<i>Retracted:</i> Mechanisms and effects of curcumin on spatial learning and memory improvement in APPswe/PS1dE9 mice
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Evidence suggests that curcumin, the phytochemical agent in the spice turmeric, might be a potential therapy for Alzheimer's disease (AD). Its antioxidant, anti-inflammatory properties have been investigated extensively. Studies have also shown that curcumin can reduce amyloid pathology in AD. The underlying mechanism, however, is complex and is still being explored. In this study, we used the APPswe/PS1dE9 double transgenic mice, an AD model, to investigate the effects and mechanisms of curcumin in the prevention and treatment of AD. The water maze test indicated that curcumin can improve spatial learning and memory ability in mice. Immunohistochemical staining and Western blot analysis were used to test major proteins in β-amyloid aggregation, β-amyloid production, and β-amyloid clearance. Data showed that, 3 months after administration, curcumin treatment reduced Aβ40 , Aβ42 , and aggregation of Aβ-derived diffusible ligands in the mouse hippocampal CA1 area; reduced the expression of the γ-secretase component presenilin-2; and increased the expression of β-amyloid-degrading enzymes, including insulin-degrading enzymes and neprilysin. This evidence suggests that curcumin, as a potential AD therapeutic method, can reduce β-amyloid pathological aggregation, possibly through mechanisms that prevent its production by inhibiting presenilin-2 and/or by accelerating its clearance by increasing degrading enzymes such as insulin-degrading enzyme and neprilysin.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".