P.040 Moncton brain tumour tissue biorepository: diagnostic and therapeutic initiatives for glioma research in New Brunswick
Bibliographic record
Abstract
Background: Improving diagnostic and therapeutic tools associated with glioblastoma multiforme (GBM), an aggressive brain tumour, is crucial as average patient survival remains slightly over a year. Challenges include early diagnosis and acquired drug resistance. Improving these challenges notably require a multidisciplinary team and a dedicated brain tumour specimen collection initiative. We hypothesize that implementing such an approach in Moncton would provide significant benefits to GBM patients and researchers in New Brunswick. Methods: A Brain Tumour Tissue Repository was instigated to collect and preserve primary tumour specimens. Storage of circulating samples from patients undergoing temozolomide (TMZ) therapy was also performed. In parallel, molecular leads were investigated in different GBM models to identify therapeutic targets. Results: Collection of 7 primary specimens was accomplished in 2016. Over 15 primary samples are housed in the tumour biorepository to date with circulating samples collected from 3 patients. Additionally, numerous deregulated non-coding RNAs were identified by qRT-PCR in GBM models and shown to be modulated following TMZ treatment warranting further investigation. Conclusions: Overall, these results provide novel therapeutic leads for GBMs and, most importantly, highlight the instigation of a New Brunswick-based brain tumor biorepository which will undoubtedly strengthen brain tumour research in the Maritimes.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".