Science AMA Series: I’m Gang Zheng, Senior Scientist at the Princess Margaret Cancer Centre in Toronto, Canada. I fight cancer using light and nanoparticles built from porphyrins; the molecules responsible for green leaves and red blood! AMA!
Bibliographic record
Abstract
Hi Reddit! I’m Gang Zheng, Senior Scientist at the Princess Margaret Cancer Center in Toronto, Canada. Our lab focused on creating clinically usable nanotechnology to combat cancer. Inspired by how plants use porphyrins to do photosynthesis, our colourful porphyrins self-assemble into biodegradable nanoparticles called “porphysomes”, which target cancer. Once they’re there, the now-coloured tumours can absorb laser light, heating and killing the tumour, and sparing healthy cells. But wait there’s more! We’ve also shown that these nanoparticles can be designed to do all sorts of medical imaging and therapeutics. We’ve used porphysomes for MRI, PET, fluorescence, photoacoustic imaging, ultrasound, photodynamic therapy, and drug delivery, all with a nanoparticle that, unlike others, can be metabolized by the body. Some have called porphysomes the “One particle to rule them all”. Check out our Lab Website HERE Whether it’s about porphyrins, cancer imaging, phototherapy, nanomedicine, or exotic food I recently attempted, I’m here to answer your questions. I’ll be back at 1 pm EST (10 am PST, 6 pm UTC) to answer your questions, ask me 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.424 | 0.224 |
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".