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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".