Bibliographic record
Abstract
Abstract : Neoplastic meningitis is a fatal complication of breast cancer for which there is no cure. The project aimed to develop a novel, safe and efficient therapy for neoplastic meningitis, that of HSV-1 oncolysis. During the 1st year of the grant we prepared virus titers (1x107, 1x108 and 1x109 pfu/ml) for studying fractionated virus particles and developed stable breast cancer cell lines together with those that express bioluminescence (Rluc) and fluorescence (mCherry) markers for in vivo molecular imaging and tested their sensitivity to the HSV-1 oncolysis. These results were published in Cancer Research (PMC4147034; 2014). We selected MDA-MB-23-Rluc-mCherry to develop a mouse model of meningeal metastases during the 2nd year of the grant while waiting for the MGH animal facilities to be set up for housing. We characterized tumor growth in this model with sequential bioluminescence and MRI. Tumor growth occurred in 3 phases; a lag, exponential and plateau phase and was comparable with disease progression in humans. The model was presented at the annual meeting of the Society of nuclear medicine and molecular imaging (SNMMI) in Vancouver (June, 2013). The manuscript is ready for submission to Cancer Research. In the 3rd year we used this model to investigate the potential therapeutic effect of HSV-1 on meningeal metastases. When HSV-1 was injected at the early growth phase (day 12) we observed a significant reduction in tumor growth compared to the non-treated mice. Data gathered from this investigation was used to study virus distribution in the brain in the rat model. The therapeutic effect of HSV-1 oncolysis on meningeal metastases was presented (oral) at the annual meeting of the World Molecular Imaging Society (Korea, June 20114). The funding received by this award has been invaluable in terms of developing a model of neoplastic meningitis in mice and investigating the potential therapeutic effect of HSV-1 oncolysis on neoplastic meningitis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".