Mapping key mitochondrial genes in Alzheimer’s disease through human tissue and iPSC derived neurons
Bibliographic record
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative condition that has become a global health challenge due to an aging world population and no available effective treatment. Mitochondrial dysfunction plays a crucial role in the development of AD due to its critical role in neuronal survival and function. However, the specific mitochondrial genes and pathways involved in AD pathogenesis remain poorly defined. In this study, we incorporated seven AD human postmortem and three AD iPSC-derived neurons (iNs) gene expression datasets to identify mitochondria-related Differentially Expressed Genes (mitoDEGs) between AD and control. The Gene Ontology (GO) analysis is conducted to investigate the AD biological mechanisms, and a random forest model is developed to assess how well the key mitoDEGs differentiate AD and control groups. Through our analysis, we identified fourteen key mitochondria related genes that show significant dysregulation in both postmortem brain tissues and iNs derived from AD patients. These genes have strong connections to oxidative stress, indicating mitochondrial dysfunction plays a crucial role in Alzheimer's disease pathology. Our study identified the key genes and pathways as promising targets for future research and therapeutic interventions, highlighting the importance of mitigating oxidative stress and restoring mitochondrial function in AD.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".