P1‐153: Identification of Novel Biomarkers Involved in the Development of Alzheimer's Disease
Bibliographic record
Abstract
The human brain has a high energy demand for the maintenance of membrane potential, synaptic transmission, production and recycling of neurotransmitters. Because of this demand, energy metabolism is highly regulated in the brain, relying on both astrocytes and neurons. Changes in brain metabolism occur throughout the aging process in healthy brains and in neurodegenerative diseases including Alzheimer’s disease (AD). Some metabolic changes may be beneficial adaptations, while others may contribute to neuronal dysfunction. To better understand the role energy metabolism plays in the development of AD, we generated a novel model of AD using 5XFAD mice with altered brain energy metabolism. Brains from mice were harvested at two week intervals after birth, for up to 8 weeks, then prepared for analysis for both metabolites and signalling pathways associated with mitochondria function. The temporal metabolic profile of brains harvested from 5XFAD mice was significantly different from the metabolic profile of brains harvested from control mice. Expression of metabolic enzymes was reflective of metabolic changes. Signalling pathways associated with mitochondria function were altered in brains of 5XFAD mice compared with control mice. Temporal alterations in brain energy metabolism were found in 5XFAD mice compared to control mice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".