Subchronic metformin pretreatment enhances novel object recognition memory task in forebrain ischemia: behavioural, molecular, and electrophysiological studies
Bibliographic record
Abstract
Metformin exerts its effect via AMP-activated protein kinase (AMPK), which is a key sensor for energy homeostasis that regulates different intracellular pathways. Metformin attenuates oxidative stress and cognitive impairment. In our experiment, rats were divided into 8 groups; some were pretreated with metformin (Met, 200 mg/kg) and (or) the AMPK inhibitor Compound C (CC) for 14 days. On day 14, rats underwent transient forebrain global ischemia. Data indicated that pretreatment of ischemic rats with metformin reduced working memory deficits in a novel object recognition test compared to group with ischemia-reperfusion (I-R) (P < 0.01). Pretreatment of the I-R animals with metformin increased phosphorylated cyclic-AMP response element-binding protein (pCREB) and c-fos levels compared to the I-R group (P < 0.001 for both). The level of CREB and c-fos was significantly lower in ischemic rats pretreated with Met + CC compared to the Met + I-R group. Field excitatory postsynaptic potential (fEPSP) amplitude and slope was significantly lower in the I-R group compared to the sham operation group (P < 0.001). Data showed that fEPSP amplitude and slope was significantly higher in the Met + I-R group compared to the I-R group (P < 0.001). Treatment of ischemic animals with Met + CC increased fEPSP amplitude and slope compared to the Met + I-R group (P < 0.01). We unravelled new aspects of the protective role of AMPK activation by metformin, further emphasizing the potency of metformin pretreatment against cerebral ischemia.
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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 | 0.001 |
| 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 | 0.001 | 0.002 |
| 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".