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Abstract The rapid failure of organic coatings in deep-sea environments complicates accurate lifetime prediction. Given the rapid cracking characteristic on the coating surface in this environment, a comprehensive “performance-structure” failure model was established. Initially, a targeted image recognition approach containing convolutional neural network (CNN) and post-processing was constructed for the crack area detection. An overall precision of 82.81% demonstrated the network’s good accuracy. The length distribution and the statistical evolution of cracks were extracted from SEM images to obtain the kinetic equation of the cracks related to coating structure degradation. In addition, the kinetics of water diffusion and coating adhesion were examined, as they represent critical parameters of coating performance. Based on this achievement, a failure model incorporating three dominant factors was integrated by the gray relational analysis method. The average prediction error of the model was 2.60%, which lays the groundwork for developing image-based methods to predict coating life.

Genetic variation is one of the important factors leading to the incidence and development of cancer. Copy number variation is an important source of genetic diversity， which can be expressed as gene amplification or deletion in structure， and is related to the occurrence and development of different tumors. High-throughput sequencing and gene chip technology can be adopted to detect the variation of copy number， and provide relevant information about tumor molecular characteristics， prognosis and treatment， which is conducive to more accurate diagnosis and treatment decisions for patients in clinical practice. Ovarian cancer is one of the female reproductive system diseases with the highest mortality rate. Understanding its pathogenesis is of significance for improving the survival rate of ovarian cancer. At present， the specific role and mechanism of copy number variation in ovarian cancer are still unclear. In this article， relevant copy number variation in ovarian cancer was reviewed based on the existing research results， aiming to provide novel ideas and methods for the prevention， diagnosis and treatment of ovarian cancer.

Abstract Enhancing farmers’ adoption of agricultural green production technology (AGPT) is crucial for the green transformation of the agricultural sector. Obtaining additional income is the primary motivation for farmers to adopt AGPTs. Therefore, clarifying the relationship between AGPTs’ adoption degree and income will facilitate the possibility of farmers’ adoption of AGPTs. Based on research data obtained from 805 citrus farmers in Sichuan Province, this study examines the impact of the degree of AGPT adoption on farmers’ income and its mechanisms using the inverse probability weighted regression adjustment (IPWRA) model. These findings indicate: (1) The greater the degree of adoption of AGPTs by farmers, the more pronounced the impact on income increase. Using 1, 2, and 3 AGTs can increase farmers’ income from citrus sales by 7.1, 10.5, and 19.8 percentage points, respectively. (2) The adoption of AGPTs leads to increased income through higher yields and sales prices, which offset the incurred costs. (3) Market channels and farmers’ operating scales can affect the revenue effect of AGPT adoption. (4) Factors (i.e., education level, physical health status, and cultivation area) can affect farmers’ adoption of AGPTs. This study presents policy recommendations based on the above conclusions, aiming to guide the government in promoting AGPTs and advancing agricultural green transformation.

Abstract Objective To conduct a bibliometric analysis of the prospects and obstacles associated with dual- and multi-energy CT in thoracic disease, emphasizing its current standing, advantages, and areas requiring attention. Methods The Web of Science Core Collection was queried for relevant publications in dual- and multi-energy CT and thoracic applications without a limit on publication date or language. The Bibliometrix packages, VOSviewer, and CiteSpace were used for data analysis. Bibliometric techniques utilized were co-authorship analyses, trend topics, thematic map analyses, thematic evolution analyses, source’s production over time, corresponding author’s countries, and a treemap of authors’ keywords. Results A total of 1992 publications and 7200 authors from 313 different sources were examined in this study. The first available document was published in November 1982, and the most cited article was cited 1200 times. Siemens AG in Germany emerged as the most prominent author affiliation, with a total of 221 published articles. The most represented scientific journals were the “European Radiology” (181 articles, h-index = 46), followed by the “European Journal of Radiology” (148 articles, h-index = 34). Most of the papers were from Germany, the USA, or China. Both the keyword and topic analyses showed the history of dual- and multi-energy CT and the evolution of its application hotspots in the chest. Conclusion Our study illustrates the latest advances in dual- and multi-energy CT and its increasingly prominent applications in the chest, especially in lung parenchymal diseases and coronary artery diseases. Photon-counting CT and artificial intelligence will be the emerging hot technologies that continue to develop in the future. Critical relevance statement This study aims to provide valuable insights into energy-based imaging in chest disease, validating the clinical application of multi-energy CT together with photon-counting CT and effectively increasing utilization in clinical practice. Key Points Bibliometric analysis is fundamental to understanding the current and future state of dual- and multi-energy CT. Research trends and leading topics included coronary artery disease, pulmonary embolism, and radiation dose. All analyses indicate a growing interest in the use of energy-based imaging techniques for thoracic applications. Graphical Abstract

Alcohol-related liver disease (ALD) is a major cause of morbidity and mortality worldwide. It encompasses conditions such as fatty liver, alcoholic hepatitis, chronic hepatitis with liver fibrosis or cirrhosis, and hepatocellular carcinoma. Numerous recent studies have demonstrated the critical role of oxidative stress, abnormal lipid metabolism, endoplasmic reticulum stress, various forms of cell death (including apoptosis, necroptosis, and ferroptosis), intestinal microbiota dysbiosis, liver immune response, cell autophagy, and epigenetic abnormalities in the pathogenesis of ALD. Currently, abstinence, corticosteroids, and nutritional therapy are the traditional therapeutic interventions for ALD. Emerging therapies for ALD mainly include the blockade of inflammatory pathways, the promotion of liver regeneration, and the restoration of normal microbiota. Summarizing the advances in animal models of ALD will facilitate a more systematic investigation of the pathogenesis of ALD and the exploration of therapeutic targets. This review summarizes the latest insight into the pathogenesis and molecular mechanisms of ALD, as well as the pros and cons of ALD rodent models, providing a basis for further research on therapeutic strategies for ALD.

BackgroundAlcoholic hepatitis is a severe inflammatory liver disease. In recent years, the incidence of AH has been on the rise, leading to an increasingly severe disease burden. Currently, there is a lack of specific biomarkers for the diagnosis and prognosis of AH in clinical practice. Therefore, the main objective of this study is to identify biomarkers closely associated with the progression of AH, to address the shortcomings in pathological diagnosis, and to identify potential therapeutic targets.MethodsBioinformatics and machine learning methods were used to comparatively study the differentially expressed genes (DEGs) between AH patients and healthy individuals by analyzing four mRNA microarray data sets obtained from the GEO database. Subsequently, the role of potential biomarkers in AH and their mechanism of action were further confirmed by AH patients and in vitro and in vivo experiments.ResultsUsing differential analysis and WGCNA of the data set, a total of 167 key genes that may be related to AH were obtained. Among 167 genes, the LASSO logistic regression algorithm identified four potential biomarkers (KCNJ10, RPL21P23, ADRB2, and AC025279.1). Notably, ADRB2 showed biomarker potential in GSE28619, GSE94397, and E-MTAB-2664 datasets, and clinical liver samples. Furthermore, AH patients and in vivo experiments demonstrated ADRB2 inhibition and suppression of SIRT1/PPARα/PGC-1α signaling pathways, accompanied by elevated inflammatory factors and lipid deposition. In vitro experiments showed that ADRB2 overexpression mitigated the inhibition of the SIRT1/PPARα/PGC-1α signaling pathway, reversing the decrease in mitochondrial membrane potential, cell apoptosis, oxidative stress, and lipid deposition induced by alcohol exposure. Besides, the results also showed that ADRB2 expression in AH was negatively correlated with the levels of inflammatory factors (e.g., CCL2, CXCL8, and CXCL10).ConclusionThis study points to ADRB2 as a promising biomarker with potential diagnostic and prognostic value in clinical cohort data. In addition, in AH patients, in vivo and in vitro experiments confirmed the key role of ADRB2 in the progression of AH. These findings suggest that ADRB2 may alleviate AH by activating the SIRT1/PPARα/PGC-1α pathway. This finding provides a new perspective for the diagnosis and treatment of AH.

Background: Medulloblastoma (MB) is a malignant pediatric central nervous system tumor that is prone to leptomeningeal metastasis. Currently, apart from magnetic resonance imaging and cerebrospinal fluid (CSF) cytology, there are no reliable biomarkers for MB progression. Cytokines are key proteins in signaling pathways in the tumor microenvironment and are closely related to tumor recurrence and progression. This study aimed to investigate the CSF cytokine profile in pediatric patients with MB to identify biomarkers of tumor progression and metastasis. Methods: In total, 10 patients were recruited for this study. Five patients had nonmetastatic MB and five had metastatic MB. A cytokine antibody array was used to detect the expression of 120 cytokines in the CSF, and differentially expressed cytokines were screened by integrated bioinformatics analysis. Results: Twenty-seven cytokines were upregulated in patients with MB compared to control individuals. Of these, eight were upregulated by > 1.5-fold (CCL2, BMP-4, beta-NGF, FGF-7, IL-12p40, eotaxin-2, M-CSF, and NT-4). Twelve cytokines were differentially expressed between patients with metastatic MB and nonmetastatic (nine cytokines were upregulated and three were downregulated). Among them, NAP-2, MIP-1α, MIP-1β, IGFBP-1, IGFBP-2 and IGFBP-3 were upregulated by more than two-fold. Gene Ontology analysis revealed that the upregulated cytokines were enriched mainly in “epithelial cell proliferation” and “chemotaxis,” and the Kyoto Encyclopedia of Genes and Genomes analysis indicated the enrichment of the “MAPK,” “PI3K-Akt,” and “Ras” signaling pathways. Conclusions: The present study investigated cytokine profiles in the CSF of pediatric patients with MB. Our results suggest that these differentially expressed cytokines may serve as novel markers for detecting MB, especially for assessing the risk of progression and metastasis.

Abstract Colorectal cancer is a leading cause of cancer-related mortality worldwide. Traditionally, colorectal cancer has been recognized as a disease caused by genetic mutations. However, recent studies have revealed the significant role of epigenetic alterations in the progression of colorectal cancer. Epithelial–mesenchymal transition, a critical step in cancer cell metastasis, has been found to be closely associated with the tumor microenvironment and immune factors, thereby playing a crucial role in many kinds of biological behaviors of cancers. In this review, we explored the impact of N6-methyladenosine and post-translational modifications (like methylation, acetylation, ubiquitination, SUMOylation, glycosylation, etc.) on the process of epithelial–mesenchymal transition in colorectal cancer and the epigenetic regulation for the transcription factors and pathways correlated to epithelial-mesenchymal transition. Furthermore, we emphasized that the complex regulation of epithelial-mesenchymal transition by epigenetics can provide new strategies for overcoming drug resistance and improving treatment outcomes. This review aims to provide important scientific evidence for the prevention and treatment of colorectal cancer based on epigenetic modifications.

Abstract The middle reaches of the Lancang‐Mekong River Basin (M‐LMRB) experienced a record‐breaking drought event in 2019, resulting in significant economic losses of approximately 650 million dollars and affecting a population of 17 million. However, the anomalous circulation and transportation processes of water vapor, which may have played a crucial role in inducing the extreme drought, have not been fully studied. In this study, we analyze the water vapor circulation during the 2019 drought event using the land‐atmosphere water balance and a backward trajectory model for moisture tracking. Our results indicate that the precipitation in the M‐LMRB from May to October 2019 was only 71.9% of the long‐term climatological mean (1959–2021). The low precipitation during this drought event can be attributed to less‐than‐normal external water vapor supply. Specifically, the backward trajectory model reveals a decrease in the amount of water vapor transported from the Indian Ocean, the Bay of Bengal, and the Pacific Ocean, which are the main moisture sources for precipitation in the region. Comparing the atmospheric circulation patterns in 2019 with the climatology, we identify anomalous anticyclone conditions in the Bay of Bengal, anomalous westerlies in the Northeast Indian Ocean, and an anomalous cyclone in the Western Pacific Ocean, collectively facilitating a stronger export of water vapor from the region. Therefore, the dynamic processes played a more significant role than thermodynamic processes in contributing to the 2019 extreme drought event.

In the wake of the COVID-19 pandemic, there has been an increasing focus towards infant immunity. The development and maintenance of the immune system are significantly influenced from birth, and is shaped by early-life infant feeding behavior. Hence, the knowledge, attitude, and practice (KAP) of parents play a crucial role in shaping the immune system of infants. A total of 2369 parents across 19 cities in China were surveyed using a self-designed online questionnaire. The KAP questionnaire assessed three domains: immunity, gut microbiota, and biotics (prebiotics, probiotics, and synbiotics). The questionnaire also included questions on infant health. An overwhelming majority of parents (97.90%) placed high value on their children’s immunity, and 40.40% of them reported an increased level of concern following the COVID-19 pandemic. Diarrhea (78.80%), colds (75.70%), nighttime crying (73.80%), regurgitation (71.70%) and food retention (66.50%) were the major health issues reported. Knowledge scores toward immunity and gut microbiota were positively correlated to attitude and practice scores, respectively. Attitude scores toward immunity, gut microbiota and biotics were positively correlated to their respective practice scores. Parental knowledge score on immunity was negatively correlated with diarrhea, colds, regurgitation and food retention. On the other hand, parental practice scores toward immunity were negatively correlated with food retention. In regards to gut microbiota, parental knowledge score was negatively correlated with diarrhea, regurgitation and food retention; parental attitude score was negatively correlated with nighttime crying whereas practice score was negatively correlated with diarrhea, regurgitation, food retention and nighttime crying. Attitude score toward biotics was negatively correlated with nighttime crying and practice scores toward biotics was negatively correlated with colds, food retention and nighttime crying. This study demonstrated that significant gaps and misunderstandings exist among parents regarding immunity, gut microbiota health, and biotics. Both public education and interventions are crucial to enhance parental knowledge and practices, thereby improving infant immunity.

Abstract Butyl butyrate is a short‐chain fatty acid ester (C8) with a fruity aroma. It has broad prospects in the fields of foods, cosmetics and biofuels. At present, butyl butyrate is produced by chemical synthesis in the industry, but it is highly dependent on petroleum‐based products. The growing concerns regarding the future scarcity of fossil fuels have been strongly promoted the transition from traditional fossil fuels and products to renewable bioenergy and biochemicals. Therefore, it is necessary to develop a green biochemical technology to replace traditional petroleum‐based materials. In recent years, microorganisms such as Escherichia coli and Clostridium have been engineered to serve as cell factories for the sustainable one‐pot production of short‐chain fatty acid esters, including butyl butyrate. This opinion highlights the recent development in the use of lipases and alcohol acyltransferases (AATs) for butyl butyrate production in microbial fermentation, as well as future perspectives.

BackgroundThis study aimed to compare the clinical outcomes and patient benefits of uncut Roux-en-Y (URY) anastomosis and Billroth-II with Braun (BB) anastomosis after distal gastrectomy.MethodsWe retrospectively reviewed the data of patients who underwent URY or BB anastomosis after distal gastrectomy between March 2015 and December 2017. Clinical characteristics, survival data, postoperative recovery data, and long-term outcomes were recorded and compared between the two groups.ResultsA total of 231 patients were included, with 167 in the URY group and 64 in the BB group. Kaplan–Meier curves for overall survival showed no differences after propensity score matching (p = 0.488). Long-term postoperative quality of life evaluation also showed no significant differences. Compared to the BB group, patients in the URY group had a significantly shorter time to start a liquid diet after propensity score matching (67.6 h vs. 46.5 h, p = 0.003), and a lower occurrence of bile reflux on follow-up gastroscopy (p

In recent years, the domestic live pig price has risen and fallen rapidly and fluctuated frequently, which has greatly impacted the live pig industry. The level of price volatility in the pig market has served as a significant indication of the progress of the agricultural sector. Identifying the dominant factors affecting the fluctuation of pig prices has become more important. Based on the monthly data of the pig industry from February 2009 to December 2022, this paper constructs an index system of influencing factors of pig price from four aspects: uncertain impact, supply factors, demand factors, and macro-environment factors. By using the transfer model of the Markov regime (MS-VAR), we obtained the probability plot of zone transition for pig price volatility, the impulse response effect diagram of factors affecting pig price, and the cumulated impulse response effect diagram of factors affecting pig price, and analyze the reasons for the ups and downs of pig price according to the above results. The findings indicate prominent features of zone transition in the price fluctuation of China's pig market. From 2017 to 2022, the domestic pig price frequently switches between rising and falling zones, and the "falling pig price stage" and "rising pig price stage" in the non-stationary state last for a relatively short and discontinuous period. There is little probability that the price of live pigs will directly change from rising to falling, and there will be a smooth buffer stage in the price rise and fall process. Among the factors that affect the fluctuation of live pig prices, the dominant factor of frequent and large fluctuation of pig prices is the pig epidemic situation in external factors. Among the internal influencing factors, the changes in farming costs have the greatest significant influence on the fluctuation of pig prices. These results provide a decision-making reference for legislators to carry out epidemic risk prevention and control better, stabilize the market pig price, and provide empirical evidence for market participants to accurately avoid price risks through multiple channels and ways and ensure stable profitability.

Abstract Salivary gland malignancies are rare and are often acompanied by poor prognoses. So, identifying the populations with risk factors and timely intervention to avoid disease progression is significant. This study provides an effective prediction model to screen the target patients and is helpful to construct a cost‐effective follow‐up strategy. We enrolled 249 patients diagnosed with salivary gland tumors and analyzed prognostic risk factors using Cox proportional hazard univariable and multivariable regression models. The patients' data were split into training and validation sets on a 7:3 ratio, and the random survival forest (RSF) model was established using the training sets and validated using the validation sets. The maximally selected rank statistics method was used to determine a cut point value corresponding to the most significant relation with survival. Univariable Cox regression suggested age, smoking, alcohol consumption, untreated, neural invasion, capsular invasion, skin invasion, tumors larger than 4 cm, advanced T and N stage, distant metastasis, and non‐mucous cell carcinoma were risk factors for poor prognosis, and multivariable analysis suggested that female, aging, smoking, untreated, and non‐mucous cell carcinoma were risk factors. The time‐dependent ROC curve showed the AUC of the RSF prediction model on 1‐, 2‐, and 3‐year survival were 0.696, 0.779, and 0.765 respectively in the validation sets. Log‐rank tests suggested that the cut point 7.42 risk score calculated from the RSF was most effective in dividing patients with significantly different prognoses. The prediction model based on the RSF could effectively screen patients with poor prognoses.