다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

The PASCAL Visual Object Classes Challenge

The COVID-19 pandemic caused by infection with SARS-CoV-2 has led to more than 200,000 deaths worldwide. Several studies have now established that the hyperinflammatory response induced by SARS-CoV-2 is a major cause of disease severity and death in infected patients. Macrophages are a population of innate immune cells that sense and respond to microbial threats by producing inflammatory molecules that eliminate pathogens and promote tissue repair. However, a dysregulated macrophage response can be damaging to the host, as is seen in the macrophage activation syndrome induced by severe infections, including in infections with the related virus SARS-CoV. Here we describe the potentially pathological roles of macrophages during SARS-CoV-2 infection and discuss ongoing and prospective therapeutic strategies to modulate macrophage activation in patients with COVID-19.

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Pathological inflammation in patients with COVID-19: a key role for monocytes and macrophages.

A new type of pneumonia caused by a novel coronavirus SARS-CoV-2 outbreaks recently in China and spreads into many other countries. This disease, named as COVID-19, is similar to patients infected by SARS-CoV and MERS-CoV, and nearly 20% of patients developed severe condition. Cardiac injury is a prevalent complication of severe patients, exacerbating the disease severity in coronavirus disease 2019 (COVID-19) patients. Angiotensin-converting enzyme 2 (ACE2), the key host cellular receptor of SARS-CoV-2, has been identified in multiple organs, but its cellular distribution in human heart is not illuminated clearly. This study performed the first state-of-art single cell atlas of adult human heart, and revealed that pericytes with high expression of ACE2 might act as the target cardiac cell of SARS-CoV-2. The pericytes injury due to virus infection may result in capillary endothelial cells dysfunction, inducing microvascular dysfunction. And patients with basic heart failure disease showed increased ACE2 expression at both mRNA and protein levels, meaning that if infected by the virus these patients may have higher risk of heart attack and critically ill condition. The finding of this study explains the high rate of severe cases among COVID-19 patients with basic cardiovascular disease; and these results also perhaps provide important reference to clinical treatment of cardiac injury among severe patients infected by SARS-CoV-2.

The ACE2 expression in human heart indicates new potential mechanism of heart injury among patients infected with SARS-CoV-2.

A computer-based information search and retrieval system and method for retrieving textual digital objects that makes full use of the projections of the documents onto both the reduced document space characterized by the singular value decomposition-based latent semantic structure and its orthogonal space. The resulting system and method has increased robustness, improving the instability of the traditional keyword search engine due to synonymy and/or polysemy of a natural language, and therefore is particularly suitable for web document searching over a distributed computer network such as the Internet.

Probabilistic information retrieval based on differential latent semantic space

A new differential LSI space-based probabilistic document classifier

Optimal referencing for stereo-electroencephalographic (SEEG) recordings.

Deep learning is well known as a method to extract hierarchical representations of data. In this paper a novel unsupervised deep learning based methodology, named Local Binary Pattern Network (LBPNet), is proposed to efficiently extract and compare high-level over-complete features in multilayer hierarchy. The LBPNet retains the same topology of Convolutional Neural Network (CNN) — one of the most well studied deep learning architectures — whereas the trainable kernels are replaced by the off-the-shelf computer vision descriptor (i.e., LBP). This enables the LBPNet to achieve a high recognition accuracy without requiring any costly model learning approach on massive data. Through extensive numerical experiments using the public benchmarks (i.e., FERET and LFW), LBPNet has shown that it is comparable to other unsupervised methods.

Liang Chen

Papers

The ACE2 expression in human heart indicates new potential mechanism of heart injury among patients infected with SARS-CoV-2.

Probabilistic information retrieval based on differential latent semantic space

A new differential LSI space-based probabilistic document classifier

Optimal referencing for stereo-electroencephalographic (SEEG) recordings.

Local binary pattern network: A deep learning approach for face recognition