BACKGROUND: Substantial evidence supports that glaucoma and dementia share pathological mechanisms and pathogenic risk factors. However, the association between glaucoma, cognitive decline and dementia has yet to be elucidated.
OBJECTIVE: This study was aimed to assess whether glaucoma increase the risk of dementia or cognitive impairment.
METHODS: PubMed, Cochrane Library, Web of Science, and EMBASE databases for cohort or case-control studies were searched from inception to March 10, 2024. The Newcastle-Ottawa Quality Assessment Scale (NOS) was used to the risk of bias. Heterogeneity was rigorously evaluated using the I2 test, while publication bias was assessed by visual inspection of the funnel plot and by Egger\' s regression asymmetry test. Subgroup analyses were applied to determine the sources of heterogeneity.
RESULTS: Twenty-seven studies covering 9,061,675 individuals were included. Pooled analyses indicated that glaucoma increased the risk of all-cause dementia, Alzheimer\'s disease, vascular dementia, and cognitive impairment. Subgroup analysis showed that the prevalence of dementia was 2.90 (95% CI: 1.45-5.77) in age ≥ 65 years and 2.07 (95% CI: 1.18-3.62) in age<65 years; the incidence rates in female glaucoma patients was 1.46 (95% CI: 1.06-2.00), respectively, which was no statistical significance in male patients. Among glaucoma types, POAG was more likely to develop dementia and cognitive impairment. There were also differences in regional distribution, with the highest prevalence in the Asia region, while glaucoma was not associated with dementia in Europe and North America regions.
CONCLUSIONS: Glaucoma increased the risk of subsequent cognitive impairment and dementia. The type of glaucoma, gender, age, and region composition of the study population may significantly affect the relationship between glaucoma and dementia.

BACKGROUND: Cognitive impairment is projected to affect a preponderant proportion of the aging population. Lifelong dietary habits have been hypothesized to play a role in preventing cognitive decline. Among the most studied dietary components, fish consumptionhas been extensively studied for its potential effects on the human brain.
OBJECTIVE: To perform a meta-analysis of observational studies exploring the association between fish intake and cognitive impairment/decline and all types of dementia.
METHODS: A systematic search of electronic databases was performed to identify observational studies providing quantitative data on fish consumption and outcomes of interest. Random effects models for meta-analyses using only extreme exposure categories, subgroup analyses, and dose-response analyses were performed to estimate cumulative risk ratios (RRs) and 95% confidence intervals (CIs).
RESULTS: The meta-analysis comprised 35 studies. Individuals reporting the highest vs. the lowest fish consumption were associated with a lower likelihood of cognitive impairment/decline (RR = 0.82, 95% CI: 0.75, 0.90, I2 = 61.1%), dementia (RR = 0.82, 95% CI: 0.73, 0.93, I2 = 38.7%), and Alzheimer\'s disease (RR = 0.80, 95% CI: 0.67, 0.96, I2 = 20.3%). The dose-response relation revealed a significantly decreased risk of cognitive impairment/decline and all cognitive outcomes across higher levels of fish intake up to 30% for 150 g/d (RR = 0.70, 95% CI: 0.52, 0.95). The results of this relation based on APOE ε4 allele status was mixed based on the outcome investigated.
CONCLUSIONS: Current findings suggest fish consumption is associated with a lower risk of cognitive impairment/decline in a dose-response manner, while for dementia and Alzheimer\'s disease there is a need for further studies to improve the strength of evidence.

UNASSIGNED: Development of SERS-based Raman nanoprobes can detect the misfolding of Amyloid beta (Aβ) 42 peptides, making them a viable diagnostic technique for Alzheimer\'s disease (AD). The detection and imaging of amyloid peptides and fibrils are expected to help in the early identification of AD.
UNASSIGNED: Here, we propose a fast, easy-to-use, and simple scheme based on the selective adsorption of Aβ42 molecules on SERS active gold nanoprobe (RB-AuNPs) of diameter 29 ± 3 nm for Detection of Alzheimer\'s Disease Biomarkers. Binding with the peptides results in a spectrum shift, which correlates with the target peptide. We also demonstrated the possibility of using silver nanoparticles (AgNPs) as precursors for the preparation of a SERS active nanoprobe with carbocyanine (CC) dye and AgNPs known as silver nanoprobe (CC-AgNPs) of diameter 25 ± 4 nm.
UNASSIGNED: RB-AuNPs probe binding with the peptides results in a spectrum shift, which correlates with the target peptide. Arginine peak appears after the conjugation confirms the binding of Aβ 42 with the nanoprobe. Tyrosine peaks appear after conjugated Aβ42 with CC-AgNPs providing binding of the peptide with the probe. The nanoprobe produced a strong, stable SERS signal. Further molecular docking was utilized to analyse the interaction and propose a structural hypothesis for the process of binding the nanoprobe to Aβ42 and Tau protein.
UNASSIGNED: This peptide-probe interaction provides a general enhancement factor and the molecular structure of the misfolded peptides. Secondary structural information may be obtained at the molecular level for specific residues owing to isotope shifts in the Raman spectra. Conjugation of the nanoprobe with Aβ42 selectively detected AD in bodily fluids. The proposed nanoprobes can be easily applied to the detection of Aβ plaques in blood, saliva, and sweat samples.

Alzheimer\'s disease (AD) is a neurological disorder that increases with age and must be treated immediately by worldwide healthcare systems. Internal neurofibrillary tau tangles and extracellular amyloid accumulation have been widely recognized as the primary causes of Alzheimer\'s disease. These degenerative age-related ailments are expected to proliferate exponentially as life expectancy rises. Experimental models of AD are essential for acquiring a deep knowledge of its pathogenesis and determining the viability of novel therapy options. Although there isn\'t a model that encompasses all the characteristics of real AD, these models are nonetheless highly helpful for the research of various modifications associated with it, even though they are only partially indicative of the disease circumstances being studied. Better knowledge of the advantages and disadvantages of each of the different models, as well as the use of more than one model to evaluate potential medications, would increase the effectiveness of therapy translation from preclinical research to patients. We outline the pathogenic characteristics and limitations of the main experimental models of AD in this review, including transgenic mice, transgenic rats, primates and non-primate models along with in-vitro cell culture models in humans. Additionally, it highlights the possible future of experimental modeling of AD and includes the co-morbid models.

Alzheimer\'s disease (AD) is a progressive neurological disorder that primarily impacts cognitive function. Currently there are no disease-modifying treatments to stop or slow its progression. Recent studies have found that several peripheral and systemic abnormalities are associated with AD, and our understanding of how these alterations contribute to AD is becoming more apparent. In this review, we focuse on amyloid‑beta (Aβ), a major hallmark of AD, summarizing recent findings on the source of brain-derived Aβ and discussing where and how the brain-derived Aβ is cleared in vivo. Based on these findings, we propose future strategies for AD prevention and treatment, from a novel perspective on Aβ metabolism.

BACKGROUND: As a currently incurable but preventable disease, the prevention and early diagnosis of Alzheimer\'s disease (AD) has long been a research hotspot. Amyloid deposition has been shown to be a major pathological feature of AD. Notably, not all the people with amyloid-beta (Aβ) pathology will have significant cognitive declines and eventually develop AD. Therefore, the aim of this study was to explore the risk factors for cognitive decline in Aβ-positive participants.
METHODS: We included 650 non-demented participants who were Aβ-positive at baseline from the Alzheimer\'s Disease Neuroimaging Initiative (ADNI) database. Mixed effects and COX regression models were applied to assess 37 potential risk factors. Mixed effects models were employed to assess the temporal associations between potential risk factors and four cognitive assessment scales. COX regression models were used to assess the impact of potential risk factors on cognitive diagnosis conversion. Univariate and multivariate analyses were applied to the above models. Additionally, we used the Cochran-Armitage trend test to examine whether the incidence of cognitive decline increased with the number concurrent of risk factors.
RESULTS: Six factors (low diastolic pressure, low body mass index, retired status, a history of drug abuse, Parkinsonism, and depression) were the identified risk factors and four factors (a history of urinary disease, musculoskeletal diseases, no major surgical history, and no prior dermatologic-connective tissue diseases) were found to be suggestive risk factors. The incidence of cognitive decline in the Aβ-positive participants gradually increased as the number of concurrent risk factors increased (p for trend = 0.0005).
CONCLUSIONS: Our study may facilitate the understanding of the potential pathological processes in AD and provide novel targets for the prevention of cognitive decline among participants with Aβ positivity.

BACKGROUND: Polygenic risk scores (PRS) and subjective cognitive decline (SCD) are associated with the risk of developing dementia. It remains to examine whether they can improve the established cardiovascular risk factors aging and dementia (CAIDE) model and how their predictive abilities compare.
METHODS: The CAIDE model was applied to a sub-sample of a large, population-based cohort study (n = 5,360; aged 50-75) and evaluated for the outcomes of all-cause dementia, Alzheimer\'s disease (AD) and vascular dementia (VD) by calculating Akaike\'s information criterion (AIC) and the area under the curve (AUC). The improvement of the CAIDE model by PRS and SCD was further examined using the net reclassification improvement (NRI) method and integrated discrimination improvement (IDI).
RESULTS: During 17 years of follow-up, 410 participants were diagnosed with dementia, including 139 AD and 152 VD diagnoses. Overall, the CAIDE model showed high discriminative ability for all outcomes, reaching AUCs of 0.785, 0.793, and 0.789 for all-cause dementia, AD, and VD, respectively. Adding information on SCD significantly increased NRI for all-cause dementia (4.4%, p = 0.04) and VD (7.7%, p = 0.01). In contrast, prediction models for AD further improved when PRS was added to the model (NRI, 8.4%, p = 0.03). When APOE ε4 carrier status was included (CAIDE Model 2), AUCs increased, but PRS and SCD did not further improve the prediction.
CONCLUSIONS: Unlike PRS, information on SCD can be assessed more efficiently, and thus, the model including SCD can be more easily transferred to the clinical setting. Nevertheless, the two variables seem negligible if APOE ε4 carrier status is available.

BACKGROUND: Posterior cortical atrophy (PCA) is a rare condition characterized by early-onset and progressive visual impairment. Individuals with PCA have relatively early-onset and progressive dementia, posing certain needs for early detection. Hence, this study aimed to investigate the association of alterations in outer retinal and choroidal structure and microvasculature with PCA neuroimaging and clinical features and the possible effects of apolipoprotein E(APOE) ε4 allele on outer retinal and choroidal alterations in participants with PCA, to detect potential ocular biomarkers for PCA screening.
METHODS: This cross-sectional study included PCA and age- and sex-matched healthy control participants from June 2022 to December 2023. All participants with PCA completed a comprehensive neurological evaluation. All participants were recorded baseline information and underwent an ophthalmic evaluation. Quantitative analyses were performed using swept-source optical coherence tomography (SS-OCT) and angiography (SS-OCTA). Adaptive optics scanning laser ophthalmoscopy (AO-SLO) was performed in some patients. In participants with PCA, the influence of APOE ε4 on outer retinal and choroidal alterations and the correlation of outer retinal and choroidal alterations with PCA neuroimaging and clinical features in participants with PCA were investigated.
RESULTS: A total of 28 participants (53 eyes) with PCA and 56 healthy control participants (112 eyes) were included in the current study. Compared with healthy control participants, participants with PCA had significantly reduced outer retinal thickness (ORT) (p < 0.001), choriocapillaris vessel density (VD) (p = 0.007), choroidal vascular index (CVI) (p = 0.005) and choroidal vascular volume (CVV) (p = 0.003). In participants with PCA, APOE ε4 carriers showed thinner ORT (p = 0.009), and increased choriocapillaris VD (p = 0.004) and CVI (p = 0.004). The PCA neuroimaging features were positively associated with the ORT, CVI and CVV. Furthermore, differential correlations were observed of PCA clinical features with the CRT, CVV and CVI.
CONCLUSIONS: Our findings highlighted the association of outer retinal and choroidal alterations with PCA neuroimaging and clinical features in participants with PCA. Noninvasive SS-OCT and SS-OCTA can provide potential biomarkers for the diagnosis and management of PCA, improving awareness of PCA syndrome among ophthalmologists, neurologists, and primary care providers.

OBJECTIVE: To identify white matter fiber injury and network changes that may lead to mild cognitive impairment (MCI) progression, then a joint model was constructed based on neuropsychological scales to predict high-risk individuals for Alzheimer\'s disease (AD) progression among older adults with MCI.
METHODS: A total of 173 MCI patients were included from the Alzheimer\'s Disease Neuroimaging Initiative(ADNI) database and randomly divided into training and testing cohorts. Forty-five progressed to AD during a 4-year follow-up period. Diffusion tensor imaging (DTI) techniques extracted relevant DTI quantitative features for each patient. In addition, brain networks were constructed based on white matter fiber bundles to extract network property features. Ensemble dimensionality reduction was applied to reduce both DTI quantitative features and network features from the training cohort, and machine learning algorithms were added to construct white matter signature. In addition, 52 patients from the National Alzheimer\'s Coordinating Center (NACC) database were used for external validation of white matter signature. A joint model was subsequently generated by combining with scale scores, and its performance was evaluated using data from the testing cohort.
RESULTS: Based on multivariate logistic regression, clinical dementia rating and Alzheimer\'s disease assessment scales (CDRS and ADAS, respectively) were selected as independent predictive factors. A joint model was constructed in combination with the white matter signature. The AUC, sensitivity, and specificity in the training cohort were 0.938, 0.937, and 0.91, respectively, and the AUC, sensitivity, and specificity in the test cohort were 0.905, 0.923, and 0.872, respectively. The Delong test showed a statistically significant difference between the joint model and CDRS or ADAS scores (P < 0.05), yet no significant difference between the joint model and the white matter signature (P = 0.341).
CONCLUSIONS: The present results demonstrate that a joint model combining neuropsychological scales can be constructed by using machine learning and DTI technology to identify MCI patients who are at high-risk of progressing to AD.

In the pathogenesis of Alzheimer\'s disease, the overexpression of glycogen synthase kinase-3β (GSK-3β) stands out due to its multifaced nature, as it contributes to the promotion of amyloid β and tau protein accumulation, as well as neuroinflammatory processes. Therefore, in the present study, we have designed, synthesized, and evaluated a new series of GSK-3β inhibitors based on the N-(pyridin-2-yl)cyclopropanecarboxamide scaffold. We identified compound 36, demonstrating an IC50 of 70 nM against GSK-3β. Subsequently, through crystallography studies and quantum mechanical analysis, we elucidated its binding mode and identified the structural features crucial for interactions with the active site of GSK-3β, thereby understanding its inhibitory potency. Compound 36 was effective in the cellular model of hyperphosphorylated tau-induced neurodegeneration, where it restored cell viability after okadaic acid treatment and showed anti-inflammatory activity in the LPS model, significantly reducing NO, IL-6, and TNF-α release. In ADME-tox in vitro studies, we confirmed the beneficial profile of 36, including high permeability in PAMPA (Pe equals 9.4) and high metabolic stability in HLMs as well as lack of significant interactions with isoforms of the CYP enzymes and lack of considerable cytotoxicity on selected cell lines (IC50 > 100 μM on HT-22 cells and 89.3 μM on BV-2 cells). Based on promising pharmacological activities and favorable ADME-tox properties, compound 36 may be considered a promising candidate for in vivo research as well as constitute a reliable starting point for further studies.