The Framework for the Prediction of the Critical Turning Period for Outbreak of COVID-19 Spread in China based on the iSEIR Model

The goal of this study is to establish a general framework for predicting the so-called critical Turning Period in an infectious disease epidemic such as the COVID-19 outbreak in China early this year. This framework enabled a timely prediction of the turning period when applied to Wuhan COVID-19 epidemic and informed the relevant authority for taking appropriate and timely actions to control the epidemic. It is expected to provide insightful information on turning period for the world's current battle against the COVID-19 pandemic. The underlying mathematical model in our framework is the individual Susceptible-Exposed- Infective-Removed (iSEIR) model, which is a set of differential equations extending the classic SEIR model. We used the observed daily cases of COVID-19 in Wuhan from February 6 to 10, 2020 as the input to the iSEIR model and were able to generate the trajectory of COVID-19 cases dynamics for the following days at midnight of February 10 based on the updated model, from which we predicted that the turning period of CIVID-19 outbreak in Wuhan would arrive within one week after February 14. This prediction turned to be timely and accurate, providing adequate time for the government, hospitals, essential industry sectors and services to meet peak demands and to prepare aftermath planning. Our study also supports the observed effectiveness on flatting the epidemic curve by decisively imposing the Lockdown and Isolation Control Program in Wuhan since January 23, 2020. The Wuhan experience provides an exemplary lesson for the whole world to learn in combating COVID-19.Comment: 24 paages, 9 figures, 10 table

Mechanism of homocysteine-mediated endothelial injury and its consequences for atherosclerosis

Homocysteine (Hcy) is an intermediate amino acid formed during the conversion from methionine to cysteine. When the fasting plasma Hcy level is higher than 15 μmol/L, it is considered as hyperhomocysteinemia (HHcy). The vascular endothelium is an important barrier to vascular homeostasis, and its impairment is the initiation of atherosclerosis (AS). HHcy is an important risk factor for AS, which can promote the development of AS and the occurrence of cardiovascular events, and Hcy damage to the endothelium is considered to play a very important role. However, the mechanism by which Hcy damages the endothelium is still not fully understood. This review summarizes the mechanism of Hcy-induced endothelial injury and the treatment methods to alleviate the Hcy induced endothelial dysfunction, in order to provide new thoughts for the diagnosis and treatment of Hcy-induced endothelial injury and subsequent AS-related diseases

Single-Cell RNA Sequencing of Peripheral Blood Mononuclear Cells From Acute Myocardial Infarction

BackgroundAcute myocardial infarction (AMI) can occur in patients with atherosclerotic disease, with or without plaque rupture. Previous studies have indicated a set of immune responses to plaque rupture. However, the specific circulating immune cell subsets that mediate inflammatory plaque rupture remain elusive.MethodsTen AMI patients were enrolled in our study (five with and five without plaque rupture; plaque characteristics were identified by optical coherence tomography). By single-cell RNA sequencing, we analyzed the transcriptomic profile of peripheral blood mononuclear cells.ResultsWe identified 27 cell clusters among 82,550 cells, including monocytes, T cells, NK cells, B cells, megakaryocytes, and CD34+ cells. Classical and non-classical monocytes constitute the major inflammatory cell types, and pro-inflammatory genes such as CCL5, TLR7, and CX3CR1 were significantly upregulated in patients with plaque rupture, while the neutrophil activation and degranulation genes FPR2, MMP9, and CLEC4D were significantly expressed in the intermediate monocytes derived from patients without plaque rupture. We also found that CD4+ effector T cells may contribute to plaque rupture by producing a range of cytokines and inflammatory-related chemokines, while CD8+ effector T cells express more effector molecules in patients without plaque rupture, such as GZMB, GNLY, and PRF1, which may contribute to the progress of plaque erosion. Additionally, NK and B cells played a significant role in activating inflammatory cells and promoting chemokine production in the plaque rupture. Cell–cell communication elaborated characteristics in signaling pathways dominated by inflammatory activation of classical monocytes in patients with plaque rupture.ConclusionsOur studies demonstrate that the circulating immune cells of patients with plaque rupture exhibit highly pro-inflammatory characteristics, while plaque erosion is mainly associated with intermediate monocyte amplification, neutrophil activation, and degranulation. These findings may provide novel targets for the precise treatment of patients with AMI

Use of a Novel Nonparametric Version of DEPTH to Identify Genomic Regions Associated with Prostate Cancer Risk.

BACKGROUND: We have developed a genome-wide association study analysis method called DEPTH (DEPendency of association on the number of Top Hits) to identify genomic regions potentially associated with disease by considering overlapping groups of contiguous markers (e.g., SNPs) across the genome. DEPTH is a machine learning algorithm for feature ranking of ultra-high dimensional datasets, built from well-established statistical tools such as bootstrapping, penalized regression, and decision trees. Unlike marginal regression, which considers each SNP individually, the key idea behind DEPTH is to rank groups of SNPs in terms of their joint strength of association with the outcome. Our aim was to compare the performance of DEPTH with that of standard logistic regression analysis. METHODS: We selected 1,854 prostate cancer cases and 1,894 controls from the UK for whom 541,129 SNPs were measured using the Illumina Infinium HumanHap550 array. Confirmation was sought using 4,152 cases and 2,874 controls, ascertained from the UK and Australia, for whom 211,155 SNPs were measured using the iCOGS Illumina Infinium array. RESULTS: From the DEPTH analysis, we identified 14 regions associated with prostate cancer risk that had been reported previously, five of which would not have been identified by conventional logistic regression. We also identified 112 novel putative susceptibility regions. CONCLUSIONS: DEPTH can reveal new risk-associated regions that would not have been identified using a conventional logistic regression analysis of individual SNPs. IMPACT: This study demonstrates that the DEPTH algorithm could identify additional genetic susceptibility regions that merit further investigation. Cancer Epidemiol Biomarkers Prev; 25(12); 1619-24. ©2016 AACR.National Health and Medical Research Council Australia (Grant ID: 1033452, Senior Principal Research Fellowship, Senior Research Fellowship), Cancer Research UK (Grant IDs: C5047/A7357, C1287/A10118, C1287/A5260, C5047/A3354, C5047/A10692, C16913/A6135 and C16913/A6835), Prostate Research Campaign UK (now Prostate Cancer UK), The Institute of Cancer Research and The Everyman Campaign, The National Cancer Research Network UK, The National Cancer Research Institute (NCRI) UK, National Institute for Health Research funding to the NIHR Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, Prostate Cancer Research Program of Cancer Council Victoria from The National Health and Medical Research Council, Australia (Grant IDs: 126402, 209057, 251533, 396414, 450104, 504700, 504702, 504715, 623204, 940394, 614296), VicHealth, Cancer Council Victoria, The Prostate Cancer Foundation of Australia, The Whitten Foundation, PricewaterhouseCoopers, Tattersall’sThis is the author accepted manuscript. The final version is available from the American Association for Cancer Research via http://dx.doi.org/10.1158/1055-9965.EPI-16-030

COMPUTING MINIMUM DESCRIPTION LENGTH FOR ROBUST LINEAR REGRESSION MODEL SELECTION

A minimum description length (MDL) and stochastic complexity approach for model selection in robust linear regression is studied in this paper. Computational aspects and implementation of this approach to practical problems are the focuses of the study. Particularly,we provide both algorithms and a package of S language programs for computing the stochastic complexity and proceeding with the associated model selection. A simulation study is then presented for illustration and comparing the MDL approach with the commonly used AIC and BIC methods. Finally, an application is given to a physiological study of triathlon athletes.