Bibliographic record
Abstract
My research focuses on understanding the biological processes that control stem cell identity and how they impact brain development. I will present my project which involves directing the differentiation of genetically engineered human pluripotent stem cells (hPSCs) into 3-dimensional brain tissues called ‘organoids’. With the hPSCs, we are able to study the early stages of brain development using human cells. These engineered stem cells carry a mutation that causes epilepsy and autism. The effects of this mutation, we believe, are present during earlier stages of brain development than what has been previously appreciated. The goal of my research is to understand how this mutation affects the earliest stages of human brain development.In my presentation I will demonstrate how, in our laboratory, we differentiate hPSCs into the mix of cell types that make up the brain. Specifically, I am measuring the amount of brain stem cells and neurons that are generated in order to understand how the disease mutation affects this balance. I will discuss how we culture stem cells in a sterile environment, and how we turn them into brain tissue using defined media formulations and engineered extracellular matrices. To analyze these brain organoids, I stain them with fluorescent antibodies that detect proteins that identify stem cells and neurons, and then image them using cutting-edge microscopy techniques. These ground- breaking technologies have been developed by current leaders in the stem cell field, many of whom work in Canada and the Vancouver region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".