Desheng Hu

Bio: Desheng Hu is an academic researcher from Huazhong University of Science and Technology. The author has contributed to research in topics: Medicine & Inflammation. The author has an hindex of 20, co-authored 50 publications receiving 2298 citations. Previous affiliations of Desheng Hu include Ludwig Maximilian University of Munich & Xiamen University.

Diabetes is a risk factor for the progression and prognosis of COVID-19.

Abstract: Backgound To figure out whether diabetes is a risk factor influencing the progression and prognosis of 2019 novel coronavirus disease (COVID-19). Methods A total of 174 consecutive patients confirmed with COVID-19 were studied. Demographic data, medical history, symptoms and signs, laboratory findings, chest computed tomography (CT) as well the treatment measures were collected and analysed. Results We found that COVID-19 patients without other comorbidities but with diabetes (n = 24) were at higher risk of severe pneumonia, release of tissue injury-related enzymes, excessive uncontrolled inflammation responses and hypercoagulable state associated with dysregulation of glucose metabolism. Furthermore, serum levels of inflammation-related biomarkers such as IL-6, C-reactive protein, serum ferritin and coagulation index, D-dimer, were significantly higher (P Conclusions Our data support the notion that diabetes should be considered as a risk factor for a rapid progression and bad prognosis of COVID-19. More intensive attention should be paid to patients with diabetes, in case of rapid deterioration.

Lymphotoxin β receptor signaling promotes tertiary lymphoid organogenesis in the aorta adventitia of aged ApoE−/− mice

Abstract: Atherosclerosis involves a macrophage-rich inflammation in the aortic intima. It is increasingly recognized that this intimal inflammation is paralleled over time by a distinct inflammatory reaction in adjacent adventitia. Though cross talk between the coordinated inflammatory foci in the intima and the adventitia seems implicit, the mechanism(s) underlying their communication is unclear. Here, using detailed imaging analysis, microarray analyses, laser-capture microdissection, adoptive lymphocyte transfers, and functional blocking studies, we undertook to identify this mechanism. We show that in aged apoE−/− mice, medial smooth muscle cells (SMCs) beneath intimal plaques in abdominal aortae become activated through lymphotoxin β receptor (LTβR) to express the lymphorganogenic chemokines CXCL13 and CCL21. These signals in turn trigger the development of elaborate bona fide adventitial aortic tertiary lymphoid organs (ATLOs) containing functional conduit meshworks, germinal centers within B cell follicles, clusters of plasma cells, high endothelial venules (HEVs) in T cell areas, and a high proportion of T regulatory cells. Treatment of apoE−/− mice with LTβR-Ig to interrupt LTβR signaling in SMCs strongly reduced HEV abundance, CXCL13, and CCL21 expression, and disrupted the structure and maintenance of ATLOs. Thus, the LTβR pathway has a major role in shaping the immunological characteristics and overall integrity of the arterial wall.

Cleavage of roquin and regnase-1 by the paracaspase MALT1 releases their cooperatively repressed targets to promote T(H)17 differentiation

Abstract: Mutations in the RNA-binding protein roquin-1 are known to result in humoral autoimmunity. Heissmeyer and colleagues show that MALT1 cleavage of roquin and regnase-1 downstream of TCR signaling releases cooperatively repressed targets to promote TH17 cell differentiation

ApoE attenuates unresolvable inflammation by complex formation with activated C1q

Abstract: Apolipoprotein-E (ApoE) has been implicated in Alzheimer’s disease, atherosclerosis, and other unresolvable inflammatory conditions but a common mechanism of action remains elusive. We found in ApoE-deficient mice that oxidized lipids activated the classical complement cascade (CCC), resulting in leukocyte infiltration of the choroid plexus (ChP). All human ApoE isoforms attenuated CCC activity via high-affinity binding to the activated CCC-initiating C1q protein (KD~140–580 pM) in vitro, and C1q–ApoE complexes emerged as markers for ongoing complement activity of diseased ChPs, Aβ plaques, and atherosclerosis in vivo. C1q–ApoE complexes in human ChPs, Aβ plaques, and arteries correlated with cognitive decline and atherosclerosis, respectively. Treatment with small interfering RNA (siRNA) against C5, which is formed by all complement pathways, attenuated murine ChP inflammation, Aβ-associated microglia accumulation, and atherosclerosis. Thus, ApoE is a direct checkpoint inhibitor of unresolvable inflammation, and reducing C5 attenuates disease burden. ApoE is a direct checkpoint inhibitor of unresolvable inflammation in neurodegenerative and cardiovascular diseases

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

Abstract: 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.

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

Abstract: Objective To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. Design Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. Data sources PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. Study selection Studies that developed or validated a multivariable covid-19 related prediction model. Data extraction At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). Results 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. Conclusion Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. Systematic review registration Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. Readers’ note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.