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Binfeng Chen

Bio: Binfeng Chen is an academic researcher from Sun Yat-sen University. The author has contributed to research in topics: Medicine & Immunology. The author has an hindex of 1, co-authored 1 publications receiving 645 citations.

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Journal ArticleDOI
TL;DR: A risk score based on characteristics of COVID-19 patients at the time of admission to the hospital was developed that may help predict a patient's risk of developing critical illness.
Abstract: Importance Early identification of patients with novel coronavirus disease 2019 (COVID-19) who may develop critical illness is of great importance and may aid in delivering proper treatment and optimizing use of resources. Objective To develop and validate a clinical score at hospital admission for predicting which patients with COVID-19 will develop critical illness based on a nationwide cohort in China. Design, Setting, and Participants Collaborating with the National Health Commission of China, we established a retrospective cohort of patients with COVID-19 from 575 hospitals in 31 provincial administrative regions as of January 31, 2020. Epidemiological, clinical, laboratory, and imaging variables ascertained at hospital admission were screened using Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression to construct a predictive risk score (COVID-GRAM). The score provides an estimate of the risk that a hospitalized patient with COVID-19 will develop critical illness. Accuracy of the score was measured by the area under the receiver operating characteristic curve (AUC). Data from 4 additional cohorts in China hospitalized with COVID-19 were used to validate the score. Data were analyzed between February 20, 2020 and March 17, 2020. Main Outcomes and Measures Among patients with COVID-19 admitted to the hospital, critical illness was defined as the composite measure of admission to the intensive care unit, invasive ventilation, or death. Results The development cohort included 1590 patients. the mean (SD) age of patients in the cohort was 48.9 (15.7) years; 904 (57.3%) were men. The validation cohort included 710 patients with a mean (SD) age of 48.2 (15.2) years, and 382 (53.8%) were men and 172 (24.2%). From 72 potential predictors, 10 variables were independent predictive factors and were included in the risk score: chest radiographic abnormality (OR, 3.39; 95% CI, 2.14-5.38), age (OR, 1.03; 95% CI, 1.01-1.05), hemoptysis (OR, 4.53; 95% CI, 1.36-15.15), dyspnea (OR, 1.88; 95% CI, 1.18-3.01), unconsciousness (OR, 4.71; 95% CI, 1.39-15.98), number of comorbidities (OR, 1.60; 95% CI, 1.27-2.00), cancer history (OR, 4.07; 95% CI, 1.23-13.43), neutrophil-to-lymphocyte ratio (OR, 1.06; 95% CI, 1.02-1.10), lactate dehydrogenase (OR, 1.002; 95% CI, 1.001-1.004) and direct bilirubin (OR, 1.15; 95% CI, 1.06-1.24). The mean AUC in the development cohort was 0.88 (95% CI, 0.85-0.91) and the AUC in the validation cohort was 0.88 (95% CI, 0.84-0.93). The score has been translated into an online risk calculator that is freely available to the public (http://118.126.104.170/) Conclusions and Relevance In this study, a risk score based on characteristics of COVID-19 patients at the time of admission to the hospital was developed that may help predict a patient’s risk of developing critical illness.

1,032 citations

Journal ArticleDOI
TL;DR: New insights are provided into the regulation of gut inflammation that TIGIT deficiency protects mice from DSS-induced colitis, which might have a potential therapeutic value in the treatment of IBDs.
Abstract: Tissue-resident memory T cells (TRM cells) have been shown to play an instrumental role in providing local immune responses for pathogen clearance in barrier tissues. However, their contribution to inflammatory bowel diseases (IBDs) and the underlying regulation are less clear. Here, we identified a critical role of T-cell immunoreceptor with immunoglobulin and ITIM (TIGIT) in regulating CD4+ TRM cells in an experimental model of intestinal inflammation. We found that CD4+ TRM cells were increased and correlated with disease activities in mice with dextran sulfate sodium (DSS)-induced colitis. Phenotypically, these CD4+ TRM cells could be classified into CD69+CD103− and CD69+CD103+ subsets. Functionally, these CD4+ TRM cells were heterogeneous. CD69+CD103− CD4+ TRM cells were pro-inflammatory and produced interferon-γ (IFNγ) and interleukin-17A (IL-17A), which accounted for 68.7% and 62.9% of total IFNγ+ and IL-17A+ CD4+ T cells, respectively, whereas CD69+CD103+ CD4+ TRM cells accounted for 73.7% Foxp3+ regulatory T cells. TIGIT expression was increased in CD4+ T cells in the gut of mice with DSS-induced colitis. TIGIT deficiency impaired IL-17A expression in CD69+CD103− CD4+ TRM cells specifically, resulting in ameliorated gut inflammation and tissue injury. Together, this study provides new insights into the regulation of gut inflammation that TIGIT deficiency protects mice from DSS-induced colitis, which might have a potential therapeutic value in the treatment of IBDs.

5 citations

Journal ArticleDOI
TL;DR: In this article , the authors identified nicotinamide phosphoribosyltransferase (NAMPT), the rate-limiting enzyme in the salvage NAD+ biosynthetic pathway, as playing a key role in controlling IFNγ production by CD4+ T cells in LN.

3 citations

Journal ArticleDOI
TL;DR: In this paper , the authors identify spleen FRC-derived acetylcholine (ACh) as a key factor that controls autoreactive B cell responses in systemic lupus erythematosus (SLE).

2 citations

Journal ArticleDOI
TL;DR: Therapeutic targeting of iron metabolism should have the potential to normalize glucose metabolism in CD4+ T‐cells and reverse their proinflammatory phenotype in IIM.
Abstract: Abstract Background T helper cells in patients with autoimmune disease of idiopathic inflammatory myopathies (IIM) are characterized with the proinflammatory phenotypes. The underlying mechanisms remain unknown. Methods RNA sequencing was performed for differential expression genes. Gene expression in CD4+ T‐cells was confirmed by quantitative real‐time PCR. CD4+ T‐cells from IIM patients or healthy controls were evaluated for metabolic activities by Seahorse assay. Glucose uptake, T‐cell proliferation and differentiation were evaluated and measured by flow cytometry. Human CD4+ T‐cells treated with iron chelators or Pfkfb4 siRNA were measured for glucose metabolism, proliferation and differentiation. Signalling pathway activation was evaluated by western blot and flow cytometry. Mouse model of experimental autoimmune myositis (EAM) were induced and treated with iron chelator or rapamycin. CD4+ T‐cell differentiation and muscle inflammation in the EAM mice were evaluated. Results RNA‐sequencing analysis revealed that iron was involved with glucose metabolism and CD4+ T‐cell differentiation. IIM patient‐derived CD4+ T‐cells showed enhanced glycolysis and mitochondrial respiration, which was inhibited by iron chelation. CD4+ T‐cells from patients with IIM was proinflammatory and iron chelation suppressed the differentiation of interferon gamma (IFNγ)‐ and interleukin (IL)‐17A‐producing CD4+ T‐cells, which resulted in an increased percentage of regulatory T (Treg) cells. Mechanistically, iron promoted glucose metabolism by an upregulation of PFKFB4 through AKT‐mTOR signalling pathway. Notably, the knockdown of Pfkfb4 decreased glucose influx and thus suppressed the differentiation of IFNγ‐ and IL‐17A‐producing CD4+ T‐cells. In vivo, iron chelation inhibited mTOR signalling pathway and reduced PFKFB4 expression in CD4+ T‐cells, resulting in reduced proinflammatory IFNγ‐ and IL‐17A‐producing CD4+ T‐cells and increased Foxp3+ Treg cells, leading to ameliorated muscle inflammation. Conclusions Iron directs CD4+ T‐cells into a proinflammatory phenotype by enhancing glucose metabolism. Therapeutic targeting of iron metabolism should have the potential to normalize glucose metabolism in CD4+ T‐cells and reverse their proinflammatory phenotype in IIM.

2 citations


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01 Jan 2020
TL;DR: Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future.
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.

4,408 citations

Journal ArticleDOI
07 Apr 2020-BMJ
TL;DR: Proposed models for covid-19 are poorly reported, at high risk of bias, and their reported performance is probably optimistic, according to a review of published and preprint reports.
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.

2,183 citations

Journal ArticleDOI
TL;DR: The extrapulmonary organ-specific pathophysiology, presentations and management considerations for patients with COVID-19 are reviewed to aid clinicians and scientists in recognizing and monitoring the spectrum of manifestations, and in developing research priorities and therapeutic strategies for all organ systems involved.
Abstract: Although COVID-19 is most well known for causing substantial respiratory pathology, it can also result in several extrapulmonary manifestations. These conditions include thrombotic complications, myocardial dysfunction and arrhythmia, acute coronary syndromes, acute kidney injury, gastrointestinal symptoms, hepatocellular injury, hyperglycemia and ketosis, neurologic illnesses, ocular symptoms, and dermatologic complications. Given that ACE2, the entry receptor for the causative coronavirus SARS-CoV-2, is expressed in multiple extrapulmonary tissues, direct viral tissue damage is a plausible mechanism of injury. In addition, endothelial damage and thromboinflammation, dysregulation of immune responses, and maladaptation of ACE2-related pathways might all contribute to these extrapulmonary manifestations of COVID-19. Here we review the extrapulmonary organ-specific pathophysiology, presentations and management considerations for patients with COVID-19 to aid clinicians and scientists in recognizing and monitoring the spectrum of manifestations, and in developing research priorities and therapeutic strategies for all organ systems involved.

2,113 citations

Journal ArticleDOI
09 Sep 2020-BMJ
TL;DR: The 4C Mortality Score outperformed existing scores, showed utility to directly inform clinical decision making, and can be used to stratify patients admitted to hospital with covid-19 into different management groups.
Abstract: OBJECTIVE:To develop and validate a pragmatic risk score to predict mortality in patients admitted to hospital with coronavirus disease 2019 (covid-19). DESIGN:Prospective observational cohort study. SETTING:International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) World Health Organization (WHO) Clinical Characterisation Protocol UK (CCP-UK) study (performed by the ISARIC Coronavirus Clinical Characterisation Consortium-ISARIC-4C) in 260 hospitals across England, Scotland, and Wales. Model training was performed on a cohort of patients recruited between 6 February and 20 May 2020, with validation conducted on a second cohort of patients recruited after model development between 21 May and 29 June 2020. PARTICIPANTS: Adults (age ≥18 years) admitted to hospital with covid-19 at least four weeks before final data extraction. MAIN OUTCOME MEASURE:In-hospital mortality. RESULTS:35 463 patients were included in the derivation dataset (mortality rate 32.2%) and 22 361 in the validation dataset (mortality rate 30.1%). The final 4C Mortality Score included eight variables readily available at initial hospital assessment: age, sex, number of comorbidities, respiratory rate, peripheral oxygen saturation, level of consciousness, urea level, and C reactive protein (score range 0-21 points). The 4C Score showed high discrimination for mortality (derivation cohort: area under the receiver operating characteristic curve 0.79, 95% confidence interval 0.78 to 0.79; validation cohort: 0.77, 0.76 to 0.77) with excellent calibration (validation: calibration-in-the-large=0, slope=1.0). Patients with a score of at least 15 (n=4158, 19%) had a 62% mortality (positive predictive value 62%) compared with 1% mortality for those with a score of 3 or less (n=1650, 7%; negative predictive value 99%). Discriminatory performance was higher than 15 pre-existing risk stratification scores (area under the receiver operating characteristic curve range 0.61-0.76), with scores developed in other covid-19 cohorts often performing poorly (range 0.63-0.73). CONCLUSIONS:An easy-to-use risk stratification score has been developed and validated based on commonly available parameters at hospital presentation. The 4C Mortality Score outperformed existing scores, showed utility to directly inform clinical decision making, and can be used to stratify patients admitted to hospital with covid-19 into different management groups. The score should be further validated to determine its applicability in other populations. STUDY REGISTRATION:ISRCTN66726260.

742 citations

Journal ArticleDOI
TL;DR: A synthesis of the current literature pertaining to factors predictive of COVID‐19 clinical course and outcomes shows findings associated with increased disease severity and/or mortality include age, multiple pre‐existing comorbidities, hypoxia, specific computed tomography findings indicative of extensive lung involvement, diverse laboratory test abnormalities, and biomarkers of end‐organ dysfunction.
Abstract: The coronavirus disease 2019 (COVID-19) pandemic is a rapidly evolving global emergency that continues to strain healthcare systems. Emerging research describes a plethora of patient factors-including demographic, clinical, immunologic, hematological, biochemical, and radiographic findings-that may be of utility to clinicians to predict COVID-19 severity and mortality. We present a synthesis of the current literature pertaining to factors predictive of COVID-19 clinical course and outcomes. Findings associated with increased disease severity and/or mortality include age > 55 years, multiple pre-existing comorbidities, hypoxia, specific computed tomography findings indicative of extensive lung involvement, diverse laboratory test abnormalities, and biomarkers of end-organ dysfunction. Hypothesis-driven research is critical to identify the key evidence-based prognostic factors that will inform the design of intervention studies to improve the outcomes of patients with COVID-19 and to appropriately allocate scarce resources.

537 citations