Diagnosis,treatment and prevention of severe acute respiratory syndrome coronavirus 2 infection in children:experts'consensus statement updatedforthe Omicron variant

It has been more than 3 years since the novel coronavirus(SARS-CoV-2)pandemic raged globally.The coronavirus disease 2019(COVID-19)has greatly influenced human society.According to data from the World Health Organization(WHO),there were over 656 million confirmed cases of COVID-19 in the world as of January 1,2023,including over 6.6 million deaths[1].

Meteorin-β/Meteorin like/IL-41 attenuates airway inflammation in house dust mite-induced allergic asthma

We sought to examine the regulatory effect of Meteorin-β(Metrnβ)/Meteorin like(Metrnl)/IL-41 on lung inflammation in allergic asthma.We found that Metrnβwas elevated significantly in asthmatic patients and in mice with allergic asthma induced by house dust mite(HDM)extract.Upon exposure to HDM,Metrnβwas secreted predominantly by airway epithelial cells and inflammatory cells,including macrophages and eosinophils.The increased Metrnβeffectively blocked the development of airway hyperreactivity(AHR)and decreased inflammatory cell airway infiltration and type 2 cytokine production,which was associated with downregulated DC-mediated adaptive immune responses.Moreover,Metrnβimpaired the maturation and function of bone marrow-derived dendritic cells in vitro.Asthmatic mice adoptively transferred with dendritic cells isolated from Metrnβ-treated allergic mice displayed decreased AHR,airway inflammation,and lung injury.Metrnβalso displayed anti-inflammatory properties in immunodeficient SCID mice with allergic asthma and in in vitro 3D ALI airway models.Moreover,blockade of Metrnβby anti-Metrnβantibody treatment promoted the development of allergic asthma.These results revealed the unappreciated protective roles of Metrnβin alleviating DC-mediated Th2 inflammation in allergic asthma,providing the novel treatment strategy of therapeutic targeting of Metrnβin allergic asthma.

Machine learning discovery of distinguishing laboratory features for severity classification of COVID-19 patients

The exponential spread of COVID-19 worldwide is evident,with devastating outbreaks primarily in the United States,Spain,Italy,the United Kingdom,France,Germany,Turkey and Russia.As of 1 May 2020,a total of 3,308,386 confirmed cases have been reported worldwide,with an accumulative mortality of 233,093.Due to the complexity and uncertainty of the pathology of COVID-19,it is not easy for front-line doctors to categorise severity levels of clinical COVID-19 that are general and severe/critical cases,with consistency.The more than 300 laboratory features,coupled with underlying disease,all combine to complicate proper and rapid patient diagnosis.However,such screening is necessary for early triage,diagnosis,assignment of appropriate level of care facility,and institution of timely intervention.A machine learning analysis was carried out with confirmed COVID-19 patient data from 10 January to 18 February 2020,who were admitted to Tongji Hospital,in Wuhan,China.A softmax neural network-based machine learning model was established to categorise patient severity levels.According to the analysis of 2662 cases using clinical and laboratory data,the present model can be used to reveal the top 30 of more than 300 laboratory features,yielding 86.30%blind test accuracy,0.8195 F1-score,and 100%consistency using a two-way patient classification of severe/critical to general.For severe/critical cases,F1-score is 0.9081(i.e.recall is 0.9050,and precision is 0.9113).This model for classification can be accomplished at a mini-second-level computational cost(in contrast to minute-level manual).Based on available COVID-19 patient diagnosis and therapy,an artificial intelligence model paradigm can help doctors quickly classify patients with a high degree of accuracy and 100%consistency to significantly improve diagnostic and classification efficiency.The discovered top 30 laboratory features can be used for greater differentiation to serve as an essential supplement to current guidelines,thus creating a more comprehensive assessment of COVID-19 cases during the early stages of infection.Such early differentiation will help the assignment of the appropriate level of care for individual patients.

Global Pediatric Pulmonology Alliance(GPPA)proposal for COVID-19 vaccination in children

Coronavirus disease 2019(COVID-19)remains a global epidemic.As of August 18,2021,the number of reported cases has exceeded 207 million globally,with more than 4.3 million deaths.COVID-19 has brought devastating losses to human society.The overall crude mortality rate is 1-3%.Although pediatric deaths from COVID-19 are rare,they do occur,as over 9,000 children have died from COVID-19 globally to date[1].With the gradual and broad application of COVID-19 vaccines around the world,the rising proportion of cases among children and unvaccinated young adults demands attention.According to World Health Organization surveillance data.

Epidemiological characteristics and clinical manifestations of pediatric patients with COVID-19 in China: A multicenter retrospective study

Importance:The Coronavirus disease 2019(COVID-19)global pandemic poses a considerable challenge for pediatricians.Objective:This study aimed to identify the epidemiological characteristics and clinical features of pediatric patients with COVID-19 in China.Methods:This multicenter retrospective study included pediatric patients from 46 hospitals in China,covering 12 provinces and two municipalities.Epidemiological,demographic,clinical,laboratory,treatment,and outcome data were analyzed.Results:In total,211 pediatric patients with COVID-19 were included in this study.The median age was 7.0 years(range:22 days to 18 years).Approximately 16.3%of the patients exhibited asymptomatic infections,23.0%had upper respiratory tract infections,and 60.7%had pneumonia,including two with severe pneumonia and one with critical illness.Approximately 78.7%of the pediatric patients occurred in familial clusters.The most three common symptoms or signs at onset in children with COVID-19 were fever(54.5%),cough(49.3%),and pharyngeal congestion(20.8%).Only 17.6%of the patients presented with decreased lymphocyte count,whereas 13.6%had increased lymphocyte count.Among the patients with pneumonia who exhibited abnormal chest computed tomography findings,18.2%(23/127)of the patients had no other symptoms.Generally,the chest radiographs showed abnormalities that affected both lungs(49.6%);ground-glass opacity(47.2%)was the most common manifestation.The cure and improvement rates were 86.7%(183/211)and 13.3%(28/211),respectively.Only one patient with an underlying condition received invasive mechanical ventilation;none of the patients died.Interpretation:Similar to adults,children of all age groups are susceptible to COVID-19.Fortunately,most pediatric patients have mild symptoms or remain asymptomatic,despite the high incidence of pneumonia.Decreased proportions of white blood cells and lymphocytes are less frequent in children than in adults.