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Gut dysbiosis, resulting from an imbalance in the gut microbiome, can induce excessive production of reactive oxygen species (ROS), leading to inflammation, DNA damage, activation of the immune system, and epigenetic alterations of critical genes involved in the metabolic pathways. Gut dysbiosis-induced inflammation can also disrupt the gut barrier integrity and increase intestinal permeability, which allows gut-derived toxic products to enter the liver and systemic circulation, further triggering oxidative stress, inflammation, and epigenetic alterations associated with metabolic diseases. However, specific gut-derived metabolites, such as short-chain fatty acids (SCFAs), lactate, and vitamins, can modulate oxidative stress and the immune system through epigenetic mechanisms, thereby improving metabolic function. Gut microbiota and diet-induced metabolic diseases, such as obesity, insulin resistance, dyslipidemia, and hypertension, can transfer to the next generation, involving epigenetic mechanisms. In this review, we will introduce the key epigenetic alterations that, along with gut dysbiosis and ROS, are engaged in developing metabolic diseases. Finally, we will discuss potential therapeutic interventions such as dietary modifications, prebiotics, probiotics, postbiotics, and fecal microbiota transplantation, which may reduce oxidative stress and inflammation associated with metabolic syndrome by altering gut microbiota and epigenetic alterations. In summary, this review highlights the crucial role of gut microbiota dysbiosis, oxidative stress, and inflammation in the pathogenesis of metabolic diseases, with a particular focus on epigenetic alterations (including histone modifications, DNA methylomics, and RNA interference) and potential interventions that may prevent or improve metabolic diseases.

Various abiotic stresses (e [...]

Earthquake-triggered landslides are one of the most significant hazards worldwide. These landslides, involving small or large volumes, can develop debris flows or avalanches with high mobility and long runout distances. The motion processes of this type of landslide are also quite complicated. This study reproduced the motion processes of two landslides using the LS-RAPID program. The Aso-Bridge landslide occurred on April 16, 2016, and it featured rapid movement and a long runout distance. The Kataragai landslide occurred on April 9, 2018, and it traveled with a flow-like behavior. This work presents the features and mechanical parameters of these two landslides by means of survey investigations and laboratory experiments. We established pre-failure models and reproduced motion processes of these two landslides using the LS-PAPID program. Two indicators, consisting of the sliding speed and traveling morphology at different intervals, were considered to describe the motion processes. Results indicated that the resultant maximum sliding speeds of these two landslides were approximately 297.1 and 43.6 m/s, respectively, and that post-failure morphologies were consistent with those observed in field investigations.

Growing evidence showed the increased prevalence of cancer incidents, particularly colorectal cancer, among type 2 diabetic mellitus patients. Antidiabetic medications such as, insulin, sulfonylureas, dipeptyl peptidase (DPP) 4 inhibitors and glucose-dependent insulinotropic peptide (GLP-1) analogues increased the additional risk of different cancers to diabetic patients. Conversely, metformin has drawn attention among physicians and researchers since its use as antidiabetic drug exhibited beneficial effect in the prevention and treatment of cancer in diabetic patients as well as an independent anticancer drug. This review aims to provide the comprehensive information on the use of metformin at preclinical and clinical stages among colorectal cancer patients. We highlight the efficacy of metformin as an anti-proliferative, chemopreventive, apoptosis inducing agent, adjuvant, and radio-chemosensitizer in various colorectal cancer models. This multifarious effects of metformin is largely attributed to its capability in modulating upstream and downstream molecular targets involved in apoptosis, autophagy, cell cycle, oxidative stress, inflammation, metabolic homeostasis, and epigenetic regulation. Moreover, the review highlights metformin intake and colorectal cancer risk based on different clinical and epidemiologic results from different gender and specific population background among diabetic and non-diabetic patients. The improved understanding of metformin as a potential chemotherapeutic drug or as neo-adjuvant will provide better information for it to be used globally as an affordable, well-tolerated, and effective anticancer agent for colorectal cancer.

Live streaming e-commerce emphasizes the role of live streaming influencers and the dynamic interactions between viewers and live streaming influencers. Utilizing data collected from 400 questionnaires, this study delves into the mechanisms through which characteristics of live streaming influencers influence consumer purchase intentions, with a focus on consumer emotional trust as a mediating variable as well as consumer education level, age, perceived risk, and live-stream engagement as moderating factors. The findings indicate that the traits of live streaming influencers have a positive effect on consumers’ intent to purchase. Emotional trust mediates the influence of live streaming influencer characteristics on purchase intention. Consumers’ educational level positively moderates the relationship between the professionalism and homogeneity of influencers and their purchase intentions, while it negatively moderates the relationship between influencers’ attraction and interactivity with purchase intentions. Additionally, the age of consumers positively moderates the link between the professionalism of influencers and purchase intentions, but negatively moderates the links between homogeneity, attraction, and interactivity of influencers and purchase intentions. Furthermore, both consumers’ educational level and age positively moderate the impact of emotional trust on purchase intentions. Lastly, perceived risk and live-stream engagement respectively exert negative and positive moderating effects on the influence of influencer professionalism and attraction on purchase intentions. The study contributes to influencer marketing theory by adopting an innovative approach to systematically investigate the collective influence of all four live streaming influencer characteristics (professionalism, homogeneity, attraction, and interactivity) within a comprehensive framework.

In this study, a green and effective extraction method was proposed to extract two main compounds, ginsenosides and polysaccharides, from American ginseng by combining deep eutectic solvents (DESs) with aqueous two-phase systems. The factors of type of DESs, water content in DESs, the solid–liquid ratio, extraction temperature, and extraction time were studied in the solid–liquid extraction. Then, the aqueous two-phase system (DESs-ethylene oxide–propylene oxide (EOPO)) and salty solution exchange (EOPO-salty solution) was applied for the purification of polysaccharides. The content of the polysaccharides and ginsenosides were analyzed by the anthrone–sulfuric acid method and HPLC method, which showed that the extraction efficiency of deep eutectic solvents (DESs) was better than conventional methods. Moreover, the antioxidant activities of ginseng polysaccharides and their cytotoxicity were further assayed. The advantages of the current study are that, throughout the whole extraction process, we avoided the usage of an organic reagent. Furthermore, the separated green solvent DESs and EOPO could be recovered and reused for a next cycle. Thus, this study proposed a new, green and recyclable extraction method for extracting ginsenosides and polysaccharides from American ginseng.

Huanglongbing (HLB), or citrus greening disease, has complex and variable symptoms, making its diagnosis almost entirely reliant on subjective experience, which results in a low diagnosis efficiency. To overcome this problem, we constructed and validated a deep learning (DL)-based method for detecting citrus HLB using YOLOv5l from digital images. Three models (Yolov5l-HLB1, Yolov5l-HLB2, and Yolov5l-HLB3) were developed using images of healthy and symptomatic citrus leaves acquired under a range of imaging conditions. The micro F1-scores of the Yolov5l-HLB2 model (85.19%) recognising five HLB symptoms (blotchy mottling, “red-nose” fruits, zinc-deficiency, vein yellowing, and uniform yellowing) in the images were higher than those of the other two models. The generalisation performance of Yolov5l-HLB2 was tested using test set images acquired under two photographic conditions (conditions B and C) that were different from that of the model training set condition (condition A). The results suggested that this model performed well at recognising the five HLB symptom images acquired under both conditions B and C, and yielded a micro F1-score of 84.64% and 85.84%, respectively. In addition, the detection performance of the Yolov5l-HLB2 model was better for experienced users than for inexperienced users. The PCR-positive rate of Candidatus Liberibacter asiaticus (CLas) detection (the causative pathogen for HLB) in the samples with five HLB symptoms as classified using the Yolov5l-HLB2 model was also compared with manual classification by experts. This indicated that the model can be employed as a preliminary screening tool before the collection of field samples for subsequent PCR testing. We also developed the ‘HLBdetector’ app using the Yolov5l-HLB2 model, which allows farmers to complete HLB detection in seconds with only a mobile phone terminal and without expert guidance. Overall, we successfully constructed a reliable automatic HLB identification model and developed the user-friendly ‘HLBdetector’ app, facilitating the prevention and timely control of HLB transmission in citrus orchards.

Background: Benign prostatic hyperplasia (BPH) is a progressive urological disease occurring in middle-aged and elderly men, which can be characterized by the non-malignant overgrowth of stromal and epithelial cells in the transition zone of the prostate. Previous studies have demonstrated that lycopene can inhibit proliferation, while curcumin can strongly inhibit inflammation. This study aims to determine the inhibitory effect of the combination of lycopene and curcumin on BPH. Method: To induce BPH models in vitro and in vivo, the BPH-1 cell line and Sprague Dawley (SD) rats were used, respectively. Rats were divided into six groups and treated daily with a vehicle, lycopene (12.5 mg/kg), curcumin (2.4 mg/kg), a combination of lycopene and curcumin (12.5 mg/kg + 2.4 mg/kg) or finasteride (5 mg/kg). Histologic sections were examined via hematoxylin and eosin (H&E) staining and immunohistochemistry. Hormone and inflammatory indicators were detected via ELISA. Network pharmacology analysis was used to fully predict the therapeutic mechanism of the combination of lycopene and curcumin on BPH. Results: Combination treatment significantly attenuated prostate hyperplasia, alleviated BPH pathological features and decreased the expression of Ki-67 in rats. The upregulation of the expression of testosterone, dihydrotestosterone (DHT), 5α-reductase, estradiol (E2) and prostate-specific antigen (PSA) in BPH rats was significantly blocked by the combination treatment. The expression levels of inflammatory factors including interleukin (IL)-1β, IL-6 and tumor necrosis factor (TNF)-α were strongly inhibited by the combination treatment. From the network pharmacology analysis, it was found that the main targets for inhibiting BPH are AKT1, TNF, EGFR, STAT3 and PTGS2, which are enriched in pathways in cancer. Conclusion: The lycopene and curcumin combination is a potential and more effective agent to prevent or treat BPH.

As a variant of Convolutional Neural Networks (CNNs) in Deep Learning, the Fully Convolutional Network (FCN) model achieved state-of-the-art performance for natural image semantic segmentation. In this paper, an accurate classification approach for high resolution remote sensing imagery based on the improved FCN model is proposed. Firstly, we improve the density of output class maps by introducing Atrous convolution, and secondly, we design a multi-scale network architecture by adding a skip-layer structure to make it capable for multi-resolution image classification. Finally, we further refine the output class map using Conditional Random Fields (CRFs) post-processing. Our classification model is trained on 70 GF-2 true color images, and tested on the other 4 GF-2 images and 3 IKONOS true color images. We also employ object-oriented classification, patch-based CNN classification, and the FCN-8s approach on the same images for comparison. The experiments show that compared with the existing approaches, our approach has an obvious improvement in accuracy. The average precision, recall, and Kappa coefficient of our approach are 0.81, 0.78, and 0.83, respectively. The experiments also prove that our approach has strong applicability for multi-resolution image classification.

The cytokinin oxidase / dehydrogenase (CKX) gene plays a principal role in controlling cyto-kinin levels and has been shown to be a major quantitative trait locus (QTL) affecting grain number in rice. However, the function and evaluation of the haplotypes of the wheat CKX gene have yet to be illustrated. In this study, TaCKX6-D1, a wheat ortholog of rice OsCKX2, was cloned and its haplotype variants were determined to be significantly associated with the 1000-grain weight on the basis of linkage mapping, association analysis and gene expression analysis. Five TaCKX6-D1 haplotypes, designated a–e, were identified. An indel marker was developed to identify haplotype a, which was associated with higher grain weight. Haplotype a showed decreased expression relative to haplotype b in seeds at 8 d after pollination. Sequence variations among modern cultivars, landraces and wild species suggest a significant domestication signature at the TaCKX6-D1 locus in Chinese wheat germplasm. TaCKX6-D1 may serve as a useful gene for the breeding of high-yielding wheat. A strategy for allele mining and utilization of TaCKX6-D1 was proposed. Our study also sheds light on the mechanisms of grain development and domestication of wheat, as well as the functional divergence of orthologs in comparative genomics.