Integrative analysis and identification of key elements and pathways regulated by Traditional Chinese Medicine (Yiqi Sanjie formula) in colorectal cancer

Introduction: The clinical efficacy of Yiqi Sanjie (YQSJ) formula in the treatment of stage III colorectal cancer (CRC) has been demonstrated. However, the underlying antitumor mechanisms remain poorly understood.Materials and methods: The aim of the present study was to comprehensively characterize the molecular and microbiota changes in colon tissues and fecal samples from CRC mice and in CRC cell lines treated with YQSJ or its main active component, peiminine. Integrative tandem mass tag-based proteomics and ultra-performance liquid chromatography coupled with time-of-flight tandem mass spectrometry metabolomics were used to analyze azoxymethane/dextran sulfate sodium-induced CRC mouse colon tissues.Results: The results showed that 0.8% (57/7568) of all detected tissue proteins and 3.2% (37/1141) of all detected tissue metabolites were significantly changed by YQSJ treatment, with enrichment in ten and six pathways associated with colon proteins and metabolites, respectively. The enriched pathways were related to inflammation, sphingolipid metabolism, and cholesterol metabolism. Metabolomics analysis of fecal samples from YQSJ-treated mice identified 121 altered fecal metabolites and seven enriched pathways including protein digestion and absorption pathway. 16S rRNA sequencing analysis of fecal samples indicated that YQSJ restored the CRC mouse microbiota structure by increasing the levels of beneficial bacteria such as Ruminococcus_1 and Prevotellaceae_UCG_001. In HCT-116 cells treated with peiminine, data-independent acquisition-based proteomics analysis showed that 1073 of the 7152 identified proteins were significantly altered and involved in 33 pathways including DNA damage repair, ferroptosis, and TGF-β signaling.Conclusion: The present study identified key regulatory elements (proteins/metabolites/bacteria) and pathways involved in the antitumor mechanisms of YQSJ, suggesting new potential therapeutic targets in CRC

Upper ocean biogeochemistry of the oligotrophic North Pacific Subtropical Gyre : from nutrient sources to carbon export

Subtropical gyres cover 26–29% of the world’s surface ocean and are conventionally regarded as ocean deserts due to their permanent stratification, depleted surface nutrients, and low biological productivity. Despite tremendous advances over the past three decades, particularly through the Hawaii Ocean Time-series and the Bermuda Atlantic Time-series Study, which have revolutionized our understanding of the biogeochemistry in oligotrophic marine ecosystems, the gyres remain understudied. We review current understanding of upper ocean biogeochemistry in the North Pacific Subtropical Gyre, considering other subtropical gyres for comparison. We focus our synthesis on spatial variability, which shows larger than expected dynamic ranges of properties such as nutrient concentrations, rates of N2 fixation, and biological production. This review provides new insights into how nutrient sources drive community structure and export in upper subtropical gyres. We examine the euphotic zone in subtropical gyres as a two-layered vertically structured system: a nutrient-depleted layer above the top of the nutricline in the well-lit upper ocean and a nutrient-replete layer below in the dimly lit waters. These layers vary in nutrient supply and stoichiometries and physical forcing, promoting differences in community structure and food webs, with direct impacts on the magnitude and composition of export production. We evaluate long-term variations in key biogeochemical parameters in both of these euphotic zone layers. Finally, we identify major knowledge gaps and research challenges in these vast and unique systems that offer opportunities for future studies

A Novel Text Clustering Approach Using Deep-Learning Vocabulary Network

Text clustering is an effective approach to collect and organize text documents into meaningful groups for mining valuable information on the Internet. However, there exist some issues to tackle such as feature extraction and data dimension reduction. To overcome these problems, we present a novel approach named deep-learning vocabulary network. The vocabulary network is constructed based on related-word set, which contains the “cooccurrence” relations of words or terms. We replace term frequency in feature vectors with the “importance” of words in terms of vocabulary network and PageRank, which can generate more precise feature vectors to represent the meaning of text clustering. Furthermore, sparse-group deep belief network is proposed to reduce the dimensionality of feature vectors, and we introduce coverage rate for similarity measure in Single-Pass clustering. To verify the effectiveness of our work, we compare the approach to the representative algorithms, and experimental results show that feature vectors in terms of deep-learning vocabulary network have better clustering performance

Solvothermal Guided V₂O₅ Microspherical Nanoparticles Constructing High-Performance Aqueous Zinc-Ion Batteries

Rechargeable aqueous zinc-ion batteries have attracted a lot of attention owing to their cost effectiveness and plentiful resources, but less research has been conducted on the aspect of high volumetric energy density, which is crucial to the space available for the batteries in practical applications. In this work, highly crystalline V2O5 microspheres were self-assembled from one-dimensional V2O5 nanorod structures by a template-free solvothermal method, which were used as cathode materials for zinc-ion batteries with high performance, enabling fast ion transport, outstanding cycle stability and excellent rate capability, as well as a significant increase in tap density. Specifically, the V2O5 microspheres achieve a reversible specific capacity of 414.7 mAh g−1 at 0.1 A g−1, and show a long-term cycling stability retaining 76.5% after 3000 cycles at 2 A g−1. This work provides an efficient route for the synthesis of three-dimensional materials with stable structures, excellent electrochemical performance and high tap density

An integrated UAV growth monitoring model of Cinnamomum camphora based on whale optimization algorithm.

To explore an effective analysis model and method for estimating Cinnamomum camphora's (C. camphora's) growth using unmanned aerial vehicle (UAV) multispectral technology, we obtained C. camphora's canopy spectral reflectance using a UAV-mounted multispectral camera and simultaneously measured four single-growth indicators: Soil and Plant Analyzer Development (SPAD)value, aboveground biomass (AGB), plant height (PH), and leaf area index (LAI). The coefficient of variation and equal weighting methods were used to construct the comprehensive growth monitoring indicators (CGMI) for C. camphora. A multispectral inversion model of integrated C. camphora growth was established using the multiple linear regression (MLR), partial least squares (PLS), support vector regression (SVR), random forest (RF), radial basis function neural network (RBFNN), back propagation neural network (BPNN), and whale optimization algorithm (WOA)-optimized BPNN models. The optimal model was selected based on the coefficient of determination (R2), normalized root mean square error (NRMSE) and mean absolute percentage error (MAPE). Our findings indicate that apparent differences in the accuracy with different model, and the WOA-BPNN model is the best model to invert the growth potential of C. camphora, the R2 of the model test set was 0.9020, the RMSE was 0.0722, and the MAPE was 7%. The R2 of the WOA-BPNN model improved by 18%, the NRMSE decreased by 33%, and the MAPE decreased by 9% compared with the BPNN model. This study provides technical support for the modern field management of C. camphora essential oil and other dwarf forestry industries

Molecular Characterization of Advanced Colorectal Cancer Using Serum Proteomics and Metabolomics

Colorectal cancer (CRC) is a growing public health concern due to its high mortality rate. Currently, there is a lack of valid diagnostic biomarkers and few therapeutic strategies are available for CRC treatment, especially for advanced CRC whose underlying pathogenic mechanisms remain poorly understood. In the present study, we investigated the serum samples from 20 patients with stage III or IV advanced CRC using data-independent acquisition (DIA)-based proteomics and ultra-performance liquid chromatography coupled to time-of-flight tandem mass spectrometry (UPLC-TOF-MS/MS) metabolomics techniques. Overall, 551 proteins and 719 metabolites were identified. Hierarchical clustering analysis revealed that the serum proteomes of advanced CRC are more diversified than the metabolomes. Ten biochemical pathways associated with cancer cell metabolism were enriched in the detected proteins and metabolites, including glycolysis/gluconeogenesis, biosynthesis of amino acids, glutathione metabolism, and arachidonic acid metabolism, etc. A protein-protein interaction network in advanced CRC serum was constructed with 80 proteins and 21 related metabolites. Correlation analysis revealed conserved roles of lipids and lipid-like molecules in a regulatory network of advanced CRC. Three metabolites (hydroquinone, leucenol and sphingomyelin) and two proteins (coagulation factor XIII A chain and plasma kallikrein) were selected to be potential biomarkers for advanced CRC, which are positively and significantly correlated with CEA and/or CA 19-9. Altogether, the results expanded our understanding of the physiopathology of advanced CRC and discovered novel potential biomarkers for further validation and application to improve the diagnosis and monitoring of advanced CRC

Unexpected side effects of the EU Ship Recycling Regulation call for global cooperation on greening the shipbreaking industry

The recent European Union Ship Recycling Regulation and other existing conventions aimed to reduce harmful environmental and health impacts of ship shipbreaking, may push the shipbreaking industry further to South Asian countries, where ecosystem and public health are threatened due to the lack of monitoring for dirty beaching methods for ship breaking. Such unsustainable patterns may continue to expand due to the mismatch of economic beneficiaries and environmental costs in the shipbreaking industry, the ineffectiveness of existing conventions and regulations, and the prospect of a large number of ships to be dismantled in the near future. Our study focuses on these emerging issues and raises the urgency of joint actions for the shipbreaking industry

Acute Oral Toxicity and Genotoxicity Test and Evaluation of <i>Cinnamomum camphora</i> Seed Kernel Oil

Cinnamomum camphora seed kernel oil (CCSKO) is one of the important natural medium chain triglycerides (MCT) resources, with more than 95.00% of medium chain fatty acids found in the world, and has various physiological effects. However, CCSKO has not been generally recognized as a safe oil or new food resource yet. The acute oral toxicity test and a standard battery of genotoxicity tests (mammalian erythrocyte micronucleus test, Ames test, and in vitro mammalian cell TK gene mutation test) of CCSKO as a new edible plant oil were used in the study. The results of the acute oral toxicity test showed that CCSKO was preliminary non-toxic, with an LD50 value higher than 21.5 g/kg body weight. In the mammalian erythrocyte micronucleus test, there was no concentration-response relationship between the dose of CCSKO and micronucleus value in polychromatic erythrocytes compared to the negative control group. No genotoxicity was observed in the Ames test in the presence or absence of S9 at 5000 μg/mL. In vitro mammalian cell TK gene mutation test showed that CCSKO did not induce in vitro mammalian cell TK gene mutation in the presence or absence of S9 at 5000 μg/mL. These results indicated that CCSKO is a non-toxic natural medium-chain oil