Transcriptomic and epigenetic analysis of various cell types affected by amyloid‐beta oligomers in the hippocampus of mice during aging
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder that affects memory and accounts for over 70% of dementia cases. It is characterized by the aggregation of amyloid-beta oligomers (Aßo) and neurofibrillary tangles. Aßo accumulate 10 to 15 years before the clinical onset of AD. Several studies have shown that Aßo contribute to synaptic loss and neuronal death in the hippocampus, a brain region crucial for memory and one of the first areas affected in AD. METHOD: In this project, Aßo were injected daily into the hippocampus of mice aged 6, 12, and 18 months for 5 days. The hippocampus was then harvested, and single-cell nuclei were isolated for simultaneous transcriptomic and epigenetic analysis (single nucleus RNA-seq + ATAC-seq; 10xGenomics). The analysis targeted neurons, astrocytes, oligodendrocytes, pericytes, endothelial cells, and microglia. RESULT: This study will identify gene expression changes and DNA accessibility sites induced specifically by Aßo in each cell type during aging in mice. CONCLUSION: A better understanding of Aß pathology could open new therapeutic avenues for preventing neurodegeneration before it causes permanent brain damage and severe memory loss, ultimately affecting cognitive health, autonomy, and quality of life in patients.
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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 | 0.000 | 0.000 |
| 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".