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1.
Biosens Bioelectron ; 222: 114979, 2022 Nov 30.
Article in English | MEDLINE | ID: covidwho-2236005

ABSTRACT

False detection of SARS-CoV-2 is detrimental to epidemic prevention and control. The scalar nature of the detected signal and the imperfect target recognition property of developed methods are the root causes of generating false signals. Here, we reported a collaborative system of CRISPR-Cas13a coupling with the stabilized graphene field-effect transistor, providing high-intensity vector signals for detecting SARS-CoV-2. In this collaborative system, SARS-CoV-2 RNA generates a "big subtraction" signal with a right-shifted feature, whereas any untargets cause the left-shifted characteristic signal. Thus, the false detection of SARS-CoV-2 is eliminated. High sensitivity with 0.15 copies/µL was obtained. In addition, the wide concerned instability of the graphene field-effect transistor for biosensing in solution environment was solved by the hydrophobic treatment to its substrate, which should be a milestone in advancing it's engineering application. This collaborative system characterized by the high-intensity vector signal and amazing stability significantly advances the accurate SARS-CoV-2 detection from the aspect of signal nature.

2.
ACS Omega ; 7(49): 45023-45035, 2022 Dec 13.
Article in English | MEDLINE | ID: covidwho-2185526

ABSTRACT

Cellular drug response (concentration required for obtaining 50% of a maximum cellular effect, EC50) can be predicted by the intracellular bioavailability (F ic) and biochemical activity (half-maximal inhibitory concentration, IC50) of drugs. In an ideal model, the cellular negative log of EC50 (pEC50) equals the sum of log F ic and the negative log of IC50 (pIC50). Here, we measured F ic's of remdesivir, favipiravir, and hydroxychloroquine in various cells and calculated their anti-SARS-CoV-2 EC50's. The predicted EC50's are close to the observed EC50's in vitro. When the lung concentrations of antiviral drugs are higher than the predicted EC50's in alveolar type 2 cells, the antiviral drugs inhibit virus replication in vivo, and vice versa. Overall, our results indicate that both in vitro and in vivo antiviral activities of drugs can be predicted by their intracellular bioavailability and biochemical activity without using virus. This virus-free strategy can help medicinal chemists and pharmacologists to screen antivirals during early drug discovery, especially for researchers who are not able to work in the high-level biosafety lab.

3.
Anal Chem ; 95(2): 966-975, 2023 01 17.
Article in English | MEDLINE | ID: covidwho-2185425

ABSTRACT

Clustered regularly interspaced short palindromic repeats (CRISPR)-based assays have been an emerging diagnostic technology for pathogen diagnosis. In this work, we developed a polydisperse droplet digital CRISPR-Cas-based assay (PddCas) for the rapid and ultrasensitive amplification-free detection of viral DNA/RNA with minimum instruments. LbaCas12a and LbuCas13a were used for the direct detection of viral DNA and RNA, respectively. The reaction mixtures were partitioned with a common vortex mixer to generate picoliter-scale polydisperse droplets in several seconds. The limit of detection (LoD) for the target DNA and RNA is approximately 100 aM and 10 aM, respectively, which is about 3 × 104-105 fold more sensitive than corresponding bulk CRISPR assays. We applied the PddCas to successfully detect severe acute respiratory syndrome coronavirus (SARS-CoV-2) and human papillomavirus type 18 (HPV 18) in clinical samples. For the 23 HPV 18-suspected cervical epithelial cell samples and 32 nasopharyngeal swabs for SARS-CoV-2, 100% sensitivity and 100% specificity were demonstrated. The dual-gene virus detection with PddCas was also established and verified. Therefore, PddCas has potential for point-of-care application and is envisioned to be readily deployed for frequent testing as part of an integrated public health surveillance program.


Subject(s)
COVID-19 , Papillomavirus Infections , Humans , DNA, Viral/genetics , RNA, Viral/genetics , CRISPR-Cas Systems/genetics , SARS-CoV-2/genetics , Human papillomavirus 18
4.
Anal Chem ; 2022 Dec 01.
Article in English | MEDLINE | ID: covidwho-2133128

ABSTRACT

As observed in the COVID-19 pandemic, RNA viruses continue to rapidly evolve through mutations. In the absence of effective therapeutics, early detection of new severely pathogenic viruses and quarantine of infected people are critical for reducing the spread of the viral infections. However, conventional detection methods require a substantial amount of time to develop probes specific to new viruses, thereby impeding immediate response to the emergence of viral pathogens. In this study, we identified multiple types of viruses by obtaining the spectral fingerprint of their surface proteins with probe-free surface-enhanced Raman scattering (SERS). In addition, the SERS-based method can remarkably distinguish influenza virus variants with several surface protein point mutations from their parental strain. Principal component analysis (PCA) of the SERS spectra systematically captured the key Raman bands to distinguish the variants. Our results show that the combination of SERS and PCA can be a promising tool for rapid detection of newly emerging mutant viruses without a virus-specific probe.

5.
Cognit Comput ; : 1-15, 2022 Nov 16.
Article in English | MEDLINE | ID: covidwho-2122241

ABSTRACT

COVID-19 created immense global challenges in 2020, and the world will live under its threat indefinitely. Much of the information on social media supported the government in addressing this major public health event. On January 9, to control the virus, the Chinese government announced universal vaccinations. However, due to a range of varied interpretations, people held different attitudes towards vaccination. Therefore, the success of the mass immunization strategy greatly depended on the public perception of the COVID-19 vaccine. This article explores the changes in people's emotional attitudes towards vaccines and the reasons behind them in the context of the global pandemic in an effort to help mankind overcome this ongoing crisis. For this article, microblogs from January to September containing Chinese people's responses to the COVID-19 vaccines were collected. Based on fuzzy logic and deep learning, we advance the hypothesis that fuzzy vector adaptive improvements will make it possible to better express language emotion and that fuzzy emotion vectors can be integrated into deep learning models, thus making these models more interpretable. Based on this assumption, we design a deep learning model with a fuzzy emotion vector. The experimental results show the positive effect of this model. By applying the model in analyses of people's attitudes towards vaccines, we can obtain people's attitudes towards vaccines in different time periods. We discovered that the most negative emotions about the vaccine appeared in April and that the most positive emotions about the vaccine appeared in February. Combined with word cloud technology and the LDA model, we can effectively explore the reasons for the changes in vaccine attitudes. Our findings show that people's negative emotions about the vaccine are always higher than their positive emotions about the vaccine and that people's attitudes towards the vaccine are closely related to the progress of the epidemic. There is also a certain relationship between people's attitudes towards the vaccine and those towards the vaccination.

6.
Biomolecules ; 12(12)2022 11 23.
Article in English | MEDLINE | ID: covidwho-2123515

ABSTRACT

The rapid spread of COVID-19 has become a major concern for people's lives and health all around the world. COVID-19 patients in various phases and severity require individualized treatment given that different patients may develop different symptoms. We employed machine learning methods to discover biomarkers that may accurately classify COVID-19 in various disease states and severities in this study. The blood gene expression profiles from 50 COVID-19 patients without intensive care, 50 COVID-19 patients with intensive care, 10 non-COVID-19 individuals without intensive care, and 16 non-COVID-19 individuals with intensive care were analyzed. Boruta was first used to remove irrelevant gene features in the expression profiles, and then, the minimum redundancy maximum relevance was applied to sort the remaining features. The generated feature-ranked list was fed into the incremental feature selection method to discover the essential genes and build powerful classifiers. The molecular mechanism of some biomarker genes was addressed using recent studies, and biological functions enriched by essential genes were examined. Our findings imply that genes including UBE2C, PCLAF, CDK1, CCNB1, MND1, APOBEC3G, TRAF3IP3, CD48, and GZMA play key roles in defining the different states and severity of COVID-19. Thus, a new point of reference is provided for understanding the disease's etiology and facilitating a precise therapy.


Subject(s)
COVID-19 , Transcriptome , Humans , COVID-19/diagnosis , COVID-19/genetics , Machine Learning , Biomarkers
7.
Cancer Cell Int ; 22(1): 331, 2022 Oct 31.
Article in English | MEDLINE | ID: covidwho-2098348

ABSTRACT

BACKGROUND: To summarize the impact of radiotherapy (RT) and chemotherapy delays on patients with nasopharyngeal carcinoma (NPC) during the COVID-19 pandemic. METHODS: We retrospectively included 233 patients with stage II-IVa NPC treated with RT and chemotherapy between December 11, 2019 and March 11, 2020. The outcomes were elevation in the EBV DNA load between two adjacent cycles of chemotherapy or during RT, and 1-year disease-free survival (DFS). RESULTS: RT delay occurred in 117 (50%) patients, and chemotherapy delay occurred in 220 (94%) patients. RT delay of ≥ 6 days was associated with a higher EBV DNA elevation rate (20.4% vs. 3.6%, odds ratio [OR] = 6.93 [95% CI = 2.49-19.32], P < 0.001), and worse 1-year DFS (91.2% vs. 97.8%, HR = 3.61 [95% CI = 1.37-9.50], P = 0.006), compared with on-schedule RT or delay of < 6 days. Chemotherapy delay of ≥ 10 days was not associated with a higher EBV DNA elevation rate (12.5% vs. 6.8%, OR = 1.94 [95% CI = 0.70-5.40], P = 0.20), or worse 1-year DFS (93.8% vs. 97.1%, HR = 3.73 [95% CI = 0.86-16.14], P = 0.059), compared with delay of < 10 days. Multivariable analyses showed RT delay of ≥ 6 days remained an independent adverse factor for both EBV DNA elevation and DFS. CONCLUSION: To ensure treatment efficacy for patients with nonmetastatic NPC, initiation of RT should not be delayed by more than 6 days; the effect of chemotherapy delay requires further investigation.

8.
J Infect Dev Ctries ; 16(9): 1417-1423, 2022 09 30.
Article in English | MEDLINE | ID: covidwho-2066671

ABSTRACT

INTRODUCTION: The treatment of acute myocardial infarction (AMI) during the COVID-19 pandemic has been affected to varying degrees. This study is the first to explore the impact of COVID-19 on the treatment and prognosis of rural and urban AMI in developing countries. METHODOLOGY: A total of 128 patients with AMI in our hospital during the COVID-19 pandemic were enrolled. A total of 197 patients diagnosed with AMI before the COVID-19 pandemic were selected as the control group and one year of follow-up was performed. RESULTS: Hospital stay and the proportion of Killip class ≥ 2 patients were increased among rural AMI patients in the 'during COVID-19' group, compared with the 'before COVID-19' group. Among ST-segment elevation myocardial infarction (STEMI) total and rural STEMI patients, the treatment time in the during-COVID-19 group was longer than that in the before-COVID-19 group, whereas only the symptom to door (S to D) total and door to balloon (D to B) were extended in urban STEMI patients. In AMI total and rural AMI patients, major adverse cardiovascular events (MACEs) and all-cause mortality were increased in the during-COVID-19 group compared with the before-COVID-19 group. Kaplan-Meier analysis revealed that the survival and occurrence of MACEs in AMI total and rural AMI patients were significantly higher in the during-COVID-19 group. CONCLUSIONS: The COVID-19 pandemic led to delayed treatment and worse prognosis in AMI patients. Rural areas appear to be at a greater risk.


Subject(s)
COVID-19 , Myocardial Infarction , ST Elevation Myocardial Infarction , COVID-19/epidemiology , Humans , Myocardial Infarction/epidemiology , Myocardial Infarction/therapy , Pandemics , Prognosis , ST Elevation Myocardial Infarction/diagnosis , ST Elevation Myocardial Infarction/epidemiology , ST Elevation Myocardial Infarction/therapy , Time Factors
9.
J Biomol Struct Dyn ; : 1-12, 2022 Sep 21.
Article in English | MEDLINE | ID: covidwho-2037155

ABSTRACT

The BA.1 × AY.4 recombinant variant (Deltacron) continues to inflict chaos globally due to its rapid transmission and infectivity. To decipher the mechanism of pathogenesis by the BA.1 × AY.4 recombinant variant (Deltacron), a protein coupling, protein structural graphs (PSG), residue communication and all atoms simulation protocols were used. We observed that the bonding network is altered by this variant; engaging new residues that helps to robustly bind. HADDOCK docking score for the wild type has been previously reported to be -111.8 ± 1.5 kcal/mol while the docking score for the Deltacron variant was calculated to be -128.3 ± 2.5 kcal/mol. The protein structural graphs revealed variations in the hub residues, number of nodes, inter and intra residues communities, and path communication perturbation caused by the acquired mutations in the Deltacron-RBD thus alter the binding approach and infectivity. Moreover, the dynamic behaviour reported a highly flexible structure with enhanced residues flexibility particularly by the loops required for interaction with ACE2. It was observed that these mutations have altered the secondary structure of the RBD mostly transited to the loops thus acquired higher flexible dynamics than the native structure during the simulation. The total binding free energy for each of these complexes, that is, WT-RBD and Deltacron-RBD were reported to be -61.38 kcal/mol and -70.47 kcal/mol. Protein's motion revealed a high trace value in the Deltacron variant that clearly depict more structural flexibility. The broad range of phase space covered by the Deltacron variant along PC1 and PC2 suggests that these mutations are important in contributing conformational heterogeneity or flexibility that consequently help the variant to bind more efficiently than the wild type. The current study provides a basis for structure-based drug designing against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

10.
J Environ Manage ; 320: 115754, 2022 Oct 15.
Article in English | MEDLINE | ID: covidwho-2015644

ABSTRACT

The COVID-19 pandemic brings a surge in household electricity consumption, thereby enabling extensive research interest on residential carbon emissions as one of the hot topics in carbon reduction. However, research on spatial-temporal driving forces for the increase of residential CO2 emissions between regions still remains unknown in terms of emissions mitigation in post-pandemic era. Therefore, we studied the residential CO2 emissions from the electricity consumption of China during the period 1997-2019. Afterward, the regional specified production emission factors, combining with electricity use pattern, living standard and household size, were modelled to reveal the spatial-temporal driving forces at national and provincial scales. We observed that the national residential electricity-related CO2 increased from 1997 to 2013, before fluctuating to a peak in 2019. Guangdong, Shandong and Jiangsu, from East China were the top emitters with 27% of the national scale. The decomposition results showed that the income improvement was the primary driving force behind the emission increase in most provinces, while the household size and production emission effects were the main negative effects. For the spatial decomposition, differences in the total households between regions further widen the gaps of total emissions. At the provincial scale of temporal decomposition, eastern developed regions exhibited the most significant decrease in production emissions. In contrast, electricity intensity effect showed negative emission influences in the east and central regions, and positive in north-eastern and western China. The research identified the different incremental patterns of residential electricity-related CO2 emissions in various Chinese provinces, thereby providing scientific ways to save energy and reduce emissions.


Subject(s)
COVID-19 , Carbon Dioxide , COVID-19/epidemiology , COVID-19/prevention & control , Carbon/analysis , Carbon Dioxide/analysis , China , Electricity , Humans , Pandemics
11.
Int J Nurs Pract ; 28(5): e13085, 2022 Oct.
Article in English | MEDLINE | ID: covidwho-1968138

ABSTRACT

AIM: To investigate the current condition and degree of fear of disease progression and associated factors in patients with mild or common type COVID-19. BACKGROUND: At the end of 2019, COVID-19 spread from Wuhan in Hubei Province throughout China. Confirmed cases and deaths have since been reported in many countries around the world. However, fear of progression in these patients has been poorly explored. METHODS: During February 2020, we recruited 114 patients with mild or common type COVID-19 admitted to a Fangcang shelter hospital. We assessed patients' degree of fear using the simplified Fear of Progression Questionnaire (Chinese version). Multiple regression analysis was applied to explore potential factors. RESULTS: The fear of disease progression scores of patients with mild or common COVID-19 was at the low-to-moderate level. Current unemployment, disease duration of 28 days or more and not having a spouse diagnosed with COVID-19 were factors potentially associated with fear of progression. CONCLUSION: With a high prevalence of fear of disease progression in patients with COVID-19, the risk of psychological effects from the pandemic is significant and fear of progression is one of the manifestations. The need for psychological support services for patients should be included in all pandemic and disaster planning.


Subject(s)
COVID-19 , COVID-19/epidemiology , China/epidemiology , Fear , Hospitals, Special , Humans , Mobile Health Units , Phobic Disorders , SARS-CoV-2
12.
Front Biosci (Landmark Ed) ; 27(7): 204, 2022 06 27.
Article in English | MEDLINE | ID: covidwho-1965057

ABSTRACT

BACKGROUND: COVID-19 displays an increased mortality rate and higher risk of severe symptoms with increasing age, which is thought to be a result of the compromised immunity of elderly patients. However, the underlying mechanisms of aging-associated immunodeficiency against Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains unclear. Epigenetic modifications show considerable changes with age, causing altered gene regulations and cell functions during the aging process. The DNA methylation patterns among patients with coronavirus 2019 disease (COVID-19) who had different ages were compared to explore the effect of aging-associated methylation modifications in SARS-CoV-2 infection. METHODS: Patients with COVID-19 were divided into three groups according to age. Boruta was used on the DNA methylation profiles of the patients to remove irrelevant features and retain essential signature sites to identify substantial aging-associated DNA methylation changes in COVID-19. Next, these features were ranked using the minimum redundancy maximum relevance (mRMR) method, and the feature list generated by mRMR was processed into the incremental feature selection method with decision tree (DT), random forest, k-nearest neighbor, and support vector machine to obtain the key methylation sites, optimal classifier, and decision rules. RESULTS: Several key methylation sites that showed distinct patterns among the patients with COVID-19 who had different ages were identified, and these methylation modifications may play crucial roles in regulating immune cell functions. An optimal classifier was built based on selected methylation signatures, which can be useful to predict the aging-associated disease risk of COVID-19. CONCLUSIONS: Existing works and our predictions suggest that the methylation modifications of genes, such as NHLH2, ZEB2, NWD1, ELOVL2, FGGY, and FHL2, are closely associated with age in patients with COVID-19, and the 39 decision rules extracted with the optimal DT classifier provides quantitative context to the methylation modifications in elderly patients with COVID-19. Our findings contribute to the understanding of the epigenetic regulations of aging-associated COVID-19 symptoms and provide the potential methylation targets for intervention strategies in elderly patients.


Subject(s)
COVID-19 , SARS-CoV-2 , Aged , COVID-19/genetics , DNA Methylation , Humans , Protein Processing, Post-Translational , SARS-CoV-2/genetics , Support Vector Machine
13.
Signal Transduct Target Ther ; 7(1): 255, 2022 07 27.
Article in English | MEDLINE | ID: covidwho-1960331

ABSTRACT

SARS-CoV-2, the culprit pathogen of COVID-19, elicits prominent immune responses and cytokine storms. Intracellular Cl- is a crucial regulator of host defense, whereas the role of Cl- signaling pathway in modulating pulmonary inflammation associated with SARS-CoV-2 infection remains unclear. By using human respiratory epithelial cell lines, primary cultured human airway epithelial cells, and murine models of viral structural protein stimulation and SARS-CoV-2 direct challenge, we demonstrated that SARS-CoV-2 nucleocapsid (N) protein could interact with Smad3, which downregulated cystic fibrosis transmembrane conductance regulator (CFTR) expression via microRNA-145. The intracellular Cl- concentration ([Cl-]i) was raised, resulting in phosphorylation of serum glucocorticoid regulated kinase 1 (SGK1) and robust inflammatory responses. Inhibition or knockout of SGK1 abrogated the N protein-elicited airway inflammation. Moreover, N protein promoted a sustained elevation of [Cl-]i by depleting intracellular cAMP via upregulation of phosphodiesterase 4 (PDE4). Rolipram, a selective PDE4 inhibitor, countered airway inflammation by reducing [Cl-]i. Our findings suggested that Cl- acted as the crucial pathological second messenger mediating the inflammatory responses after SARS-CoV-2 infection. Targeting the Cl- signaling pathway might be a novel therapeutic strategy for COVID-19.


Subject(s)
COVID-19 , Chlorine/metabolism , MicroRNAs , Animals , COVID-19/genetics , Humans , Inflammation/pathology , Mice , MicroRNAs/metabolism , Nucleocapsid Proteins , Respiratory Mucosa/metabolism , Respiratory Mucosa/pathology , SARS-CoV-2
14.
Front Mol Biosci ; 9: 908080, 2022.
Article in English | MEDLINE | ID: covidwho-1952442

ABSTRACT

The occurrence of coronavirus disease 2019 (COVID-19) has become a serious challenge to global public health. Definitive and effective treatments for COVID-19 are still lacking, and targeted antiviral drugs are not available. In addition, viruses can regulate host innate immunity and antiviral processes through the epigenome to promote viral self-replication and disease progression. In this study, we first analyzed the methylation dataset of COVID-19 using the Monte Carlo feature selection method to obtain a feature list. This feature list was subjected to the incremental feature selection method combined with a decision tree algorithm to extract key biomarkers, build effective classification models and classification rules that can remarkably distinguish patients with or without COVID-19. EPSTI1, NACAP1, SHROOM3, C19ORF35, and MX1 as the essential features play important roles in the infection and immune response to novel coronavirus. The six significant rules extracted from the optimal classifier quantitatively explained the expression pattern of COVID-19. Therefore, these findings validated that our method can distinguish COVID-19 at the methylation level and provide guidance for the diagnosis and treatment of COVID-19.

15.
Int J Mol Sci ; 23(13)2022 Jul 05.
Article in English | MEDLINE | ID: covidwho-1934138

ABSTRACT

Long-chain noncoding RNAs (lncRNAs) are RNAs that do not code for proteins, widely present in eukaryotes. They regulate gene expression at multiple levels through different mechanisms at epigenetic, transcription, translation, and the maturation of mRNA transcripts or regulation of the chromatin structure, and compete with microRNAs for binding to endogenous RNA. Adipose tissue is a large and endocrine-rich functional tissue in mammals. Excessive accumulation of white adipose tissue in mammals can cause metabolic diseases. However, unlike white fat, brown and beige fats release energy as heat. In recent years, many lncRNAs associated with adipogenesis have been reported. The molecular mechanisms of how lncRNAs regulate adipogenesis are continually investigated. In this review, we discuss the classification of lncRNAs according to their transcriptional location. lncRNAs that participate in the adipogenesis of white or brown fats are also discussed. The function of lncRNAs as decoy molecules and RNA double-stranded complexes, among other functions, is also discussed.


Subject(s)
Adipogenesis , RNA, Long Noncoding , Adipocytes/metabolism , Adipocytes, Brown/metabolism , Adipogenesis/genetics , Adipose Tissue, Brown/metabolism , Adipose Tissue, White/metabolism , Animals , Mammals/metabolism , RNA, Long Noncoding/genetics , RNA, Long Noncoding/metabolism
16.
Biochem Mol Biol Educ ; 50(4): 414-420, 2022 07.
Article in English | MEDLINE | ID: covidwho-1894578

ABSTRACT

This study aimed to investigate how international students enrolled on medical and surgical bachelor's degree programs (MBBS) in China perceived online medical education course, compared to native Chinese students during the Covid-19 pandemic. The perceptions of 38 MBBS and 31 Chinese sophomores were surveyed using the Chaoxing platform. The international student group's mean satisfaction with online teaching was 2.737 on a 5-point scale, much lower than the Chinese students' mean score of 4.355 (p < 0.05). Similarly, the international students expressed less satisfaction than the Chinese learners with other aspects of the course, including the teacher's level, at 3.964 ± 0.818 versus 4.445 ± 0.548 (p < 0.05); curriculum organization, at 3.651 ± 0.848 versus 4.333 ± 0.568 (p < 0.05); and self-learning level, at 3.634 ± 0.996 versus 3.686 ± 0.949 (p > 0.05), respectively. There were also noteworthy differences between the progress made by the international students in Chinese language learning, which was positively correlated with satisfaction with teaching on the online medical education (p < 0.05). The results suggest that, while online teaching was a necessary response to the Covid-19 pandemic, satisfaction with this mode of education is lower among international students than their Chinese counterparts.


Subject(s)
COVID-19 , Education, Distance , Education, Medical , Students, Medical , COVID-19/epidemiology , Education, Distance/methods , Humans , Pandemics , Students
17.
Huan Jing Ke Xue ; 43(6): 2831-2839, 2022 Jun 08.
Article in Chinese | MEDLINE | ID: covidwho-1876197

ABSTRACT

The Chinese government triggered the immediate implementation of a lockdown policy in China following the outbreak of the COVID-19 pandemic, leading to drastic decreases in air pollutant emissions. However, concentrations of PM2.5 and other pollutants increased during the COVID-19 lockdown over the Jing-Jin-Ji region compared with those averaged over 2015-2019, and two PM2.5 pollution events occurred during the lockdown. Using the ERA5 reanalysis data, we found that the Jing-Jin-Ji region during the COVID-19 lockdown was characterized by higher relative humidity, lower planetary boundary layer height, and anomalous updraft. These conditions were favorable for condensation and the secondary formation of aerosols and prevented turbulent diffusion of pollutants. Furthermore, we conducted sensitivity tests using the WRF-Chem model and found that ρ(PM2.5) increased by 20-55 µg·m-3(60%-170%) over the middle region of Jing-Jin-Ji during the COVID-19 lockdown due to changes in meteorological conditions. Furthermore, the enhanced aerosol chemistry and unfavorable diffusion conditions were identified as the key factors driving increases in PM2.5 concentrations during the lockdown. Planetary boundary layer height and relative humidity may become the important factors in forecasting PM2.5 pollution events over the Jing-Jin-Ji region under the background of emission reduction.


Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Aerosols/analysis , Air Pollutants/analysis , Air Pollution/analysis , COVID-19/epidemiology , COVID-19/prevention & control , China/epidemiology , Communicable Disease Control , Environmental Monitoring , Humans , Pandemics/prevention & control , Particulate Matter/analysis
18.
Micromachines (Basel) ; 13(5)2022 Apr 28.
Article in English | MEDLINE | ID: covidwho-1875706

ABSTRACT

Flexible wearable pressure sensors play a pivotal role in healthcare monitoring, disease prevention, and humanmachine interactions. However, their narrow sensing ranges, low detection sensitivities, slow responses, and complex preparation processes restrict their application in smart wearable devices. Herein, a capacitive pressure sensor with high sensitivity and flexibility that uses an ionic collagen fiber material as the dielectric layer is proposed. The sensor exhibits a high sensitivity (5.24 kPa-1), fast response time (40 ms), long-term stability, and excellent repeatability over 3000 cycles. Because the sensor is resizable, flexible, and has a simple preparation process, it can be flexibly attached to clothes and the human body for wearable monitoring. Furthermore, the practicality of the sensor is proven by attaching it to different measurement positions on the human body to monitor the activity signal.

19.
Life (Basel) ; 12(6)2022 May 28.
Article in English | MEDLINE | ID: covidwho-1869692

ABSTRACT

SARS-CoV-2 shows great evolutionary capacity through a high frequency of genomic variation during transmission. Evolved SARS-CoV-2 often demonstrates resistance to previous vaccines and can cause poor clinical status in patients. Mutations in the SARS-CoV-2 genome involve mutations in structural and nonstructural proteins, and some of these proteins such as spike proteins have been shown to be directly associated with the clinical status of patients with severe COVID-19 pneumonia. In this study, we collected genome-wide mutation information of virulent strains and the severity of COVID-19 pneumonia in patients varying depending on their clinical status. Important protein mutations and untranslated region mutations were extracted using machine learning methods. First, through Boruta and four ranking algorithms (least absolute shrinkage and selection operator, light gradient boosting machine, max-relevance and min-redundancy, and Monte Carlo feature selection), mutations that were highly correlated with the clinical status of the patients were screened out and sorted in four feature lists. Some mutations such as D614G and V1176F were shown to be associated with viral infectivity. Moreover, previously unreported mutations such as A320V of nsp14 and I164ILV of nsp14 were also identified, which suggests their potential roles. We then applied the incremental feature selection method to each feature list to construct efficient classifiers, which can be directly used to distinguish the clinical status of COVID-19 patients. Meanwhile, four sets of quantitative rules were set up, which can help us to more intuitively understand the role of each mutation in differentiating the clinical status of COVID-19 patients. Identified key mutations linked to virologic properties will help better understand the mechanisms of infection and will aid in the development of antiviral treatments.

20.
Biomed Res Int ; 2022: 6089242, 2022.
Article in English | MEDLINE | ID: covidwho-1832691

ABSTRACT

COVID-19 is hypothesized to be linked to the host's excessive inflammatory immunological response to SARS-CoV-2 infection, which is regarded to be a major factor in disease severity and mortality. Numerous immune cells play a key role in immune response regulation, and gene expression analysis in these cells could be a useful method for studying disease states, assessing immunological responses, and detecting biomarkers. Here, we developed a machine learning procedure to find biomarkers that discriminate disease severity in individual immune cells (B cell, CD4+ cell, CD8+ cell, monocyte, and NK cell) using single-cell gene expression profiles of COVID-19. The gene features of each profile were first filtered and ranked using the Boruta feature selection method and mRMR, and the resulting ranked feature lists were then fed into the incremental feature selection method to determine the optimal number of features with decision tree and random forest algorithms. Meanwhile, we extracted the classification rules in each cell type from the optimal decision tree classifiers. The best gene sets discovered in this study were analyzed by GO and KEGG pathway enrichment, and some important biomarkers like TLR2, ITK, CX3CR1, IL1B, and PRDM1 were validated by recent literature. The findings reveal that the optimal gene sets for each cell type can accurately classify COVID-19 disease severity and provide insight into the molecular mechanisms involved in disease progression.


Subject(s)
COVID-19 , Algorithms , Biomarkers , COVID-19/genetics , Humans , Machine Learning , SARS-CoV-2/genetics
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