Mortality burden due to ambient nitrogen dioxide pollution in China: Application of high-resolution models.

BACKGROUND: A large gap exists between the latest Global Air Quality Guidelines (AQG 2021) and Chinese air quality standards for NO2. Assessing whether and to what extent air quality standards for NO2 should be tightened in China requires a comprehensive understanding of the spatiotemporal characteristics of population exposure to ambient NO2 and related health risks, which have not been studied to date. OBJECTIVE: We predicted ground NO2 concentrations with high resolution in mainland China, explored exposure characteristics to NO2 pollution, and assessed the mortality burden attributable to NO2 exposure. METHODS: Daily NO2 concentrations in 2019 were predicted at 1-km spatial resolution in mainland China using random forest models incorporating multiple predictors. From these high-resolution predictions, we explored the spatiotemporal distribution of NO2, population and area percentages with NO2 exposure exceeding criterion levels, and premature deaths attributable to long- and short-term NO2 exposure in China. RESULTS: The cross-validation R2and root mean squared error of the NO2 predicting model were 0.80 and 7.78 µg/m3, respectively,at the daily level in 2019.The percentage of people (population number) with annual NO2 exposure over 40 µg/m3 in mainland China in 2019 was 10.40 % (145,605,200), and it reached 99.68 % (1,395,569,840) with the AQG guideline value of 10 µg/m3. NO2 levels and population exposure risk were elevated in urban areas than in rural. Long- and short-term exposures to NO2 were associated with 285,036 and 121,263 non-accidental deaths, respectively, in China in 2019. Tightening standards in steps gradually would increase the potential health benefit. CONCLUSION: In China, NO2 pollution is associated with significant mortality burden. Spatial disparities exist in NO2 pollution and exposure risks. China's current air quality standards may no longer objectively reflect the severity of NO2 pollution and exposure risk. Tightening the national standards for NO2 is needed and will lead to significant health benefits.

Lateral flow immunoassays for antigens, antibodies and haptens detection.

Lateral flow immunoassay (LFIA) is widely used as a rapid point-of-care testing (POCT) technique in food safety, veterinary and clinical detection on account of the accessible, fast and low-cost characteristics. After the outbreak of the coronavirus disease 2019 (COVID-19), different types of LFIAs have attracted considerable interest because of their ability of providing immediate diagnosis directly to users, thereby effectively controlling the outbreak. Based on the introduction of the principles and key components of LFIAs, this review focuses on the major detection formats of LFIAs for antigens, antibodies and haptens. With the rapid innovation of detection technologies, new trends of novel labels, multiplex and digital assays are increasingly integrated with LFIAs. Therefore, this review will also introduce the development of new trends of LFIAs as well as its future perspectives.

Comparison of the immunogenicity of nasal-spray rVSV vector, adenovirus vector, and inactivated COVID-19-based vaccines in rodent models.

Intranasal (i.n.) vaccines can induce mucosal and systemic immunity against respiratory pathogens. Previously, we demonstrated that the recombinant vesicular stomatitis virus (rVSV)-based COVID-19 vaccine rVSV-SARS-CoV-2, with poor immunogenicity via the intramuscular route (i.m.), is more suitable for i.n. administration in mice and nonhuman primates. Here, we found that the rVSV-SARS-CoV-2 Beta variant was more immunogenic than the wild-type strain and other variants of concern (VOCs) in golden Syrian hamsters. Furthermore, the immune responses elicited by rVSV-based vaccine candidates via the i.n. route were significantly higher than those of two licensed vaccines: the inactivated vaccine KCONVAC delivered via the i.m. route and the adenovirus-based Vaxzevria delivered i.n. or i.m. We next assessed the booster efficacy of rVSV following two i.m. doses of KCONVAC. Twenty-eight days after receiving two i.m. doses of KCONVAC, hamsters were boosted with a third dose of KCONVAC (i.m.), Vaxzevria (i.m. or i.n.), or rVSVs (i.n.). Consistent with other heterologous booster studies, Vaxzevria and rVSV elicited significantly higher humoral immunity than the homogenous KCONVAC. In summary, our results confirmed that two i.n. doses of rVSV-Beta elicited significantly higher humoral immune responses than commercial inactivated and adeno-based COVID vaccines in hamsters. As a heterologous booster dose, rVSV-Beta induced potent, persistent, and broad-spectrum humoral and mucosal neutralizing responses against all VOCs, highlighting its potential to be developed into a nasal-spray vaccine.

Wireless and Multi‐Person Respiration Monitoring using Facemask‐Integrated Flexible Meta‐Antennas

Respiration monitoring of a large population is important in containing the spread of viral respiratory infections such as the coronavirus disease 2019 (COVID‐19). Current technologies, however, lack the ability in respiration monitoring of multiple human subjects in a long‐term, robust, and low‐cost manner. Herein, wireless respiration monitoring of multiple human subjects using facemask‐integrated flexible meta‐antennas is demonstrated. The flexible meta‐antenna has an architecture of multi‐layered anisotropic hole‐array, which is optimized by theory and simulations to achieve high performances including good antenna gain, robustness against body interferences, and high air permeability favorable for facemask integration. A person's respiration patterns and respiration rates are wirelessly obtained by the meta‐antenna integrated with a temperature‐sensor‐embedded chip. Respiration monitoring of multiple subjects in long range and long term during daily activities is simultaneously demonstrated. In addition, a real‐time data processing system is introduced in which a local server, a cloud server, and an application layer are implemented for the real‐time display of respiration patterns and automatic recognition of abnormal status. The design of flexible meta‐antennas may lead to a distinct class of physiological sensors over a large population for applications in pandemic control and personalized healthcare. [ABSTRACT FROM AUTHOR] Copyright of Advanced Materials Technologies is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Critical review on quality of methodology and recommendations of clinical practice guidelines for peri-implantitis.

BACKGROUND: Peri-implantitis is of high prevalence with the popularity of dental implants nowadays. Guidelines or consensus have been developed in succession, and we are little-known about their quality. The objective of this study is to evaluate the methodological quality of these guidelines and analyze the consistency of the clinical recommendations. METHODS: We searched for guidelines or consensus on prevention, diagnosis, and/or treatment of peri-implantitis through PubMed, Web of Science, Cochrane Library until January 15th, 2022. In addition, we also searched the websites of the American Dental Association, International Team for Implantology, FDI World Dental Federation, and some guideline collection databases. Appraisal of Guidelines for Research & Evaluation II methodological quality instrument was used to assess the selected guidelines. Furthermore, we described the consistency of recommendations across the included guidelines. RESULTS: In total, 15 guidelines were included. The mean values of the six domains score all below 50%. The mean scores of Applicability were lowest (mean:15%, range:4-29%). As to the overall quality, eleven (73%) were recommended after being modified, and four (27%) were not recommended. Among the clinical recommendations, 53 (67.09%) are for treatment of peri-implantitis, 13 (16.46%) for monitoring issue, 7 (8.86%) for diagnosis, 3 (3.80%) for the disease prevention. CONCLUSIONS: Improving methodology quality and strengthening clinical evidence is essential in the future guideline development in a range of disciplines for improving the treatment effectiveness of people with peri-implantitis. And there is a lack of integrated guidelines in the case of the COVID-19 pandemic.

Identification of Genes Associated with the Impairment of Olfactory and Gustatory Functions in COVID-19 via Machine-Learning Methods.

The coronavirus disease 2019 (COVID-19), as a severe respiratory disease, affects many parts of the body, and approximately 20-85% of patients exhibit functional impairment of the senses of smell and taste, some of whom even experience the permanent loss of these senses. These symptoms are not life-threatening but severely affect patients' quality of life and increase the risk of depression and anxiety. The pathological mechanisms of these symptoms have not been fully identified. In the current study, we aimed to identify the important biomarkers at the expression level associated with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection-mediated loss of taste or olfactory ability, and we have suggested the potential pathogenetic mechanisms of COVID-19 complications. We designed a machine-learning-based approach to analyze the transcriptome of 577 COVID-19 patient samples, including 84 COVID-19 samples with a decreased ability to taste or smell and 493 COVID-19 samples without impairment. Each sample was represented by 58,929 gene expression levels. The features were analyzed and sorted by three feature selection methods (least absolute shrinkage and selection operator, light gradient boosting machine, and Monte Carlo feature selection). The optimal feature sets were obtained through incremental feature selection using two classification algorithms: decision tree (DT) and random forest (RF). The top genes identified by these multiple methods (H3-5, NUDT5, and AOC1) are involved in olfactory and gustatory impairments. Meanwhile, a high-performance RF classifier was developed in this study, and three sets of quantitative rules that describe the impairment of olfactory and gustatory functions were obtained based on the optimal DT classifiers. In summary, this study provides a new computation analysis and suggests the latent biomarkers (genes and rules) for predicting olfactory and gustatory impairment caused by COVID-19 complications.

Infection of SARS-CoV-2 causes severe pathological changes in mouse testis.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has affected more than 600 million people worldwide. Several organs including lung, intestine, and brain are infected by SARS-CoV-2. It has been reported that SARS-CoV-2 receptor angiotensin-converting enzyme-2 (ACE2) is expressed in human testis. However, whether testis is also affected by SARS-CoV-2 is still unclear. In this study, we generate a human ACE2 (hACE2) transgenic mouse model in which the expression of hACE2 gene is regulated by hACE2 promoter. Sertoli and Leydig cells from hACE2 transgenic mice can be infected by SARS-CoV-2 pseudovirus in vitro, and severe pathological changes are observed after injecting the SARS-CoV-2 pseudovirus into the seminiferous tubules. Further studies reveal that Sertoli and Leydig cells from hACE2 transgenic mice are also infected by authentic SARS-CoV-2 virus in vitro. After testis interstitium injection, authentic SARS-CoV-2 viruses are first disseminated to the interstitial cells, and then detected inside the seminiferous tubules which in turn cause germ cell loss and disruption of seminiferous tubules. Our study demonstrates that testis is most likely a target of SARS-CoV-2 virus. Attention should be paid to the reproductive function in SARS-CoV-2 patients.

SARS-COV-2 spike protein promotes RPE cell senescence via the ROS/P53/P21 pathway.

SARS-Cov-2 infection, which has caused the COVID-19 global pandemic, triggers cellular senescence. In this study, we investigate the role of the SARS-COV-2 spike protein (S-protein) in regulating the senescence of RPE cells. The results showed that administration or overexpression of S-protein in ARPE-19 decreased cell proliferation with cell cycle arrest at the G1 phase. S-protein increased SA-ß-Gal positive ARPE-19 cells with high expression of P53 and P21, senescence-associated inflammatory factors (e.g., IL-1ß, IL-6, IL-8, ICAM, and VEGF), and ROS. Elimination of ROS by N-acetyl cysteine (NAC) or knocking down p21 by siRNA diminished S-protein-induced ARPE cell senescence. Both administrated and overexpressed S-protein colocalize with the ER and upregulate ER-stress-associated BIP, CHOP, ATF3, and ATF6 expression. S-protein induced P65 protein nuclear translocation. Inhibition of NF-κB by bay-11-7082 reduced S-protein-mediated expression of senescence-associated factors. Moreover, the intravitreal injection of S-protein upregulates senescence-associated inflammatory factors in the zebrafish retina. In conclusions, the S-protein of SARS-Cov-2 induces cellular senescence of ARPE-19 cells in vitro and the expression of senescence-associated cytokines in zebrafish retina in vivo likely by activating ER stress, ROS, and NF-κb. These results may uncover a potential association between SARS-cov-2 infection and development of AMD.

Rational identification of potent and broad sarbecovirus-neutralizing antibody cocktails from SARS convalescents.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron sublineages have escaped most receptor-binding domain (RBD)-targeting therapeutic neutralizing antibodies (NAbs), which proves that previous NAb drug screening strategies are deficient against the fast-evolving SARS-CoV-2. Better broad NAb drug candidate selection methods are needed. Here, we describe a rational approach for identifying RBD-targeting broad SARS-CoV-2 NAb cocktails. Based on high-throughput epitope determination, we propose that broad NAb drugs should target non-immunodominant RBD epitopes to avoid herd-immunity-directed escape mutations. Also, their interacting antigen residues should focus on sarbecovirus conserved sites and associate with critical viral functions, making the antibody-escaping mutations less likely to appear. Following these criteria, a featured non-competing antibody cocktail, SA55+SA58, is identified from a large collection of broad sarbecovirus NAbs isolated from SARS-CoV-2-vaccinated SARS convalescents. SA55+SA58 potently neutralizes ACE2-utilizing sarbecoviruses, including circulating Omicron variants, and could serve as broad SARS-CoV-2 prophylactics to offer long-term protection, especially for individuals who are immunocompromised or with high-risk comorbidities.

Thymosin-α1 binds with ACE and downregulates the expression of ACE2 in human respiratory epithelia.

BACKGROUND: Thymosin-α1 has been implicated into the treatment of novel respiratory virus Coronavirus Disease 2019 (COVID-19), but the underlying mechanisms are still disputable. AIM: Herein we aimed to reveal a previously unrecognized mechanism that thymosin-α1 prevents COVID-19 by binding with angiotensin-converting enzyme (ACE), which was inspired from the tool of network pharmacology. METHODS: KEGG pathway enrichment of thymosin-α1 treating COVID-19 was analyzed by Database of Functional Annotation Bioinformatics Microarray Analysis, then core targets were validated by ligand binding kinetics assay and fluorometric detection of ACE and ACE2 enzymatic activity. The production of angiotensin I, angiotensin II, angiotensin (1-7) and angiotensin (1-9) were detected by enzyme linked immunosorbent assay. RESULTS: We found that thymosin-α1 impaired the expressions of angiotensin-converting enzyme 2 and angiotensin (1-7) of human lung epithelial cells in a dose-dependent way (p < 0.001). In contrast, thymosin-α1 had no impact on their ACE and angiotensin (1-9) expressions but significantly inhibited the enzymatic activity of ACE (p > 0.05). CONCLUSION: The bioinformatic findings of network pharmacology and the corresponding pharmacological validations have revealed that thymosin-α1 treatment could decrease ACE2 expression in human lung epithelial cells, which strengthens the potential clinical applications of thymosin-α1 to prevent severe acute respiratory syndrome coronavirus 2 infection.

Growth, Antigenicity, and Immunogenicity of SARS-CoV-2 Spike Variants Revealed by a Live rVSV-SARS-CoV-2 Virus.

SARS-CoV-2 is an emerging coronavirus threatening human health and the economy worldwide. As an RNA virus, variants emerge during the pandemic and potentially influence the efficacy of the anti-viral drugs and vaccines. Eight spike variants harboring highly recurrent mutations were selected and introduced into a replication-competent recombinant VSV in place of the original G protein (rVSV-SARS-CoV-2). The resulting mutant viruses displayed similar growth curves in vitro as the wild-type virus and could be neutralized by sera from convalescent COVID-19 patients. Several variants, especially Beta strain, showed resistance to human neutralizing monoclonal antibodies targeting the receptor-binding domain (RBD). A single dose of rVSV-SARS-CoV-2 Beta variant could elicit enhanced and broad-spectrum neutralizing antibody responses in human ACE2 knock-in mice and golden Syrian hamsters, while other mutants generated antibody levels comparable to the wild-type. Therefore, our results will be of value to the development of next-generation vaccines and therapeutic antibodies.

Enhanced protective immunity against SARS-CoV-2 elicited by a VSV vector expressing a chimeric spike protein.

SARS-CoV-2 and SARS-CoV are genetically related coronavirus and share the same cellular receptor ACE2. By replacing the VSV glycoprotein with the spikes (S) of SARS-CoV-2 and SARS-CoV, we generated two replication-competent recombinant viruses, rVSV-SARS-CoV-2 and rVSV-SARS-CoV. Using wild-type and human ACE2 (hACE2) knock-in mouse models, we found a single dose of rVSV-SARS-CoV could elicit strong humoral immune response via both intranasal (i.n.) and intramuscular (i.m.) routes. Despite the high genetic similarity between SARS-CoV-2 and SARS-CoV, no obvious cross-neutralizing activity was observed in the immunized mice sera. In macaques, neutralizing antibody (NAb) titers induced by one i.n. dose of rVSV-SARS-CoV-2 were eight-fold higher than those by a single i.m. dose. Thus, our data indicates that rVSV-SARS-CoV-2 might be suitable for i.n. administration instead of the traditional i.m. immunization in human. Because rVSV-SARS-CoV elicited significantly stronger NAb responses than rVSV-SARS-CoV-2 in a route-independent manner, we generated a chimeric antigen by replacing the receptor binding domain (RBD) of SARS-CoV S with that from the SARS-CoV-2. rVSV expressing the chimera (rVSV-SARS-CoV/2-RBD) induced significantly increased NAbs against SARS-CoV-2 in mice and macaques than rVSV-SARS-CoV-2, with a safe Th1-biased response. Serum immunized with rVSV-SARS-CoV/2-RBD showed no cross-reactivity with SARS-CoV. hACE2 mice receiving a single i.m. dose of either rVSV-SARS-CoV-2 or rVSV-SARS-CoV/2-RBD were fully protected against SARS-CoV-2 challenge without obvious lesions in the lungs. Our results suggest that transplantation of SARS-CoV-2 RBD into the S protein of SARS-CoV might be a promising antigen design for COVID-19 vaccines.

Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods.

COVID-19, a severe respiratory disease caused by a new type of coronavirus SARS-CoV-2, has been spreading all over the world. Patients infected with SARS-CoV-2 may have no pathogenic symptoms, i.e., presymptomatic patients and asymptomatic patients. Both patients could further spread the virus to other susceptible people, thereby making the control of COVID-19 difficult. The two major challenges for COVID-19 diagnosis at present are as follows: (1) patients could share similar symptoms with other respiratory infections, and (2) patients may not have any symptoms but could still spread the virus. Therefore, new biomarkers at different omics levels are required for the large-scale screening and diagnosis of COVID-19. Although some initial analyses could identify a group of candidate gene biomarkers for COVID-19, the previous work still could not identify biomarkers capable for clinical use in COVID-19, which requires disease-specific diagnosis compared with other multiple infectious diseases. As an extension of the previous study, optimized machine learning models were applied in the present study to identify some specific qualitative host biomarkers associated with COVID-19 infection on the basis of a publicly released transcriptomic dataset, which included healthy controls and patients with bacterial infection, influenza, COVID-19, and other kinds of coronavirus. This dataset was first analysed by Boruta, Max-Relevance and Min-Redundancy feature selection methods one by one, resulting in a feature list. This list was fed into the incremental feature selection method, incorporating one of the classification algorithms to extract essential biomarkers and build efficient classifiers and classification rules. The capacity of these findings to distinguish COVID-19 with other similar respiratory infectious diseases at the transcriptomic level was also validated, which may improve the efficacy and accuracy of COVID-19 diagnosis.

Detecting the Multiomics Signatures of Factor-Specific Inflammatory Effects on Airway Smooth Muscles.

Smooth muscles are a specific muscle subtype that is widely identified in the tissues of internal passageways. This muscle subtype has the capacity for controlled or regulated contraction and relaxation. Airway smooth muscles are a unique type of smooth muscles that constitute the effective, adjustable, and reactive wall that covers most areas of the entire airway from the trachea to lung tissues. Infection with SARS-CoV-2, which caused the world-wide COVID-19 pandemic, involves airway smooth muscles and their surrounding inflammatory environment. Therefore, airway smooth muscles and related inflammatory factors may play an irreplaceable role in the initiation and progression of several severe diseases. Many previous studies have attempted to reveal the potential relationships between interleukins and airway smooth muscle cells only on the omics level, and the continued existence of numerous false-positive optimal genes/transcripts cannot reflect the actual effective biological mechanisms underlying interleukin-based activation effects on airway smooth muscles. Here, on the basis of newly presented machine learning-based computational approaches, we identified specific regulatory factors and a series of rules that contribute to the activation and stimulation of airway smooth muscles by IL-13, IL-17, or the combination of both interleukins on the epigenetic and/or transcriptional levels. The detected discriminative factors (genes) and rules can contribute to the identification of potential regulatory mechanisms linking airway smooth muscle tissues and inflammatory factors and help reveal specific pathological factors for diseases associated with airway smooth muscle inflammation on multiomics levels.

Identifying Transcriptomic Signatures and Rules for SARS-CoV-2 Infection.

The world-wide Coronavirus Disease 2019 (COVID-19) pandemic was triggered by the widespread of a new strain of coronavirus named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Multiple studies on the pathogenesis of SARS-CoV-2 have been conducted immediately after the spread of the disease. However, the molecular pathogenesis of the virus and related diseases has still not been fully revealed. In this study, we attempted to identify new transcriptomic signatures as candidate diagnostic models for clinical testing or as therapeutic targets for vaccine design. Using the recently reported transcriptomics data of upper airway tissue with acute respiratory illnesses, we integrated multiple machine learning methods to identify effective qualitative biomarkers and quantitative rules for the distinction of SARS-CoV-2 infection from other infectious diseases. The transcriptomics data was first analyzed by Boruta so that important features were selected, which were further evaluated by the minimum redundancy maximum relevance method. A feature list was produced. This list was fed into the incremental feature selection, incorporating some classification algorithms, to extract qualitative biomarker genes and construct quantitative rules. Also, an efficient classifier was built to identify patients infected with SARS-COV-2. The findings reported in this study may help in revealing the potential pathogenic mechanisms of COVID-19 and finding new targets for vaccine design.

Establishment of replication-competent vesicular stomatitis virus-based recombinant viruses suitable for SARS-CoV-2 entry and neutralization assays.

Replication-competent vesicular stomatitis virus (VSV)-based recombinant viruses are useful tools for studying emerging and highly pathogenic enveloped viruses in level 2 biosafety facilities. Here, we used a replication-competent recombinant VSVs (rVSVs) encoding the spike (S) protein of SARS-CoV-2 in place of the original G glycoprotein (rVSV-eGFP-SARS-CoV-2) to develop a high-throughput entry assay for SARS-CoV-2. The S protein was incorporated into the recovered rVSV-eGFP-SARS-CoV-2 particles, which could be neutralized by sera from convalescent COVID-19 patients. The recombinant SARS-CoV-2 also displayed entry characteristics similar to the wild type virus, such as cell tropism and pH-dependence. The neutralizing titers of antibodies and sera measured by rVSV-eGFP-SARS-CoV-2 were highly correlated with those measured by wild-type viruses or pseudoviruses. Therefore, this is a safe and convenient screening tool for SARS-CoV-2, and it may promote the development of COVID-19 vaccines and therapeutics.

Identification of COVID-19 Infection-Related Human Genes Based on a Random Walk Model in a Virus-Human Protein Interaction Network.

Coronaviruses are specific crown-shaped viruses that were first identified in the 1960s, and three typical examples of the most recent coronavirus disease outbreaks include severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and COVID-19. Particularly, COVID-19 is currently causing a worldwide pandemic, threatening the health of human beings globally. The identification of viral pathogenic mechanisms is important for further developing effective drugs and targeted clinical treatment methods. The delayed revelation of viral infectious mechanisms is currently one of the technical obstacles in the prevention and treatment of infectious diseases. In this study, we proposed a random walk model to identify the potential pathological mechanisms of COVID-19 on a virus-human protein interaction network, and we effectively identified a group of proteins that have already been determined to be potentially important for COVID-19 infection and for similar SARS infections, which help further developing drugs and targeted therapeutic methods against COVID-19. Moreover, we constructed a standard computational workflow for predicting the pathological biomarkers and related pharmacological targets of infectious diseases.