Ana Cristina Andreazza: Driven by curiosity – transforming mental health through mitochondrial innovation
Bibliographic record
Abstract
Dr. Ana Cristina Andreazza is a Professor of Pharmacology Toxicology and Psychiatry at the University of Toronto, holding the Thomas C. Zachos Chair in Mitochondrial Research and a Tier II Canada Research Chair in Molecular Pharmacology of Mood Disorders. As the visionary Founder and Scientific Director of the Mitochondrial Innovation Initiative (Mito2i), she leads pioneering research on the role of mitochondrial dysfunction in neurological and psychiatric diseases, organ transplants, and novel therapeutic strategies. Her groundbreaking work has revolutionized our understanding of the relationship between mitochondrial function and mental health disorders, particularly bipolar disorder. Dr. Andreazza's career was inspired by early curiosity, family influence, and a commitment to reduce the stigma surrounding metabolic and psychiatric conditions. Her innovative work bridges multiple disciplines, aiming to discover biomarkers that could enable personalized treatments in mental health. A recipient of numerous prestigious awards, including membership in the Royal Society of Canada College of New Scholars, Dr. Andreazza has published over 200 peer-reviewed papers and is internationally recognized for her contributions to metabolic psychiatry. In this Genomic Press Interview, she shares insights into her remarkable journey from studying wine chemistry in Brazil to becoming a leading force in mitochondrial research while discussing her perspectives on collaborative science and innovation. Driven by a passion for teaching and collaborative science, Dr. Andreazza continues to foster innovation and mentorship in the mitochondrial research community.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".