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Background: Mounting evidence underscores the importance of cell communication within the tumor microenvironment, which is pivotal in tumor proliferation, invasion, and metastasis. Exosomes play a crucial role in cell-to-cell communication. Although single-cell RNA sequencing (scRNA-seq) provides insights into individual cell transcriptional characteristics, it falls short of comprehensively capturing exosome-mediated intercellular communication. Method: We analyzed Pancreatic Ductal Adenocarcinoma (PDAC) tissues, separating supernatant and precipitate for exosome purification and single-cell nucleus suspension. We then constructed Single-nucleus RNA sequencing (snRNA-seq) and small RNA-seq libraries from these components. Our bioinformatic analysis integrated these sequences with ligand-receptor analysis and public miRNA data to map the cell communication network. Results: We established intercellular communication networks using bioinformatic analysis to track exosome miRNA effects and ligand-receptor pairs. Significantly, hsa-miR-1293 emerged as a prognostic biomarker for pancreatic cancer, linked to immune evasion, increased myeloid-derived suppressor cells, and poorer prognosis. Targeting this miRNA may enhance anti-tumor immunity and improve outcomes. Conclusion: Our study offers a novel approach to constructing intercellular communication networks using snRNA-seq and exosome-small RNA sequencing. By integrating miRNA tracing with ligand-receptor analysis, we illuminate the complex interactions in the pancreatic cancer microenvironment, highlighting the pivotal role of miRNAs and identifying potential biomarkers and therapeutic targets.

Abstract Background Current observational data indicates that ischemic stroke (IS) affects a significant proportion of people with COVID-19. The current study sought to evaluate the causal relationship between COVID-19 and IS. Methods A two-sample Mendelian randomization (2 S-MR) approach was used to probe the relationship between genetic determinants of three COVID-19 parameters (SARS-CoV‐2 infection, COVID-19 hospitalization, and severe COVID-19) and the incidence of IS based on genome-wide association studies (GWAS) data. Using this 2 S-MR technique, expression quantitative trait loci (eQTL) and GWAS studies were further assessed for overlap to identify common causative genes associated with severe COVID-19 and IS. Results IVW approaches indicated the genetic variants linked to COVID-19 hospitalization (OR 1.04, 95% CI 1.01–1.08, p = 0.023) and severe COVID-19 (OR 1.03, 95% CI 1.01–1.05, p = 0.007) were both significantly linked to greater odds of IS. In contrast, there was no causal association between genetic SARS-CoV-2 infection susceptibility and the occurrence of IS (OR 0.99, 95% CI 0.92–1.06, p = 0.694). Ten shared causal genes (TNFSF8, CFL2, TPM1, C15orf39, LHFPL6, FAM20C, SPAG9, KCNJ2, PELI1, and HLA-L) were established as possible mediators of the interplay between severe COVID-19 and the development of IS, with these genes primarily being enriched in immune-related and renin-angiotensin-aldosterone system pathways. Conclusion These findings indicate a possible causative relationship between IS risk and COVID-19 severity, offering crucial new information for managing COVID-19 patients. Promising options for therapeutic therapies for severe COVID-19 complicated by IS include the common genes found in the present study.

Abstract Background Autophagy is crucial for controlling the manifestation of tuberculosis. This study intends to discover autophagy-related molecular clusters as biomarkers for discriminating between latent tuberculosis (LTBI) and active tuberculosis (ATB) in children through gene expression profile analysis. Methods The expression of autophagy modulators was examined in pediatric patients with LTBI and ATB utilizing public datasets from the Gene Expression Omnibus (GEO) collection (GSE39939 and GSE39940). Results In a training dataset (GSE39939), patients with LTBI and ATB exhibited the expression of autophagy-related genes connected with their active immune responses. Two molecular clusters associated with autophagy were identified. Compared to Cluster 1, Cluster 2 was distinguished through decreased adaptive cellular immune response and enhanced inflammatory activation, according to single-sample gene set enrichment analysis (ssGSEA). Per the study of gene set variation, Cluster 2’s differentially expressed genes (DEGs) played a role in synthesizing transfer RNA, DNA repair and recombination, and primary immunodeficiency. The peak variation efficiency, root mean square error, and area under the curve (AUC) (AUC = 0.950) were all lowered in random forest models. Finally, a seven-gene-dependent random forest profile was created utilizing the CD247, MAN1C1, FAM84B, HSZFP36, SLC16A10, DTX3, and SIRT4 genes, which performed well against the validation dataset GSE139940 (AUC = 0.888). The nomogram calibration and decision curves performed well in identifying ATB from LTBI. Conclusions In summary, according to the present investigation, autophagy and the immunopathology of TB might be correlated. Furthermore, this investigation established a compelling prediction expression profile for measuring autophagy subtype development risks, which might be employed as possible biomarkers in children to differentiate ATB from LTBI.

IntroductionThe immune response regulates the severity of COVID-19 (sCOVID-19). This study examined the cause-and-effect relationship between immune cell traits (ICTs) and the risk of severe COVID-19. Additionally, we discovered the potential role of plasma metabolome in modulating this risk.MethodsEmploying data from a genome-wide association study (GWAS), we conducted a two-sample Mendelian randomization (MR) assessment of 731 genetic ICTs and sCOVID-19 (5,101 cases, 1,383,241 controls) incidence. The MR analysis was utilized to further quantitate the degree of plasma metabolome-mediated regulation of immune traits in sCOVID-19.ResultsThe inverse variance weighted method recognized 2 plasma metabolites (PMs) responsible for casual associations between immune cells and sCOVID-19 risk. These included Tridecenedioate (C13:1-DC) which regulated the association between CD27 on IgD- CD38br (OR 0.804, 95% CI 0.699–0.925, p = 0.002) and sCOVID-19 risk (mediated proportion: 18.7%); arginine to citrulline ratio which controlled the relationship of CD39 on monocyte (OR 1.053, 95% CI 1.013–1.094, p = 0.009) with sCOVID-19 risk (mediated proportion: -7.11%). No strong evidence that genetically predicted sCOVID-19 influenced the aforementioned immune traits.ConclusionIn this study, we have successfully identified a cause-and-effect relationship between certain ICTs, PMs, and the likelihood of contracting severe COVID-19. Our findings can potentially improve the accuracy of COVID-19 prognostic evaluation and provide valuable insights into the underlying mechanisms of the disease.

Currently, chemotherapy remains occupying a pivotal place in the treatment of pancreatic ductal adenocarcinoma (PDAC). Nonetheless, the emergence of drug resistance in recent years has limited the clinical efficacy of chemotherapeutic agents, especially gemcitabine (GEM). Through bioinformatics analysis, AT-rich Interactive Domain-containing Protein 3A (ARID3A), one of transcription factors, is discovered to possibly participate in this progress. This study thoroughly investigates the potential role of ARID3A in the malignant progression and GEM chemoresistance of PDAC and explores the underlying mechanisms. The results indicate that ARID3A knockdown suppresses tumor development and enhances the sensitivity of PDAC cells to GEM in vitro and vivo. Mechanically, CUT&Tag profiling sequencing, RNA‐sequencing and functional studies demonstrates that decreased ARID3A expression alleviates the transcriptional inhibition of phosphatase and tensin homolog (PTEN), consequently leading to glutathione peroxidase 4 (GPX4) depletion and increased lipid peroxidation levels. Activated ferroptosis induced by the inhibition of GPX4 subsequently restricts tumor progression and reduces GEM resistance in PDAC. This research identifies the ferroptosis regulatory pathway of ARID3A-PTEN-GPX4 axis and reveals its critical role in driving the progression and chemoresistance of pancreatic cancer. Notably, both inhibition of ARID3A and enhancement of ferroptosis can increase chemosensitivity to GEM, which offers a promising opportunity for developing therapeutic strategies to combat acquired chemotherapy resistance in pancreatic cancer.

Abstract Background Severe influenza is a serious global health issue that leads to prolonged hospitalization and mortality on a significant scale. The pathogenesis of this infectious disease is poorly understood. Therefore, this study aimed to identify the key genes associated with severe influenza patients necessitating invasive mechanical ventilation. Methods The current study utilized two publicly accessible gene expression profiles (GSE111368 and GSE21802) from the Gene Expression Omnibus database. The research focused on identifying the genes exhibiting differential expression between severe and non-severe influenza patients. We employed three machine learning algorithms, namely the Least Absolute Shrinkage and Selection Operator regression model, Random Forest, and Support Vector Machine-Recursive Feature Elimination, to detect potential key genes. The key gene was further selected based on the diagnostic performance of the target genes substantiated in the dataset GSE101702. A single-sample gene set enrichment analysis algorithm was applied to evaluate the participation of immune cell infiltration and their associations with key genes. Results A total of 44 differentially expressed genes were recognized; among them, we focused on 10 common genes, namely PCOLCE2, HLA_DPA1, LOC653061, TDRD9, MPO, HLA_DQA1, MAOA, S100P, RAP1GAP, and CA1. To ensure the robustness of our findings, we employed overlapping LASSO regression, Random Forest, and SVM-RFE algorithms. By utilizing these algorithms, we were able to pinpoint the aforementioned 10 genes as potential biomarkers for distinguishing between both cases of influenza (severe and non-severe). However, the gene HLA_DPA1 has been recognized as a crucial factor in the pathological condition of severe influenza. Notably, the validation dataset revealed that this gene exhibited the highest area under the receiver operating characteristic curve, with a value of 0.891. The use of single-sample gene set enrichment analysis has provided valuable insights into the immune responses of patients afflicted with severe influenza that have further revealed a categorical correlation between the expression of HLA_DPA1 and lymphocytes. Conclusion The findings indicated that the HLA_DPA1 gene may play a crucial role in the immune-pathological condition of severe influenza and could serve as a promising therapeutic target for patients infected with severe influenza.

In order to reveal the propagation law of coal and gas outburst impact airflow, the distribution laws of velocity field, pressure field, impact force field, concentration field of impact airflow in the process of coal and gas outburst are simulated by using the high Mach flow and dense material transfer module in COMSOL software. The simulation results show that the impact airflow in the roadway is jet like, with a velocity of 378.6 m/s, and with the increase of distance, it presents a multi peak vibration attenuation trend. Periodic expansion wave zone and compression wave zone appear in the jet, and the tail of the jet zone develops into turbulence and instability, and moves disorderly in the roadway. The impact force of the protruding blast flow can be as high as 246.3 kPa, and the shock wave moves in the roadway at the local sound speed. The maximum gas diffusion distance is 133.6 m, and the gas volume fraction in the middle of the roadway is close to 100%. Due to the pressure difference in the roadway at the later stage of the outburst, the gas returns, and the high volume fraction gas is further mixed with the air in the roadway.

Eukaryotic chromatin is organized into functional domains, that are characterized by distinct proteomic compositions and specific nuclear positions. In contrast to cellular organelles surrounded by lipid membranes, the composition of distinct chromatin domains is rather ill described and highly dynamic. To gain molecular insight into these domains and explore their composition, we developed an antibody-based proximity biotinylation method targeting the RNA and proteins constituents. The method that we termed antibody-mediated proximity labelling coupled to mass spectrometry (AMPL-MS) does not require the expression of fusion proteins and therefore constitutes a versatile and very sensitive method to characterize the composition of chromatin domains based on specific signature proteins or histone modifications. To demonstrate the utility of our approach we used AMPL-MS to characterize the molecular features of the chromocenter as well as the chromosome territory containing the hyperactive X chromosome in Drosophila. This analysis identified a number of known RNA-binding proteins in proximity of the hyperactive X and the centromere, supporting the accuracy of our method. In addition, it enabled us to characterize the role of RNA in the formation of these nuclear bodies. Furthermore, our method identified a new set of RNA molecules associated with the Drosophila centromere. Characterization of these novel molecules suggested the formation of R-loops in centromeres, which we validated using a novel probe for R-loops in Drosophila. Taken together, AMPL-MS improves the selectivity and specificity of proximity ligation allowing for novel discoveries of weak protein–RNA interactions in biologically diverse domains.

Summary: Background: The widespread involvement of tumor-infiltrating B cells highlights their potential role in tumor behavior. However, B cell heterogeneity in PDAC remains unexplored. Studying TIL-Bs in PDAC aims to identify new treatment strategies. Methods: We performed single-cell RNA sequencing to study the heterogeneity of B cells in PDAC. The prognostic and immunologic value of the identified CD38+ B cells was explored in FUSCC (n = 147) and TCGA (n = 176) cohorts. Flow cytometry was conducted to characterize the relationship between CD38+ B cells and other immune cells, as well as their phenotypic features. In vitro and in vivo experiments were performed to assess the putative effect of CD38+ B cells on antitumor immunity. Findings: The presence of CD38+ B cells in PDAC was associated with unfavorable clinicopathological features and poorer overall survival (p

Abstract The visual modeling method enables flexible interactions with rich graphical depictions of data and supports the exploration of the complexities of epidemiological analysis. However, most epidemiology visualizations do not support the combined analysis of objective factors that might influence the transmission situation, resulting in a lack of quantitative and qualitative evidence. To address this issue, we developed a portrait-based visual modeling method called +msRNAer. This method considers the spatiotemporal features of virus transmission patterns and multidimensional features of objective risk factors in communities, enabling portrait-based exploration and comparison in epidemiological analysis. We applied +msRNAer to aggregate COVID-19-related datasets in New South Wales, Australia, combining COVID-19 case number trends, geo-information, intervention events, and expert-supervised risk factors extracted from local government area-based censuses. We perfected the +msRNAer workflow with collaborative views and evaluated its feasibility, effectiveness, and usefulness through one user study and three subject-driven case studies. Positive feedback from experts indicates that +msRNAer provides a general understanding for analyzing comprehension that not only compares relationships between cases in time-varying and risk factors through portraits but also supports navigation in fundamental geographical, timeline, and other factor comparisons. By adopting interactions, experts discovered functional and practical implications for potential patterns of long-standing community factors regarding the vulnerability faced by the pandemic. Experts confirmed that +msRNAer is expected to deliver visual modeling benefits with spatiotemporal and multidimensional features in other epidemiological analysis scenarios.

This review presents an overview of advanced air mobility broadband noise (BBN) prediction and control techniques, highlighting significant advancements in various prediction models. Methods such as the semi-empirical Brooks–Pope–Marcolini (BPM) model, analytical Amiet model, and time-domain models based on the FW-H equation have been extensively studied. Machine learning (ML) shows promise in BBN prediction but requires extensive data training and application to noise source mechanisms. Passive control methods, such as leading and trailing edge serrations and blade tip designs, have been partially successful but often compromise the aerodynamic performance. Active control methods, like suction and blowing control, trim adjustments, and dielectric barrier discharge (DBD) plasma actuators, show great potential, with the latter two being particularly effective for reducing BBN in thin propeller structures. Overall, while progress has been made in understanding and predicting BBN, further research is needed to refine these methods and develop comprehensive noise control strategies. These advancements hold significant promise for effective and efficient noise mitigation in future AAM vehicles.

Environmental degradation remains a pressing global concern, prompting many nations to adopt measures to mitigate carbon emissions. In response to international pressure, China has committed to achieving a carbon peak by 2030 and carbon neutrality by 2060. Despite extensive research on China’s overall carbon emissions, there has been limited focus on individual provinces, particularly less developed provinces. Jiangxi Province, an underdeveloped province in southeastern China, recorded the highest GDP (Gross Domestic Product) growth rate in the country in 2022, and it holds significant potential for carbon emission reduction. This study uses data from Jiangxi Province’s 14th Five-Year Plan and Vision 2035 to create three carbon emission reduction scenarios and predict emissions. The extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology), along with various visualisation techniques, is employed to analyse the impacts of population size, primary electricity application level, GDP per capita, the share of the secondary industry in fixed-asset investment, and the number of civilian automobiles owned on carbon emissions. The study found that there is an inverted U-shaped curve relationship between GDP per capita and carbon emissions, car ownership is not a major driver of carbon emissions, and the development of primary electricity has significant potential as a means for reducing carbon emissions in Jiangxi Province. If strict environmental protection measures are implemented, Jiangxi Province can reach its peak carbon target by 2029, one year ahead of the national target. These findings provide valuable insights for policymakers in Jiangxi Province to ensure that their environmental objectives are met.

Abstract Background Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy. Radical surgical resection offers the only potential cure. There is increasing agreement that radical antegrade modular pancreatosplenectomy (RAMPS) may benefit patients with tumors in the body and tail of the pancreas. To address this, the Chinese Study Group for Pancreatic Cancer (CSPAC)-3 trial is proposed to compare the effect of RAMPS and standard retrograde pancreatosplenectomy (SRPS) on patient survival and preoperative safety Methods The randomized controlled trial will be multicenter and two-armed with blinded outcomes and intention-to-treat analysis. Three hundred patients with resectable body and tail pancreatic adenocarcinoma will be enrolled and randomly assigned to RAMPS or SRPS. Adjuvant chemotherapy based on an initial regimen will be recommended 4–6 weeks after surgery if no serious complication occurs. The hypothesis that RAMPS improves survival outcomes compared with SRPS will be tested using a superiority trial. The primary outcome will be overall survival (OS). Secondary outcomes will include recurrence-free survival (RFS), R0 resection rate, the number of harvested lymph nodes, postoperative complications, and quality of life scores. Discussion The use of RAMPS has increased over the past decade. It is reported that RAMPS is superior to SRPS in improving both the rate of R0 resection and lymph node yield. Despite these advantages, however, there is little high-level documentation of the superiority of RAMPS in terms of survival and this needs to be investigated. To address this issue, CSPAC has instigated the first prospective, randomized phase III control trials, aiming to explore the optimal surgical strategy for improving the prognosis and OS of patients with left-sided pancreatic cancer Trial registration Chinese Clinical Trial Registry ChiCTR2100053844; pre-results. Registered on December 1, 2021.

Abstract Background Cell death plays a crucial role in the progression of active tuberculosis (ATB) from latent infection (LTBI). Cuproptosis, a novel programmed cell death, has been reported to be associated with the pathology of various diseases. We aimed to identify cuproptosis-related molecular subtypes as biomarkers for distinguishing ATB from LTBI in pediatric patients. Method The expression profiles of cuproptosis regulators and immune characteristics in pediatric patients with ATB and LTBI were analyzed based on GSE39939 downloaded from the Gene Expression Omnibus. From the 52 ATB samples, we investigated the molecular subtypes based on differentially expressed cuproptosis-related genes (DE-CRGs) via consensus clustering and related immune cell infiltration. Subtype-specific differentially expressed genes (DEGs) were found using the weighted gene co-expression network analysis. The optimum machine model was then determined by comparing the performance of the eXtreme Gradient Boost (XGB), the random forest model (RF), the general linear model (GLM), and the support vector machine model (SVM). Nomogram and test datasets (GSE39940) were used to verify the prediction accuracy. Results Nine DE-CRGs (NFE2L2, NLRP3, FDX1, LIPT1, PDHB, MTF1, GLS, DBT, and DLST) associated with active immune responses were ascertained between ATB and LTBI patients. Two cuproptosis-related molecular subtypes were defined in ATB pediatrics. Single sample gene set enrichment analysis suggested that compared with Subtype 2, Subtype 1 was characterized by decreased lymphocytes and increased inflammatory activation. Gene set variation analysis showed that cluster-specific DEGs in Subtype 1 were closely associated with immune and inflammation responses and energy and amino acids metabolism. The SVM model exhibited the best discriminative performance with a higher area under the curve (AUC = 0.983) and relatively lower root mean square and residual error. A final 5-gene-based (MAN1C1, DKFZP434N035, SIRT4, BPGM, and APBA2) SVM model was created, demonstrating satisfactory performance in the test datasets (AUC = 0.905). The decision curve analysis and nomogram calibration curve also revealed the accuracy of differentiating ATB from LTBI in children. Conclusion Our study suggested that cuproptosis might be associated with the immunopathology of Mycobacterium tuberculosis infection in children. Additionally, we built a satisfactory prediction model to assess the cuproptosis subtype risk in ATB, which can be used as a reliable biomarker for the distinguishment between pediatric ATB and LTBI.

BackgroundApoptosis is associated with the pathogenesis of Mycobacterium tuberculosis infection. This study aims to identify apoptosis-related genes as biomarkers for differentiating active tuberculosis (ATB) from latent tuberculosis infection (LTBI).MethodsThe tuberculosis (TB) datasets (GSE19491, GSE62525, and GSE28623) were downloaded from the Gene Expression Omnibus (GEO) database. The diagnostic biomarkers differentiating ATB from LTBI were identified by combining the data of protein-protein interaction network, differentially expressed gene, Weighted Gene Co-Expression Network Analysis (WGCNA), and receiver operating characteristic (ROC) analyses. Machine learning algorithms were employed to validate the diagnostic ability of the biomarkers. Enrichment analysis for biomarkers was established, and potential drugs were predicted. The association between biomarkers and N6-methyladenosine (m6A) or 5-methylated cytosine (m5C) regulators was evaluated.ResultsSix biomarkers including CASP1, TNFSF10, CASP4, CASP5, IFI16, and ATF3 were obtained for differentiating ATB from LTBI. They showed strong diagnostic performances, with area under ROC (AUC) values > 0.7. Enrichment analysis demonstrated that the biomarkers were involved in immune and inflammation responses. Furthermore, 24 drugs, including progesterone and emricasan, were predicted. The correlation analysis revealed that biomarkers were positively correlated with most m6A or m5C regulators.ConclusionThe six ARGs can serve as effective biomarkers differentiating ATB from LTBI and provide insight into the pathogenesis of Mycobacterium tuberculosis infection.

Abstract Purpose Stenotrophomonas maltophilia is a Gram-negative pathogen that most commonly causes hospital-acquired infections that can be extremely challenging to treat, contributing to underrecognized mortality throughout the world. The relative benefits of monotherapy as compared to combination therapy in patients diagnosed with S. maltophilia pneumonia, however, have yet to be established. Methods Data from 307 patients diagnosed with S. maltophilia hospital-acquired pneumonia (HAP) across four Chinese teaching hospitals from 2016 to 2022 were retrospectively analyzed. Results Of the analyzed patients, 55.7% (171/307) were administered combination definitive therapy, with a 30-day all-cause mortality rate of 41.0% (126/307). A propensity score weighting analysis revealed that compared with monotherapy, combination definitive therapy was associated with a comparable 30-day mortality risk in the overall patient cohort (OR 1.124, 95% CI 0.707–1.786, P = 0.622), immunocompetent patients (OR 1.349, 95% CI 0.712–2.554, P = 0.359), and patients with APACHE II scores

Growing global concern over greenhouse gas emissions has led to a demand for understanding and addressing carbon emissions, with China being one of the main contributors to global carbon emissions, committed to reach the carbon peak by 2030. As a result, much previous research has delved into the drivers of carbon emissions in China; however, few studies have included new energy factors in the extended STIRPAT model when analysing the data, employed more advanced visualisation techniques such as force-directed diagrams, and explored factors outside of the industrial and energy sectors in determining China’s ability to reach their environmental goals. In this study, we use the extended STIRPAT model to analyse a more diverse range of drivers for carbon emissions in China and discuss methods to reach peak carbon emissions through the implementation of environmental policies. Using data from China’s 14th Five-Year Plan and Vision 2035 to set up two simulation scenarios, we predict China’s carbon emissions, introducing ridge regression to ensure validity, and employing big data and visualisation techniques to aid in interpreting results. Our findings suggest that China needs to implement more stringent environmental policies to meet its commitment to reach peak carbon emissions by 2030, revealing that factors such as per capita arable land area, per capita GDP, the proportion of people living in extreme poverty, the level of tourism development, the use of fossil fuels, and new energy technologies have a significant impact on China’s carbon emissions. As such, we can recommend more stringent policies relating to the agricultural, energy, and tourism sectors to help China achieve their goal of carbon peak by 2030.

The surface morphology of perovskite films significantly influences the performance of perovskite light-emitting diodes (PeLEDs). However, the thin perovskite thickness (~10 nm) results in low surface coverage on the substrate, limiting the improvement of photoelectric performance. Here, we propose a molecular additive strategy that employs pentafluorophenyl diphenylphosphinate (FDPP) molecules as additives. P=O and Pentafluorophenyl (5F) on FDPP can coordinate with Pb2+ to slow the crystallization process of perovskite and enhance surface coverage. Moreover, FDPP reduces the defect density of perovskite and enhances the crystalline quality. The maximum brightness, power efficiency (PE), and external quantum efficiency (EQE) of the optimal device reached 24,230 cd m−2, 82.73 lm W−1, and 21.06%, respectively. The device maintains an EQE of 19.79% at 1000 cd m−2 and the stability is further enhanced. This study further extends the applicability of P=O-based additives.

Background: Autophagy is closely involved in the control of mycobacterial infection. Objectives: Here, a diagnostic model was developed using the levels of autophagy-related genes (ARGs) in the blood to differentiate active tuberculosis (ATB) and latent tuberculosis infection (LTBI). Design: Secondary data analysis of three prospective cohorts. Methods: The expression of ARGs in patients with ATB and LTBI were analyzed using the GSE37250, GSE19491, and GSE28623 datasets from the GEO database. Results: Twenty-two differentially expressed ARGs were identified in the training dataset GSE37250. Using least absolute shrinkage and selection operator and multivariate logistic regression, three ARGs ( FOXO1, CCL2 , and ITGA3 ) were found that were positively associated with adaptive immune-related lymphocytes and negatively associated with myeloid and inflammatory cells. A nomogram was constructed using the three ARGs. The accuracy, consistency, and clinical relevance of the nomogram were evaluated using receiver operating characteristic curves, the C-index, calibration curves, and validation in the datasets GSE19491 and GSE28623. The nomogram showed good predictive performance. Conclusion: The nomogram was able to accurately differentiate between ATB and LTBI patients. These findings provide evidence for future study on the pathology of autophagy in tuberculosis infection.

Channel catfish virus (CCV, Ictalurid herpesvirus 1) is the causative pathogen of channel catfish virus disease, which has caused high mortality and substantial economic losses in the catfish aquaculture industry. Due to the lack of licensed prophylactic vaccines and therapeutic drugs, the prevention and control of CCV infection seem to remain stagnant. Active compounds from medicinal plants offer eligible sources of pharmaceuticals and lead drugs to fight against endemic and pandemic diseases and exhibit excellent effect against viral infection. In this study, we evaluated the antiviral ability of 12 natural compounds against CCV with cell models in vitro and found kaempferol exhibited the strongest inhibitory compound against CCV infection among all the tested compounds. Correspondingly, kaempferol decreased transcription levels of viral genes and the synthesis of viral proteins, as well as reduced proliferation and release of viral progeny, the severity of the CPE induced by CCV in a dose-dependent manner, based on quantitative real-time PCR (RT-qPCR), western blotting, viral cytopathic effects (CPE) and viral titer assessment. Moreover, time-of-drug-addition assays, virus attachment, and penetration assays revealed that kaempferol exerted anti-CCV activity probably by blocking attachment and internalization of the viral entry process. Altogether, the present results indicated that kaempferol may be a promising candidate antiviral agent against CCV infection, which shed light on the development of a novel and potent treatment for fish herpesvirus infection.

Precipitation can lead to significant leaching of heavy metals from abandoned tailings，resulting in a decline in the quality of the surrounding environment. This study aimed to simulate and quantify the migration patterns and fate of heavy metals in tailings caused by precipitation in various environmental media (tailings, air, water, soil, and sediments) using leaching tests, source apportionment, and a fugacity model. Results revealed that the average contents of Cd, Cu, As, Pb, Zn, and Cr in the un-weathered tailings were 3.43, 495.56, 160.70, 138.94, 536.57, and 69.52 mg/kg, respectively. The ecological risk factors in the tailings as well as in sediments and soils, were in the following order: Cd >Cu >As >Pb >Zn >Cr. A fugacity model based on the mass-balance methods was established, achieving a good agreement between simulation and measured values. The total amounts of Cd, Cu, As, Pb, and Zn leached from abandoned tailings over the 30-year evaluation period were estimated to be 1.09, 62.44, 0.16, 0.94, and 102.12 t, respectively. Soil and sediments are important reservoirs for heavy metals. The sum of the As, Cd, Cu, Pb, and Zn storage capacities in the soil and sediment accounted for 77.28%, 75.63%, 73.94%, 69.39%, and 57.80% of the total storage capacity, respectively. This study could provide the means for the establishment of a targeted pollution control plan, a guide for restoration projects, and will aid in controlling pollution risk and improving the surrounding environment.

A novel form of cell death, cuproptosis, was recently identified to be mediated by the binding of copper to lipoylated enzymes of the tricarboxylic acid cycle. Cuproptosis-related genes (CRGs) may play a crucial role in the progression of pancreatic adenocarcinoma (PAAD), which often exhibits metabolic reprogramming. In the present study, univariate Cox regression analysis and Kaplan–Meier survival analysis were performed to identify prognostic CRGs. Data from the Cancer Therapeutics Response Portal and the Genomics of Drug Sensitivity in Cancer database were downloaded for drug sensitivity analysis. DLAT was identified as the only prognostic CRG in PAAD (HR = 2.72; 95% CI, 1.10–6.74). Functional enrichment analyses indicated that the basic function of DLAT is closely related to metabolism, and multiple tumor-promoting and immune response-related pathways were enriched in DLAT-high PAAD samples. The influence of DLAT and related genes on cancer immunity was evaluated by comprehensive immune infiltration analyses, which revealed the value of these genes as biomarkers for evaluating the sensitivity to immunotherapy. Additionally, high DLAT expression induced drug resistance, and significantly increased resistance to commonly used chemotherapeutics in PAAD, such as gemcitabine, oxaliplatin, 5-fluorouracil, and irinotecan. In conclusion, our study preliminarily revealed the prognostic value of DLAT, which is correlated with PAAD progression, chemoresistance, and immune infiltration, providing a valuable reference for PAAD treatment. However, our findings need to be confirmed by further in vivo and in vitro experiments.

Intratumor heterogeneity of positron emission tomography-computed tomography (PET-CT) is reflected by variable 18F-fluorodeoxyglucose (FDG) uptake. Increasing evidence has shown that neoplastic and non-neoplastic components can affect the total 18F-FDG uptake in tumors. Cancer-associated fibroblasts (CAFs) is considered as the main non-neoplastic components in tumor microenvironment (TME) of pancreatic cancer. Our study aims to explore the impact of metabolic changes in CAFs on heterogeneity of PET-CT. A total of 126 patients with pancreatic cancer underwent PET-CT and endoscopic ultrasound elastography (EUS-EG) before treatment. High maximum standardized uptake value (SUVmax) from the PET-CT was positively correlated with the EUS-derived strain ratio (SR) and indicated poor prognosis of patients. In addition, single-cell RNA analysis showed that CAV1 affected glycolytic activity and correlated with glycolytic enzyme expression in fibroblasts in pancreatic cancer. We also observed the negative correlation between CAV1 and glycolytic enzyme expression in the tumor stroma by using immunohistochemistry (IHC) assay in the SUVmax-high and SUVmax-low groups of pancreatic cancer patients. Additionally, CAFs with high glycolytic activity contributed to pancreatic cancer cell migration, and blocking CAF glycolysis reversed this process, suggesting that glycolytic CAFs promote malignant biological behavior in pancreatic cancer. In summary, our research demonstrated that the metabolic reprogramming of CAFs affects total 18F-FDG uptake in tumors. Thus, an increase in glycolytic CAFs with decreased CAV1 expression promotes tumor progression, and high SUVmax may be a marker for therapy targeting the neoplastic stroma. Further studies should clarify the underlying mechanisms.

Growing evidence indicates a potential correlation between necroptosis and pancreatic cancer, and the relationship between necroptosis, immune infiltration and the microenvironment in pancreatic cancer has drawn increasing attention. However, two-dimensional phenotype and prognostic assessment systems based on a combination of necroptosis and immunity have not been explored. In our present study, we explored the pancancer genomics signature of necroptosis-related molecules, identifying necroptosis-related molecule mutation profiles, expression profiles, and correlations between expression levels and methylation/CNV levels. We identified distinct necroptotic as well as immune statuses in pancreatic cancer, and a high necroptosis phenotype and high immunity phenotype both indicated better prognosis than a low necroptosis phenotype and low immunity phenotype. The two-dimensional phenotype we constructed has ideal discriminative effects on pancreatic cancer prognosis, inflammation, and the immune microenvironment. The ''high-necroptosis and high-immunity (HNHI)'' group exhibited the best prognosis and the highest proportion of infiltrating immune cells. The NI score can be used to predict patient prognosis and is correlated with the immune microenvironment score, chemotherapeutic drug IC50, and tumor mutational burden. In addition, it may be useful for predicting the effect of individualized chemotherapy and immunotherapy. Our study also revealed that SLC2A1 is associated with both necroptosis and immunity and acts as a potential oncogene in pancreatic cancer. In conclusion, the two-dimensional phenotype and NI score we developed are promising tools for clinical multiomics applications and prediction of chemotherapy and immunotherapy response and present benefits in terms of precision medicine and individualized treatment decision-making for pancreatic cancer patients.

Abstract As a critical component of the tumor microenvironment (TME), cancer‐associated fibroblasts (CAFs) play important roles in cancer initiation and progression. Well‐known signaling pathways, including the transforming growth factor‐β (TGF‐β), Hedgehog (Hh), Notch, Wnt, Hippo, nuclear factor kappa‐B (NF‐κB), Janus kinase (JAK)/signal transducer and activator of transcription (STAT), mitogen‐activated protein kinase (MAPK), and phosphoinositide 3‐kinase (PI3K)/AKT pathways, as well as transcription factors, including hypoxia‐inducible factor (HIF), heat shock transcription factor 1 (HSF1), P53, Snail, and Twist, constitute complex regulatory networks in the TME to modulate the formation, activation, heterogeneity, metabolic characteristics and malignant phenotype of CAFs. Activated CAFs remodel the TME and influence the malignant biological processes of cancer cells by altering the transcriptional and secretory characteristics, and this modulation partially depends on the regulation of signaling cascades. The results of preclinical and clinical trials indicated that therapies targeting signaling pathways in CAFs demonstrated promising efficacy but were also accompanied by some failures (e.g., NCT01130142 and NCT01064622). Hence, a comprehensive understanding of the signaling cascades in CAFs might help us better understand the roles of CAFs and the TME in cancer progression and may facilitate the development of more efficient and safer stroma‐targeted cancer therapies. Here, we review recent advances in studies of signaling pathways in CAFs and briefly discuss some future perspectives on CAF research.

The COVID-19 pandemic is one of the most severe infectious diseases in recent decades, and has had a significant impact on the global economy, and the stock market. Most existing studies on stock market volatility during the pandemic have been conducted from a data science perspective, with statistical analysis and mathematical models often revealing the superficial relationship between Covid and the stock market at the data level. In contrast, few studies have explored the relationship between more specialised aspects of the pandemic. Specifically, the relationship found between major social events and the stock market. In this work, a multi-source, data-based relationship analysis method is proposed, that collects historical data on significant social events and related stock data in China and the USA, to further explore the potential correlation between stock market index fluctuations and the impact of social events by analysing cross-timeline data. The results suggest and offer more evidence that social events do indeed impact equity markets, and that the indices in both China and the USA were also affected more by the epidemic in 2020 than in 2021, and these indices became less affected by the epidemic as it became the world adapted. Moreover, these relationships may also be influenced by a variety of other factors not covered in this study. This research, so far, is in its initial stage, and the methodology is not rigorous and cannot be applied as an individual tool for decision; however, it could potentially serve as a supplementary tool and provide a multi-dimensional basis for stock investors and policymakers to make decisions.

Abstract Background Autophagy regulators play important roles in the occurrence and development of a variety of tumors and are involved in immune regulation and drug resistance. However, the modulatory roles and prognostic value of autophagy regulators in pancreatic cancer have not been identified. Methods Transcriptomic data and survival information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used to construct a risk score model. Important clinical features were analyzed to generate a nomogram. In addition, we used various algorithms, including ssGSEA, CIBERSORT, XCELL, EPIC, TIMER, and QUANTISEQ, to evaluate the roles of autophagy regulators in the pancreatic cancer immune microenvironment. Furthermore, the mutation landscape was compared between different risk groups. Results Pan cancer analysis indicated that most of the autophagy regulators were upregulated in pancreatic cancer and were correlated with methylation and CNV level. MET, TSC1, and ITGA6 were identified as the prognostic autophagy regulators and used to construct a risk score model. Some critical clinical indicators, such as age, American Joint Committee on Cancer (AJCC) T stage, AJCC N stage, alcohol and sex, were combined with the risk model to establish the nomogram, which may offer clinical guidance. In addition, our study demonstrated that the low score groups exhibited high immune activity and high abundances of various immune cells, including T cells, B cells, and NK cells. Patients with high risk scores exhibited lower half inhibitory concentration (IC50) values for paclitaxel and had downregulated expression profiles of PD1, CTLA4, and LAG3. Mutation investigation indicated that the high risk groups exhibited a higher mutation burden and higher mutation number compared to the low risk groups. additionally, we verified our risk stratification method using cytology and histology data from our center, and the results are satisfactory. Conclusion We speculated that autophagy regulators have large effects on the prognosis, immune landscape and drug sensitivity of pancreatic cancer. Our model, which combines critical autophagy regulators and clinical indicators, will provide guidance for clinical treatment.

Abstract Background Influenza is a contagious disease that affects people of all ages and is linked to considerable mortality during epidemics and occasional outbreaks. Moreover, effective immunological biomarkers are needed for elucidating aetiology and preventing and treating severe influenza. Herein, we aimed to evaluate the key genes linked with the disease severity in influenza patients needing invasive mechanical ventilation (IMV). Three gene microarray data sets (GSE101702, GSE21802, and GSE111368) from blood samples of influenza patients were made available by the Gene Expression Omnibus (GEO) database. The GSE101702 and GSE21802 data sets were combined to create the training set. Hub indicators for IMV patients with severe influenza were determined using differential expression analysis and Weighted correlation network analysis (WGCNA) from the training set. The receiver operating characteristic curve (ROC) was also used to evaluate the hub genes from the test set's diagnostic accuracy. Different immune cells' infiltration levels in the expression profile and their correlation with hub gene markers were examined using single-sample gene set enrichment analysis (ssGSEA). Results In the present study, we evaluated a total of 447 differential genes. WGCNA identified eight co-expression modules, with the red module having the strongest correlation with IMV patients. Differential genes were combined to obtain 3 hub genes (HLA-DPA1, HLA-DRB3, and CECR1). The identified genes were investigated as potential indicators for patients with severe influenza who required IMV using the least absolute shrinkage and selection operator (LASSO) approach. The ROC showed the diagnostic value of the three hub genes in determining the severity of influenza. Using ssGSEA, it has been revealed that the expression of key genes was negatively correlated with neutrophil activation and positively associated with adaptive cellular immune response. Conclusion We evaluated three novel hub genes that could be linked to the immunopathological mechanism of severe influenza patients who require IMV treatment and could be used as potential biomarkers for severe influenza prevention and treatment.

Pancreatic ductal adenocarcinoma (PDAC) is a highly heterogeneous cancer with limited understanding of its classification and tumor microenvironment. Here, by analyzing single-nucleus RNA sequencing of 43, 817 tumor cells from 15 PDAC tumors and non-tumor, we find that hypoxia signatures were heterogeneous across samples and were potential regulators for tumor progression and more aggressive phenotype. Hypoxia-high PDAC tends to present a basal/squamous-like phenotype and has significantly increased outgoing signaling, which enhances tumor cell stemness and promotes metastasis, angiogenesis, and fibroblast differentiation in PDAC. Hypoxia is related to an extracellular matrix enriched microenvironment, and increased possibility of TP53 mutation in PDAC. TP63 is a specific marker of squamous-like phenotype, and presents elevated transcriptome levels in most hypoxia PDAC tumors. In summary, our research highlights the potential linkage of hypoxia, tumor progression and genome alteration in PDAC, leading to further understand of the formation of inter-tumoral and intra-tumoral heterogenous in PDAC. Our study extends the understanding of the diversity and transition of tumor cells in PDAC, which provides insight into future PDAC management.

Abstract Background RNA methylation refers to a form of methyl modification in RNA that modulates various epigenetic alterations. Mounting studies have focused on its potential mechanisms in cancer initiation and progression. However, the prognostic value and potential role of RNA methylation in the immune microenvironment of pancreatic cancer remain unclear. Methods Comprehensive bioinformatics analysis was performed to illuminate the expression profiles of RNA methylation modulators. In addition, the ConsensusClusterPlus algorithm was utilized to identify two remarkably different subtypes, and a feasible risk stratification method was established to accurately estimate prognosis. In addition, we validated our signature at the cytology and histology levels and conducted functional experiments to explore the biological functions of our key genes. Results Two subtypes with remarkable survival differences were identified by the consensus clustering algorithm. Cluster 2 tended to have higher expression levels of RNA methylation regulators and to be the high RNA methylation group. In addition, cluster 1 exhibited a significantly higher abundance of almost all immune cells and increased immune checkpoint expression compared to cluster 2. Chemotherapeutic sensitivity analysis indicated that there were significant differences in the sensitivity of four of the six drugs between different subgroups. Mutation investigation revealed a higher mutation burden and a higher number of mutations in cluster 2. An accurate and feasible risk stratification method was established based on the expression of key genes of each subtype. Patients with low risk scores exhibited longer survival times in one training (TCGA) and two validation cohorts (ICGC, GSE57495), with p values of 0.001, 0.0081, and 0.0042, respectively. In addition, our signature was further validated in a cohort from Fudan University Shanghai Cancer Center. The low-risk group exhibited higher immune cell abundance and immune checkpoint levels than the high-risk group. The characteristics of the low-risk group were consistent with those of cluster 1: higher stromal score, estimate score, and immune score and lower tumor purity. Additionally, cell function investigations suggested that knockdown of CDKN3 remarkably inhibited the proliferation and migration of pancreatic cancer cells. Conclusions RNA methylation has a close correlation with prognosis, immune infiltration and therapy in pancreatic cancer. Our subtypes and risk stratification method can accurately predict prognosis and the efficacy of immune therapy and chemotherapy.

BackgroundUrinary incontinence (UI) is a highly prevalent health concern commonly observed during and after pregnancy that can substantially impact women’s physical and psychological well-being and quality of life. Owing to its numerous advantages, mobile health may be a promising solution; however, it is unclear whether the app-based intervention can effectively improve UI symptoms during and after pregnancy. ObjectiveThis study aimed to evaluate the effectiveness of the Urinary Incontinence for Women (UIW) app–based intervention for UI symptom improvement among pregnant women in China. MethodsSingleton pregnant women without incontinence before pregnancy who were aged ≥18 years and between 24 and 28 weeks of gestation were recruited from a tertiary public hospital in China and were randomly allocated (1:1) to either an experimental group (n=63) or a control group (n=63). The experimental group received the UIW app intervention and oral pelvic floor muscle training (PFMT) instructions, whereas the control group received oral PFMT instructions alone. Neither the participants nor the researchers were blinded to the intervention. The primary outcome was UI severity. The secondary outcomes included quality of life, self-efficacy with PFMT, and knowledge of UI. All data were collected at baseline, 2 months after randomization, and 6 weeks post partum through electronic questionnaires or by checking the electronic medical record system. Data analysis followed the intention-to-treat principle. A linear mixed model was used to examine the intervention effect on primary and secondary outcomes. ResultsParticipants in the experimental and control groups were comparable at baseline. Of the 126 overall participants, 117 (92.9%) and 103 (81.7%) women completed follow-up visits at 2 months after randomization and 6 weeks after delivery, respectively. A statistically significant difference in UI symptom severity was observed between the experimental group and control group (2 months after randomization: mean difference −2.86, 95% CI −4.09 to −1.64, P

Increasing evidence has confirmed that cancer-associated fibroblasts (CAFs) recruit and induce regulatory T cells (Tregs) and macrophages but inhibit cytotoxic T lymphocyte infiltration to a certain extent, indicating that CAFs have a significant influence on the immunosuppressive microenvironment. However, the effect of CAFs on the immune microenvironment and immunotherapy response in pancreatic cancer remains unclear. Our research identified remarkable variation in CAF-associated molecules in multiple cancer types at the genetic and transcriptome levels. Two phenotypes were identified for 476 pancreatic cancer samples, and the different phenotypes exhibited significant variation in immune and inflammatory characteristics. Phenotype 1 exhibited higher levels of immune infiltration and lower expression of tumor-associated gene signatures than phenotype 2. We used a multipart approach to assess the prognostic value of CAF-associated molecules and constructed a CAF score model that could accurately predict patient prognosis. The CAF score accurately predicted infiltrating immune cell abundance, chemosensitivity, and the response to immunotherapy. Additionally, we found that the CAF-associated molecule FGFR4 may promote the proliferation and migration and inhibit the apoptosis of pancreatic cancer cells and is correlated with immune infiltration, suggesting its potential role as an oncogene. CAFs may promote the malignant biological behavior of pancreatic cancer through FGFR4. In summary, our research highlights potential relationships of the dysregulation of CAF-associated molecules with genome alterations and carcinogenesis in multiple malignancies. Our CAF-associated phenotypes and scoring system may enhance the understanding of pancreatic cancer chemotherapy sensitivity and immunotherapy response, providing new insights for personalized chemotherapy and immunotherapy.

IntroductionAdolescence is a crucial period for establishing ego identity and becoming a social individual. However, numerous adolescents suffer from mental health problems, especially after the conditions surrounding the COVID-19 outbreak. Personality assessments are often used when adolescents look for psychological self-help services. However, the meaning and mechanism of these personality assessments remain unknown. Taking the increasingly popular MBTI personality assessment as an entry point, the current study examined the potential sequential mediation relationship of Barnum effect – ego identity on the link between personality assessment usage and mental health.MethodsThe current study surveyed 308 Chinese high school students, including 109 males and 199 females. MBTI use, Barnum effect, ego-identity, and mental health (subjective well-being, depression, and anxiety) were measured by seven questionnaires, respectively. Sequential mediation models were constructed to analyze the relationship.ResultsThe results indicate that the Barnum effect and ego identity together function as a sequential mediation path between personality assessment use and teenagers’ mental health, including subjective well-being, depression, and anxiety. Specifically, a higher level of MBTI use triggers a stronger Barnum effect. The Barnum effect then promotes adolescents’ ego identity, ultimately increasing subjective well-being levels and reducing anxiety and depression.DiscussionOur findings suggest that by properly using personality assessment and stimulating the Barnum effect, we can enhance adolescents’ mental health. The theoretical and practical implications of our findings are discussed.

Abstract Cancer-associated fibroblasts (CAFs), a stromal cell population with cell-of-origin, phenotypic and functional heterogeneity, are the most essential components of the tumor microenvironment (TME). Through multiple pathways, activated CAFs can promote tumor growth, angiogenesis, invasion and metastasis, along with extracellular matrix (ECM) remodeling and even chemoresistance. Numerous previous studies have confirmed the critical role of the interaction between CAFs and tumor cells in tumorigenesis and development. However, recently, the mutual effects of CAFs and the tumor immune microenvironment (TIME) have been identified as another key factor in promoting tumor progression. The TIME mainly consists of distinct immune cell populations in tumor islets and is highly associated with the antitumor immunological state in the TME. CAFs interact with tumor-infiltrating immune cells as well as other immune components within the TIME via the secretion of various cytokines, growth factors, chemokines, exosomes and other effector molecules, consequently shaping an immunosuppressive TME that enables cancer cells to evade surveillance of the immune system. In-depth studies of CAFs and immune microenvironment interactions, particularly the complicated mechanisms connecting CAFs with immune cells, might provide novel strategies for subsequent targeted immunotherapies. Herein, we shed light on recent advances regarding the direct and indirect crosstalk between CAFs and infiltrating immune cells and further summarize the possible immunoinhibitory mechanisms induced by CAFs in the TME. In addition, we present current related CAF-targeting immunotherapies and briefly describe some future perspectives on CAF research in the end.

Pyroptosis is a form of programmed cell death associated with inflammatory alterations. However, the intrinsic mechanisms and underlying correlation of pyroptosis-related lncRNAs (PRLs) in pancreatic ductal adenocarcinoma (PDAC) remain unclear. The objective of the current research was to identify pyroptosis-related lncRNAs and a prognostic model to predict the prognosis of patients. We extracted pyroptosis-related lncRNAs to construct a risk model and validated them at Fudan University Shanghai Cancer Center. Crosstalk between lncRNA SNHG10 and GSDMD was found to regulate pyroptosis levels. A new algorithm was used to establish a 0 or 1 PRL pair matrix and prognostic model. Six pyroptosis-related lncRNA pairs were identified and utilized to construct a risk model. The low-risk groups exhibited better prognoses than the high-risk groups. The area under the curve (AUC) indicated extremely high accuracy, reaching 0.810 at 1 year, 0.850 at 2 years, and 0.850 at 3 years in the training set. Patients with different risk scores exhibited distinct metabolic, inflammatory, and immune microenvironments as well as tumor mutation landscapes. Additionally, 9 commonly used chemotherapeutic drugs exhibited different sensitivities between the high- and low-risk groups. To conclude, we propose that pyroptosis exhibits a close correlation with PDAC. Our risk model based on PRL pairs may be beneficial for the accurate estimation of prognostic outcomes, the immune microenvironment, and drug sensitivity, bringing therapeutic hope for patients with PDAC.

Abstract Background Extensive studies have demonstrated the pivotal roles of circular RNAs (circRNAs) in the occurrence and development of different human cancers. However, the expression and regulatory roles of circRNAs in pancreatic ductal adenocarcinoma (PDAC) are unclear. Methods CircEYA3 was explored based on Gene Expression Omnibus (GEO) dataset analysis. qRT-PCR was applied to determine the expression of circRNAs, miRNAs and mRNAs in PDAC cells and tissues. The biological roles of circEYA3 in vitro and in vivo were determined by performing a series of functional experiments. Further, dual luciferase reporter, fluorescence in situ hybridization (FISH), RNA pull-down assays, and RNA immunoprecipitation (RIP) assays were used to confirm the interaction of circEYA3 with miR-1294. Results CircEYA3 was elevated in PDAC tissues and cells, and a higher level of circEYA3 was significantly associated with a poorer prognosis in patients with PDAC. Functionally, circEYA3 increased energy production via ATP synthesis to promote PDAC progression in vitro and in vivo. Mechanistically, circEYA3 functions as an endogenous miR-1294 sponge to elevate c-Myc expression, thus exerting its oncogenic functions. Conclusion CircEYA3 promotes the progression of PDAC through the miR-1294/c-Myc signalling axis, and circEYA3 may be an efficient molecular therapeutic target in PDAC.

Abstract Background High tumor mutation burden (TMB) has gradually become a sensitive biomarker for predicting the response to immunotherapy in many cancers, including lung, bladder and head and neck cancers. However, whether high TMB predicts the response to immunotherapy and prognosis in pancreatic ductal adenocarcinoma (PDAC) remained obscure. Hence, it is significant to investigate the role of genes related to TMB (TRGs) in PDAC. Methods The transcriptome and mutation data of PDAC was downloaded from The Cancer Genome Atlas-Pancreatic Adenocarcinoma (TCGA). Five independent external datasets of PDAC were chosen to validate parts of our results. qRT-PCR and immunohistochemical staining were also performed to promote the reliability of this study. Results The median overall survival (OS) was significantly increased in TMB_low group compared with the counterpart with higher TMB score after tumor purity adjusted (P = 0.03). 718 differentially expressed TRGs were identified and functionally enriched in some oncogenic pathways. 67 TRGs were associated with OS in PDAC. A prognostic model for the OS was constructed and showed a high predictive accuracy (AUC = 0.849). We also found TMB score was associated with multiple immune components and signatures in tumor microenvironment. In addition, we identified a PDAC subgroup featured with TMBlowMicrosatellite instabilityhigh (MSIhigh) was associated with prolonged OS and a key molecule, ANKRD55, potentially mediating the survival benefits. Conclusion This study analyzed the biological function, prognosis value, implications for mutation landscape and potential influence on immune microenvironment of TRGs in PDAC, which contributed to get aware of the role of TMB in PDAC. Future studies are expected to investigate how these TRGs regulate the initiation, development or repression of PDAC.

Abstract Background Postherpetic neuralgia (PHN) is a devastating complication after varicella-zoster virus infection. Brain-derived neurotrophic factor (BDNF) has been shown to participate in the pathogenesis of PHN. A truncated isoform of the tropomyosin receptor kinase B (TrkB) receptor TrkB.T1, as a high-affinity receptor of BDNF, is upregulated in multiple nervous system injuries, and such upregulation is associated with pain. Acid-sensitive ion channel 3 (ASIC3) is involved in chronic neuropathic pain, but its relation with BDNF/TrkB.T1 in the peripheral nervous system (PNS) during PHN is unclear. This study aimed to investigate whether BDNF/TrkB.T1 contributes to PHN through regulating ASIC3 signaling in dorsal root ganglia (DRGs). Methods Resiniferatoxin (RTX) was used to induce rat PHN models. Mechanical allodynia was assessed by measuring the paw withdrawal thresholds (PWTs). Thermal hyperalgesia was determined by detecting the paw withdrawal latencies (PWLs). We evaluated the effects of TrkB.T1-ASIC3 signaling inhibition on the behavior, neuronal excitability, and inflammatory response during RTX-induced PHN. ASIC3 short hairpin RNA (shRNA) transfection was used to investigate the effect of exogenous BDNF on inflammatory response in cultured PC-12 cells. Results RTX injection induced mechanical allodynia and upregulated the protein expression of BDNF, TrkB.T1, ASIC3, TRAF6, nNOS, and c-Fos, as well as increased neuronal excitability in DRGs. Inhibition of ASIC3 reversed the abovementioned effects of RTX, except for BDNF and TrkB.T1 protein expression. In addition, inhibition of TrkB.T1 blocked RTX-induced mechanical allodynia, activation of ASIC3 signaling, and hyperexcitability of neurons. RTX-induced BDNF upregulation was found in both neurons and satellite glia cells in DRGs. Furthermore, exogenous BDNF activated ASIC3 signaling, increased NO level, and enhanced IL-6, IL-1β, and TNF-α levels in PC-12 cells, which was blocked by shRNA-ASIC3 transfection. Conclusion These findings demonstrate that inhibiting BDNF/TrkB.T1 reduced inflammation, decreased neuronal hyperexcitability, and improved mechanical allodynia through regulating the ASIC3 signaling pathway in DRGs, which may provide a novel therapeutic target for patients with PHN.

While graph drawing focuses more on the aesthetic representation of node-link diagrams, graph visualization takes into account other visual metaphors making them useful for graph exploration tasks in information visualization and visual analytics. Although there are aesthetic graph drawing criteria that describe how a graph should be presented to make it faster and more reliably explorable, many controlled and uncontrolled empirical user studies flourished over the past years. The goal of them is to uncover how well the human user performs graph-specific tasks, in many cases compared to previously designed graph visualizations. Due to the fact that many parameters in a graph dataset as well as the visual representation of them might be varied and many user studies have been conducted in this space, a state-of-the-art survey is needed to understand evaluation results and findings to inform the future design, research, and application of graph visualizations. In this article, we classify the present literature on the topmost level into graph interpretation, graph memorability, and graph creation where the users with their tasks stand in focus of the evaluation, not the computational aspects. As another outcome of this work, we identify the white spots in this field and sketch ideas for future research directions.

In advanced driver-assistance systems (ADAS), the accuracy and real-time performance of pedestrian- and vehicle-detection algorithms based on vision sensors are crucial for safety. Here, a lightweight detection algorithm based on aggregated channel features (ACFs),consisting of a context pixel ACF (CP-ACF) pedestrian detector and a multiview ACF (Mv-ACF) vehicle detector, is proposed to rapidly and precisely understand road scenes. The former fuses local and context information to improve the robustness to pedestrian deformation, while the latter contains a number of subclass detectors to alleviate intraclass differences due to different viewing angles. Compared to the original ACF, the CP-ACF pedestrian detector reduces the average miss rate (AMR) by 6.34%. The Mv-ACF vehicle detector improves the average precision (AP) by 40.26% on average at easy, moderate and hard levels. This remarkable effectiveness is due to the spectrum clustering of multiview samples and the resulting integration of these subclass detectors via confidence score calibration, which reduces the intraclass differences of vehicles. Since feature extraction takes up 68.8% of the total detection time, a mechanism of feature sharing between pedestrian and vehicle detectors is advanced to reduce the time spent in feature extraction. A strategy based on ground-plane constraints (GPCs) is proposed to control false detection of pedestrians and vehicles by incorporating road prior information, which reduces the AMR by 1.07% for CP-ACF pedestrian detectors and improves the AP by 0.27% on average for Mv-ACF vehicle detectors. Thus, the proposed algorithm can effectively control false detection by road prior information.

Background: Increasing numbers of studies have elucidated the role of competitive endogenous RNA (ceRNA) networks in carcinogenesis. However, the potential role of the paclitaxel-related ceRNA network in the innate mechanism and prognosis of pancreatic cancer has not been identified. Methods: Comprehensive bioinformatics analyses were performed to identify drug-related miRNAs (DRmiRNAs), drug-related mRNAs (DRmRNAs) and drug-related lncRNAs (DRlncRNAs) and construct a ceRNA network. The ssGSEA and CIBERSORT algorithms were utilized for immune cell infiltration analysis. Additionally, we validated our paclitaxel-related ceRNA regulatory axis at the gene expression level; functional experiments were conducted to explore the biological functions of the key genes. Results: A total of 182 mRNAs, 13 miRNAs, and 53 lncRNAs were confirmed in the paclitaxel-related ceRNA network. In total, 6 mRNAs, 4 miRNAs, and 6 lncRNAs were identified to establish a risk signature and exhibited optimal prognostic effects. The mRNA signature can predict the abundance of immune cell infiltration and the sensitivity of different chemotherapeutic drugs and may also have a guiding effect in immune checkpoint therapy. A potential PART1/hsa-mir-21/SCRN1 axis was confirmed according to the ceRNA theory and was verified by qPCR. The results indicated that PART1 knockdown markedly increased hsa-mir-21 expression but inhibited SCRN1 expression, weakening the proliferation and migration abilities. Conclusions: We hypothesized that the paclitaxel-related ceRNA network strongly influences the innate mechanism, prognosis, and immune infiltration of pancreatic cancer. Our risk signatures can accurately predict survival outcomes and provide a clinical basis.

Abstract As the most important component of the stromal cell population in the tumor microenvironment (TME), cancer-associated fibroblasts (CAFs) are crucial players in tumor initiation and progression. The interaction between CAFs and tumor cells, as well as the resulting effect, is much greater than initially expected. Numerous studies have shown that noncoding RNAs (ncRNAs) play an irreplaceable role in this interplay, and related evidence continues to emerge and advance. Under the action of ncRNAs, normal fibroblasts are directly or indirectly activated into CAFs, and their metabolic characteristics are changed; thus, CAFs can more effectively promote tumor progression. Moreover, via ncRNAs, activated CAFs can affect the gene expression and secretory characteristics of cells, alter the TME and enhance malignant biological processes in tumor cells to contribute to tumor promotion. Previously, ncRNA dysregulation was considered the main mechanism by which ncRNAs participate in the crosstalk between CAFs and tumor cells. Recently, however, exosomes containing ncRNAs have been identified as another vital mode of interaction between these two types of cells, with a more direct and clear function. Gaining an in-depth understanding of ncRNAs in CAFs and the complex regulatory network connecting CAFs with tumor cells might help us to establish more effective and safer approaches for cancer therapies targeting ncRNAs and CAFs and offer new hope for cancer patients.

Abstract In recent years, cancer immunotherapy based on immune checkpoint inhibitors (ICIs) has achieved considerable success in the clinic. However, ICIs are significantly limited by the fact that only one third of patients with most types of cancer respond to these agents. The induction of cell death mechanisms other than apoptosis has gradually emerged as a new cancer treatment strategy because most tumors harbor innate resistance to apoptosis. However, to date, the possibility of combining these two modalities has not been discussed systematically. Recently, a few studies revealed crosstalk between distinct cell death mechanisms and antitumor immunity. The induction of pyroptosis, ferroptosis, and necroptosis combined with ICIs showed synergistically enhanced antitumor activity, even in ICI-resistant tumors. Immunotherapy-activated CD8+ T cells are traditionally believed to induce tumor cell death via the following two main pathways: (i) perforin-granzyme and (ii) Fas-FasL. However, recent studies identified a new mechanism by which CD8+ T cells suppress tumor growth by inducing ferroptosis and pyroptosis, which provoked a review of the relationship between tumor cell death mechanisms and immune system activation. Hence, in this review, we summarize knowledge of the reciprocal interaction between antitumor immunity and distinct cell death mechanisms, particularly necroptosis, ferroptosis, and pyroptosis, which are the three potentially novel mechanisms of immunogenic cell death. Because most evidence is derived from studies using animal and cell models, we also reviewed related bioinformatics data available for human tissues in public databases, which partially confirmed the presence of interactions between tumor cell death and the activation of antitumor immunity.

Abstract Pancreatic cancer is a highly lethal disease with a poor prognosis, and existing therapies offer only limited effectiveness. Mutation gene sequencing has shown several gene associations that may account for its carcinogenesis, revealing a promising research direction. Poly (ADP-ribose) polymerase (PARP) inhibitors target tumor cells with a homologous recombination repair (HRR) deficiency based on the concept of synthetic lethality. The most prominent target gene is BRCA, in which mutations were first identified in breast cancer and ovarian cancer. PARP inhibitors can trap the PARP-1 protein at a single-stranded break/DNA lesion and disrupt its catalytic cycle, ultimately leading to replication fork progression and consequent double-strand breaks. For tumor cells with BRCA mutations, HRR loss would result in cell death. Pancreatic cancer has also been reported to have a strong relationship with BRCA gene mutations, which indicates that pancreatic cancer patients may benefit from PARP inhibitors. Several clinical trials are being conducted and have begun to yield results. For example, the POLO (Pancreatic Cancer Olaparib Ongoing) trial has demonstrated that the median progression-free survival was observably longer in the olaparib group than in the placebo group. However, PARP inhibitor resistance has partially precluded their use in clinical applications, and the major mechanism underlying this resistance is the restoration of HRR. Therefore, determining how to use PARP inhibitors in more clinical applications and how to avoid adverse effects, as well as prognosis and treatment response biomarkers, require additional research. This review elaborates on future prospects for the application of PARP inhibitors in pancreatic cancer.

Summary: Objective: Laparoscopic common bile duct exploration (LCBDE) has been demonstrated safety and effective for patients with gallbladder stones and extrahepatic bile duct stones, however few studies reported its suitability for the treatment of elderly patients. Thus, our study aims to investigate the safety and feasibility of primary closure after LCBDE in the treatment of elderly patients. Methods: 408 out of 499 patients with Gallbladder stones complicated with choledocholithiasis who were undergone LCBDE and primary closure were divided into two groups: Group A ( 0.05). There were also no statistical differences between both groups in time to removal of drainage, postoperative hospital stay, readmission within 30 days and mortality (p > 0.05). Conclusions: It is safe and feasible to treat the elderly patients with common bile duct stones under the premise of strict surgical indications, skilled laparoscopic procedures and accurate endoscopic suture techniques. Keywords: Choledocholithiasis, Elderly patients, Laparoscopic common bile duct exploration, Primary closure, Stent

In order to more effectively design the structure of vehicle ISD (Inerter Spring Damper) suspension system using the inerter, this paper proposed a design method using a fractional-order electrical network structure of a mechatronic inerter for fractional-order electrical network components, according to the characteristics that the external electrical network of a mechatronic inerter can simulate the corresponding mechanical network structure equivalently. First, the 1/4 dynamic model of the suspension is constructed. The improved Oustaloup filtering algorithm is used to simulate fractional calculus, and the fractional order components are simulated. Then, the simulation model of the vehicle mechatronic ISD suspension is established. In order to simplify the electrical network, one resistance, one fractional inductance and one fractional capacitance are limited in the design of the fractional electrical network at the outer end of the mechatronic inerter. The structure-immittance approach is used to obtain two general layouts of all possible structures of three elements. At the same time, the optimal fractional electrical network structure and parameters are obtained by combining the optimization algorithm. The simulation results verify the performance of the fractional ISD suspension with the optimized structure, which can provide a new idea for the structural design of a fractional-order electrical network applied in vehicle mechatronic ISD suspension.

Background The number of patients diagnosed with T1 stage adenocarcinoma of esophagogastric junction (AEGJ) has been increasing. This study was conducted to investigate the effect of different treatment options (surgery, chemoradiation, and surgery+chemoradiation) on long-term survival in patients with T1-stage AEGJ. Methods We searched the Surveillance, Epidemiology, and End Results (SEER) database to identify the records of patients with T1-stage AEGJ between 2010 and 2018. Patient demographics and cancer parameters were compared among the three groups. The Kaplan–Meier method and Cox proportional hazard modeling were used to compare long-term survival. Results Data from 925 T1 stage AEGJ patients (surgery: n=516, surgery+chemoradiation: n=206, chemoradiation: n=203) were collected. We found that the OS and CSS rates of three treatment options had significant difference. Besides, positive nodal status also showed lower OS and CSS rat. Multivariate Cox regression analysis showed that surgery group has much lower risk of death compared with chemoradiation group and similar risk of death compared with surgery+chemoradiation group. Subgroup analysis suggested that in patients with N1–N3 status had higher OS and CSS rates in surgery+chemoradiation group. Conclusion Using SEER data, we identified a significant survival advantage with the use of surgery compared to chemoradiation in patients with T1-stage AEGJ while the long-term survival of patients after surgery+chemoradiation group was not significantly different and low risk of death in positive nodal status.

Abstract Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer-related death and is one of the most difficult-to-treat cancers. Surgical resection and adjuvant therapy have limited effects on the overall survival of PDAC patients. PDAC exhibits an immunosuppressive microenvironment, the immune response predicts survival, and activation of immune system has the potential to produce an efficacious PDAC therapy. However, chimeric antigen receptor T (CAR-T) cell immunotherapy and immune checkpoint blockade (ICB), which have produced unprecedented clinical benefits in a variety of different cancers, produce promising results in only some highly selected patients with PDAC. This lack of efficacy may be because existing immunotherapies mainly target the interactions between cancer cells and immune cells. However, PDAC is characterized by an abundant tumor stroma that includes a heterogeneous mixture of immune cells, fibroblasts, endothelial cells, neurons and some molecular events. Immune cells engage in extensive and dynamic crosstalk with stromal components in the tumor tissue in addition to tumor cells, which subsequently impacts tumor suppression or promotion to a large extent. Therefore, exploration of the interactions between the stroma and immune cells may offer new therapeutic opportunities for PDAC. In this review, we discuss how infiltrating immune cells influence PDAC development and explore the contributions of complex components to the immune landscape of tumor tissue. The roles of stromal constituents in immune modulation are emphasized. We also predict potential therapeutic strategies to target signals in the immune network in the abundant stromal microenvironment of PDAC.

Abstract Background Programmed cell death protein 1 (PD-1) is a key immune checkpoint that regulates peripheral tolerance and protects against autoimmunity. Programmed death ligand-2 (PD-L2) is a less studied ligand to PD-1 and has yet to be fully explored, especially in pancreatic ductal adenocarcinoma (PDAC). Methods In this study, we performed immunohistochemistry to detect the PD-L2, CD3, CD8, transforming growth factor-β2 (TGF-β2) and FOXP3 levels in paraffin sections from 305 patients with resected PDAC as a training set. Expression levels of intratumoral and stromal immune markers were compared in relation to survival using Kaplan-Meier curves, random survival forest model and survival tree analysis. A multivariable Cox proportional-hazards model of associated markers was used to calculate the risk scores. Results PD-L2 was expressed in 71.5% of PDAC samples and showed strong correlations with CD3+, CD8+ T cells and FOXP3+ regulatory T cell densities. High levels of intratumoral PD-L2 and FOXP3 were related to poor survival; only stromal FOXP3 overexpression was associated with worse prognosis. Four patterns generated from survival tree analysis demonstrated that PD-L2low stromalFOXP3low patients had the longest survival, while PD-L2high intratumoralCD3low patients had the shortest survival (P

Abstract Microbiota is just beginning to be recognized as an important player in carcinogenesis and the interplay among microbes is greater than expected. Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease for which mortality closely parallels incidence. Early detection would provide the best opportunity to increase survival rates. Specific well-studied oral, gastrointestinal, and intrapancreatic microbes and some kinds of hepatotropic viruses and bactibilia may have potential etiological roles in pancreatic carcinogenesis, or modulating individual responses to oncotherapy. Concrete mechanisms mainly involve perpetuating inflammation, regulating the immune system-microbe-tumor axis, affecting metabolism, and altering the tumor microenvironment. The revolutionary technology of omics has generated insight into cancer microbiomes. A better understanding of the microbiota in PDAC might lead to the establishment of screening or early-stage diagnosis methods, implementation of cancer bacteriotherapy, adjustment of therapeutic efficacy even alleviating the adverse effects, creating new opportunities and fostering hope for desperate PDAC patients.