Coffee increases antioxidant enzyme capacity in the brain of male G93A mice, an animal model of amyotrophic lateral sclerosis (ALS)
Bibliographic record
Abstract
Oxidative stress is implicated in several neurodegenerative diseases including ALS. Coffee (COF) consumption increases antioxidant status and reduces oxidative stress. To determine whether this effect is due to its single or combined constituents, we studied the effect of COF, caffeine (CAF) and chlorogenic acid (CHLA) 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; CAT; GR) in the brain of 108‐d old male G93A mice. Starting at age 40‐d, 21 mice were randomly divided into control (CON, 6), COF (5), CAF (5) or CHLA (5). Compounds were added to the food, equivalent to amounts found in 5‐10 cups of coffee/day (mg/g BW). FI increased over time (P < 0.001); all groups consumed more than CON (COF, 21%, P = 0.014; CAF, 22%, P = 0.018; CHLA, 12%, P = 0.086). BW increased and BC decreased over time (P < 0.001) with no differences between groups. MP increased over time (P < 0.001) and was higher in COF vs. all groups (CON, 6%; CAF, 27%; CHLA, 40%; NS). CAT was higher in COF vs. all groups (CON, 61%, P = 0.002; CAF, 25%, P = 0.028; CHLA, 22%, P = 0.023). There were no significant differences in CS, AM, MnSOD and GR. We conclude that COF, independent of CAF or CHLA, is protective in male G93A mice by improving motor performance and increasing antioxidant enzyme capacity. Grant Funding Source 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".