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• In the early stage of cirrhosis, there seems to be a possibility of cirrhosis reversal. • The development of cirrhosis is related to the intestinal microbiota and activation of toll-like receptors (TLRs) pathways, which could regulate cell proliferation, apoptosis, expression of the hepatomitogen epiregulin, and liver inflammation. • In this paper, we first reviewed the dynamic change of intestinal microbiota and TLRs during cirrhosis progression. And further discussed the interaction between them and potential therapeutic targets to reverse early staged cirrhosis. • The interference of toll-like receptors (TLRs) and its intracellular signaling pathway can regulate the liver inflammatory response and cirrhosis process. • Drug treatment of intestinal dysbiosis and TLRs activation may slow cirrhosis progression. • With the further study of intestinal microbiota and TLRs on the progression of cirrhosis, it offers a new way of preventing and treating inflammatory response and fibrosis diseases. Cirrhosis is a progressive chronic liver disease caused by one or more causes and characterized by diffuse fibrosis, pseudolobules, and regenerated nodules. Once progression to hepatic decompensation, the function of the liver and other organs is impaired and almost impossible to reverse and recover, which often results in hospitalization, impaired quality of life, and high mortality. However, in the early stage of cirrhosis, there seems to be a possibility of cirrhosis reversal. The development of cirrhosis is related to the intestinal microbiota and activation of toll-like receptors (TLRs) pathways, which could regulate cell proliferation, apoptosis, expression of the hepatomitogen epiregulin, and liver inflammation. Targeting regulation of intestinal microbiota and TLRs pathways could affect the occurrence and development of cirrhosis and its complications. In this paper, we first reviewed the dynamic change of intestinal microbiota and TLRs during cirrhosis progression. And further discussed the interaction between them and potential therapeutic targets to reverse early staged cirrhosis. [ABSTRACT FROM AUTHOR]

Artificial reefs (ARs) are widespread globally and play a positive role in enhancing fish communities and restoring habitat. However, the effect of ARs on phytoplankton, which are fundamental to the marine food chain, remains inconclusive. Conducting a literature review and meta-analysis, this study investigates how ARs influence phytoplankton community dynamics by comparing the biomass, density, and diversity of phytoplankton between ARs and natural water bodies across varying deployment durations, constituent materials, and climatic zones. The study findings suggest that, overall, ARs enhance the biomass, density, and diversity of phytoplankton communities, with no significant differences observed compared to natural water bodies. The enhancement effect of ARs on phytoplankton communities becomes progressively more pronounced with increasing deployment time, with the overall status of phytoplankton communities being optimal when artificial reefs are deployed for 5 years or longer. Concrete and stone ARs can significantly enhance the biomass and diversity of phytoplankton, respectively. The effect of ARs on phytoplankton diversity is unrelated to climatic zones. However, deploying ARs in temperate waters significantly enhances phytoplankton biomass, while in tropical waters, it significantly reduces phytoplankton density. The research findings provide practical implications for the formulation of artificial reef construction strategies tailored to the characteristics of different aquatic ecosystems, emphasizing the need for long-term deployment and appropriate material selection. This study offers a theoretical basis for optimizing AR design and deployment to achieve maximum ecological benefits., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Artificial intelligence (AI) powered drug development has received remarkable attention in recent years. It addresses the limitations of traditional experimental methods that are costly and time-consuming. While there have been many surveys attempting to summarize related research, they only focus on general AI or specific aspects such as natural language processing and graph neural network. Considering the rapid advance on computer vision, using the molecular image to enable AI appears to be a more intuitive and effective approach since each chemical substance has a unique visual representation. In this paper, we provide the first survey on image-based molecular representation for drug development. The survey proposes a taxonomy based on the learning paradigms in computer vision and reviews a large number of corresponding papers, highlighting the contributions of molecular visual representation in drug development. Besides, we discuss the applications, limitations and future directions in the field. We hope this survey could offer valuable insight into the use of image-based molecular representation learning in the context of drug development., (© The Author(s) 2024. Published by Oxford University Press.)

Peanut root rot , commonly referred to as rat tail or root rot , is caused by a range of Fusarium species. A strain of bacteria (named TG5) was isolated from crop rhizosphere soil in Mount Taishan, Shandong Province, China, through whole genome sequencing that TG5 was identified as Bacillus thuringiensis , which can specifically produce chloramphenicol, bacitracin, clarithromycin, lichen VK 21 A 1 and bacitracin, with good biological control potential. Based on liquid chromatography tandem mass spectrometry metabonomics analysis and transcriptome conjoint analysis, the mechanism of TG5 and carbendazim inducing peanut plants to resist F. oxysporum stress was studied. In general, for peanut root rot caused by F. oxysporum , B. thuringiensis TG5 has greater advantages than carbendazim and is environmentally friendly. These findings provide new insights for peanut crop genetics and breeding, and for microbial pesticides to replace traditional highly toxic and highly polluting chemical pesticides. Based on the current background of agricultural green cycle and sustainable development, it has significant practical significance and broad application prospects., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Du and Li.)

Alcoholic liver disease (ALD) serves as the leading cause of chronic liver diseases-related morbidity and mortality, which threatens the life of millions of patients in the world. However, the molecular mechanisms underlying ALD progression remain unclear. Here, we applied microarray analysis and experimental approaches to identify miRNAs and related regulatory signaling that associated with ALD. Microarray analysis identified that the expression of miR-99b was elevated in the ALD mouse model. The AML-12 cells were treated with EtOH and the expression of miR-99b was enhanced in the cells. The expression of miR-99b was positively correlated with ALT levels in the ALD mice. The microarray analysis identified the abnormally expressed mRNAs in ALD mice and the overlap analysis was performed with based on the differently expressed mRNAs and the transcriptional factors of miR-99b, in which STAT1 was identified. The elevated expression of STAT1 was validated in ALD mice. Meanwhile, the treatment of EtOH induced the expression of STAT1 in the AML-12 cells. The expression of STAT1 was positively correlated with ALT levels in the ALD mice. The positive correlation of STAT1 and miR-99b expression was identified in bioinformatics analysis and ALD mice. The expression of miR-99b and pri-miR-99b was promoted by the overexpression of STAT1 in AML-12 cells. ChIP analysis confirmed the enrichment of STAT1 on miR-99b promoter in AML-12 cells. Next, we found that the expression of mitogen-activated protein kinase kinase 1 (MAP2K1) was negatively associated with miR-99b. The expression of MAP2K1 was downregulated in ALD mice. Consistently, the expression of MAP2K1 was reduced by the treatment of EtOH in AML-12 cells. The expression of MAP2K1 was negative correlated with ALT levels in the ALD mice. We identified the binding site of MAP2K1 and miR-99b. Meanwhile, the treatment of miR-99b mimic repressed the luciferase activity of MAP2K1 in AML-12 cells. The expression of MAP2K1 was suppressed by miR-99b in the cells. We observed that the expression of MAP2K1 was inhibited by the overexpression of STAT1 in AML-12 cells. Meanwhile, the apoptosis of AML-12 cells was induced by the treatment of EtOH, while miR-99b mimic promoted but the overexpression of MAP2K1 attenuated the effect of EtOH in the cells. In conclusion, we identified the correlation and effect of STAT1, miR-99b, and MAP2K1 in ALD mouse model and hepatocyte. STAT1, miR-99b, and MAP2K1 may serve as potential therapeutic target of ALD.

Thyroid cancer is one of the common types of cancer worldwide, and Ultrasound (US) imaging is a modality normally used for thyroid cancer diagnostics. The American College of Radiology Thyroid Imaging Reporting and Data System (ACR TIRADS) has been widely adopted to identify and classify US image characteristics for thyroid nodules. This paper presents novel methods for detecting the characteristic descriptors derived from TIRADS. Our methods return descriptions of the nodule margin irregularity, margin smoothness, calcification as well as shape and echogenicity using conventional computer vision and deep learning techniques. We evaluate our methods using datasets of 471 US images of thyroid nodules acquired from US machines of different makes and labeled by multiple radiologists. The proposed methods achieved overall accuracies of 88.00%, 93.18%, and 89.13% in classifying nodule calcification, margin irregularity, and margin smoothness respectively. Further tests with limited data also show a promising overall accuracy of 90.60% for echogenicity and 100.00% for nodule shape. This study provides an automated annotation of thyroid nodule characteristics from 2D ultrasound images. The experimental results showed promising performance of our methods for thyroid nodule analysis. The automatic detection of correct characteristics not only offers supporting evidence for diagnosis, but also generates patient reports rapidly, thereby decreasing the workload of radiologists and enhancing productivity., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Efficient Neural Architecture Search (ENAS) is a recent development in searching for optimal cell structures for Convolutional Neural Network (CNN) design. It has been successfully used in various applications including ultrasound image classification for breast lesions. However, the existing ENAS approach only optimizes cell structures rather than the whole CNN architecture nor its trainable hyperparameters. This paper presents a novel framework for automatic design of CNN architectures by combining strengths of ENAS and Bayesian Optimization in two-folds. Firstly, we use ENAS to search for optimal normal and reduction cells. Secondly, with the optimal cells and a suitable hyperparameter search space, we adopt Bayesian Optimization to find the optimal depth of the network and optimal configuration of the trainable hyperparameters. To test the validity of the proposed framework, a dataset of 1522 breast lesion ultrasound images is used for the searching and modeling. We then evaluate the robustness of the proposed approach by testing the optimized CNN model on three external datasets consisting of 727 benign and 506 malignant lesion images. We further compare the CNN model with the default ENAS-based CNN model, and then with CNN models based on the state-of-the-art architectures. The results (error rate of no more than 20.6% on internal tests and 17.3% on average of external tests) show that the proposed framework generates robust and light CNN models., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Two-dimensional graphdiyne (GDY) formed by sp and sp 2 hybridized carbon has been found to be an efficient toxic gas sensing material by density functional theory (DFT). However, little experimental research concerning its gas sensing capability has been reported owing to the complex preparation process and harsh experimental conditions. Herein, porous GDY nanosheets are successfully synthesized through a facile solvothermal synthesis technique by using CuO microspheres (MSs) as both template and source of catalyst. The porous GDY nanosheets exhibit a broadband optical absorption, rendering it suitable for the light-driven optoelectronic gas sensing applications. The GDY-based gas sensor was demonstrated to have excellent reversible to NO 2 behaviors at 25 °C for the first time. More importantly, higher response value and faster response-recovery time once exposed to NO 2 gas molecules are achieved by the illumination of UV light. In this way, our work paves the way for the exploration of GDY-based gas detection experimentally., (© 2023 Wiley-VCH GmbH.)

Background: Natural killer (NK) cells are a type of innate immune cell that recognize and eliminate tumor cells and infected cells, without prior sensitization or activation. Herein, we aimed to construct a predictive model based on NK cell-related genes for hepatocellular carcinoma (HCC) patients and assess the feasibility of utilizing this model for prognosis prediction. Methods: Single-cell RNA-seq data were obtained from the Gene Expression Omnibus (GEO) database to identify marker genes of NK cells. Univariate Cox and lasso regression were performed to further establish a signature in the TCGA dataset. Subsequently, qPCR and immunohistochemistry (IHC) staining were employed to validate the expression levels of prognosis signature genes in HCC. The effectiveness of the model was further validated using two external cohorts from the GEO and ICGC datasets. Clinical characteristics, prognosis, tumor mutation burden, immune microenvironments, and biological function were compared for different genetic subtypes and risk groups. Finally, molecular docking was performed to evaluate the binding affinity between the hub gene and chemotherapeutic drugs. Results: A total of 161 HCC-related NK cell marker genes (NKMGs) were identified, 28 of which were significantly associated with overall survival in HCC patients. Based on differences in gene expression characteristics, HCC patients were classified into three subtypes. Ten prognosis genes (KLRB1, CD7, LDB2, FCER1G, PFN1, FYN, ACTG1, PABPC1, CALM1, and RPS8) were screened to develop a prognosis model. The model not only demonstrated excellent predictive performance on the training dataset, but also were successfully validated on two independent external datasets. The risk scores derived from the model were shown to be an independent prognosis factor for HCC and were correlated with pathological severity. Moreover, qPCR and IHC staining confirmed that the expression of the prognosis genes was generally consistent with the results of the bioinformatic analysis. Finally, molecular docking revealed favorable binding energies between the hub gene ACTG1 and chemotherapeutic drugs. Conclusion: In this study, we developed a model for predicting the prognosis of HCC based on NK cells. The utilization of NKMGs as innovative biomarkers showed promise in the prognosis assessment of HCC., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Li, Du, Gan, Li, Zao and Ye.)

Background and Aims: Patients with chronic hepatitis B (CHB) in the immune tolerant (IT) phase were previously thought to have no or slight inflammation or fibrosis in the liver. In fact, some CHB patients with normal ALT levels still experience liver fibrosis. This study aimed to develop and validate a non-invasive model for identifying pseudo-immune tolerance (pseudo-IT) of CHB by predicting significant liver fibrosis., Methods: This multi-center study enrolled a total of 445 IT-phase patients who had undergone liver biopsy for the training cohort ( n  = 289) and validation cohort ( n  = 156) during different time periods. A risk model (IT-3) for predicting significant liver fibrosis (Ishak score ≥ 3) was developed using high-risk factors which were identified using multivariate stepwise logistic regression. Next, an online dynamic nomogram was created for the clinical usage. The receiver operating characteristic (ROC) curve, net reclassification improvement and integrated discrimination improvement were used to assess the discrimination of the IT-3 model. Calibration curves were used to evaluate the models' calibration. The clinical practicability of the model was evaluated using decision curve analysis and clinical impact curves., Results: 8.8% (39 of 445) patients presented with significant liver fibrosis in this study. Aspartate aminotransferase (AST), hepatitis B e-antigen (HBeAg), and platelet (PLT) were included in the prediction model (IT-3). The IT-3 model showed good calibration and discrimination both in the training and validation cohorts (AUC = 0.888 and 0.833, respectively). The continuous NRI and IDI showed that the IT-3 model had better predictive accuracy than GPR, APRI, and FIB-4 ( p  < 0.001). Decision curve analysis and clinical impact curves were used to demonstrate the clinical usefulness. At a cut-off value of 106 points, the sensitivity and specificity were 91.7 and 70.2%, respectively., Conclusion: The IT-3 model proved an accurate non-invasive method in identifying pseudo-IT of CHB, which can help to formulate more appropriate treatment strategies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Li, Li, Du, Zao, Gan, Yang, Li, Xing and Ye.)

The Chinese traditional medicine KangXianYiAi formula (KXYA) is used to treat hepatic disease in the clinic. Here we aim to confirm the therapeutic effects and explore the pharmacological mechanisms of KXYA on hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). We first collected and analyzed clinical data of 40 chronic hepatitis B (CHB) patients with precancerous liver lesions under KXYA treatment. Then, the cell viability, migration, cell cycle, and apoptosis of HepAD38 cells with KXYA treatment were examined. Next, we performed network pharmacological analysis based on database mining to obtain the key target pathways and genes of KXYA treatment on HBV-related HCC. We finally analyzed the expression of the key genes between normal and HBV-related HCC tissues in databases and measured the mRNA expression of the key genes in HepAD38 cells after KXYA treatment. The KXYA treatment could reduce the liver nodule size of CHB patients, suppress the proliferation and migration capabilities, and promote apoptosis of HepAD38 cells. The key pathways of KXYA on HBV-related HCC were Cancer, Hepatitis B, Viral carcinogenesis, Focal adhesion, and PI3K-Akt signaling, and KXYA treatment could regulate the expression of the key genes including HNF4A, MAPK8, NR3C1, PTEN, EGFR , and HDAC1 . The KXYA exhibited a curative effect via inhibiting proliferation, migration, and promoting apoptosis of HBV-related HCC and the pharmacological mechanism was related to the regulation of the expression of HNF4A, MAPK8, NR3C1, PTEN, EGFR, and HDAC1 ., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Cao, Chen, Li, Li, Yang, Jin, Liang, Zhang, Zhou, Zhang, Chen, Du, Zao and Ye.)

Background: Hepatocellular carcinoma is the world's leading cause of cancer-related death. Chronic hepatitis B virus and hepatitis C virus infection cause liver cancer. The aim of this study was to investigate the relationship between statins and the risk of hepatocellular carcinoma in patients with hepatitis B or C., Methods: We systematically searched Web of Science, Embase, PubMed, Cochrane Library, China National Knowledge Infrastructure, and Wanfang Database from their inception to January 2019. We included studies that reported the hepatocellular carcinoma incidence among hepatitis B virus- or hepatitis C virus-infected patients or hepatitis B virus- or hepatitis C virus-related cirrhotic patients, evaluated and clearly defined exposure to statins, provided effective comparison groups, and reported risk estimates. Inclusion was not otherwise restricted. Summary relative risk estimates with 95% CIs were calculated using a random-effects model., Results: Meta-analysis of 10 studies showed that statin users had a significantly lower risk of hepatocellular carcinoma (relative risk = 0.47, 95% CI = 0.38-0.56) with significant heterogeneity. In 7 hepatitis studies, using statin was associated with a 53% reduction in the incidence of hepatocellular carcinoma (relative risk = 0.47, 95% CI = 0.43-0.50) with substantial heterogeneity. In 3 cirrhosis studies, the incidence of hepatocellular carcinoma in statin users was significantly reduced by 55% (relative risk = 0.45, 95% CI = 0.30-0.61) with no heterogeneity., Conclusion: Statins reduce the hepatocellular carcinoma risk among patients infected with hepatitis B virus or hepatitis C virus. This chemoprotective association is more pronounced in hepatitis B virus or hepatitis C virus-associated cirrhotic patients.