Multidrug resistance‐1 gene expression does not increase during tumor progression in the MGH‐OGS murine osteosarcoma tumor model
Bibliographic record
Abstract
In addition to its possible role in drug resistance, expression of the multidrug resistance-1 gene may also be associated with a more malignant phenotype and tumor progression. This study evaluated its expression during tumor progression in the MGH-OGS transplantable murine osteosarcoma tumor model. Three variables of tumor progression were analyzed: tumor size, local recurrence, and metastasis. With a highly sensitive reverse transcription-polymerase chain reaction method, mRNA levels of multidrug resistance-1 were compared in primary tumors of different sizes. In addition, the levels were compared in primary, locally recurrent, and metastatic tumors isolated from individual mice. No significant difference was found in the levels of expression with increasing primary tumor size. In addition, the levels in primary, locally recurrent, and metastatic tumors were not significantly different. Our results indicate that--at least in the MGH-OGS tumor model, which is analogous to the majority of spontaneously occurring human osteosarcomas in that it has low levels of multidrug resistance-1/P-glycoprotein and is sensitive to doxorubicin--there is no evidence of upregulation of multidrug resistance-1 expression during tumor progression.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".