Acute lung injury (ALI) is a clinically severe lung illness with high incidence rate and mortality. Especially, coronavirus disease 2019 (COVID-19) poses a serious threat to world wide governmental fitness. It has distributed to almost from corner to corner of the universe, and the situation in the prevention and control of COVID-19 remains grave. Traditional Chinese medicine plays a vital role in the precaution and therapy of sicknesses. At present, there is a lack of drugs for treating these diseases, so it is necessary to develop drugs for treating COVID-19 related ALI. Fagopyrum dibotrys (D. Don) Hara is an annual plant of the Polygonaceae family and one of the long-history used traditional medicine in China. In recent years, its rhizomes (medicinal parts) have attracted the attention of scholars at home and abroad due to their significant anti-inflammatory, antibacterial and anticancer activities. It can work on SARS-COV-2 with numerous components, targets, and pathways, and has a certain effect on coronavirus disease 2019 (COVID-19) related acute lung injury (ALI). However, there are few systematic studies on its aerial parts (including stems and leaves) and its potential therapeutic mechanism has not been studied. The phytochemical constituents of rhizome of F. dibotrys were collected using TCMSP database. And metabolites of F. dibotrys\' s aerial parts were detected by metabonomics. The phytochemical targets of F. dibotrys were predicted by the PharmMapper website tool. COVID-19 and ALI-related genes were retrieved from GeneCards. Cross targets and active phytochemicals of COVID-19 and ALI related genes in F. dibotrys were enriched by gene ontology (GO) and KEGG by metscape bioinformatics tools. The interplay network entre active phytochemicals and anti COVID-19 and ALI targets was established and broke down using Cytoscape software. Discovery Studio (version 2019) was used to perform molecular docking of crux active plant chemicals with anti COVID-19 and ALI targets. We identified 1136 chemicals from the aerial parts of F. dibotrys, among which 47 were active flavonoids and phenolic chemicals. A total of 61 chemicals were searched from the rhizome of F. dibotrys, and 15 of them were active chemicals. So there are 6 commonly key active chemicals at the aerial parts and the rhizome of F. dibotrys, 89 these phytochemicals\'s potential targets, and 211 COVID-19 and ALI related genes. GO enrichment bespoken that F. dibotrys might be involved in influencing gene targets contained numerous biological processes, for instance, negative regulation of megakaryocyte differentiation, regulation of DNA metabolic process, which could be put down to its anti COVID-19 associated ALI effects. KEGG pathway indicated that viral carcinogenesis, spliceosome, salmonella infection, coronavirus disease - COVID-19, legionellosis and human immunodeficiency virus 1 infection pathway are the primary pathways obsessed in the anti COVID-19 associated ALI effects of F. dibotrys. Molecular docking confirmed that the 6 critical active phytochemicals of F. dibotrys, such as luteolin, (+) -epicatechin, quercetin, isorhamnetin, (+) -catechin, and (-) -catechin gallate, can combine with kernel therapeutic targets NEDD8, SRPK1, DCUN1D1, and PARP1. In vitro activity experiments showed that the total antioxidant capacity of the aerial parts and rhizomes of F. dibotrys increased with the increase of concentration in a certain range. In addition, as a whole, the antioxidant capacity of the aerial part of F. dibotrys was stronger than that of the rhizome. Our research afford cues for farther exploration of the anti COVID-19 associated ALI chemical compositions and mechanisms of F. dibotrys and afford scientific foundation for progressing modern anti COVID-19 associated ALI drugs based on phytochemicals in F. dibotrys. We also fully developed the medicinal value of F. dibotrys\' s aerial parts, which can effectively avoid the waste of resources. Meanwhile, our work provides a new strategy for integrating metabonomics, network pharmacology, and molecular docking techniques which was an efficient way for recognizing effective constituents and mechanisms valid to the pharmacologic actions of traditional Chinese medicine.

The current, rapidly diversifying pandemic has accelerated the need for efficient and effective identification of potential drug candidates for COVID-19. Knowledge on host-immune response to SARS-CoV-2 infection, however, remains limited with few drugs approved to date. Viable strategies and tools are rapidly arising to address this, especially with repurposing of existing drugs offering significant promise. Here we introduce a systems biology tool, the PHENotype SIMulator, which -by leveraging available transcriptomic and proteomic databases-allows modeling of SARS-CoV-2 infection in host cells in silico to i) determine with high sensitivity and specificity (both>96%) the viral effects on cellular host-immune response, resulting in specific cellular SARS-CoV-2 signatures and ii) utilize these cell-specific signatures to identify promising repurposable therapeutics. Powered by this tool, coupled with domain expertise, we identify several potential COVID-19 drugs including methylprednisolone and metformin, and further discern key cellular SARS-CoV-2-affected pathways as potential druggable targets in COVID-19 pathogenesis.

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Research evidence suggests that adipocytes in obesity might facilitate SARS-CoV-2 replication, for it was only found in adipose tissue of individuals with overweight or obesity but not lean individuals who died from COVID-19. As lipid metabolism is key to adipocyte function, and viruses are capable of exploiting and manipulating lipid metabolism of host cells for their own benefit of infection, we hypothesize that adipocytes could not only impair host immune defense against viral infection, but also facilitate SARS-CoV-2 entry, replication and assembly as a reservoir to boost the viral infection in obesity. The latter of which could mainly be mediated by SARS-CoV-2 hijacking the abnormal lipid metabolism in the adipocytes. If these were to be confirmed, an approach to combat COVID-19 in people with obesity by taking advantage of the abnormal lipid metabolism in adipocytes might be considered, as well as modifying lipid metabolism of other host cells as a potential adjunctive treatment for COVID-19.

Combination drugs have been used for several diseases for many years since they produce better therapeutic effects. However, it is still a challenge to discover candidates to form a combination drug. This study aimed to investigate whether using a comprehensive in silico approach to identify novel combination drugs from a Chinese herbal formula is an appropriate and creative strategy. We, therefore, used Toujie Quwen Granules for the main protease (Mpro) of SARS-CoV-2 as an example. We first used molecular docking to identify molecular components of the formula which may inhibit Mpro. Baicalein (HQA004) is the most favorable inhibitory ligand. We also identified a ligand from the other component, cubebin (CHA008), which may act to support the proposed HQA004 inhibitor. Molecular dynamics simulations were then performed to further elucidate the possible mechanism of inhibition by HQA004 and synergistic bioactivity conferred by CHA008. HQA004 bound strongly at the active site and that CHA008 enhanced the contacts between HQA004 and Mpro. However, CHA008 also dynamically interacted at multiple sites, and continued to enhance the stability of HQA004 despite diffusion to a distant site. We proposed that HQA004 acted as a possible inhibitor, and CHA008 served to enhance its effects via allosteric effects at two sites. Additionally, our novel wavelet analysis showed that as a result of CHA008 binding, the dynamics and structure of Mpro were observed to have more subtle changes, demonstrating that the inter-residue contacts within Mpro were disrupted by the synergistic ligand. This work highlighted the molecular mechanism of synergistic effects between different herbs as a result of allosteric crosstalk between two ligands at a protein target, as well as revealed that using the multi-ligand molecular docking, simulation, free energy calculations and wavelet analysis to discover novel combination drugs from a Chinese herbal remedy is an innovative pathway.

The coronavirus disease-2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has seriously affected public health around the world. In-depth studies on the pathogenic mechanisms of SARS-CoV-2 is urgently necessary for pandemic prevention. However, most laboratory studies on SARS-CoV-2 have to be carried out in bio-safety level 3 (BSL-3) laboratories, greatly restricting the progress of relevant experiments. In this study, we used a bacterial artificial chromosome (BAC) method to assemble a SARS-CoV-2 replication and transcription system in Vero E6 cells without virion envelope formation, thus avoiding the risk of coronavirus exposure. Furthermore, an improved real-time quantitative reverse transcription PCR (RT-qPCR) approach was used to distinguish the replication of full-length replicon RNAs and transcription of subgenomic RNAs (sgRNAs). Using the SARS-CoV-2 replicon, we demonstrated that the nucleocapsid (N) protein of SARS-CoV-2 facilitates the transcription of sgRNAs in the discontinuous synthesis process. Moreover, two high-frequency mutants of N protein, R203K and S194L, can obviously enhance the transcription level of the replicon, hinting that these mutations likely allow SARS-CoV-2 to spread and reproduce more quickly. In addition, remdesivir and chloroquine, two well-known drugs demonstrated to be effective against coronavirus in previous studies, also inhibited the transcription of our replicon, indicating the potential applications of this system in antiviral drug discovery. Overall, we developed a bio-safe and valuable replicon system of SARS-CoV-2 that is useful to study the mechanisms of viral RNA synthesis and has potential in novel antiviral drug screening.

UNASSIGNED: When diagnosing Coronavirus disease 2019(COVID-19), radiologists cannot make an accurate judgments because the image characteristics of COVID-19 and other pneumonia are similar. As machine learning advances, artificial intelligence(AI) models show promise in diagnosing COVID-19 and other pneumonias. We performed a systematic review and meta-analysis to assess the diagnostic accuracy and methodological quality of the models.
UNASSIGNED: We searched PubMed, Cochrane Library, Web of Science, and Embase, preprints from medRxiv and bioRxiv to locate studies published before December 2021, with no language restrictions. And a quality assessment (QUADAS-2), Radiomics Quality Score (RQS) tools and CLAIM checklist were used to assess the quality of each study. We used random-effects models to calculate pooled sensitivity and specificity, I2 values to assess heterogeneity, and Deeks\' test to assess publication bias.
UNASSIGNED: We screened 32 studies from the 2001 retrieved articles for inclusion in the meta-analysis. We included 6737 participants in the test or validation group. The meta-analysis revealed that AI models based on chest imaging distinguishes COVID-19 from other pneumonias: pooled area under the curve (AUC) 0.96 (95 % CI, 0.94-0.98), sensitivity 0.92 (95 % CI, 0.88-0.94), pooled specificity 0.91 (95 % CI, 0.87-0.93). The average RQS score of 13 studies using radiomics was 7.8, accounting for 22 % of the total score. The 19 studies using deep learning methods had an average CLAIM score of 20, slightly less than half (48.24 %) the ideal score of 42.00.
UNASSIGNED: The AI model for chest imaging could well diagnose COVID-19 and other pneumonias. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the quality of research methodology and further improve the generalizability of the developed predictive models.

Vaccination is considered as the ultimate weapon to end the pandemic. However, the role of vaccines in the pandemic remains controversial. To explore the impact of vaccination on the COVID-19 pandemic, we used logistic regression models to predict numbers of population-adjusted confirmed cases, deaths, intensive care unit (ICU) cases, case fatality rates and ICU admission rates of COVID-19 in the 50 U.S. states, based on 17 related variables. The logistic regression analysis showed that percentages of people vaccinated correlated inversely with the numbers of COVID-19 deaths and case fatality rates but showed no significant correlation with numbers of confirmed cases or ICU cases, or ICU admission rates. The Spearman correlation analysis showed that the percentages of people vaccinated correlated inversely with the numbers of COVID-19 deaths, ICU cases, ICU case rates, and case fatality rates but showed no significant correlation with numbers of confirmed cases. The number of deaths and mortality in the group after the vaccine usage were significantly lower than those in the group before the vaccine usage. However, after delta became the dominant strain, there were no longer significant differences in the number of deaths and the mortality rate between before and after delta became the dominant strain, although vaccines were used in both periods. Vaccination can significantly reduce COVID-19 deaths and mortality, while it cannot reduce the risk of COVID-19 infection. In addition to vaccination, other measures, such as social distancing, remain important in containing COVID-19 transmission and lower the risk of COVID-19 severe outcomes.

The SARS-CoV-2 is constantly mutating, and the new coronavirus such as Omicron has spread to many countries around the world. Anexelekto (AXL) is a transmembrane protein with biological functions such as promoting cell growth, migration, aggregation, metastasis and adhesion, and plays an important role in cancers and coronavirus disease 2019 (COVID-19). Unlike angiotensin-converting enzyme 2 (ACE2), AXL was highly expressed in respiratory system cells. In this study, we verified the AXL expression in cancer and normal tissues and found AXL expression was strongly correlated with cancer prognosis, tumor mutation burden (TMB), the microsatellite instability (MSI) in most tumor types. Immune infiltration analysis also demonstrated that there was an inextricable link between AXL expression and immune scores in cancer patients, especially in BLCA, BRCA and CESC. The NK-cells, plasmacytoid dendritic cells, myeloid dendritic cells, as one of the important components of the tumor microenvironment, were highly expressed AXL. In addition, AXL-related tumor neoantigens were identified and might provide the novel potential targets for tumor vaccines or SARS-Cov-2 vaccines research in cancer patients.

UNASSIGNED: Few studies have investigated the impacts of metabolic syndrome (MS) on coronavirus disease 2019 (COVID-19). We described the clinical features and prognosis of confirmed COVID-19 patients with MS during hospitalization and after discharge.
UNASSIGNED: Two hundred and thirty-three COVID-19 patients from the hospitals in 8 cities of Jiangsu, China were retrospectively included. Clinical characteristics of COVID-19 patients were described and risk factors of severe illness were analyzed by logistic regression analysis.
UNASSIGNED: Forty-five (19.3%) of 233 COVID-19 patients had MS. The median age of COVID-19 patients with MS was significantly higher than non-MS patients (53.0 years vs. 46.0 years, P = 0.004). There were no significant differences of clinical symptoms, abnormal chest CT images, and treatment drugs between two groups. More patients with MS had severe illness (33.3% vs. 6.4%, P < 0.001) and critical illness (4.4% vs. 0.5%, P = 0.037) than non-MS patients. The proportions of respiratory failure and acute respiratory distress syndrome in MS patients were also higher than non-MS patients during hospitalization. Multivariate analysis showed that concurrent MS (odds ratio [OR] 7.668, 95% confidence interval [CI] 3.062-19.201, P < 0.001) and lymphopenia (OR 3.315, 95% CI 1.306-8.411, P = 0.012) were independent risk factors of severe illness of COVID-19. At a median follow-up of 28 days after discharge, bilateral pneumonia was found in 95.2% of MS patients, while only 54.7% of non-MS patients presented bilateral pneumonia.
UNASSIGNED: 19.3% of COVID-19 patients had MS in our study. COVID-19 patients with MS are more likely to develop severe complications and have worse prognosis. More attention should be paid to COVID-19 patients with MS.
UNASSIGNED: Pocos estudios han investigado el impacto del síndrome metabólico (SM) en la enfermedad por coronavirus 2019 (COVID-19). Describimos las características clínicas y el pronóstico de los pacientes con COVID-19 confirmados con SM durante la hospitalización y después del alta.
UNASSIGNED: Se incluyó de forma retrospectiva a 233 pacientes con COVID-19 de los hospitales de 8 ciudades de Jiangsu (China). Se describieron sus características clínicas y se analizaron los factores de riesgo de enfermedad grave mediante un análisis de regresión logística.
UNASSIGNED: De los 233 pacientes, 45 (19,3%) tenían EM. La mediana de edad de estos pacientes con EM fue significativamente mayor que la de los pacientes sin él (53,0 años frente a 46,0 años; p = 0,004). No hubo diferencias significativas en cuanto a los síntomas clínicos, las imágenes de TC torácica anormales y los fármacos de tratamiento entre los 2 grupos. Hubo más pacientes con EM que tuvieron enfermedades graves (33,3% frente a 6,4%; p < 0,001) y críticas (4,4% frente a 0,5%; p = 0,037) que los pacientes sin EM. Las proporciones de insuficiencia respiratoria y síndrome de dificultad respiratoria aguda en los pacientes con EM también fueron mayores que en los pacientes sin EM durante la hospitalización. El análisis multivariante mostró que la EM concurrente (odds ratio [OR] 7,668; intervalo de confianza [IC] del 95%: 3,062-19,201; p < 0,001) y la linfopenia (OR 3,315; IC del 95%: 1,306-8,411; p = 0,012) eran factores de riesgo independientes de COVID-19 grave. En una mediana de seguimiento de 28 días tras el alta, se encontró neumonía bilateral en el 95,2% de los pacientes con EM, mientras que solo la presentaron el 54,7% de los pacientes sin EM.
UNASSIGNED: El 19,3% de los pacientes con COVID-19 tenían EM en nuestro estudio. Los pacientes con COVID-19 y EM son más propensos a desarrollar complicaciones graves y tienen peor pronóstico. Se debe prestar más atención a los pacientes con COVID-19 y EM.