HIGH CONTENT QUANTITATIVE FRAMEWORK FOR UNDERSTANDING MITOCHONDRIAL DYSFUNCTION IN HUMAN CELLS
Bibliographic record
Abstract
Cellular and systemic stress disrupt function and impact health. This multidisciplinary thesis investigates the effects of internal and external stress in two contexts, mitochondrial dysfunction in Huntington Disease (HD) and publication pressure experienced by Canadian researchers during the COVID-19 pandemic. Standardized tools for accurately quantifying mitochondrial morphology remain limited, despite recent interest in understanding the role of mitochondrial dysfunction in HD. We first developed a high content and semi-automated analysis system for assessing mitochondrial morphology in patient-derived human fibroblasts. We compared control and HD cells, where HD fibroblasts were highly fragmented, and carried out orthogonal assays to measure energy production and mitochondrial membrane potential, validating the morphological results. We next evaluated how mitochondrial morphology responds to various stressors, including DNA damage, mitochondrial toxins, and nutrient and metabolite deprivation. The difference in the morphological response between control and diseased cells provided mechanistic insight into mitochondrial dysfunction in HD. Shifting focus to another form of stress, we also measured academic pressure during the COVID-19 pandemic. We explored disparities in perceived publication pressure reported between various populations, such as gender and disability status, using a nationwide survey of Canadian researchers. This thesis offers a dual biological and academic perspective on stress in the context of adaptation and resilience. Our interdisciplinary approach provides novel insights into mitochondrial morphology in HD and contributes to ongoing discussions about sustainable research cultures.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".