Metabolic network changes that are strongly associated with Dementia Lewy Body determined through signed distance and partial correlation analysis
Bibliographic record
Abstract
INTRODUCTION Determining significant metabolic changes in Dementia with Lewy Body (DLB), a complex and multifactorial neurodegenerative disease, requires, in addition to the analysis of concentration changes, a deep understanding of functional changes in the context of metabolic networks. METHODS Brain metabolomics data from DLB patients and controls was analysed using novel approaches to determine metabolites with the largest changes in their network in DLB. Signed distance correlation (SiDCo) method is provided at: http://complimet.ca/SiDCo RESULTS Novel clustering and correlation network analysis shows major change in the metabolic network in DLB brain relative to matching controls with the largest interaction network alterations for fructose, propylene-glycol, pantothenate and O-acetylcarnitine in spite of no statistically significant change in their concentrations. DISCUSSION Network and correlation analyses indicate major changes in the purine degradation pathway, propanoate and -alanine metabolism as well as an increased role of fructose and reduced significance of glucose in DLB affected brain.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".