Neural Plastic Effects of Cognitive Training on Aging Brain.

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Patient Perception of Physician Attire Before and After Disclosure of the Risks of Microbial Contamination

Background: The white coat is traditionally considered to be the appropriate attire for physicians but it may also be contaminated with microbes and act as a potential source of infection. We aimed to study patients’ acceptance of physicians’ attire, their underlying reasons, and their reactions to an educational intervention with regards to the risks of contamination. Methods: We conducted a voluntary questionnaire survey at a university teaching hospital in Hong Kong from February to July 2012. 262 patient-responses from adult inpatients and outpatients across various specialties were analysed. Results: White coats were highly favoured (90.8%) when compared with scrubs (22.1%), smart casual (7.6%) and formal (7.3%) wears. ’Professional image’ and ‘ease of identification’ were the main attributes of the white coat. Most patients (92.2%) would prefer doctors washing their white coats every few days, whilst 80.9% believed that doctors were actually doing so. After patients were informed of the potential risk of microbial contamination, white coats remained as the most favoured attire (66.4%), but with scrubs doubling in popularity (45.8%). Smart casual (9.2%) and formal attire (4.6%) remain the least accepted. Conclusion: Despite cross-infections being a significant concern within the healthcare environments, patients’ predominant acceptance and perceived attributes towards the white coat were maintained after an educational intervention on the risks of microbial contamination.published_or_final_versio

Development and validation of sex-specific hip fracture prediction models using electronic health records: a retrospective, population-based cohort study

Background: Hip fracture is associated with immobility, morbidity, mortality, and high medical cost. Due to limited availability of dual-energy X-ray absorptiometry (DXA), hip fracture prediction models without using bone mineral density (BMD) data are essential. We aimed to develop and validate 10-year sex-specific hip fracture prediction models using electronic health records (EHR) without BMD. Methods: In this retrospective, population-based cohort study, anonymized medical records were retrieved from the Clinical Data Analysis and Reporting System for public healthcare service users in Hong Kong aged ≥60 years as of 31 December 2005. A total of 161,051 individuals (91,926 female; 69,125 male) with complete follow-up from 1 January 2006 till the study end date on 31 December 2015 were included in the derivation cohort. The sex-stratified derivation cohort was randomly divided into 80% training and 20% internal testing datasets. An independent validation cohort comprised 3046 community-dwelling participants aged ≥60 years as of 31 December 2005 from the Hong Kong Osteoporosis Study, a prospective cohort which recruited participants between 1995 and 2010. With 395 potential predictors (age, diagnosis, and drug prescription records from EHR), 10-year sex-specific hip fracture prediction models were developed using stepwise selection by logistic regression (LR) and four machine learning (ML) algorithms (gradient boosting machine, random forest, eXtreme gradient boosting, and single-layer neural networks) in the training cohort. Model performance was evaluated in both internal and independent validation cohorts. Findings: In female, the LR model had the highest AUC (0.815; 95% Confidence Interval [CI]: 0.805–0.825) and adequate calibration in internal validation. Reclassification metrics showed the LR model had better discrimination and classification performance than the ML algorithms. Similar performance was attained by the LR model in independent validation, with high AUC (0.841; 95% CI: 0.807–0.87) comparable to other ML algorithms. In internal validation for male, LR model had high AUC (0.818; 95% CI: 0.801–0.834) and it outperformed all ML models as indicated by reclassification metrics, with adequate calibration. In independent validation, the LR model had high AUC (0.898; 95% CI: 0.857–0.939) comparable to ML algorithms. Reclassification metrics demonstrated that LR model had the best discrimination performance. Interpretation: Even without using BMD data, the 10-year hip fracture prediction models developed by conventional LR had better discrimination performance than the models developed by ML algorithms. Upon further validation in independent cohorts, the LR models could be integrated into the routine clinical workflow, aiding the identification of people at high risk for DXA scan. Funding: Health and Medical Research Fund, Health Bureau, Hong Kong SAR Government (reference: 17181381)

Using blood transcriptome analysis for Alzheimer's disease diagnosis and patient stratification

INTRODUCTION: Blood protein biomarkers demonstrate potential for Alzheimer's disease (AD) diagnosis. Limited studies examine the molecular changes in AD blood cells. METHODS: Bulk RNA-sequencing of blood cells was performed on AD patients of Chinese descent (n = 214 and 26 in the discovery and validation cohorts, respectively) with normal controls (n = 208 and 38 in the discovery and validation cohorts, respectively). Weighted gene co-expression network analysis (WGCNA) and deconvolution analysis identified AD-associated gene modules and blood cell types. Regression and unsupervised clustering analysis identified AD-associated genes, gene modules, cell types, and established AD classification models. RESULTS: WGCNA on differentially expressed genes revealed 15 gene modules, with 6 accurately classifying AD (areas under the receiver operating characteristics curve [auROCs] > 0.90). These modules stratified AD patients into subgroups with distinct disease states. Cell-type deconvolution analysis identified specific blood cell types potentially associated with AD pathogenesis. DISCUSSION: This study highlights the potential of blood transcriptome for AD diagnosis, patient stratification, and mechanistic studies. Highlights: We comprehensively analyze the blood transcriptomes of a well-characterized Alzheimer's disease cohort to identify genes, gene modules, pathways, and specific blood cells associated with the disease. Blood transcriptome analysis accurately classifies and stratifies patients with Alzheimer's disease, with some gene modules achieving classification accuracy comparable to that of the plasma ATN biomarkers. Immune-associated pathways and immune cells, such as neutrophils, have potential roles in the pathogenesis and progression of Alzheimer's disease

Large-scale plasma proteomic profiling identifies a high-performance biomarker panel for Alzheimer's disease screening and staging

INTRODUCTION: Blood proteins are emerging as candidate biomarkers for Alzheimer's disease (AD). We systematically profiled the plasma proteome to identify novel AD blood biomarkers and develop a high-performance, blood-based test for AD. METHODS: We quantified 1160 plasma proteins in a Hong Kong Chinese cohort by high-throughput proximity extension assay and validated the results in an independent cohort. In subgroup analyses, plasma biomarkers for amyloid, tau, phosphorylated tau, and neurodegeneration were used as endophenotypes of AD. RESULTS: We identified 429 proteins that were dysregulated in AD plasma. We selected 19 “hub proteins” representative of the AD plasma protein profile, which formed the basis of a scoring system that accurately classified clinical AD (area under the curve = 0.9690–0.9816) and associated endophenotypes. Moreover, specific hub proteins exhibit disease stage-dependent dysregulation, which can delineate AD stages. DISCUSSION: This study comprehensively profiled the AD plasma proteome and serves as a foundation for a high-performance, blood-based test for clinical AD screening and staging

European bone mineral density loci are also associated with BMD in East-Asian populations

Most genome-wide association (GWA) studies have focused on populations of European ancestry with limited assessment of the influence of the sequence variants on populations of other ethnicities. To determine whether markers that we have recently shown to associate with Bone Mineral Density (BMD) in Europeans also associate with BMD in East-Asians we analysed 50 markers from 23 genomic loci in samples from Korea (n = 1,397) and two Chinese Hong Kong sample sets (n = 3,869 and n = 785). Through this effort we identified fourteen loci that associated with BMD in East-Asian samples using a false discovery rate (FDR) of 0.05; 1p36 (ZBTB40, P = 4.3×10 -9), 1p31 (GPR177, P = 0.00012), 3p22 (CTNNB1, P = 0.00013), 4q22 (MEPE, P = 0.0026), 5q14 (MEF2C, P = 1.3×10 -5), 6q25 (ESR1, P = 0.0011), 7p14 (STARD3NL, P = 0.00025), 7q21 (FLJ42280, P = 0.00017), 8q24 (TNFRSF11B, P = 3.4×10 -5), 11p15 (SOX6, P = 0.00033), 11q13 (LRP5, P = 0.0033), 13q14 (TNFSF11, P = 7.5×10 -5), 16q24 (FOXL1, P = 0.0010) and 17q21 (SOST, P = 0.015). Our study marks an early effort towards the challenge of cataloguing bone density variants shared by many ethnicities by testing BMD variants that have been established in Europeans, in East-Asians. © 2010 Styrkarsdottir et al.published_or_final_versio

Genetic and polygenic risk score analysis for Alzheimer's disease in the Chinese population

Introduction: Dozens of Alzheimer's disease (AD)-associated loci have been identified in European-descent populations, but their effects have not been thoroughly investigated in the Hong Kong Chinese population. Methods: TaqMan array genotyping was performed for known AD-associated variants in a Hong Kong Chinese cohort. Regression analysis was conducted to study the associations of variants with AD-associated traits and biomarkers. Lasso regression was applied to establish a polygenic risk score (PRS) model for AD risk prediction. Results: SORL1 is associated with AD in the Hong Kong Chinese population. Meta-analysis corroborates the AD-protective effect of the SORL1 rs11218343 C allele. The PRS is developed and associated with AD risk, cognitive status, and AD-related endophenotypes. TREM2 H157Y might influence the amyloid beta 42/40 ratio and levels of immune-associated proteins in plasma. Discussion: SORL1 is associated with AD in the Hong Kong Chinese population. The PRS model can predict AD risk and cognitive status in this population

A blood-based multi-pathway biomarker assay for early detection and staging of Alzheimer's disease across ethnic groups

INTRODUCTION: Existing blood-based biomarkers for Alzheimer's disease (AD) mainly focus on its pathological features. However, studies on blood-based biomarkers associated with other biological processes for a comprehensive evaluation of AD status are limited. METHODS: We developed a blood-based, multiplex biomarker assay for AD that measures the levels of 21 proteins involved in multiple biological pathways. We evaluated the assay's performance for classifying AD and indicating AD-related endophenotypes in three independent cohorts from Chinese or European-descent populations. RESULTS: The 21-protein assay accurately classified AD (area under the receiver operating characteristic curve [AUC] = 0.9407 to 0.9867) and mild cognitive impairment (MCI; AUC = 0.8434 to 0.8945) while also indicating brain amyloid pathology. Moreover, the assay simultaneously evaluated the changes of five biological processes in individuals and revealed the ethnic-specific dysregulations of biological processes upon AD progression. DISCUSSION: This study demonstrated the utility of a blood-based, multi-pathway biomarker assay for early screening and staging of AD, providing insights for patient stratification and precision medicine. HIGHLIGHTS: The authors developed a blood-based biomarker assay for Alzheimer's disease. The 21-protein assay classifies AD/MCI and indicates brain amyloid pathology. The 21-protein assay can simultaneously assess activities of five biological processes. Ethnic-specific dysregulations of biological processes in AD were revealed

GWAS of bone size yields twelve loci that also affect height, BMD, osteoarthritis or fractures

© 2019, The Author(s). Bone area is one measure of bone size that is easily derived from dual-energy X-ray absorptiometry (DXA) scans. In a GWA study of DXA bone area of the hip and lumbar spine (N ≥ 28,954), we find thirteen independent association signals at twelve loci that replicate in samples of European and East Asian descent (N = 13,608 – 21,277). Eight DXA area loci associate with osteoarthritis, including rs143384 in GDF5 and a missense variant in COL11A1 (rs3753841). The strongest DXA area association is with rs11614913[T] in the microRNA MIR196A2 gene that associates with lumbar spine area (P = 2.3 × 10−42, β = −0.090) and confers risk of hip fracture (P = 1.0 × 10−8, OR = 1.11). We demonstrate that the risk allele is less efficient in repressing miR-196a-5p target genes. We also show that the DXA area measure contributes to the risk of hip fracture independent of bone density

Chemical genetics strategies for identification of molecular targets

Chemical genetics is an emerging field that can be used to study the interactions of chemical compounds, including natural products, with proteins. Usually, the identification of molecular targets is the starting point for studying a drug’s mechanism of action and this has been a crucial step in understanding many biological processes. While a great variety of target identification methods have been developed over the last several years, there are still many bioactive compounds whose target proteins have not yet been revealed because no routine protocols can be adopted. This review contains information concerning the most relevant principles of chemical genetics with special emphasis on the different genomic and proteomic approaches used in forward chemical genetics to identify the molecular targets of the bioactive compounds, the advantages and disadvantages of each and a detailed list of successful examples of molecular targets identified with these approaches