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Gastric cancer is the fifth most common cancer and the third most common cause of cancer death globally. Patients with advanced gastric cancer have poor outcomes and short survival times. This case report presents a remarkable clinical response in a patient with advanced Epstein-Barr virus (EBV)-associated gastric adenocarcinoma treated with a combination of chemotherapy, Claudin18.2-targeted therapy (TST001), and PD-1 inhibition (nivolumab). The patient, initially diagnosed with stage IA disease (pT1bN0M0) after Billroth II gastric resection was performed due to early carcinoma, later developed metastases to the liver, cervicothoracic lymph nodes and abdominal lymph nodes. The patient received seven cycles of CAPOX (capecitabine + oxaliplatin), nivolumab, and TST001, achieving partial response (PR) after treatment. Treatment was discontinued due to aortic dissection requiring surgery. Surprisingly, despite no further antitumor therapy, follow-up imaging over 19 months revealed continued tumor shrinkage, culminating in a near complete response (CR). Therefore, the combination of chemotherapy, Claudin18.2-targeted therapy, and PD-1 inhibitor may be a good treatment strategy for gastric cancer.

Fructose, especially industrial fructose (sucrose and high fructose corn syrup) is commonly used in all kinds of beverages and processed foods. Liver is the primary organ for fructose metabolism, recent studies suggest that excessive fructose intake is a driving force in non-alcoholic fatty liver disease (NAFLD). Dietary fructose metabolism begins at the intestine, along with its metabolites, may influence gut barrier and microbiota community, and contribute to increased nutrient absorption and lipogenic substrates overflow to the liver. Overwhelming fructose and the gut microbiota-derived fructose metabolites (e.g., acetate, butyric acid, butyrate and propionate) trigger the de novo lipogenesis in the liver, and result in lipid accumulation and hepatic steatosis. Fructose also reprograms the metabolic phenotype of liver cells (hepatocytes, macrophages, NK cells, etc.), and induces the occurrence of inflammation in the liver. Besides, there is endogenous fructose production that expands the fructose pool. Considering the close association of fructose metabolism and NAFLD, the drug development that focuses on blocking the absorption and metabolism of fructose might be promising strategies for NAFLD. Here we provide a systematic discussion of the underlying mechanisms of dietary fructose in contributing to the development and progression of NAFLD, and suggest the possible targets to prevent the pathogenetic process.

The tumor suppressor p53 is the central hub of a molecular network, which controls cell proliferation and death, and also plays an important role in the occurrence and development of liver fibrosis. The abundant post-translational processing and modification endow the functional diversity of p53. Considering the relationship between p53 and liver fibrosis, drug intervention targeting p53 or management of p53 regulation might be effective strategies to treat liver fibrosis. Here, we systematically discuss the regulation of p53 in different liver cells (hepatocytes, immune cells, HSCs, etc ) and the role of p53 in the development of liver fibrosis, and propose possible interventions to prevent the pathogenic processes of liver fibrosis.

An a priori model for multipoint statistics (MPS) modeling approaches is a training image. Before using MPS modeling, it must be determined whether the training images satisfy the spatial statistical stationarity. Modeling can be performed using the regular MPS approach if a training image is stationary. Otherwise, an enhanced method of nonstationary modeling is required. For instance, partition-based nonstationary modeling is an option. This study proposes a nonstationary evaluation metric based on pattern tile distances. It is possible to more accurately quantify the characteristics of the various distributions of spatial structure features in the entire space and achieve the goal of quantitatively evaluating the nonstationary metrics of training images by quantifying the distances of lower-level subpatterns in the pattern. Furthermore, an automatic partitioning approach based on pattern tile discrepancy is proposed for nonstationary training images to avoid the subjective and inefficient issues of manual partitioning when the training images cannot meet the stationary requirement of MPS modeling.

In the experiments reported here, we integrated work on hierarchy, culture, and the enforcement of group cooperation by examining patterns of punishment. Studies in Western contexts have shown that having high status can temper acts of dominance, suggesting that high status may decrease punishment by the powerful. We predicted that high status would have the opposite effect in Asian cultures because vertical collectivism permits the use of dominance to reinforce the existing hierarchical order. Across two experiments, having high status decreased punishment by American participants but increased punishment by Chinese and Indian participants. Moreover, within each culture, the effect of status on punishment was mediated by feelings of being respected. A final experiment found differential effects of status on punishment imposed by Asian Americans depending on whether their Asian or American identity was activated. Analyzing enforcement through the lens of hierarchy and culture adds insight into the vexing puzzle of when and why people engage in punishment.

Interstitial microstructures in igneous rocks record key evidence of late-stage evolution processes. In order to constrain the late-stage evolution of a Baima gabbro pegmatite (Panxi region, SW (Southwestern Sichuan Province), China), we investigated its mineral compositions and late-stage microstructures. These microstructures include replacive symplectites and fish-hook clinopyroxene. Replacive symplectites include fine-grained lamellar intergrowths of anorthite (An)-rich plagioclase + clinopyroxene/amphibole/biotite that are rooted to Fe-Ti oxides and replacing nearby plagioclase primocrysts (Type I) and intergrowths of An-rich plagioclase + clinopyroxene/amphibole that are rooted to olivine and replacing nearby plagioclase primocrysts (Type II). Rare replacive symplectites composed of biotite + plagioclase are also present. Those replacive symplectites and fish-hook clinopyroxene grew at a late magmatic stage with temperatures of 867–1023 °C. An-rich plagioclase in the replacive symplectites and fish-hook textures have An contents up to 94 mol.%, much higher than those of plagioclase primocrysts. Interstitial microstructures are interpreted to reflect interaction between primocrysts and an Fe-rich residual interstitial liquid, consistent with separation and migration of conjugate immiscible melts in a crystal mush. We propose that the hydrous interstitial melt in the Baima gabbro pegmatite may have undergone silicate immiscibility during late-stage magma crystallization. As the crystal fraction increased, crystal-mush compaction and porous melt migration likely became the primary controls on the evolution of the late-stage interstitial melt, rather than convection or diffusion.

With the rapid development of the Internet-of-Things (IoT), a massive amount of transient data is transmitted in edge networks. Transient data are highly time-sensitive, such as monitoring data generated by industrial devices. Due to their inefficiency, traditional caching strategies in edge networks are inadequate for handling transient data. Thus, to improve the efficiency of transient data caching, we construct a freshness model of transient data and propose a maximum entropy Actor–Critic-based caching strategy, TD-MEAC-which can improve the freshness of cached data and reduce the long-term caching cost. Simulation results show that the proposed TD-MEAC achieves a higher cache hit rate and maintains a higher average freshness of cached transient data compared with the existing DRL and baseline caching strategies.

The variogram function is a crucial tool for quantifying spatial correlation and a key component of Kriging interpolation, directly influencing the accuracy of interpolation results. However, traditional variogram fitting methods, such as least squares and weighted polynomial regression, often face limitations when handling complex or non-stationary spatial structures, which can lead to unstable fitting outcomes. To address these challenges, this paper proposes an automatic fitting method for experimental variogram functions based on deep learning. The variogram fitting process is inherently a nonlinear optimization problem, where the goal is to optimize the alignment between experimental and theoretical variogram functions. In this study, we construct a deep learning model designed to capture the nonlinear characteristics of experimental variograms with the objective of minimizing fitting errors and improving both the accuracy and stability of the fitting process. Extensive experimental results demonstrate that the proposed neural network model can effectively learn to fit a variety of experimental variograms and accurately represent different theoretical variogram functions. Compared to traditional methods, this approach consistently achieves superior fitting performance across multiple datasets, validating its effectiveness and robustness under diverse conditions. The stronger generalization capability and improved accuracy of the proposed method significantly enhance the automatic fitting of experimental variogram functions.

Ulcerative colitis (UC) is a kind of inflammatory bowel condition characterized by inflammation within the mucous membrane, rectal bleeding, diarrhea, and pain experienced in the abdominal region. Existing medications for UC have limited treatment efficacy and primarily focus on symptom relief. Limonium bicolor (LB), an aquatic traditional Chinese medicine (TCM), exerts multi-targeted therapeutic effects with few side effects and is used to treat anemia and hemostasis. Nevertheless, the impact of LB on UC and its mechanism of action remain unclear. Therefore, the objective of this study was to investigate the anti-inflammatory effects and mechanism of action of ethanol extract of LB (LBE) in lipopolysaccharide-induced RAW 264.7 macrophages and dextran sulfate sodium (DSS)-induced UC. The results showed that LBE suppressed the secretion of cytokines in LPS-stimulated RAW 264.7 cells in a dose-dependent manner. LBE had protective effects against DSS-induced colitis in mice, decreased the disease activity index (DAI) score, alleviated symptoms, increased colon length, and improved histological characteristics, thus having protective effects against DSS-induced colitis in mice. In addition, it reversed disturbances in the abundance of proteobacteria and probiotics such as Lactobacillus and Blautia in mice with DSS-induced UC. Based on the results of network pharmacology analysis, we identified four main compounds in LBE that are associated with five inflammatory genes (Ptgs2, Plg, Ppar-γ, F2, and Gpr35). These results improve comprehension of the biological activity and functionality of LB and may facilitate the development of LB-based compounds for the treatment of UC.

Shale reservoirs provide critical storage space for unconventional oil and gas, yet their frequent vertical facies alternations and complex spatial architectures make it difficult for conventional two-point geostatistical methods to reproduce thin interbedding and reservoir-scale continuity. Multiple-point geostatistics can incorporate structural information through training images (TIs), but practical 3D shale modeling is often hindered by the limited availability of representative 3D TIs. Using the F2 Member in the Qintong Sag, Subei Basin, eastern China, as a case study, we propose a hierarchical 2D-to-3D geological modeling workflow that combines mixed-point geostatistical simulation (MIXSIM) for generating vertical 2D facies sections and a sequential 2D simulation strategy with conditioning data (s2Dcd) for propagating section-based patterns into 3D space under hard well constraints. In the workflow, vertical sections serve as TI carriers to explicitly capture bedding-scale alternations, while well data are imposed as hard conditioning information during 3D simulation. Quantitative evaluation is performed in terms of (i) conditioning-data consistency, (ii) vertical facies-transition statistics quantified by transition counts and Markov transition probability matrices, (iii) global facies proportions summarized as the mean of 10 realizations, and (iv) connectivity characterized by connected geobody analysis. The realizations honor the conditioning data exactly, reproduce vertical transition behavior with a transition-matrix discrepancy of DMAE=0.0396, and maintain global facies proportions close to well-based estimates with a maximum deviation of 2.36%. These results demonstrate that the proposed MIXSIM–s2Dcd workflow provides a practical solution for well-data-driven, high-resolution 3D shale facies modeling when 3D training images are unavailable.