The Mechanism of Memory Impairment Induced by Aβ Chronic Administration Involves Imbalance between Cytokines and Neurotrophins in the Rat Hippocampus
Bibliographic record
Abstract
It has been demonstrated that the onset and progression of Alzheimer's disease (AD) are associated with inflammatory disorders in the brain. Although the interactions of inflammatory cytokines with neurotrophins have been reported in vitro, the balance change between inflammatory cytokines and neurotrophic factors (NTFs), such as nerve growth factor (NGF), brain derived neurotrophic factor (BDNF), and glial cell line-derived neurotrophic factor (GDNF), due to amyloid β (Aβ) chronic administration in vivo is still unclear. The hypothesis of the present study was that the accumulation of Aβ activated glial cells to produce inflammatory mediators and NTFs to maintain the neurons survival, however the failure of crosstalk between NTFs and inflammatory cytokines might occur in the brain and the NTFs expressions would decrease after Aβ chronic treatment, which, therefore, would contribute to the neuronal death and memory impairments. Thus, the present study measured the learning and memory behavior, glial cells activities, cytokines (IL-1α, IL-1β and TNF-α) concentrations and NTFs (NGF, BDNF and GDNF) gene and protein levels in rats after i.c.v injection of Aβ(25-35) for 14 days. The results showed that Aβ (25-35)-treated animals exhibited failure of balance between inflammatory cytokines and NTFs system (increased cytokines levels, decreased NGF protein expression and reduced NTFs gene transcriptions), which might contribute to the cognitive impairments. The findings from this study provide valuable evidence that correct regulation of the crosstalk between inflammatory cytokines and NTFs could be a direction for AD therapy in the future.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".