Highly specific and reliable biomarker of human MM cells in NOD/SCID animal models through the cancer/testis antigen AKAP-4: new opportunities for pharmacological studies (165.17)
Bibliographic record
Abstract
Abstract Despite recent findings for the treatment of multiple myeloma (MM) it still remains a deadly disease. In the effort to develop new and more effective therapies for human tumors, murine animal models have been proven critical for a variety of neoplasias. Unfortunately, although recent studies have described different animal models of human MM, none is available for tracking the tumor progression with a specific biomarker employing human MM cells. This lack hampers the development of new approaches for treating MM. Here we describe a human MM model in NOD/SCID mice that allows for highly sensitive and specific monitoring of disease progression through the endogenously-expressed tumor specific cancer/testis antigen AKAP-4. The human MM cells U266 were subcutaneously injected in NOD/SCID animals. AKAP-4 expression resulted to be a reliable and sensitive antigen to monitor tumor growth and spread by different and independent techniques, namely flow-cytometry, E.L.I.S.A., Western blot and RT-PCR. The relevance of our findings stems from the suitability of this innovative model for a thorough pre-clinical evaluation of experimental pharmacological strategies for the treatment of human MM.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".