Worldwide Prevalence of Familial Hypercholesterolemia: Meta-Analyses of 11 Million Subjects

BackgroundDespite the greater prevalence of familial hypercholesterolemia (FH) in subjects with ischemic heart disease (IHD), premature IHD, and severe hypercholesterolemia (low-density lipoprotein ≥190 mg/dl), overall prevalence estimates are not available.ObjectivesThe aim of this study was to provide worldwide estimates of FH prevalence in subjects with IHD, premature IHD, and severe hypercholesterolemia compared with those in the general population.MethodsIn this systematic review and meta-analyses, Embase, PubMed, and the Web of Science were searched until June 3, 2019, for peer-reviewed papers and conference abstracts reporting heterozygous FH prevalence in nonfounder populations, revealing 104 studies eligible for inclusion.ResultsEstimates of FH prevalence were pooled using random-effects meta-analyses and were 0.32% (95% confidence interval [CI]: 0.26% to 0.39% [corresponding to 1:313]) among 10,921,310 unique subjects in the general population (33,036 patients with FH) on the basis of 44 studies, 3.2% (95% CI: 2.2% to 4.3% [1:31]) among 84,479 unique subjects with IHD (2,103 patients with FH) on the basis of 28 studies, 6.7% (95% CI: 4.9% to 8.7% [1:15]) among 31,316 unique subjects with premature IHD (1,471 patients with FH) on the basis of 32 studies, and 7.2% (95% CI: 4.6% to 10.8% [1:14]) among 17,728 unique subjects with severe hypercholesterolemia (920 patients with FH) on the basis of 7 studies. FH prevalence in the general population was similar using genetic versus clinical diagnoses. Seventeen of 195 countries (9%) in the world have reported FH prevalence for the general population, leaving 178 (91%) countries in the world with unknown prevalence.ConclusionsCompared with 1:313 among subjects in the general population, FH prevalence is 10-fold higher among those with IHD, 20-fold higher among those with premature IHD, and 23-fold higher among those with severe hypercholesterolemia. The prevalence of FH is unknown in 90% of countries in the world.     

Organoids in modelling infectious diseases

Approaches based on animal and two-dimensional (2D) cell culture models cannot ensure reliable results in modeling novel pathogens or in drug testing in the short term; therefore, there is rising interest in platforms such as organoids. To develop a toolbox that can be used successfully to overcome current issues in modeling various infections, it is essential to provide a framework of recent achievements in applying organoids. Organoids have been used to study viruses, bacteria, and protists that cause, for example, respiratory, gastrointestinal, and liver diseases. Their future as models of infection will be associated with improvements in system complexity, including abilities to model tissue structure, a dynamic microenvironment, and coinfection.Teaser.Organoids are a flexible tool for modelling viral, bacterial and protist infections. They can provide fast and reliable information on the biology of pathogens and in drug screening, and thus have become essential in combatting emerging infectious diseases.     

发热伴血小板减少综合征研究热点的可视化分析

[摘要]　目的　对发热伴血小板减少综合征（severe fever with thrombocytopenia syndrome, SFTS）的相关文献进行文献计量学和可视化分析，探寻近年来的研究现状、热点及趋势，为临床治疗和基础研究提供参考。方法?以Web of Science（WOS）核心合集和中国知网（China national knowledge infrastructure, CNKI）数据库为文献来源，检索2011年1月1日—2021年12月31日有关SFTS的文献，导入CiteSpace.5.7.R2软件，以国家、作者、文献共被引、关键词为节点进行可视化分析，并绘制相关图谱。结果?在WOS核心合集共检索到797篇文献，在CNKI数据库共检索到714篇文献。相关领域发文量总体呈上升趋势，中国发文量居首位，美国和日本之间机构合作密切。研究热点集中在发病机制、抗体、特异性治疗等领域。非结构蛋白、临床预后、血小板减少、SFTS感染的靶细胞等将是未来的研究重点。结论?国内外关于SFTS的研究逐渐成熟，新型布尼亚病毒、免疫功能、预后是研究重点，但是对SFTS发病机制和病毒受体尚不清楚，仍须进一步探索。     

重症肺炎与肺梗死

肺梗死是儿童重症肺炎的少见并发症，多累及下叶近胸膜处，常常继发于肺血栓栓塞，诊断依靠于胸部增强CT检查。关于儿童重症肺炎并发肺梗死的研究较少，相关病因学、流行病学、临床特点等数据缺乏。基于成人的研究提示肺梗死与肺血栓栓塞的临床表现、治疗及预后类似。儿科医师应提高对本病的认识，通过真实世界研究方法发掘客观规律，积累儿科诊治经验。     

Pediatric problematic severe asthma: Recent advances in management

Problematic severe asthma remains a significant challenge to manage, accounting for the majority of healthcare utilization among children with asthma. The heterogeneity is recognized and the clinical phenotypes of “difficult-to-treat” asthma (DA) and “severe therapy-resistant asthma” (STRA) help to guide management. Recent evidence supports molecular distinctions between these phenotypes and shows poor correlations between peripheral and airway markers of inflammation, especially in STRA. Airway neutrophils in the context of childhood severe asthma have been explored, but their role in disease causation, protection, or as bystanders remain unknown, and thus, treatment implications are unclear. Several novel management strategies, including once-daily maintenance therapy, single-device maintenance and reliever therapy, and novel biological treatments are being increasingly used for DA and STRA. However, pediatric data for efficacy of novel treatments is scarce, and when available, is restricted to adolescents. The aim of this review is to highlight recent advances in objective biomarkers that aid stratification and management of childhood severe asthma and to highlight gaps in pediatric evidence. Specifically, the urgent need for efficacy studies to improve the management of problematic severe asthma in children younger than 12 years.     

Switching between biologics in severe asthma patients. When the first choice is not proven to be the best

During the last decades, new treatments targeting disease mechanisms referred as biologics have been introduced in the therapy of asthma and currently, five monoclonal antibodies have been approved. Although these therapeutic agents have been formulated to target specific asthma endotypes, it is often difficult for the treating physician to identify which patient is the best candidate for each one of these specific treatments especially in the clinical scenario of a patient in whom clinical characteristics overlap between different endotypes, allowing the selection of more than one biologic agent. As no head-to-head comparisons between these biologics have been attempted, there is no evidence on the superiority of one biologic agent over the other. Furthermore, a physician's first therapeutic decision, no matter how carefully has been made, may often result in suboptimal clinical response and drug discontinuation, indicating the need for switching to a different biologic. In this short review, we discuss the available evidence regarding the switching between biologics in patients with severe asthma and we propose a simple algorithm on switching possibilities in case that the physicians’ initial choice is proven not to be the best.     

Risk factors related to the severity of COVID-19 in Wuhan

Objective: To evaluate the characteristics at admission of patients with moderate COVID-19 in Wuhan and to explore risk factors associated with the severe prognosis of the disease for prognostic prediction.Methods: In this retrospective study, moderate and severe disease was defined according to the report of the WHO-China Joint Mission on COVID-19. Clinical characteristics and laboratory findings of 172 patients with laboratory-confirmed moderate COVID-19 were collected when they were admitted to the Cancer Center of Wuhan Union Hospital between February 13, 2020 and February 25, 2020. This cohort was followed to March 14, 2020. The outcomes, being discharged as mild cases or developing into severe cases, were categorized into two groups. The data were compared and analyzed with univariate logistic regression to identify the features that differed significantly between the two groups. Based on machine learning algorithms, a further feature selection procedure was performed to identify the features that can contribute the most to the prediction of disease severity.Results: Of the 172 patients, 112 were discharged as mild cases, and 60 developed into severe cases. Four clinical characteristics and 18 laboratory findings showed significant differences between the two groups in the statistical test (P<0.01) and univariate logistic regression analysis (P<0.01). In the further feature selection procedure, six features were chosen to obtain the best performance in discriminating the two groups with a linear kernel support vector machine. The mean accuracy was 91.38%, with a sensitivity of 0.90 and a specificity of 0.94. The six features included interleukin-6, high-sensitivity cardiac troponin I, procalcitonin, high-sensitivity C-reactive protein, chest distress and calcium level.Conclusions: With the data collected at admission, the combination of one clinical characteristic and five laboratory findings contributed the most to the discrimination between the two groups with a linear kernel support vector machine classifier. These factors may be risk factors that can be used to perform a prognostic prediction regarding the severity of the disease for patients with moderate COVID-19 in the early stage of the disease.