Metabolic and Microbiota Profiles from Plasma and Fecal Samples of McGill-R-Thy1-APP Transgenic Rats Exposed to a High-Fat or Control Diet for 6 Months
Bibliographic record
Abstract
This work, part of Lorenzo Campanelli's doctoral thesis (former CONICET doctoral fellow supervised by Dr. Laura Morelli and Dr. Pablo Galeano), comprises the identification of core protein-metabolite networks associated with Alzheimer’s disease-like cerebral amyloidosis using a transgenic rat model. The study involved metabolomic and bacterial genotyping analyses, revealing networks related to immune responses, with CD36 serving as a key hub. The findings highlight the role of immune and metabolic pathways in AD pathology. Overall, it provides new insights into the molecular mechanisms connecting diet, microbiota, and neurodegeneration. The datasets comprise the script (01) for the pre-processing of metabolome and microbiome data; (02) for sPLS-DA analysis described in results (Figure 2), and (03) the dot plots of the protein networks (Figure 4 and 5) (1, 2 and 3 are contained in the folder “Scripts”). On the other hand, we uploaded tables that were input for sPLS-DA analysis (abundance and normalized metabolome and microbiome data, and group sample tables) (contained in the folder “Tables”).
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) | 0.000 | 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) | 0.003 | 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".