Machine learning-aided detection of heart failure (LVEF ≤ 49%) by using ballistocardiography and respiratory effort signals.

Purpose: Under the influence of COVID-19 and the in-hospital cost, the in-home detection of cardiovascular disease with smart sensing devices is becoming more popular recently. In the presence of the qualified signals, ballistocardiography (BCG) can not only reflect the cardiac mechanical movements, but also detect the HF in a non-contact manner. However, for the potential HF patients, the additional quality assessment with ECG-aided requires more procedures and brings the inconvenience to their in-home HF diagnosis. To enable the HF detection in many real applications, we proposed a machine learning-aided scheme for the HF detection in this paper, where the BCG signals recorded from the force sensor were employed without the heartbeat location, and the respiratory effort signals separated from force sensors provided more HF features due to the connection between the heart and the lung systems. Finally, the effectiveness of the proposed HF detection scheme was verified in comparative experiments. Methods: First, a piezoelectric sensor was used to record a signal sequences of the two-dimensional vital sign, which includes the BCG and the respiratory effort. Then, the linear and the non-linear features w.r.t. BCG and respiratory effort signals were extracted to serve the HF detection. Finally, the improved HF detection performance was verified through the LOO and the LOSO cross-validation settings with different machine learning classifiers. Results: The proposed machine learning-aided scheme achieved the robust performance in the HF detection by using 4 different classifiers, and yielded an accuracy of 94.97% and 87.00% in the LOO and the LOSO experiments, respectively. In addition, experimental results demonstrated that the designed respiratory and cardiopulmonary features are beneficial to the HF detection (LVEF ≤ 49 % ). Conclusion: This study proposed a machine learning-aided HF diagnostic scheme. Experimental results demonstrated that the proposed scheme can fully exploit the relationship between the heart and the lung systems to potentially improve the in-home HF detection performance by using both the BCG, the respiratory and the cardiopulmonary-related features.

Machine learning-aided detection of heart failure (LVEF ≤ 49%) by using ballistocardiography and respiratory effort signals

Purpose: Under the influence of COVID-19 and the in-hospital cost, the in-home detection of cardiovascular disease with smart sensing devices is becoming more popular recently. In the presence of the qualified signals, ballistocardiography (BCG) can not only reflect the cardiac mechanical movements, but also detect the HF in a non-contact manner. However, for the potential HF patients, the additional quality assessment with ECG-aided requires more procedures and brings the inconvenience to their in-home HF diagnosis. To enable the HF detection in many real applications, we proposed a machine learning-aided scheme for the HF detection in this paper, where the BCG signals recorded from the force sensor were employed without the heartbeat location, and the respiratory effort signals separated from force sensors provided more HF features due to the connection between the heart and the lung systems. Finally, the effectiveness of the proposed HF detection scheme was verified in comparative experiments. Methods: First, a piezoelectric sensor was used to record a signal sequences of the two-dimensional vital sign, which includes the BCG and the respiratory effort. Then, the linear and the non-linear features w.r.t. BCG and respiratory effort signals were extracted to serve the HF detection. Finally, the improved HF detection performance was verified through the LOO and the LOSO cross-validation settings with different machine learning classifiers. Results: The proposed machine learning-aided scheme achieved the robust performance in the HF detection by using 4 different classifiers, and yielded an accuracy of 94.97% and 87.00% in the LOO and the LOSO experiments, respectively. In addition, experimental results demonstrated that the designed respiratory and cardiopulmonary features are beneficial to the HF detection (LVEF Conclusion: This study proposed a machine learning-aided HF diagnostic scheme. Experimental results demonstrated that the proposed scheme can fully exploit the relationship between the heart and the lung systems to potentially improve the in-home HF detection performance by using both the BCG, the respiratory and the cardiopulmonary-related features.

An efficient method for qualitation and quantitation of multi-components of the herbal medicine Qingjin Yiqi Granules.

Qingjin Yiqi Granules (QJYQ) is a Traditional Chinese Medicines (TCMs) prescription for the patients with post-COVID-19 condition. It is essential to carry out the quality evaluation of QJYQ. A comprehensive investigation was conducted by establishing deep-learning assisted mass defect filter (deep-learning MDF) mode for qualitative analysis, ultra-high performance liquid chromatography and scheduled multiple reaction monitoring method (UHPLC-sMRM) for precise quantitation to evaluate the quality of QJYQ. Firstly, a deep-learning MDF was used to classify and characterize the whole phytochemical components of QJYQ based on the mass spectrum (MS) data of ultra-high performance liquid chromatography quadrupole time of flight tandem mass spectrometry (UHPLC-Q-TOF/MS). Secondly, the highly sensitive UHPLC-sMRM data-acquisition method was established to quantify the multi-ingredients of QJYQ. Totally, nine major types of phytochemical compounds in QJYQ were intelligently classified and 163 phytochemicals were initially identified. Furthermore, fifty components were rapidly quantified. The comprehensive evaluation strategy established in this study would provide an effective tool for accurately evaluating the quality of QJYQ as a whole.

Comparison study of Beninese and Chinese herbal medicines in treating COVID-19.

ETHNOPHARMACOLOGICAL RELEVANCE: The worldwide use of natural remedies is an alternative therapeutic solution to strengthen immunity, fight, and prevent this disease. The rapid spread of the coronavirus disease worldwide has promoted the search for therapeutic solutions following different approaches. China and Benin have seen the use of natural remedies such as Chinese herbal medicine and local endemic plants as alternative solutions in treating COVID-19. AIM OF THE STUDY: The present study was designed to identify the prevalence of medicinal plant use in four municipalities of Benin most affected by COVID-19 and compare them with traditional Chinese medicine and finally verify the efficacy of the main components of the six plants most frequently used, via in vitro experiments. MATERIALS AND METHODS: This study targeting market herbalists and traditional healers was conducted in the form of an ethnomedicinal survey in four representative communities (Cotonou, Abomey-Calavi, Zè, and Ouidah) of southern Benin. The chemical compositions of the six most commonly used herbs were investigated using network pharmacology. Network-based global prediction of disease genes and drug, target, function, and pathway enrichment analysis of the top six herbs was conducted using databases including IPA and visualised using Cytoscape software. The natural botanical drugs involved three medicines and three formulas used in the treatment of COVID-19 in China from the published literature were compared with the top six botanical drugs used in Benin to identify similarities between them and guide the clinical medication in both countries. Finally, the efficacy of the common ingredients in six plants was verified by measuring the viability of BEAS-2B cells and the release of inflammatory factors after administration of different ingredients. Binding abilities of six components to COVID-19 related targets were verified by molecular docking. RESULTS: According to the medication survey investigation, the six most used herbs were Citrus aurantiifolia (13.18%), Momordica charantia (7.75%), Ocimum gratissimum (7.36%), Crateva adansonii (6.59%), Azadirachta indica (5.81%), and Zanthoxylum zanthoxyloides (5.42%). The most represented botanical families were Rutaceae, Lamiaceae, Cucurbitaceae, Meliaceae, and Capparaceae. The network pharmacology of these six herbal plants showed that the flavonoids quercetin, kaempferol, and ß-sitosterol were the main active ingredients of the Benin herbal medicine. Chinese and Beninese herbal medicine are similar in that they have the same targets and pathways in inflammation and oxidative stress relief. Mild COVID-19-related targets come from C. aurantiifolia and M. charantia, and severe COVID-19-related targets come from A. indica A. Juss. Cell viability and enzyme-linked immunosorbent assay results confirmed that six major compounds could protect BEAS-2B cells against injury by inhibiting the expression of inflammatory factors, among which quercetin and isoimperatorin were more effective. Docking verified that the six compounds have good binding potential with COVID-19 related targets. CONCLUSIONS: These results suggest that Benin herbal medicine and Chinese herbal medicine overlap in compounds, targets, and pathways to a certain extent. Among the commonly used plants in Benin, C. aurantiifolia and M. charantia may have a good curative effect on the treatment of mild COVID-19, while for severe COVID-19, A. indica can be added on this basis.

Association Between Ischemic Stroke and COVID-19 in China: A Population-Based Retrospective Study.

Background and Purpose: To investigate the effect of prior ischemic stroke on the outcomes of patients hospitalized with coronavirus disease 2019 (COVID-19), and to describe the incidence, clinical features, and risk factors of acute ischemic stroke (AIS) following COVID-19. Methods: In this population-based retrospective study, we included all the hospitalized positive patients with COVID-19 at Wuhan City from December 29, 2019 to April 15, 2020. Clinical data were extracted from administrative datasets coordinated by the Wuhan Health Commission. The propensity score matching and multivariate logistic regression analyses were used to adjust the confounding factors. Results: There are 36,358 patients in the final cohort, in which 1,160 (3.2%) had a prior stroke. After adjusting for available baseline characteristics, patients with prior stroke had a higher proportion of severe and critical illness and mortality. We found for the first time that the premorbid modified Rankin Scale (MRS) grouping (odds ratio [OR] = 1.796 [95% CI 1.334-2.435], p < 0.001) and older age (OR = 1.905 [95% CI 1.211-3.046], p = 0.006) imparted increased risk of death. AIS following COVID-19 occurred in 124 (0.34%) cases, and patients with prior stroke had a much higher incidence of AIS (3.4%). Logistic regression analyses confirmed an association between the severity of COVID-19 with the incidence of AIS. COVID-19 patients with AIS had a significantly higher mortality compared with COVID-19 patients without stroke and AIS patients without COVID-19. Conclusions: Coronavirus disease 2019 patients with prior stroke, especially those with the higher premorbid MRS or aged, have worse clinical outcomes. Furthermore, COVID-19 increases the incidence of AIS, and the incidence is positively associated with the severity of COVID-19.

A Study of Public Attitudes toward Shanghai's Image under the Influence of COVID-19: Evidence from Comments on Sina Weibo.

With the advent of the Internet era, Chinese users tend to choose to express their opinions on social media platforms represented by Sina Weibo. The changes in people's emotions toward cities from the microblogging texts can reflect the image of cities presented on mainstream social media, and thus target a good image of cities. In this paper, we collected microblog data containing "Shanghai" from 1 January 2019 to 1 September 2022 by Python technology, and we used three methods: Term Frequency-Inverse Document Frequency keyword statistics, Latent Dirichlet Allocation theme model construction, and sentiment analysis by Zhiwang Sentiment Dictionary. We also explore the impact of the COVID-19 epidemic on Shanghai's urban image in the context of the "Shanghai Territorial Static Management", an important public opinion topic during the COVID-19 epidemic. The results of the study show that the "Shanghai-wide static management" of COVID-19 epidemic has significantly reduced the public's perception of Shanghai and negatively affected the city's image. By analyzing the data results, we summarize the basic characteristics of Shanghai's city image and provide strategies for communicating Shanghai's city image in the post-epidemic era.

Research on Influencing Factors of Satisfaction with the Use of Public Health Internet Platform: Evidence from Ding Xiang Doctor (DXY) Internet Medical Platform.

With the rapid development of Internet information technology, Internet medical platforms are gradually entering daily life. Especially after the outbreak of the COVID-19 pandemic, it becomes very difficult for patients to go out for medical treatment, and the Internet medical platform plays an important role. The study of the use and influencing factors of Internet medical platforms has become a new topic. In this study, evidence from the Chinese Internet medical platform Ding Xiang Doctor(DXY) is combined with an integrated approach of hierarchical analysis and the entropy value method to construct evaluation indexes and questionnaires from four dimensions of perceived quality, perceived value, user trust, and user involvement to analyze the factors influencing users' satisfaction with Internet medical platforms. The questionnaires were distributed online, and 556 questionnaires were distributed from June to August 2022; 520 questionnaires were collected; the questionnaires' recovery rate was 93.53%; after excluding some invalid questionnaires, 424 questionnaires remained; the questionnaire efficiency was 81.54%; the Cronbach coefficient was 0.978; the KMO(Kaiser-Meyer-Olkin) value was 0.977; and the reliability performance was good. The study concluded that: (1) Users pay more attention to the content of perceived value, including the cost of time, economy, expense, and effort spent, and emphasize the degree of personal benefit. (2) Users are less satisfied with the information accessibility, design aesthetics, information timeliness, information comprehensiveness, and classification clarity of the DXY platform. (3) Users pay most attention to the protection of personal privacy by the platform side in the dimension of perceived value. (4) Users' trust in the platform is relatively high, their willingness to use the platform in the future is strong, and the dimensions of online interactive discussion, willingness to pay, and paid services are highly recognized.

Xuanfei Baidu Decoction suppresses complement overactivation and ameliorates IgG immune complex-induced acute lung injury by inhibiting JAK2/STAT3/SOCS3 and NF-κB signaling pathway.

BACKGROUND: The significant clinical efficacy of Xuanfei Baidu Decoction (XFBD) is proven in the treatment of patients with coronavirus disease 2019 (COVID-19) in China. However, the mechanisms of XFBD against acute lung injury (ALI) are still poorly understood. METHODS: In vivo, the mouse model of ALI was induced by IgG immune complexes (IgG-IC), and then XFBD (4g/kg, 8g/kg) were administered by gavage respectively. 24 h after inducing ALI, the lungs were collected for histological and molecular analysis. In vitro, alveolar macrophages inflammation models induced by IgG-IC were performed and treated with different dosage of XFBD-containing serum to investigate the protective role and molecular mechanisms of XFBD. RESULTS: The results revealed that XFBD mitigated lung injury and significantly downregulated the production of pro-inflammatory mediators in lung tissues and macrophages upon IgG-IC stimulation. Notably, XFBD attenuated C3a and C5a generation, inhibited the expression of C3aR and C5aR and suppressed the activation of JAK2/STAT3/SOCS3 and NF-κB signaling pathway in lung tissues and macrophages induced by IgG-IC. Moreover, in vitro experiments, we verified that Colivelin TFA (CAF, STAT3 activator) and C5a treatment markedly elevated the IgG-IC-triggered inflammatory responses in macrophages and XFBD weakened the effects of CAF or C5a. CONCLUSION: XFBD suppressed complement overactivation and ameliorated IgG immune complex-induced acute lung injury by inhibiting JAK2/STAT3/SOCS3 and NF-κB signaling pathway. These data contribute to understanding the mechanisms of XFBD in COVID-19 treatment.

Emerging nanophotonic biosensor technologies for virus detection

Highly infectious viral diseases are a serious threat to mankind as they can spread rapidly among the community, possibly even leading to the loss of many lives. Early diagnosis of a viral disease not only increases the chance of quick recovery, but also helps prevent the spread of infections. There is thus an urgent need for accurate, ultrasensitive, rapid, and affordable diagnostic techniques to test large volumes of the population to track and thereby control the spread of viral diseases, as evidenced during the COVID-19 and other viral pandemics. This review paper critically and comprehensively reviews various emerging nanophotonic biosensor mechanisms and biosensor technologies for virus detection, with a particular focus on detection of the SARS-CoV-2 (COVID-19) virus. The photonic biosensing mechanisms and technologies that we have focused on include: (a) plasmonic field enhancement via localized surface plasmon resonances, (b) surface enhanced Raman scattering, (c) nano-Fourier transform infrared (nano-FTIR) near-field spectroscopy, (d) fiber Bragg gratings, and (e) microresonators (whispering gallery modes), with a particular emphasis on the emerging impact of nanomaterials and two-dimensional materials in these photonic sensing technologies. This review also discusses several quantitative issues related to optical sensing with these biosensing and transduction techniques, notably quantitative factors that affect the limit of detection (LoD), sensitivity, specificity, and response times of the above optical biosensing diagnostic technologies for virus detection. We also review and analyze future prospects of cost-effective, lab-on-a-chip virus sensing solutions that promise ultrahigh sensitivities, rapid detection speeds, and mass manufacturability.

Evaluation Study on the Use of Non-Contact Prevention and Protection Products in the Context of COVID-19: A Comprehensive Evaluation Method from AHP and Entropy Weight Method.

In the post-epidemic era, there is an endless supply of epidemic prevention products that cover a wide range of public areas. The introduction of such products has eased the tense pattern of virus proliferation in the context of the epidemic, and effectively demonstrated the initiatives implemented by the Chinese people in response to the outbreak. This paper therefore begins with the study of contactless epidemic prevention products, which appear in a form that meets the needs of contemporary society and offers a new mode of living to it. It enriches the measures for epidemic prevention and control. By obtaining satisfaction ratings from the user community, the performance of such products can be understood in time to provide a substantial basis for the subsequent upgrading and optimization or transformation of such products. This study uses the KJ method and questionnaires to construct an index system for contactless epidemic prevention products, grasp users' needs for epidemic prevention products in real time, classify and identify such products, and select such products as epidemic prevention smart security gates, medical delivery robots, infrared handheld thermometers, thermographic body temperature screening, contactless inductive lift buttons, and contactless medical vending machines. The questionnaire was designed with four dimensions: safety, intelligence, aesthetics and economy. A sample size of 262 was collected through the distribution of questionnaires. We used AHP and entropy weighting methods for the comprehensive evaluation; AHP basically tells us how satisfied most users are with this type of product. The use of the entropy weighting method can achieve objectivity in the weighting process. Combining the two approaches helps to improve the scientific nature of the weighting of the evaluation indexes for contactless and epidemic-proof products. It is clear from the AHP analysis that, firstly, there are differences in the perceptions of the performance of this type of product between different age groups. Secondly, the user group rated the perceived performance of the product presented as high (Bn>0.200), which users can subjectively and directly perceive. Next, the perceived future sustainable economic development of this product category is low (Bn≤0.200), and users place low importance on its economic aspects as an objective additional condition. The entropy method of analysis shows that, under reasonable government control of the market for intelligent products, the safety, intelligence and aesthetic effects of these products are significant (Cm≤0.100); further, the economic presentation of these products has yet to be optimized and upgraded (Cm>0.100).

Xuanfei Baidu Decoction suppresses complement overactivation and ameliorates IgG immune complex-induced acute lung injury by inhibiting JAK2/STAT3/SOCS3 and NF-κB Signaling Pathway

Background The significant clinical efficacy of Xuanfei Baidu Decoction (XFBD) is proven in the treatment of patients with coronavirus disease 2019 (COVID-19) in China. However, the mechanisms of XFBD against acute lung injury (ALI) are still poorly understood. Methods In vivo, the mouse model of ALI was induced by IgG immune complexes (IgG-IC), and then XFBD (4g/kg, 8g/kg) were administered by gavage respectively. 24h after inducing ALI, the lungs were collected for histological and molecular analysis. In vitro, alveolar macrophages inflammation models induced by IgG-IC were performed and treated with different dosage of XFBD-Containing Serum to investigate the protective role and molecular mechanisms of XFBD. Results The results revealed that XFBD mitigated lung injury and significantly downregulated the production of pro-inflammatory mediators in lung tissues and macrophages upon IgG-IC stimulation. Notably, XFBD attenuated C3a and C5a generation, inhibited the expression of C3aR and C5aR and suppressed the activation of JAK2/STAT3/SOCS3 and NF-κB signaling pathway in lung tissues and macrophages induced by IgG-IC. Moreover, in vitro experiments, we verified that Colivelin TFA (CAF, STAT3 activator) and C5a treatment markedly elevated the IgG-IC-triggered inflammatory responses in macrophages and XFBD weakened the effects of CAF or C5a. Conclusion XFBD suppressed complement overactivation and ameliorated IgG immune complex-induced acute lung injury by inhibiting JAK2/STAT3/SOCS3 and NF-κB Signaling Pathway. These data contribute to understanding the mechanisms of XFBD in COVID-19 treatment. Graphical Image, graphical Schematic representation of proposed mechanism underlying the protective effects of XFBD on the IgG-IC-induced ALI. XFBD suppressed complement overactivation and protected against IgG immune complex-induced acute lung injury by inhibiting JAK2/STAT3/SOCS3 and NF-κB Signaling Pathway.

Network pharmacology analysis on mechanisms of xuanfei baidu prescription in treatment of SARS, MERS and COVID-19

Objective: To explore the effective components, target and signal pathway of Xuanfei Baidu Prescription in treatment of coronavirus infection, and to explain its mechanism of action. Methods A network of Character, taste, and meridian of Xuanfei Baidu Prescription was constructed using Cytoscape. Effective components and related targets of Xuanfei Baidu Prescription were selected by using TCMSP database, SwissADME database, and Swiss Target Prediction database. Disease targets of SARS, MERS and COVID-19 were collected using GeneCards database and CTD database. Drug targets and disease targets were intersected, and Cytoscape software was used to construct the network diagram. Using String database, the network model of protein-protein interaction (PPI) was constructed for potential targets. Metascape database was used for GO and KEGG enrichment analysis of potential targets, and Cytoscape was used to construct the network diagram. Results The results showed that 10 ingredients in Xuanfeibaidu Prescription are associated with the Lung meridian. 167 active components and 242 potential targets were screened out. The core drugs were Glycyrrhiza uralensis Fisch.,Ephedrae Herba, Artemisia annua L, Verbena officinalis L., Polygonum cuspidatum Sieb. et Zucc.. The core components were quercetin, stigmasterol, kaempferol, luteolin, isorhamnetin. The core targets were AKT1, IL-6, TP53, VEGFA, TNF. The possible mechanism of action is related to several signaling pathways such as PI3K-Akt signaling pathway, HIF-1 signaling pathway, TNF signaling pathway, and so on. Conclusion This study explored the potential common mechanism of Xuanfei Baidu Prescription on SARS, MERS and COVID-19, reflecting the multi-component, multi-target and multi-pathway characteristics of TCM.

Genetically predicted high IGF-1 levels showed protective effects on COVID-19 susceptibility and hospitalization: a Mendelian randomisation study with data from 60 studies across 25 countries.

Background: Epidemiological studies observed gender differences in COVID-19 outcomes, however, whether sex hormone plays a causal in COVID-19 risk remains unclear. This study aimed to examine associations of sex hormone, sex hormones-binding globulin (SHBG), insulin-like growth factor-1 (IGF-1), and COVID-19 risk. Methods: Two-sample Mendelian randomization (TSMR) study was performed to explore the causal associations between testosterone, estrogen, SHBG, IGF-1, and the risk of COVID-19 (susceptibility, hospitalization, and severity) using genome-wide association study (GWAS) summary level data from the COVID-19 Host Genetics Initiative (N=1,348,701). Random-effects inverse variance weighted (IVW) MR approach was used as the primary MR method and the weighted median, MR-Egger, and MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test were conducted as sensitivity analyses. Results: Higher genetically predicted IGF-1 levels have nominally significant association with reduced risk of COVID-19 susceptibility and hospitalization. For one standard deviation increase in genetically predicted IGF-1 levels, the odds ratio was 0.77 (95% confidence interval [CI], 0.61-0.97, p=0.027) for COVID-19 susceptibility, 0.62 (95% CI: 0.25-0.51, p=0.018) for COVID-19 hospitalization, and 0.85 (95% CI: 0.52-1.38, p=0.513) for COVID-19 severity. There was no evidence that testosterone, estrogen, and SHBG are associated with the risk of COVID-19 susceptibility, hospitalization, and severity in either overall or sex-stratified TSMR analysis. Conclusions: Our study indicated that genetically predicted high IGF-1 levels were associated with decrease the risk of COVID-19 susceptibility and hospitalization, but these associations did not survive the Bonferroni correction of multiple testing. Further studies are needed to validate the findings and explore whether IGF-1 could be a potential intervention target to reduce COVID-19 risk. Funding: We acknowledge support from NSFC (LR22H260001), CRUK (C31250/A22804), SHLF (Hjärt-Lungfonden, 20210351), VR (Vetenskapsrådet, 2019-00977), and SCI (Cancerfonden).

Xuanfei Baidu Formula attenuates LPS-induced acute lung injury by inhibiting the NF-κB signaling pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: Acute lung injury (ALI) is a common manifestation of COVID-19. Xuanfei Baidu Formula(XFBD) is used in China to treat mild or common damp-toxin obstructive pulmonary syndrome in COVID-19 patients. However, the active ingredients of XFBD have not been extensively studied, and its mechanism of action in the treatment of ALI is not well understood. AIM OF THE STUDY: The purpose of this study was to investigate the mechanism of action of XFBD in treating ALI in rats, by evaluating its active components. MATERIALS AND METHODS: Firstly, the chemical composition of XFBD was identified using ultra-high performance liquid chromatography with quadrupole time-of-flight mass spectrometry. The potential targets of XFBD for ALI treatment were predicted using network pharmacological analysis. Finally, the molecular mechanism of XFBD was validated using a RAW264.7 cell inflammation model and a mouse ALI model. RESULTS: A total of 113 compounds were identified in XFBD. Network pharmacology revealed 34 hub targets between the 113 compounds and ALI. The results of Kyoto Encyclopedia of Genes and Genomes and gene ontology analyses indicated that the NF-κB signaling pathway was the main pathway for XFBD in the treatment of ALI. We found that XFBD reduced proinflammatory factor levels in LPS-induced cellular models. By examining the lung wet/dry weight ratio and pathological sections in vivo, XFBD was found that XFBD could alleviate ALI. Immunohistochemistry results showed that XFBD inhibited ALI-induced increases in p-IKK, p-NF-κB p65, and iNOS proteins. In vitro experiments demonstrated that XFBD inhibited LPS-induced activation of the NF-κB pathway. CONCLUSION: This study identified the potential practical components of XFBD, combined with network pharmacology and experimental validation to demonstrate that XFBD can alleviate lung injury caused by ALI by inhibiting the NF-κB signaling pathway.

Factors Influencing Use of Fitness Apps by Adults under Influence of COVID-19.

During the coronavirus disease 2019 (COVID-19) pandemic, many countries imposed restrictions and quarantines on the population, which led to a decrease in people's physical activity (PA) and severely damaged their mental health. As a result, people engaged in fitness activities with the help of fitness apps, which improved their resistance to the virus and reduced the occurrence of psychological problems, such as anxiety and depression. However, the churn rate of fitness apps is high. As such, our purpose in this study was to analyze the factors that influence the use of fitness apps by adults aged 18-65 years in the context of COVID-19, with the aim of contributing to the analysis of mobile fitness user behavior and related product design practices. We constructed a decision target program model using the analytic hierarchy process (AHP), and we analyzed and inductively screened 11 evaluation indicators, which we combined with an indicator design questionnaire. We distributed 420 questionnaires; of the respondents, 347 knew about or used fitness apps. Among these 347, we recovered 310 valid questionnaires after removing invalid questionnaires with a short completion time, for an effective questionnaire recovery rate of 89.33%. We used the AHP and entropy method to calculate and evaluate the weight coefficient of each influencing factor and to determine an influencing factor index. Our conclusions were as follows: first, the effect of perceived usefulness on the use of fitness apps by the study groups was the most notable. Second, personal motivation and perceived ease of use considerably influenced the adult group's willingness to use fitness apps. Finally, the perceived cost had relatively little effect on the use of fitness apps by adults, and the study group was much more concerned with the privacy cost than the expense cost.

Visceral adipose tissue and risk of COVID-19 susceptibility, hospitalization, and severity: A Mendelian randomization study.

Background: Coronavirus Disease 2019 (COVID-19) has rapidly evolved as a global pandemic. Observational studies found that visceral adipose tissue (VAT) increased the likelihood of worse clinical outcomes in COVID-19 patients. Whereas, whether VAT is causally associated with the susceptibility, hospitalization, or severity of COVID-19 remains unconfirmed. We aimed to investigate the causal associations between VAT and susceptibility, hospitalization, and severity of COVID-19. Methods: We applied a two-sample Mendelian randomization (MR) study to infer causal associations between VAT and COVID-19 outcomes. Single-nucleotide polymorphisms significantly associated with VAT were derived from a large-scale genome-wide association study. The random-effects inverse-variance weighted method was used as the main MR approach, complemented by three other MR methods. Additional sensitivity analyses were also performed. Results: Genetically predicted higher VAT mass was causally associated with higher risks of COVID-19 susceptibility [odds ratios (ORs) = 1.13; 95% confidence interval (CI), 1.09-1.17; P = 4.37 × 10-12], hospitalization (OR = 1.51; 95% CI = 1.38-1.65; P = 4.14 × 10-20), and severity (OR = 1.58; 95% CI = 1.38-1.82; P = 7.34 × 10-11). Conclusion: This study provided genetic evidence that higher VAT mass was causally associated with higher risks of susceptibility, hospitalization, and severity of COVID-19. VAT can be a useful tool for risk assessment in the general population and COVID-19 patients, as well as an important prevention target.

Xuanfei Baidu formula alleviates impaired mitochondrial dynamics and activated NLRP3 inflammasome by repressing NF-κB and MAPK pathways in LPS-induced ALI and inflammation models.

BACKGROUND: Xuanfei Baidu Formula (XBF) is an effective traditional Chinese medicine (TCM) remedy for treating coronavirus disease 2019 (COVID-19) in China. This herbal medicine has shown effects in reducing clinical symptoms and shortening the average length of hospital stay for COVID-19 patients. Previous studies have demonstrated that XBF alleviates acute lung injury (ALI) by regulating macrophage-mediated immune inflammation, but the mechanisms of action remain elusive. PURPOSE: This study aimed to evaluate the lung-protective and anti-inflammatory effects of XBF and its underlying mechanisms. METHODS: Here, XBF's effects were investigated in an ALI mouse model induced by inhalation of atomized lipopolysaccharide (LPS). Besides, the LPS-induced inflammation model in RAW264.7 cells was used to clarify the underlying mechanisms of XBF against ALI. RESULTS: Our results showed that XBF treatment alleviated LPS-induced lung injury, as evidenced by reduced histopathological changes, pulmonary alveoli permeability, fibrosis, and apoptosis in the lung tissues. In addition, inflammation was alleviated as shown by decreased levels of tumor necrosis factor (TNF)-α, interleukin (IL)-6, IL-1ß in serum and bronchoalveolar lavage fluid (BALF), and reduced white blood cell (WBC) count in BALF. Furthermore, consistent with the in vivo assay, XBF inhibited LPS-induced inflammatory cytokines release and pro-inflammatory polarization in RAW264.7 cells. Mechanistically, XBF increased mitochondrial fusion by upregulating Mfn1 and attenuated NLRP3 inflammasome activation by repressing Casp11, respectively, to inhibit NF-κB and MAPK pathways, thus repressing pro-inflammatory macrophage polarization. CONCLUSION: In this study, we demonstrate that XBF exerts anti-ALI and -inflammatory effects by recovering mitochondrial dynamics and reducing inflammasome activation, providing a biological illustration of the clinical efficacy of XBF in treating COVID-19 patients.

A Review of Non-Invasive Drug Delivery through Respiratory Routes.

With rapid and non-invasive characteristics, the respiratory route of administration has drawn significant attention compared with the limitations of conventional routes. Respiratory delivery can bypass the physiological barrier to achieve local and systemic disease treatment. A scientometric analysis and review were used to analyze how respiratory delivery can contribute to local and systemic therapy. The literature data obtained from the Web of Science Core Collection database showed an increasing worldwide tendency toward respiratory delivery from 1998 to 2020. Keywords analysis suggested that nasal and pulmonary drug delivery are the leading research topics in respiratory delivery. Based on the results of scientometric analysis, the research hotspots mainly included therapy for central nervous systems (CNS) disorders (Parkinson's disease, Alzheimer's disease, depression, glioblastoma, and epilepsy), tracheal and bronchial or lung diseases (chronic obstructive pulmonary disease, asthma, acute lung injury or respiratory distress syndrome, lung cancer, and idiopathic pulmonary fibrosis), and systemic diseases (diabetes and COVID-19). The study of advanced preparations contained nano drug delivery systems of the respiratory route, drug delivery barriers investigation (blood-brain barrier, BBB), and chitosan-based biomaterials for respiratory delivery. These results provided researchers with future research directions related to respiratory delivery.

Modulating effect of Xuanfei Baidu granule on host metabolism and gut microbiome in rats.

Xuanfei Baidu granule (XFBD) is a recommended patented drug for the prevention and treatment of Corona Virus Disease 2019 (COVID-19), which is approved by the National Medical Products Administration. XFBD suppresses the over-activated immune response caused by inflammatory factor storms in COVID-19 infection. The intestine plays a crucial role in the immune system. The mass spectrometry based fecal metabolomics with 16S rDNA sequencing were combined to evaluate the effects of XFBD on host metabolism and gut microbiome. Short-chain fatty acids (SCFAs) contents in fecal matter were quantified by gas chromatography-mass spectrometry (GC-MS). Plasma samples were used to detect immune and inflammatory levels. The results were verified with a rat model of intestinal disorder. Results indicated that XFBD could increase the immune level of Immunoglobulin A (IgA), Immunoglobulin G (IgG) and Immunoglobulin M (IgM) (p < 0.05). The OPLS-DA analysis results showed that a total of 271 differential metabolites (178 up-regulated and 93 down-regulated) were identified based on the VIP ≥1, p < 0.05, FC ≥ 2 and FC ≤ 0.5. The metabolic pathways mainly involved D-Glutamine and D-glutamate metabolism, Arginine biosynthesis, Biotin metabolism, et al. XFBD modified the gut bacteria structure according to the principal component analysis (PCA), that is, 2 phyla, 3 classes, 5 orders, 11 families and 14 genera were significantly different based on taxonomic assignment. In addition, it could partially callback the relative abundance of intestinal microflora in bacterial disorder rats caused by antibiotics. It is suggested that the intervention mechanism of XFBD might be related to the regulation of intestinal flora composition. The evidence obtained in the study provides a useful reference for understanding the mechanism of XFBD.