Dietary magnesium and copper affect survival time and neuroinflammation in chronic wasting disease
Bibliographic record
Abstract
Chronic wasting disease (CWD), the only known wildlife prion disease, affects deer, elk and moose. The disease is an ongoing and expanding problem in both wild and captive North American cervid populations and is difficult to control in part due to the extreme environmental persistence of prions, which can transmit disease years after initial contamination. The role of exogenous factors in CWD transmission and progression is largely unexplored. In an effort to understand the influence of environmental and dietary constituents on CWD, we collected and analyzed water and soil samples from CWD-negative and positive captive cervid facilities, as well as from wild CWD-endozootic areas. Our analysis revealed that, when compared with CWD-positive sites, CWD-negative sites had a significantly higher concentration of magnesium, and a higher magnesium/copper (Mg/Cu) ratio in the water than that from CWD-positive sites. When cevidized transgenic mice were fed a custom diet devoid of Mg and Cu and drinking water with varied Mg/Cu ratios, we found that higher Mg/Cu ratio resulted in significantly longer survival times after intracerebral CWD inoculation. We also detected reduced levels of inflammatory cytokine gene expression in mice fed a modified diet with a higher Mg/Cu ratio compared to those on a standard rodent diet. These findings indicate a role for dietary Mg and Cu in CWD pathogenesis through modulating inflammation in the brain.
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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".