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1.
China CDC Weekly ; 4(30):649-654, 2022.
Article in English | China CDC Weekly | ID: covidwho-1965174

ABSTRACT

< -type="Summary"> <sec> What is already known about this topic? Hong Kong Special Administrative Region (SAR), China and Singapore are both facing considerable Omicron variant epidemic. However, the overwhelmed medical system and high case fatality ratio (CFR) just occurred in Hong Kong SAR, China but not in Singapore.</sec><sec> What is added by this report? The low vaccination coverage in Hong Kong SAR, China, especially among the older adults, is shown to be a primary reason of its recent high CFR.</sec><sec> What are the implications for public health practice? Facing the potential epidemic risk, non-vaccinated, non-fully-vaccinated, and non-booster-vaccinated people in China, especially the elderly, should get any type of accessible vaccine, which could save lives when the infection unfortunately befalls.</sec>

2.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-330371

ABSTRACT

Population antibody response is believed to be important in selection of new variant viruses. We identified that SARS-CoV-2 infections elicit a population immune response mediated by a lineage of VH1-69 germline antibodies. The representative antibody R1-32 targets a novel semi-cryptic epitope defining a new class of RBD targeting antibodies. Binding to this non-ACE2 competing epitope leading to spike destruction impairing virus entry. Based on epitope location, neutralization mechanism and analysis of antibody binding to spike variants we propose that recurrent substitutions at 452 and 490 are associated with immune evasion of this population antibody response. These substitutions, including L452R found in the Delta variant, disrupt interaction mediated by the VH1-69 specific hydrophobic HCDR2 to impair antibody-antigen association allowing variants to escape. Lacking 452/490 substitutions, the Omicron variant is sensitive to this class of antibodies. Our results provide new insights into SARS-CoV-2 variant genesis and immune evasion.

3.
World J Psychiatry ; 11(11): 1106-1115, 2021 Nov 19.
Article in English | MEDLINE | ID: covidwho-1561918

ABSTRACT

BACKGROUND: Studies have indicated that childhood exposure to domestic violence is a common factor in posttraumatic growth (PTG) and posttraumatic stress disorder (PTSD), but it is unclear whether PTG and PTSD share a common/different underlying mechanism. AIM: To explore the common/different underlying mechanism of PTG and PTSD. METHODS: Between February 12 and 17, 2020, a nationwide cross-sectional online survey was conducted in China among 2038 university students, and a self-administered questionnaire was used for the data collection. The data included demographic characteristics, such as age, gender, and subjective social economic status, and childhood exposure to domestic violence scale that was selected from the Chinese version of revised Adverse Childhood Experiences Question, Self-compassion Scale, Connor-Davidson Resilience Scale, Posttraumatic Growth Inventory, and the Abbreviated PTSD Checklist-Civilian version. A structural equation model was used to test the hypotheses. RESULTS: Exposure to domestic violence was significantly associated with PTG and PTSD via a 1-step indirect path of self-compassion (PTG: ß = -0.023, 95%CI: -0.44 to -0.007; PTSD: ß = 0.008, 95%CI: 0.002, 0.014) and via a 2-step indirect path from self-compassion to resilience (PTG: ß = -0.008, 95%CI: -0.018 to -0.002; PTSD: ß = 0.013, 95%CI: 0.004-0.024). However, resilience did not mediate the relationship between exposure to domestic violence and PTG and PTSD. CONCLUSION: PTG and PTSD are common results of childhood exposure to domestic violence, which may be influenced by self-compassion and resilience.

4.
Comput Intell Neurosci ; 2021: 4529107, 2021.
Article in English | MEDLINE | ID: covidwho-1511536

ABSTRACT

Frequent occurrence and long-term existence of respiratory diseases such as COVID-19 and influenza require bus drivers to wear masks correctly during driving. To quickly detect whether the mask is worn correctly on resource-constrained devices, a lightweight target detection network SAI-YOLO is proposed. Based on YOLOv4-Tiny, the network incorporates the Inception V3 structure, replaces two CSPBlock modules with the RES-SEBlock modules to reduce the number of parameters and computational difficulty, and adds a convolutional block attention module and a squeeze-and-excitation module to extract key feature information. Moreover, a modified ReLU (M-ReLU) activation function is introduced to replace the original Leaky_ReLU function. The experimental results show that SAI-YOLO reduces the number of network parameters and calculation difficulty and improves the detection speed of the network while maintaining certain recognition accuracy. The mean average precision (mAP) for face-mask-wearing detection reaches 86% and the average precision (AP) for mask-wearing normative detection reaches 88%. In the resource-constrained device Raspberry Pi 4B, the average detection time after acceleration is 197 ms, which meets the actual application requirements.


Subject(s)
Automobile Driving , COVID-19 , Humans , Recognition, Psychology , SARS-CoV-2
5.
Complexity ; 2021, 2021.
Article in English | ProQuest Central | ID: covidwho-1358939

ABSTRACT

The spread of the COVID-19 epidemic, since December 2019, has caused much damage around the world, disturbed every aspect of daily life, and has become a serious health threat. The COVID-19 epidemic impacted nearly 150 countries around the globe between December 2019 and March 2020. Since December 2019, researchers have been trying to develop new suitable statistical models to adequately describe the behavior of this deadly pandemic. In this paper, a flexible statistical model has been proposed that can be used to model the lifetime events associated with this deadly pandemic. The new distribution is derived from the combination of an extended Weibull distribution and a trigonometric strategy referred to as the arcsine-X approach. Hence, the new model may be referred to as the arcsine new flexible extended Weibull model. The proposed model is capable of capturing five different behaviors of the hazard rate function. The model parameters are estimated via the maximum likelihood approach. Furthermore, a Monte Carlo study is conducted to assess the behavior of the estimators. Finally, the applicability of the new model is demonstrated using the data of fifty-three patients taken from a hospital in China.

6.
PLoS One ; 16(7): e0254999, 2021.
Article in English | MEDLINE | ID: covidwho-1325438

ABSTRACT

Over the past few months, the spread of the current COVID-19 epidemic has caused tremendous damage worldwide, and unstable many countries economically. Detailed scientific analysis of this event is currently underway to come. However, it is very important to have the right facts and figures to take all possible actions that are needed to avoid COVID-19. In the practice and application of big data sciences, it is always of interest to provide the best description of the data under consideration. The recent studies have shown the potential of statistical distributions in modeling data in applied sciences, especially in medical science. In this article, we continue to carry this area of research, and introduce a new statistical model called the arcsine modified Weibull distribution. The proposed model is introduced using the modified Weibull distribution with the arcsine-X approach which is based on the trigonometric strategy. The maximum likelihood estimators of the parameters of the new model are obtained and the performance these estimators are assessed by conducting a Monte Carlo simulation study. Finally, the effectiveness and utility of the arcsine modified Weibull distribution are demonstrated by modeling COVID-19 patients data. The data set represents the survival times of fifty-three patients taken from a hospital in China. The practical application shows that the proposed model out-classed the competitive models and can be chosen as a good candidate distribution for modeling COVID-19, and other related data sets.


Subject(s)
COVID-19/epidemiology , COVID-19/mortality , Models, Statistical , Pandemics , SARS-CoV-2/pathogenicity , COVID-19/diagnosis , COVID-19/physiopathology , China/epidemiology , Cough/diagnosis , Cough/physiopathology , Fatigue/diagnosis , Fatigue/physiopathology , Fever/diagnosis , Fever/physiopathology , Hospitals , Humans , Monte Carlo Method , Survival Analysis
7.
Proteins ; 89(11): 1541-1556, 2021 11.
Article in English | MEDLINE | ID: covidwho-1303290

ABSTRACT

The expansion of three-dimensional protein structures and enhanced computing power have significantly facilitated our understanding of protein sequence/structure/function relationships. A challenge in structural genomics is to predict the function of uncharacterized proteins. Protein function deconvolution based on global sequence or structural homology is impracticable when a protein relates to no other proteins with known function, and in such cases, functional relationships can be established by detecting their local ligand binding site similarity. Here, we introduce a sequence order-independent comparison algorithm, PocketShape, for structural proteome-wide exploration of protein functional site by fully considering the geometry of the backbones, orientation of the sidechains, and physiochemical properties of the pocket-lining residues. PocketShape is efficient in distinguishing similar from dissimilar ligand binding site pairs by retrieving 99.3% of the similar pairs while rejecting 100% of the dissimilar pairs on a dataset containing 1538 binding site pairs. This method successfully classifies 83 enzyme structures with diverse functions into 12 clusters, which is highly in accordance with the actual structural classification of proteins classification. PocketShape also achieves superior performances than other methods in protein profiling based on experimental data. Potential new applications for representative SARS-CoV-2 drugs Remdesivir and 11a are predicted. The high accuracy and time-efficient characteristics of PocketShape will undoubtedly make it a promising complementary tool for proteome-wide protein function inference and drug repurposing study.


Subject(s)
Algorithms , Antiviral Agents/pharmacology , Drug Repositioning/methods , Proteins/metabolism , Adenosine Monophosphate/analogs & derivatives , Adenosine Monophosphate/chemistry , Adenosine Monophosphate/metabolism , Adenosine Monophosphate/pharmacology , Alanine/analogs & derivatives , Alanine/chemistry , Alanine/metabolism , Alanine/pharmacology , Antiviral Agents/chemistry , Binding Sites , Coronavirus 3C Proteases/chemistry , Coronavirus 3C Proteases/metabolism , Databases, Protein , GTP Phosphohydrolases/chemistry , GTP Phosphohydrolases/metabolism , Phosphoglycerate Mutase/chemistry , Phosphoglycerate Mutase/metabolism , Proteins/chemistry , Proteins/classification , ROC Curve , SARS-CoV-2/drug effects
8.
Front Psychiatry ; 11: 803, 2020.
Article in English | MEDLINE | ID: covidwho-732830

ABSTRACT

OBJECTIVES: To investigate the prevalence and risk factors for poor mental health of Chinese university students during the Corona Virus Disease 2019 (COVID-19) pandemic. METHOD: Chinese nation-wide on-line cross-sectional survey on university students, collected between February 12th and 17th, 2020. Primary outcome was prevalence of clinically-relevant posttraumatic stress disorder symptoms. Secondary outcomes on poor mental health included prevalence of clinically-relevant anxiety and depressive symptoms, while posttraumatic growth was considered as indicator of effective coping reaction. RESULTS: Of 2,500 invited Chinese university students, 2,038 completed the survey. Prevalence of clinically-relevant PTSD, anxiety, and depressive symptoms, and post traumatic growth (PTG) was 30.8, 15.5, 23.3, and 66.9% respectively. Older age, knowing people who had been isolated, more ACEs, higher level of anxious attachment, and lower level of resilience all predicted primary outcome (all p < 0.01). CONCLUSIONS: A significant proportion of young adults exhibit clinically relevant posttraumatic stress disorder (PTSD), anxious or depressive symptoms, but a larger portion of individuals showed to effectively cope with COVID-19 pandemic. Interventions promoting resilience should be provided, even remotely, to those subjects with specific risk factors to develop poor mental health during COVID-19 or other pandemics with social isolation.

9.
Chin. Trad. Herbal Drugs ; 6(51): 1386-1396, 20200328.
Article in Chinese | WHO COVID, ELSEVIER | ID: covidwho-379238

ABSTRACT

Objective: To explore the potential material basis of Kangbingdu Granules for the treatment of coronavirus disease 2019 (COVID-19) through network pharmacology and molecular docking technology. Methods: The chemical constituents and action targets of Isatidis Radix, Forsythiae Fructus, Gypsum Fibrosum, Anemarrhenae Rhizoma, Phragmitis Rhizoma, Rehmanniae Radix Praeparata, Pogostemon cablin, Acoritataninowii Rhizoma and Curcumae Radix in Kangbingdu Granules were searched by TCMSP. The gene corresponding to the target was searched by UniProt database, and Cytoscape 3.6.1 was used to build a medicinal material-compound-target (gene) network. DAVID was used to perform gene ontology (GO) function enrichment analysis and KEGG pathway enrichment analysis to predict its mechanism. Molecular docking of the top 15 components was carried out in the medicinal material-compound-target network with SARS-CoV-2 3CL hydrolase, and molecular docking with bicuculline, luteolin, quercetin and angiotensin-converting enzyme II (ACE2) was performed. Results: The medicinal material-compound-target (gene) network contained eight medicinal materials, 75 compounds and 255 targets. GO function enrichment analysis revealed 161 GO items (P < 0.05), including 65 biological process (BP) items, 36 cell composition (CC) items, and 60 molecular function (MF) items. KEGG pathway enrichment screened 131 signaling pathways (P < 0.05). The results of molecular docking showed that the core active compounds such as bicuculline, luteolin, and quercetin in the Kangbingdu Granules had similar affinities with those recommended by COVID-19. Conclusion: The active compounds in Kangbingdu Granules can interact with angiotensin-converting enzyme II (ACE2) via targets PTGS2, HSP90AB1, and PTGS1 to regulate multiple signaling pathways, thereby exerting therapeutic effects on COVID-19.

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