Deep learning implementations using convolutional neural nets have recently demonstrated promise in many areas of medical imaging. In this article we lay out the methods by which we have achieved consistently high quality, high throughput computation of intra-cranial segmentation from whole head magnetic resonance images, an essential but typically time-consuming bottleneck for brain image analysis. We refer to this output as "production-level" because it is suitable for routine use in processing pipelines. Training and testing with an extremely large archive of structural images, our segmentation algorithm performs uniformly well over a wide variety of separate national imaging cohorts, giving Dice metric scores exceeding those of other recent deep learning brain extractions. We describe the components involved to achieve this performance, including size, variety and quality of ground truth, and appropriate neural net architecture. We demonstrate the crucial role of appropriately large and varied datasets, suggesting a less prominent role for algorithm development beyond a threshold of capability.

Health care-associated methicillin-resistant Staphylococcus aureus (MRSA) infections are a burden on the health care system. Clinical laboratories play a key role in reducing this burden, as the timely identification of MRSA colonization or infection facilitates infection control practices that are effective at limiting invasive MRSA infections. The Xpert MRSA NxG assay recently received FDA clearance for the direct detection of MRSA from nasal swabs. This multicenter study evaluated the clinical performance characteristics of the Xpert MRSA NxG assay with prospectively collected rayon nasal swabs (n = 1,103) and flocked swab (ESwab) nasal specimens (n = 846). Culture-based identification methods and antimicrobial susceptibility testing were used as the reference standards for comparison. According to the reference method, the positivity rates for MRSA in the population evaluated were 11.1% (122/1,103) for rayon swabs and 11.6% (98/846) for flocked swabs. The overall sensitivity and specificity of the rayon swabs were 91.0% (95% confidence interval [CI], 84.6 to 94.9%) and 96.9% (95% CI, 95.7 to 97.8%), respectively, across eight testing sites. The flocked swab specimens were 92.9% sensitive (95% CI, 86.0 to 96.5%) and 97.6% specific (95% CI, 96.2 to 98.5%) for MRSA detection across six testing sites. The sensitivity and specificity of the combined flocked and rayon swab data were 91.8% (95% CI, 87.4 to 94.8%) and 97.2% (95% CI, 96.3 to 97.9%), respectively. The positive predictive value (PPV) for rayon swabs was 78.7%, versus 83.5% for ESwabs. The negative predictive values (NPVs) for rayon swabs and ESwab specimens were 98.9% and 99.1%, respectively. In conclusion, the Xpert MRSA NxG assay is a sensitive and specific assay for the direct detection of MRSA from nasal swab specimens.

OBJECTIVE: To investigate the role of adipocyte Pcpe2 (procollagen C-endopeptidase enhancer 2) in SR-BI (scavenger receptor class BI)-mediated HDL-C (high-density lipoprotein cholesterol) uptake and contributions to adipose lipid storage. APPROACH AND RESULTS: Pcpe2, a glycoprotein devoid of intrinsic proteolytic activity, is believed to participate in extracellular protein-protein interactions, supporting SR-BI- mediated HDL-C uptake. In published studies, Pcpe2 deficiency increased the development of atherosclerosis by reducing SR-BI-mediated HDL-C catabolism, but the biological impact of this deficiency on adipocyte SR-BI-mediated HDL-C uptake is unknown. Differentiated cells from Ldlr-/-/Pcpe2-/- (Pcpe2-/-) mouse adipose tissue showed elevated SR-BI protein levels, but significantly reduced HDL-C uptake compared to Ldlr-/- (control) adipose tissue. SR-BI-mediated HDL-C uptake was restored by preincubation of cells with exogenous Pcpe2. In diet-fed mice lacking Pcpe2, significant reductions in visceral, subcutaneous, and brown adipose tissue mass were observed, despite elevations in plasma triglyceride and cholesterol concentrations. Significant positive correlations exist between adipose mass and Pcpe2 expression in both mice and humans. CONCLUSIONS: Overall, these findings reveal a novel and unexpected function for Pcpe2 in modulating SR-BI expression and function as it relates to adipose tissue expansion and cholesterol balance in both mice and humans.

The HDL (high-density lipoprotein) Workshop was established in 2009 as a forum for candid discussions among academic basic scientists, clinical investigators, and industry researchers about the role of HDL in cardiovascular disease. This ninth HDL Workshop was held on May 16 to 17, 2019 in Boston, MA, and included outstanding oral presentations from established and emerging investigators. The Workshop featured 5 sessions with topics that tackled the role of HDL in the vasculature, its structural complexity, its role in health and disease states, and its interaction with the intestinal microbiome. The highlight of the program was awarding the Jack Oram Award to the distinguished professor emeritus G.S. Getz from the University of Chicago. The tenth HDL Workshop will be held on May 2020 in Chicago and will continue the focus on intellectually stimulating presentations by established and emerging investigators on novel roles of HDL in cardiovascular and noncardiovascular health and disease states.

Introduction

Although dementia-related proteinopathy has a strong negative impact on public health, and is highly heritable, understanding of the related genetic architecture is incomplete.

Methods

We applied multidimensional generalized partial credit modeling (GPCM) to test genetic associations with dementia-related proteinopathies. Data were analyzed to identify candidate single nucleotide variants for the following proteinopathies: Aβ, tau, α-synuclein, and TDP-43.

Results

Final included data comprised 966 participants with neuropathologic and WGS data. Three continuous latent outcomes were constructed, corresponding to TDP-43-, Aβ/Tau-, and α-synuclein-related neuropathology endophenotype scores. This approach helped validate known genotype/phenotype associations: for example, TMEM106B and GRN were risk alleles for TDP-43 pathology; and GBA for α-synuclein/Lewy bodies. Novel suggestive proteinopathy-linked alleles were also discovered, including several (SDHAF1, TMEM68, and ARHGEF28) with colocalization analyses and/or high degrees of biologic credibility.

Discussion

A novel methodology using GPCM enabled insights into gene candidates for driving misfolded proteinopathies.

Highlights

Latent factor scores for proteinopathies were estimated using a generalized partial credit model. The three latent continuous scores corresponded well with proteinopathy severity. Novel genes associated with proteinopathies were identified. Several genes had high degrees of biologic credibility for dementia risk factors.

The process of identifying suitable genome-wide association (GWA) studies and formatting the data to calculate multiple polygenic risk scores on a single genome can be laborious. Here, we present a centralized polygenic risk score calculator currently containing over 250,000 genetic variant associations from the NHGRI-EBI GWAS Catalog for users to easily calculate sample-specific polygenic risk scores with comparable results to other available tools. Polygenic risk scores are calculated either online through the Polygenic Risk Score Knowledge Base (PRSKB; https://prs.byu.edu ) or via a command-line interface. We report study-specific polygenic risk scores across the UK Biobank, 1000 Genomes, and the Alzheimer's Disease Neuroimaging Initiative (ADNI), contextualize computed scores, and identify potentially confounding genetic risk factors in ADNI. We introduce a streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies, which we anticipate will facilitate a wider adaptation of polygenic risk scores in future disease research.

Background

Deep learning has shown potential in various scientific domains but faces challenges when applied to complex, high-dimensional multi-omics data. Alzheimer's Disease (AD) is a neurodegenerative disorder that lacks targeted therapeutic options. This study introduces the Circular-Sliding Window Association Test (c-SWAT) to improve the classification accuracy in predicting AD using serum-based metabolomics data, specifically lipidomics.

Methods

The c-SWAT methodology builds upon the existing Sliding Window Association Test (SWAT) and utilizes a three-step approach: feature correlation analysis, feature selection, and classification. Data from 997 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) served as the basis for model training and validation. Feature correlations were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA), and Convolutional Neural Networks (CNN) were employed for feature selection. Random Forest was used for the final classification.

Findings

The application of c-SWAT resulted in a classification accuracy of up to 80.8% and an AUC of 0.808 for distinguishing AD from cognitively normal older adults. This marks a 9.4% improvement in accuracy and a 0.169 increase in AUC compared to methods without c-SWAT. These results were statistically significant, with a p-value of 1.04 × 10ˆ-4. The approach also identified key lipids associated with AD, such as Cer(d16:1/22:0) and PI(37:6).

Interpretation

Our results indicate that c-SWAT is effective in improving classification accuracy and in identifying potential lipid biomarkers for AD. These identified lipids offer new avenues for understanding AD and warrant further investigation.

Funding

The specific funding of this article is provided in the acknowledgements section.

Mechanisms of resilience against tau pathology in individuals across the Alzheimer's disease spectrum are insufficiently understood. Longitudinal data are necessary to reveal which factors relate to preserved cognition (i.e. cognitive resilience) and brain structure (i.e. brain resilience) despite abundant tau pathology, and to clarify whether these associations are cross-sectional or longitudinal. We used a longitudinal study design to investigate the role of several demographic, biological and brain structural factors in yielding cognitive and brain resilience to tau pathology as measured with PET. In this multicentre study, we included 366 amyloid-β-positive individuals with mild cognitive impairment or Alzheimer's disease dementia with baseline 18F-flortaucipir-PET and longitudinal cognitive assessments. A subset (n = 200) additionally underwent longitudinal structural MRI. We used linear mixed-effects models with global cognition and cortical thickness as dependent variables to investigate determinants of cognitive resilience and brain resilience, respectively. Models assessed whether age, sex, years of education, APOE-ε4 status, intracranial volume (and cortical thickness for cognitive resilience models) modified the association of tau pathology with cognitive decline or cortical thinning. We found that the association between higher baseline tau-PET levels (quantified in a temporal meta-region of interest) and rate of cognitive decline (measured with repeated Mini-Mental State Examination) was adversely modified by older age (Stβinteraction = -0.062, P = 0.032), higher education level (Stβinteraction = -0.072, P = 0.011) and higher intracranial volume (Stβinteraction = -0.07, P = 0.016). Younger age, higher education and greater cortical thickness were associated with better cognitive performance at baseline. Greater cortical thickness was furthermore associated with slower cognitive decline independent of tau burden. Higher education also modified the negative impact of tau-PET on cortical thinning, while older age was associated with higher baseline cortical thickness and slower rate of cortical thinning independent of tau. Our analyses revealed no (cross-sectional or longitudinal) associations for sex and APOE-ε4 status on cognition and cortical thickness. In this longitudinal study of clinically impaired individuals with underlying Alzheimer's disease neuropathological changes, we identified education as the most robust determinant of both cognitive and brain resilience against tau pathology. The observed interaction with tau burden on cognitive decline suggests that education may be protective against cognitive decline and brain atrophy at lower levels of tau pathology, with a potential depletion of resilience resources with advancing pathology. Finally, we did not find major contributions of sex to brain nor cognitive resilience, suggesting that previous links between sex and resilience might be mainly driven by cross-sectional differences.