Potential herb‒drug interactions between anti-COVID-19 drugs and traditional Chinese medicine.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread worldwide. Effective treatments against COVID-19 remain urgently in need although vaccination significantly reduces the incidence, hospitalization, and mortality. At present, antiviral drugs including Nirmatrelvir/Ritonavir (PaxlovidTM), Remdesivir, and Molnupiravir have been authorized to treat COVID-19 and become more globally available. On the other hand, traditional Chinese medicine (TCM) has been used for the treatment of epidemic diseases for a long history. Currently, various TCM formulae against COVID-19 such as Qingfei Paidu decoction, Xuanfei Baidu granule, Huashi Baidu granule, Jinhua Qinggan granule, Lianhua Qingwen capsule, and Xuebijing injection have been widely used in clinical practice in China, which may cause potential herb-drug interactions (HDIs) in patients under treatment with antiviral drugs and affect the efficacy and safety of medicines. However, information on potential HDIs between the above anti-COVID-19 drugs and TCM formulae is lacking, and thus this work seeks to summarize and highlight potential HDIs between antiviral drugs and TCM formulae against COVID-19, and especially pharmacokinetic HDIs mediated by metabolizing enzymes and/or transporters. These well-characterized HDIs could provide useful information on clinical concomitant medicine use to maximize clinical outcomes and minimize adverse and toxic effects.

Application of deep learning-based diagnostic systems in screening asymptomatic COVID-19 patients among oversea returnees.

INTRODUCTION: Our study aimed to investigate the performance of deep learning (DL)-based diagnostic systems in alerting against COVID-19, especially among asymptomatic individuals coming from overseas, and to analyze the features of identified asymptomatic patients in detail. METHODOLOGY: DL diagnostic systems were deployed to assist in the screening of COVID-19, including the pneumonia system and pulmonary nodules system. 1,917 overseas returnees who underwent CT examination and rRT-PCR tests were enrolled. DL pneumonia system promptly alerted clinicians to suspected COVID-19 after CT examinations while the performance was evaluated with rRT-PCR results as the reference. The radiological features of asymptomatic COVID-19 cases were described according to the Nomenclature of the Fleischner Society. RESULTS: Fifty-three cases were confirmed as COVID-19 patients by rRT-PCR tests, including 5 asymptomatic cases. DL pneumonia system correctly alerted 50 cases as suspected COVID-19 with a sensitivity of 0.9434 and specificity of 0.9592 (within 2 minutes per case); while the pulmonary nodules system alerted 2 of the 3 missed asymptomatic cases. Additionally, five asymptomatic patients presented different characteristics such as elevated creatine kinase level and prolonged prothrombin time, as well as atypical radiological features. CONCLUSIONS: DL diagnostic systems are promising complementary approaches for prompt screening of imported COVID-19 patients, even the imported asymptomatic cases. Unique clinical and radiological characteristics of asymptomatic cases might be of great value in screening as well. ADVANCES IN KNOWLEDGE: DL-based systems are practical, efficient, and reliable to assist radiologists in screening COVID-19 patients. Differential features of asymptomatic patients might be useful to clinicians in the frontline to differentiate asymptomatic cases.

Multicriteria Model for Shared Parking and Parking Route Recommender Systems

In many congested areas, shared parking has gotten increasing attention because of its potential to alleviate parking resource shortages. However, managing parking resources remains a challenge when simultaneously considering multiple decision-making criteria of public travelers in allocating parking spaces and recommending optimal parking routes. To fill this gap, from four perspectives, i.e., driving, among shared parking lots, at a shared parking lot, between shared parking spaces and destinations, we proposed nine criteria for shared parking space allocations and parking route recommendations, and we also gave the quantitative models for different criteria. Furthermore, an analytic hierarchy process Entropy-TOPSIS grey relational analysis (AHP-Entropy-TOPSIS-GRA) method and an improved ant colony algorithm were proposed to solve the proposed allocation of parking spaces and recommend optimal parking routes, respectively. Finally, the validity of our proposed models and algorithms was tested by empirical parking data and road traffic data collected in Huai’an City, Jiangsu province, China. The research helps provide a theoretical foundation for implementing shared parking initiatives and improving public travelers’ parking satisfaction.

Reconstruction of the Transmission Chain of COVID-19 Outbreak in Beijing's Xinfadi Market, China.

OBJECTIVES: The aim of the study was to reconstruct the complete transmission chain of the COVID-19 outbreak in Beijing's Xinfadi Market using data from epidemiological investigations, which contributes to reflecting transmission dynamics and transmission risk factors. METHODS: We set up a transmission model, and the model parameters are estimated from the survey data via Markov chain Monte Carlo sampling. Bayesian data augmentation approaches are used to account for uncertainty in the source of infection, unobserved onset, and infection dates. RESULTS: The rate of transmission of COVID-19 within households is 9.2%. Older people are more susceptible to infection. The accuracy of our reconstructed transmission chain was 67.26%. In the gathering place of this outbreak, the Beef and Mutton Trading Hall of Xinfadi market, most of the transmission occurs within 20 m, only 19.61% of the transmission occurs over a wider area (>20 m), with an overall average transmission distance of 13.00 m. The deepest transmission generation is 9. In this outbreak, there were 2 abnormally high transmission events. CONCLUSIONS: The statistical method of reconstruction of transmission trees from incomplete epidemic data provides a valuable tool to help understand the complex transmission factors and provides a practical guideline for investigating the characteristics of the development of epidemics and the formulation of control measures.

Weighted Centrality and Retail Store Locations in Beijing, China: A Temporal Perspective from Dynamic Public Transport Flow Networks

The spatial relationship between transport networks and retail store locations is an important topic in studies related to commercial activities. Much effort has been made to study physical street networks, but they are seldom empirically discussed with considerations of transport flow networks from a temporal perspective. By using Beijing’s bus and subway smart card data (SCD) and point of interest (POI) data, this study examined the location patterns of various retail stores and their daily dynamic relationships with three weighted centrality indices in the networks of public transport flows: degree, betweenness, and closeness. The results indicate that most types of retail stores are highly correlated with weighted centrality indices. For the network constructed by total public transport flows in the week, supermarkets, convenience stores, electronics stores, and specialty stores had the highest weighted degree value. By contrast, building material stores and shopping malls had the weighted closeness and weighted betweenness values, respectively. From a temporal perspective, most retail types’ largest correlations on weekdays occurred during the after-work period of 19:00 to 21:00. On weekends, shopping malls and electronics stores changed their favorite periods to the daytime, while specialty stores favored the daytime on both weekdays and weekends. In general, the higher store type level of the shopping malls correlates more to weighted closeness or betweenness, and the lower-level store type of convenience stores correlates more to weighted degree. This study provides a temporal analysis that surpasses previous studies on street centrality and can help with urban commercial planning.

Disulfiram-loaded lactoferrin nanoparticles for treating inflammatory diseases.

Sepsis is a dysregulated immune response to infection and potentially leads to life-threatening organ dysfunction, which is often seen in serious Covid-19 patients. Disulfiram (DSF), an old drug that has been used to treat alcohol addiction for decades, has recently been identified as a potent inhibitor of the gasdermin D (GSDMD)-induced pore formation that causes pyroptosis and inflammatory cytokine release. Therefore, DSF represents a promising therapeutic for the treatment of inflammatory disorders. Lactoferrin (LF) is a multifunctional glycoprotein with potent antibacterial and anti-inflammatory activities that acts by neutralizing circulating endotoxins and activating cellular responses. In addition, LF has been well exploited as a drug nanocarrier and targeting ligands. In this study, we developed a DSF-LF nanoparticulate system (DSF-LF NP) for combining the immunosuppressive activities of both DSF and LF. DSF-LF NPs could effectively block pyroptosis and inflammatory cytokine release from macrophages. Treatment with DSF-LF NPs showed remarkable therapeutic effects on lipopolysaccharide (LPS)-induced sepsis. In addition, this therapeutic strategy was also applied to treat ulcerative colitis (UC), and substantial treatment efficacy was achieved in a murine colitis model. The underlying mode of action of these DSF-LF-NPs may contribute to efficiently suppressing macrophage-mediated inflammatory responses and ameliorating the complications caused by sepsis and UC. As macrophage pyroptosis plays a pivotal role in inflammation, this safe and effective biomimetic nanomedicine may offer a versatile therapeutic strategy for treating various inflammatory diseases by repurposing DSF.

Neutralization of SARS-CoV-2 pseudovirus using ACE2-engineered extracellular vesicles.

The spread of coronavirus disease 2019 (COVID-19) throughout the world has resulted in stressful healthcare burdens and global health crises. Developing an effective measure to protect people from infection is an urgent need. The blockage of interaction between angiotensin-converting enzyme 2 (ACE2) and S protein is considered an essential target for anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) drugs. A full-length ACE2 protein could be a potential drug to block early entry of SARS-CoV-2 into host cells. In this study, a therapeutic strategy was developed by using extracellular vesicles (EVs) with decoy receptor ACE2 for neutralization of SARS-CoV-2. The EVs embedded with engineered ACE2 (EVs-ACE2) were prepared; the EVs-ACE2 were derived from an engineered cell line with stable ACE2 expression. The potential effect of the EVs-ACE2 on anti-SARS-CoV-2 was demonstrated by both in vitro and in vivo neutralization experiments using the pseudovirus with the S protein (S-pseudovirus). EVs-ACE2 can inhibit the infection of S-pseudovirus in various cells, and importantly, the mice treated with intranasal administration of EVs-ACE2 can suppress the entry of S-pseudovirus into the mucosal epithelium. Therefore, the intranasal EVs-ACE2 could be a preventive medicine to protect from SARS-CoV-2 infection. This EVs-based strategy offers a potential route to COVID-19 drug development.

Adaptively temporal graph convolution model for epidemic prediction of multiple age groups

Introduction Multivariate time series prediction of infectious diseases is significant to public health, and the deep learning method has attracted increasing attention in this research field. Material and Methods An adaptively temporal graph convolution (ATGCN) model, which learns the contact patterns of multiple age groups in a graph-based approach, was proposed for COVID- 19 and influenza prediction. We compared ATGCN with autoregressive models, deep sequence learning models, and experience- based ATGCN models in short-term and long-term prediction tasks. Results Results showed that the ATGCN model performed better than the autoregressive models and the deep sequence learning models on two datasets in both short-term (12.5% and 10% improvements on RMSE) and long-term (12.4% and 5% improvements on RMSE) prediction tasks. And the RMSE of ATGCN predictions fluctuated least in different age groups of COVID- 19 (0.029 ± 0.003) and influenza (0.059±0.008). Compared with the Ones-ATGCN model or the Pre-ATGCN model, the ATGCN model was more robust in performance, with RMSE of 0.0293 and 0.06 on two datasets when horizon is one. Discussion Our research indicates a broad application prospect of deep learning in the field of infectious disease prediction. Transmission characteristics and domain knowledge of infectious diseases should be further applied to the design of deep learning models and feature selection. Conclusions The ATGCN model addressed the multivariate time series forecasting in a graph-based deep learning approach and achieved robust prediction on the confirmed cases of multiple age groups, indicating its great potentials for exploring the implicit interactions of multivariate variables.

Minor Feelings in the Wake of the Atlanta Attack: How a Mom of Asian Descent Spent the First 100 hours in the Aftermath

This autoethnographic writing documents how a family of Chinese descent spent their first 100 hours after the Atlanta Shooting on March 16, 2021, in which a White gunman killed eight people, including six Asian women. It bears witness to the rise of the anti-Asian racism in the United States during the COVID-19 pandemic and offers a snapshot of the private life of a family of Asian descent in the dawn of the Stop Asian Hate Movement. Drawing on Korean American poet Cathy Park Hong’s term minor feelings, this essay explores how emotions, rooted in racialized lived experience and triggered by the mass shooting, evolved, shifted, and fueled the sentiments that gave rise to the Stop Asian Hate Movement. Compared with the more visible violence against Asians and Asian Americans displayed on social media, it interrogates the less visible traumatic experience that haunts Asian and Asian American communities. [ABSTRACT FROM AUTHOR] Copyright of Cultural Studies/Critical Methodologies is the property of Sage Publications 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.)

Exploring an Integrative Therapy for Treating COVID-19: A Randomized Controlled Trial.

OBJECTIVES: To develop a new Chinese medicine (CM)-based drug and to evaluate its safety and effect for suppressing acute respiratory distress syndrome (ARDS) in COVID-19 patients. METHODS: A putative ARDS-suppressing drug Keguan-1 was first developed and then evaluated by a randomized, controlled two-arm trial. The two arms of the trial consist of a control therapy (alpha interferon inhalation, 50 µg twice daily; and lopinavir/ritonavir, 400 and 100 mg twice daily, respectively) and a testing therapy (control therapy plus Keguan-1 19.4 g twice daily) by random number table at 1:1 ratio with 24 cases each group. After 2-week treatment, adverse events, time to fever resolution, ARDS development, and lung injury on newly diagnosed COVID-19 patients were assessed. RESULTS: An analysis of the data from the first 30 participants showed that the control arm and the testing arm did not exhibit any significant differences in terms of adverse events. Based on this result, the study was expanded to include a total of 48 participants (24 cases each arm). The results show that compared with the control arm, the testing arm exhibited a significant improvement in time to fever resolution (P=0.035), and a significant reduction in the development of ARDS (P=0.048). CONCLUSIONS: Keguan-1-based integrative therapy was safe and superior to the standard therapy in suppressing the development of ARDS in COVID-19 patients. (Trial registration No. NCT04251871 at www.clinicaltrials.gov ).