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1.
J Environ Public Health ; 2022: 1126489, 2022.
Article in English | MEDLINE | ID: covidwho-1861688

ABSTRACT

Agricultural finance is in an embarrassing position in the current financial environment, especially during the process of COVID-19. Based on a small-scale peasant economy, it can no longer meet the rapidly rising demand of farmers for agricultural finance. Moreover, there has been a serious disconnection between the financial system of secondary and tertiary industries, and the quality of development needs to be improved urgently. The agricultural loan risk assessment has always been the main problem that we pay great attention to in the innovation of agricultural finance. Agricultural loans are an indispensable element in supporting agricultural development and promoting rural revitalization strategy. However, financial institutions have certain credit risks in reviewing and issuing agricultural loans. This article studies the speech emotion recognition of farmers in loan review to assess loan risk. As for emotional confusion caused by speech segmentation, a special method of data connection between Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (Bi-LSTM) Networks is designed, and a variable-length speech emotion recognition model including CNN and Bi-LSTM is designed. Experimental results show that the proposed algorithm can effectively improve the risk assessment of farmers in loan review.


Subject(s)
Artificial Intelligence , COVID-19 , Agriculture , COVID-19/epidemiology , Emotions , Humans , Risk Assessment
2.
Microbiol Spectr ; : e0195621, 2022 May 17.
Article in English | MEDLINE | ID: covidwho-1846337

ABSTRACT

Coronavirus disease 2019 (COVID-19) is a respiratory infectious disease responsible for many infections worldwide. Differences in respiratory microbiota may correlate with disease severity. Samples were collected from 20 severe and 51 mild COVID-19 patients. High-throughput sequencing of the 16S rRNA gene was used to analyze the bacterial community composition of the upper and lower respiratory tracts. The indices of diversity were analyzed. When one genus accounted for >50% of reads from a sample, it was defined as a super dominant pathobiontic bacterial genus (SDPG). In the upper respiratory tract, uniformity indices were significantly higher in the mild group than in the severe group (P < 0.001). In the lower respiratory tract, uniformity indices, richness indices, and the abundance-based coverage estimator were significantly higher in the mild group than in the severe group (P < 0.001). In patients with severe COVID-19, SDPGs were detected in 40.7% of upper and 63.2% of lower respiratory tract samples. In patients with mild COVID-19, only 10.8% of upper and 8.5% of lower respiratory tract samples yielded SDPGs. SDPGs were present in both upper and lower tracts in seven patients (35.0%), among which six (30.0%) patients possessed the same SDPG in the upper and lower tracts. However, no patients with mild infections had an SDPG in both tracts. Staphylococcus, Corynebacterium, and Acinetobacter were the main SDPGs. The number of SDPGs identified differed significantly between patients with mild and severe COVID-19 (P < 0.001). SDPGs in nasopharyngeal microbiota cause secondary bacterial infection in COVID-19 patients and aggravate pneumonia. IMPORTANCE The nasopharyngeal microbiota is composed of a variety of not only the true commensal bacterial species but also the two-face pathobionts, which are one a harmless commensal bacterial species and the other a highly invasive and deadly pathogen. In a previous study, we found that the diversity of nasopharyngeal microbiota was lost in severe influenza patients. We named the genus that accounted for over 50% of microbiota abundance as super dominant pathobiontic genus, which could invade to cause severe pneumonia, leading to high fatality. Similar phenomena were found here for SARS-CoV-2 infection. The diversity of nasopharyngeal microbiota was lost in severe COVID-19 infection patients. SDPGs in nasopharyngeal microbiota were frequently detected in severe COVID-19 patients. Therefore, the SDPGs in nasopharynx microbiota might invade into low respiratory and be responsible for secondary bacterial pneumonia in patients with SARS-CoV-2 infection.

3.
Parasit Vectors ; 15(1): 78, 2022 Mar 05.
Article in English | MEDLINE | ID: covidwho-1789129

ABSTRACT

BACKGROUND: This study explored the effect of a continuous mitigation and containment strategy for coronavirus disease 2019 (COVID-19) on five vector-borne diseases (VBDs) in China from 2020 to 2021. METHODS: Data on VBDs from 2015 to 2021 were obtained from the National Health Commission of the People's Republic of China, and the actual trend in disease activity in 2020-2021 was compared with that in 2015-2019 using a two-ratio Z-test and two proportional tests. Similarly, the estimated trend in disease activity was compared with the actual trend in disease activity in 2020. RESULTS: There were 13,456 and 3684 average yearly cases of VBDs in 2015-2019 and 2020, respectively. This represents a decrease in the average yearly incidence of total VBDs of 72.95% in 2020, from 0.9753 per 100,000 population in 2015-2019 to 0.2638 per 100,000 population in 2020 (t = 75.17, P < 0.001). The observed morbidity rates of the overall VBDs were significantly lower than the predicted rates (47.04% reduction; t = 31.72, P < 0.001). The greatest decline was found in dengue, with a 77.13% reduction (observed rate vs predicted rate: 0.0574 vs. 0.2510 per 100,000; t = 41.42, P < 0.001). Similarly, the average yearly mortality rate of total VBDs decreased by 77.60%, from 0.0064 per 100,000 population in 2015-2019 to 0.0014 per 100,000 population in 2020 (t = 6.58, P < 0.001). A decreasing trend was also seen in the monthly incidence of total VBDs in 2021 compared to 2020 by 43.14% (t = 5.48, P < 0.001). CONCLUSIONS: The results of this study verify that the mobility and mortality rates of VBDs significantly decreased from 2015-2019 to 2020-2021, and that they are possibly associated to the continuous COVID-19 mitigation and contamination strategy implemented in China in 2020-2021.


Subject(s)
COVID-19 , Epidemics , Vector Borne Diseases , COVID-19/epidemiology , COVID-19/prevention & control , China/epidemiology , Humans , SARS-CoV-2 , Vector Borne Diseases/epidemiology , Vector Borne Diseases/prevention & control
4.
J Med Virol ; 94(5): 2201-2211, 2022 May.
Article in English | MEDLINE | ID: covidwho-1777589

ABSTRACT

The public health interventions to mitigate coronavirus disease 2019 (COVID-19) could also potentially reduce the global activity of influenza. However, this strategy's impact on other common infectious diseases is unknown. We collected data of 10 respiratory infectious (RI) diseases, influenza-like illnesses (ILIs), and seven gastrointestinal infectious (GI) diseases during 2015-2020 in China and applied two proportional tests to check the differences in the yearly incidence and mortality, and case-fatality rates (CFRs) over the years 2015-2020. The results showed that the overall RI activity decreased by 7.47%, from 181.64 in 2015-2019 to 168.08 per 100 000 in 2020 (p < 0.001); however, the incidence of influenza was seen to have a 16.08% escalation (p < 0.001). In contrast, the average weekly ILI percentage and positive influenza virus rate decreased by 6.25% and 61.94%, respectively, in 2020 compared to the previous 5 years (all p < 0.001). The overall incidence of GI decreased by 45.28%, from 253.73 in 2015-2019 to 138.84 in 2020 per 100 000 (p < 0.001), and with the greatest decline seen in hand, foot, and mouth disease (HFMD) (64.66%; p < 0.001). The mortality and CFRs from RI increased by 128.49% and 146.95%, respectively, in 2020, compared to 2015-2019 (p < 0.001). However, the mortality rates and CFRs of seven GI decreased by 70.56% and 46.12%, respectively (p < 0.001). In conclusion, China's COVID-19 elimination/containment strategy is very effective in reducing the incidence rates of RI and GI, and ILI activity, as well as the mortality and CFRs of GI diseases.


Subject(s)
COVID-19 , Communicable Diseases , Influenza, Human , COVID-19/epidemiology , COVID-19/prevention & control , China/epidemiology , Communicable Diseases/epidemiology , Humans , Influenza, Human/epidemiology , Influenza, Human/prevention & control , Public Health , SARS-CoV-2
5.
J Geriatr Psychiatry Neurol ; 35(2): 196-205, 2022 03.
Article in English | MEDLINE | ID: covidwho-1731434

ABSTRACT

OBJECTIVES: The Coronavirus Disease 2019 (COVID-19) pandemic has profound negative effects on the mental health of clinically stable older patients with psychiatric disorders. This study examined the influential nodes of psychiatric problems and their associations in this population using network analysis. METHODS: Clinically stable older patients with psychiatric disorders were consecutively recruited from four major psychiatric hospitals in China from May 22 to July 15, 2020. Depressive and anxiety syndromes (depression and anxiety hereafter), insomnia, posttraumatic stress symptoms (PTSS), pain, and fatigue were measured using the Patient Health Questionnaire, General Anxiety Disorder, Insomnia Severity Index, Posttraumatic Stress Disorder Checklist - Civilian Version, and Numeric Rating Scales for pain and fatigue, respectively. RESULTS: A total of 1063 participants were included. The network analysis revealed that depression was the most influential node followed by anxiety as indicated by the centrality index of strength. In contrast, the edge connecting depression and anxiety was the strongest edge, followed by the edge connecting depression and insomnia, and the edge connecting depression and fatigue as indicated by edge-weights. The network structure was invariant by gender based on the network structure invariance test (M = .14, P = .20) and global strength invariance tests (S = .08, P = .30). CONCLUSIONS: Attention should be paid to depression and its associations with anxiety, insomnia, and fatigue in the screening and treatment of mental health problems in clinically stable older psychiatric patients affected by the COVID-19 pandemic.


Subject(s)
COVID-19 , Sleep Initiation and Maintenance Disorders , Stress Disorders, Post-Traumatic , Anxiety/epidemiology , Anxiety Disorders/epidemiology , COVID-19/complications , COVID-19/epidemiology , Cross-Sectional Studies , Depression/epidemiology , Disease Outbreaks , Fatigue/epidemiology , Fatigue/etiology , Humans , Pain , Pandemics , SARS-CoV-2 , Sleep Initiation and Maintenance Disorders/epidemiology , Stress Disorders, Post-Traumatic/epidemiology
6.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-323639

ABSTRACT

To investigate the changes of the respiratory infectious diseases (RID) and air quality during the COVID-19 outbreak over Yangtze River Delta Region, China. We investigate the impact of COVID-19 control measures on changes of RID and air quality by constructing two proportional test and fitting ARIMA and piecewise regression models. A total of 81,345 and 1,048,511 cases of RID were identified in Shanghai and Zhejiang Province, respectively. The incidence of seven RID and influenza decreased by 37.80% and 49.57% in 2020 in Shanghai and decreased by 20.39% and 43.40% in Zhejiang Province, respectively. The monthly concentrations of overall air pollutants decreased by 12.7% and 12.85% in 2020 Shanghai and Zhejiang compared to the 2017–2019 period;the most rapid decrease was observed in SO 2 concentrations (32.39% and 33.37% in Shanghai Province and Zhejiang Province, respectively). A moderate correlation was seen between influenza incidence and monthly SO 2 concentrations in Shanghai (r = 0.59). A 10 μg/m 3 decrease of SO 2 was significantly associated with the reduction of influenza incidence(2907.76 per 100,000). This study provided the additional evidences that the measures taken for COVID-19 were effective in improving the air quality and reducing spread of other common respiratory diseases, but direct causality is not established.

7.
Int J Environ Res Public Health ; 19(3)2022 01 24.
Article in English | MEDLINE | ID: covidwho-1649078

ABSTRACT

The Yangtze River Delta is one of the top five Chinese regions affected by COVID-19, as it is adjacent to Hubei Province, where COVID-19 first emerged. We investigated the impact of COVID-19 non-pharmaceutical interventions (NPIs) on changes in respiratory infectious diseases (RIDs) incidence and air quality in the Yangtze River Delta by constructing two proportional tests and fitting ARIMA and linear regression models. Compared with the pre-COVID-19 period, the average monthly incidence of seven RIDs decreased by 37.80% (p < 0.001) and 37.11% (p < 0.001) during the COVID-19 period and the post-vaccination period, respectively, in Shanghai, and decreased by 20.39% (p < 0.001) and 22.86% (p < 0.001), respectively, in Zhejiang. Similarly, compared with the pre-COVID-19 period, the monthly overall concentrations of six air pollutants decreased by 12.7% (p = 0.003) and 18.79% (p < 0.001) during the COVID-19 period and the post-vaccination period, respectively, in Shanghai, and decreased by 12.85% (p = 0.008) and 15.26% (p = 0.001), respectively, in Zhejiang. Interestingly, no significant difference in overall incidence of RIDs and concentrations of air quality was shown between the COVID-19 period and the post-vaccination period in either Shanghai or Zhejiang. This study provides additional evidence that the NPIs measures taken to control COVID-19 were effective in improving air quality and reducing the spread of RIDs. However, a direct causal relationship has not been established.


Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Communicable Diseases , Air Pollutants/analysis , Air Pollution/analysis , Air Pollution/prevention & control , China/epidemiology , Environmental Monitoring , Humans , Incidence , Particulate Matter/analysis , SARS-CoV-2
8.
Arch Virol ; 167(2): 577-581, 2022 Feb.
Article in English | MEDLINE | ID: covidwho-1639483

ABSTRACT

Outbreaks of acute hemorrhagic conjunctivitis (AHC) are associated with a high disease burden. In this study, we investigated the association between enhanced public health intervention and the incidence of AHC during the COVID-19 pandemic in China. A total of 212,526 AHC cases were reported in China during 2015-2020. The overall yearly incidence rate and number of AHC cases decreased by 23.08% and 22.15%, respectively, during the COVID-19 epidemic, compared with the previous 5 years (all p < 0.001). Significant reductions in AHC incidence were found both during the emergency period and after the relaxation of emergency measures in 2020 compared to the previous 5 years (22.22% and 28.00% reduction, respectively; p < 0.001). Enhanced public health initiatives during the COVID-19 pandemic in China were therefore associated with lower transmission of pathogens causing AHC.


Subject(s)
COVID-19 , Conjunctivitis, Acute Hemorrhagic , China/epidemiology , Conjunctivitis, Acute Hemorrhagic/epidemiology , Conjunctivitis, Acute Hemorrhagic/prevention & control , Disease Outbreaks , Humans , Incidence , Pandemics , Public Health , SARS-CoV-2
9.
Open Forum Infect Dis ; 8(11): ofab499, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1584163

ABSTRACT

Background: Community-acquired pneumonia (CAP) is a leading infectious cause of hospitalization and death worldwide. Knowledge about the incidence and etiology of CAP in China is fragmented. Methods: A multicenter study performed at 4 hospitals in 4 regions in China and clinical samples from CAP patients were collected and used for pathogen identification from July 2016 to June 2019. Results: A total of 1674 patients were enrolled and the average annual incidence of hospitalized CAP was 18.7 (95% confidence interval, 18.5-19.0) cases per 10000 people. The most common viral and bacterial agents found in patients were respiratory syncytial virus (19.2%) and Streptococcus pneumoniae (9.3%). The coinfections percentage was 13.8%. Pathogen distribution displayed variations within age groups as well as seasonal and regional differences. The severe acute respiratory syndrome coronavirus 2 was not detected. Respiratory virus detection was significantly positively correlated with air pollutants (including particulate matter ≤2.5 µm, particulate matter ≤10 µm, nitrogen dioxide, and sulfur dioxide) and significantly negatively correlated with ambient temperature and ozone content; bacteria detection was opposite. Conclusions: The hospitalized CAP incidence in China was higher than previously known. CAP etiology showed that differences in age, seasons, regions, and respiratory viruses were detected at a higher rate than bacterial infection overall. Air pollutants and temperature have an influence on the detection of pathogens.

10.
Adv Sci (Weinh) ; 8(23): e2102593, 2021 12.
Article in English | MEDLINE | ID: covidwho-1559092

ABSTRACT

Fast and accurate identification of microbial pathogens is critical for the proper treatment of infections. Traditional culture-based diagnosis in clinics is increasingly supplemented by metagenomic next-generation-sequencing (mNGS). Here, RNA/cDNA-targeted sequencing (meta-transcriptomics using NGS (mtNGS)) is established to reduce the host nucleotide percentage in clinic samples and by combining with Oxford Nanopore Technology (ONT) platforms (meta-transcriptomics using third-generation sequencing, mtTGS) to improve the sequencing time. It shows that mtNGS improves the ratio of microbial reads, facilitates bacterial identification using multiple-strategies, and discovers fungi, viruses, and antibiotic resistance genes, and displaying agreement with clinical findings. Furthermore, longer reads in mtTGS lead to additional improvement in pathogen identification and also accelerate the clinical diagnosis. Additionally, primary tests utilizing direct-RNA sequencing and targeted sequencing of ONT show that ONT displays important potential but must be further developed. This study presents the potential of RNA-targeted pathogen identification in clinical samples, especially when combined with the newest developments in ONT.


Subject(s)
Bronchoalveolar Lavage Fluid/microbiology , High-Throughput Nucleotide Sequencing/methods , Infections/genetics , Metagenomics/methods , RNA/genetics , Sequence Analysis, RNA/methods , Aged , Bronchoalveolar Lavage/methods , Female , Humans , Male , Metagenome/genetics , Middle Aged
11.
J Glob Health ; 11: 03114, 2021.
Article in English | MEDLINE | ID: covidwho-1478402
12.
Front Psychiatry ; 12: 735973, 2021.
Article in English | MEDLINE | ID: covidwho-1472406

ABSTRACT

Background: Depression has been a common mental health problem during the COVID-19 epidemic. From a network perspective, depression can be conceptualized as the result of mutual interactions among individual symptoms, an approach that may elucidate the structure and mechanisms underlying this disorder. This study aimed to examine the structure of depression among residents in Wuhan, the epicenter of the COVID-19 outbreak in China, in the later stage of the COVID-19 pandemic. Methods: A total of 2,515 participants were recruited from the community via snowball sampling. The Patient Health Questionnaire was used to assess self-reported depressive symptoms with the QuestionnaireStar program. The network structure and relevant centrality indices of depression were examined in this sample. Results: Network analysis revealed Fatigue, Sad mood, Guilt and Motor disturbances as the most central symptoms, while Suicide and Sleep problems had the lowest centrality. No significant differences were found between women and men regarding network structure (maximum difference = 0.11, p = 0.44) and global strength (global strength difference = 0.04; female vs. male: 3.78 vs. 3.83, p = 0.51), a finding that suggests there are no gender differences in the structure or centrality of depressive symptoms. Limitations: Due to the cross-sectional study design, causal relationships between these depressive symptoms or dynamic changes in networks over time could not be established. Conclusions: Fatigue, Sad mood, Guilt, and Motor disturbances should be prioritized as targets in interventions and prevention efforts to reduce depression among residents in Wuhan, in the later stage of the COVID-19 pandemic.

13.
J Environ Sci (China) ; 122: 115-127, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-1472038

ABSTRACT

The coronavirus (COVID-19) pandemic is disrupting the world from many aspects. In this study, the impact of emission variations on PM2.5-bound elemental species and health risks associated to inhalation exposure has been analyzed based on real-time measurements at a remote coastal site in Shanghai during the pandemic. Most trace elemental species decreased significantly and displayed almost no diel peaks during the lockdown. After the lockdown, they rebounded rapidly, of which V and Ni even exceeded the levels before the lockdown, suggesting the recovery of both inland and shipping activities. Five sources were identified based on receptor modeling. Coal combustion accounted for more than 70% of the measured elemental concentrations before and during the lockdown. Shipping emissions, fugitive/mineral dust, and waste incineration all showed elevated contributions after the lockdown. The total non-carcinogenic risk (HQ) for the target elements exceeded the risk threshold for both children and adults with chloride as the predominant species contributing to HQ. Whereas, the total carcinogenic risk (TR) for adults was above the acceptable level and much higher than that for children. Waste incineration was the largest contributor to HQ, while manufacture processing and coal combustion were the main sources of TR. Lockdown control measures were beneficial for lowering the carcinogenic risk while unexpectedly increased the non-carcinogenic risk. From the perspective of health effects, priorities of control measures should be given to waste incineration, manufacture processing, and coal combustion. A balanced way should be reached between both lowering the levels of air pollutants and their health risks.


Subject(s)
Air Pollutants , COVID-19 , Adult , Air Pollutants/analysis , COVID-19/epidemiology , Carcinogens , Child , China/epidemiology , Coal/analysis , Communicable Disease Control , Environmental Monitoring , Humans , Pandemics , Particulate Matter/analysis , Risk Assessment , Seasons
14.
Atmos Environ (1994) ; 266: 118750, 2021 Dec 01.
Article in English | MEDLINE | ID: covidwho-1432963

ABSTRACT

The coronavirus disease (COVID-19) spread rapidly worldwide in the first half of 2020. Stringent national lockdown policies imposed by China to prevent the spread of the virus reduced anthropogenic emissions and improved air quality. A weather research and forecasting model coupled with chemistry was applied to evaluate the impact of meteorology and emissions on air quality during the COVID-19 outbreak (from January 23 to February 29, 2020) in mid-eastern China. The results show that air pollution episodes still occurred on polluted days and accounted for 31.6%-60.5% of the total number of outbreak days in mid-eastern China from January 23 to February 29, 2020. However, anthropogenic emissions decreased significantly, indicating that anthropogenic emission reduction cannot completely offset the impact of unfavorable meteorological conditions on air quality. Favorable meteorological conditions in 2019 improved the overall air quality for a COVID-19 outbreak in 2019 instead of 2020. PM2.5 concentrations decreased by 4.2%-29.2% in Beijing, Tianjin, Shijiazhuang, and Taiyuan, and increased by 6.1%-11.5% in Jinan and Zhengzhou. PM2.5 concentrations increased by 10.9%-20.5% without the COVID-19 outbreak of 2020 in mid-eastern China, and the frequency of polluted days increased by 5.3%-18.4%. Source apportionment of PM2.5 during the COVID-19 outbreak showed that industry and residential emissions were the dominant PM2.5 contributors (32.7%-49.6% and 26.0%-44.5%, respectively) followed by agriculture (18.7%-24.0%), transportation (7.7%-15.5%), and power (4.1%-5.9%). In Beijing, industrial and residential contributions to PM2.5 concentrations were lower (32.7%) and higher (44.5%), respectively, than in other cities (38.7%-49.6% for industry and 26.0%-36.2% for residential). Therefore, enhancing regional cooperation and implementing a united air pollution control are effective emission mitigation measures for future air quality improvement, especially the development of new technologies for industrial and cooking fumes.

15.
Sci Adv ; 7(35)2021 Aug.
Article in English | MEDLINE | ID: covidwho-1373925

ABSTRACT

The 2019 novel coronavirus pandemic (COVID-19) negatively affected global public health and socioeconomic development. Lockdowns and travel restrictions to contain COVID-19 resulted in reduced human activity and decreased anthropogenic emissions. However, the secondary effects of these restrictions on the biophysical environment are uncertain. Using remotely sensed big data, we investigated how lockdowns and traffic restrictions affected China's spring vegetation in 2020. Our analyses show that travel decreased by 58% in the first 18 days following implementation of the restrictions across China. Subsequently, atmospheric optical clarity increased and radiation levels on the vegetation canopy were augmented. Furthermore, the spring of 2020 arrived 8.4 days earlier and vegetation 17.45% greener compared to 2015-2019. Reduced human activity resulting from COVID-19 restrictions contributed to a brighter, earlier, and greener 2020 spring season in China. This study shows that short-term changes in human activity can have a relatively rapid ecological impact at the regional scale.

16.
Ethiopian Journal of Health Development ; 34(4):236-243, 2020.
Article in English | GIM | ID: covidwho-1300010

ABSTRACT

Background: Quick and precise identification of people suspected of having COVID-19 plays a key function in imposing quarantine at the right time and providing medical treatment, and results not only in societal benefits but also helps in the development of an improved health system. Building a deep-learning framework for automated identification of COVID-19 using chest computed tomography (CT) is beneficial in tackling the epidemic. Aim: To outline a novel deep-learning model created using 3D CT volumes for COVID-19 classification and localization of swellings.

17.
BMC Geriatr ; 21(1): 355, 2021 06 10.
Article in English | MEDLINE | ID: covidwho-1266472

ABSTRACT

BACKGROUND: Since the outbreak of COVID-19, it has been documented that old age and underlying illnesses are associated with poor prognosis among COVID-19 patients. However, it is unknown whether sarcopenia, a common geriatric syndrome, is associated with poor prognosis among older COVID-19 patients. The aim of our prospective cohort study is to investigate the association between sarcopenia risk and severe disease among COVID-19 patients aged ≥60 years. METHOD: A prospective cohort study of 114 hospitalized older patients (≥60 years) with confirmed COVID-19 pneumonia between 7 February, 2020 and 6 April, 2020. Epidemiological, socio-demographic, clinical and laboratory data on admission and outcome data were extracted from electronic medical records. All patients were assessed for sarcopenia on admission using the SARC-F scale and the outcome was the development of the severe disease within 60 days. We used the Cox proportional hazards model to identify the association between sarcopenia and progression of disease defined as severe cases in a total of 2908 person-days. RESULT: Of 114 patients (mean age 69.52 ± 7.25 years, 50% woman), 38 (33%) had a high risk of sarcopenia while 76 (67%) did not. We found that 43 (38%) patients progressed to severe cases. COVID-19 patients with higher risk sarcopenia were more likely to develop severe disease than those without (68% versus 22%, p < 0.001). After adjustment for demographic and clinical factors, higher risk sarcopenia was associated with a higher hazard of severe condition [hazard ratio = 2.87 (95% CI, 1.33-6.16)]. CONCLUSION: We found that COVID-19 patients with higher sarcopenia risk were more likely to develop severe condition. A clinician-friendly assessment of sarcopenia could help in early warning of older patients at high-risk with severe COVID-19 pneumonia.


Subject(s)
COVID-19 , Sarcopenia , Aged , Female , Geriatric Assessment , Humans , Proportional Hazards Models , Prospective Studies , SARS-CoV-2 , Sarcopenia/diagnosis , Sarcopenia/epidemiology , Surveys and Questionnaires
18.
The Ethiopian Journal of Health Development ; 34(4):235, 2020.
Article in English | ProQuest Central | ID: covidwho-1190849

ABSTRACT

Background: Quick and precise identification of people suspected of having COVID-19 plays a key function in imposing quarantine at the right time and providing medical treatment, and results not only in societal benefits but also helps in the development of an improved health system. Building a deep-learning framework for automated identification of COVID-19 using chest computed tomography (CT) is beneficial in tackling the epidemic. Aim: To outline a novel deep-learning model created using 3D CT volumes for COVID-19 classification and localization of swellings. Methods: In all cases, subjects' chest areas were segmented by means of a pre-trained U-Net;the segmented 3D chest areas were submitted as inputs to a 3D deep neural network to forecast the likelihood of infection with COVID-19;the swellings were restricted by joining the initiation areas within the classification system and the unsupervised linked elements. A total of 499 3D CT scans were utilized for training worldwide and 131 3D CT scans were utilized for verification. Results: The algorithm took only 1.93 seconds to process the CT amount of a single affected person using a special graphics processing unit (GPU). Interesting results were obtained in terms of the development of societal challenges and better health policy. Conclusions: The deep-learning model can precisely forecast COVID-19 infectious probabilities and detect swelling areas in chest CT, with no requirement for training swellings. The easy-to-train and high-functioning deep-learning algorithm offers a fast method to classify people affected by COVID-19, which is useful to monitor the SARS-CoV-2 epidemic.

19.
Signal Transduct Target Ther ; 6(1): 123, 2021 03 15.
Article in English | MEDLINE | ID: covidwho-1135650

ABSTRACT

The emergence of SARS-CoV-2 has resulted in the COVID-19 pandemic, leading to millions of infections and hundreds of thousands of human deaths. The efficient replication and population spread of SARS-CoV-2 indicates an effective evasion of human innate immune responses, although the viral proteins responsible for this immune evasion are not clear. In this study, we identified SARS-CoV-2 structural proteins, accessory proteins, and the main viral protease as potent inhibitors of host innate immune responses of distinct pathways. In particular, the main viral protease was a potent inhibitor of both the RLR and cGAS-STING pathways. Viral accessory protein ORF3a had the unique ability to inhibit STING, but not the RLR response. On the other hand, structural protein N was a unique RLR inhibitor. ORF3a bound STING in a unique fashion and blocked the nuclear accumulation of p65 to inhibit nuclear factor-κB signaling. 3CL of SARS-CoV-2 inhibited K63-ubiquitin modification of STING to disrupt the assembly of the STING functional complex and downstream signaling. Diverse vertebrate STINGs, including those from humans, mice, and chickens, could be inhibited by ORF3a and 3CL of SARS-CoV-2. The existence of more effective innate immune suppressors in pathogenic coronaviruses may allow them to replicate more efficiently in vivo. Since evasion of host innate immune responses is essential for the survival of all viruses, our study provides insights into the design of therapeutic agents against SARS-CoV-2.


Subject(s)
Immunity, Innate , Membrane Proteins/immunology , Nucleotidyltransferases/immunology , RNA, Viral/immunology , SARS-CoV-2/immunology , Signal Transduction/immunology , Viral Proteins/immunology , A549 Cells , Animals , Chickens , HEK293 Cells , HeLa Cells , Humans , Ligases/immunology , Mice
20.
J Affect Disord ; 287: 145-157, 2021 05 15.
Article in English | MEDLINE | ID: covidwho-1126896

ABSTRACT

The coronavirus disease 2019 (COVID-19) and Severe Acute Respiratory Syndrome (SARS) are associated with various psychiatric comorbidities. This is a systematic review and meta-analysis comparing the prevalence of psychiatric comorbidities in all subpopulations during the SARS and COVID-19 epidemics. A systematic literature search was conducted in major international (PubMed, EMBASE, Web of Science, PsycINFO) and Chinese (China National Knowledge Internet [CNKI] and Wanfang) databases to identify studies reporting prevalence of psychiatric comorbidities in all subpopulations during the SARS and COVID-19 epidemics. Data analyses were conducted using the Comprehensive Meta-Analysis Version 2.0 (CMA V2.0). Eighty-two studies involving 96,100 participants were included. The overall prevalence of depressive symptoms (depression hereinafter), anxiety symptoms (anxiety hereinafter), stress, distress, insomnia symptoms, post-traumatic stress symptoms (PTSS) and poor mental health during the COVID-19 epidemic were 23.9% (95% CI: 18.4%-30.3%), 23.4% (95% CI: 19.9%-27.3%), 14.2% (95% CI: 8.4%-22.9%), 16.0% (95% CI: 8.4%-28.5%), 26.5% (95% CI: 19.1%-35.5%), 24.9% (95% CI: 11.0%-46.8%), and 19.9% (95% CI: 11.7%-31.9%), respectively. Prevalence of poor mental health was higher in general populations than in health professionals (29.0% vs. 11.6%; Q=10.99, p=0.001). The prevalence of depression, anxiety, PTSS and poor mental health were similar between SARS and COVID-19 epidemics (all p values>0.05). Psychiatric comorbidities were common in different subpopulations during both the SARS and COVID-19 epidemics. Considering the negative impact of psychiatric comorbidities on health and wellbeing, timely screening and appropriate interventions for psychiatric comorbidities should be conducted for subpopulations affected by such serious epidemics.


Subject(s)
COVID-19 , Epidemics , Severe Acute Respiratory Syndrome , Anxiety , China , Depression , Humans , Prevalence , SARS-CoV-2 , Severe Acute Respiratory Syndrome/epidemiology
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