Marein, a novel natural product for restoring chemo-sensitivity to cancer cells through competitive inhibition of ABCG2 function.

The pivotal roles of ATP-binding cassette (ABC) transporters in drug resistance have been widely appreciated. Here we report that marein, a natural product from Coreopsis tinctoria Nutt, is a potent chemo-sensitizer in drug resistant cancer cells overexpressing ABCG2 transporter. We demonstrate that marein can competitively inhibit efflux activity of ABCG2 protein and increase the intracellular accumulation of the chemotherapeutic drugs that belong to substrate of this transporter. We further show that marein can bind to the conserved amino acid residue F439 of ABCG2, a critical site for drug-substrate interaction. Moreover, marein can significantly sensitize the ABCG2-expressing tumor cells to chemotherapeutic drugs such as topotecan, mitoxantrone, and olaparib. This study reveals a novel role and mechanism of marein in modulating drug resistance, and may have important implications in treatment of cancers that are resistant to chemotherapeutic drugs that belong to the substrates of ABCG2 transporters.

Computational identification and clinical validation of a novel risk signature based on coagulation-related lncRNAs for predicting prognosis, immunotherapy response, and chemosensitivity in colorectal cancer patients.

Background: Coagulation is critically involved in the tumor microenvironment, cancer progression, and prognosis assessment. Nevertheless, the roles of coagulation-related long noncoding RNAs (CRLs) in colorectal cancer (CRC) remain unclear. In this study, an integrated computational framework was constructed to develop a novel coagulation-related lncRNA signature (CRLncSig) to stratify the prognosis of CRC patients, predict response to immunotherapy and chemotherapy in CRC, and explore the potential molecular mechanism. Methods: CRC samples from The Cancer Genome Atlas (TCGA) were used as the training set, while the substantial bulk or single-cell RNA transcriptomics from Gene Expression Omnibus (GEO) datasets and real-time quantitative PCR (RT-qPCR) data from CRC cell lines and paired frozen tissues were used for validation. We performed unsupervised consensus clustering of CRLs to classify patients into distinct molecular subtypes. We then used stepwise regression to establish the CRLncSig risk model, which stratified patients into high- and low-risk groups. Subsequently, diversified bioinformatics algorithms were used to explore prognosis, biological pathway alteration, immune microenvironment, immunotherapy response, and drug sensitivity across patient subgroups. In addition, weighted gene coexpression network analysis was used to construct an lncRNA-miRNA-mRNA competitive endogenous network. Expression levels of CRLncSig, immune checkpoints, and immunosuppressors were determined using RT-qPCR. Results: We identified two coagulation subclusters and constructed a risk score model using CRLncSig in CRC, where the patients in cluster 2 and the low-risk group had a better prognosis. The cluster and CRLncSig were confirmed as the independent risk factors, and a CRLncSig-based nomogram exhibited a robust prognostic performance. Notably, the cluster and CRLncSig were identified as the indicators of immune cell infiltration, immunoreactivity phenotype, and immunotherapy efficiency. In addition, we identified a new endogenous network of competing CRLs with microRNA/mRNA, which will provide a foundation for future mechanistic studies of CRLs in the malignant progression of CRC. Moreover, CRLncSig strongly correlated with drug susceptibility. Conclusion: We developed a reliable CRLncSig to predict the prognosis, immune landscape, immunotherapy response, and drug sensitivity in patients with CRC, which might facilitate optimizing risk stratification, guiding the applications of immunotherapy, and individualized treatments for CRC.

Isotopic Signatures and Outputs of Lead from Coal Fly Ash Disposal in China, India, and the United States.

Despite extensive research and technology to reduce the atmospheric emission of Pb from burning coal for power generation, minimal attention has been paid to Pb associated with coal ash disposal in the environment. This study investigates the isotopic signatures and output rates of Pb in fly ash disposal in China, India, and the United States. Pairwise comparison between feed coal and fly ash samples collected from coal-fired power plants from each country shows that the Pb isotope composition of fly ash largely resembles that of feed coal, and its isotopic distinction allows for tracing the release of Pb from coal fly ash into the environment. Between 2000 and 2020, approx. 236, 56, and 46 Gg Pb from fly ash have been disposed in China, India, and the U.S., respectively, posing a significant environmental burden. A Bayesian Pb isotope mixing model shows that during the past 40 to 70 years, coal fly ash has contributed significantly higher Pb (∼26%) than leaded gasoline (∼7%) to Pb accumulation in the sediments of five freshwater lakes in North Carolina, U.S.A. This implies that the release of disposed coal fly ash Pb at local and regional scales can outweigh that of other anthropogenic Pb sources.

Identification of hub genes and immune infiltration in ulcerative colitis using bioinformatics.

Ulcerative colitis (UC) is a chronic inflammatory disease of the intestine, whose pathogenesis is not fully understood. Given that immune infiltration plays a key role in UC progression, our study aimed to assess the level of immune cells in UC intestinal mucosal tissues and identify potential immune-related genes. The GSE65114 UC dataset was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between healthy and UC tissues were identified using the "limma" package in R, while their Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were determined with the clusterProfiler package. Protein-protein interaction network analysis and visualization were performed with STRING and Cytoscape. Immune cell infiltration was calculated with CIBERSORT. The relationship between hub genes and immune-infiltrated cells in UC was determined by Pearson correlation. A total of 206 DEGs were identified, of which 174 were upregulated and 32 downregulated. GO and KEGG functional classification indicated DEG enrichment in immune response pathways, including Toll-like receptor signaling, IL-17 signaling, and immune system process and chemokine signaling. 13 hub genes were identified. Infiltration matrix analysis of immune cells showed abundant plasma cells, memory B cells, resting CD4 memory T cells, γδ T cells, M0 and M1 macrophages, and neutrophils in UC intestinal tissues. Correlation analysis revealed 13 hub genes associated with immune-infiltrated cells in UC. 13 hub genes associated with immune-infiltrated cells in UC were identified; they included CXCL13, CXCL10, CXCL9, CXCL8, CCL19, CTLA4, CCR1, CD69, CD163, IL7R, PECAM1, TLR8 and TLR2. These genes could potentially serve as markers for the diagnosis and treatment of UC.

Developing an m5C regulator-mediated RNA methylation modification signature to predict prognosis and immunotherapy efficacy in rectal cancer.

Background: Currently, a very small number of patients with colorectal cancer (CRC) respond to immune checkpoint inhibitor (ICI) treatment. Therefore, there is an urgent need to investigate effective biomarkers to determine the responsiveness to ICI treatment. Recently, aberrant 5-methylcytosine (m5C) RNA modification has emerged as a key player in the pathogenesis of cancer. Thus, we aimed to explore the predictive signature based on m5C regulator-related genes for characterizing the immune landscapes and predicting the prognosis and response to therapies. Methods: The Cancer Genome Atlas (TCGA) cohort was used as the training set, while GEO data sets, real-time quantitative PCR (RT-qPCR) analysis from paired frozen tissues, and immunohistochemistry (IHC) data from tissue microarray (TMA) were used for validation. We constructed a novel signature based on three m5C regulator-related genes in patients with rectal adenocarcinoma (READ) using a least absolute shrinkage and selection operator (LASSO)-Cox regression and unsupervised consensus clustering analyses. Additionally, we correlated the three-gene signature risk model with the tumor immune microenvironment, immunotherapy efficiency, and potential applicable drugs. Results: The m5C methylation-based signature was an independent prognostic factor, where low-risk patients showed a stronger immunoreactivity phenotype and a superior response to ICI therapy. Conversely, the high-risk patients had enriched pathways of cancer hallmarks and presented immune-suppressive state, which demonstrated that they are more insensitive to immunotherapy. Additionally, the signature markedly correlated with drug susceptibility. Conclusions: We developed a reliable m5C regulator-based risk model to predict the prognosis, clarify the molecular and tumor microenvironment status, and identify patients who would benefit from immunotherapy or chemotherapy. Our study could provide vital guidance to improve prognostic stratification and optimize personalized therapeutic strategies for patients with rectal cancer.

Efficacy and Safety of CMAB008 Compared with Innovator Infliximab in Patients with Moderate-to-Severe Rheumatoid Arthritis Receiving Concomitant Methotrexate: A Randomized, Double-blind, Multi-center, Phase III Non-inferiority Study.

OBJECTIVES: The aim of this work is to verify the non-inferior efficacy and safety of CMAB008 compared with innovator infliximab in rheumatoid arthritis patients combined with methotrexate. METHODS: We conducted a randomized, double-blinded, parallel, positive control design, multicenter study, with a stable dose of methotrexate. Patients were enrolled randomly with a ratio of 1:1 to receive intravenously CMAB008 3 mg/kg or innovator infliximab 3 mg/kg at weeks 0, 2, 6, 14, 22 and 30. The primary efficacy endpoint was American College of Rheumatology 20% improvement criteria (ACR20) response rate at week 30. The non-inferiority was established if the lower limit of the one-sided 97.5% confidence interval (CI) for the difference was more than - 15% and the equivalence was established if the two-sided 95% CI was within ± 15% in an exploratory equivalence analysis. The secondary endpoints included other efficacy assessment parameters, as well as immunogenicity, safety, and pharmacokinetics. RESULTS: In the full analysis population (FAS), 110 (57.6%) of 191 patients in the CMAB008 group and 120 (62.2%) of 193 patients in the innovator infliximab group reached the primary outcome of ACR20 at week 30. The differences of the rates were - 4.6% and the lower limit of one-sided 97.5% confidence interval was - 14.29%, not less than the lower limit of the non-inferiority margin (- 15%); so CMAB008 was non-inferior to innovator infliximab. Further, CMAB008 was equivalent to innovator infliximab both in FAS (difference - 4.6%, 95% CI - 14.29% to 5.12%) and PPS (difference - 3.3%, 95% CI - 13.18% to 6.62%). The efficacy, safety, immunogenicity, and pharmacokinetics are highly similar between CMAB008 and innovator infliximab. CONCLUSIONS: Non-inferior efficacy of CMAB008 to innovator infliximab is illustrated with similar early and lasting therapeutic effects, and the equivalence is further demonstrated. CMAB008 is well tolerated and has semblable safety compared with the innovator infliximab. TRIAL REGISTRATION NUMBER: NCT03478111.

Characterization of sialylation-related long noncoding RNAs to develop a novel signature for predicting prognosis, immune landscape, and chemotherapy response in colorectal cancer.

Aberrant sialylation plays a key biological role in tumorigenesis and metastasis, including tumor cell survival and invasion, immune evasion, angiogenesis, and resistance to therapy. It has been proposed as a possible cancer biomarker and a potential therapeutic target of tumors. Nevertheless, the prognostic significance and biological features of sialylation-related long noncoding RNAs (lncRNAs) in colorectal cancer (CRC) remain unclear. This study aimed to develop a novel sialylation-related lncRNA signature to accurately evaluate the prognosis of patients with CRC and explore the potential molecular mechanisms of the sialylation-related lncRNAs. Here, we identified sialylation-related lncRNAs using the Pearson correlation analysis on The Cancer Genome Atlas (TCGA) dataset. Univariate and stepwise multivariable Cox analysis were used to establish a signature based on seven sialylation-related lncRNAs in the TCGA dataset, and the risk model was validated in the Gene Expression Omnibus dataset. Kaplan-Meier curve analysis revealed that CRC patients in the low-risk subgroup had a better survival outcome than those in the high-risk subgroup in the training set, testing set, and overall set. Multivariate analysis demonstrated that the sialylation-related lncRNA signature was an independent prognostic factor for overall survival, progression-free survival, and disease-specific survival prediction. The sialylation lncRNA signature-based nomogram exhibited a robust prognostic performance. Furthermore, enrichment analysis showed that cancer hallmarks and oncogenic signaling were enriched in the high-risk group, while inflammatory responses and immune-related pathways were enriched in the low-risk group. The comprehensive analysis suggested that low-risk patients had higher activity of immune response pathways, greater immune cell infiltration, and higher expression of immune stimulators. In addition, we determined the sialylation level in normal colonic cells and CRC cell lines by flow cytometry combined with immunofluorescence, and verified the expression levels of seven lncRNAs using real-time quantitative polymerase chain reaction. Finally, combined drug sensitivity analysis using the Genomics of Drug Sensitivity in Cancer, Cancer Therapeutics Response Portal, and Profiling Relative Inhibition Simultaneously in Mixtures indicated that the sialylation-related lncRNA signature could serve as a potential predictor for chemosensitivity. Collectively, this is the first sialylation lncRNA-based signature for predicting the prognosis, immune landscape, and chemotherapeutic response in CRC, and may provide vital guidance to facilitate risk stratification and optimize individualized therapy for CRC patients.

Comprehensive Analysis of a Cancer-Immunity Cycle-Based Signature for Predicting Prognosis and Immunotherapy Response in Patients With Colorectal Cancer.

Immune checkpoint blockade (ICB) has been recognized as a promising immunotherapy for colorectal cancer (CRC); however, most patients have little or no clinical benefit. This study aimed to develop a novel cancer-immunity cycle-based signature to stratify prognosis of patients with CRC and predict efficacy of immunotherapy. CRC samples from The Cancer Genome Atlas (TCGA) were used as the training set, while the RNA data from Gene Expression Omnibus (GEO) data sets and real-time quantitative PCR (RT-qPCR) data from paired frozen tissues were used for validation. We built a least absolute shrinkage and selection operator (LASSO)-Cox regression model of the cancer-immunity cycle-related gene signature in CRC. Patients who scored low on the risk scale had a better prognosis than those who scored high. Notably, the signature was an independent prognostic factor in multivariate analyses, and to improve prognostic classification and forecast accuracy for individual patients, a scoring nomogram was created. The comprehensive results revealed that the low-risk patients exhibited a higher degree of immune infiltration, a higher immunoreactivity phenotype, stronger expression of immune checkpoint-associated genes, and a superior response to ICB therapy. Furthermore, the risk model was closely related to the response to multiple chemotherapeutic drugs. Overall, we developed a reliable cancer-immunity cycle-based risk model to predict the prognosis, the molecular and immune status, and the immune benefit from ICB therapy, which may contribute greatly to accurate stratification and precise immunotherapy for patients with CRC.

Comparison of DMA-80 and ICP-MS Combined with Closed-Vessel Microwave Digestion for the Determination of Mercury in Coal.

As one of the most widely used techniques for concentration determination of trace elements in coal, inductively coupled plasma mass spectrometry (ICP-MS) has also been used in several studies for the determination of mercury concentration in coal. ICP-MS after closed-vessel microwave digestion and a Milestone DMA-80 are employed in this study to determine the mercury concentration in coal. Three NIST standard references of coal samples were selected as references to verify the accuracy of the test results. The Au rinse solution (200 µg/L, 5% HNO3) can diminish mercury memory effects to a blank level within 80 seconds. The results showed that ICP-MS can accurately determine the mercury content in mercury standard solutions, but the mercury concentration in most NIST samples after microwave digestion is lower than the detection level of the ICP-MS. The inaccuracy may be due to volatilization of mercury during solid sample digestion process. By contrast, the determined concentrations in NIST samples by the Milestone DMA-80 are very close to the verified values. Therefore, ICP-MS is not recommended to analyze mercury in coal after digestion even in a closed-vessel digestion system, but the mercury direct analyzer (without digestion) is recommended to analyze mercury in coal.

Picroside II alleviates liver injury induced by alpha-naphthylisothiocyanate through AMPK-FXR pathway.

Alpha-naphthylisothiocyanate (ANIT) is a typical hepatotoxicant that causes cholestasis, which causes toxic bile acid accumulation in the liver and leads to liver injury. Picroside II (PIC), one of the dominant effective components extracted from Picrorhiza scrophulariiflora Pennell, exhibits many pharmacological effects. However, the role of AMP-activated protein kinase (AMPK)-Farnesoid X receptor (FXR) pathway in the hepatoprotective effect of PIC against ANIT-induced cholestasis remains largely unknown. This study aimed to investigate the mechanisms of PIC on ANIT-induced cholestasis in vivo and in vitro. Our results showed that PIC protected against ANIT-induced liver injury in primary mouse hepatocytes, and decreased serum biochemical markers and lessened histological injuries in mice. ANIT inhibited FXR and its target genes of bile acid synthesis enzymes sterol-12α-hydroxylase (CYP8B1), and increase bile acid uptake transporter Na + -dependent taurocholate transporter (NTCP), efflux transporter bile salt export pump (BSEP) and bile acid metabolizing enzymes UDP-glucuronosyltransferase 1a1 (UGT1A1) expressions. PIC prevented its downregulation of FXR, NTCP, BSEP and UGT1A1, and further reduced CYP8B1 by ANIT. Furthermore, ANIT activated AMPK via ERK1/2-LKB1 pathway. PIC inhibited ERK1/2, LKB1 and AMPK phosphorylation in ANIT-induced cholestasis in vivo and in vitro. AICAR, an AMPK agonist, blocked PIC-mediated changes in FXR, CYP8B1 and BSEP expression in vitro. Meanwhile, U0126, an ERK1/2 inhibitor, further repressed ERK1/2-LKB1-AMPK pathway phosphorylation. In conclusion, PIC regulated bile acid-related transporters and enzymes to protect against ANIT-induced liver injury, which related to ERK1/2-LKB1-AMPK pathway. Thus, this study extends the understanding of the anti-cholestasis effect of PIC and provides new therapeutic targets for cholestasis treatment.

Inhibition of heparanase protects against pancreatic beta cell death in streptozotocin-induced diabetic mice via reducing intra-islet inflammatory cell infiltration.

BACKGROUND AND PURPOSE: Intra-islet heparan sulfate (HS) plays an important role in the maintenance of pancreatic islet function. The aim of this study was to investigate the effect mechanism of HS loss on the functioning of islets in diabetic mice. EXPERIMENTAL APPROACH: The hypoglycaemic effect of a heparanase inhibitor, OGT2115, was tested in a streptozotocin-induced diabetic mouse model. The islets in pancreatic sections were also stained to reveal their morphology. An insulinoma cell line (MIN6) and primary isolated murine islets were used to investigate the effect of OGT2115 in vitro. KEY RESULTS: Intra-islet HS was clearly lost in streptozotocin-induced diabetic mice due to the increased heparanase expression in damaged islets. OGT2115 prevented intra-islet HS loss and improved the glucose profile and insulin secretion in streptozotocin-treated mice. The apoptosis of pancreatic beta cells and the infiltration of mononuclear macrophages, CD4- and CD8-positive T-cells in islets was reduced by OGT2115 in streptozotocin-treated mice, but OGT2115 did not alter the direct streptozotocin-induced damage in vitro. The expression of heparanase was increased in high glucose-treated isolated islets but not in response to direct streptozotocin stimulation. Further experiments showed that high glucose stimuli could decreased expression of PPARγ in cultured islets, thereby relieving the PPARγ-induced inhibition of heparanase gene expression. CONCLUSION AND IMPLICATIONS: Hyperglycaemia could cause intra-islet HS loss by elevating the expression of heparanase, thereby aggravating inflammatory cell infiltration and islet damage. Inhibition of heparanase might provide benefit for pancreatic beta cell protection in Type 1 diabetes.

Effect of Catadioptric Component Postposition on Lens Focal Length and Imaging Surface in a Mirror Binocular System.

The binocular vision system is widely used in three-dimensional measurement, drone navigation, and many other fields. However, due to the high cost, large volume, and inconvenient operation of the two-camera system, it is difficult to meet the weight and load requirements of the UAV system. Therefore, the study of mirror binocular with single camera was carried out. Existing mirror binocular systems place the catadioptric components in front of the lens, which makes the volume of measurement system still large. In this paper, a catadioptric postposition system is designed, which places the prism behind the lens to achieve mirror binocular imaging. The influence of the post prism on the focal length and imaging surface of the optical system is analyzed. The feasibility of post-mirror binocular imaging are verified by experiments, and it is reasonable to compensate the focal length change by changing the back focal plane position. This research laid the foundation for the subsequent research on the 3D reconstruction of the novel mirror binocular system.

Multi-Scale Feature Fusion for Coal-Rock Recognition Based on Completed Local Binary Pattern and Convolution Neural Network.

Automatic coal-rock recognition is one of the critical technologies for intelligent coal mining and processing. Most existing coal-rock recognition methods have some defects, such as unsatisfactory performance and low robustness. To solve these problems, and taking distinctive visual features of coal and rock into consideration, the multi-scale feature fusion coal-rock recognition (MFFCRR) model based on a multi-scale Completed Local Binary Pattern (CLBP) and a Convolution Neural Network (CNN) is proposed in this paper. Firstly, the multi-scale CLBP features are extracted from coal-rock image samples in the Texture Feature Extraction (TFE) sub-model, which represents texture information of the coal-rock image. Secondly, the high-level deep features are extracted from coal-rock image samples in the Deep Feature Extraction (DFE) sub-model, which represents macroscopic information of the coal-rock image. The texture information and macroscopic information are acquired based on information theory. Thirdly, the multi-scale feature vector is generated by fusing the multi-scale CLBP feature vector and deep feature vector. Finally, multi-scale feature vectors are input to the nearest neighbor classifier with the chi-square distance to realize coal-rock recognition. Experimental results show the coal-rock image recognition accuracy of the proposed MFFCRR model reaches 97.9167%, which increased by 2%-3% compared with state-of-the-art coal-rock recognition methods.

A New Dictionary Construction Based Multimodal Medical Image Fusion Framework.

Training a good dictionary is the key to a successful image fusion method of sparse representation based models. In this paper, we propose a novel dictionary learning scheme for medical image fusion. First, we reinforce the weak information of images by extracting and adding their multi-layer details to generate the informative patches. Meanwhile, we introduce a simple and effective multi-scale sampling to implement a multi-scale representation of patches while reducing the computational cost. Second, we design a neighborhood energy metric and a multi-scale spatial frequency metric for clustering the image patches with a similar brightness and detail information into each respective patch group. Then, we train the energy sub-dictionary and detail sub-dictionary, respectively by K-SVD. Finally, we combine the sub-dictionaries to construct a final, complete, compact and informative dictionary. As a main contribution, the proposed online dictionary learning can not only obtain an informative as well as compact dictionary, but can also address the defects, such as superfluous patch issues and low computation efficiency, in traditional dictionary learning algorithms. The experimental results show that our algorithm is superior to some state-of-the-art dictionary learning based techniques in both subjective visual effects and objective evaluation criteria.