Identification and quantitative analysis of bioactive components from Potentilla kleiniana Wight et Arn with anti HIV-1 proteases activity.

Potentilla kleiniana Wight et Arn（PK, 'Wu Pi Feng' in Chinese） was recorded as Miao ethnic medicine for treatment of fever, cough, ulcer, and erysipelas for thousands years. This study aimed to evaluate the antiviral activity of four PK extracts and seven compounds by using HIV-1 protease (HIV-1 PR). In addition, Ultra-High Performance Liquid Chromatography and High Resolution Mass Spectrometry (UPLC-HRMS) was employed to identify the bioactive components. The toxicity assessment of the extracts was done before antiviral screening using a highly specific human aspartyl protease, renin protease by fluorimetric method. As a result, seven compounds and four extracts of PK inhibited HIV-1 PR with IC50 range from 0.009 to 0.36 mg/mL, and did not appreciably inhibit the general human protease renin. This study first demonstrated that four PK extracts, ellagic acid and ursolic acid potent inhibit HIV-1 protease, could be used as an efficacious drug candidate to treat SARS-CoV-2 infection.

Identification of Flavonoids from Scutellaria barbata D. Don as Inhibitors of HIV-1 and Cathepsin L Proteases and Their Structure-Activity Relationships.

Scutellaria barbata D. Don (SB, Chinese: Ban Zhi Lian), a well-known medicinal plant used in traditional Chinese medicine, is rich in flavonoids. It possesses antitumor, anti-inflammatory, and antiviral activities. In this study, we evaluated the inhibitory activities of SB extracts and its active components against HIV-1 protease (HIV-1 PR) and SARS-CoV2 viral cathepsin L protease (Cat L PR). UPLC/HRMS was used to identify and quantify the major active flavonoids in different SB extracts, and fluorescence resonance energy transfer (FRET) assays were used to determine HIV-1 PR and Cat L PR inhibitions and identify structure-activity relationships. Molecular docking was also performed, to explore the diversification in bonding patterns of the active flavonoids upon binding to the two PRs. Three SB extracts (SBW, SB30, and SB60) and nine flavonoids inhibited HIV-1 PR with an IC50 range from 0.006 to 0.83 mg/mL. Six of the flavonoids showed 10~37.6% inhibition of Cat L PR at a concentration of 0.1 mg/mL. The results showed that the introduction of the 4'-hydroxyl and 6-hydroxyl/methoxy groups was essential in the 5,6,7-trihydroxyl and 5,7,4'-trihydroxyl flavones, respectively, to enhance their dual anti-PR activities. Hence, the 5,6,7,4'-tetrahydroxyl flavone scutellarein (HIV-1 PR, IC50 = 0.068 mg/mL; Cat L PR, IC50 = 0.43 mg/mL) may serve as a lead compound to develop more effective dual protease inhibitors. The 5,7,3',4'-tetrahydroxyl flavone luteolin also showed a potent and selective inhibition of HIV-1 PR (IC50 = 0.039 mg/mL).

Fibrotic-Like Pulmonary Radiographic Patterns Are Not Associated With Adverse Outcomes in COVID-19 Chronic Critical Illness.

OBJECTIVES: Pulmonary fibrosis is a feared complication of COVID-19. To characterize the risks and outcomes associated with fibrotic-like radiographic abnormalities in patients with COVID-19-related acute respiratory distress syndrome (ARDS) and chronic critical illness. DESIGN: Single-center prospective cohort study. SETTING: We examined chest CT scans performed between ICU discharge and 30 days after hospital discharge using established methods to quantify nonfibrotic and fibrotic-like patterns. PATIENTS: Adults hospitalized with COVID-19-related ARDS and chronic critical illness (> 21 d of mechanical ventilation, tracheostomy, and survival to ICU discharge) between March 2020 and May 2020. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We tested associations of fibrotic-like patterns with clinical characteristics and biomarkers, and with time to mechanical ventilator liberation and 6-month survival, controlling for demographics, comorbidities, and COVID-19 therapies. A total of 141 of 616 adults (23%) with COVID-19-related ARDS developed chronic critical illness, and 64 of 141 (46%) had a chest CT a median (interquartile range) 66 days (42-82 d) after intubation. Fifty-five percent had fibrotic-like patterns characterized by reticulations and/or traction bronchiectasis. In adjusted analyses, interleukin-6 level on the day of intubation was associated with fibrotic-like patterns (odds ratio, 4.40 per quartile change; 95% CI, 1.90-10.1 per quartile change). Other inflammatory biomarkers, Sequential Organ Failure Assessment score, age, tidal volume, driving pressure, and ventilator days were not. Fibrotic-like patterns were not associated with longer time to mechanical ventilator liberation or worse 6-month survival. CONCLUSIONS: Approximately half of adults with COVID-19-associated chronic critical illness have fibrotic-like patterns that are associated with higher interleukin-6 levels at intubation. Fibrotic-like patterns are not associated with longer time to liberation from mechanical ventilation or worse 6-month survival.

Evaluation of the correlation between sleep quality and work engagement among nurses in Shanghai during the post-epidemic era.

AIM: To examine the status quo and influencing factors of sleep quality and work engagement of nurses participating in COVID-19 during the post-epidemic era and to study the relationship between them. DESIGN: We conducted a cross-sectional survey and correlational and predictive logic to determine the association between sleep quality and work engagement among nurses in Shanghai during the post-epidemic era. METHODS: This design involved 1060 frontline nurses in Shanghai. The Pittsburgh Sleep Quality Index questionnaire and the Utrecht Work Engagement Scale-9 scales were used for data collection. RESULTS: This study found that the sleep quality of frontline nurses was impaired and the nurses with poor sleep accounted for 48.20% during the post-epidemic era. The work engagement of frontline nurses was at the medium level. Factors affecting nurses' sleep quality were the number of nurse night shifts, family support and nurse health. The factors affecting the nurse work engagement were monthly income, profession title, family support and self-health status. There was a positive correlation between nurses' sleep quality and work engagement.

Dynamic chest computed tomography change analysis and prediction of length of stay for delta variant COVID-19 patients

OBJECTIVE: As hospital admission rate is high during the COVID-19 pandemic, hospital length of stay (LOS) is a key indicator of medical resource allocation. This study aimed to elucidate specific dynamic longitudinal computed tomography (CT) imaging changes for patients with COVID-19 over in-hospital and predict individual LOS of COVID-19 patients with Delta variant of SARS-CoV-2 using the machine learning method. MATERIALS AND METHODS: This retrospective study recruited 448 COVID-19 patients with a total of 1761 CT scans from July 14, 2021 to August 20, 2021 with an averaged hospital LOS of 22.5 ± 7.0 days. Imaging features were extracted from each CT scan, including CT morphological characteristics and artificial intelligence (AI) extracted features. Clinical features were obtained from each patient's initial admission. The infection distribution in lung fields and progression pattern tendency was analyzed. Then, to construct a model to predict patient LOS, each CT scan was considered as an independent sample to predict the LOS from the current CT scan time point to hospital discharge combining with the patients' corresponding clinical features. The 1761 follow-up CT data were randomly split into training set and testing set with a ratio of 7:3 at patient-level. A total of 85 most related clinical and imaging features selected by Least Absolute Shrinkage and Selection Operator were used to construct LOS prediction model. RESULTS: Infection-related features were obtained, such as the percentage of the infected region of lung, ground-glass opacity (GGO), consolidation and crazy-paving pattern, and air bronchograms. Their longitudinal changes show that the progression changes significantly in the earlier stages (0–3 days to 4–6 days), and then, changes tend to be statistically subtle, except for the intensity range between (−470 and −70) HU which exhibits a significant increase followed by a continuous significant decrease. Furthermore, the bilateral lower lobes, especially the right lower lobe, present more severe. Compared with other models, combining the clinical, imaging reading, and AI features to build the LOS prediction model achieved the highest R2 of 0.854 and 0.463, Pearson correlation coefficient of 0.939 and 0.696, and lowest mean absolute error of 2.405 and 4.426, and mean squared error of 9.176 and 34.728 on the training and testing set. CONCLUSION: The most obvious progression changes were significantly in the earlier stages (0–3 days to 4–6 days) and the bilateral lower lobes, especially the right lower lobe. GGO, consolidation, and crazy-paving pattern and air bronchograms are the most main CT findings according to the longitudinal changes of infection-related features with LOS (day). The LOS prediction model of combining clinical, imaging reading, and AI features achieved optimum performance.

Large-scale screening of COVID-19 from community acquired pneumonia using infection size-aware classification.

The worldwide spread of coronavirus disease (COVID-19) has become a threatening risk for global public health. It is of great importance to rapidly and accurately screen patients with COVID-19 from community acquired pneumonia (CAP). In this study, a total of 1658 patients with COVID-19 and 1027 CAP patients underwent thin-section CT. All images were preprocessed to obtain the segmentations of infections and lung fields. A set of handcrafted location-specific features was proposed to best capture the COVID-19 distribution pattern, in comparison to conventional CT severity score (CT-SS) and Radiomics features. An infection Size Aware Random Forest method (iSARF) was used for classification. Experimental results show that the proposed method yielded best performance when using the handcrafted features with sensitivity of 91.6%, specificity of 86.8%, and accuracy of 89.8% over state-of-the-art classifiers. Additional test on 734 subjects with thick slice images demonstrates great generalizability. It is anticipated that our proposed framework could assist clinical decision making. Furthermore, the data of extracted features will be made available after the review process.

Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification With Chest CT.

Chest computed tomography (CT) becomes an effective tool to assist the diagnosis of coronavirus disease-19 (COVID-19). Due to the outbreak of COVID-19 worldwide, using the computed-aided diagnosis technique for COVID-19 classification based on CT images could largely alleviate the burden of clinicians. In this paper, we propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. Specifically, we first extract location-specific features from CT images. Then, in order to capture the high-level representation of these features with the relatively small-scale data, we leverage a deep forest model to learn high-level representation of the features. Moreover, we propose a feature selection method based on the trained deep forest model to reduce the redundancy of features, where the feature selection could be adaptively incorporated with the COVID-19 classification model. We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP). The accuracy (ACC), sensitivity (SEN), specificity (SPE), AUC, precision and F1-score achieved by our method are 91.79%, 93.05%, 89.95%, 96.35%, 93.10% and 93.07%, respectively. Experimental results on the COVID-19 dataset suggest that the proposed AFS-DF achieves superior performance in COVID-19 vs. CAP classification, compared with 4 widely used machine learning methods.

Structural Attention Graph Neural Network for Diagnosis and Prediction of COVID-19 Severity.

With rapid worldwide spread of Coronavirus Disease 2019 (COVID-19), jointly identifying severe COVID-19 cases from mild ones and predicting the conversion time (from mild to severe) is essential to optimize the workflow and reduce the clinician's workload. In this study, we propose a novel framework for COVID-19 diagnosis, termed as Structural Attention Graph Neural Network (SAGNN), which can combine the multi-source information including features extracted from chest CT, latent lung structural distribution, and non-imaging patient information to conduct diagnosis of COVID-19 severity and predict the conversion time from mild to severe. Specifically, we first construct a graph to incorporate structural information of the lung and adopt graph attention network to iteratively update representations of lung segments. To distinguish different infection degrees of left and right lungs, we further introduce a structural attention mechanism. Finally, we introduce demographic information and develop a multi-task learning framework to jointly perform both tasks of classification and regression. Experiments are conducted on a real dataset with 1687 chest CT scans, which includes 1328 mild cases and 359 severe cases. Experimental results show that our method achieves the best classification (e.g., 86.86% in terms of Area Under Curve) and regression (e.g., 0.58 in terms of Correlation Coefficient) performance, compared with other comparison methods.

Inhibitors of HIV-1 and Cathepsin L Proteases Identified from the Insect Gall of Hypericum kouytchense.

Hypericum kouytchense Lévl is a semi-evergreen plant of the Hypericaceae family. Its roots and seeds have been used in a number of traditional remedies for antipyretic, detoxification, anti-inflammatory, antimicrobial and antiviral functions. However, to date, no bioactivity compounds have been characterized from the insect gall of H. kouytchens. In this study, we evaluated the antiviral activities of different extracts from the insect gall of H. kouytchen against cathepsin L, HIV-1 and renin proteases and identified the active ingredients using UPLC-HRMS. Four different polar extracts (HW, H30, H60 and H85) of the H. kouytchense insect gall exhibited antiviral activities with IC50 values of 10.0, 4.0, 3.2 and 17.0 µg/mL against HIV-1 protease; 210.0, 34.0, 24.0 and 30.0 µg/mL against cathepsin L protease; and 180.0, 65.0, 44.0 and 39.0 µg/mL against human renin, respectively. Ten compounds were identified and quantified in the H. kouytchense insect gall extracts. Epicatechin, eriodictyol and naringenin chalcone were major ingredients in the extracts with contents ranging from 3.9 to 479.2 µg/mg. For HIV-1 protease, seven compounds showed more than 65% inhibition at a concentration of 1000.0 µg/mL, especially for hypericin and naringenin chalcone with IC50 values of 1.8 and 33.0 µg/mL, respectively. However, only hypericin was active against cathepsin L protease with an IC50 value of 17100.0 µg/mL, and its contents were from 0.99 to 11.65 µg/mg. Furthermore, we attempted to pinpoint the interactions between the active compounds and the proteases using molecular docking analysis. Our current results imply that the extracts and active ingredients could be further formulated and/or developed for potential prevention and treatment of HIV or SARS-CoV-2 infections.

Exploring the Association between Negative Emotions and COVID-19 Vaccine Acceptance: A Cross-Sectional Analysis of Unvaccinated Adults in Sweden.

The coronavirus disease 2019 (COVID-19) pandemic has had a significant impact on individuals' mental health. This study aimed to investigate how negative emotions toward the COVID-19 pandemic, including feeling anxious, depressed, upset, and stressed, were associated with COVID-19 vaccine acceptance in Sweden. The study is a cross-sectional online survey conducted between 21-28 May 2021, using three nested hierarchical logistic regression models to assess the association. The study included 965 unvaccinated individuals, 51.2% (n = 494) of whom reported their intention to get vaccinated. We observed graded positive associations between reported negative emotions and vaccine acceptance. Individuals who experienced economic stress had lower odds of vaccine acceptance while having a positive opinion of the government's response to COVID-19 was associated with higher odds of being vaccine-acceptant. In conclusion, unvaccinated individuals experiencing negative emotions about the pandemic were more willing to get the vaccine. On the contrary, those with a negative opinion about the government's response, and those that had experienced economic stress were less likely to accept the immunization.

Dual Inhibition of HIV-1 and Cathepsin L Proteases by Sarcandra glabra.

The COVID-19 pandemic continues to impose a huge threat on human health due to rapid viral mutations. Thus, it is imperative to develop more potent antivirals with both prophylactic and treatment functions. In this study, we screened for potential antiviral compounds from Sarcandra glabra (SG) against Cathepsin L and HIV-1 proteases. A FRET assay was applied to investigate the inhibitory effects and UPLC-HRMS was employed to identify and quantify the bioactive components. Furthermore, molecular docking was carried out to get a glimpse of the binding of active compounds to the proteases. Our results showed that the SG extracts (SGW, SG30, SG60, and SG85) inhibited HIV-1 protease with an IC50 of 0.003~0.07 mg/mL and Cathepsin L protease with an IC50 of 0.11~0.26 mg/mL. Fourteen compounds were identified along with eight quantified from the SG extracts. Chlorogenic acid, which presented in high content in the extracts (12.7~15.76 µg/mg), possessed the most potent inhibitory activity against HIV-1 protease (IC50 = 0.026 mg/mL) and Cathepsin L protease (inhibition: 40.8% at 0.01 mg/mL). Thus, SG extracts and the active ingredients could potentially be used to prevent/treat viral infections, including SARS-CoV-2, due to their dual-inhibition functions against viral proteases.

Pan-Cancer Analysis, Reveals COVID-19-Related BSG as a Novel Marker for Treatment and Identification of Multiple Human Cancers.

Background: Coronavirus disease 2019 (COVID-19) has been a public threat and healthcare concern caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. During the period of the pandemic of COVID-19, cancer patients should be paid more attention as more severe events are found in cancer patients infected with SARS-CoV-2. Basigin (BSG) is an essential factor for the infection and progression of COVID-19 and tumorigenesis of multiple tumors, which may serve as a novel target for the effective treatment against COVID-19 and multiple human cancers. Methods: A total of 19,020 samples from multiple centers were included in our research for the comprehensive investigation of the differences in BSG expression among human organs, cancer cells, cancer tissues, and normal tissues. Cox regression analysis and Kaplan-Meier curves were utilized to explore the prognosis factor of BSG in cancers. Correlation analyses were used to determine associations of BSG expression with tumor mutational burden, the immune microenvironment, etc. Gene set enrichment analysis was applied to explore the underlying mechanisms of BSG in cancers. Results: Compared with normal tissues, BSG expression was high in 13 types of cancers (cholangiocarcinoma, etc.) and low in colon adenocarcinoma and rectum adenocarcinoma. BSG expression was related to the prognosis of eight cancers (e.g., invasive breast carcinoma) (p < 0.05). The gene also demonstrated a pronounced effect in identifying 12 cancers (cholangiocarcinoma, etc.) from their control samples (AUC >0.7). The BSG expression was associated with DNA methyltransferases, mismatch repair genes, immune infiltration levels, tumor mutational burden, microsatellite instability, neoantigen, and immune checkpoints, suggesting the potential of BSG as an exciting target for cancer treatment. BSG may play its role in several cancers by affecting several signaling pathways such as drug cytochrome metabolism P450 and JAK-STAT. Conclusion: BSG may be a novel biomarker for treating and identifying multiple human cancers.

Association between health behaviours and the COVID-19 vaccination: risk compensation among healthcare workers in Taizhou, China.

This study is conducted to explore the association between health behaviors and the COVID-19 vaccination based on the risk compensation concept among health-care workers in Taizhou, China. We conducted a self-administered online survey to estimate the health behaviors among the staff in a tertiary hospital in Taizhou, China, from May 18 to 21 May 2021. A total of 592 out of 660 subjects (89.7%) responded to the questionnaire after receiving an e-poster on WeChat. Subjects who had been inoculated with the COVID-19 vaccine were asked to mention the differences in their health behaviors before and after the vaccination. The results showed that there were no statistical differences in health behaviors between vaccinated and unvaccinated groups, except in terms of the type of gloves they used (62.8% in the vaccinated group and 49.2% in the unvaccinated group, p = .048). Subjects who received earlier COVID-19 vaccinations exhibited better health behaviors (22.40% increased for duration of wearing masks (P = .007), 25.40% increased for times of washing hands (P = .01), and 20.90% increased for times of wearing gloves (P = .01)). Subjects also revealed better health behaviors (washing hands, wearing gloves, and wearing masks) after vaccination compared to that before. In conclusion, concept of risk compensation was not applied in our findings. The health behaviors did not reduce after the COVID-19 vaccination, which even may improve health behaviors among health-care workers in the hospital setting.

Cross-Site Severity Assessment of COVID-19 From CT Images via Domain Adaptation.

Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on computed tomography (CT) images offers a great help to the estimation of intensive care unit event and the clinical decision of treatment planning. To augment the labeled data and improve the generalization ability of the classification model, it is necessary to aggregate data from multiple sites. This task faces several challenges including class imbalance between mild and severe infections, domain distribution discrepancy between sites, and presence of heterogeneous features. In this paper, we propose a novel domain adaptation (DA) method with two components to address these problems. The first component is a stochastic class-balanced boosting sampling strategy that overcomes the imbalanced learning problem and improves the classification performance on poorly-predicted classes. The second component is a representation learning that guarantees three properties: 1) domain-transferability by prototype triplet loss, 2) discriminant by conditional maximum mean discrepancy loss, and 3) completeness by multi-view reconstruction loss. Particularly, we propose a domain translator and align the heterogeneous data to the estimated class prototypes (i.e., class centers) in a hyper-sphere manifold. Experiments on cross-site severity assessment of COVID-19 from CT images show that the proposed method can effectively tackle the imbalanced learning problem and outperform recent DA approaches.

Separation, Purification, Structure Analysis, In Vitro Antioxidant Activity and circRNA-miRNA-mRNA Regulatory Network on PRV-Infected RAW264.7 Cells of a Polysaccharide Derived from Arthrospira platensis.

To investigate the structure of Arthrospira platensis polysaccharide (PAP) (intracellular polysaccharide) and the antioxidant activity of the first component of PAP (PAP-1) on pseudorabies virus (PRV) -infected RAW264.7 cells. The PAP was separated and purified by the Cellulose DE-52 chromatography column and Sephacryl S-200 high-resolution gel column to obtain PAP-1. The antioxidant activity and regulation of PAP-1 on PRV-infected RAW264.7 cells of circRNA-miRNA-mRNA network were investigated by chemical kit, Q-PCR, and ce-RNA seq. The results indicated that the molecular weight (Mw) of PAP-1, which was mainly composed of glucose and eight other monosaccharides, was 1.48 × 106 Da. The main glycosidic bond structure of PAP-1 was →4)-α-D-Glcp-(1→. PAP-1 may be increased the antioxidant capacity by regulating the circRNA-miRNA-mRNA network in PRV-infected RAW264.7 cells. This study provided a scientific foundation for further exploring the antioxidant activity of PAP-1 based on its structure.

Pulmonary fibrosis 4 months after COVID-19 is associated with severity of illness and blood leucocyte telomere length.

The risk factors for development of fibrotic-like radiographic abnormalities after severe COVID-19 are incompletely described and the extent to which CT findings correlate with symptoms and physical function after hospitalisation remains unclear. At 4 months after hospitalisation, fibrotic-like patterns were more common in those who underwent mechanical ventilation (72%) than in those who did not (20%). We demonstrate that severity of initial illness, duration of mechanical ventilation, lactate dehydrogenase on admission and leucocyte telomere length are independent risk factors for fibrotic-like radiographic abnormalities. These fibrotic-like changes correlate with lung function, cough and measures of frailty, but not with dyspnoea.

Long-term health impact of PM2.5 under whole-year COVID-19 lockdown in China.

The health impact of changes in particulate matter with an aerodynamic diameter <2.5 µm (PM2.5) pollution associated with the COVID-19 lockdown has aroused great interest, but the estimation of the long-term health effects is difficult because of the lack of an annual mean air pollutant concentration under a whole-year lockdown scenario. We employed a time series decomposition method to predict the monthly PM2.5 concentrations in urban cities under permanent lockdown in 2020. The premature mortality attributable to long-term exposure to ambient PM2.5 was quantified by the risk factor model from the latest epidemiological studies. Under a whole-year lockdown scenario, annual mean PM2.5 concentrations in cites ranged from 5.4 to 68.0 µg m-3, and the national mean concentration was reduced by 32.2% compared to the 2015-2019 mean. The Global Exposure Mortality Model estimated that 837.3 (95% CI: 699.8-968.4) thousand people in Chinese cities would die prematurely from illnesses attributable to long-term exposure to ambient PM2.5. Compared to 2015-2019 mean levels, 140.2 (95% CI: 122.2-156.0) thousand premature deaths (14.4% of the annual mean deaths from 2015 to 2019) attributable to long-term exposure to PM2.5 were avoided. Because PM2.5 concentrations were still high under the whole-year lockdown scenario, the health benefit is limited, indicating that continuous emission-cutting efforts are required to reduce the health risks of air pollution. Since a similar scenario may be achieved through promotion of electric vehicles and the innovation of industrial technology in the future, the estimated long-term health impact under the whole year lockdown scenario can establish an emission-air quality-health impact linkage and provide guidance for future emission control strategies from a health protection perspective.

Sex Differences in Adverse Reactions to an Inactivated SARS-CoV-2 Vaccine Among Medical Staff in China.

Objective: We investigated whether there were sex differences in adverse reactions to an inactivated SARS-CoV-2 vaccine among medical staff in China. Methods: From 24 February to 7 March 2021 an online cross-sectional survey was conducted with a self-administered COVID-19 vaccine questionnaire among medical staff in Taizhou, China. In total, 1397 interviewees (1,107 women and 290 men) participated in the survey. Results: In our study, 178 (16.1%) women and 23 (7.9%) men reported adverse reactions following their first vaccination, and 169 (15.3%) women and 35 (12.1%) men reported adverse reactions following their second vaccination. After adjusting for confounding factors, adverse reactions to other vaccines, worry about adverse reactions, knowledge of the inactivated vaccine being used in the hospital, taking the vaccine for one's family proactively and receiving an influenza vaccination were significantly related to adverse reactions to both injections in women. In contrast, in men, concerns about adverse reactions independently increased the risk of adverse reactions following either vaccination, and a history of adverse reactions to other vaccines also increased the risk of adverse reactions to both injections. Conclusions: Sex differences in the frequency of reported adverse reactions to an inactivated SARS-CoV-2 vaccine and potential factors were demonstrated in a sample of medical staff.

Clinical significance and molecular mechanism of angiotensin-converting enzyme 2 in hepatocellular carcinoma tissues.

During the pandemic of the coronavirus disease 2019, there exist quite a few studies on angiotensin-converting enzyme 2 (ACE2) and SARS-CoV-2 infection, while little is known about ACE2 in hepatocellular carcinoma (HCC). The detailed mechanism among ACE2 and HCC still remains unclear, which needs to be further investigated. In the current study with a total of 6,926 samples, ACE2 expression was downregulated in HCC compared with non-HCC samples (standardized mean difference = -0.41). With the area under the curve of summary receiver operating characteristic = 0.82, ACE2 expression showed a better ability to differentiate HCC from non-HCC. The mRNA expression of ACE2 was related to the age, alpha-fetoprotein levels and cirrhosis of HCC patients, and it was identified as a protected factor for HCC patients via Kaplan-Meier survival, Cox regression analyses. The potential molecular mechanism of ACE2 may be relevant to catabolic and cell division. In all, decreasing ACE2 expression can be seen in HCC, and its protective role for HCC patients and underlying mechanisms were explored in the study.

Sources of PM2.5 and its responses to emission reduction strategies in the Central Plains Economic Region in China: Implications for the impacts of COVID-19.

The Central Plains Economic Region (CPER) located along the transport path to the Beijing-Tianjin-Hebei area has experienced severe PM2.5 pollution in recent years. However, few modeling studies have been performed on the sources of PM2.5, especially the impacts of emission reduction strategies. In this study, the Nested Air Quality Prediction Model System (NAQPMS) with an online tracer-tagging module was adopted to investigate source sectors of PM2.5 and a series of sensitivity tests were conducted to investigate the impacts of different sector-based mitigation strategies on PM2.5 pollution. The response surfaces of pollutants to sector-based emission changes were built. The results showed that resident-related sector (resident and agriculture), fugitive dust, traffic and industry emissions were the main sources of PM2.5 in Zhengzhou, contributing 49%, 19%, 15% and 13%, respectively. Response surfaces of pollutants to sector-based emission changes in Henan revealed that the combined reduction of resident-related sector and industry emissions efficiently decreased PM2.5 in Zhengzhou. However, reduced emissions in only the Henan region barely satisfied the national air quality standard of 75 µg/m3, whereas a 50%-60% reduction in resident-related sector and industry emissions over the whole region could reach this goal. On severely polluted days, even a 60% reduction in these two sectors over the whole region was insufficient to satisfy the standard of 75 µg/m3. Moreover, a reduction in traffic emissions resulted in an increase in the O3 concentration. The results of the response surface method showed that PM2.5 in Zhengzhou decreased by 19% in response to the COVID-19 lockdown, which approached the observed reduction of 21%, indicating that the response surface method could be employed to study the impacts of the COVID-19 lockdown on air pollution. This study provides a scientific reference for the formulation of pollution mitigation strategies in the CPER.