Cluster Analysis of Cortical Amyloid Burden for Identifying Imaging-driven Subtypes in Mild Cognitive Impairment.

Over the past decade, Alzheimer's disease (AD) has become increasingly severe and gained greater attention. Mild Cognitive Impairment (MCI) serves as an important prodromal stage of AD, highlighting the urgency of early diagnosis for timely treatment and control of the condition. Identifying the subtypes of MCI patients exhibits importance for dissecting the heterogeneity of this complex disorder and facilitating more effective target discovery and therapeutic development. Conventional method uses clinical measurements such as cognitive score and neurophysical assessment to stratify MCI patients into two groups with early MCI (EMCI) and late MCI (LMCI), which shows their progressive stages. However, such clinical method is not designed to de-convolute the heterogeneity of the disorder. This study uses a data-driven approach to divide MCI patients into a novel grouping of two subtypes based on an amyloid dataset of 68 cortical features from positron emission tomography (PET), where each subtype has a homogeneous cortical amyloid burden pattern. Experimental evaluation including visual two-dimensional cluster distribution, Kaplan-Meier plot, genetic association studies, and biomarker distribution analysis demonstrates that the identified subtypes performs better across all metrics than the conventional EMCI and LMCI grouping.

Is there a significant difference in microbiota between water and microplastic surfaces in winter? The possibility of spreading offshore into the ocean.

Environmental problems caused by microplastics (MPs) are attracting global attention. The ecological risks of bacteria attached to MPs have not been studied in detail under low temperature conditions. Here, MPs in surface water were sampled in winter from the Changjiang (or Yangtze) River Estuary. The physical and chemical characteristics of the MPs were identified, and the diversity and species composition of bacteria on the surface water MPs were analyzed. Phenotypic prediction analysis was used to analyze the potential risk of bacteria in the biofilm on the surfaces of MPs. The main chemical composition in the MPs in the surface water were PP (polypropylene), PE (polyethylene), PS (polystyrene) and other light weight MPs. Sampling sites played a decisive role in the bacterial species composition. The potential plastic-degrading bacterium Acinetobacter and the potential pathogenic bacterium Pseudomonas showed significant differences across different sampling sites. Microbial communities on the surfaces of MPs in winter were not significantly different from planktonic bacteria in the water body. Phenotypic prediction results showed that bacteria on the surface of MPs had a marked capacity to form biofilms, but a low pathogenicity risk. Based on the results of biodiversity analysis and phenotypic prediction, the potential ecological risk of bacteria in biofilms on MP surfaces is lower at low temperatures. In addition, the numerical simulation results show that the possibility of bacteria attached to MPs from the Changjiang River entering the Pacific Ocean in winter is small. MPs attached bacteria in the Changjiang estuary have low ecological risk to the estuary and the Pacific Ocean in winter.

Mining High-Level Imaging Genetic Associations via Clustering AD Candidate Variants with Similar Brain Association Patterns.

Brain imaging genetics examines associations between imaging quantitative traits (QTs) and genetic factors such as single nucleotide polymorphisms (SNPs) to provide important insights into the pathogenesis of Alzheimer's disease (AD). The individual level SNP-QT signals are high dimensional and typically have small effect sizes, making them hard to be detected and replicated. To overcome this limitation, this work proposes a new approach that identifies high-level imaging genetic associations through applying multigraph clustering to the SNP-QT association maps. Given an SNP set and a brain QT set, the association between each SNP and each QT is evaluated using a linear regression model. Based on the resulting SNP-QT association map, five SNP-SNP similarity networks (or graphs) are created using five different scoring functions, respectively. Multigraph clustering is applied to these networks to identify SNP clusters with similar association patterns with all the brain QTs. After that, functional annotation is performed for each identified SNP cluster and its corresponding brain association pattern. We applied this pipeline to an AD imaging genetic study, which yielded promising results. For example, in an association study between 54 AD SNPs and 116 amyloid QTs, we identified two SNP clusters with one responsible for amyloid beta clearances and the other regulating amyloid beta formation. These high-level findings have the potential to provide valuable insights into relevant genetic pathways and brain circuits, which can help form new hypotheses for more detailed imaging and genetics studies in independent cohorts.

Identifying genetic markers enriched by brain imaging endophenotypes in Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is a complex neurodegenerative disorder and the most common type of dementia. AD is characterized by a decline of cognitive function and brain atrophy, and is highly heritable with estimated heritability ranging from 60 to 80[Formula: see text]. The most straightforward and widely used strategy to identify AD genetic basis is to perform genome-wide association study (GWAS) of the case-control diagnostic status. These GWAS studies have identified over 50 AD related susceptibility loci. Recently, imaging genetics has emerged as a new field where brain imaging measures are studied as quantitative traits to detect genetic factors. Given that many imaging genetics studies did not involve the diagnostic outcome in the analysis, the identified imaging or genetic markers may not be related or specific to the disease outcome. RESULTS: We propose a novel method to identify disease-related genetic variants enriched by imaging endophenotypes, which are the imaging traits associated with both genetic factors and disease status. Our analysis consists of three steps: (1) map the effects of a genetic variant (e.g., single nucleotide polymorphism or SNP) onto imaging traits across the brain using a linear regression model, (2) map the effects of a diagnosis phenotype onto imaging traits across the brain using a linear regression model, and (3) detect SNP-diagnosis association via correlating the SNP effects with the diagnostic effects on the brain-wide imaging traits. We demonstrate the promise of our approach by applying it to the Alzheimer's Disease Neuroimaging Initiative database. Among 54 AD related susceptibility loci reported in prior large-scale AD GWAS, our approach identifies 41 of those from a much smaller study cohort while the standard association approaches identify only two of those. Clearly, the proposed imaging endophenotype enriched approach can reveal promising AD genetic variants undetectable using the traditional method. CONCLUSION: We have proposed a novel method to identify AD genetic variants enriched by brain-wide imaging endophenotypes. This approach can not only boost detection power, but also reveal interesting biological pathways from genetic determinants to intermediate brain traits and to phenotypic AD outcomes.

Application of contrast-enhanced ultrasonography and ultrasonography scores in rheumatoid arthritis.

OBJECTIVE: To investigate diagnostic value of ultrasonography scores (US) and contrast-enhanced ultrasonography (CEUS) in evaluating rheumatoid arthritis (RA) activity. METHODS: 39 patients with RA were included and the metacarpophalangeal, proximal interphalangeal, wrist, elbow and knee joints of them were examined by high frequency ultrasound. The severe joints and the related indexes (synovial thickness, synovial blood flow, joint effusion and bone erosion) were exposed. Then scores (0~3) were obtained and the sum was calculated. For 12 patients of the 39, 2.4 ml SonoVue was intravenously injected with observation of synovial enhancing. ROIs time-intensity curve (TIC) was obtained and the parameters including area under curve (AUC), peak intensity (PI) and time to peak (TTP) were analyzed. For 39 patients, the relationships among each parameters, ultrasonography scores, DAS28 scores and biochemical examinations (ESR, CRP, RF, anti-CCP) were analyzed. RESULTS: The US were significantly correlated with DAS28 Scores (r=0.823, P<0.01=. The correlation between US and CRP was better than that between DAS28 scores and CRP (rUS =0.692, rDAS28=0.526, P<0.01). The synovial thickness in US were correlated with DAS28 Scores and biochemical examinations (ESR, CRP) (rDAS28=0.852, rESR=0.779, rCRP=0.587, P<0.01. The AUC and PI in CEUS were significantly correlated with US (rAUC=0.832, rPI=0.809, P<0.01=. The correlations among AUC, PI and ESR were better than that between US and ESR (rAUC=0.907, rPI=0.851, rUS=0.836, P<0.01=. The correlations among AUC, PI and CRP were better than that between US and CRP (rAUC=0.855, rPI=0.854, rUS=0.692, P<0.01. CONCLUSIONS: US was almost identical with DAS28 Scores and biochemical examinations (ESR, CRP) in diagnosis of RA activity, while CEUS was almost identical with DAS28 Scores and biochemical examinations (ESR, CRP). In diagnosis of RA, US may be better than DAS28 Scores, while CEUS better than US. Both of them were useful for evaluation of RA activity.