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Tribo-tests play a crucial role in the evaluation of the material performance of tribo-pairs. Traditionally, tribo-tests are performed onsite and tightly depend on experienced human operators. However, some public health emergencies such as covid-19 have a substantial impact on daily human life, including product and service systems. Therefore, this chapter aims to develop a new tribo-test service pattern, that is, digital twin enhanced remote tribo-test. A digital twin enhanced tribo-test service framework is proposed, which includes the modeling stage and the application stage of tribo-test service. A case study is presented to showcase how to implement the proposed framework. © 2022 Elsevier Inc. All rights reserved.
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Background Socioeconomic development,lifestyle changes and the COVID-19 pandemic all have an impact on people's mental and physical health,which may affect the prevalence of mental disorders. Currently,there is still no sufficient epidemiological information of mental disorders in Xinjiang. Objective To investigate the prevalence and influencing factors of common mental disorders among people aged 15 and above in northern Xinjiang,then compare the data with those of their counterparts in southern Xinjiang,and summarize the overall prevalence of common mental disorders in Xinjiang,providing a scientific basis for the formulation of corresponding mental health plans. Methods From November 2021 to July 2022,a multistage,stratified,random sampling method was used to select 3 853 residents from northern Xinjiang to attend a survey. General Demographic Questionnaire,and self-assessment scales(the 12-Item General Health Questionnaire,Mood Disorder Questionnaire,Symptom Checklist-90,etc.) and other assessment scales(Hamilton Depression Inventory,Bech-Rafaelsen Mania Rating Scale,Brief Psychiatric Rating Scale,etc.) were used as survey instruments. Mental disorders were diagnosed by the ICD-10 classification of mental and behavioral disorders by two psychiatrists with at least five years' working experience, or by a chief or associate chief psychiatrist when there is an inconsistency between the diagnoses made by the two psychiatrists. Results The point prevalence rate and age-adjusted rate of common mental disorders in northern Xinjiang were 9.71% (374/3 853) and 10.07%,respectively. The point prevalence rate and age-adjusted rate of common mental disorders in the whole Xinjiang were 9.69%(750/7 736)and 9.90%,respectively. The point prevalence rates of mood disorders,anxiety disorders,schizophrenia,organic mental disorders,and mental retardation in northern Xinjiang were 4.83%(374/7 736),3.63% (281/7 736),0.63%(49/7 736),0.23%(18/7 736),and 0.36%(28/7 736),respectively. Multivariate Logistic regression analysis for northern Xinjiang showed that:the risk of mood disorders in females was 1.854 times higher than that in males 〔95%CI(1.325,2.593)〕;The risk of mood disorders increased by 5.210 times in 25-34-year-olds 〔95%CI(1.348, 20.143)〕 and 3.863 times in 35-44-year-olds 〔95%CI(1.030,14.485)〕 compared with that in those aged ≥ 65 years;The risk of mood disorders increased by 0.199 times in those with high school or technical secondary school education 〔95%CI (0.078,0.509)〕 and 0.147 times in those with two- or three-year college and above education 〔95%CI(0.056,0.388)〕 compared with that in illiteracies. The risk of anxiety disorder in females was 1.627 times higher than that in males 〔95%CI (1.144, 2.315)〕;The risk of anxiety disorder increased by 0.257 times in 15-24-year-olds 〔95%CI(0.091,0.729)〕,0.243 times in 45-54-year-olds 〔95%CI(0.101,0.583)〕,and 0.210 times in 55-64-year-olds 〔95%CI(0.067,0.661)〕 compared to that of those aged ≥ 65 years old. The risk of schizophrenia among people living in villages or towns was 4.762 times higher than that of those living in cities 〔95%CI(1.705,13.300)〕;The risk of schizophrenia among people with high school or technical secondary school education was 0.079 times higher than that of illiteracies 〔95%CI(0.015,0.405)〕. Conclusion The prevalence of mood disorders and anxiety disorders is high among all types of mental disorders in Xinjiang. Females,rural people,or low educated people in northern Xinjiang are more prone to various types of mental disorders. © 2023 Chinese General Practice. All rights reserved.
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The outbreak of COVID-19 has caused a worldwide pandemic. The widespread infection of the medical staff has caused great attention from all quarters of society. There is a particular concern when considering intubation treatment in the emergency operating room, where a significant amount of virus droplets are typically spread within the room, exposing the medical staff to a high risk of infection. Hence, there is currently a pressing need to develop an effective protection mechanism for the medical staff to prevent them from being infected during routine work. In order to understand the spread of droplets and aerosols when different oxygen supply devices are used for intubation therapy, this study uses particle image velocimetry (PIV) technology to analyze the airflow distribution between the medical staff and the patient. In the experiment, a simple version of the respirator was established to reproduce the breathing of human lungs. This model used oil to create smoke as a tracer aerosol, then a high-sensitivity camera was used to record the scattering light from this smoke (which is irradiated by the green laser sheet). Ultimately, after applying post-processing techniques, the airflow distribution is analyzed. PAO aerosol is the primary aerosol source in this experiment, and it is used to quantify the patient's breathing;the concentration of PAO aerosol was measured at three different points: head, trunk, and feet. In addition, flow field visualization can effectively present the flow field distribution of the entire operating room;also, the results can be mutually verified with the PAO concentration measurement results. Aerosol concentrations were measured for six different oxygen supply devices with various tidal volumes of the artificial respirator, and the results were ranked from high to low concentrations for different oxygen supply devices and their operational oxygen supply flowrates: HFNC (70 l/min) > CPAP (40 l/min) > HFNC (30 l/min) > nasal cannula (15 l/min) > NRM (15 l/min) > VAPOX (28 l/min). © 2022, The Author(s).
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Objective: Single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics have identified novel cell subtypes and microenvironments which compartmentalize diverse functions at the maternal-fetal interface. We aimed to combine these high-resolution technologies with a rigorous classification of transcription alterations associated with diabetes subtypes in pregnancy. We hypothesized characteristic transcriptome profiles in specific cell populations would be linked to these classifications. Study Design: We clinically validated gestational diabetes mellitus type 1 (GDMA1), GDMA2, and type 2 diabetes (T2DM) classes within a cohort of placentae and compared them to healthy controls by bulk RNA-seq (N=53). We then integrated our non-diabetic term placentae spatial transcriptomics data (N=12) with 273,944 publicly available transcriptomes from term placenta scRNA-seq or single-nuclei RNA-seq (snRNA-seq) datasets (accessions phs001886, GSE173193, EGAS00001002449) with control, GDM, or SARS-CoV-2 positive subjects to create a placental transcriptomic catalog. Result(s): In bulk, we identified 104 significantly differentially expressed transcripts (-2< log2fold-change >2,p< 0.05) in our GDMA1 samples, 102 with GDMA2, and 121 with T2DM (Fig. 1a). Comparisons revealed 88 transcripts uniquely marking GDMA1, 68 for GDMA2, and 85 for T2DM, while FGA and CYP1A1 perturbations were shared across diabetes classes (Fig. 1b). We then compared these bulk GDM subtype markers with the 5,211 significantly differentially expressed transcripts associated with 22 cell-type clusters in our term placenta atlas (Fig. 2), and 20 unique GDM bulk markers aligned with extravillous trophoblast, stromal, endometrial epithelia, endothelial, NK, and dendritic single-cell placental markers. Conclusion(s): Together, these results detail the gene expression profiles and the cell types in the maternal-fetal milieu of pregnancies affected by diabetes. Consistent with their distinct clinical outcomes, GDM and T2DM have unique cellular transcriptomes and would thus be targets for new therapeutics. [Formula presented] [Formula presented] Copyright © 2022
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Bipolar disorder (BD) is a highly pathological disorder that is often misdiagnosed or undiagnosed. The main treatment is a combination of psychotherapy and medication. Traditional psychotherapy is affected by factors such as time, space, shortage of professional psychotherapists and patients' stigma, and has low availability. In terms of drug therapy, patients' medication compliance is poor, leading to repeated illness. The epidemic of coronavirus in 2019, closed management and telemedicine provide new ideas for the treatment of BD. Telemedicine can provide convenient medical services, promote disease rehabilitation, effectively guide patients' self-management, improve patients' treatment compliance, and prevent disease recurrence. This paper analyzes and summarizes the research related to telemedicine in BD, including the origin and significance, technical methods and application effects. To provide a reference for the application of telemedicine in patients with BD. © 2022 ACM.
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PurposeTaking a global perspective, this paper aims to examine the impact of COVID-19 on Airbnb booking activities through three critical perspectives - the initial Wuhan lockdown, local COVID-19 cases and local lockdowns. Design/methodology/approachUsing Airbnb reviews and cancellations as proxies for Airbnb bookings on a global scale, econometrics was used to examine the impacts of the initial Wuhan lockdown, local COVID-19 cases and local lockdowns on Airbnb bookings. FindingsThe authors find that local lockdowns result in a 57.8% fall in global booking activities. Every doubling of newly infected cases is associated with a 4.16% fall in bookings. The sensitivity of bookings to COVID-19 decreases with geographic distance to Wuhan and increases with government stringency of lockdown policies and human mobility within a market. Practical implicationsThe empirical evidence from this research can provide governments with insights into more accurate assessment of the financial loss of Airbnb hosts so that proper support can be offered based on the financial needs because of due to sudden lockdown. Originality/valueThis research contributes to new knowledge on peer-to-peer accommodation during a time of crisis and provides much needed global evidence to understand the impacts of COVID-19 on the accommodation industry.
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The COVID-19 lockdown has opened a unique window for investigating aerosol formation and evolution with controlled anthropogenic emissions in urban areas. Here, variations of PM2.5 chemical compositions, gaseous pollutants, meteorological conditions, and secondary organic aerosol (SOA) molecular tracers were monitored during three stages at an urban site (Pudong) and a suburban site (Qingpu) in Shanghai, which were defined as pre-COVID lockdown (PL), during COVID lockdown (DL), and after COVID lockdown (AL) in 2020. Abundances of pollutants during the same periods back in 2019 were also analyzed for a more comprehensive intercomparison and evaluation of the impact of the 2020 COVID-19 lockdown on regional air quality. With the sudden cessation of anthropogenic activities during the lockdown, significant reductions in PM2.5 were observed compared to both PL in 2020 (32% in Pudong and 36% in Qingpu) and the DL period back in 2019 (31% in Pudong and 35% in Qingpu), which was accompanied by the significantly reduced PM2.5 components (29-44% and 14-44% reductions in sulfate, nitrate, ammonium, organic carbon, and elemental carbon for Pudong and Qingpu, respectively). In particular, with the reduced secondary inorganic aerosol (SIA), the time series of SOA molecular tracers also underwent significant reduction that was characteristic to the lockdown. Amid the uncontrolled biogenic emissions and even slightly enhanced atmospheric oxidation capacity during the 2020 DL period, controlling anthropogenic emissions exhibits synergistic effects on the reduction of SIA and SOA, which could be further attributed to the changes in the aerosol aqueous-phase environment, such as aerosol liquid water content (ALWC), ionic strength, sulfate content, and particulate NH4+. Based on thermodynamic modeling, greatly reduced ALWC was observed during 2020 DL, which can prevent the partitioning of oxygenated organics into the condensed phase as well as the aqueous-phase formation of SOA. Higher ionic strength in 2020 DL may have a "salting-out" effect on gas- particle partitioning of oxygenated organics. The reduced SOA during 2020 DL at both sites can generally be reflected by the predicted heterogeneous reaction kinetics (gamma) of the isoprene SOA formation pathway. Overall, our study showed a synergistic effect in suppressing SIA and SOA formation upon the reduction of anthropogenic emissions during the COVID-19 lockdown, which shed light on the importance of controlling anthropogenic emissions in regulating secondary aerosol formation in typical urban areas of East China.
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In response to the research and testing imperatives brought on by the coronavirus (COVID-19), the University of Maryland (UMD) School of Public Health requires a standalone space in which to conduct its disease-related research. UMD’s School of Architecture proposed and undertook the challenge of designing a lightweight, compact, modular clinic structure based on origami science. Such origami structure could be deployed with minimal time and construction labor or expertise. Origami, an ancient art of paper folding, has evolved into a science that has inspired architectural, structural, engineering, and medical device design for decades. The underlying principles of origami design have led to broad practical applications from disaster relief shelters to foldable aerospace solar panels. This origami design provides the proof of concept for a foldable, origami-inspired, mobile clinic for use in responding to the COVID-19. This experimental design has two aims: (1) demonstrate how to use constructability and practicality as design constraints to inspire the design solution and (2) find alternative ways of using design as a tool to address social needs. © 2023 selection and editorial matter, AnnaMarie Bliss and Dak Kopec;individual chapters, the contributors.
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Objective: To clarify the epidemiological characteristics and spatiotemporal clustering dynamics of COVID-19 in Shanghai in 2022. Methods: The COVID-19 data presented on the official websites of Municipal Health Commissions of Shanghai during March 1, 2022 and May 31, 2022 were collected for a spatial autocorrelation analysis by GeoDa software. A logistic growth model was used to fit the epidemic situation and make a comparison with the actual infection situation. Results: Pudong district had the highest number of symptomatic and asymptomatic infectants, accounting for 29.30% and 35.58% of the total infectants. Differences in cumulative attack rates and infection rates among 16 districts (P<0.001) were significant. The rates were significantly higher in Huangpu district than in other districts. The attack rate of COVID-19 from March 1, 2022 to May 31, 2022 had a global spatial positive correlation (P<0.05). Spatial distribution of COVID-19 attack rate was different at different periods. The global autocorrelation coefficient from March 16 to March 29, April 6 to April 12 and May 18 to May 24 had no statistical significance (P>0.05). Our local autocorrelation analysis showed that 22 high-high clustering areas were detected in eight periods.The high-risk hot-spot areas have experienced a "less-more-less" change process. The growth model fitting results were consistent with the actual infection situation. Conclusion: There was a clear spatiotemporal correlation in the distribution of COVID-19 in Shanghai. The comprehensive prevention and control measures of COVID-19 epidemic in Shanghai have effectively prohibited the growth of the epidemic, not only curbing the spatially spread of high-risk epidemic areas, but also reducing the risk of transmission to other cities.
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COVID-19 , Epidemics , Humans , COVID-19/epidemiology , China/epidemiology , Disease Outbreaks , Spatial AnalysisABSTRACT
As emerging pollutants, microplastics (MPs) are widely distributed in water, soil and atmosphere, and have become a popularly concerned environmental and social issue. The research on atmospheric microplastics (AMPs) started later than that on the MPs in soil and water, but AMPs’ potential environmental impacts are explored in an even wider range. Based on the literatures on AMPs since 2015 as well as those about MPs in water and soil, this paper systematically reviews the distribution, source, transport of AMPS and the environmental and ecological impacts of AMPs. The results show that AMPs are distributed in global atmosphere, and have been detected in the atmosphere of urban, suburban, remote areas and indoor air. The concentrations of AMPs were detected in a range 2 to 77000 n m–2 d–1 or 0 to 1583 n m–3. The distribution characteristics of MPs in atmosphere are affected by environmental factors such as indoor and outdoor environment, underlying surface type and airflow, etc. In general, the concentration and the diversity of AMPs’ shape and composition are higher in the places near to MPs the source, but the wind, precipitation and even local animals could reshape the characters of AMPs. The sources of AMPs are mainly the production, use and recycling processes of plastic products, as well as land and sea where MPs accumulated. Studies also showed that abrasion of vehicle tires and the use of synthetic textile are major sources. What’s noteworthy is that the COVID-19 pandemic has made masks as necessities of life, which indirectly exacerbated the pollution of AMPs. The transport of MPs can occur in atmospheric environment, such as suspension, deposition and diffusion, and is affected by the morphology of MPs, wind direction, precipitation and other atmospheric factors. The diffusion of MPs in atmosphere, also known as atmospheric transport, is an important part of the global plastic cycle. AMPs’ transport path is mostly studied of Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) by conducting backward trajectory simulation, and their transport volume is estimated mainly through deposition and aerodynamic model. In addition, AMPs have unique physical and chemical properties, which can affect regional atmospheric environmental quality, change regional and global climate. It could also adsorb heavy metals, organic pollutants and harmful microorganisms during transport, resulting in greater health risks to human. Also, AMPs could affect atmospheric ecosystems through food chains and providing microbial niches, and alter structure and functions of terrestrial forest and water ecosystems through deposition. There are still some unsolved scientific and technical questions. Due to the lack of standardized sampling and identification means, the past research methods on AMPs are different on sampling and physical analysis, which make information comparison difficult. The observations of AMPs’ environmental behaviors, the atmospheric transport, source attribution and trans-regional effects of AMPs are still limited. Therefore, some conclusions from laboratory researches cannot fully explain the uncertainty of in natural environment. Based on the analysis, it is suggested that future scientific research on AMPs should focus on standardization of research methods, the establishment of source list, transport mechanism and environmental and ecological impacts. It is necessary for the study of AMPs to establish a set of scientifically credible and technically feasible monitoring techniques as well. Because AMPs could be transported to different ecosystems and could enter the human body through a variety of ways, it is urgent to study the physiological and ecological status of human body and ecosystems which are continuously exposed to AMPs pollution. © 2022 Chinese Academy of Sciences. All rights reserved.
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Objective To construct an XGBoost prediction model to predict disease severity of COVID-19 based on clinical characteristics dataset of COVID-19 patients.Methods A total of 347 laboratory-confirmed COVID-19 patients with complete medical information admitted from Feb 10 to April 5, 2020 were screened from the medical record system of Huoshenshan Hospital.Firstly, 21 features with significant differences were screened out as input features for the training model.Bayesian optimization was performed on the constructed XGBoost model to adjust the parameters, and the optimal combination of features was filtered based on feature importance.To further analyze the positive and negative effects of the numerical size of each feature on the prediction results, each feature importance was quantified and attributed by using SHapley Additive explanations (SHAP).Finally, the performance of the XGBoost prediction model was evaluated, and the model was compared and discussed with other machine learning methods, including support vector machine (SVM), naive Bayes ( NB ) , logical regression ( LR) , and k-nearest neighbors ( KNN ).Results In this study, 21 features with significant differences between the severe and non-severe groups were selected for training and validation.The optimal subset with 10 features in the k-nearest neighbor model obtained the highest value of area under curve ( AUG) among the 4 models in the validation set.XGBoost and support vector machine were better than other machine learning methods in terms of prediction performance (AUG;0.942 0, and 0.959 4 on the test set, respectively) , and the training speed of XGBoost was significantly faster.Conclusion A prediction model based on XGBoost is successfully built to achieve early prediction of disease severity of GOVID-19 patients. © 2022 Journal of Army Medical University. All rights reserved.
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Large-scale infectious diseases pose a tremendous risk to humans, with global outbreaks of COVID-19 causing millions of deaths and trillions of dollars in economic losses. To minimize the damage caused by large-scale infectious diseases, it is necessary to develop infectious disease prediction models to provide assistance for prevention. In this paper, we propose an XGBoost-LSTM mixed framework that predicts the spread of infectious diseases in multiple cities and regions. According to big traffic data, it was found that population flow is closely related to the spread of infectious diseases. Clustering and dividing cities according to population flow can significantly improve prediction accuracy. Meanwhile, an XGBoost is used to predict the transmission trend based on the key features of infection. An LSTM is used to predict the transmission fluctuation based on infection-related multiple time series features. The mixed model combines transmission trends and fluctuations to predict infections accurately. The proposed method is evaluated on a dataset of highly pathogenic infectious disease transmission published by Baidu and compared with other advanced methods. The results show that the model has an excellent predictive effect and practical value for large-scale infectious disease prediction.
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Objective This study is conducted against a case of positive nucleic acid detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) of environmental samples in a medical institution in Chengdu. Epidemiological investigation methods and laboratory tests are used to investigate the source and analyze the cause of the case, to explore the nucleic acid monitoring mode and the disposal scheme of abnormal conditions of SARS-CoV-2 in the medical institution environment. Methods Chengdu and Shuangliu district CDC jointly investigate A Medical Institution (A refers to a specific anonymous medical institute). Epidemiological surveys were conducted though related influencing factors of the medical institution. SARS-CoV-2 nucleic acid detection kits were used for detection. Sequencing was carried out on a second-generation sequencing platform. Results From Jan.18th 2021 to Jan.20th 2021, a total of 62 smear samples of environment and articles were collected, among which 30 samples were positive for SARS-CoV-2 nucleic acid. 30 positive samples were divided into Gongwei building (9) and Zhonghe building (21) according to the sampling location. The samples' Ct values of ORF1ab gene in Gongwei building were lower than that in Zhonghe building, and the difference was statistically significant (t=2.452, P=0.036). According to the nature of the specimens, they were divided into external environment smear samples (24 samples) and cleaning tool smear samples (6 samples). The N gene Ct values of external environment smear samples were lower than that of cleaning tools, and the difference was statistically significant (Z=-2.204, P=0.028). Through gene sequencing analysis, the sequence of SARS-CoV-2 nucleic acid positive environmental samples detected this time is highly homologous with SARS-CoV-2 vaccine (> 99.9%). Conclusions The positive environmental samples of SARS-CoV-2 nucleic acid in the medical institution are caused by the damage and leakage of COVID-19 vaccine ampoules in the process of vaccination, which led to the contamination of the vaccination room of public health building, and then transmit to the hospital environment of fever clinic and complex building through cleaning tools by cleaning workers. With the progress of COVID-19 vaccine vaccination, there is a high probability of environmental pollution of vaccine liquid in the vaccination area of medical institutions. Therefore, it is necessary in combination with the current normalization monitoring requirements of domestic COVID-19 epidemic situation to refine the specific implementation plan, conduct vaccination in a scientific and orderly manner, and reduce the social impact. © 2022, Publication Centre of Anhui Medical University. All rights reserved.
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The world has been struck by multiple crises that crippled the socio-economy of nations in the past. The impact of these crises was so significant that they initiated numerous policy changes worldwide. The radical crises in this context refer to the Spanish flu, the Asian financial crisis, the global financial crisis, and the current COVID-19 pandemic. Due to their small capital structure with limited resources and fragile nature, SMEs were severely impacted by these crises. Many SMEs were forced to close down their business operations. Somehow, the remaining SMEs managed to persist and survive through the crises. Moving forward, SMEs can better prepare for future crises by understanding and learning from the predicaments of these past crises. Consequently, SMEs must also be adaptive to new business environments and responding promptly to crises by realigning their strategies to achieve business sustainability in the long term. © 2021, IGI Global.
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BackgroundSARS-CoV-2 Omicron variant BA.1 first emerged on the Chinese mainland in January 2022 in Tianjin and caused a large wave of infections. During mass PCR testing, a total of 430 cases infected with Omicron were recorded between January 8 and February 7, 2022, with no new infections detected for the following 16 days. Most patients had been vaccinated with SARS-CoV-2 inactivated vaccines. The disease profile associated with BA.1 infection, especially after vaccination with inactivated vaccines, is unclear. Whether BA.1 breakthrough infection after receiving inactivated vaccine could create a strong enough humoral immunity barrier against Omicron is not yet investigated. MethodsWe collected the clinical information and vaccination history of the 430 COVID-19 patients infected with Omicron BA.1. Re-positive cases and inflammation markers were monitored during the patients convalescence phase. Ordered multiclass logistic regression model was used to identify risk factors for COVID-19 disease severity. Authentic virus neutralization assays against SARS-CoV-2 wildtype, Beta and Omicron BA.1 were conducted to examine the plasma neutralizing titers induced after post-vaccination Omicron BA.1 infection, and were compared to a group of uninfected healthy individuals who were selected to have a matched vaccination profile. FindingsAmong the 430 patients, 316 (73.5%) were adults with a median age of 47 years, and 114 (26.5%) were under-age with a median age of 10 years. Female and male patients account for 55.6% and 44.4%, respectively. Most of the patients presented with mild (47.7%) to moderate diseases (50.2%), with only 2 severe cases (0.5%) and 7 (1.6%) asymptomatic infections. No death was recorded. 341 (79.3%) of the 430 patients received inactivated vaccines (54.3% BBIBP-CorV vs. 45.5% CoronaVac), 49 (11.4%) received adenovirus-vectored vaccines (Ad5-nCoV), 2 (0.5%) received recombinant protein subunit vaccines (ZF2001), and 38 (8.8%) received no vaccination. No vaccination is associated with a substantially higher ICU admission rate among Omicron BA.1 infected patients (2.0% for vaccinated patients vs. 23.7% for unvaccinated patients, P<0.001). Compared with adults, child patients presented with less severe illness (82.5% mild cases for children vs. 35.1% for adults, P<0.001), no ICU admission, fewer comorbidities (3.5% vs. 53.2%, P<0.001), and less chance of turning re-positive on nucleic acid tests (12.3% vs. 22.5%, P=0.019). For adult patients, compared with no prior vaccination, receiving 3 doses of inactivated vaccine was associated with significantly lower risk of severe disease (OR 0.227 [0.065-0.787], P=0.020), less ICU admission (OR 0.023 [0.002-0.214], P=0.001), lower re-positive rate on PCR (OR 0.240 [0.098-0.587], P=0.002), and shorter duration of hospitalization and recovery (OR 0.233 [0.091-0.596], P=0.002). At the beginning of the convalescence phase, patients who had received 3 doses of inactivated vaccine had substantially lower systemic immune-inflammation index (SII) and C-reactive protein than unvaccinated patients, while CD4+/CD8+ ratio, activated Treg cells and Th1/Th2 ratio were higher compared to their 2-dose counterparts, suggesting that receipt of 3 doses of inactivated vaccine could step up inflammation resolution after infection. Plasma neutralization titers against Omicron, Beta, and wildtype significantly increased after breakthrough infection with Omicron. Moderate symptoms were associated with higher plasma neutralization titers than mild symptoms. However, vaccination profiles prior to infection, whether 2 doses versus 3 doses or types of vaccines, had no significant effect on post-infection neutralization titer. Among recipients of 3 doses of CoronaVac, infection with Omicron BA.1 largely increased neutralization titers against Omicron BA.1 (8.7x), Beta (4.5x), and wildtype (2.2x), compared with uninfected healthy individuals who have a matched vaccination profile. InterpretationReceipt of 3-dose inactivated vaccines can substantially reduce the disease severity of Omicron BA.1 infection, with most vaccinated patients presenting with mild to moderate illness. Child patients present with less severe disease than adult patients after infection. Omicron BA.1 convalescents who had received inactivated vaccines showed significantly increased plasma neutralizing antibody titers against Omicron BA.1, Beta, and wildtype SARS-CoV-2 compared with vaccinated healthy individuals. FundingThis research is supported by Changping Laboratory (CPL-1233) and the Emergency Key Program of Guangzhou Laboratory (EKPG21-30-3), sponsored by the Ministry of Science and Technology of the Peoples Republic of China. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSPrevious studies (many of which have not been peer-reviewed) have reported inconsistent findings regarding the effect of inactivated vaccines against the Omicron variant. On Mar 6, 2022, we searched PubMed with the query "(SARS-CoV-2) AND ((Neutralisation) OR (Neutralisation)) AND ((Omicron) OR (BA.1)) AND (inactivated vaccine)", without date or language restrictions. This search identified 18 articles, of which 13 were directly relevant. Notably, the participants in many of these studies have received only one or two doses of inactivated vaccine with heterologous booster vaccination; other studies have a limited number of participants receiving inactivated vaccines. Added value of this studyTo date, this is the first study to report on the protective effect of inactivated vaccines against the severe disease caused by the Omicron variant. We examine and compare the disease profile of adults and children. Furthermore, we estimate the effect of post-vaccination omicron infection on plasma neutralization titers against Omicron and other SARS-COV-2 variants. Specifically, the disease profile of Omicron convalescents who had received two-dose primary series of inactivated vaccines with or without a booster dose prior to infection is compared with unvaccinated patients. We also analyzed the effect of infection on neutralizing activity by comparing vaccinated convalescents with vaccinated healthy individuals with matched vaccination profiles. Implications of all the available evidenceCompared with adults, child patients infected with Omicron tend to present with less severe disease and are less likely to turn re-positive on nucleic acid tests. Receipt of two-dose primary series or three doses of inactivated vaccine is a protective factor against severe disease, ICU admission, re-positive PCR and longer hospitalization. The protection afforded by a booster dose is stronger than two-dose primary series alone. Besides vaccination, infection with Omicron is also a key factor for elevated neutralizing antibody titers, enabling cross-neutralization against Omicron, wildtype (WT) and the Beta variant.
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Infections , Breakthrough Pain , COVID-19 , InflammationABSTRACT
We demonstrate the THz near-field nano-imaging of Bacillus cereus and Corona Virus Disease 2019 (COVID-19) spike fake virus utilizing THz scattering near-field optical microscopy (SNOM). Here, it shows that bacteria and virus can be distinguished from other substances by THz near-field imaging. And we can use the THz time-domain spectrometer (TDS) scattering near field microscope(s-SNOM) to obtain the spectrum of different substances (bacteria and their substrate), then analyzing the differences between them from their specific responses in THz. This is of great significance to development of the terahertz near-field biology. © 2021 European Union
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Cough is a common symptom of respiratory and lung diseases. Cough detection is important to prevent, assess and control epidemic, such as COVID-19. This paper proposes a model to detect cough events from cough audio signals. The models are trained by the dataset combined ESC-50 dataset with self-recorded cough recordings. The test dataset contains inpatient cough recordings collected from inpatients of the respiratory disease department in Ruijin Hospital. We totally build 15 cough detection models based on different feature numbers selected by Random Frog, Uninformative Variable Elimination (UVE), and Variable influence on projection (VIP) algorithms respectively. The optimal model is based on 20 features selected from Mel Frequency Cepstral Coefficients (MFCC) features by UVE algorithm and classified with Support Vector Machine (SVM) linear two-class classifier. The best cough detection model realizes the accuracy, recall, precision and F1-score with 94.9%, 97.1%, 93.1% and 0.95 respectively. Its excellent performance with fewer dimensionality of the feature vector shows the potential of being applied to mobile devices, such as smartphones, thus making cough detection remote and non-contact. © 2021 IEEE.
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The global spread of coronavirus disease has become a major threat to global public health. There are more than 137 million confirmed cases worldwide at the time of writing. The spread of COVID-19 has resulted in a huge medical load due to the numerous suspected examinations and community screening. Deep learning methods to automatically classify COVID-19 have become an effective assistive technology. However, the current researches on data quality and the use of CT data to diagnose COVID-19 with convolutional neural networks are poor. This study is based on CT scan data of COVID-19 patients, patients with other lung diseases, and healthy people. In this work, we find that data smoothing can improve the quality of CT images of COVID-19 and improve the accuracy of the model. Specifically, an interpolation smoothing method is proposed using the bilinear interpolation algorithm. Besides, we propose an improved ResNet structure to improve the model feature extraction and fusion by optimizing the structure of the input stem and downsampling parts. Compared with the baseline ResNet, the model improves the accuracy of the three-class classification by 3.8% to 93.83%. Our research has particular significance for research on the automatic diagnosis of COVID-19 infectious diseases. © 2021, Springer Nature Switzerland AG.