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Objective:To understand the epidemiology, clinical characteristics and associated risk factors of severe adenovirus(ADV)pneumonia in children, providing the basis for targeted prevention and treatment.Methods:Clinical features of children with ADV pneumonia at Children′s Hospital of Soochow University from January 2011 to December 2020 were retrospectively analyzed.According to the severity of the disease, cases were divided into severe ADV pneumonia group and common ADV pneumonia group.The epidemiological and clinical characteristics of two groups were compared, and risk factors for the occurrence of severe ADV pneumonia were analyzed.Results:A total of 1 158 patients with ADV pneumonia were enrolled, including severe ADV pneumonia 104 cases(8.98%) and ordinary ADV pneumonia 1 054 cases(91.02%).The median age of severe ADV pneumonia group was 1.17 (0.83, 2.73) years, which was significantly younger than that of common ADV pneumonia group 3.16 (1.50, 4.50) years( P<0.05), and 77.89% (81/104) of them were younger than 3 years old.The occurrence of severe ADV pneumonia was predominant in winter and spring, accounting for 71.15% (74/104).Cough was present in 89.42% (93/104) and fever in 99.01% (103/104) of the severe ADV pneumonia group.Compared with the common ADV pneumonia group, the severe ADV pneumonia group had a significantly longer febrile time[10(6, 14)d vs. 5(4, 7)d, P<0.05], significantly higher incidence of shortness of breath, wheezing, convulsions/coma[100% vs. 2.09%, 45.19% vs. 13.57%, 10.57% vs. 1.99%, P<0.05], and significantly higher incidences of emphysema, pleural effusion, bronchial signs, pulmonary solids, and atelectasis [21.15% vs. 2.09%, 5.77% vs. 0.19%, 4.81% vs. 0, 3.85% vs. 0.09%, P<0.05].Multivariable Logistic regression showed that age younger than 1.71 years old, wheezing, and the presence of underlying diseases (moderate to severe anaemia, congenital heart disease, neurological disease) were risk factors for the development of severe ADV pneumonia ( P<0.05).Receiver operating characteristic curve analysis showed that the sensitivity and specificity of age<1.71 years old(20 months old) for predicting the occurrence of severe ADV pneumonia were 65.4% and 71.5%, respectively. Conclusion:The age of most severe ADV pneumonia is less 3 years in Suzhou.It usually occurres in winter and spring, with fever, cough, shortness of breath, and wheezing as the main symptoms.Pulmonary manifestations such as pleural effusion, emphysema, pulmonary consolidation, and atelectasis may occur.The underlying disease, wheezing, and age of onset less than 1.71 years (20 months) old are independent risk factors for severe ADV pneumonia.
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Objective:To understand the current level of work engagement of new nurses and analyze the relationship between the variables of emotion regulation difficulties, job crafting, and work engagement, to provide a theoretical basis for nursing managers to develop interventions to enhance the level of work engagement of new nurses.Methods:The convenience sampling method was applied to select a total of 637 new nurses from 11 tertiary levels A hospitals in Beijing City, Zhejiang Province, Guangxi Zhuang Autonomous Region, Anhui Province, and Hubei Province from April to June 2022 as the respondents, and the general information questionnaire, Emotion Regulation Difficulties Scale, Job Crafting Scale and Work Engagement Scale were used to conduct a cross-sectional survey. The mediation effect was analyzed by AMOS 26.0 structural equation model.Results:Finally, 626 new nurses were included, and scored (31.08 ± 10.94) points on the Work Engagement Scale, with a mean entry score of (3.45 ± 1.22) points. Work engagement was significantly negatively associated with difficulties in emotion regulation ( r=-0.291, P<0.01) and positively associated with job crafting ( r=0.724, P<0.01). Analysis of mediating effects revealed that job crafting partially mediated the relationship between difficulties in emotion regulation and work engagement ( β=-0.424, P<0.001). Conclusions:New nurses' work engagement was at an intermediate level, and job crafting partially mediated the relationship between emotion regulation difficulties and work engagement. Nursing managers should strengthen training on emotion regulation strategies for new nurses and construct intervention strategies based on job crafting, to improve new nurses′ work engagement.
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Objective:To investigate the clinical significance and possible mechanisms of elevated homocysteine(Hcy) levels in peripheral blood of children with sepsis.Methods:The clinical data of 51 children with sepsis (sepsis group) admitted to PICU at Xuzhou Children′s Hospital from January 2019 to December 2019 were analyzed, and the levels of Hcy in plasma were compared with 50 non-septic children (common infection group) and 50 healthy children (healthy control group) during the same period.The possible mechanism of metabolic disorders about Hcy was analyzed by detecting the levels of the key rate-limiting enzymes cystathionine-β-synthase(CBS) and cystathionine-γ-lyase(CSE), which were in the downstream of metabolism in septic mouse model induced by lipopolysaccharide.Results:The level of Hcy in plasma was (12.62±5.46)μmol/L in sepsis group, which was significantly higher than those in common infection group[(9.42±2.28) μmol/L] and healthy control group[(8.14±1.60) μmol/L]( P<0.05). The level of Hcy in plasma of 12 children with acute kidney injury in sepsis group was significantly higher than that of 39 children without acute kidney injury in sepsis group[(16.48±5.87)μmol/L vs.(11.62±4.74) μmol/L, P<0.05]. The level of Hcy in plasma of six children with acute liver failure in sepsis group was significant higher than that of 45 children without acute liver failure in sepsis group[(18.35±7.10) μmol/L vs.(11.84±4.78) μmol/L, P<0.05]. The level of Hcy in serum significantly increased in septic mouse models ( P<0.01). The transcription and protein expression levels of key rate-limiting Hcy transcription enzymes CBS and CSE in liver and kidney tissues of septic mouse were significantly down-regulated ( P<0.05). Conclusion:The level of Hcy in peripheral blood of children with sepsis increases, which is more obviously in children with acute kidney injury or acute liver injury.When patients developed sepsis, the expression of CBS and CSE will be restrained, leading to disorders related to transsulfuration metabolism and elevated level of Hcy in peripheral blood.
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Artificial intelligence can potentially provide a substantial role in streamlining chest computed tomography (CT) diagnosis of COVID-19 patients. However, several critical hurdles have impeded the development of robust AI model, which include deficiency, isolation, and heterogeneity of CT data generated from diverse institutions. These bring about lack of generalization of AI model and therefore prevent it from applications in clinical practices. To overcome this, we proposed a federated learning-based Unified CT-COVID AI Diagnostic Initiative (UCADI, http://www.ai-ct-covid.team/), a decentralized architecture where the AI model is distributed to and executed at each host institution with the data sources or client ends for training and inferencing without sharing individual patient data. Specifically, we firstly developed an initial AI CT model based on data collected from three Tongji hospitals in Wuhan. After model evaluation, we found that the initial model can identify COVID from Tongji CT test data at near radiologist-level (97.5% sensitivity) but performed worse when it was tested on COVID cases from Wuhan Union Hospital (72% sensitivity), indicating a lack of model generalization. Next, we used the publicly available UCADI framework to build a federated model which integrated COVID CT cases from the Tongji hospitals and Wuhan Union hospital (WU) without transferring the WU data. The federated model not only performed similarly on Tongji test data but improved the detection sensitivity (98%) on WU test cases. The UCADI framework will allow participants worldwide to use and contribute to the model, to deliver a real-world, globally built and validated clinic CT-COVID AI tool. This effort directly supports the United Nations Sustainable Development Goals number 3, Good Health and Well-Being, and allows sharing and transferring of knowledge to fight this devastating disease around the world.