Science AMA Series: We’re scientists and doctors researching nano medicine, Ask Us Anything!
Bibliographic record
Abstract
Hi Reddit we are scientists from Toronto/Boston working on improving the use of nanomedicine in the clinic. If you’re curious about our list of credentials: Shawn Stapleton PhD, Research Fellow at Harvard Medical School/Massachusetts General Hospital, who’s currently looking to transition into faculty. https://www.researchgate.net/profile/Shawn_Stapleton https://www.linkedin.com/in/staplet David Jaffray PhD, Senior Scientist and Director of the TECHNA Institute, University Health Network and University of Toronto. https://www.uhnresearch.ca/researcher/david-jaffray http://technainstitute.com/people/david-jaffray/ Michael Milosevic MD, Clinician and Scientist, University of Toronto and Princess Margaret Cancer Center. https://www.uhnresearch.ca/researcher/michael-f-milosevic http://www.radonc.utoronto.ca/content/michael-milosevic Our collaborative research focuses on using imaging, mathematical modeling and physiological/molecular measurements of the tumor microenvironment to understand where nanomedicines end up in a tumour. We are using this knownledge to (1) develope strategies to improve nanomedicine drug delivery to tumours; and (2) develop new clinically relevant imaging methods to help guide drug delivery in patients. Ultimately we’d like to be able to use imaging methods like CT, MRI, or PET to bring drug delivery to the same level of precision achieved with radiation therapy and surgery. We’ve recently published a review describing how radiation can be used to improve nanomedicine drug delivery to tumors, leading to improved tumor response. The manuscript, titled “Radiation effects on the tumor microenvironment: Implications for nanomedicine delivery. ”, can be found in Advanced Drug Delivery Reviews . Check it out! http://www.sciencedirect.com/science/article/pii/S0169409X16301818 This is exciting area of research that will allow us to use clinical methods, such as radiotherapy, to guide where nanoparticles go in the tumor AND increase local drug concentrations without increasing toxicity. We are here to answer your questions about drug delivery, nanomedicine, imaging, radiotherapy, oncology, the pains/pleasures of research, transitioning to/making it in academia, why Toronto is an exciting for biomedical research, and more! Ask US Anything!
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.422 | 0.370 |
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".