neuroectoderm-derived tumors. The involvement of GD2 in cancer stem cell propertiesand the roles of GD2 in the signal modulation to bring about cancer stemness are now some of the most fascinating research topics. Cancer immunotherapy targeting GD2 by anti-GD2 antibody or anti-GD2 CAR-T is now widely being challenged with various modifications such as combination with cytokines, chemotherapy, or immune checkpoint blocking.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".