Bibliographic record
Abstract
Inhibitions or blockages of ligand-receptor interactions on cancer cell surfaces by exogenous competetors or antibodies often result in apoptosis or “programmed cell death.” The underlying mechanisms of action for cellular apoptosis depend greatly on the molecular nature of specific ligand-receptor interactions and the signal transduction pathways involved. Two such unrelated systems which are potentially involved in apoptosis of cancer cells are described in this review. They are, respectively, gonadotropinreleasing hormone (GnRH) receptor and cancerous immunoglobulins, or CA215, both of which are widely expressed on the surface of cancer cells from diversified tissue origins. Bindings of GnRH or its decapeptide analogs as ligands to GnRH receptor were known to induce apoptosis of several extrapituitary cell types in gonadal tissues, as well as different cancer cells. Monoclonal antibodies against the GnRH receptor of cancer cells were shown to induce apoptosis, similar to the action of GnRH analogs. In contrast, RP215 monoclonal antibody reacts specifically with the carbohydrate-associated epitope of cancerous immunoglobulins and is known to induce apoptosis of cancer cells in vitro. It also causes growth inhibition of tumor cells in nude mouse experimental models. Elucidations of the specific mechanisms of apoptosis in cancer cells of these two molecular interaction systems will not only lead to a better understanding of cancer biology but also benefit patients in cancer monitoring and therapy.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".