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Hao Wu, Yanna Shen, Li Zhang, Mouzhe Yang, Yougang Ren, Feng Mao, Zhongxiao Wu Department of Dermatology, Ningbo No.6 Hospital, Ningbo, People’s Republic of ChinaCorrespondence: Zhongxiao Wu, Department of Dermatology, Ningbo No.6 Hospital, No. 1059, Zhongshan East Road, Ningbo, People’s Republic of China, Email 2016123801@jou.edu.cnAbstract: Psoriasis is relatively common in clinical practice, whereas niacin deficiency is relatively rare. We describe the clinical case of a patient with plaque psoriasis for over 20 years who also had a concomitant latent tuberculosis infection. After secukinumab and anti-tuberculosis treatment for 1 year, the psoriatic rash mostly resolved, but atypical symptoms of niacin deficiency suddenly appeared. The patient’s symptoms rapidly subsided after experimental treatment with niacin. After 2 weeks, the patient suddenly developed an erythroderma-like rash, manifesting as large areas of erythema and plaque psoriasis throughout the body. The patient was admitted to the hospital and treated with an anti-inflammatory, biologic adalimumab, tripterygium glycoside, and sodium thiosulfate. The patient was discharged after a week. This case suggests the need for caution and to look out for the emergence of new symptoms when treating patients with moderate-to-severe plaque psoriasis, especially with biologics.Keywords: psoriasis, niacin deficiency, case report

Toll-like receptors (TLRs) play a key role in the innate immune response by sensing bacterial ligands. The mechanisms involved in the TLR-mediated cytokine response are well established; however, the possible contribution of TLR-dependent recognition of bacteria to macrophage phagocytosis remains unclear. Listeria monocytogenes is an intracellular, parasitic, Gram-positive bacterium recognized mainly by TLR2. In this study, we investigated whether TLR2-dependent signaling is involved in the phagocytosis of L. monocytogenes by macrophages. We found no difference in the number of L. monocytogenes cells associating with wild-type (WT) and TLR2(-/-) macrophages 1 h after infection. However, the number of L. monocytogenes cells phagocytosed in TLR2(-/-) and MyD88(-/-) macrophages was significantly lower than that of WT macrophages. In addition, lipopolysaccharide (LPS) treatment restored impaired phagocytic activity of TLR2(-/-) macrophages but did not enhance the activity of MyD88(-/-) macrophages. The efficiency of phagocytosis was suppressed by inhibitors of phosphatidylinositol 3-kinase (PI3K) and the small Rho GTPases but not by cycloheximide. Moreover, functional activation of PI3K and Rac1 was impaired in TLR2(-/-) and MyD88(-/-) macrophages. In an in vivo infection model, we found significantly lower numbers of L. monocytogenes cells phagocytosed in peritoneal macrophages of TLR2(-/-) and MyD88(-/-) mice after intraperitoneal infection. Moreover, a lower number of bacteria were detected in the spleens of TLR2(-/-) mice 1 day after intravenous infection than in WT mice. These results clearly indicated that TLR2-MyD88-dependent signaling enhances the basal level of phagocytosis of L. monocytogenes by macrophages through activation of PI3K and Rac1, not by synthesis of proinflammatory cytokines or expression of phagocytic receptors.

Additional file 1. Figure S1: Transmission electron microscopy showed excess glycogen accumulation in DCM rats. Figure S2: The effect of high osmolarity on Corin and ANP expression levels in neonatal rat cardiomyocytes.

This paper investigates Bayesian modeling of unknown causes of events in the context of disease-outbreak detection. We introduce a Bayesian approach that models and detects both (1) known diseases (e.g., influenza and anthrax) by using informative prior probabilities and (2) unknown diseases (e.g., a new, highly contagious respiratory virus that has never been seen before) by using relatively non-informative prior probabilities. We report the results of simulation experiments which support that this modeling method can improve the detection of new disease outbreaks in a population. A key contribution of this paper is that it introduces a Bayesian approach for jointly modeling both known and unknown causes of events. Such modeling has broad applicability in medical informatics, where the space of known causes of outcomes of interest is seldom complete.