Background Retinal biomarkers of Alzheimer’s disease (AD) have been extensively investigated in recent decades. Retinal nervous and vascular parameters can reflect brain conditions, and they can facilitate early diagnosis of AD. Objective Our study aimed to evaluate the difference in retinal neuro-layer thickness and vascular parameters of patients with AD and healthy controls (HCs). Methods Non-invasive optical coherence tomography angiography (OCTA) was used to determine the combined thickness of the retinal nerve fiber layer (RNFL) and ganglion cell layer (GCL), as well as the full retinal thickness (FRT). The vascular branching (VB), vascular curvature (VC), and vascular density (VD) for AD and HC groups were also obtained. The Mini-Mental State Examination (MMSE) was used to evaluate the cognitive performance of all the participants. After obtaining all the parameters, two-way analysis of variance (ANOVA) was used to compare the mean values of all the retinal parameters of the patients with AD and the HCs. Pearson's correlation was used to test the association between retinal parameters, MMSE scores, and vascular parameters. Results Seventy-eight eyes from 39 participants (19 AD and 20 HC; male, 52.6% in AD and 45.0% in HC; mean [standard deviation] age of 73.79 [7.22] years in AD and 74.35 [6.07] years in HC) were included for the analysis. The average RNFL + GCL thickness (106.32 ± 7.34 μm), FRTs of the four quadrants (290.35 ± 13.05 μm of inferior quadrant, 294.68 ± 9.37 μm of superior quadrant, 302.97 ± 6.52 μm of nasal quadrant, 286.02 ± 13.74 μm of temporal quadrant), and retinal VD (0.0148 ± 0.003) of patients with AD, compared with the HCs, were significantly reduced (p < 0.05). Retinal thickness was significantly correlated with the MMSE scores (p < 0.05). Meanwhile, retinal VD was significantly correlated with the average RNFL + GCL thickness (r2 = 0.2146, p < 0.01). When the vascular parameters were considered, the sensitivity of the AD diagnosis was increased from 0.874 to 0.892. Conclusion Our study suggested that the patients with AD, compared with age-matched HCs, had significantly reduced RNFL + GCL thickness and vascular density. These reductions correlated with the cognitive performance of the participants. By combining nerve and vessel parameters, the diagnosis of AD can be improved using OCTA technology. Trail registration Name of the registry: Chinese Clinical Trail Registry, Trial registration number: ChiCTR2000035243, Date of registration: Aug. 5, 2020. URL of trial registry record: http://www.chictr.org.cn/index.aspx
Objectives. Retina abnormalities are related to cognitive disorders in patients with Alzheimer’s disease (AD). Retinal amyloid beta (Aβ) can be labeled by curcumin. We measured Aβ content in the cerebrum and retina of APPswe/PS1dE9 (APP) transgenic mice with early age to investigate the correlation between cerebrum and retina. Methods. APP mice and age-matched wild-type mice were investigated every month from age 2 months to 6 months to assess changes in Aβ content in the retina and cerebrum. At the beginning of each month, mice were fed a curcumin diet (50 mg/kg/day) for 7 consecutive days. The Aβ levels in the retina and cerebrum were measured by ELISAs. Correlations were identified between retinal and cerebral Aβ contents using Pearson’s correlation. Results. In the absence of curcumin, there was a significant correlation between Aβ contents in the retina and cerebrum of APP mice (r=0.7291, P=0.0014). With increasing age, Aβ-mediated degenerative change in the cerebrum (P<0.001 in 5 months) and retina (P<0.01 in 5 months) increased significantly. The inhibitory effect of curcumin on the Aβ level was significant in the cerebrum (P<0.001) and retina (P<0.01) of older APP mice in the early stage of life. Conclusion. We observed a significant correlation between the Aβ content in the retina and Aβ content in the cerebrum of APP mice. Our data suggest an appropriate time to measure retinal Aβ. Although curcumin can label Aβ in the retina, it also suppresses Aβ levels and weakens the degree of correlation between Aβ in cerebrum and retina tissues.
BackgroundCurcumin has multifunctional pharmacological properties, including anti-oxidant, anti-inflammatory, and anti-diabetic properties. We investigated whether curcumin can improve pathological changes associated with Alzheimer's disease, including amyloid β (Aβ), protein kinase B (PKB, also termed Akt), and glycogen synthase kinase 3β (GSK3β) levels and expression. MethodsAlzheimer’s transgenic APPSWE/PS1ΔE9 mice and wild type mice were treated with curcumin by intragastric administration for 2 weeks at 2 and 5 months of age. Aβ plaques and contents in the brain and retina were measured by immunohistochemistry and enzyme-linked immune sorbent assay, respectively, while the expression of Akt and GSK3β was tested by RNA isolation and quantitative real-time poly merase chain reaction. Bloods of patients with AD and age-matched health controls were used to determine the contents of Aβ, Akt and GSK3β. ResultsCurcumin treatment decreased Aβ accumulation in the early stages of AD at 5 months (p < 0.001). It also improved AD-associated pathological changes, including upregulation of Akt (p < 0.01) and downregulation of GSK3β (p < 0.01). In addition to AD-associated changes, the proinflammatory cytokine IL-1β was significantly decreased by curcumin treatment (p < 0.05). ConclusionsCurcumin can suppress Aβ accumulation during the early stages of AD, upregulate the expression of Akt, downregulate the expression of GSK3β, and inhibit the proinflammatory cytokine IL-1β. Curcumin can be used to improve pathological features of AD in the early stages of disease
Relevance Model (RM) is one of typical and generally stable methods for the query expansion in information retrieval (IR). This paper presents a novel information retrieval model based on the quantum probability theory, and makes a preliminary exploration on the application of quantum model in pseudo-relevance feedback. In the document weight allocation framework, we propose two re-ranking approaches based on linear weight allocation and quantum interference weight allocation, respectively. The experimental results on standard TREC datasets show that the proposed model is effective and potential in information retrieval tasks.
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