Network Analysis of International Financial Markets Contagion Based on Volatility Indexes

In this paper, five volatility indexes are used as the proxy to examine the risk contagion among major stock markets. Both static and dynamic connectedness methods are employed. First, the overall connectedness can help monitor the dynamic systemic risk. Sharpe increases in total spillover correspond to actual risk events, e.g., the outbreak of COVID-19. Second, Hong Kong bridges the domestic and international stock markets, and forms a protective barrier for the mainland market to some extend. Third, developed economies mainly act as the net risk transmitters in the network. Fourth, Europe shows greater power in risk contagion after the pandemic.

Self-directed learning: Alternative for traditional classroom learning in undergraduate ophthalmic education during the COVID-19 pandemic in China

In recent years, the COVID-19 pandemic has led to a shift in medical education from traditional face-to-face to online or remote learning, which provided challenges to faculty and students that have traditionally given face-to-face instruction. Self-directed learning (SDL) has gained popularity in undergraduate education such as nursing and adult education. Although the application of SDL in many medical teachings is practical, the application of SDL in undergraduate ophthalmology education has not been well investigated. COVID-19 pandemic led to changes in the learning style of undergraduate medical students to adapt to the shift from traditional classroom learning (TCL) to online or remote learning. Self-directed learning is a process in which individuals take the initiative in diagnosing their learning needs, formulating learning goals, identifying resources for learning, choosing and implementing appropriate learning strategies and evaluating learning outcomes. This study compared students' perspectives and study outcomes of the SDL and TCL to preliminarily investigate the effect of SDL in undergraduate ophthalmology education. The students showed equal perspectives and satisfaction with both learning models. There was no difference in learning outcomes at the end of the study. The students with different interests in ophthalmology had different perspectives on SDL and TCL. Self-directed learning is an essential alternative to traditional classroom learning in undergraduate ophthalmic education during the COVID-19 pandemic in China.

Exploring Potential Biomarkers and Molecular Mechanisms of Ischemic Cardiomyopathy and COVID-19 Comorbidity Based on Bioinformatics and Systems Biology.

Cardiovascular complications combined with COVID-19 (SARS-CoV-2) lead to a poor prognosis in patients. The common pathogenesis of ischemic cardiomyopathy (ICM) and COVID-19 is still unclear. Here, we explored potential molecular mechanisms and biomarkers for ICM and COVID-19. Common differentially expressed genes (DEGs) of ICM (GSE5406) and COVID-19 (GSE164805) were identified using GEO2R. We performed enrichment and protein-protein interaction analyses and screened key genes. To confirm the diagnostic performance for these hub genes, we used external datasets (GSE116250 and GSE211979) and plotted ROC curves. Transcription factor and microRNA regulatory networks were constructed for the validated hub genes. Finally, drug prediction and molecular docking validation were performed using cMAP. We identified 81 common DEGs, many of which were enriched in terms of their relation to angiogenesis. Three DEGs were identified as key hub genes (HSP90AA1, HSPA9, and SRSF1) in the protein-protein interaction analysis. These hub genes had high diagnostic performance in the four datasets (AUC > 0.7). Mir-16-5p and KLF9 transcription factor co-regulated these hub genes. The drugs vindesine and ON-01910 showed good binding performance to the hub genes. We identified HSP90AA1, HSPA9, and SRSF1 as markers for the co-pathogenesis of ICM and COVID-19, and showed that co-pathogenesis of ICM and COVID-19 may be related to angiogenesis. Vindesine and ON-01910 were predicted as potential therapeutic agents. Our findings will contribute to a deeper understanding of the comorbidity of ICM with COVID-19.

Geographical and Temporal Analysis of Tweets Related to COVID-19 and Cardiovascular Disease in the US

The COVID-19 pandemic has resulted in more than 600 million confirmed cases worldwide since December 2021. Cardiovascular disease (CVD) is both a risk factor for COVID-19 mortality and a complication that many COVID-19 patients develop. This study uses Twitter data to identify the spatiotemporal patterns and correlation of related tweets with daily COVID-19 cases and deaths at the national, regional, and state levels. We collected tweets mentioning both COVID-19 and CVD-related words from February to July 2020 (Eastern Time) and geocoded the tweets to the state level using GIScience techniques. We further proposed and validated that the Twitter user registration state can be a feasible proxy of geotags. We applied geographical and temporal analysis to investigate where and when people talked about COVID-19 and CVD. Our results indicated that the trend of COVID-19 and CVD-related tweets is correlated to the trend of COVID-19, especially the daily deaths. These social media messages revealed widespread recognition of CVD's important role in the COVID-19 pandemic, even before the medical community started to develop consensus and theory supports about CVD aspects of COVID-19. The second wave of the pandemic caused another rise in the related tweets but not as much as the first one, as tweet frequency increased from February to April, decreased till June, and bounced back in July. At the regional level, four regions (Northeast, Midwest, North, and West) had the same trend of related tweets compared to the country as a whole. However, only the Northeast region had a high correlation (0.8–0.9) between the tweet count, new cases, and new deaths. For the second wave of confirmed new cases, the major contributing regions, South and West, did not ripple as many related tweets as the first wave. Our understanding is that the early news attracted more attention and discussion all over the U.S. in the first wave, even though some regions were not impacted as much as the Northeast at that time. The study can be expanded to more geographic and temporal scales, and with more physical and socioeconomic variables, with better data acquisition in the future.

Heterogeneously informed trading and the stock market efficiency during the COVID-19 pandemic

This study investigates the U.S. stock market efficiency from the symmetric and asymmetric perspectives during the COVID-19 pandemic. We explore that the pandemic boosts (hurts) the information role of symmetrically (asymmetrically) informed trading. Specifically, we find that the epidemic outbreak and infection scale strengthen (weaken) the stock return reaction to symmetrically (asymmetrically) informed trading. Evidence also indicates that the effect of symmetrically (asymmetrically) informed trading on stocks' permanent price shocks and price informational efficiency is enhanced (impaired) during the pandemic. Moreover, all these effects are consistently more intensive to informed buys.

Heterogeneously informed trading and the stock market efficiency during the COVID-19 pandemic.

This study investigates the U.S. stock market efficiency from the symmetric and asymmetric perspectives during the COVID-19 pandemic. We explore that the pandemic boosts (hurts) the information role of symmetrically (asymmetrically) informed trading. Specifically, we find that the epidemic outbreak and infection scale strengthen (weaken) the stock return reaction to symmetrically (asymmetrically) informed trading. Evidence also indicates that the effect of symmetrically (asymmetrically) informed trading on stocks' permanent price shocks and price informational efficiency is enhanced (impaired) during the pandemic. Moreover, all these effects are consistently more intensive to informed buys.

Are commodity futures a hedge against inflation? A Markov-switching approach

This study examines the inflation hedging ability of various commodity futures using Markov-switching vector error correction models (MS-VECM). We find that total commodity futures fail to provide a hedge against inflation over the sample period between January 1983 and December 2021. However, industrial metals and precious metals are able to hedge against inflation. Other sub-indexes, including energy, agriculture, and livestock, do not have a significant inflation hedging ability. The inflation hedging capacity of industrial metals exhibits substantial variation over time, with most of the inflation hedging power occurring during the relatively longer and more common regimes covering the Great Moderation, the post-subprime crisis, and the periods after the outbreak of the COVID-19 pandemic. We further evaluate the inflation hedge ability of commodity futures by including stocks and bonds in the model. Our results suggest that industrial metals are more reliable inflation hedges.

Viral reactivation in the lungs of patients with severe pneumonia is associated with increased mortality, a multicenter, retrospective study.

PURPOSE: Viral reactivation is widespread in patients with severe pneumonia, yet the landscape of viral reactivation in the lungs is not well-known. This study aims to assess the landscape and clinical features of viral reactivation in the early onset of severe pneumonia in ICU patients. METHODS: The clinical data from 97 patients were collected retrospectively from the intensive care units of five teaching hospitals between June 2018 and July 2021. Metagenomic next-generation sequencing (mNGS) of the bronchoalveolar lavage fluid (BALF) was performed at the onset of severe pneumonia. RESULTS: Cytomegalovirus (CMV), herpes simplex virus-1 (HSV-1), and Epstein-Barr virus (EBV) were the most common reactivated viruses in the lower respiratory tract of patients with severe pneumonia. After adjusting for the risk of confounding and competition of age, sex, sequential organ failure assessment, acute physiology chronic health assessment II and immunosuppression status, viral reactivation resulted in an overall 2.052-fold increase in 28-day all-cause mortality (95% CI: 1.004-4.194). CONCLUSION: This study showed that CMV, HSV-1, and EBV were the most common reactivated viruses in the lungs of patients with severe pneumonia. The existence of viral reactivations was associated with an increased risk of mortality. The simultaneous reactivation of multiple viruses needs to be considered in the design of clinical trials. This article is protected by copyright. All rights reserved.

COVID-19 and stock market performance: Evidence from the RCEP countries☆

As the world's largest trading bloc, the agreement of RCEP, which was formalized in September 2020, is believed to play a non-neglectable role in the post-pandemic recovery. Real economies and the capital markets of the participating countries will have greater interactions due to tariff reduction and negative lists. By looking into the shocks in early 2020 that affect the stock markets of RCEP participating countries, we measure the stock market reaction to common risks just before the RCEP agreement was formalized. Following return-based, volume-based and liquidity-based event-study approaches, we use daily data from 11 Asia-Pacific countries to examine the stock market reactions. We find that RCEP economies for which the agreement took effect on January 1st 2022 showed better risk resistance in response to COVID-19 shocks. In the long run, trading benefits brought by the RCEP agreement are expected to form and strengthen a system of circular flow of international trading activities among the participating countries, which will in turn increase the risk resistance ability of their stock markets.

Contrastive learning and subtyping of post-COVID-19 lung computed tomography images.

Patients who recovered from the novel coronavirus disease 2019 (COVID-19) may experience a range of long-term symptoms. Since the lung is the most common site of the infection, pulmonary sequelae may present persistently in COVID-19 survivors. To better understand the symptoms associated with impaired lung function in patients with post-COVID-19, we aimed to build a deep learning model which conducts two tasks: to differentiate post-COVID-19 from healthy subjects and to identify post-COVID-19 subtypes, based on the latent representations of lung computed tomography (CT) scans. CT scans of 140 post-COVID-19 subjects and 105 healthy controls were analyzed. A novel contrastive learning model was developed by introducing a lung volume transform to learn latent features of disease phenotypes from CT scans at inspiration and expiration of the same subjects. The model achieved 90% accuracy for the differentiation of the post-COVID-19 subjects from the healthy controls. Two clusters (C1 and C2) with distinct characteristics were identified among the post-COVID-19 subjects. C1 exhibited increased air-trapping caused by small airways disease (4.10%, p = 0.008) and diffusing capacity for carbon monoxide %predicted (DLCO %predicted, 101.95%, p < 0.001), while C2 had decreased lung volume (4.40L, p < 0.001) and increased ground glass opacity (GGO%, 15.85%, p < 0.001). The contrastive learning model is able to capture the latent features of two post-COVID-19 subtypes characterized by air-trapping due to small airways disease and airway-associated interstitial fibrotic-like patterns, respectively. The discovery of post-COVID-19 subtypes suggests the need for different managements and treatments of long-term sequelae of patients with post-COVID-19.

Association of Workplace Bullying with Suicide Ideation and Attempt Among Chinese Nurses During the COVID-19 Pandemic.

Nurses experience a high incidence of workplace bullying and are at a higher risk of suicide than the general population. However, there is no empirical evidence on how exposure to workplace bullying is associated with suicide ideation and attempts among nurses. Nurses were recruited from tertiary hospitals in Shandong Province, China, using stratified cluster sampling. Suicide ideation and attempts were assessed using two items, and the Workplace Psychologically Violent Behaviors Instrument was used to measure subtypes of workplace bullying. The prevalence of workplace bullying, suicide ideation, and suicide attempts was 30.6%, 16.8%, and 10.8%, respectively. After adjusting for covariates, victims of workplace bullying were at a high risk of suicide ideation and attempts. Among workplace bullying subtypes, individuals' isolation from work and direct negative behaviors were predictors of both suicide ideation and attempts; attack on personality only predicted suicide attempts. The more bullying subtypes experienced by nurses, the greater their likelihood of suicide ideation and attempts. These findings suggested that workplace bullying was associated with an increased risk of suicide ideation and attempts in nurses, with both independent and cumulative risks. Interventions should focus on prevention and managing the effects of workplace bullying among nurses.

Geographical and Temporal Analysis of Tweets Related to COVID-19 and Cardiovascular Disease in the US.

The COVID-19 pandemic has resulted in more than 600 million confirmed cases worldwide since December 2021. Cardiovascular disease (CVD) is both a risk factor for COVID-19 mortality and a complication that many COVID-19 patients develop. This study uses Twitter data to identify the spatiotemporal patterns and correlation of related tweets with daily COVID-19 cases and deaths at the national, regional, and state levels. We collected tweets mentioning both COVID-19 and CVD-related words from February to July 2020 (Eastern Time) and geocoded the tweets to the state level using GIScience techniques. We further proposed and validated that the Twitter user registration state can be a feasible proxy of geotags. We applied geographical and temporal analysis to investigate where and when people talked about COVID-19 and CVD. Our results indicated that the trend of COVID-19 and CVD-related tweets is correlated to the trend of COVID-19, especially the daily deaths. These social media messages revealed widespread recognition of CVD's important role in the COVID-19 pandemic, even before the medical community started to develop consensus and theory supports about CVD aspects of COVID-19. The second wave of the pandemic caused another rise in the related tweets but not as much as the first one, as tweet frequency increased from February to April, decreased till June, and bounced back in July. At the regional level, four regions (Northeast, Midwest, North, and West) had the same trend of related tweets compared to the country as a whole. However, only the Northeast region had a high correlation (0.8-0.9) between the tweet count, new cases, and new deaths. For the second wave of confirmed new cases, the major contributing regions, South and West, did not ripple as many related tweets as the first wave. Our understanding is that the early news attracted more attention and discussion all over the U.S. in the first wave, even though some regions were not impacted as much as the Northeast at that time. The study can be expanded to more geographic and temporal scales, and with more physical and socioeconomic variables, with better data acquisition in the future.

Genetic characterization of SARS-CoV-2 from cross-border trucks and containers in Kashgar Prefecture, Xinjiang Uygur Autonomous Region, China

Kashgar is a prefecture in Xinjiang Uygur Autonomous Region. China. Kashgar Prefecture (KP) is a land-cargo port connecting China with central Asian countries and Europe. Frequent transportation of cargo has increased the risk of severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) introduction into China, which has increased the pressure on coronavirus disease-2019 (COVID-19) prevention and control. In November 2020, an imported virus-induced COVID-19 outbreak occurred in KP. To investigate the genetic characterization of SARS-CoV-2 that contaminated the trucks and containers, and the potential of border rapid logistics system to serve as carriers for SARS-CoV-2 transmission, thirty-five SARS-CoV-2-positive nucleic-acid samples collected from KP cross-border trucks and containers from 6-10 November 2020 were subjected into SARS-CoV-2 genomic sequencing and comparative analyses. The results showed that the median (minimum to maximum) Ct value of ORF1ab was 37.64 (28.91-39.81) . and that of the N gene was 36.50 (26.35-39.30), and the median (minimum to maximum) of the reads mapping ratio to SARS-CoV-2 was 51.95% (0.86%-99.31%), which indicated low viral loads in these environmental samples. Eighteen of 35 samples had genomic coverage >70%. According to the Pango nomenclature, 18 SARS-CoV-2 sequences belonged to six lineages (B.1, B.I.1, B.1.9. B.1.1.220, B.1.153 and B.1.465), three of which (B.I. B.1.1 and 8.1.153) were found in case samples from the same period of four China-neighboring countries. Analyses of nucleotide mutations and phylogenetic trees showed that the genome sequences of SARS-CoV-2 collected from the same location were similar. Four of 18 sequences were in a sub-lineage with the representative strain of COVID-19 outbreak in KP, one of which had 1 or 2 differences in nucleotide mutation sites with the strain that caused the COVID-19 outbreak in KP, which indicated high homology in the viral genome. We showed that cross-border trucks and containers were contaminated by various genotypes of SARS-CoV-2 from other countries during the outbreak in KP. and in which contained the parental virus of the KP cases. These trucks and containers served as carriers for SARS-CoV-2 introduction from other countries to cause local transmission. Our results provide important references for COVID-19 prevention-and-control strategies in border ports and tracing of outbreak sources in China.

Emotional-Health-Oriented Urban Design: A Novel Collaborative Deep Learning Framework for Real-Time Landscape Assessment by Integrating Facial Expression Recognition and Pixel-Level Semantic Segmentation.

Emotional responses are significant for understanding public perceptions of urban green space (UGS) and can be used to inform proposals for optimal urban design strategies to enhance public emotional health in the times of COVID-19. However, most empirical studies fail to consider emotion-oriented landscape assessments under dynamic perspectives despite the fact that individually observed sceneries alter with angle. To close this gap, a real-time sentimental-based landscape assessment framework is developed, integrating facial expression recognition with semantic segmentation of changing landscapes. Furthermore, a case study using panoramic videos converted from Google Street View images to simulate changing scenes was used to test the viability of this framework, resulting in five million big data points. The result of this study shows that through the collaboration of deep learning algorithms, finer visual variables were classified, subtle emotional responses were tracked, and better regression results for valence and arousal were obtained. Among all the predictors, the proportion of grass was the most significant predictor for emotional perception. The proposed framework is adaptable and human-centric, and it enables the instantaneous emotional perception of the built environment by the general public as a feedback survey tool to aid urban planners in creating UGS that promote emotional well-being.

Density-based detection of cell transition states to construct disparate and bifurcating trajectories.

Tree- and linear-shaped cell differentiation trajectories have been widely observed in developmental biologies and can be also inferred through computational methods from single-cell RNA-sequencing datasets. However, trajectories with complicated topologies such as loops, disparate lineages and bifurcating hierarchy remain difficult to infer accurately. Here, we introduce a density-based trajectory inference method capable of constructing diverse shapes of topological patterns including the most intriguing bifurcations. The novelty of our method is a step to exploit overlapping probability distributions to identify transition states of cells for determining connectability between cell clusters, and another step to infer a stable trajectory through a base-topology guided iterative fitting. Our method precisely re-constructed various benchmark reference trajectories. As a case study to demonstrate practical usefulness, our method was tested on single-cell RNA sequencing profiles of blood cells of SARS-CoV-2-infected patients. We not only re-discovered the linear trajectory bridging the transition from IgM plasmablast cells to developing neutrophils, and also found a previously-undiscovered lineage which can be rigorously supported by differentially expressed gene analysis.

Novel skewed usage of B-cell receptors in COVID-19 patients with various clinical presentations.

B cell-mediated immune responses play important roles in controlling SARS-CoV infection. Here, we performed the single-cell B cell receptor sequencing (scBCR-seq) of the PBMC samples from eleven healthy controls, five asymptomatic subjects and 33 symptomatic COVID-19 patients with various clinical presentations, and subsequently analyzed the abundance and diversity of the BCR repertoires in different groups, respectively. We revealed the skewed usage of the IGHV, IGLV and IGKV genes and identified a number of heavy or light chain VDJ gene pairs and combinational preference in each group, such as IGKV3-7 and IGKV2-24 enriched in the asymptomatic subjects, whereas IGHV3-13, IGHV3-23-IGHJ4, IGHV1-18-IGLV3-19, IGHV1-18-IGLV3-21, and IGHV1-18-IGLV3-25 enriched in the recovery patients with severe diseases. We also observed the differential expression of IGHV3-23 in various B cell clusters by analysis of the scRNA-seq data. Additional dock analysis indicated that IGHV3-13 could bind to the spike protein of SARS-CoV-2. These findings may advance our understanding of the humoral immune responses in COVID-19 patients and help develop novel vaccine candidates as well as therapeutical antibodies against SASR-CoV-2 infections.

SARS-CoV-2 and Plasmodium falciparum Co-Infection in a Returning Traveler

Since December 2019, the Coronavirus Disease 2019 (COVID-19) pandemic has become a non-neglectable context for the whole healthcare system. Under the background of COVID-19, the detection and diagnosis of malaria cases are under challenge. Here, we reported a COVID-19 and malaria co-infection traveler who has a long living history in Cameroon. The case was administered with dihydroartemisinin and piperaquine tablets for malaria, Lopinavir and Ritonavir tablets, Arbidol, recombinant human interferon α-2b and Compound Maxing Yifei mixture for COVID-19, and Zolpidem Tartrate tablets, Diazepam, Paroxetine Hydrochloride tablets, Thymosin α1, and Lianhua Qinwen Jiaonang during the second hospitalization of the patient since the patient has a certain level of anxiety and insomnia with no evidence of inflammatory reactions. After being tested negative two times for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 48 h, the patient met China's COVID-19 discharge standards and was discharged with stable vital signs and mental state. Since most countries in the sub-Saharan region have a fragile health system, co-infection for both Plasmodium and SARS-CoV-2 may not be uncommon, and raise a challenge in diagnosis, treatment, and prevention for both diseases. We add to the literature on co-infection of P. falciparum malaria and COVID-19 and offer operational advice on diagnosis, prevention, and treatment for the co-infection.

Identification of TCR repertoires in asymptomatic COVID-19 patients by single-cell T-cell receptor sequencing.

The T cell-mediated immune responses associated with asymptomatic infection (AS) of SARS-CoV-2 remain largely unknown. The diversity of T-cell receptor (TCR) repertoire is essential for generating effective immunity against viral infections in T cell response. Here, we performed the single-cell TCR sequencing of the PBMC samples from five AS subjects, 33 symptomatic COVID-19 patients and eleven healthy controls to investigate the size and the diversity of TCR repertoire. We subsequently analyzed the TCR repertoire diversity, the V and J gene segment deference, and the dominant combination of αß VJ gene pairing among these three study groups. Notably, we revealed significant TCR preference in the AS group, including the skewed usage of TRAV1-2-J33-TRBV6-4-J2-2 and TRAV1-2-J33-TRBV6-1-J2-3. Our findings may shed new light on understanding the immunopathogenesis of COVID-19 and help identify optimal TCRs for development of novel therapeutic strategies against SARS-CoV-2 infection.

Epitope-focused immunogen design based on the ebolavirus glycoprotein HR2-MPER region.

The three human pathogenic ebolaviruses: Zaire (EBOV), Bundibugyo (BDBV), and Sudan (SUDV) virus, cause severe disease with high fatality rates. Epitopes of ebolavirus glycoprotein (GP) recognized by antibodies with binding breadth for all three ebolaviruses are of major interest for rational vaccine design. In particular, the heptad repeat 2 -membrane-proximal external region (HR2-MPER) epitope is relatively conserved between EBOV, BDBV, and SUDV GP and targeted by human broadly-neutralizing antibodies. To study whether this epitope can serve as an immunogen for the elicitation of broadly-reactive antibody responses, protein design in Rosetta was employed to transplant the HR2-MPER epitope identified from a co-crystal structure with the known broadly-reactive monoclonal antibody (mAb) BDBV223 onto smaller scaffold proteins. From computational analysis, selected immunogen designs were produced as recombinant proteins and functionally validated, leading to the identification of a sterile alpha motif (SAM) domain displaying the BDBV-HR2-MPER epitope near its C terminus as a promising candidate. The immunogen was fused to one component of a self-assembling, two-component nanoparticle and tested for immunogenicity in rabbits. Robust titers of cross-reactive serum antibodies to BDBV and EBOV GPs and moderate titers to SUDV GP were induced following immunization. To confirm the structural composition of the immunogens, solution NMR studies were conducted and revealed structural flexibility in the C-terminal residues of the epitope. Overall, our study represents the first report on an epitope-focused immunogen design based on the structurally challenging BDBV-HR2-MPER epitope.

Impact of Azithromycin on Forsythiaside Pharmacokinetics in Rats: A Population Modeling Method.

OBJECTIVE: Lianhuaqingwen and Shuanghuanglian are drug treatment options for Corona Virus Disease 2019 (COVID-19). In China, use of traditional Chinese medicine with Shuanghuanglian or Lianhuaqingwen (for them, forsythiaside is the active antiviral and antibacterial component) in combination with azithromycin is common for the treatment of pediatric pneumonia. It is important to understand the reason why the combination of these compounds is better than a single drug treatment. This study aimed to explore the pharmacokinetic interaction between forsythiaside and azithromycin. METHODS: Twelve male Sprague-Dawley rats were randomly divided into an experimental group (Forsythia suspensa extract and azithromycin) and a control group (a single dose of Forsythia suspensa extract in 5% glucose solution). Plasma samples were collected at scheduled time points, and the high-performance liquid chromatography combined with ultraviolet method was used to determine the plasma forsythiaside concentration. Non-compartmental analysis and population pharmacokinetic methods were used to investigate the forsythiaside pharmacokinetic difference between the experimental and control group. RESULTS: Compared with a single administration, the area under the curve and half-life of forsythiaside increased, and forsythiaside clearance decreased significantly after co-administration with azithromycin. The in vivo behavior of forsythiaside could be described by the one compartment model. The forsythiaside clearance decreased when combined with azithromycin. Visual evaluation and bootstrap results suggested that the final model was precise and stable. CONCLUSION: Co-administration of azithromycin can significantly decrease the forsythiaside clearance and increase drug exposure. A lower dose of azithromycin can obtain sufficient forsythiaside concentration to provide antiviral and antibacterial activity.