Covid-19 CasesTo rapidly communicate information on the global clinical effort against Covid-19, the Journal has initiated a series of case reports that offer important teaching points or novel findings. The case reports should be viewed as observations rather than as recommendations for evaluation or treatment. In the interest of timeliness, these reports are evaluated by in-house editors, with peer review reserved for key points as needed. Coagulopathy and Antiphospholipid Antibodies in Patients with Covid-19We describe a patient with Covid-19 and clinically significant coagulopathy, antiphospholipid antibodies, and multiple infarcts. He was one of three patients with these findings in an intensive care unit designated for patients with Covid-19. This unit, which was managed by a multidisciplinary team from Peking Union Medical College Hospital in the Sino-French New City Branch of Tongji Hospital in Wuhan, China, was set up on an emergency basis to accept the most critically ill patients during the outbreak of Covid-19. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection was confirmed in all the patients by reverse-transcriptase-polymerase-chain-reaction (RT-PCR) assay or serologic testing.A 69-year-old man with a history of hypertension, diabetes, and stroke presented with fever, cough, dyspnea, diarrhea, and headache. Covid-19 was diagnosed in the patient on January 25, 2020, on the basis of RT-PCR testing that detected SARS-CoV-2. The initial treatment was supportive; however, the illness subsequently progressed to hypoxemic respiratory failure warranting the initiation of invasive mechanical ventilation.

IntroductionCD200 is a type I transmembrane glycoprotein that can regulate the activation threshold of inflammatory immune responses, polarize cytokine production, and maintain immune homeostasis. We therefore evaluated the functional status of CD200/CD200 receptor 1 (CD200R1) interactions in subjects with systemic lupus erythematosus (SLE).MethodsSerum CD200 level was detected by ELISA. The expression of CD200/CD200R1 by CD4+ T cells and dendritic cells (DCs) was examined by flow cytometry, and then compared between SLE patients and healthy controls. Peripheral blood mononuclear cells were stained with carboxyfluorescein diacetate succinimidyl ester and annexin V/propidium iodide for evaluation of the effect of CD200 on cell proliferation and apoptosis. In addition, the effect of CD200 on DC function was determined by transwell migration assay as well as by measurement of binding and phagocytosis of apoptotic cells.ResultsIn SLE patients, the number of CD200+ cells and the level of soluble CD200 were significantly higher than in healthy controls, whereas the expression of CD200R1 by CD4+ T cells and DCs was decreased. Furthermore, the increased CD200 expression by early apoptotic cells contributed to their diminished binding and phagocytosis by DCs in SLE. Importantly, the engagement of CD200 receptor on CD4+ T cells with CD200-Fc fusion protein in vitro reduced the differentiation of T-helper type 17 cells and reversed the defective induction of CD4+CD25highFoxP3+ T cells by transforming growth factor beta in SLE patients. Conversely, blockade of CD200-CD200R1 interaction with anti-CD200R1 antibody promoted CD4+ T-cell proliferation.ConclusionCD200 and CD200R1 expression and function are abnormal in SLE and may contribute to the immunologic abnormalities in SLE.

This work proposes the concept of continuous conditional generative adversarial network (CcGAN), the first generative model for image generation conditional on continuous, scalar conditions (termed regression labels). Existing conditional GANs (cGANs) are mainly designed for categorical conditions (e.g., class labels); conditioning on regression labels is mathematically distinct and raises two fundamental problems: (P1) Since there may be very few (even zero) real images for some regression labels, minimizing existing empirical versions of cGAN losses (a.k.a. empirical cGAN losses) often fails in practice; (P2) Since regression labels are scalar and infinitely many, conventional label input mechanisms (e.g., combining a hidden map of the generator/discriminator with a one-hot encoded label) are not applicable. To solve the above problems, by (S1) reformulating existing empirical cGAN losses to be appropriate for the continuous scenario; and (S2) proposing a naive label input (NLI) mechanism and an improved label input (ILI) mechanism to incorporate regression labels into the generator and the discriminator, we propose 4 CcGANs with employing different proposed losses and label input mechanisms. The reformulation in (S1) leads to two novel empirical discriminator losses, termed the hard vicinal discriminator loss (HVDL) and the soft vicinal discriminator loss (SVDL) respectively, and a novel empirical generator loss. The error bounds of the discriminator trained with HVDL and SVDL respectively are derived under mild assumptions in this work. To evaluate the performance of CcGANs, two new benchmark datasets (RC-49 and Cell-200) are proposed. A novel evaluation metric (Sliding Fr échet Inception Distance) is also proposed to replace Intra-FID when Intra-FID is not applicable. Our extensive experiments on several benchmark datasets (i.e., the Circular 2-D Gaussians, RC-49, UTKFace, Cell-200, and Steering Angle datasets) support the following observations: The proposed CcGAN is able to generate diverse, high-quality samples from the image distribution conditional on a given regression label; and CcGAN substantially outperforms cGAN both visually and quantitatively.

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