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Public Health Public Health

Improving the trans-ancestry portability of polygenic risk scores by prioritizing variants in predicted cell-type-specific regulatory elements.

In Nature genetics ; h5-index 174.0

Poor trans-ancestry portability of polygenic risk scores is a consequence of Eurocentric genetic studies and limited knowledge of shared causal variants. Leveraging regulatory annotations may improve portability by prioritizing functional over tagging variants. We constructed a resource of 707 cell-type-specific IMPACT regulatory annotations by aggregating 5,345 epigenetic datasets to predict binding patterns of 142 transcription factors across 245 cell types. We then partitioned the common SNP heritability of 111 genome-wide association study summary statistics of European (average n ≈ 189,000) and East Asian (average n ≈ 157,000) origin. IMPACT annotations captured consistent SNP heritability between populations, suggesting prioritization of shared functional variants. Variant prioritization using IMPACT resulted in increased trans-ancestry portability of polygenic risk scores from Europeans to East Asians across all 21 phenotypes analyzed (49.9% mean relative increase in R2). Our study identifies a crucial role for functional annotations such as IMPACT to improve the trans-ancestry portability of genetic data.

Amariuta Tiffany, Ishigaki Kazuyoshi, Sugishita Hiroki, Ohta Tazro, Koido Masaru, Dey Kushal K, Matsuda Koichi, Murakami Yoshinori, Price Alkes L, Kawakami Eiryo, Terao Chikashi, Raychaudhuri Soumya

2020-Nov-30

General General

Automatic dementia screening and scoring by applying deep learning on clock-drawing tests.

In Scientific reports ; h5-index 158.0

Dementia is one of the most common neurological syndromes in the world. Usually, diagnoses are made based on paper-and-pencil tests and scored depending on personal judgments of experts. This technique can introduce errors and has high inter-rater variability. To overcome these issues, we present an automatic assessment of the widely used paper-based clock-drawing test by means of deep neural networks. Our study includes a comparison of three modern architectures: VGG16, ResNet-152, and DenseNet-121. The dataset consisted of 1315 individuals. To deal with the limited amount of data, which also included several dementia types, we used optimization strategies for training the neural network. The outcome of our work is a standardized and digital estimation of the dementia screening result and severity level for an individual. We achieved accuracies of 96.65% for screening and up to 98.54% for scoring, overcoming the reported state-of-the-art as well as human accuracies. Due to the digital format, the paper-based test can be simply scanned by using a mobile device and then be evaluated also in areas where there is a staff shortage or where no clinical experts are available.

Chen Shuqing, Stromer Daniel, Alabdalrahim Harb Alnasser, Schwab Stefan, Weih Markus, Maier Andreas

2020-11-30

Radiology Radiology

A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images.

In Nature communications ; h5-index 260.0

Intracranial aneurysm is a common life-threatening disease. Computed tomography angiography is recommended as the standard diagnosis tool; yet, interpretation can be time-consuming and challenging. We present a specific deep-learning-based model trained on 1,177 digital subtraction angiography verified bone-removal computed tomography angiography cases. The model has good tolerance to image quality and is tested with different manufacturers. Simulated real-world studies are conducted in consecutive internal and external cohorts, in which it achieves an improved patient-level sensitivity and lesion-level sensitivity compared to that of radiologists and expert neurosurgeons. A specific cohort of suspected acute ischemic stroke is employed and it is found that 99.0% predicted-negative cases can be trusted with high confidence, leading to a potential reduction in human workload. A prospective study is warranted to determine whether the algorithm could improve patients' care in comparison to clinicians' assessment.

Shi Zhao, Miao Chongchang, Schoepf U Joseph, Savage Rock H, Dargis Danielle M, Pan Chengwei, Chai Xue, Li Xiu Li, Xia Shuang, Zhang Xin, Gu Yan, Zhang Yonggang, Hu Bin, Xu Wenda, Zhou Changsheng, Luo Song, Wang Hao, Mao Li, Liang Kongming, Wen Lili, Zhou Longjiang, Yu Yizhou, Lu Guang Ming, Zhang Long Jiang

2020-11-30

General General

Prediction and validation of mouse meiosis-essential genes based on spermatogenesis proteome dynamics.

In Molecular & cellular proteomics : MCP

The molecular mechanism associated with mammalian meiosis has yet to be fully explored, and one of the main reasons for this lack of exploration is that some meiosis-essential genes are still unknown. The profiling of gene expression during spermatogenesis has been performed in previous studies, yet few studies have aimed to find new functional genes. Since there is a huge gap between the number of genes that are able to be quantified and the number of genes that can be characterized by phenotype screening in one assay, an efficient method to rank quantified genes according to phenotypic relevance is of great importance. We proposed to rank genes by the probability of their function in mammalian meiosis based on global protein abundance using machine learning. Here, nine types of germ cells focusing on continual substages of meiosis prophase I were isolated, and the corresponding proteomes were quantified by high-resolution mass spectrometry. By combining meiotic labels annotated from the MGI mouse knockout database and the spermatogenesis proteomics dataset, a supervised machine learning package, FuncProFinder, was developed to rank meiosis-essential candidates. Of the candidates whose functions were unannotated, four of ten genes with the top prediction scores, Zcwpw1, Tesmin, 1700102P08Rik and Kctd19, were validated as meiosis-essential genes by knockout mouse models. Therefore,  mammalian meiosis-essential genes could be efficiently predicted based on the protein abundance dataset, which provides a paradigm for other functional gene mining from a related abundance dataset.

Fang Kailun, Li Qidan, Wei Yu, Zhou Changyang, Guo Wenhui, Shen Jiaqi, Wu Ruoxi, Ying Wenqin, Yu Lu, Zi Jin, Zhang Yuxing, Yang Hui, Liu Siqi, Chen Charlie Degui

2020-Nov-30

Cell division, Data evaluation, Developmental biology, Label-free quantification, Molecular Dynamics*, Mouse models, meiosis

General General

Protocol for PD SENSORS: Parkinson's Disease Symptom Evaluation in a Naturalistic Setting producing Outcome measuRes using SPHERE technology. An observational feasibility study of multi-modal multi-sensor technology to measure symptoms and activities of daily living in Parkinson's disease.

In BMJ open

INTRODUCTION : The impact of disease-modifying agents on disease progression in Parkinson's disease is largely assessed in clinical trials using clinical rating scales. These scales have drawbacks in terms of their ability to capture the fluctuating nature of symptoms while living in a naturalistic environment. The SPHERE (Sensor Platform for HEalthcare in a Residential Environment) project has designed a multi-sensor platform with multimodal devices designed to allow continuous, relatively inexpensive, unobtrusive sensing of motor, non-motor and activities of daily living metrics in a home or a home-like environment. The aim of this study is to evaluate how the SPHERE technology can measure aspects of Parkinson's disease.

METHODS AND ANALYSIS : This is a small-scale feasibility and acceptability study during which 12 pairs of participants (comprising a person with Parkinson's and a healthy control participant) will stay and live freely for 5 days in a home-like environment embedded with SPHERE technology including environmental, appliance monitoring, wrist-worn accelerometry and camera sensors. These data will be collected alongside clinical rating scales, participant diary entries and expert clinician annotations of colour video images. Machine learning will be used to look for a signal to discriminate between Parkinson's disease and control, and between Parkinson's disease symptoms 'on' and 'off' medications. Additional outcome measures including bradykinesia, activity level, sleep parameters and some activities of daily living will be explored. Acceptability of the technology will be evaluated qualitatively using semi-structured interviews.

ETHICS AND DISSEMINATION : Ethical approval has been given to commence this study; the results will be disseminated as widely as appropriate.

Morgan Catherine, Craddock Ian, Tonkin Emma L, Kinnunen Kirsi M, McNaney Roisin, Whitehouse Sam, Mirmehdi Majid, Heidarivincheh Farnoosh, McConville Ryan, Carey Julia, Horne Alison, Rolinski Michal, Rochester Lynn, Maetzler Walter, Matthews Helen, Watson Oliver, Eardley Rachel, Whone Alan L

2020-Nov-30

information technology, “parkinsons disease”, qualitative research, statistics & research methods

Surgery Surgery

Lung transplantation for patients with severe COVID-19.

In Science translational medicine ; h5-index 138.0

Lung transplantation can potentially be a life-saving treatment for patients with non-resolving COVID-19-associated respiratory failure. Concerns limiting lung transplantation include recurrence of SARS-CoV-2 infection in the allograft, technical challenges imposed by viral-mediated injury to the native lung, and the potential risk for allograft infection by pathogens causing ventilator-associated pneumonia in the native lung. Importantly, the native lung might recover, resulting in long-term outcomes preferable to those of transplant. Here, we report the results of lung transplantation in three patients with non-resolving COVID-19-associated respiratory failure. We performed single molecule fluorescent in situ hybridization (smFISH) to detect both positive and negative strands of SARS-CoV-2 RNA in explanted lung tissue from the three patients and in additional control lung tissue samples. We conducted extracellular matrix imaging and single cell RNA sequencing on explanted lung tissue from the three patients who underwent transplantation and on warm post-mortem lung biopsies from two patients who had died from COVID-19-associated pneumonia. Lungs from these five patients with prolonged COVID-19 disease were free of SARS-CoV-2 as detected by smFISH, but pathology showed extensive evidence of injury and fibrosis that resembled end-stage pulmonary fibrosis. Using machine learning, we compared single cell RNA sequencing data from the lungs of patients with late stage COVID-19 to that from the lungs of patients with pulmonary fibrosis and identified similarities in gene expression across cell lineages. Our findings suggest that some patients with severe COVID-19 develop fibrotic lung disease for which lung transplantation is their only option for survival.

Bharat Ankit, Querrey Melissa, Markov Nikolay S, Kim Samuel, Kurihara Chitaru, Garza-Castillon Rafael, Manerikar Adwaiy, Shilatifard Ali, Tomic Rade, Politanska Yuliya, Abdala-Valencia Hiam, Yeldandi Anjana V, Lomasney Jon W, Misharin Alexander V, Budinger G R Scott

2020-Nov-30