Post-acute COVID-19 symptom risk in hospitalized and non-hospitalized COVID-19 survivors: A systematic review and meta-analysis.

Background: Post-acute coronavirus disease 2019 (COVID-19) symptoms occurred in most of the COVID-19 survivors. However, few studies have examined the issue of whether hospitalization results in different post-acute COVID-19 symptom risks. This study aimed to compare potential COVID-19 long-term effects in hospitalized and non-hospitalized COVID-19 survivors. Methods: This study is designed as a systematic review and meta-analysis of observational studies. A systematic search of six databases was performed for identifying articles published from inception until April 20th, 2022, which compared post-acute COVID-19 symptom risk in hospitalized and non-hospitalized COVID-19 survivors using a predesigned search strategy included terms for SARS-CoV-2 (eg, COVID, coronavirus, and 2019-nCoV), post-acute COVID-19 Syndrome (eg, post-COVID, post COVID conditions, chronic COVID symptom, long COVID, long COVID symptom, long-haul COVID, COVID sequelae, convalescence, and persistent COVID symptom), and hospitalization (hospitalized, in hospital, and home-isolated). The present meta-analysis was conducted according to The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement using R software 4.1.3 to create forest plots. Q statistics and the I 2 index were used to evaluate heterogeneity in this meta-analysis. Results: Six observational studies conducted in Spain, Austria, Switzerland, Canada, and the USA involving 419 hospitalized and 742 non-hospitalized COVID-19 survivors were included. The number of COVID-19 survivors in included studies ranged from 63 to 431, and follow-up data were collected through visits in four studies and another two used an electronic questionnaire, visit and telephone, respectively. Significant increase in the risks of long dyspnea (OR = 3.18, 95% CI = 1.90-5.32), anxiety (OR = 3.09, 95% CI = 1.47-6.47), myalgia (OR = 2.33, 95% CI = 1.02-5.33), and hair loss (OR = 2.76, 95% CI = 1.07-7.12) risk were found in hospitalized COVID-19 survivors compared with outpatients. Conversely, persisting ageusia risk was significantly reduced in hospitalized COVID-19 survivors than in non-hospitalized patients. Conclusion: The findings suggested that special attention and patient-centered rehabilitation service based on a needs survey should be provided for hospitalized COVID-19 survivors who experienced high post-acute COVID-19 symptoms risk.

How has the COVID-19 pandemic affected young people?-Mapping knowledge structure and research framework by scientometric analysis.

Since the outbreak of COVID-19, there has been a large body of literature focusing on the relationship between the COVID-19 pandemic and young people. The purpose of this study is to explore the current research status and the specific mechanism of COVID-19's effects on young people based on related literature. This paper mainly used VOS viewer and CiteSpace software to conduct a scientometric analysis of 5,077 publications retrieved from the Web of Science database. The results show that the main contributors to the field were mainly from North America and Europe, and the trend of research focus was from shallow to deep. The five main research areas in the field were summarized by keyword clustering analysis as follows: lifestyle changes due to lockdown; changes in stress and emotions; psychological illness and trauma; risk perception and practice toward the epidemic; interventions and social support. Finally, they were linked by four pathways to form a framework that integrates the relationships between the five domains and between elements within each of them, revealing the mechanism of COVID-19's effect on young people. In addition, less studied but promising elements are also presented in the framework, such as research on special groups (disadvantaged socioeconomic groups and sexual minority youth) and extreme suicidal tendencies that deserve our further attention.

Global Myocardial Strain in Multisystem Inflammatory Syndrome in Children, Kawasaki Disease, and Healthy Children: A Network Meta-Analysis.

Background: Nearly 6,000 multisystem inflammatory syndrome in children (MIS-C) have been reported in the United States by November 2021. Left ventricular global myocardial strain has been proved to be one of the best evidence of the diagnostic and prognostic implications for cardiac dysfunction. The global myocardial strain change of MIS-C in the acute phase was still unclear. Methods: PubMed and other sources were searched. A network meta-analysis was conducted. MIS-C was divided into two groups according to left ventricular ejection fraction (LVEF): MIS-C with depressed ejection fraction (MIS-C dEF) and MIS-C with preserved ejection fraction (MIS-C pEF). Global longitudinal strain (GLS) and global circumferential strain (GCS) were compared among MIS-C, Kawasaki disease (KD), and healthy children. Results: In total, nine case-control studies were included, published between 2014 and 2021. These studies involved 107 patients with MIS-C, 188 patients with KD, and 356 healthy children. After Bayesian analysis, MIS-C dEF group was found to have a lower LVEF, higher GLS and GCS than the KD groups. Both MIS-C pEF and KD had similar GLS and GCS, which were higher than healthy controls. There was no difference of LVEF among MIS-C pEF, KD, and healthy controls. Conclusion: MIS-C dEF was more severe than KD, both in LVEF and global myocardial strain. MIS-C pEF and KD were similar with mild impaired left ventricular myocardial strain compared with the healthy children. Global myocardial strain may be a monitoring index for MIS-C. Systematic Review Registration: [https://www.crd.york.ac.uk/prospero/], identifier [CRD42021264760].

A Smart Helmet-Based PLS-BPNN Error Compensation Model for Infrared Body Temperature Measurement of Construction Workers during COVID-19

In the context of the long-term coexistence between COVID-19 and human society, the implementation of personnel health monitoring in construction sites has become one of the urgent needs of current construction management. The installation of infrared temperature sensors on the helmets required to be worn by construction personnel to track and monitor their body temperature has become a relatively inexpensive and reliable means of epidemic prevention and control, but the accuracy of measuring body temperature has always been a problem. This study developed a smart helmet equipped with an infrared temperature sensor and conducted a simulated construction experiment to collect data of temperature and its influencing factors in indoor and outdoor construction operation environments. Then, a Partial Least Square–Back Propagation Neural Network (PLS-BPNN) temperature error compensation model was established to correct the temperature measurement results of the smart helmet. The temperature compensation effects of different models were also compared, including PLS-BPNN with Least Square Regression (LSR), Partial Least Square Regression (PLSR), and single Back Propagation Neural Network (BPNN) models. The results showed that the PLS-BPNN model had higher accuracy and reliability, and the determination coefficient of the model was 0.99377. After using PLS-BPNN model for compensation, the relative average error of infrared body temperature was reduced by 2.745 °C and RMSE was reduced by 0.9849. The relative error range of infrared body temperature detection was only 0.005~0.143 °C.