Impact of Sleep Deprivation on the Brain’s Inflammatory Response Triggered by Lipopolysaccharide and Its Consequences on Spatial Learning and Memory and Long-Term Potentiation in Male Rats
Bibliographic record
Abstract
INTRODUCTION: Both sleep deprivation (SD) and inflammation can negatively affect cognitive function. This study aimed to investigate how SD impacts the brain's inflammatory response to lipopolysaccharide (LPS) and its subsequent effects on cognitive functions. METHODS: To this end, male rats were tested through a Morris water maze (MWM) to assess their spatial learning and memory. Also, in vivo field potential recordings (to evaluate synaptic plasticity) were done in the Saline, SD, LPS1 (1 mg/kg/7 days), and LPS1+SD groups. Cytokine levels were measured using an enzyme-linked immunosorbent assay (ELISA). RESULTS: Based on the results, the LPS1+SD group showed increased total distance and escape latency compared to the other groups in the MWM test. Besides, the LPS1+SD group exhibited a significant decrease in long-term potentiation (LTP) induction and maintenance in the CA1 area of the brain. Finally, the inflammatory cytokine interleukin-1β (IL-1β) levels were significantly higher in the LPS1+SD group than in the Saline group. CONCLUSION: These findings suggest that the combined effects of SD and brain inflammatory response can have more harmful effects on cognitive function, LTP, and inflammatory factors than either SD or LPS1 alone.
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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) | 0.000 | 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 | 0.001 |
| 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".