Supernatants of tumours treated with chemotherapy can alter tumour growth and development in vivo.
Bibliographic record
Abstract
BACKGROUND: Tumour-derived supernatants are comprised of bioactive substances that have the capacity to transform host systems rendering them more supportive of tumour growth. Certain chemotherapies are able to alter the make-up of these supernatants. MATERIALS AND METHODS: We explored the effects that vaccination with supernatants derived from tumours may have on tumour growth in a BALB/c model. RESULTS: A number of cytokines were detected in the supernatants capable of increasing B-cell lymphoma 2 (BCL2) protein expression in cancer cells; of note, significantly higher levels of granulocyte-macrophage colony stimulating factor (GM-CSF) were detected in chemotherapy-treated supernatants compared to controls. Vaccinating mice with supernatants from untreated tumours significantly impeded the growth of sub-cutaneous-implanted tumours. However, this anticancer effect was significantly diminished if the supernatants used were from cancer cells treated with gemcitabine. CONCLUSION: The study lends in vivo support to the idea that tumours produce bioactive components that can influence host biology and that certain chemotherapies can negate these.
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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) | 0.000 | 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".