Optimisation and molecular signalling of apoptosis in sequential cryotherapy and chemotherapy combination in human A549 lung cancer xenografts in SCID mice
Bibliographic record
Abstract
We define the optimal parameters for combination of cryotherapy (nitrous oxide) with chemotherapy (vinorelbine ditartrate, VNB) treatment and characterise some of the signals involved for apoptosis activation. No advantage appeared when cryotherapy and VNB were combined simultaneously compared to cryosurgery alone. In contrast, tumour volumes were reduced after a sequential treatment schedule, where each individual treatment was separated by 48 h. No significant benefit appeared when the sequential treatment was separated by 24 h, although some individual mice showed a good response. The sequence of treatment had no impact on the observed tumour growth inhibition in mice. The number of apoptotic cells was significantly augmented in the sequential treatment schedule where VNB was administered 48 h before cryotherapy. In this sequential treatment, the number of apoptotic cells correlated with heightened expression of the BH3-only Puma, Noxa and Bim-EL, at both the mRNA and protein levels. No significant change in Bax, Bcl-xL and Bcl-2 mRNA expression was apparent, whereas Mcl-1 expression increased only slightly to a much lower level than BH3-only mRNAs. Our data indicate that 48 h sequential rather than simultaneous cryotherapy with VNB in future cancer cryochemotherapy schedules will enhance the tumour response, and argue that VNB administration, 48 h before cryotherapy, will provoke apoptosis more efficiently.
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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 | 0.000 |
| 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".