A combination of lycopene and human amniotic epithelial cells can ameliorate cognitive deficits and suppress neuroinflammatory signaling by choroid plexus in Alzheimer's disease rat
Bibliographic record
Abstract
Neuroinflammation characterized by glial activation and release of proinflammatory mediators is considered to be correlated with cognitive deficits in Alzheimer's disease (AD). Previously, some studies have demonstrated that lycopene (LYCO) or human amniotic epithelial cells (HAECs) could attenuate inflammation in AD. Specifically, the choroid plexus (CP), an epithelial layer that forms the blood-cerebrospinal fluid barrier, is able to modulate the cognitive function, through changes in the neuroinflammatory response and in brain immune surveillance. However, it is unclear if LYCO can interact with HAECs to improve neuroinflammation at the CP. Thus, this study chose the region of interest, considered the feasibility of using a combination of LYCO and HAECs, as a therapeutic agent for immunomodulatory effects at the CP in an acutely induced AD rat model. Results showed that oral administration of LYCO, HAECs transplantation, and their combination significantly improved cognitive deficits in water maze test, decreased the level of proinflammatory mediators (TNF-α and IL-1β), increased the level of anti-inflammatory mediators (IL-10 and TGF-β1) in the cerebro-spinal fluid, and hippocampal tissue. Interestingly, LYCO administration, HAECs transplantation and their combination reversed the Aβ1–42 induced up-regulation of Toll like receptor 4 and nuclear factor-κB p65 mRNA and protein expressions at the CP. This study provided the novel experimental evidence for the influence of co-treatment with LYCO and HAECs on immunomodulatory capabilities of CP. It could also warrant therapeutic window for the pathophysiology of AD and the associated underlying mechanisms at the CP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".