Correlations of blood and brain NMR metabolomics with Alzheimer’s disease mouse models
Bibliographic record
Abstract
Alzheimer's disease (AD) is a complex, progressive neurodegenerative disorder, impacting millions of geriatric patients globally. Unfortunately, AD can only be diagnosed post-mortem, through the analysis of autopsied brain tissue in human patients. This renders early detection and countering disease progression difficult. As AD progresses, the metabolomic profile of the brain and other organs can change. These alterations can be detected in peripheral systems (i.e., blood) such that biomarkers of the disease can be identified and monitored with minimal invasion. In this work, High-Resolution Magic Angle Spinning (HRMAS) Nuclear Magnetic Resonance (NMR) spectroscopy is used to correlate biochemical changes in mouse brain tissues, from the cortex and hippocampus, with blood plasma. Ten micrograms of each brain tissue and ten microliters of blood plasma were obtained from 5XFAD Tg AD mice models (n = 15, 8 female, 7 male) and female C57/BL6 wild-type mice (n = 8). Spectral regions-of-interest (ROI, n = 51) were identified, and 121 potential metabolites were assigned using the Human Metabolome Database and tabulated according to their trends (increase/decrease, false discovery rate significance). This work identified several metabolites that impact glucose oxidation (lactic acid, pyruvate, glucose-6-phosphate), allude to oxidative stress resulting in brain dysfunction (L-cysteine, galactitol, propionic acid), as well as those interacting with other neural pathways (taurine, dimethylamine). This work also suggests correlated metabolomic changes within blood plasma, proposing an avenue for biomarker detection, ideally leading to improved patient diagnosis and prognosis in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".