Methyl rosmarinate is an allosteric inhibitor of SARS-CoV-2 3 CL protease as a potential candidate against SARS-cov-2 infection.

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been ongoing for more than three years and urgently needs to be addressed. Traditional Chinese medicine (TCM) prescriptions have played an important role in the clinical treatment of patients with COVID-19 in China. However, it is difficult to uncover the potential molecular mechanisms of the active ingredients in these TCM prescriptions. In this paper, we developed a new approach by integrating the experimental assay, virtual screening, and the experimental verification, exploring the rapid discovery of active ingredients from TCM prescriptions. To achieve this goal, 4 TCM prescriptions in clinical use for different indications were selected to find the antiviral active ingredients in TCMs. The 3-chymotrypsin-like protease (3CLpro), an important target for fighting COVID-19, was utilized to determine the inhibitory activity of the TCM prescriptions and single herb. It was found that 10 single herbs had better inhibitory activity than other herbs by using a fluorescence resonance energy transfer (FRET) - based enzymatic assay of SARS-CoV-2 3CLpro. The ingredients contained in 10 herbs were thus virtually screened and the predicted active ingredients were experimentally validated. Thus, such a research strategy firstly removed many single herbs with no inhibitory activity against SARS-CoV-2 3CLpro at the very beginning by FRET-based assay, making our subsequent virtual screening more effective. Finally, 4 active components were found to have stronger inhibitory effects on SARS-CoV-2 3CLpro, and their inhibitory mechanism was subsequently investigated. Among of them, methyl rosmarinate as an allosteric inhibitor of SARS-CoV-2 3CLpro was confirmed and its ability to inhibit viral replication was demonstrated by the SARS-CoV-2 replicon system. To validate the binding mode via docking, the mutation experiment, circular dichroism (CD), enzymatic inhibition and surface plasmon resonance (SPR) assay were performed, demonstrating that methyl rosmarinate bound to the allosteric site of SARS-CoV-2 3CLpro. In conclusion, this paper provides the new ideas for the rapid discovery of active ingredients in TCM prescriptions based on a specific target, and methyl rosmarinate has the potential to be developed as an antiviral therapeutic candidate against SARS-CoV-2 infection.

Danshensu inhibits SARS-CoV-2 by targeting its main protease as a specific covalent inhibitor and discovery of bifunctional compounds eliciting antiviral and anti-inflammatory activity.

The coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has posed a serious threat to human. Since there are still no effective treatment options against the new emerging variants of SARS-CoV-2, it is necessary to devote a continuous endeavor for more targeted drugs and the preparation for the next pandemic. Salvia miltiorrhiza and its active ingredients possess wide antiviral activities, including against SARS-CoV-2. Danshensu, as one of the most important active ingredients in Salvia miltiorrhiza, has been reported to inhibit the entry of SARS-CoV-2 into ACE2 (angiotensin-converting enzyme 2)-overexpressed HEK-293T cells and Vero-E6 cells. However, there is a paucity of information regarding its detailed target and mechanism against SARS-CoV-2. Here, we present Danshensu as a covalent inhibitor of 3-chymotrypsin-like protease (3CLpro) against SARS-CoV-2 by the time-dependent inhibition assay (TDI) and mass spectrometry analysis. Further molecular docking, site-directed mutagenesis, circular dichroism (CD) and fluorescence spectra revealed that Danshensu covalently binds to C145 of SARS-CoV-2 3CLpro, meanwhile forming the hydrogen bonds with S144, H163 and E166 in the S1 site. Structure-based optimization of Danshensu led to the discovery of the promising compounds with good inhibitory activity and microsomal stability in vitro. Due to Danshensu inhibiting lung inflammation in the mouse model, we found that Danshensu derivatives also showed better anti-inflammatory activity than Danshensu in lipopolysaccharide (LPS)-stimulated RAW264.7 macrophage cells. Thus, our study provides not only the clue of the efficacy of Salvia miltiorrhiza against SARS-CoV-2, but also a detailed mechanistic insight into the covalent mode of action of Danshensu for design of covalent inhibitors against SARS-CoV-2 3CLpro, highlighting its potential as a bifunctional molecule with antivirus and anti-inflammation.

Discovery of the covalent SARS-CoV-2 Mpro inhibitors from antiviral herbs via integrating target-based high-throughput screening and chemoproteomic approaches.

The main proteases (Mpro ) are highly conserved cysteine-rich proteins that can be covalently modified by numerous natural and synthetic compounds. Herein, we constructed an integrative approach to efficiently discover covalent inhibitors of Mpro from complex herbal matrices. This work begins with biological screening of 60 clinically used antiviral herbal medicines, among which Lonicera japonica Flos (LJF) demonstrated the strongest anti-Mpro effect (IC50 = 37.82 µg/mL). Mass spectrometry (MS)-based chemical analysis and chemoproteomic profiling revealed that LJF extract contains at least 50 constituents, of which 22 exhibited the capability to covalently modify Mpro . We subsequently verified the anti-Mpro effects of these covalent binders. Gallic acid and quercetin were found to potently inhibit severe acute respiratory syndrome coronavirus 2 Mpro in dose- and time- dependent manners, with the IC50 values below 10 µM. The inactivation kinetics, binding affinity and binding mode of gallic acid and quercetin were further characterized by fluorescence resonance energy transfer, surface plasmon resonance, and covalent docking simulations. Overall, this study established a practical approach for efficiently discovering the covalent inhibitors of Mpro from herbal medicines by integrating target-based high-throughput screening and MS-based assays, which would greatly facilitate the discovery of key antiviral constituents from medicinal plants.

Predicting prognosis of nasopharyngeal carcinoma based on deep learning: peritumoral region should be valued.

BACKGROUND: The purpose of this study was to explore whether incorporating the peritumoral region to train deep neural networks could improve the performance of the models for predicting the prognosis of NPC. METHODS: A total of 381 NPC patients who were divided into high- and low-risk groups according to progression-free survival were retrospectively included. Deeplab v3 and U-Net were trained to build segmentation models for the automatic segmentation of the tumor and suspicious lymph nodes. Five datasets were constructed by expanding 5, 10, 20, 40, and 60 pixels outward from the edge of the automatically segmented region. Inception-Resnet-V2, ECA-ResNet50t, EfficientNet-B3, and EfficientNet-B0 were trained with the original, segmented, and the five new constructed datasets to establish the classification models. The receiver operating characteristic curve was used to evaluate the performance of each model. RESULTS: The Dice coefficients of Deeplab v3 and U-Net were 0.741(95%CI:0.722-0.760) and 0.737(95%CI:0.720-0.754), respectively. The average areas under the curve (aAUCs) of deep learning models for classification trained with the original and segmented images and with images expanded by 5, 10, 20, 40, and 60 pixels were 0.717 ± 0.043, 0.739 ± 0.016, 0.760 ± 0.010, 0.768 ± 0.018, 0.802 ± 0.013, 0.782 ± 0.039, and 0.753 ± 0.014, respectively. The models trained with the images expanded by 20 pixels obtained the best performance. CONCLUSIONS: The peritumoral region NPC contains information related to prognosis, and the incorporation of this region could improve the performance of deep learning models for prognosis prediction.

Ginkgolic acids inhibit SARS-CoV-2 and its variants by blocking the spike protein/ACE2 interplay.

Targeting the interaction between the spike protein receptor binding domain (S-RBD) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and angiotensin-converting enzyme 2 (ACE2) is a potential therapeutic strategy for treating coronavirus disease 2019 (COVID-19). However, we still lack small-molecule drug candidates for this target due to the missing knowledge in the hot spots for the protein-protein interaction. Here, we used NanoBiT technology to identify three Ginkgolic acids from an in-house traditional Chinese medicine (TCM) library, and they interfere with the S-RBD/ACE2 interplay. Our pseudovirus assay showed that one of the compounds, Ginkgolic acid C17:1 (GA171), significantly inhibits the entry of original SARS-CoV-2 and its variants into the ACE2-overexpressed HEK293T cells. We investigated and proposed the binding sites of GA171 on S-RBD by combining molecular docking and molecular dynamics simulations. Site-directed mutagenesis and surface plasmon resonance revealed that GA171 specifically binds to the pocket near R403 and Y505, critical residues of S-RBD for S-RBD interacting with ACE2. Thus, we provide structural insights into developing new small-molecule inhibitors and vaccines against the proposed S-RBD binding site.

Deep learning for locally advanced nasopharyngeal carcinoma prognostication based on pre- and post-treatment MRI.

PURPOSE: We aimed to predict the prognosis of advanced nasopharyngeal carcinoma (stage Ⅲ-Ⅳa) using Pre- and Post-treatment MR images based on deep learning (DL). METHODS: A total of 206 patients with primary nasopharyngeal carcinoma who were diagnosed and treated at the Renmin Hospital of Wuhan University between June 2012 and January 2018 were retrospectively selected. A rectangular region of interest (ROI), which included the tumor area, surrounding tissues and organs, was delineated on each Pre- and Post-treatment MR image. Two Inception-Resnet-V2 based transfer learning models, named Pre-model and Post-model, were trained with the Pre-treatment images and the Post-treatment images, respectively. In addition, an ensemble learning model based on the Pre-model and Post-models was established. The three established models were evaluated by receiver operating characteristic curve (ROC), confusion matrix, and Harrell's concordance indices (C-index). High-risk-related gradient-weighted class activation mapping (Grad-CAM) images were developed according to the DL models. RESULTS: The Pre-model, Post-model, and ensemble model displayed a C-index of 0.717 (95% CI: 0.639 to 0.795), 0.811 (95% CI: 0.745-0.877), 0.830 (95% CI: 0.767-0.893), and AUC of 0.741 (95% CI: 0.584-0.900), 0.806 (95% CI: 0.670-0.942), and 0.842 (95% CI: 0.718-0.967) for the test cohort, respectively. In comparison with the models, the performance of Post-model was better than the performance of Pre-model, which indicated the importance of Post-treatment images for prognosis prediction. All three DL models performed better than the TNM staging system (0.723, 95% CI: 0.567-0.879). The captured features presented on Grad-CAM images suggested that the areas around the tumor and lymph nodes were related to the prognosis of the tumor. CONCLUSIONS: The three established DL models based on Pre- and Post-treatment MR images have a better performance than TNM staging. Post-treatment MR images are of great significance for prognosis prediction and could contribute to clinical decision-making.

A Radiomics Approach to Assess High Risk Carotid Plaques: A Non-invasive Imaging Biomarker, Retrospective Study.

Objective: This study aimed to construct a radiomics-based MRI sequence from high-resolution magnetic resonance imaging (HRMRI), combined with clinical high-risk factors for non-invasive differentiation of the plaque of symptomatic patients from asyptomatic patients. Methods: A total of 115 patients were retrospectively recruited. HRMRI was performed, and patients were diagnosed with symptomatic plaques (SPs) and asymptomatic plaques (ASPs). Patients were randomly divided into training and test groups in the ratio of 7:3. T2WI was used for segmentation and extraction of the texture features. Max-Relevance and Min-Redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) were employed for the optimized model. Radscore was applied to construct a diagnostic model considering the T2WI texture features and patient demography to assess the power in differentiating SPs and ASPs. Results: SPs and ASPs were seen in 75 and 40 patients, respectively. Thirty texture features were selected by mRMR, and LASSO identified a radscore of 16 radiomics features as being related to plaque vulnerability. The radscore, consisting of eight texture features, showed a better diagnostic performance than clinical information, both in the training (area under the curve [AUC], 0.923 vs. 0.713) and test groups (AUC, 0.989 vs. 0.735). The combination model of texture and clinical information had the best performance in assessing lesion vulnerability in both the training (AUC, 0.926) and test groups (AUC, 0.898). Conclusion: This study demonstrated that HRMRI texture features provide incremental value for carotid atherosclerotic risk assessment.

Anatomical Partition-Based Deep Learning: An Automatic Nasopharyngeal MRI Recognition Scheme.

BACKGROUND: Training deep learning (DL) models to automatically recognize diseases in nasopharyngeal MRI is a challenging task, and optimizing the performance of DL models is difficult. PURPOSE: To develop a method of training anatomical partition-based DL model which integrates knowledge of clinical anatomical regions in otorhinolaryngology to automatically recognize diseases in nasopharyngeal MRI. STUDY TYPE: Single-center retrospective study. POPULATION: A total of 2485 patients with nasopharyngeal diseases (age range 14-82 years, female, 779[31.3%]) and 600 people with normal nasopharynx (age range 18-78 years, female, 281[46.8%]) were included. SEQUENCE: 3.0 T; T2WI fast spin-echo sequence. ASSESSMENT: Full images (512 × 512) of 3085 patients constituted 100% of the dataset, 50% and 25% of which were randomly retained as two new datasets. Two new series of images (seg112 image [112 × 112] and seg224 image [224 × 224]) were automatically generated by a segmentation model. Four pretrained neural networks for nasopharyngeal diseases classification were trained under the nine datasets (full image, seg112 image, and seg224 image, each with 100% dataset, 50% dataset, and 25% dataset). STATISTICAL TESTS: The receiver operating characteristic curve was used to evaluate the performance of the models. Analysis of variance was used to compare the performance of the models built with different datasets. Statistical significance was set at P < 0.05. RESULTS: When the 100% dataset was used for training, the performances of the models trained with the seg112 images (average area under the curve [aAUC] 0.949 ± 0.052), seg224 images (aAUC 0.948 ± 0.053), and full images (aAUC 0.935 ± 0.053) were similar (P = 0.611). When the 25% dataset was used for training, the mean aAUC of the models that were trained with seg112 images (0.823 ± 0.116) and seg224 images (0.765 ± 0.155) was significantly higher than the models that were trained with full images (0.640 ± 0.154). DATA CONCLUSION: The proposed method can potentially improve the performance of the DL model for automatic recognition of diseases in nasopharyngeal MRI. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 1.

Changes in Resting-State Spontaneous Brain Activity in Patients With Allergic Rhinitis: A Pilot Neuroimaging Study.

BACKGROUND: Allergic rhinitis (AR) is an inflammatory disorder of the nose caused by immunoglobulin E (IgE)-mediated immune response to allergens. Apart from the typical symptoms of sneezing, itching, rhinorrhea, and nasal congestion, behavioral complications were also reported to be associated with the progression of AR, such as cognitive deficits, mood changes, memory decline, attention deficiency, poor school performance, anxiety, and depression. Recent human studies have suggested that alterations in brain function caused by allergen exposure may precipitate high levels of anxiety and emotional reactivity in asthma patients. But until now, there is no direct evidence of the relationship between brain activity and allergic rhinitis. METHODS: Resting-state functional magnetic resonance imaging (rs-fMRI) was used to excavate whether there remain functional changes of brain activity in AR patients. We measured the amplitude of low-frequency fluctuation (ALFF) and the z conversion of ALFF (zALFF) in 20 patients with AR and 20 age- and sex-matched healthy controls (HCs) using the rs-fMRI data. RESULTS: Compared with healthy controls, AR patients exhibited lower ALFF values in the precuneus (PCUN) and higher ALFF values in the anterior cingulate cortex (ACC). The ALFF values of these features were significantly correlated with the visual analog scale (VAS) scores, the Rhinoconjunctivitis Quality of Life Questionnaire (RQLQ) scores, the subscales of RQLQ, and specific IgE, partly. CONCLUSION: We found changes in resting-state spontaneous brain activity in AR patients with hypoactivity in the PCUN and hyperactivity of the ACC. The brain-related symptoms of AR might be another potential clinical intervention target for improving the life quality of AR patients. Further attention to brain activity is essential for a deeper understanding of AR.

Experimental Study on Damage Identification of Nano-SiO2 Concrete Filled GFRP Tube Column Using Piezoceramic Transducers.

This paper proposes a new approach to damage detection of nano-SiO2 concrete-filled glass fiber reinforced polymer (GFRP) tube column using piezoceramic transducers. Stress waves are emitted and received by a pair of piezoceramic transducers embedded in the concrete-filled GFRP tube, and the energy and damage indices at different levels of loading in the tube are obtained by wavelet packet to evaluate the damage degree of GFRP tube nano-SiO2 concrete column. Through the experimental studies, the effects of different nano-SiO2 contents, concrete grades, and superplasticizer on the damage were analyzed to gain load-displacement curves, load-energy index curves, and load-damage index curves. The results show that the wave method can be adopted to monitor the damage of GFRP tube nano-SiO2 concrete column. The specimens with 3% nano-SiO2 content have the smallest energy change rate, indicating that adding 3% nano-SiO2 content into concrete can effectively delay the development of damage. After the addition of superplasticizer, with the increase in the strength grade of concrete, the cracks in the specimen tend to develop slowly, and therefore the specimens have a stronger resistance to damage. The damage of the specimens with the nano-SiO2 content of 1% appeared the latest, while the damage without the nano-SiO2 specimen appeared the fastest. The experimental results show that this method can better monitor the damage of the Nano-SiO2 concrete in the glass fiber reinforced polymer (GFRP) tube.

[Dynamics of unprotected soil organic carbon with the restoration process of Pinus massoniana plantation in red soil erosion area].

By the method of spatiotemporal substitution and taking the bare land and secondary forest as the control, we measured light fraction and particulate organic carbon in the topsoil under the Pinus massoniana woodlands of different ages with similar management histories in a red soil erosion area, to determine their dynamics and evaluate the conversion processes from unprotected to protected organic carbon. The results showed that the content and storage of soil organic carbon increased significantly along with ages in the process of vegetation restoration (P < 0.01). The unprotected soil organic carbon content and distribution proportion to the total soil organic carbon increased significantly (P < 0.05) after 7-11 years' restoration but stabilized after 27 and 30 years of restoration. It suggested that soil organic carbon mostly accumulated in the form of unprotected soil organic carbon during the initial restoration period, and reached a stable level after long-term vegetation restoration. Positive correlations were found between restoration years and the rate constant for C transferring from the unprotected to the protected soil pool (k) in 0-10 cm and 10-20 cm soil layers, which demonstrated that the unprotected soil organic carbon gradually transferred to the protected soil organic carbon in the process of vegetation restoration.

Effects of Cd and Zn on oxygen consumption and ammonia excretion in sipuncula (Phascolosoma esculenta).

Physiological responses (oxygen consumption and ammonia excretion) of the sipuncula (Phascolosoma esculenta) exposed to four concentrations of Cd (0.45, 0.96, 2.04, and 4.46 mg L(-1)) and four concentrations of Zn (1.09, 2.34, 4.96, and 10.91 mg L(-1)) were monitored for 21 days, respectively. Oxygen consumption rates of sipuncula at all concentrations of Cd decreased from day 1 to day 6. At low concentrations of Cd (0.45 and 0.96 mg L(-1)), the oxygen consumption rate was promoted. Time and concentration were significant in affecting oxygen consumption rate, respectively. Oxygen consumption rate decreased significantly with time for Zn-exposed individuals and also decreased significantly with the interaction between the concentration of Cd and time for Cd-exposed individuals. Changes occurred in the ammonia excretion rates and O:N ratios with individual sipuncula experiencing different metal concentrations over time. Although low O:N ratios (<30) were obtained in most of the treatments, no predictable correlation was found between concentrations of metals and values of O:N obtained. The value of using O:N ratio as a stress index is questioned.