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The novel deubiquitinase enzyme, motif interacting with ubiquitin-containing novel DUB family-1 (MINDY1), is highly expressed in liver cancer tissues and plays a crucial role in maintaining the stemness of liver cancer cells. Programmed death ligand-1 (PD-L1) is an immunosuppressive molecule overexpressed by tumour cells. The potential role of MINDY1 in inhibiting the stemness of liver cancer cells by deubiquitinating PD-L1 has not yet been reported. To investigate the mechanism by which MINDY1 mediates immune escape in liver cancer through the regulation of PD-L1 ubiquitination, we examined the expression levels of MINDY1 and PD-L1 in liver cancer and adjacent tissues from 50 hepatocellular carcinoma (HCC) patients using protein imprinting and immunohistochemistry. We analyzed the relationship between the expression levels of MINDY1 and PD-L1 in liver cancer tissues and their correlation with the 5-year tumor-free survival rates of patients. Subsequently, MINDY1 expression was knocked down in Huh7 cells using small interfering RNA (siRNA) interference or upregulated through transfection with a MINDY1 overexpression plasmid. The effects of MINDY1 knockdown or overexpression on the proliferation, apoptosis, migration, and invasion of HCC cells, as well as the regulation of PD-L1 binding and ubiquitination, were assessed. The 5-year tumor-free survival rates were significantly lower in both the high MINDY1 expression group and the high PD-L1 expression group (χ2 = 4.919 and 13.158, respectively). A significant difference in survival was observed between the high and low MINDY1 expression groups (χ2= 27.415). MINDY1 was found to directly interact with PD-L1, with MINDY1 gene knockdown promoting PD-L1 ubiquitination and MINDY1 overexpression inhibiting PD-L1 ubiquitination. All comparisons yielded statistically significant results (P < 0.05). In conclusion, MINDY1 inhibits the malignant progression of liver cancer by inhibiting PD-L1 ubiquitination and mediating immune escape.

Drug prediction and treatment using bioinformatics and large-scale modeling have emerged as pivotal research areas. This study proposes a novel multi-level collaboration framework named Syn-COM for feature extraction and data integration of diseases and drugs. The framework aims to explore optimal drug combinations and interactions by integrating molecular virtuality, similarity clustering, overlap area, and network distance. It uniquely combines the characteristics of Chinese herbal medicine with clinical experience and innovatively assesses drug interaction and correlation through a synergy matrix. Gouty arthritis (GA) was used as a case study to validate the framework’s reliability, leading to the identification of an effective drug combination for GA treatment, comprising Tamaricis Cacumen (Si = 0.73), Cuscutae Semen (Si = 0.68), Artemisiae Annuae Herba (Si = 0.62), Schizonepetae Herba (Si = 0.73), Gleditsiae Spina (Si = 0.89), Prunellae Spica (Si = 0.75), and Achyranthis Bidentatae Radix (Si = 0.62). The efficacy of the identified drug combination was confirmed through animal experiments and traditional Chinese medicine (TCM) component analysis. Results demonstrated significant reductions in the blood inflammatory factors IL1A, IL6, and uric acid, as well as downregulation of TGFB1, PTGS2, and MMP3 expression (p < 0.05), along with improvements in ankle joint swelling in GA mice. This drug combination notably enhances therapeutic outcomes in GA by targeting key genes, underscoring the potential of integrating traditional medicine with modern bioinformatics for effective disease treatment.

Abstract The conventional numerical solvers for partial differential equations encounter a formidable challenge, as their computational efficiency and accuracy are heavily contingent on grid size. Recently, machine learning (ML) has exhibited substantial promise in addressing partial differential equations. Nevertheless, substantial hurdles persist in practical applications. In this work, we endeavor to establish a deep learning framework founded on the Fourier neural operator (FNO) for resolving the intricacies of simulating real landslide dynamic processes. Our findings demonstrate that the current FNO approach adeptly replicates landslide dynamic processes and boasts exceptional computational efficiency. Additionally, it is noteworthy that this data‐driven ML methodology can seamlessly incorporate data from other experimental sources or numerical simulation techniques. Consequently, this work underscores the significant potential of utilizing ML methodologies to supplant conventional numerical simulation methods.

Abstract The processing quality of the block hole system affects the working performance of the marine diesel engine block directly. Choosing an appropriate combination of process parameters is a prerequisite to improving the accuracy of the block hole system. Uncertain fluctuations of process parameters during the machining process would affect the process reliability of the block hole system, resulting in an ultra-poor accuracy. For this reason, the RBF method is used to establish the relationship between the verticality of the cylinder hole and process parameters, including cutting speed, depth of cut, and feed rate. The minimum cylinder hole verticality is taken as the goal and the process reliability constraints of the cylinder hole are set based on Monte Carlo, a reliability optimization model of processing parameters for cylinder hole is established in this paper. Meanwhile, an improved particle swarm algorithm was designed to solve the model, and eventually, the global optimal combination of process parameters for the cylinder hole processing of the diesel engine block in the reliability stable region was obtained.

Abstract Background Waste anesthetic gases (WAGs) leaked from new-type halogenated inhalational anesthetics such as sevoflurane have been were reported to pose a risk for the health of operating room personnel. The effects of WAGs on peripheral blood lymphocytes, however, remain yet controversial. The present study was undertaken to examine the effects of occupational sevoflurane exposure on the peripheral blood lymphocytes of medical personnel who work in the operating room. Methods A cohort of 56 medical residents were divided into exposed group (n = 28) and control group (non-exposed group) (n = 28). Gas chromatography was used to measure the concentration of sevoflurane in the medical resident’s breathing zone during surgeries under inhalation anesthesia in the exposure group. The gas collection lasted an hour. Peripheral blood lymphocytes were isolated from venous blood, and then apoptosis and cell cycle were analyzed by flow cytometry. EDTA-anticoagulated whole blood was harvested to analyze the lymphocyte subsets by flow cytometry. Immunoglobulins (IgA, IgM, IgG) were quantified by immunoturbidimetry. Results The average concentration of sevoflurane in the exposed group was 1.03 ppm with a range from 0.03 ppm to 2.24 ppm. No significant effects were found on the apoptosis rates or cell cycles of peripheral blood lymphocytes in the exposed group relative to the control group (P > 0.05). Similarly, there were no significant differences in the lymphocyte subsets or the levels of immunoglobulins (IgA, IgM, IgG) between the two groups (P > 0.05). Conclusions Occupational exposure to low-level sevoflurane has no significant effect on the peripheral blood lymphocytes of operating room staff, but this conclusion needs to be confirmed by multicenter and long-term follow-up studies with large samples. Trial registration number and date of registration ChiCTR2000040772 , December 9, 2020 (Retrospective registration).

Abstract Background Hepatocellular carcinoma (HCC) is a common malignant tumor with poor prognosis. Magnetic resonance imaging (MRI) is one of the most effective imaging methods for the early diagnosis of HCC. However, the current MR contrast agents are still facing challenges in the early diagnosis of HCC due to their relatively low sensitivity and biosafety. Thus, the development of effective MR agents is highly needed for the early diagnosis of HCC. Results Herein, we fabricated an HCC-targeted nanocomplexes containing SPIO-loaded mesoporous polydopamine (MPDA@SPIO), sialic acid (SA)-modified polyethyleneimine (SA-PEI), and alpha-fetoprotein regulated ferritin gene (AFP-Fth) which was developed for the early diagnosis of HCC. It was found that the prepared nanocomplexes (MPDA@SPIO/SA-PEI/AFP-Fth) has an excellent biocompatibility towards the liver cells. In vivo and in vivo studies revealed that the transfection of AFP-Fth gene in hepatic cells significantly upregulated the expression level of ferritin, thereby resulting in an enhanced contrast on T2-weighted images via the formed endogenous MR contrast. Conclusions The results suggested that MPDA@SPIO/SA-PEI/AFP-Fth had a superior ability to enhance the MR contrast of T2-weighted images of tumor region than the other preparations, which was due to its HCC-targeted ability and the combined T2 contrast effect of endogenous ferritin and exogenous SPIO. Our study proved that MPDA@SPIO/SA-PEI/AFP-Fth nanocomplexes could be used as an effective MR contrast agent to detect HCC in the early stage.

No single technology can be rich enough to segment accurately due to the challenges of liver segmentation, which include low contrast with neighboring organs and the presence of pathology as well as highly varied shapes between subjects. This paper presents a Multi-stage framework for location and segmentation. First, Faster RCNN is employed to locate the liver region. Then, the Gaussian mixture model-based signed distance function is proposed to increase the flexibility of shape prior models. To reach the long and narrow ravine liver regions, the Gaussian pseudo variance level set is applied. Experimental results demonstrate the efficiency of the proposed method. More specifically, the proposed method is evaluated on 40 CT scan images, which are publicly available on three databases: SLIVER07, 3Dircadb, and LiTS. Our method has a slightly superior performance compared with other newly published methods.

In view of the high precision requirement of the marine diesel engine body and the difficulty of quality control, a quality prediction method of the body, based on a process error transfer network, is proposed. First, according to the processing information of the body, the network nodes and edges are abstracted to establish the process error transfer network of the body. Then, the key quality control points and key quality features of the diesel engine body are determined by the PageRank and node degree. The key quality features obtained from the network analysis are taken as the output, and the corresponding process errors and process parameters are taken as the input. Finally, the quality prediction model of the body is established based on SVR algorithm, and the C, g parameters of SVR algorithm are optimized by the K-fold cross-validation method and grid search method to improve the prediction accuracy of the body processing quality.

On August 21, 2020, a landslide occurred in Zhonghai village of Hanyuan County, Sichuan Province, China. The landslide is triggered by two successive rounds of heavy rainfall. This landslide can be clearly divided into an initial landslide and a main landslide. The main landslide is activated by the intense impact and overloading of the initial landslide. The depth-integrated continuum method is adopted to simulate the dynamic process. Due to the complicated failure process, it is found that there is no proper unified parameters in Coulomb model which could well reproduce the two successive landslides. It implies that the dynamic process of landslides is highly associated with the characteristics of sliding bodies. Here, an implement of variable frictional coefficients for different parts is proposed and the parameters are calibrated. It is demonstrated that results from numerical modeling match well with the field investigation. The complicated landslides in two different stage can both be efficiently revealed by depth-integrated continuum modeling.

Abstract Background Recent evidence suggests early screening of preeclampsia and small-for-gestational-age (SGA) would benefit pregnancies followed by subsequent prophylactic use of aspirin. Multi-marker models have shown capability of predicting preeclampsia and SGA in first trimester. Yet the clinical feasibility of combined screening model for Chinese pregnancies has not been fully assessed. The aim of this study is to evaluate the applicability of a multi-marker screening model to the prediction of preeclampsia and SGA in first trimester particularly among Chinese population. Methods Three thousand two hundred seventy pregnancies meeting the inclusion criteria took first-trimester screening of preeclampsia and SGA. A prior risk based on maternal characteristics was evaluated, and a posterior risk was assessed by combining prior risk with multiple of median (MoM) values of mean arterial pressure (MAP), serum placental growth factor (PLGF) and pregnancy associated plasma protein A (PAPP-A). Both risks were calculated by Preeclampsia PREDICTOR™ software, Perkin Elmer. Screening performance of prior and posterior risks for early and late preeclampsia by using PREDICTOR software was shown by Receiver Operating Characteristics (ROC) curves. The estimation of detection rates and false positive rates of delivery with both preeclampsia and SGA was made. Results Eight cases developed early preeclampsia (0.24%) and 35 were diagnosed as late preeclampsia (1.07%). Five with early preeclampsia and ten with late preeclampsia later delivered SGA newborns (0.46%); 84 without preeclampsia gave birth to the SGAs (2.57%). According to ROC curves, posterior risks performed better than prior risks in terms of preeclampsia, especially in early preeclampsia. At 10% false positive rate, detection rates of early and late preeclampsia were 87.50 and 48.57%, detection rates of early and late SGA were 41.67 and 28.00%, respectively. For SGA, detection rates in cases with preeclampsia were much higher than those in absence of it. Conclusions This study demonstrates that combined screening model could be useful for predicting early preeclampsia in Chinese pregnancies. Furthermore, the performance of SGA screening by same protocol is strongly associated with preeclampsia.

The outbreak of novel coronavirus disease 2019 (COVID-19) has become the largest health threat worldwide, with more than 34.40 million positive cases and over 1.02 million deaths confirmed. In this study, we confirmed that significantly differentially expressed genes in COVID-19 patients were mainly involved in the regulation of immune and inflammation-related signaling pathways. It is worth noting that many infected COVID-19 patients have malignant tumors, and their prognosis is poor. To explore the susceptibility factors of cancer patients, we assessed the expression of ACE2, TMPRSS2, and the endocytic regulator AAK1 in lung adenocarcinoma (LUAD) patients and explored their effects on immune infiltration. We found that the expression of ACE2 and TMPRSS2 in LUAD patients was significantly increased, which may explain why LUAD patients are susceptible to SARS-CoV-2, and the patients with high-expression genes presented increased infiltration of immune cells such as B cells and CD4 T cells. In addition, we also identified miR-432-5p as a potential targeted molecule and bexarotene as a potential targeted drug of the three genes through bioinformatic analysis and further verified the anti-inflammatory effect of bexarotene, providing new ideas for the treatment of COVID-19.

Hepatocellular carcinoma (HCC) is the fifth common cause of tumor-related death worldwide. ZFP36, a RNA-binding protein, decreases in many cancers and its role in HCC remains unclear. This study aimed to investigate the underlying mechanisms by which ZFP36 inhibited HCC progression and increased fluorouracil (5-Fu) sensitivity. We found that ZFP36 was downregulated and PRC1 was upregulated in HCC tissues compared with adjacent non-tumor tissues. In vitro investigation presented that ZFP36 acted as a tumor suppressor, while overexpression of PRC1 increased cell proliferation, colony formation and invasion. Further investigations demonstrated that overexpression of ZFP36 inhibited tumor growth and promoted 5-Fu sensitivity in xenograft tumor mice model, which could be reversed when PRC1 overexpressed simultaneously. Luciferase reporter assays and Ribonucleoprotein immunoprecipitation analysis indicated that ZFP36 could bind to adenylate uridylate-rich elements located in PRC1 mRNA 3′UTR to downregulate PRC1 expression. Taken together, our findings identified that ZFP36 regulated PRC1 to exert anti-tumor effect, which suggested a potential therapeutic strategy for treating HCC by exploiting ZFP36/PRC1 axis.

Background/Aims: Poor response to chemotherapy leads to the relapse and metastatic progression of tumors. Reprogrammed glucose metabolism is one of the important hallmarks of cancer that facilitates cancer cell survival, proliferation and chemoresistance. However, the precise fate of glucose metabolism and its role in therapy responsiveness in cancers remains largely unexplored. Methods: The glycolytic phenotype of doxorubicin (ADR)-resistant breast cancer cells and their parental cells was assessed by measuring glucose uptake, lactate release, and extracellular acidification rate (ECAR). Protein expression was detected by Western blotting analysis and mRNA expression was detected using q-PCR. Cell survival ratio was determined by the cell counting kit 8 assay. The role of fibroblast growth factor receptor 4 (FGFR4) in glycolysis, chemoresistance, and the underlying mechanisms were studied by using gene expression microarray and short hairpin RNA-mediated gene knockdown. Results: We found that glycolytic flux are increased in two doxorubicin (ADR)-resistant breast cancer cell lines compared with their parental wild type cells, as demonstrated by increased glucose uptake, lactate release, and extracellular acidification rate (ECAR). By gene expression microarray, we identified FGFR4 as a critical modulator of ADR resistance and enhanced glucose metabolism. Genetic silencing of FGFR4 increased the chemosensitivity and suppressed the enhanced glycolytic flux in ADR-resistant cells. Mechanistically, activation of FGFR4 signaling in ADR-resistant cells led to the phosphorylation of FGF receptor substrate 2 (FRS2) and further activated the downstream MAPK/ERK signaling. Pharmacological inhibition of FGFR4-FRS2-ERK signaling pathway significantly blocked the chemoresistant and glycolytic phenotypes of ADR-resistant cells. Conclusion: Our findings suggest that high levels of FGFR4 can increase glucose metabolism and lead to chemoresistance in breast cancer and reveal the mechanistic basis for targeting FGFR4 as a therapeutic opportunity for chemoresistant tumors.

War chess gaming has so far received insufficient attention but is a significant component of turn-based strategy games (TBS) and is studied in this paper. First, a common game model is proposed through various existing war chess types. Based on the model, we propose a theory frame involving combinational optimization on the one hand and game tree search on the other hand. We also discuss a key problem, namely, that the number of the branching factors of each turn in the game tree is huge. Then, we propose two algorithms for searching in one turn to solve the problem: (1) enumeration by order; (2) enumeration by recursion. The main difference between these two is the permutation method used: the former uses the dictionary sequence method, while the latter uses the recursive permutation method. Finally, we prove that both of these algorithms are optimal, and we analyze the difference between their efficiencies. An important factor is the total time taken for the unit to expand until it achieves its reachable position. The factor, which is the total number of expansions that each unit makes in its reachable position, is set. The conclusion proposed is in terms of this factor: Enumeration by recursion is better than enumeration by order in all situations.

The anticorrosion properties of polymer emulsion modified cement-based coating with healing effect on carbon steel were investigated. The change of linear polarization resistance (Rp) and electrochemical impedance spectroscopy (EIS) have been used to measure the inhibitive and curative effect of electrodes painted with the coating in 3.5% NaCl solution. The results indicated the high alkalinity of filler (cement) in coating can obviously improve the anticorrosion performance of coated steel electrodes. And the inhibitive ability of cement-based coating with fluoride acrylic emulsion is superior to that of the coating with epoxy emulsion. Moreover, by adding antirust pigments and rust converters in the fluoride acrylic emulsion modified cement-based coating, the inhibitive property was improved and the curative effect can be observed.

Background: The present study aimed to explore the changes in the hepatic metabolic profile during the evolution of diabetes mellitus (DM) and verify the key metabolic pathways. Methods: Liver samples were collected from diabetic rats induced by streptozotocin (STZ) and rats in the control group at 1, 5, and 9 weeks after STZ administration. Proton nuclear magnetic resonance spectroscopy (1H NMR)-based metabolomics was used to examine the metabolic changes during the evolution of DM, and partial least squares-discriminate analysis (PLS-DA) was performed to identify the key metabolites. Results: We identified 40 metabolites in the 1H NMR spectra, and 11 metabolites were further selected by PLS-DA model. The levels of α-glucose and β-glucose, which are two energy-related metabolites, gradually increased over time in the DM rats, and were significantly greater than those of the control rats at the three-time points. The levels of choline, betaine, and methionine decreased in the DM livers, indicating that the protective function in response to liver injury may be undermined by hyperglycemia. The levels of the other amino acids (leucine, alanine, glycine, tyrosine, and phenylalanine) were significantly less than those of the control group during DM development. Conclusions: Our results suggested that the hepatic metabolic pathways of glucose, choline-betaine-methionine, and amino acids were disturbed during the evolution of diabetes, and that choline-betaine-methionine metabolism may play a key role. [ABSTRACT FROM AUTHOR]

A mathematical model for the transport process of migrating corrosion inhibitor (MCI) in cement-based material and a two-dimensional numerical solution method using commercially-available software are proposed. The numerical analysis results are in a good agreement with experimental data obtained from the two-dimensional absorption tests for nonreactive MCI but do not coincide with the data for reactive MCI. The reason behind may be that the latter reacted with calcium hydroxide (CH) in cement mortar and hindered the transport process. Therefore a modified transport model involving the pore-blocking effect of reactive MCI in cement-based materials is established according to the chemometrics method. The numerical and test results prove that the amendatory model is reasonable to understand the mass transfer of reactive MCI in cement mortar. Variance analyses of the factors in the model are also performed to find the key factor affecting the MCI transport process. A basic transport model and numerical analysis method of MCI in concrete is proposed. The model is corrected involving pore-blocking effect for reactive MCI based on chemical metrology. The numerical calculation results of the proposed model have a fine agreement with experimental results. [ABSTRACT FROM AUTHOR]

Numerical analysis was used to study the deposition and burning characteristics of combining co-combustion with slagging combustion technologies in this paper. The pyrolysis and burning kinetic models of different fuels were implanted into the WBSF-PCC2 (wall burning and slag flow in pulverized co-combustion) computation code, and then the slagging and co-combustion characteristics—especially the wall burning mechanism of different solid fuels and their effects on the whole burning behavior in the cylindrical combustor at different mixing ratios under the condition of keeping the heat input same—were simulated numerically. The results showed that adding wood powder at 25% mass fraction can increase the temperature at the initial stage of combustion, which is helpful to utilize the front space of the combustor. Adding wood powder at a 25% mass fraction can increase the reaction rate at the initial combustion stage; also, the coal ignitability is improved, and the burnout efficiency is enhanced by about 5% of suspension and deposition particles, which is helpful for coal particles to burn entirely and for combustion devices to minimize their dimensions or sizes. The results also showed that adding wood powder at a proper ratio is helpful to keep the combustion stability, not only because of the enhancement for the burning characteristics, but also because the running slag layer structure can be changed more continuously, which is very important for avoiding the abnormal slag accumulation in the slagging combustor. The theoretic analysis in this paper proves that unification of co-combustion and slagging combustion technologies is feasible, though more comprehensive and rigorous research is needed. [ABSTRACT FROM AUTHOR]

The article focuses on a research study which investigates the effect of providing power mobility on body weight in adults who were first-time qualifiers of power mobility in the U.S. The authors state that assistive mobility devices such as wheelchairs, are used to increase mobility, increase participation, and improve quality of life for patients with mobility impairments. They conclude that there was no significant weight change in adults who were first-time users of power mobility.

In recent years, intelligent unmanned systems such as drones, autonomous vehicles, and unmanned boats have played a significant role in various fields, including military, industrial and agricultural production, and logistics transportation. However, due to the limitations in resources and capabilities of individual intelligent unmanned systems, they are unable to meet the increasingly complex demands of real-world applications. it has become a trend to organize multiple intelligent unmanned systems into clusters to achieve collaborative work and swarm intelligence, of which swarm navigation and positioning is an important basis for swarm intelligence.Traditional satellite navigation systems are inadequate to meet the application requirements of intelligent unmanned systems in complex environments. Therefore, autonomous navigation of intelligent unmanned swarm systems has become an urgent problem to solve. Inspired by the neural mechanisms and principles of animal navigation systems and collective coordination, brain-inspired swarm navigation has emerged as a promising research field. It aims to achieve efficient and intelligent navigation in complex and unknown environments, fulfilling the navigation needs of intelligent unmanned systems operating in groups. This article provides an overview of the concept and mechanisms of brain-inspired swarm navigation. It explores the spatial neural mechanisms involved in brain-inspired swarm navigation, relevant simulation models, and swarm intelligence algorithms for brain-inspired swarm decision-making. Finally, the future directions of brain-inspired swarm navigation are discussed.