Caffeine reduces motor performance and antioxidant enzyme capacity in the brain of female G93A mice, an animal model of amyotrophic lateral sclerosis (ALS)
Bibliographic record
Abstract
Oxidative stress is associated with several neurodegenerative diseases including Parkinson's (PD), Alzheimer's (AD) and ALS. Caffeine (CAF) consumption is protective in PD and AD, however findings from our lab suggest that CAF in G93A mice may not be protective, since CAF supplementation shortened life span in females by 6 days (4.4%) vs. control (CON) mice. We therefore investigated the effect of CAF on food intake (FI), body weight (BW), body condition (BC), motor performance (MP), ability to move (AM), clinical score (CS), and antioxidant enzyme protein content (MnSOD; catalase, CAT; glutathione reductase, GR) using Western blotting techniques in the brain of sixteen 108‐d old female G93A mice. Starting at age 40 d, mice were equally divided into CON or CAF. CAF was added to the food, equivalent to amounts found in 5 ‐ 10 cups of coffee/day (mg/g body weight). FI increased over time (P < 0.001); CAF consumed 29% more vs. CON (P = 0.001). BW increased over time (P < 0.001); CAF were 5% heavier vs. CON (P = 0.032). MP was non‐significantly lower in CAF vs. CON over time (NS), with a very strong trend at age 107 d (47%, P = 0.053). CAF reduced GR by 35% (P = 0.008), CAT by 32% (P = 0.022), and MnSOD by 18% (P = 0.062) vs. CON. BC, AM, and CS were not significant between groups. We conclude that although CAF increases FI and BW, motor performance is hindered and antioxidant enzyme capacity is reduced. (Supported by HHSF, NSERC, York U‐Health).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".