Single-cell RNA analysis reveals the potential risk of organ-specific cell types vulnerable to SARS-CoV-2 infections

Uncovering strain- and age-dependent innate immune responses to SARS-CoV-2 infection in air-liquid-interface cultured nasal epithelia

Continuous assessment of the impact of SARS-CoV-2 on the host at the cell-type level is crucial for understanding key mechanisms involved in host defense responses to viral infection. We investigated host response to ancestral-strain and Alpha-variant SARS-CoV-2 infections within air-liquid-interface human nasal epithelial cells from younger adults (26-32 Y) and older children (12-14 Y) using single-cell RNA-sequencing. Ciliated and secretory-ciliated cells formed the majority of highly infected cell-types, with the latter derived from ciliated lineages. Strong innate immune responses were observed across lowly infected and uninfected bystander cells and heightened in Alpha-infection. Alpha highly infected cells showed increased expression of protein-refolding genes compared with ancestral-strain-infected cells in children. Furthermore, oxidative phosphorylation-related genes were down-regulated in bystander cells versus infected and mock-control cells, underscoring the importance of these biological functions for viral replication. Overall, this study highlights the complexity of cell-type-, age- and viral strain-dependent host epithelial responses to SARS-CoV-2.

A comprehensive survey of dimensionality reduction and clustering methods for single-cell and spatial transcriptomics data

In recent years, the application of single-cell transcriptomics and spatial transcriptomics analysis techniques has become increasingly widespread. Whether dealing with single-cell transcriptomic or spatial transcriptomic data, dimensionality reduction and clustering are indispensable. Both single-cell and spatial transcriptomic data are often high-dimensional, making the analysis and visualization of such data challenging. Through dimensionality reduction, it becomes possible to visualize the data in a lower-dimensional space, allowing for the observation of relationships and differences between cell subpopulations. Clustering enables the grouping of similar cells into the same cluster, aiding in the identification of distinct cell subpopulations and revealing cellular diversity, providing guidance for downstream analyses. In this review, we systematically summarized the most widely recognized algorithms employed for the dimensionality reduction and clustering analysis of single-cell transcriptomic and spatial transcriptomic data. This endeavor provides valuable insights and ideas that can contribute to the development of novel tools in this rapidly evolving field.

Serum AXL is a potential molecular marker for predicting COVID-19 progression

Background

The severity, symptoms, and outcome of COVID-19 is thought to be closely linked to how the virus enters host cells. This process involves the key roles of angiotensin-converting enzyme 2 (ACE2) and the Tyrosine protein kinase receptor UFO (AXL) receptors. However, there is limited research on the circulating levels of ACE2 and AXL and their implications in COVID-19.

Methods

A control group of 71 uninfected individuals was also included in the study. According to the Guidance for Corona Virus Disease 2019 (10th edition), a cohort of 358 COVID-19 patients were categorized into non-severe and severe cases. Serum ACE2/AXL levels in COVID-19 patients were detected by enzyme-linked immunosorbent assay (ELISA) at different time points post-COVID-19 infection, including days 0-7, 8-15, 31-179 and >180 days. Serum SARS-CoV-2 IgG/IgM antibodies in COVID-19 patients at the same intervals were assessed by using an iFlash 3000 Chemiluminescence Immunoassay Analyzer. The receiver operating characteristic (ROC) curves were used to assess the diagnostic value of the biological markers, and the association between laboratory parameters and illness progression were explored.

Results

Compared with the uninfected group, the levels of ACE2 and AXL in the COVID-19 group were decreased, and the SARS-COV-2 IgG level was increased. AXL (AUC = 0.774) demonstrated a stronger predictive ability for COVID-19 than ACE2. In the first week after infection, only the level of AXL was statistically different between severe group and non-severe group. After first week, the levels of ACE2 and AXL were different in two groups. Moreover, in severe COVID-19 cases, the serum ACE2, AXL, and SARS-COV-2 IgM levels reached a peak during days 8-15 before declining, whereas serum SARS-COV-2 IgG levels continued to rise, reaching a peak at day 31-180 days before decreasing. In addition, the AXL level continued to decrease and the SARS-COV-2 IgG level continued to increase in the infected group after 180 days compared to the uninfected group.

Conclusions

The levels of serum ACE2 and AXL correlate with COVID-19 severity. However, AXL can also provide early warning of clinical deterioration in the first week after infection. AXL appears to be a superior potential molecular marker for predicting COVID-19 progression.

Integrating single-cell RNA sequencing data to genome-wide association analysis data identifies significant cell types in influenza A virus infection and COVID-19

With the global pandemic of COVID-19, the research on influenza virus has entered a new stage, but it is difficult to elucidate the pathogenesis of influenza disease. Genome-wide association studies (GWASs) have greatly shed light on the role of host genetic background in influenza pathogenesis and prognosis, whereas single-cell RNA sequencing (scRNA-seq) has enabled unprecedented resolution of cellular diversity and in vivo following influenza disease. Here, we performed a comprehensive analysis of influenza GWAS and scRNA-seq data to reveal cell types associated with influenza disease and provide clues to understanding pathogenesis. We downloaded two GWAS summary data, two scRNA-seq data on influenza disease. After defining cell types for each scRNA-seq data, we used RolyPoly and LDSC-cts to integrate GWAS and scRNA-seq. Furthermore, we analyzed scRNA-seq data from the peripheral blood mononuclear cells (PBMCs) of a healthy population to validate and compare our results. After processing the scRNA-seq data, we obtained approximately 70 000 cells and identified up to 13 cell types. For the European population analysis, we determined an association between neutrophils and influenza disease. For the East Asian population analysis, we identified an association between monocytes and influenza disease. In addition, we also identified monocytes as a significantly related cell type in a dataset of healthy human PBMCs. In this comprehensive analysis, we identified neutrophils and monocytes as influenza disease-associated cell types. More attention and validation should be given in future studies.

CDSKNNXMBD: a novel clustering framework for large-scale single-cell data based on a stable graph structure

BACKGROUND

Accurate and efficient cell grouping is essential for analyzing single-cell transcriptome sequencing (scRNA-seq) data. However, the existing clustering techniques often struggle to provide timely and accurate cell type groupings when dealing with datasets with large-scale or imbalanced cell types. Therefore, there is a need for improved methods that can handle the increasing size of scRNA-seq datasets while maintaining high accuracy and efficiency.

METHODS

We propose CDSKNNXMBD (Community Detection based on a Stable K-Nearest Neighbor Graph Structure), a novel single-cell clustering framework integrating partition clustering algorithm and community detection algorithm, which achieves accurate and fast cell type grouping by finding a stable graph structure.

RESULTS

We evaluated the effectiveness of our approach by analyzing 15 tissues from the human fetal atlas. Compared to existing methods, CDSKNN effectively counteracts the high imbalance in single-cell data, enabling effective clustering. Furthermore, we conducted comparisons across multiple single-cell datasets from different studies and sequencing techniques. CDSKNN is of high applicability and robustness, and capable of balancing the complexities of across diverse types of data. Most importantly, CDSKNN exhibits higher operational efficiency on datasets at the million-cell scale, requiring an average of only 6.33 min for clustering 1.46 million single cells, saving 33.3% to 99% of running time compared to those of existing methods.

CONCLUSIONS

The CDSKNN is a flexible, resilient, and promising clustering tool that is particularly suitable for clustering imbalanced data and demonstrates high efficiency on large-scale scRNA-seq datasets.

CODENET: A deep learning model for COVID-19 detection

Conventional COVID-19 testing methods have some flaws: they are expensive and time-consuming. Chest X-ray (CXR) diagnostic approaches can alleviate these flaws to some extent. However, there is no accurate and practical automatic diagnostic framework with good interpretability. The application of artificial intelligence (AI) technology to medical radiography can help to accurately detect the disease, reduce the burden on healthcare organizations, and provide good interpretability. Therefore, this study proposes a new deep neural network (CNN) based on CXR for COVID-19 diagnosis - CodeNet. This method uses contrastive learning to make full use of latent image data to enhance the model's ability to extract features and generalize across different data domains. On the evaluation dataset, the proposed method achieves an accuracy as high as 94.20%, outperforming several other existing methods used for comparison. Ablation studies validate the efficacy of the proposed method, while interpretability analysis shows that the method can effectively guide clinical professionals. This work demonstrates the superior detection performance of a CNN using contrastive learning techniques on CXR images, paving the way for computer vision and artificial intelligence technologies to leverage massive medical data for disease diagnosis.

MFD-GDrug: multimodal feature fusion-based deep learning for GPCR-drug interaction prediction

The accurate identification of drug-protein interactions (DPIs) is crucial in drug development, especially concerning G protein-coupled receptors (GPCRs), which are vital targets in drug discovery. However, experimental validation of GPCR-drug pairings is costly, prompting the need for accurate predictive methods. To address this, we propose MFD-GDrug, a multimodal deep learning model. Leveraging the ESM pretrained model, we extract protein features and employ a CNN for protein feature representation. For drugs, we integrated multimodal features of drug molecular structures, including three-dimensional features derived from Mol2vec and the topological information of drug graph structures extracted through Graph Convolutional Neural Networks (GCN). By combining structural characterizations and pretrained embeddings, our model effectively captures GPCR-drug interactions. Our tests on leading GPCR-drug interaction datasets show that MFD-GDrug outperforms other methods, demonstrating superior predictive accuracy.

FEOpti-ACVP: identification of novel anti-coronavirus peptide sequences based on feature engineering and optimization

Anti-coronavirus peptides (ACVPs) represent a relatively novel approach of inhibiting the adsorption and fusion of the virus with human cells. Several peptide-based inhibitors showed promise as potential therapeutic drug candidates. However, identifying such peptides in laboratory experiments is both costly and time consuming. Therefore, there is growing interest in using computational methods to predict ACVPs. Here, we describe a model for the prediction of ACVPs that is based on the combination of feature engineering (FE) optimization and deep representation learning. FEOpti-ACVP was pre-trained using two feature extraction frameworks. At the next step, several machine learning approaches were tested in to construct the final algorithm. The final version of FEOpti-ACVP outperformed existing methods used for ACVPs prediction and it has the potential to become a valuable tool in ACVP drug design. A user-friendly webserver of FEOpti-ACVP can be accessed at http://servers.aibiochem.net/soft/FEOpti-ACVP/.

Blood microbial analyses reveal long-term effects of SARS-CoV-2 infection on patients who recovered from COVID-19

OBJECTIVE

Few symptoms persist for a long time after patients recover from COVID-19, called "long COVID". We explored the potential microbial risk factors for COVID-19 for a deeper understanding and assistance in the follow-up treatment of these sequelae.

METHODS

Microbiome re-annotation was performed using whole blood RNA-Seq data collected from recovered COVID-19 patients and healthy controls at multiple time points. Subsequently, a series of downstream analyses were conducted to reveal the microbial characteristics of patients who recovered from SARS-CoV-2 infection.

RESULTS

The blood microbiome at 12 weeks post-infection was most evidently disturbed, including an increasing ratio of Bacillota/Bacteroidota and a higher microbial alpha diversity. In addition, a group of pathogenic microbes at 12 weeks post-infection were identified, including Staphylococcus aureus, Klebsiella pneumoniae, Streptococcus pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa, which were positively associated with host genes involved in immune regulatory and olfactory transduction pathways. Several microbes, such as Streptococcus pneumoniae were associated with infiltrating immune cells, such as M2 macrophages.

CONCLUSION

This study provides insights into the relationship between the blood microbiome and COVID-19 sequelae. Several pathogenic microbes were enriched in recovered COVID-19 patients and thus affected host genes participating in the immune and olfactory transduction pathways, which play critical roles in COVID-19 sequelae.

CompCorona: A web application for comparative transcriptome analyses of coronaviruses reveals SARS-CoV-2-specific host response

Background/aim

Understanding the mechanism of host transcriptomic response to infection by the SARS-CoV-2 virus is crucial, especially for patients suffering from long-term effects of COVID-19, such as long COVID or pericarditis inflammation, potentially linked to side effects of the SARS-CoV-2 spike proteins. We conducted comprehensive transcriptome and enrichment analyses on lung and peripheral blood mononuclear cells (PBMCs) infected with SARS-CoV-2, as well as on SARS-CoV and MERS-CoV, to uncover shared pathways and elucidate their common disease progression and viral replication mechanisms.

Materials and methods

We developed CompCorona, the first interactive online tool for visualizing gene response variance among the family Coronaviridae through 2D and 3D principal component analysis (PCA) and exploring systems biology variance using pathway plots. We also made preprocessed datasets of lungs and PBMCs infected by SARS-CoV-2, SARS-CoV, and MERS-CoV publicly available through CompCorona.

Results

One remarkable finding from the lung and PBMC datasets for infections by SARS-CoV-2, but not infections by other coronaviruses (CoVs), was the significant downregulation of the angiogenin (ANG) and vascular endothelial growth factor A (VEGFA) genes, both directly involved in epithelial and vascular endothelial cell dysfunction. Suppression of the TNF signaling pathway was also observed in cells infected by SARS-CoV-2, along with simultaneous activation of complement and coagulation cascades and pertussis pathways. The ribosome pathway was found to be universally suppressed across all three viruses. The CompCorona online tool enabled the comparative analysis of 9 preprocessed host transcriptome datasets of cells infected by CoVs, revealing the specific host response differences in cases of SARS-CoV-2 infection. This included identifying markers of epithelial dysfunction via interactive 2D and 3D PCA, Venn diagrams, and pathway plots.

Conclusion

Our findings suggest that infection by SARS-CoV-2 might induce pulmonary epithelial dysfunction, a phenomenon not observed in cells infected by other CoVs. The publicly available CompCorona tool, along with the preprocessed datasets of cells infected by various CoVs, constitutes a valuable resource for further research into CoV-associated syndromes.

NRTPredictor: identifying rice root cell state in single-cell RNA-seq via ensemble learning

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) measurements of gene expression show great promise for studying the cellular heterogeneity of rice roots. How precisely annotating cell identity is a major unresolved problem in plant scRNA-seq analysis due to the inherent high dimensionality and sparsity.

RESULTS

To address this challenge, we present NRTPredictor, an ensemble-learning system, to predict rice root cell stage and mine biomarkers through complete model interpretability. The performance of NRTPredictor was evaluated using a test dataset, with 98.01% accuracy and 95.45% recall. With the power of interpretability provided by NRTPredictor, our model recognizes 110 marker genes partially involved in phenylpropanoid biosynthesis. Expression patterns of rice root could be mapped by the above-mentioned candidate genes, showing the superiority of NRTPredictor. Integrated analysis of scRNA and bulk RNA-seq data revealed aberrant expression of Epidermis cell subpopulations in flooding, Pi, and salt stresses.

CONCLUSION

Taken together, our results demonstrate that NRTPredictor is a useful tool for automated prediction of rice root cell stage and provides a valuable resource for deciphering the rice root cellular heterogeneity and the molecular mechanisms of flooding, Pi, and salt stresses. Based on the proposed model, a free webserver has been established, which is available at https://www.cgris.net/nrtp .

Identification of drug-side effect association via correntropy-loss based matrix factorization with neural tangent kernel

Adverse drug reactions include side effects, allergic reactions, and secondary infections. Severe adverse reactions can cause cancer, deformity, or mutation. The monitoring of drug side effects is an important support for post marketing safety supervision of drugs, and an important basis for revising drug instructions. Its purpose is to timely detect and control drug safety risks. Traditional methods are time-consuming. To accelerate the discovery of side effects, we propose a machine learning based method, called correntropy-loss based matrix factorization with neural tangent kernel (CLMF-NTK), to solve the prediction of drug side effects. Our method and other computational methods are tested on three benchmark datasets, and the results show that our method achieves the best predictive performance.

Multi-view local hyperplane nearest neighbor model based on independence criterion for identifying vesicular transport proteins

Vesicular transport proteins participate in various biological processes and play a significant role in the movement of substances within cells. These proteins are associated with numerous human diseases, making their identification particularly important. In this study, we developed a novel strategy for accurately identifying vesicular transport proteins. We developed a novel multi-view classifier called graph-regularized k-local hyperplane distance nearest neighbor model (HSIC-GHKNN), which combines the Hilbert-Schmidt independence criterion (HSIC)-based multi-view learning method with a local hyperplane distance nearest-neighbor classifier. We first extracted protein evolution information using two feature extraction methods, pseudo-position-specific scoring matrix (PsePSSM) and AATP, and addressed dataset imbalance using the Edited Nearest Neighbors (ENN) algorithm. Subsequently, we employed a local hyperplane distance nearest-neighbor classifier for each view identification and added an HSIC term to maintain independence between views. We then assessed the performance of our identification strategy and analyzed the PsePSSM and AATP feature sets to determine the influencing factors of the classification results. The experimental results demonstrate that the accurate and Matthew correlation coefficients of our strategy on the independent test set are 85.8 % and 0.548, respectively. Our approach outperformed existing methods in most evaluation metrics. In addition, the proposed multi-view classification model can easily be applied to similar identification tasks.

Identification of DNA-binding protein based multiple kernel model

DNA-binding proteins (DBPs) play a critical role in the development of drugs for treating genetic diseases and in DNA biology research. It is essential for predicting DNA-binding proteins more accurately and efficiently. In this paper, a Laplacian Local Kernel Alignment-based Restricted Kernel Machine (LapLKA-RKM) is proposed to predict DBPs. In detail, we first extract features from the protein sequence using six methods. Second, the Radial Basis Function (RBF) kernel function is utilized to construct pre-defined kernel metrics. Then, these metrics are combined linearly by weights calculated by LapLKA. Finally, the fused kernel is input to RKM for training and prediction. Independent tests and leave-one-out cross-validation were used to validate the performance of our method on a small dataset and two large datasets. Importantly, we built an online platform to represent our model, which is now freely accessible via http://8.130.69.121:8082/.

Alkali metal-doped borospherenes M@C4B32 (M = K, Na, and Li) as a highly efficient alternative for the drug delivery

CONTEXT

Nanomaterials enjoy a great surface-to-surface area ratio, small size, extremely high stability, satisfactory bio-compatibility, improved permeability, specificity in receptor targeting, and tunable lifetime. This paper investigates alkali metal-doped borospherenes M@C4B32 (in which M denotes K, Na, and Li) as a highly efficient alternative for the delivery of drugs using density functional theory (DFT) calculations. A borospherene with a B₃₆ nanocage doped with four C atoms (i.e., C4B32) recently showed promising performance. Therefore, the present work investigates C4B32 nanoclusters doped with alkali metals for the effective delivery of drugs.

METHODS

This paper primarily seeks to evaluate the interaction between thioguanine (TG) as a cancer drug and pristine M@C4B32 through DFT (PBE/6-31 + G (d)) calculations. The UV-Vis spectroscopy indicated a redshift in the complex electronic spectra to higher wavelengths (i.e., lower energy levels). Hence, K@C4B32 was concluded to be effective in TG delivery.

A Machine Learning Method to Identify Umami Peptide Sequences by Using Multiplicative LSTM Embedded Features

Umami peptides enhance the umami taste of food and have good food processing properties, nutritional value, and numerous potential applications. Wet testing for the identification of umami peptides is a time-consuming and expensive process. Here, we report the iUmami-DRLF that uses a logistic regression (LR) method solely based on the deep learning pre-trained neural network feature extraction method, unified representation (UniRep based on multiplicative LSTM), for feature extraction from the peptide sequences. The findings demonstrate that deep learning representation learning significantly enhanced the capability of models in identifying umami peptides and predictive precision solely based on peptide sequence information. The newly validated taste sequences were also used to test the iUmami-DRLF and other predictors, and the result indicates that the iUmami-DRLF has better robustness and accuracy and remains valid at higher probability thresholds. The iUmami-DRLF method can aid further studies on enhancing the umami flavor of food for satisfying the need for an umami-flavored diet.

High Expression of Heterogeneous Nuclear Ribonucleoprotein A1 Facilitates Hepatocellular Carcinoma Growth

Purpose

Hepatocellular carcinoma (HCC) represents one of the most common tumors in the world. Our study aims to explore new markers and therapeutic targets for HCC. Heterogeneous Nuclear ribonucleoprotein A1 (hnRNPA1) has recently been found to be involved in the progression of several types of cancer, but its role in HCC remains uncovered.

Methods

We performed bioinformatic analysis to preliminarily show the relationship between hnRNPA1 and liver cancer. Then the correlation of the hnRNPA1 gene expression with clinicopathological characteristics of HCC patients was verified by human liver cancer tissue microarrays. The functional role of this gene was evaluated by in vivo and vitro experiments.

Results

Results showed that the expression of hnRNPA1 was upregulated in HCC tissues and was associated with pathological stage of HCC patients. Knockdown of hnRNPA1 gene markedly inhibited tumor growth in vivo, and reversed the effects on proliferation, migration and invasion and promoted apoptosis in vitro. Furthermore, down-regulation of hnRNPA1 gene expression can inhibit the activity of the MEK/ERK pathway.

Conclusion

In our work, we combined bioinformatic analysis with in vivo and in vitro experiments to initially elucidate the function of hnRNPA1 in liver cancer, which may help to explore biomarkers and therapeutic targets for HCC patients.

Insight into the mechanism of DNA methylation and miRNA-mRNA regulatory network in ischemic stroke

BACKGROUND

Epigenetic changes, such as DNA methylation and miRNA-target gene mechanisms, have recently emerged as key provokers in Ischemic stroke (IS) onset. However, cellular and molecular events harboring these epigenetic alterations are poorly understood. Therefore, the present study aimed to explore the potential biomarkers and therapeutic targets for IS.

METHODS

miRNAs, mRNAs and DNA methylation datasets of IS were derived from the GEO database and normalized by PCA sample analysis. Differentially expressed genes (DEGs) were identified, and GO and KEGG enrichment analyses were performed. The overlapped genes were utilized to construct a protein-protein interaction network (PPI). Meanwhile, differentially expressed mRNAs and miRNAs interaction pairs were obtained from the miRDB, TargetScan, miRanda, miRMap and miTarBase databases. We constructed differential miRNA-target gene regulatory networks based on mRNA-miRNA interactions.

RESULTS

A total of 27 up-regulated and 15 down-regulated differential miRNAs were identified. Dataset analysis identified 1053 and 132 up-regulated and 1294 and 9068 down-regulated differentially expressed genes in the GSE16561 and GSE140275 datasets, respectively. Moreover, 9301 hypermethylated and 3356 hypomethylated differentially methylated sites were also identified. Moreover, DEGs were enriched in terms related to translation, peptide biosynthesis, gene expression, autophagy, Th1 and Th2 cell differentiation, primary immunodeficiency, oxidative phosphorylation and T cell receptor signaling pathway. MRPS9, MRPL22, MRPL32 and RPS15 were identified as hub genes. Finally, a differential miRNA-target gene regulatory network was constructed.

CONCLUSIONS

RPS15, along with hsa-miR-363-3p and hsa-miR-320e have been identified in the differential DNA methylation protein interaction network and miRNA-target gene regulatory network, respectively. These findings strongly posit the differentially expressed miRNAs as potential biomarkers to improve ischemic stroke diagnosis and prognosis.

Constructing discriminative feature space for LncRNA-protein interaction based on deep autoencoder and marginal fisher analysis

Long non-coding RNAs (lncRNAs) play important roles by regulating proteins in many biological processes and life activities. To uncover molecular mechanisms of lncRNA, it is very necessary to identify interactions of lncRNA with proteins. Recently, some machine learning methods were proposed to detect lncRNA-protein interactions according to the distribution of known interactions. The performances of these methods were largely dependent upon: (1) how exactly the distribution of known interactions was characterized by feature space; (2) how discriminative the feature space was for distinguishing lncRNA-protein interactions. Because the known interactions may be multiple and complex model, it remains a challenge to construct discriminative feature space for lncRNA-protein interactions. To resolve this problem, a novel method named DFRPI was developed based on deep autoencoder and marginal fisher analysis in this paper. Firstly, some initial features of lncRNA-protein interactions were extracted from the primary sequences and secondary structures of lncRNA and protein. Secondly, a deep autoencoder was exploited to learn encode parameters of the initial features to describe the known interactions precisely. Next, the marginal fisher analysis was employed to optimize the encode parameters of features to characterize a discriminative feature space of the lncRNA-protein interactions. Finally, a random forest-based predictor was trained on the discriminative feature space to detect lncRNA-protein interactions. Verified by a series of experiments, the results showed that our predictor achieved the precision of 0.920, recall of 0.916, accuracy of 0.918, MCC of 0.836, specificity of 0.920, sensitivity of 0.916 and AUC of 0.906 respectively, which outperforms the concerned methods for predicting lncRNA-protein interaction. It may be suggested that the proposed method can generate a reasonable and effective feature space for distinguishing lncRNA-protein interactions accurately. The code and data are available on https://github.com/D0ub1e-D/DFRPI.

Mesenchymal stem cell-released oncolytic virus: an innovative strategy for cancer treatment

Oncolytic viruses (OVs) infect, multiply, and finally remove tumor cells selectively, causing no damage to normal cells in the process. Because of their specific features, such as, the ability to induce immunogenic cell death and to contain curative transgenes in their genomes, OVs have attracted attention as candidates to be utilized in cooperation with immunotherapies for cancer treatment. This treatment takes advantage of most tumor cells' inherent tendency to be infected by certain OVs and both innate and adaptive immune responses are elicited by OV infection and oncolysis. OVs can also modulate tumor microenvironment and boost anti-tumor immune responses. Mesenchymal stem cells (MSC) are gathering interest as promising anti-cancer treatments with the ability to address a wide range of cancers. MSCs exhibit tumor-trophic migration characteristics, allowing them to be used as delivery vehicles for successful, targeted treatment of isolated tumors and metastatic malignancies. Preclinical and clinical research were reviewed in this study to discuss using MSC-released OVs as a novel method for the treatment of cancer. Video Abstract.

High expression of homeobox B2 predicts poor survival of colon adenocarcinoma by enhancing tumor proliferation and invasion

OBJECTIVE

Homeobox B2 (HOXB2) is known to be correlated with the development and prognosis of various cancers. However, its role in colon cancer remains unclear.

AIMS

In this study, we explored the prognostic value of HOXB2 in colon adenocarcinoma (COAD).

METHODS

A total of 264 colon adenocarcinoma cases were retrospectively enrolled to evaluate HOXB2 expression and clinical significance. Chi-square test was applied to identify relationship between clinical features and HOXB2 expression. The effect of HOXB2 expression and clinical features on the survival of COAD patients was evaluated using Kaplan-Meier and Cox regression analyses. Cellular assays and mice models were conducted to validate the tumor-related role of HOXB2 in COAD.

RESULTS

Higher expression of HOXB2 in COAD tissues was significantly associated with tumor size, invasion depth, and lymph node metastasis (all P < 0.05). Univariate and multivariate analyses showed that high expression of HOXB2 was significantly correlated with a poor overall survival. In vitro cellular assays combined with knockdown strategies demonstrated that HOXB2 can promote tumor proliferation and invasion of COAD, which was further confirmed by in vivo xenograft experiments.

CONCLUSIONS

HOXB2 may be a valuable biomarker and potential therapeutic target for the treatment of COAD.

Ultrafast and absolute quantification of SARS-CoV-2 on food using hydrogel RT-LAMP without pre-lysis

With rapid growing of environmental contact infection, more and more attentions are focused on the precise and absolute quantification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus on cold chain foods via point-of-care test (POCT). In this work, we propose a hydrogel-mediated reverse transcription loop-mediated isothermal amplification (RT-LAMP) for ultrafast and absolute quantification of SARS-CoV-2. Cross-linked hydrogel offers opportunities for digital single molecule amplification in nanoconfined spaces, facilitating the virus lysis, RNA reverse transcription and amplification process, which is about 3.4-fold faster than conventional bulk RT-LAMP. Ultrafast quantification of SARS-CoV-2 is accomplished in 15 min without virus pre-lysis and RNA extraction. The sensitivity can accurately quantify SARS-CoV-2 down to 0.5 copy/μL. Furthermore, the integrated system has an excellent specificity, reproducibility and storage stability, which can be also used to test SARS-CoV-2 on various cold chain fruits. The developed ultrafast and simple hydrogel RT-LAMP will be an enormous potential for surveillance of virus or other hazardous microbes in environmental, agricultural and food industry.

A systematic pan-cancer analysis reveals the clinical prognosis and immunotherapy value of C-X3-C motif ligand 1 (CX3CL1)

It is now widely known that C-X3-C motif ligand 1 (CX3CL1) plays an essential part in the process of regulating pro-inflammatory cells migration across a wide range of inflammatory disorders, including a number of malignancies. However, there has been no comprehensive study on the correlation between CX3CL1 and cancers on the basis of clinical features. In order to investigate the potential function of CX3CL1 in the clinical prognosis and immunotherapy, I evaluated the expression of CX3CL1 in numerous cancer types, methylation levels and genetic alterations. I found CX3CL1 was differentially expressed in numerous cancer types, which indicated CX3CL1 may plays a potential role in tumor progression. Furthermore, CX3CL1 was variably expressed in methylation levels and gene alterations in most cancers according to The Cancer Genome Atlas (TCGA). CX3CL1 was robustly associated with clinical characteristics and pathological stages, suggesting that it was related to the degree of tumor malignancy and the physical function of patients. As determined by the Kaplan-Meier method of estimating survival, high CX3CL1 expression was associated with either favorable or unfavorable outcomes depending on the different types of cancer. It suggests the correlation between CX3CL1 and tumor prognosis. Significant positive correlations of CX3CL1 expression with CD4⁺ T cells, M1 macrophage cells and activated mast cells have been established in the majority of TCGA malignancies. Which indicates CX3CL1 plays an important role in tumor immune microenvironment. Gene Ontology (GO) terms and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis suggested that the chemokine signaling pathway may shed light on the pathway for CX3CL1 to exert function. In a conclusion, our study comprehensively summarizes the potential role of CX3CL1 in clinical prognosis and immunotherapy, suggesting that CX3CL1 may represent a promising pharmacological treatment target of tumors.

Pharmacological basis of bergapten in gastrointestinal diseases focusing on H+/K+ ATPase and voltage-gated calcium channel inhibition: A toxicological evaluation on vital organs

Aim and objectives: This study aimed to establish a pharmacological basis for evaluating the effects of bergapten (5-methoxypsoralen) in gastrointestinal diseases and assessment of its toxicological profile.

Methods: The pharmacokinetic profile was evaluated using the SwissADME tool. AUTODOCK and PyRx were used for evaluating the binding affinities. The obtained results were further investigated for a post-dock analysis using Discovery Studio Visualizer 2016. The Desmond software package was used to conduct molecular dynamic simulations of best bound poses. Bergapten was further investigated for antidiarrheal, anti-secretory, charcoal meal transit time, anti-ulcer, anti-H. pylori activity.

Results: Bergapten at a dose of 50, 100, and 200 mg/kg was proved effective in reducing diarrheal secretions, intestinal secretions, and distance moved by charcoal meal. Bergapten at the aforementioned doses acts as a gastroprotective agent in the ethanol-induced ulcer model that can be attributed to its effectiveness against H. pylori. Bergapten shows concentration-dependent relaxation of both spontaneous and K+ (80 mM)-induced contractions in the isolated rabbit jejunum model; the Ca2+ concentration–response curves (CRCs) were shifted to the right showing potentiating effect similar to papaverine. For molecular investigation, the H+/K+ ATPase inhibitory assay indicated inhibition of the pump comparable to omeprazole. Oxidative stress markers GST, GSH, and catalase showed increased expression, whereas the expression of LPO (lipid peroxidation) was reduced. Histopathological examination indicated marked improvement in cellular morphology. ELISA and western blot confirmed the reduction in inflammatory mediator expression. RT-PCR reduced the mRNA expression level of H+/K+ ATPase, confirming inhibition of the pump. The toxicological profile of bergapten was evaluated by an acute toxicity assay and evaluated for behavioral analysis, and the vital organs were used to analyze biochemical, hematological, and histopathological examination.

Conclusion: Bergapten at the tested doses proved to be an antioxidant, anti-inflammatory, anti-ulcer, and antidiarrheal agent and relatively safe in acute toxicity assay.

Multifaceted role of polyphenols in the treatment and management of neurodegenerative diseases

Neurodegenerative diseases (NDDs) are conditions that cause neuron structure and/or function to deteriorate over time. Genetic alterations may be responsible for several NDDs. However, a multitude of physiological systems can trigger neurodegeneration. Several NDDs, such as Huntington's, Parkinson's, and Alzheimer's, are assigned to oxidative stress (OS). Low concentrations of reactive oxygen and nitrogen species are crucial for maintaining normal brain activities, as their increasing concentrations can promote neural apoptosis. OS-mediated neurodegeneration has been linked to several factors, including notable dysfunction of mitochondria, excitotoxicity, and Ca²⁺ stress. However, synthetic drugs are commonly utilized to treat most NDDs, and these treatments have been known to have side effects during treatment. According to providing empirical evidence, studies have discovered many occurring natural components in plants used to treat NDDs. Polyphenols are often safer and have lesser side effects. As, epigallocatechin-3-gallate, resveratrol, curcumin, quercetin, celastrol, berberine, genistein, and luteolin have p-values less than 0.05, so they are typically considered to be statistically significant. These polyphenols could be a choice of interest as therapeutics for NDDs. This review highlighted to discusses the putative effectiveness of polyphenols against the most prevalent NDDs.

Anti-arthritic and toxicological evaluation of ethanolic extract of Alternanthera bettzickiana in rats

In many developing countries, medicinal plants have long been used for therapeutic purposes due to their low cost and toxicity. This study evaluated the safety and anti-arthritic potential of Alternanthera bettzickiana ethanolic extract (ABEE). Acute oral toxicity (OECD 425) was tested in the safety evaluation. A limit test was used to identify the LD50 value. For an acute oral toxicity study a dose of 2000 mg/kg of ABEE was given orally to the treatment group, and the control group received distilled water at a rate of 10 ml/kg. Biochemical, hematological, and histopathological analyses were performed after 14 days. A formaldehyde 2% w/v solution was injected via i.p. to rats of all groups to prepare the arthritic model. Five groups were divided into control (D.H2O), standard (Diclofenac), and three groups receiving the plant extract at dose levels of 125 mg/kg, 250 mg/kg, and 500 mg/kg respectively. Treatment was continued for 10 days. Paw diameter and hematological and biochemical variables were quantified. ELISA was performed for the estimation of inflammatory cytokines. In the acute oral toxicity study, no mortality or morbidity were observed, so the LD50 of this plant was greater than 2000 mg/kg. ABEE decreased the paw diameter with the restoration of hematological and biochemical changes. SOD and CAT levels were increased while decreasing the MDA, NO, TNF-α, and IL-6 levels in arthritic rats. It is concluded that the use of A. bettzickiana has low toxicity, and it can be used for the treatment of arthritis.

Antiviral COVID-19 protein and molecular docking: In silico characterization of various antiviral compounds extracted from Arisaema jacquemontii Blume

Arisaema jacquemontii Blume is a highly medicinal and poisonous plant belong to the family Araceae. It is used to treat several deadly diseases, including viral infections. It has antioxidant, anti-cancerous, antimalarial, anti-vermicidal, and antiviral activities. Therefore, five parts of the Arisaema jacquemontii Blume plant, such as leaf, seed, stem, pulp, and rhizome extract, were evaluated for metabolic and in silico characterization of probable compounds using gas chromatography-mass spectrometry (GC-MS) analysis. A total of 22 compounds were isolated from the methanolic extracts of A. jacquemontii Blume. A selected antiviral COVID-19 protein i.e., protease (6LU7) was docked against the obtained compounds. Different affinities were obtained through various compounds. The best results were shown by three different compounds identified in the rhizome. The maximum binding affinity of these compounds is 8.1 kJ/mol. Molecular docking (MD) indicate that these molecules have the highest binding energies and hydrogen bonding interactions. The binding mode of interaction was discovered to be reasonably effective for counteracting the SARS virus COVID-19. The findings of this study could be extremely useful in the development of more phytochemical-based COVID-19 therapeutics.

Forecasting the transmission trends of respiratory infectious diseases with an exposure-risk-based model at the microscopic level

Respiratory infectious diseases (e.g., COVID-19) have brought huge damages to human society, and the accurate prediction of their transmission trends is essential for both the health system and policymakers. Most related studies focus on epidemic trend forecasting at the macroscopic level, which ignores the microscopic social interactions among individuals. Meanwhile, current microscopic models are still not able to sufficiently decipher the individual-based spreading process and lack valid quantitative tests. To tackle these problems, we propose an exposure-risk-based model at the microscopic level, including 4 modules: individual movement, virion-laden droplet movement, individual exposure risk estimation, and prediction of transmission trends. Firstly, the front two modules reproduce the movements of individuals and the droplets of infectors' expiratory activities, respectively. Then, the outputs are fed to the third module to estimate the personal exposure risk. Finally, the number of new cases is predicted in the final module. By predicting the new COVID- 19 cases in the United States, the performances of our model and 4 other existing macroscopic or microscopic models are compared. Specifically, the mean absolute error, root mean square error, and mean absolute percentage error provided by the proposed model are respectively 2454.70, 3170.51, and 3.38% smaller than the minimum results of comparison models. The quantitative results reveal that our model can accurately predict the transmission trends from a microscopic perspective, and it can benefit the further investigation of many microscopic disease transmission factors (e.g., non-walkable areas and facility layouts).

The Renshen Chishao Decoction Could Ameliorate the Acute Lung Injury but Could Not Reduce the Neutrophil Extracellular Traps Formation

The acute lung injury (ALI) causes severe pulmonary diseases, leading to a high mortality rate. The Renshen and Chishao have protective and anti-inflammatory effects against the ALI. To explore the protective effects of the Renshen Chishao (RC) decoction against the ALI, we established the lipopolysaccharide-indued ALI model and randomly divided the mice into seven groups: control group, ALI group, high-dose RC group, middle-dose RC group, low-dose RC group, middle-dose RC group + CXCR2 antagonist group, and ALI + CXCR2 antagonist group. We estimated the lung injury by the hematoxylin and eosin staining, the neutrophil extracellular traps (NETs) formations by the immunofluorescence colocalization and enzyme-linked immunosorbent assay (ELISA), and the CXCR2/CXCL2 pathway by the flow cytometry, ELISA, and real-time polymerase chain reaction. We conducted the high-throughput sequencing and enrichment analyses to explore the potential mechanisms. The results showed that the RC decoction pathologically ameliorated the lipopolysaccharide-induced lung injury and inflammatory response but failed to reduce the circulating and lung tissue NETs formation and the blood neutrophil percent. The high-dose RC decoction increased the plasma CXCL2 level, but the RC decoction had no effects on the neutrophilic CXCR2 levels. Under the inhibition of the CXCR2, the middle-dose RC decoction still decreased the lung injury score but as yet had unobvious influence on the NETs formation. Other potential mechanisms of the RC decoction against the ALI involved the pathways of ribosome and coronavirus disease 2019 (COVID-19); the target genes of inflammatory factors, such as Ccl17, Cxcl17, Cd163, Cxcr5, and Il31ra, and lncRNAs; and the regulations of the respiratory cilia. In conclusion, the RC decoction pathologically ameliorated the lipopolysaccharide-induced lung inflammatory injury via upregulating the CXCL2/CXCR2 pathway but could not reduce the circulating or lung tissue NETs formation.

Nutritional Management Mode of Early Cardiac Rehabilitation in Patients with Stanford Type A Aortic Dissection

Malnutrition and metabolic disorders are common problems faced by patients with Stanford type A aortic dissection after surgery. Some patients have dietary problems such as malnutrition, unbalanced diet, and poor eating habits before surgery. Therefore, the nutritional management of early heart health can improve the nutritional support for perioperative recovery, to improve the pertinence. Therefore, active nutritional support after surgery will help to change malnutrition and metabolism and is of great significance to postoperative recovery and quality of life. This paper is aimed at studying the nutritional management mode of early cardiac rehabilitation in patients with Stanford type A aortic dissection. Based on the analysis of the pathogenesis of aortic dissection and the diagnosis of aortic dissection, two groups of patients were given individualized nutritional management scheme and routine nutritional scheme, respectively, and the nutritional risk differences between the two groups under different schemes were compared. The results showed that there was a statistical difference between the two groups at discharge. The NRS-2002 score of 14 cases in the observation group was less than 3 after nutritional intervention, indicating that there was no nutritional risk at discharge.

Effect Evaluation of Bronchial Artery Embolization for Hemoptysis of Lung Cancer and Changes in Serum Tumor Markers and miR-34 Levels

The clinical efficacy, serum tumor markers, and miR-34 expression levels of bronchial artery embolization (BAE) in patients with lung cancer with hemoptysis are investigated. 92 patients with lung cancer hemoptysis treated in our hospital from January 2019 to December 2021 are randomly selected, and 92 patients are randomly divided into the conservative group and the BAE group according to the number table method, with 46 patients in each group. The efficacy, overall survival (OS) rate, coagulation function, hemoptysis volume, serum tumor markers, and miR-34 expression are compared among all groups at different time points. The experimental results show that the BAE treatment can promote the expression of miR-34 and inhibit the expression of tumor markers, so it can improve the efficacy of patients with lung cancer hemoptysis, improve the symptoms of hemoptysis and coagulation function, and prolong the life cycle of patients.

ConSIG: consistent discovery of molecular signature from OMIC data

The discovery of proper molecular signature from OMIC data is indispensable for determining biological state, physiological condition, disease etiology, and therapeutic response. However, the identified signature is reported to be highly inconsistent, and there is little overlap among the signatures identified from different biological datasets. Such inconsistency raises doubts about the reliability of reported signatures and significantly hampers its biological and clinical applications. Herein, an online tool, ConSIG, was constructed to realize consistent discovery of gene/protein signature from any uploaded transcriptomic/proteomic data. This tool is unique in a) integrating a novel strategy capable of significantly enhancing the consistency of signature discovery, b) determining the optimal signature by collective assessment, and c) confirming the biological relevance by enriching the disease/gene ontology. With the increasingly accumulated concerns about signature consistency and biological relevance, this online tool is expected to be used as an essential complement to other existing tools for OMIC-based signature discovery. ConSIG is freely accessible to all users without login requirement at https://idrblab.org/consig/.

Towards the discovery of potential RdRp inhibitors for the treatment of COVID-19: structure guided virtual screening, computational ADME and molecular dynamics study

Coronavirus disease 2019 (COVID-19) has become a major challenge affecting almost every corner of the world, with more than five million deaths worldwide. Despite several efforts, no drug or vaccine has shown the potential to check the ever-mutating SARS-COV-2. The emergence of novel variants is a major concern increasing the need for the discovery of novel therapeutics for the management of this pandemic. Out of several potential drug targets such as S protein, human ACE2, TMPRSS2 (transmembrane protease serine 2), 3CLpro, RdRp, and PLpro (papain-like protease), RNA-dependent RNA polymerase (RdRP) is a vital enzyme for viral RNA replication in the mammalian host cell and is one of the legitimate targets for the development of therapeutics against this disease. In this study, we have performed structure-based virtual screening to identify potential hit compounds against RdRp using molecular docking of a commercially available small molecule library of structurally diverse and drug-like molecules. Since non-optimal ADME properties create hurdles in the clinical development of drugs, we performed detailed in silico ADMET prediction to facilitate the selection of compounds for further studies. The results from the ADMET study indicated that most of the hit compounds had optimal properties. Moreover, to explore the conformational dynamics of protein-ligand interaction, we have performed an atomistic molecular dynamics simulation which indicated a stable interaction throughout the simulation period. We believe that the current findings may assist in the discovery of drug candidates against SARS-CoV-2.

Omadacycline Efficacy against Streptococcus Agalactiae Isolated in China: Correlation between Resistance and Virulence Gene and Biofilm Formation

This study aimed to evaluate the activity, resistance, clonality of MIC distribution, and the correlation between virulence and resistance genes and biofilm formation of omadacycline (OMC) in clinics for Streptococcus agalactiae isolates from China. 162 isolates were collected retrospectively in China. The S. agalactiae were collected from the body's cervical secretions, wound secretions, ear swabs, secretions, semen, venous blood, cerebrospinal fluid, pee, etc. The MIC of OMC against S. agalactiae was determined by broth microdilution. The inhibition zone diameters of OMC and other common antibiotics were measured using filter paper. D-test was performed to determine the phenotype of cross resistance between erythromycin and clindamycin. In Multilocus sequence typing (MLST), some commonly-detected resistance genes and virulence gene of these S. agalactiae isolates were investigated using polymerase chain reaction (PCR). Biofilms were detected by crystal violet staining. Our data demonstrated the correalation of the biofilm formation and OMA antimicrobial susceptibility of S.agalactiae clinical isolates with the carrier of virulence gene scpB. Conclusively, OMC exhibits the robust antimcirobial activity against clinical S. agalactiae isolates from China compared with DOX or MIN, and the carrier of the virulence gene scpB might correlate with the biofilm formation in OMC-resistant S. agalactiae.

DNA repair and damage pathways in mesothelioma development and therapy

Malignant mesothelioma (MMe) is an aggressive neoplasm that occurs through the transformation of mesothelial cells. Asbestos exposure is the main risk factor for MMe carcinogenesis. Other important etiologies for MMe development include DNA damage, over-activation of survival signaling pathways, and failure of DNA damage response (DDR). In this review article, first, we will describe the most important signaling pathways that contribute to MMe development and their interaction with DDR. Then, the contribution of DDR failure in MMe progression will be discussed. Finally, we will review the latest MMe therapeutic strategies that target the DDR pathway.

Protein Phosphatase PPM1B Inhibits Gastric Cancer Progression and Serves as a Favorable Prognostic Biomarker

BACKGROUND

Protein phosphatase PPM1B, also named as PP2Cβ, is a member of serine/threonine phosphatase family. Dysregulated expression of PPM1B has been reported in several malignancies; nevertheless, its role in gastric cancer remains unknown. Here, we aimed to initially investigate the expression and function of PPM1B in gastric adenocarcinoma.

METHODS

We firstly evaluated the protein expression of PPM1B in our enrolled retrospective cohort (n=161) via immunohistochemistry staining. Univariate and multivariate analyses were conducted to assess its prognostic value. Cellular experiments and xenografts in mice model were also performed to validate the role of PPM1B in gastric adenocarcinoma progression.

RESULTS

The advanced tumor stage was characterized with a lower PPM1B level. Lower PPM1B was associated with poor prognosis in both The Cancer Genome Atlas (TCGA) dataset and our cohort (P<0.05). Furthermore, Cox regression analysis demonstrated that PPM1B was a novel independent prognostic factor for gastric adenocarcinoma patients (hazard ratio=0.35, P=0.001). Finally, cellular and xenografts data confirmed that overexpressing PPM1B can remarkably attenuated gastric adenocarcinoma growth.

CONCLUSION

Low expression of PPM1B may be a potential molecular marker for poor prognosis in gastric adenocarcinoma.

Quantitative Scintigraphy Evaluated the Relationship between 131I Therapy and Salivary Glands Function in DTC Patients: A Retrospective Analysis

Purpose

Quantitative scintigraphy to evaluate salivary gland function changes in patients with differentiated thyroid cancer (DTC) after iodine-131 (¹³¹I) treatment.

Methods

A total of 458 patients with DTC grouped by sex and age were included. Salivary gland scintigraphy was performed to evaluate salivary gland function before and after ¹³¹I treatment. The uptake fraction (UF), uptake index (UI), and excretion fraction (EF) of two pairs of parotid glands and submandibular glands were measured and compared. The Chi-square test was conducted according to function impairment count.

Results

Salivary gland function in different age groups and sexes were quite different, especially for women <55 years old, who had decreased UF, UI, and EF of all four glands without basal injury. The secretion or uptake function of some salivary glands with basic function impairment before ¹³¹I treatment was increased after iodine treatment. Only a small percentage of males showed reduced functional parameters after several treatments. The most significant difference in the count of impairment for the four salivary glands were the first and third examinations, which was more evident in women. The submandibular gland had the most significant reduction in uptake.

Conclusion

Changes in salivary gland function are more common in young females being treated for DTC. Impairment of salivary gland function is correlated with the number of treatments and the cumulative dose of ¹³¹I. Some salivary gland functions impaired before ¹³¹I treatment were enhanced in the early treatment.

Study on the Mechanism and Oxidative Stress of Compound Danshen on the Degradation of Cx43 and Improvement of Myocardial Apoptosis in ICM Rats

In order to investigate the mechanism by which Salvia miltiorrhiza degrades connexin 43 (Cx43) and improves myocardial cell apoptosis and oxidative stress in rats with myocardial ischemia (ICM), male SD rats of pure grade are selected (32 rats), adaptively bred for 1 week, and then bred in SPF medium. They are randomly divided into a normal group, a model group, a low-dose Danshen group, and a high-dose group, with 8 rats in each group. HE staining and immunohistochemistry are performed. Serum samples are detected using kits according to instructions, including nitric oxide (NO) and endothelin-1 (ET-1); malondialdehyde (MDA), ELISA for superoxide dismutase (SOD), and vascular cell adhesion molecules (VCAM-1); western blot for detection of cardiac Cx43 protein expression; immunohistochemistry to detect Cx43 expression; and detection of myocardial ischemia. For ICM rats, the application of high-dose compound Salvia miltiorrhiza can effectively prevent the degradation of Cx43 protein, improve the apoptosis of myocardial cells in rats, and have a certain protective effect on ischemic myocardium in rats.

Exploration on the Effect of Nonselective β-Receptor Blockers (NSBBs) on Hemodynamic Parameters in Complicated Liver Cirrhosis

Esophageal-gastric variceal bleeding occurs in 5–15% of patients with liver cirrhosis annually, and the mortality rate is as high as 20% within 6 weeks of the first bleed. The more compromised the liver function, the higher the mortality. Effective control of bleeding is pivotal for reducing mortality in patients with liver cirrhosis. To explore the effect of nonselective β-receptor blockers (NSBBs) on hemodynamic parameters in liver cirrhosis complicated with esophageal-gastric variceal bleeding and the association with hepatorenal syndrome (HRS), this retrospective study assessed the clinical data of 248 patients with liver cirrhosis and esophageal-gastric variceal hemorrhage admitted to our hospital for research. 112 patients are treated with somatostatin (control group) and 136 with somatostatin+propranolol (study group). The success rate of hemostasis, changes of hemodynamic parameters before and after treatment, and incidence of HRS are compared between the groups. Logistic regression analysis is used to explore the use of propranolol when HRS occurred. NSBBs combined with somatostatin are more effective than somatostatin alone in the treatment of liver cirrhosis complicated with esophageal-gastric variceal bleeding; NSBBs may be associated with the occurrence of HRS.

Association of Hypertension and Breast Cancer: Antihypertensive Drugs as an Effective Adjunctive in Breast Cancer Therapy

Breast cancer (BC) is the most common malignancy affecting women, and its incidence in younger women is rising worldwide. Early-onset of BC is a multi-step process involving various biological aggressive tumors such as triple negative and human epidermal growth factor 2 (HER2)-positive cancers. BC prevention is still arduous across the globe. A series of observational studies have established a conclusive non-genetic clinical link between hypertension (HTN) and the development of invasive BC. Those clinical associations have driven a pharmacological seek to use the anti-hypertension (AHTN) drugs as an effective adjunctive in BC therapy. The use of AHTN, especially beta-blockers and thiazides, has been recognized as a potent anti-tumor drug to mitigate BC progression, reduce the side effects of cancer treatment, and stop the reoccurrence of cancer in the survivors. Considering the dire need to disseminate the research on how AHTN drugs can be opted as the effective adjunctive therapy to cure the BC, the current review aimed to provide an update on novel understandings on association and mechanisms of AHTN-drugs against BC as an additional cancer therapy.

Cytotoxicity evaluation of environmentally friendly synthesis Copper/Zinc bimetallic nanoparticles on MCF-7 cancer cells

Bimetallic nanoparticles offer unique chemical, physical and optical properties that are not available for monometallic nanoparticles. Bimetallic nanoparticles play a major role in various therapeutic, industrial and energy fields. Recently, nanoparticles of Copper/Zinc bimetallic nanoparticles have attracted attention in various fields, especially medicine. In this study, bimetallic CuO/ZnO nanostructures were biosynthesized using plant extracts. The plant-mediated synthesis nanoparticles were characterized by Transmission electron microscopy (TEM), X-ray diffraction analysis (XRD), Field Emission Scanning Electron Microscopy (FESEM) and Energy-Dispersive Spectroscopy (EDAX). The cytotoxicity of plant-mediated synthesis bimetallic nanoparticles and the synergistic effects of these nanoparticles in combination with the anticancer drug doxorubicin on MCF-7 cancer cells were evaluated by MTT assay.

Diffusion Tensor Imaging Observation of Frontal Lobe Multidirectional Transcranial Direct Current Stimulation in Stroke Patients with Memory Impairment

Stroke is a group of diseases caused by the sudden rupture or blockage of blood vessels in the brain that prevent blood from flowing into the brain, resulting in brain tissue damage and dysfunction. Stroke has the characteristics of high morbidity, high disability, and high mortality. To investigate the effect of multidirectional transcranial direct current stimulation (tDCS) of the prefrontal lobe in stroke memory disorder. We evaluated 60 patients with poststroke memory impairment who underwent magnetic resonance diffusion tensor imaging (DTI) during their admission to our hospital between January 2018 and December 2020. The patients were divided into the prefrontal group (n = 15), dorsolateral group (n = 15), prefrontal + dorsolateral group (n = 15), and pseudostimulation group (n = 15). Assessments using the Rivermead Behavioral Memory Test (RBMT), Montreal Cognitive Assessment Scale (MoCA), Lovingston Occupational Therapy Cognitive Scale (LOTCA), and frontal lobe fractional anisotropy (FA) were performed before and after treatment. The RBMT, MoCA, and LOTCA scores in the prefrontal + dorsolateral group were significantly higher than those in the dorsolateral, prefrontal, and sham groups (all P < 0.05). The posttreatment FA value of the frontal lobe was significantly higher in the prefrontal + dorsolateral group than in the dorsolateral, prefrontal, and sham stimulation groups (all P < 0.05). The FA value of the frontal lobe was significantly lower in patients with severe memory impairment than in patients with mild-moderate memory impairment (P < 0.05). The area under the receiver operating characteristic curve was 0.801 (95% CI: 0.678-0.925, P < 0.05), and the optimal cut-off value was 0.34, with a sensitivity and specificity of 81.60% and 72.70%, respectively. Prefrontal lobe + dorsolateral tDCS is beneficial in the treatment of post-stroke memory impairment. The DTI FA value can be useful in determining the degree of memory impairment.