Dicoumarol is an effective post-exposure prophylactic for SARS-CoV-2 Omicron infection in human airway epithelium.

Repurposing existing drugs to inhibit SARS-CoV-2 infection in airway epithelial cells (AECs) is a quick way to find novel treatments for COVID-19. Computational screening has found dicoumarol (DCM), a natural anticoagulant, to be a potential SARS-CoV-2 inhibitor, but its inhibitory effects and possible working mechanisms remain unknown. Using air-liquid interface culture of primary human AECs, we demonstrated that DCM has potent antiviral activity against the infection of multiple Omicron variants (including BA.1, BQ.1 and XBB.1). Time-of-addition and drug withdrawal assays revealed that early treatment (continuously incubated after viral absorption) of DCM could markedly inhibit Omicron replication in AECs, but DCM did not affect the absorption, exocytosis and spread of viruses or directly eliminate viruses. Mechanistically, we performed single-cell sequencing analysis (a database of 77,969 cells from different airway locations from 10 healthy volunteers) and immunofluorescence staining, and showed that the expression of NAD(P)H quinone oxidoreductase 1 (NQO1), one of the known DCM targets, was predominantly localised in ciliated AECs. We further found that the NQO1 expression level was positively correlated with both the disease severity of COVID-19 patients and virus copy levels in cultured AECs. In addition, DCM treatment downregulated NQO1 expression and disrupted signalling pathways associated with SARS-CoV-2 disease outcomes (e.g., Endocytosis and COVID-19 signalling pathways) in cultured AECs. Collectively, we demonstrated that DCM is an effective post-exposure prophylactic for SARS-CoV-2 infection in the human AECs, and these findings could help physicians formulate novel treatment strategies for COVID-19.

Identification of a Necroptosis-Related Prognostic Index and Associated Regulatory Axis in Kidney Renal Clear Cell Carcinoma.

Background: Kidney renal clear cell carcinoma (KIRC) is one of the most common aggressive malignancies in the genitourinary system with the high degree of immune infiltration. However, the role of necroptosis-related genes in the immune infiltration of KIRC and the impact on overall survival have not been adequately studied. Methods: Differentially expressed necroptosis-related genes were identified based on The Cancer Genome Atlas (TCGA). Then, we constructed a necroptosis-related prognostic index (NRPI) through Lasso Cox regression analysis. The KIRC patients were divided into NRPI-high and NRPI-low groups by the median. Univariate and multivariate Cox regression analyses were used to determine NRPI as an independent prognostic factor. The role of NRPI was assessed through nomogram, GO/KEGG enrichment analyses, and immune cells infiltration. The efficacy of immunotherapy in KIRC patients was evaluated by TIDE. The immunohistochemistry was performed to verify the difference in protein expression between tumor samples and normal tissues from our hospital. Results: We found that NRPI-high patients had higher mortality. The multivariate Cox regression between the signature and multiple clinicopathological characteristics proved that NRPI could effectively and independently predict the prognosis of KIRC. The protein expression of three necroptosis-related genes constituting NRPI was significantly different between tumor and normal tissues. NRPI was closely related to immunologically relevant pathways and functions. The tumor microenvironment and immune infiltrating cells showed clear distinctions in NRPI-high and NRPI-low patients. The analysis of clinical treatments found that NRPI-low patients responded better to immunotherapy, while NRPI-high patients were more sensitive to targeted therapy. Furthermore, we identified a lncRNAs/miRNA/mRNA regulatory axis for KIRC. Conclusion: In general, NRPI was a promising biomarker in predicting the prognosis and responses to treatments in KIRC.

Characterization of a Pyroptosis-Related Signature for Prognosis Prediction and Immune Microenvironment Infiltration in Prostate Cancer.

This study was aimed at constructing a pyroptosis-related signature for prostate cancer (PCa) and elucidating the prognosis and immune landscape and the sensitivity of immune checkpoint blockade (ICB) therapy in signature-define subgroups of PCa. We identified 22 differentially expressed pyroptosis-related genes in PCa from The Cancer Genome Atlas (TCGA) database. The pyroptosis-related genes could divide PCa patients into two clusters with differences in survival. Seven genes were determined to construct a signature that was confirmed by qRT-PCR to be closely associated with the biological characteristics of malignant PCa. The signature could effectively and independently predict the biochemical recurrence (BCR) of PCa, which was validated in the GSE116918 and GSE21034. We found that patients in the high-risk group were more prone to BCR and closely associated with high-grade and advanced-stage disease progression. Outperforming clinical characteristics and nine published articles, our signature demonstrated excellent predictive performance. The patients in the low-risk group were strongly related to the high infiltration of various immune cells including CD8+ T cells and plasma B cells. Furthermore, the high-risk group with higher TMB levels and expression of immune checkpoints was more likely to benefit from immune checkpoint therapy such as PD-1 and CTLA-4 inhibitors. The sensitivity to chemotherapy, endocrine, and targeted therapy showed significant differences in the two risk groups. Our signature was a novel therapeutic strategy to distinguish the prognosis and guide treatment strategies.

An Immune-Related lncRNA Signature to Predict the Biochemical Recurrence and Immune Landscape in Prostate Cancer.

PURPOSE: This study aims to construct an immune-related signature to provide comprehensive insights into the immune landscape of prostate cancer, which can predict biochemical recurrence (BCR) and clinical treatment. METHODS: Based on The Cancer Genome Atlas (TCGA) dataset, a signature constructed by DEirlncRNAs pairs was determined. The receiver operating characteristic curve analysis, Kaplan-Meier analysis, nomogram, and decision curve analysis were used to analyze it. Then, immunophenoscore (IPS), immune cell infiltration, tumor mutation burden (TMB), and immune function were investigated. Finally, we evaluated the role of the signature in medical treatment. RESULTS: A signature constructed by 10 valid DEirlncRNAs pairs was identified in the training set and validated well in the testing and entire set. The signature was a reliable and independent prognostic indicator to predict the BCR of prostate cancer, which was better than the clinicopathological characteristics. After dividing the patients into low- and high-risk groups by median value, we found that the high-risk group had shorter BCR-free time and higher TMB levels. Furthermore, the high-risk group was negatively associated with plasma B cells and CD+8 T cells. IPS and immune functions, such as immune checkpoints and human leukocyte antigen, were significantly different between the two groups. Low-risk group was more sensitive to endocrine therapy and immunotherapy, while high-risk group was more inclined to targeted drugs. Both groups had their own sensitive chemotherapy. CONCLUSION: We established a novel signature to predict BCR and validated its role in the immune landscape of prostate cancer, which could help patients receive personalized medical treatment.

Carbon Nanospheres Exert Antitumor Effects Associated with Downregulation of 4E-BP1 Expression on Prostate Cancer.

INTRODUCTION: Although carbon nanospheres (CNPs) are promising nanomaterials in cancer treatment, how they affect prostate cancer (PCa) remains unclear. METHODS: In this study, scanning electron microscopy (SEM), X-ray diffraction (XRD), and Raman spectroscopy were used to confirm the successful synthesis of CNPs. CCK-8, flow cytometry, Transwell, wound healing, Western blot and immunohistochemistry (IHC) assays were performed to evaluate the antitumor effect of CNPs toward the two kinds of prostate cancer cell lines PC3 and DU145. RESULTS: Our results showed that CNPs inhibited cell growth, invasion, and migration and induced apoptosis and autophagy in PCa cells. Multifactor detection of a single Akt phosphorylation pathway and Western blot results suggested the suppression of 4E-BP1 in PCa cells after incubation with CNPs. The results from animal experiments also suggested the antitumor effect of CNPs and reduced 4E-BP1 expression in PCa tissue samples from BALB/c nude mice administered a local subcutaneous injection of CNPs.

A Novel and Practical Scheme for Resolving the Quality of Samples in Background Modeling.

In view of its important application value, background modeling is studied so widely that many techniques have emerged, which mainly concentrate on the selections of the basic model, the granularity of processing, the components in a framework, etc. However, the quality of samples (QoS) for training has long been ignored. There are two aspects regarding this issue, which are how many samples are suitable and which samples are reliable. To tackle the "how many" problem, in this paper, we propose a convergent method, coined Bi-Variance (BV), to decide an appropriate endpoint in the training sequence. In this way, samples in the range from the first frame to the endpoint can be used for model establishment, rather than using all the samples. With respect to the "which" problem, we construct a pixel histogram for each pixel and subtract one from each bin (called number of intensity values (NoIV-1)), which can efficiently get rid of outliers. Furthermore, our work is plug-and-play in nature, so that it could be applied to diverse sample-based background subtraction methods. In experiments, we integrate our scheme into several state-of-the-art methods, and the results show that the performance of these methods in three indicators, recall, precision, and F-measure, improved from 4.95% to 16.47%, from 5.39% to 26.54%, and from 12.46% to 20.46%, respectively.

A Novel Nonlinear Parameter Estimation Method of Soft Tissues.

The elastic parameters of soft tissues are important for medical diagnosis and virtual surgery simulation. In this study, we propose a novel nonlinear parameter estimation method for soft tissues. Firstly, an in-house data acquisition platform was used to obtain external forces and their corresponding deformation values. To provide highly precise data for estimating nonlinear parameters, the measured forces were corrected using the constructed weighted combination forecasting model based on a support vector machine (WCFM_SVM). Secondly, a tetrahedral finite element parameter estimation model was established to describe the physical characteristics of soft tissues, using the substitution parameters of Young's modulus and Poisson's ratio to avoid solving complicated nonlinear problems. To improve the robustness of our model and avoid poor local minima, the initial parameters solved by a linear finite element model were introduced into the parameter estimation model. Finally, a self-adapting Levenberg-Marquardt (LM) algorithm was presented, which is capable of adaptively adjusting iterative parameters to solve the established parameter estimation model. The maximum absolute error of our WCFM_SVM model was less than 0.03 Newton, resulting in more accurate forces in comparison with other correction models tested. The maximum absolute error between the calculated and measured nodal displacements was less than 1.5 mm, demonstrating that our nonlinear parameters are precise.