What do we know about IL-6 in COVID-19 so far?

Interleukin 6 (IL-6) is a cytokine with dual functions of pro-inflammation and anti-inflammation. It is mainly produced by mononuclear macrophages, Th2 cells, vascular endothelial cells and fibroblasts. IL-6 binds to glycoprotein 130 and one of these two receptors, membrane-bound IL-6R or soluble IL-6R, forming hexamer (IL-6/IL-6R/gp130), which then activates different signaling pathways (classical pathway, trans-signaling pathway) to exert dual immune-modulatory effects of anti-inflammation or pro-inflammation. Abnormal levels of IL-6 can cause multiple pathological reactions, including cytokine storm. Related clinical studies have found that IL-6 levels in severe COVID-19 patients were much higher than in healthy population. A large number of studies have shown that IL-6 can trigger a downstream cytokine storm in patients with COVID-19, resulting in lung damages, aggravating clinical symptoms and developing excessive inflammation and acute respiratory distress syndrome (ARDS). Monoclonal antibodies against IL-6 or IL-6R, such as tocilizumab, sarilumab, siltuximab and olokizumab may serve as therapeutic options for COVID-19 infection.

Case clustering, contact stratification, and transmission heterogeneity of SARS-CoV-2 Omicron BA.5 variants in Urumqi, China: An observational study.

Background: From August to September 2022, Urumqi, the capital of the Xinjiang Uygur Autonomous Region in China, faced its largest COVID-19 outbreak caused by the emergence of the SARS-CoV-2 Omicron BA.5.2 variants. Although the superspreading of COVID-19 played an important role in triggering large-scale outbreaks, little was known about the superspreading potential and heterogeneity in the transmission of Omicron BA.5 variants. Methods: In this retrospective observational, contact tracing study, we identified 1139 laboratory-confirmed COVID-19 cases of Omicron BA.5.2 variants, and 51 323 test-negative close contacts in Urumqi from 7 August to 7 September 2022. By using detailed contact tracing information and exposure history of linked case-contact pairs, we described stratification in contact and heterogeneity in transmission across different demographic strata, vaccine statuses, and contact settings. We adopted beta-binomial models to characterise the secondary attack rate (SAR) distribution among close contacts and modelled COVID-19 transmission as a branching process with heterogeneity in transmission governed by negative binomial models. Results: After the city lockdown, the mean case cluster size decreased from 2.0 (before lockdown) to 1.6, with decreased proportions of contacts in workplace and community settings compared with household settings. We estimated that 14% of the most infectious index cases generated 80% transmission, whereas transmission in the community setting presented the highest heterogeneity, with 5% index cases seeding 80% transmission. Compared with zero, one, and two doses of inactivated vaccine (Sinopharm), index cases with three doses of vaccine had a lower risk of generating secondary cases in terms of the reproduction number. Contacts of female cases, cases with ages 0-17 years, and household settings had relatively higher SAR. Conclusions: In the context of intensive control measures, active case detection, and relatively high vaccine coverage, but with an infection-naive population, our findings suggested high heterogeneity in the contact and transmission risks of Omicron BA.5 variants across different demographic strata, vaccine statuses, and contact settings. Given the rapid evolution of SARS-CoV-2, investigating the distribution of transmission not only helped promote public awareness and preparedness among high-risk groups, but also highlighted the importance of continuously monitoring the transmission characteristics of genetic variants of SARS-CoV-2.

Full-coverage mapping and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China

Ozone (O₃) is an important trace and greenhouse gas in the atmosphere, posing a threat to the ecological environment and human health at the ground level. Large-scale and long-term studies of O₃ pollution in China are few due to highly limited direct ground and satellite measurements. This study offers a new perspective to estimate ground-level O₃ from solar radiation intensity and surface temperature by employing an extended ensemble learning of the space-time extremely randomized trees (STET) model, together with ground-based observations, remote sensing products, atmospheric reanalysis, and an emission inventory. A full-coverage (100%), high-resolution (10 km) and high-quality daily maximum 8-h average (MDA8) ground-level O₃ dataset covering China (called ChinaHighO₃) from 2013 to 2020 was generated. Our MDA8 O₃ estimates (predictions) are reliable, with an average out-of-sample (out-of-station) coefficient of determination of 0.87 (0.80) and root-mean-square error of 17.10 (21.10) μg/m³ in China. The unique advantage of the full coverage of our dataset allowed us to accurately capture a short-term severe O₃ pollution exposure event that took place from 23 April to 8 May in 2020. Also, a rapid increase and recovery of O₃ concentrations associated with variations in anthropogenic emissions were seen during and after the COVID-19 lockdown, respectively. Trends in O₃ concentration showed an average growth rate of 2.49 μg/m³/yr (p < 0.001) from 2013 to 2020, along with the continuous expansion of polluted areas exceeding the daily O₃ standard (i.e., MDA8 O₃ = 160 μg/m³). Summertime O₃ concentrations and the probability of occurrence of daily O₃ pollution have significantly increased since 2015, especially in the North China Plain and the main air pollution transmission belt (i.e., the "2 + 26” cities). However, a decline in both was seen in 2020, mainly due to the coordinated control of air pollution and ongoing COVID-19 effects. This carefully vetted and smoothed dataset is valuable for studies on air pollution and environmental health in China.

Study on the Distribution of Fresh Food Support System—An Example of Shanghai during the Epidemic Closure of 2022

One of the key indicators to measure the sustainability and resilience of a city during a public health crisis is how well it can meet the daily needs of its residents. During the COVID-19 lockdown in Shanghai in 2022, e-commerce shopping and delivery became the most important method for ensuring the city's material supplies. This article uses the distribution data of a fresh e-commerce platform's pre-warehouse and static population distribution data to establish a basic material supply system evaluation model for the city and explore its resilience potential. Focusing on the central urban area of Shanghai, this study uses a population heat map with geographic coordinates to reflect the static distribution of residents and obtains the distribution data of the e-commerce pre-warehouses. Using kernel density analysis, the relationship between the pre-warehouses and the residents' needs is established. Through analysis, it was found that the supply capacity of fresh food in different areas of Shanghai during the lockdown could be categorized as insufficient, adequate, or excessive. Based on these three categories, improvement strategies were proposed. Finally, this article suggests establishing a scientific supply security system to promote urban sustainability and prepare for future challenges.

Severity prediction of pulmonary diseases using chest CT scans via cost-sensitive label multi-kernel distribution learning.

BACKGROUND AND OBJECTIVES: The progression of pulmonary diseases is a complex progress. Timely predicting whether the patients will progress to the severe stage or not in its early stage is critical to take appropriate hospital treatment. However, this task suffers from the "insufficient and incomplete" data issue since it is clinically impossible to have adequate training samples for one patient at each day. Besides, the training samples are extremely imbalanced since the patients who will progress to the severe stage is far less than those who will not progress to the non-severe stage. METHOD: We consider the severity prediction of pulmonary diseases as a time estimation problem based on CT scans. To handle the issue of "insufficient and incomplete" training samples, we introduced label distribution learning (LDL). Specifically, we generate a label distribution for each patient, making a CT image contribute to not only the learning of its chronological day, but also the learning of its neighboring days. In addition, a cost-sensitive mechanism is introduced to explore the imbalance data issue. To identify the importance of pulmonary segments in pulmonary disease severity prediction, multi-kernel learning in composite kernel space is further incorporated and particle swarm optimization (PSO) is used to find the optimal kernel weights. RESULTS: We compare the performance of the proposed CS-LD-MKSVR algorithm with several classical machine learning algorithms and deep learning (DL) algorithms. The proposed method has obtained the best classification results on the in-house data, fully indicating its effectiveness in pulmonary disease severity prediction. CONTRIBUTIONS: The severity prediction of pulmonary diseases is considered as a time estimation problem, and label distribution is introduced to describe the conversion time from non-severe stage to severe stage. The cost-sensitive mechanism is also introduced to handle the data imbalance issue to further improve the classification performance.

Knowledge, attitudes and practice regarding environmental friendly disinfectants for household use among residents of China in the post-pandemic period.

Background: A sharp rise in household consumption of disinfectants triggered by COVID-19 pandemic has generated tremendous environmental burden and risks of disinfectant emissions in the post-pandemic period. To address this emerging challenge, replacing highly hazardous disinfectants with more environmental friendly alternatives has been accepted as an inherently effective solution to environment issues posed by disinfectant emerging contaminants. However, no study has yet been done to explore the potential customers' attitudes and the market prospect of environmental friendly disinfectants until now. Methods: This cross-sectional questionnaire-based survey was conducted from January to March, 2022, among resident volunteers in China, to explore the practices, knowledge and attitudes of the public regarding environmental friendly disinfectants for household use. Results: Among a total of 1,861 Chinese residents finally included in the analyses, 18% agreed or strongly agreed that they paid special attention to the environmental certification label on the product, and only bought the environmental certified disinfectant products; 16% and 10% were using environmental friendly disinfectants for hand sanitization and environmental disinfection, respectively. The mean self-assessed and actual knowledge scores were 2.42 ± 1.74 and 2.12 ± 1.97, respectively, out of a total of 5. Participants having good practices of consuming environmental friendly disinfectants achieved higher knowledge scores. Residents' overall attitudes toward the development, consumption and application of environmental friendly disinfectants were very positive. "Possible conflict between disinfection effectiveness and environmental factor of disinfectants in a context of severe COVID-19 pandemic" was considered as the most important barrier jeopardizing the participants' usage intention for environmental friendly disinfectants. Conclusions: These data suggested most residents of China had a positive attitude, poor knowledge and practices toward environmental friendly disinfectants. More should be done to enhance the residents' environmental knowledge levels about disinfectants, and to further develop and promote disinfectant products with both excellent disinfection activity and environmentally friendly attributes.

Wastewater surveillance and an automated robot: effectively tracking SARS-CoV-2 transmission in the post-epidemic era.

Wastewater-based epidemiology (WBE) has exhibited great utility in the early and rapid identification of SARS-CoV-2. However, the efficacy of wastewater surveillance under China's previous strict epidemic prevention policy remains to be described. We collected the WBE data of wastewater treatment plants (WWTPs) in the Third People's Hospital of Shenzhen and several communities to determine the significant effectiveness of routine wastewater surveillance in monitoring the local spread of SARS-CoV-2 under tight containment of the epidemic. The results of 1 month of continuous wastewater surveillance showed that positive signals for SARS-CoV-2 RNA were detected in the wastewater samples, and a significant positive correlation was observed between the virus concentration and the number of daily cases. In addition, the community's domestic wastewater surveillance results were confirmed even 3 days before, or simultaneously with, the infected patient being confirmed as having the virus. Meanwhile, an automated sewage virus detection robot, ShenNong No.1 robot, was developed, showing a high degree of agreement with experimental data, offering the possibility of large-scale multi-point surveillance. Overall, our results illustrated the clear indicative role of wastewater surveillance in combating COVID-19 and provided a practical basis for rapidly expanding the feasibility and value of routine wastewater surveillance for future emerging infectious diseases.

FlipGFP protease assay for evaluating in vitro inhibitory activity against SARS-CoV-2 Mpro and PLpro.

FlipGFP assay characterizes the intracellular drug target engagement to Mpro and PLpro and can be performed in the biosafety level 1/2 settings. Here, we provide the detailed protocol for the cell-based FlipGFP assay to identify and characterize SARS-CoV-2 Mpro and PLpro inhibitors. We describe steps for cell passage and seeding, transfection, addition of compounds, and their incubation and timing. We then detail the quantification of the fluorescence signal of the assay For complete details on the use and execution of this protocol, please refer to Ma et al.1.

Q-Rung Interval-Valued Probabilistic Dual Hesitant Fuzzy Sets: A New Tool for Multiattribute Group Decision-Making

This paper aims at proposing a novel multiattribute group decision-making (MAGDM) method in complex decision-making environments. To this end, we first introduce a tool, called q-rung interval-valued probabilistic dual hesitant fuzzy sets (q-RIVPDHFSs), for decision makers to express their evaluation information over a set of finite alternatives in MAGDM procedures. The q-RIVPDHFS consists of some possible membership and nonmembership degrees, along with their interval-valued probabilistic information. Due to this structure, q-RIVPDHFSs are more powerful and flexible than the traditional q-rung probabilistic q-rung dual hesitant fuzzy sets, in which probabilistic information of membership and nonmembership degree is denoted by crisp numbers. Second, some other related concepts of q-RIVPDHFSs, such as operational laws, comparison method, distance measure, and aggregation operators, are introduced. Third, based on these novel concepts, two MAGDM methods (Algorithms 1 and 2) are put forward. Last but not least, a practical decision-making example is provided to show the effectiveness of our proposed MAGDM method. We also compare our Algorithms 1 and 2 with some existing decision-making methods to explain why our methods are more powerful and useful.

Global Atmospheric Composition Needs from Future Ultraviolet–Visible–Near-Infrared (UV–Vis–NIR) NOAA Satellite Instruments

Presentations spanned a range of applications: the public health impacts of poor air quality and environmental justice;greenhouse gas measuring, monitoring, reporting, and verification (GHG MMRV);stratospheric ozone monitoring;and various applications of satellite observations to improve models, including data assimilation in global Earth system models. The combination of methane (CH4), carbon dioxide (CO2), carbon monoxide (CO), and NO2 retrievals can improve confidence in emissions inventories and model performance, and together these data products would be of use in future air quality management tools. The ability to retrieve additional trace gases (e.g., ethane, isoprene, and ammonia) in the thermal IR along with those measured in the UV–Vis–NIR region would be extremely useful for air quality applications, including source apportionment analysis (e.g., for oil/natural gas extraction, biogenic, and agricultural sources). Ground-level ozone is one of six criteria pollutants for which the EPA sets National Ambient Air Quality Standards (NAAQS) to protect against human health and welfare effects.

Separating Daily 1 km PM2.5 Inorganic Chemical Composition in China since 2000 via Deep Learning Integrating Ground, Satellite, and Model Data.

Fine particulate matter (PM2.5) chemical composition has strong and diverse impacts on the planetary environment, climate, and health. These effects are still not well understood due to limited surface observations and uncertainties in chemical model simulations. We developed a four-dimensional spatiotemporal deep forest (4D-STDF) model to estimate daily PM2.5 chemical composition at a spatial resolution of 1 km in China since 2000 by integrating measurements of PM2.5 species from a high-density observation network, satellite PM2.5 retrievals, atmospheric reanalyses, and model simulations. Cross-validation results illustrate the reliability of sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), and chloride (Cl-) estimates, with high coefficients of determination (CV-R2) with ground-based observations of 0.74, 0.75, 0.71, and 0.66, and average root-mean-square errors (RMSE) of 6.0, 6.6, 4.3, and 2.3 µg/m3, respectively. The three components of secondary inorganic aerosols (SIAs) account for 21% (SO42-), 20% (NO3-), and 14% (NH4+) of the total PM2.5 mass in eastern China; we observed significant reductions in the mass of inorganic components by 40-43% between 2013 and 2020, slowing down since 2018. Comparatively, the ratio of SIA to PM2.5 increased by 7% across eastern China except in Beijing and nearby areas, accelerating in recent years. SO42- has been the dominant SIA component in eastern China, although it was surpassed by NO3- in some areas, e.g., Beijing-Tianjin-Hebei region since 2016. SIA, accounting for nearly half (∼46%) of the PM2.5 mass, drove the explosive formation of winter haze episodes in the North China Plain. A sharp decline in SIA concentrations and an increase in SIA-to-PM2.5 ratios during the COVID-19 lockdown were also revealed, reflecting the enhanced atmospheric oxidation capacity and formation of secondary particles.

Octyl gallate targeting the 3C-like protease exhibits highly efficient antiviral activity against swine enteric coronavirus PEDV.

Infection with porcine epidemic diarrhea virus (PEDV) causes severe watery diarrhea in newborn piglets, leading to substantial financial losses for the swine industry. In this study, we screened small molecule drugs targeting 3 C-like protease (3CLpro) by molecular docking, and further evaluated the antiviral activity of the screened drugs against PEDV. Results showed that octyl gallate (OG), a widely used food additive, exhibited strong binding affinity with the 3CLpro active sites of PEDV. Bio-layer interferometry and fluorescence resonance energy transfer revealed that OG directly interacts with PEDV 3CLpro (KD = 549 nM) and inhibits 3CLpro activity (IC50 = 22.15 µM). OG showed a strong inhibition of PEDV replication in vitro. Virus titers were decreased by 0.58 and 0.71 log10 TCID50/mL for the CV777 and HM2017 strains, respectively. In vivo, all piglets in the PEDV-infected group died at 48 h post-infection (hpi), while 75% of piglets in the OG treatment group showed significant relief from the clinical symptoms, pathological damage, and viral loads in the jejunum and ileum. Moreover, the western blotting results showed that OG also has strong antiviral activity against other swine enteric coronaviruses, including transmissible gastroenteritis virus (TGEV), porcine deltacoronavirus (PDCoV), and swine acute diarrhea syndrome coronavirus (SADS-CoV). Our findings revealed that OG could be developed as a novel antiviral drug against PEDV. The OG exhibited a potential broad-spectrum antiviral drug for control of other swine enteric coronaviruses.

The mediating role of coping strategies between depression and social support and the moderating effect of the parent–child relationship in college students returning to school: During the period of the regular prevention and control of COVID-19

Objective According to the WHO, compared to before the COVID-19 pandemic, young people showed a significant increase in depressive symptoms. In light of the recent coronavirus pneumonia pandemic, this study was conducted to determine how social support, coping style, parent-child relationships, and depression are associated. We investigated how these factors interacted and affected the prevalence of depression during this challenging and unheard-of time. Our research may help both individuals and healthcare professionals better comprehend and assist those who are coping with the pandemic's psychological effects. Design and main outcome measures 3,763 students from a medical college in Anhui Province were investigated with Social Support Rate Scale, Trait Coping Style Questionnaire, and Self-rating Depression Scale. Results When the pandemic situation was normalizing, social support was associated with depression and the coping style of college students (p < 0.01). During the period of pandemic normalization, the parent–child relationship moderated the relationship between social support and positive coping (t = −2.45, p < 0.05);the parent–child relationship moderated the relationship between social support and negative coping (t = −4.29, p < 0.01);and the parent–child relationship moderated the association between negative coping and depression (t = 2.08, p < 0.05). Conclusion Social support has an impact on depression in the period of the regular prevention and control of COVID-19 through the mediating role of coping style and the moderating effect of the parent–child relationship.

Associated factors of burnout among Chinese vaccination staff during COVID-19 epidemic: A cross-sectional study.

Objective: During the COVID-19 epidemic, vaccination staff had three main aspects of work: routine vaccination for children and adults, COVID-19 vaccination and COVID-19 prevention and control. All these works significantly increased the workload of vaccination staff. This study aimed to investigate the prevalence and influencing factors of burnout among vaccination staff in Hangzhou, China. Methods: A total of 501 vaccination staff from 201 community/township healthcare centers in Hangzhou were recruited using a cross-sectional survey through WeChat social platform. The Maslach Burnout Inventory-General Scale (MBI-GS) was used to assess the level of burnout. Descriptive statistics were made on the characteristics of participants. Univariate analysis using the chi-square test and multivariable analysis using binary logistic regression were conducted to determine the relative predictors of burnout. Univariate analysis and multiple linear regression were used to determine the relative predictors of exhaustive emotion, cynicism, and personal accomplishment. Results: During the COVID-19 pandemic, 20.8% of the vaccination staff experienced burnout. Educational level above undergraduate education level, medium professional title, and more working time in COVID-19 vaccination work reported a higher degree of job burnout. The vaccination staff was experiencing a high degree of exhaustive emotion, cynicism, and low personal accomplishment. Professional title, working place, and working time for COVID-19 vaccination were associated with exhaustive emotion and cynicism. Professional title and participation time for COVID-19 prevention and control were associated with personal accomplishment. Conclusions: Our findings suggest that the prevalence rate of burnout is high among vaccination staff during the COVID-19 pandemic, especially with a low level of personal accomplishment. Psychological intervention for vaccination staff is urgently needed.

Neurological and Psychiatric Manifestations of Post-COVID-19 Conditions.

BACKGROUND: We aimed to investigate the factors associated with neurological manifestations of post-coronavirus disease 2019 (COVID-19) conditions. METHODS: We retrospectively collected data from 440 patients who visited our post-COVID-19 clinic more than 4 weeks after severe acute respiratory syndrome coronavirus 2 infection. We analyzed the prevalence of different neurological symptoms (brain fog, memory impairment, headache, and dizziness) and assessed the associated factors. RESULTS: Brain fog was the most common symptom, observed in 170 patients (38.6%), followed by headaches (n = 137, 31.1%), dizziness (n = 128, 29%), and memory impairment (n = 104, 23.6%). Brain fog was associated with hyposmia or hypogeusia (odds ratio [OR], 2.54; P < 0.001), Fatigue Severity Scale (FSS) (OR, 1.06; P < 0.001), and Hospital Anxiety and Depression Scale-Anxiety (OR, 1.09; P = 0.037). Memory impairment was associated with sleep problems (OR, 2.83; P < 0.001), FSS (OR, 1.05; P < 0.001), and age (OR, 1.02; P = 0.015). Headache was associated with sleep problems (OR, 2.28; P = 0.001), sex (OR, 1.68; P = 0.042), and FSS (OR, 1.04; P < 0.001). Dizziness was associated with sleep problems (OR, 2.88; P < 0.001), and FSS (OR, 1.04; P < 0.001). The incidence of brain fog (P < 0.001), memory impairment (P < 0.001), dizziness (P = 0.007), and headache (P = 0.045) accompanied by hyposmia and hypogeusia was higher in patients with the aforementioned symptoms than in those without. CONCLUSION: This study suggests that there is a relationship between neurological symptoms and other clinical factors, such as fatigue, depression, anxiety, hyposmia, and hypogeusia.

The history, mechanism, and perspectives of nirmatrelvir (PF-07321332): an orally bioavailable main protease inhibitor used in combination with ritonavir to reduce COVID-19-related hospitalizations.

The rapid development of effective vaccines to combat the SARS-CoV-2 virus has been an effective counter measure to decrease hospitalization and the mortality rate in many countries. However, with the risk of mutated strains decreasing the efficacy of the vaccine, there has been an increasing demand for antivirals to treat COVID-19. While antivirals, such as remdesivir, have had some success treating COVID-19 patients in hospital settings, there is a need for orally bioavailable, cost-effective antivirals that can be administered in outpatient settings to minimize COVID-19-related hospitalizations and death. Nirmatrelvir (PF-07321332) is an orally bioavailable Mpro (also called 3CLpro) inhibitor developed by Pfizer. It is administered in combination with ritonavir, a potent CYP3A4 inhibitor that decreases the metabolism of nirmatrelvir. This review seeks to outline the history of the rational design, the target selectivity, synthesis, drug resistance, and future perspectives of nirmatrelvir. Graphical abstract.

mRNA cancer vaccines: Advances, trends and challenges.

Patients exhibit good tolerance to messenger ribonucleic acid (mRNA) vaccines, and the choice of encoded molecules is flexible and diverse. These vaccines can be engineered to express full-length antigens containing multiple epitopes without major histocompatibility complex (MHC) restriction, are relatively easy to control and can be rapidly mass produced. In 2021, the U.S. Food and Drug Administration (FDA) approved the first mRNA-based coronavirus disease 2019 (COVID-19) vaccine produced by Pfizer and BioNTech, which has generated enthusiasm for mRNA vaccine research and development. Based on the above characteristics and the development of mRNA vaccines, mRNA cancer vaccines have become a research hotspot and have undergone rapid development, especially in the last five years. This review analyzes the advances in mRNA cancer vaccines from various perspectives, including the selection and expression of antigens/targets, the application of vectors and adjuvants, different administration routes, and preclinical evaluation, to reflect the trends and challenges associated with these vaccines.

Development of the Safe and Broad-Spectrum Aldehyde and Ketoamide Mpro inhibitors Derived from the Constrained α, γ-AA Peptide Scaffold.

SARS-CoV-2 is still wreaking havoc all over the world with surging morbidity and high mortality. The main protease (Mpro ) is essential in the replication of SARS-CoV-2, enabling itself an active target for antiviral development. Herein, we reported the design and synthesis of a new class of peptidomimetics-constrained α, γ-AA peptides, based on which a series of aldehyde and ketoamide inhibitors of the Mpro of SARS-CoV-2 were prepared. The lead compounds showed excellent inhibitory activity in the FRET-based Mpro enzymatic assay not only for the Mpro of SARS-CoV-2 but also for SARS-CoV and MERS-CoV, along with HCoVs like HCoV-OC43, HCoV-229E, HCoV-NL63 and HKU1. The X-ray crystallographic results demonstrated that our compounds form a covalent bond with the catalytic Cys145. They also demonstrated effective antiviral activity against live SARS-CoV-2. Overall, the results suggest that α, γ-AA peptide could be a promising molecular scaffold in designing novel Mpro inhibitors of SARS-CoV-2 and other coronaviruses.

Transmission Characteristics and Inactivated Vaccine Effectiveness Against Transmission of SARS-CoV-2 Omicron BA.5 Variants in Urumqi, China.

Importance: In 2022, Omicron variants circulated globally, and Urumqi, China, experienced a COVID-19 outbreak seeded by Omicron BA.5 variants, resulting in the highest number of infections in the city's record before the exit of the zero COVID-19 strategy. Little was known about the characteristics of Omicron variants in mainland China. Objective: To evaluate transmission characteristics of Omicron BA.5 variants and the effectiveness of inactivated vaccine (mainly BBIBP-CorV) against their transmission. Design, Setting, and Participants: This cohort study was conducted using data from an Omicron-seeded COVID-19 outbreak in Urumqi from August 7 to September 7, 2022. Participants included all individuals with confirmed SARS-CoV-2 infections and their close contacts identified between August 7 and September 7, 2022 in Urumqi. Exposures: A booster dose was compared vs 2 doses (reference level) of inactivated vaccine and risk factors were evaluated. Main Outcomes and Measures: Demographic characteristics, timeline records from exposure to laboratory testing outcomes, contact tracing history, and contact setting were obtained. The mean and variance of the key time-to-event intervals of transmission were estimated for individuals with known information. Transmission risks and contact patterns were assessed under different disease-control measures and in different contact settings. The effectiveness of inactivated vaccine against the transmission of Omicron BA.5 was estimated using multivariate logistic regression models. Results: Among 1139 individuals diagnosed with COVID-19 (630 females [55.3%]; mean [SD] age, 37.4 [19.9] years) and 51 323 close contacts who tested negative for COVID-19 (26 299 females [51.2%]; mean [SD] age, 38.4 [16.0] years), the means of generation interval, viral shedding period, and incubation period were estimated at 2.8 days (95% credible interval [CrI], 2.4-3.5 days), 6.7 days (95% CrI, 6.4-7.1 days), and 5.7 days (95% CrI, 4.8-6.6 days), respectively. Despite contact tracing, intensive control measures, and high vaccine coverage (980 individuals with infections [86.0%] received ≥2 doses of vaccine), high transmission risks were found in household settings (secondary attack rate, 14.7%; 95% CrI, 13.0%-16.5%) and younger (aged 0-15 years; secondary attack rate, 2.5%; 95% CrI, 1.9%-3.1%) and older age (aged >65 years; secondary attack rate, 2.2%; 95% CrI, 1.5%-3.0%) groups. Vaccine effectiveness against BA.5 variant transmission for the booster-dose vs 2 doses was 28.9% (95% CrI, 7.7%-45.2%) and 48.5% (95% CrI, 23.9%-61.4%) for 15-90 days after booster dose. No protective outcome was detected beyond 90 days after the booster dose. Conclusions and Relevance: This cohort study revealed key transmission characteristics of SARS-CoV-2 as they evolved, as well as vaccine effectiveness against variants. These findings suggest the importance of continuously evaluating vaccine effectiveness against emerging SARS-CoV-2 variants.

An Overview of Antivirals against Monkeypox Virus and Other Orthopoxviruses.

The current monkeypox outbreaks during the COVID-19 pandemic have reignited interest in orthopoxvirus antivirals. Monkeypox belongs to the Orthopoxvirus genus of the Poxviridae family, which also includes the variola virus, vaccinia virus, and cowpox virus. Two orally bioavailable drugs, tecovirimat and brincidofovir, have been approved for treating smallpox infections. Given their human safety profiles and in vivo antiviral efficacy in animal models, both drugs have also been recommended to treat monkeypox infection. To facilitate the development of additional orthopoxvirus antivirals, we summarize the antiviral activity, mechanism of action, and mechanism of resistance of orthopoxvirus antivirals. This perspective covers both direct-acting and host-targeting antivirals with an emphasis on drug candidates showing in vivo antiviral efficacy in animal models. We hope to speed the orthopoxvirus antiviral drug discovery by providing medicinal chemists with insights into prioritizing proper drug targets and hits for further development.