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BackgroundInterferon-γ (IFN-γ) is a key cytokine with diverse biological functions, including antiviral defense, antitumor activity, immune regulation, and modulation of cellular processes. Nonetheless, its role in pancreatic cancer (PC) therapy remains debated. Therefore, it is worthwhile to explore the role of Interferon-γ related genes (IFN-γGs) in the progression of PC development.MethodologyTranscriptomic data from 930 PC were sourced from TCGA, GEO, ICGC, and ArrayExpress, and 93 IFN-γGs were obtained from the MSigDB. We researched the characteristics of IFN-γGs in pan-cancer. Subsequently, the cohort of 930 PC was stratified into two distinct subgroups using the NMF algorithm. We then examined disparities in the activation of cancer-associated pathways within these subpopulations through GSVA analysis. We scrutinized immune infiltration in both subsets and probed classical molecular target drug sensitivity variations. Finally, we devised and validated a novel IFN-γ related prediction model using LASSO and Cox regression analyses. Furthermore, we conducted RT-qPCR and immunohistochemistry assays to validate the expression of seven target genes included in the prediction model.ResultsWe demonstrated the CNV, SNV, methylation, expression levels, and prognostic characteristics of IFN-γGs in pan-cancers. Notably, Cluster 2 demonstrated superior prognostic outcomes and heightened immune cell infiltration compared to Clusters 1. We also assessed the IC50 values of classical molecular targeted drugs to establish links between IFN-γGs expression levels and drug responsiveness. Additionally, by applying our prediction model, we segregated PC patients into high-risk and low-risk groups, identifying potential benefits of cisplatin, docetaxel, pazopanib, midostaurin, epothilone.B, thapsigargin, bryostatin.1, and AICAR for high-risk PC patients, and metformin, roscovitine, salubrinal, and cyclopamine for those in the low-risk group. The expression levels of these model genes were further verified through HPA website data and qRT-PCR assays in PC cell lines and tissues.ConclusionThis study unveils IFN-γGs related molecular subsets in pancreatic cancer for the first time, shedding light on the pivotal role of IFN-γGs in the progression of PC. Furthermore, we establish an IFN-γGs related prognostic model for predicting the survival of PC, offering a theoretical foundation for exploring the precise mechanisms of IFN-γGs in PC.

Abstract Background It is useful to advance simulated patient (SP) participation in teaching to improve the communication skills of medical students, so this study aims to explore the current state of Chinese mainland SP education. Methods A cross sectional survey was designed utilizing well defined quantitative research methods and descriptive statistics. The questionnaire sought information which elucidated the current status of SP-based education, the origin of SP-based learning, SP training, challenges of this learning strategy and future developments. Questionnaires were distributed to 79 medical colleges in mainland China, and 68 were returned. Of these, 64 constituted valid responses (81%). Results The number of SP-based education activities in medical colleges offering 5-year、7-year and 8-year clinical medicine programs was significantly higher than that in medical colleges which offered only a single 5-year program (p

Abstract Intrahepatic cholangiocarcinoma (CHOL) remains a rare malignancy, ranking as the leading lethal primary liver cancer worldwide. However, the biological functions of integrator complex subunit 8 (INTS8) in CHOL remain unknown. Thus, this research aimed to explore the potential role of INTS8 as a novel diagnostic or therapeutic target in CHOL. Differentially expressed genes (DEGs) in two Gene Expression Omnibus (GEO) datasets were obtained by the “RRA” package in R software. The “maftools” package was used to visualize the CHOL mutation data from The Cancer Genome Atlas (TCGA) database. The expression of INTS8 was detected by performing quantitative reverse transcription-PCR (qRT-PCR) and immunohistochemistry in cell lines and human samples. The association between subtypes of tumour-infiltrating immune cells (TIICs) and INTS8 expression in CHOL was determined by using CIBERSORT tools. We evaluated the correlations between INTS8 expression and mismatch repair (MMR) genes and DNA methyltransferases (DNMTs) in pan-cancer analysis. Finally, the pan-cancer prognostic signature of INTS8 was identified by univariate analysis. We obtained the mutation landscapes of an RRA gene set in CHOL. The expression of INTS8 was upregulated in CHOL cell lines and human CHOL samples. Furthermore, INTS8 expression was closely associated with a distinct landscape of TIICs, MMR genes, and DNMTs in CHOL. In addition, the high INTS8 expression group presented significantly poor outcomes, including overall survival (OS), disease-specific survival (DSS) and disease-free interval (DFI) (p

The extracellular matrix (ECM) is a vital component of the tumor microenvironment, which interplays with stromal and tumor cells to stimulate the capacity of cancer cells to proliferate, migrate, invade, and undergo angiogenesis. Nevertheless, the crucial functions of ECM-related genes (ECMGs) in pancreatic adenocarcinoma (PAAD) have not been systematically evaluated. Hence, a comprehensive evaluation of the ECMGs is required in pan-cancer, especially in PAAD. First, a pan-cancer overview of ECMGs was explored through the integration of expression profiles, prognostic values, mutation information, methylation levels, and pathway-regulation relationships. Seven ECMGs (i.e. LAMB3, LAMA3, ITGB6, ITGB4, ITGA2, LAMC2, and COL11A1) were identified to be hub genes of PAAD, which were obviously up-regulated in PAAD and considerably linked to tumor stage as well as prognosis. Subsequently, patients with PAAD were divided into 3 clusters premised on ECMG expression and ECM scores. Cluster 2 was the subtype with the best prognosis accompanied by the lowest ECM scores, further verifying ECM’s significant contribution to the pathophysiological processes of PAAD. Significant differences were observed for oncogene and tumor suppressor gene expression, immune microenvironment, and chemotherapy sensitivity across three ECM subtypes. After applying a variety of bioinformatics methods, a novel and robust ECM-associated mRNA-lncRNA-based prognostic panel (ECM-APP) was developed and validated for accurately predicting clinical outcomes of patients with PAAD. Patients with PAAD were randomly categorized into the train, internal validation, and external validation cohorts; meanwhile, each patient was allocated into high-risk (unfavorable prognosis) and low-risk (favorable prognosis) populations premised on the expression traits of ECM-related mRNAs and lncRNAs. The discrepancy in the tumor mutation burden and immune microenvironment might be responsible for the difference in prognoses across the high-risk and low-risk populations. Overall, our findings identified and validated seven ECMGs remarkably linked to the onset and progression of PAAD. ECM-based molecular classification and prognostic panel aid in the prognostic assessment and personalized intervention of patients with PAAD.

Abstract Stomach adenocarcinoma (STAD) is a malignant tumor with high histological heterogeneity. However, the potential mechanism of STAD tumorigenesis remains to be elucidated. The purpose of our research was to identify candidate genes associated with the diagnosis, progression, prognosis, and immunotherapeutic targets of STAD. Based on tumor samples from the GSE28541 dataset, weighted gene co‐expression network analysis revealed 16 modules related to STAD stage and grade. The salmon module emerged as the most relevant module (cor = 0.34), and functional enrichment analysis showed that the genes in the salmon were primarily related to major histocompatibility complex, immune response, and cell differentiation. Toll‐like receptor 7 (TLR7) was recognized as the real hub gene in the salmon module. Compared to normal stomach tissues, the transcriptional and translational levels of TLR7 were significantly elevated in STAD. Receiver operating characteristic curves verified that TLR7 displayed remarkable sensitivity and specificity for the diagnosis of STAD. The functions of TLR7 were primarily enriched in the regulation of Toll‐like receptor signaling pathway, pattern recognition receptor signaling pathway, and innate immune response. Overexpression of TLR7 tended to indicate more advanced STAD, higher degree of STAD, and poorer prognosis of STAD. In addition, TLR7 expression was positively correlated with immune cell infiltration and immune checkpoint expression. Somatic copy number alteration of TLR7 was also significantly related to immune cell infiltration. In conclusion, this study revealed the crucial role of TLR7 in STAD and provided new perspectives for the selection of biomarkers, progression and prognosis indicators, and immunotherapeutic targets for STAD.

Abstract Hepatocellular carcinoma (HCC) is one of the main causes of cancer deaths globally. Immunotherapy is becoming increasingly important in the cure of advanced HCC. Thus it is essential to identify biomarkers for treatment response and prognosis prediction. We searched publicly available databases and retrieved 465 samples of genes from The Cancer Genome Atlas (TCGA) database and 115 tumor samples from Gene Expression Omnibus (GEO). Meanwhile, we used the ImmPort database to determine the immune-related genes as well. Weighted gene correlation network analysis, Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis were used to identify the key immune related genes (IRGs) which are closely related to prognosis. Gene set enrichment analysis (GSEA) was implemented to explore the difference of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway between Immune high- and low-risk score groups. Finally, we made a prognostic nomogram including Immune-Risk score and other clinicopathologic factors. A total of 318 genes from prognosis related modules were identified through weighted gene co-expression network analysis (WGCNA). 46 genes were strongly linked to prognosis after univariate Cox analysis. We constructed a seven genes prognostic signature which showed powerful prediction ability in both training cohort and testing cohort. 16 significant KEGG pathways were identified between high- and low- risk score groups using GSEA analysis. This study identified and verified seven immune-related prognostic biomarkers for the patients with HCC, which have potential value for immune modulatory and therapeutic targets.

Abstract Background The prediction of drug-target interaction from chemical and biological data can advance our search for potential drug, contributing to a therapeutic strategy for pancreatic adenocarcinoma (PAAD). We aim to identify hub genes of PAAD and search for potential drugs from distinct databases. The docking simulation is adopted to validate our findings from computable perspective. Methods Differently expressed genes (DEGs) of PAAD were performed based on TCGA. With two Cytoscape plugins of CentiScaPe and MCODE, hub genes were analyzed and visualized by STRING analysis of Protein–protein Interaction (PPI). The hub genes were further selected with significant prognostic values. In addition, we examined the correlation between hub genes and immune infiltration in PAAD. Subsequently, we searched for the hub gene-targeted drugs in Connectivity map (Cmap) and cBioportal, which provided a large body of candidate drugs. The hub gene, which was covered in the above two databases, was estimated in Traditional Chinese Medicine Systems Pharmacology (TCMSP) and Herbal Ingredients’ Targets (HIT) database, which collected natural herbs and related ingredients. After obtaining molecular structures, the potential ingredient from TCMSP was applied for a docking simulation. We finalized a network connectivity of ingredient and its targets. Results A total of 2616 DEGs of PAAD were identified, then we further determined and visualized 24 hub genes by a connectivity analysis of PPI. Based on prognostic value, we identified 5 hub genes including AURKA (p = 0.0059), CCNA2 (p = 0.0047), CXCL10 (p = 0.0044), ADAM10 (p = 0.00043), and BUB1 (p = 0.0033). We then estimated tumor immune correlation of these 5 hub genes, because the immune effector process was one major result of GO analysis. Subsequently, we continued to search for candidate drugs from Cmap and cBioportal database. BUB1, not covered in the above two databases, was estimated in TCMSP and HIT databases. Our results revealed that genistein was a potential drug of BUB1. Next, we generated two docking modes to validate drug-target interaction based on their 3D structures. We eventually constructed a network connectivity of BUB1 and its targets. Conclusions All 5 hub genes that predicted poor prognosis had their potential drugs, especially our findings showed that genistein was predicted to target BUB1 based on TCMSP and docking simulation. This study provided a reasonable approach to extensively retrieve and initially validate putative therapeutic agents for PAAD. In future, these drug-target results should be investigated with solid data from practical experiments.

IntroductionThe coronavirus disease 2019 (COVID-19), emerged in late 2019, was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The risk factors for idiopathic pulmonary fibrosis (IPF) and COVID-19 are reported to be common. This study aimed to determine the potential role of differentially expressed genes (DEGs) common in IPF and COVID-19.Materials and methodsBased on GEO database, we obtained DEGs from one SARS-CoV-2 dataset and five IPF datasets. A series of enrichment analysis were performed to identify the function of upregulated and downregulated DEGs, respectively. Two plugins in Cytoscape, Cytohubba and MCODE, were utilized to identify hub genes after a protein-protein interaction (PPI) network. Finally, candidate drugs were predicted to target the upregulated DEGs.ResultsA total of 188 DEGs were found between COVID-19 and IPF, out of which 117 were upregulated and 71 were downregulated. The upregulated DEGs were involved in cytokine function, while downregulated DEGs were associated with extracellular matrix disassembly. Twenty-two hub genes were upregulated in COVID-19 and IPF, for which 155 candidate drugs were predicted (adj.P.value < 0.01).ConclusionIdentifying the hub genes aberrantly regulated in both COVID-19 and IPF may enable development of molecules, encoded by those genes, as therapeutic targets for preventing IPF progression and SARS-CoV-2 infections.

Skin cutaneous melanoma (SKCM) is a common cancer of which mortality is increasing continuously. Our study conducted a series of analyses on the clinical significance of Serine/threonine kinase 17B (STK17B) in SKCM to provide a new biomarker for diagnosis and treatment. The RNA-sequence data were obtained from The Cancer Genome Atlas and Genotype-Tissue Expression databases. The data of 468 SKCM patients were divided into STK17B high- and low-expression groups and analyzed by Bioconductor package to identify the differential expressed genes. The R package of "clusterProfiler" was used for Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene-Set Enrichment Analysis analyses. A protein-protein interaction network and immune infiltration landscape were respectively constructed via STRING database and ssGSEA. STK17B had lower expression in SKCM than normal tissues. Besides, STK17B expression was significantly related to some clinicopathological characteristics in SKCM patients including T stage, Breslow depth, radiation therapy, melanoma Clark level, and pathologic stage. The Kaplan-Meier curve analyses revealed that the low expression of STK17B was correlated with poor overall survival and disease-specific survival. We constructed nomograms to predict the 1-, 3-, and 5-year survival of SKCM patients. The function enrichment analyses showed STK17B-related differential expressed genes were enriched in cellular differentiation and immune-related progress. STK17B expression level were positively correlated with infiltrating level of immune cells. In this study, we found that STK17B, which played an important role in immune infiltration, could be a new biomarker for diagnosis and prognosis in SKCM patients.

Uveal melanoma (UVM), the most common primary intraocular malignancy, has a high mortality because of a high propensity to metastasize. Our study analyzed prognostic value and immune-related characteristics of CARD11 in UVM, hoping to provide a potential management and research direction. The RNA-sequence data of 80 UVM patients were downloaded from The Cancer Genome Atlas database and divided them into high- and low-expression groups. We analyzed the differentially expressed genes, enrichment analyses and the infiltration of immune cells using the R package and Gene-Set Enrichment Analysis. A clinical prediction nomogram and protein-protein interaction network were constructed and the first 8 genes were considered as the hub-genes. Finally, we constructed a competing endogenous RNA (ceRNA) network by Cytoscape and analyzed the statistical data via the R software. Here we found that CARD11 expression had notable correlation with UVM clinicopathological features, which was also an independent predictor for overall survival (OS). Intriguingly, CARD11 had a positively correlation to autophagy, cellular senescence and apoptosis. Infiltration of monocytes was significantly higher in low CARD11 expression group, and infiltration of T cells regulatory was lower in the same group. Functional enrichment analyses revealed that CARD11 was positively related to T cell activation pathways and cell adhesion molecules. The expressions of hub-genes were all increased in the high CARD11 expression group and the ceRNA network showed the interaction among mRNA, miRNA and lncRNA. These findings show that high CARD11 expression in UVM is associated with poor OS, indicating that CARD11 may serve as a potential biomarker for the diagnosis and prognosis of the UVM.

Background The functional significance of ADAMs family members in the immune infiltration of pancreatic adenocarcinoma (PAAD) awaits elucidation. Methods ADAMs family members with significant expression were identified among differentially expressed genes of PAAD based on The Cancer Genome Atlas (TCGA) database followed by a verification based on the Oncomine database. The correlation of ADAMs in PAAD was estimated with the Spearman’s rho value. The pathway enrichment of ADAMs was performed by STRING and GSEALite, respectively. The protein–protein interaction and Gene Ontology analyses of ADAMs and their similar genes were exanimated in STRING and visualized by Cytoscape. Subsequently, the Box-Whisker plot was used to show a correlation between ADAMs and different tumor grade 1/2/3/4 with Student’s t-test. TIMER was applied to estimate a correlation of ADAMs expressions with immune infiltrates and immune checkpoint blockade (ICB) immunotherapy-related molecules. Furthermore, the effect of copy number variation (CNV) of ADAMs genes was assessed on the immune infiltration levels. Result ADAM8/9/10/12/15/19/28/TS2/TS12 were over-expressed in PAAD. Most of the nine ADAMs had a significant correlation. ADAM8/12/15/19 expression was remarkably increased in the comparison between grade 1 and grade 2/3 of PAAD. ADAM8/9/10/12/19/28/TS2/TS12 had a positive correlation with almost five immune infiltrates. ADAM12/19/TS2/TS12 dramatically related with ICB immunotherapy-related molecules. CNV of ADAMs genes potentially influenced the immune infiltration levels. Conclusion Knowledge of the expression level of the ADAMs family could provide a reasonable strategy for improved immunotherapies to PAAD.

Pancreatic disorders cause a broad spectrum of clinical diseases, mainly including acute and chronic pancreatitis and pancreatic cancer, and are associated with high global rates of morbidity and mortality. Unfortunately, the pathogenesis of pancreatic disease remains obscure, and there is a lack of specific treatments. T lymphocytes (T cells) play a vital role in the adaptive immune systems of multicellular organisms. During pancreatic disease development, local imbalances in T-cell subsets in inflammatory and tumor environments and the circulation have been observed. Furthermore, agents targeting T cells have been shown to reverse the natural course of pancreatic diseases. In this review, we have discussed the clinical relevance of T-cell alterations as a potential outcome predictor and the underlying mechanisms, as well as the present status of immunotherapy targeting T cells in pancreatitis and neoplasms. The breakthrough findings summarized in this review have important implications for innovative drug development and the prospective use of immunotherapy for pancreatitis and pancreatic cancer.

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal human cancers. Aberrant expression of genes plays important role in the procession of PDAC. The analysis of gene expression profile will contribute to the research of carcinoma mechanism. Objective: This present study is focused to investigate the differentially expressed genes (DEGs) from 3 PDAC microarray datasets, which would provide candidate genes for putative biomarkers to understand the mechanism of PDAC and potential targets of treatment. Method: Based on the overlap genes obtained from 3 GEO datasets, the hub genes were identified using STRING and Cytoscape plugin MCODE. The enrichment and function analysis were applied using DAVID. The protein-protein interaction network was performed using cBioPortal and UCSC Xena. The Oncomine was finally used to determine the candidate gene by analyzing their expression between pancreas sample and PDAC sample. Results: 25 hub genes were selected from a total of 1006 DEGs from 3 GEO datasets, consisting of 14 upregulated genes and 11 downregulated genes. The overall decline of hub gene expression enriched in G1 phase of cell cycle in other subtypes of pancreatic cancer. Oncomine database was ultimately performed to determine the 8 candidate genes, including CXCL5, CCL20, NMU, F2R, ANXA1, EDNRA, LPAR6, and GNA15. Conclusions: Conclusively, 8 candidate genes would become the potential PDAC combined biomarkers for diagnosis and therapeutic strategies.

Pancreatitis is an inflammatory disease that is responsible for substantial morbidity and mortality, and it can induce pancreatic necrosis that starts within pancreatic acinar cells in severe cases. Emodin, a pleiotropic natural product isolated from the Chinese herb Rheum palmatum L., has effective anti-inflammatory activities. In this paper, we investigated the protective effects and molecular mechanism of emodin against sodium taurocholate (STC)-induced pancreatic acinar cells injury in vitro and in vivo; and the results showed that emodin could significantly alleviate STC-induced pancreatic acinar cells injury through decreasing trypsin, amylase and the release of inflammatory factors (tumor necrosis factor alpha, interleukin-1β, and interleukin-6). Also, we found that emodin could significantly downregulate the HTRA1, interleukin-33, myeloid differentiation primary response gene 88, TNF receptor-associated factor-6, and nuclear factor kappa-B protein levels, but upregulate the transforming growth factor beta 1 (TGF-β1) protein level. These results indicated that emodin alleviated pancreatic acinar cells injury mainly through inhibiting HTRA1/TGF-β1 signaling pathway, and this finding was further proved by the HTRA1 overexpression experiments. In addition, the inflammatory regulator microRNA-30a-5p (miR-30a-5p) was confirmed to be a transcriptional brake that controls the HTRA1 gene through using a dual luciferase reporter assay, and it was upregulated by emodin in pancreatic acinar cells. Furthermore, the pancreatic protective effects and anti-inflammatory activities of emodin were all abrogated with both miR-30a-5p inhibitor in vitro and miR-30a-5p antagomir in vivo. Collectively, these results demonstrate that miR-30a-5p/HTRA1 are the target of emodin-mediated attenuation of pancreatic acinar cell injury in pancreatitis, thus providing the foundation for further development of this natural product for medical therapy.

Acute pancreatitis (AP) is a common gastrointestinal disorder that featured by acute inflammatory responses leading to systemic inflammatory response syndrome (SIRS) or multiple organ failure. A worldwide increase in annual incidence has been observed during the past decade with high acute hospitalization and mortality. Lack of any specific treatment for AP, even to this day, is a reminder that there is much to be learned about the exact pathogenesis of AP. Fortunately, the discovery of microRNA (miRNA) has started an entirely new thought process regarding the molecular mechanism associated with the disease processes. Given the extensive effort made on miRNA research, certain types of miRNA have been identified across a variety of biological processes, including cell differentiation, apoptosis, metabolism, and inflammatory responses. Mutations in miRNA sequences or deregulation of miRNA expression may contribute to the alteration of a pivotal physiological function leading to AP. Designing miRNA-related tools for AP diagnosis and treatment presents a novel and potential research frontier. In this mini-review, we summarize the current knowledge of various miRNAs closely interacting with AP and the possible development of targeted miRNA therapies in this disease, which may benefit the development of potential disease biomarkers and novel treatment targets for future medical implications.

Acute pancreatitis (AP) is a commonly occurring gastrointestinal disorder. An increase in the annual incidence of AP has been observed, and it causes acute hospitalization and high mortality. The diagnosis and treatment guidelines for AP recommend conservative medical treatments focused on reducing pancreatic secretion and secondary injury, as a primary therapeutic approach. Unfortunately, the existing treatment options have limited impact on the incidence and severity of AP due to the complex and multifaceted pathological process of this disease. In recent decades, Chinese herbal medicines (CHMs) have been used as efficient therapeutic agents to attenuate AP in Asian countries. Despite early cell culture, animal models, and clinical trials, CHMs are capable of interacting with numerous molecular targets participating in the pathogenesis of AP; however, comprehensive, up-to-date communication in this field is not yet available. This review focuses on the pharmacological activities of CHMs against AP in vitro and in vivo and the underlying mechanisms. A computational prediction of few selected and promising plant-derived molecules (emodin, baicalin, resveratrol, curcumin, ligustrazine, and honokiol) to target numerous proteins or networks involved in AP was initially established based on a network pharmacology simulation. Moreover, we also summarized some potential toxic natural products for pancreas in order to more safe and reasonable medication. These breakthrough findings may have important implications for innovative drug research and the future development of treatments for AP.

Yin-Chen-Hao Tang (YCHT) is a classical Chinese medicine compound that has a long history of clinical use in China for the treatment of inflammatory diseases. However, the efficacy and mechanisms of YCHT for the treatment of severe acute pancreatitis (SAP) are not known. The current study investigated the pharmacological properties of YCHT against SAP and its underlying mechanisms. A computational prediction of potential targets of YCHT was initially established based on a network pharmacology simulation. The model suggested that YCHT attenuated SAP progress by apoptosis inducement, anti-inflammation, anti-oxidation and blood lipid regulation. These effects were validated in SAP rats. YCHT administration produced the following results: (1) significantly inhibited the secretion of pancreatic enzymes and protected pancreatic tissue; (2) obviously increased the number of in situ terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL)-positive cells and induced apoptosis; (3) markedly inhibited neutrophil infiltration to the impaired pancreas and reduced the inflammatory reaction; (4) notably enhanced the activities of antioxidant enzymes and decreased the nitric oxide synthase levels; (5) significantly reduced the levels of triglycerides, total cholesterol and low-density lipoprotein and increased high-density lipoprotein; and (6) significantly up-regulated peroxisome proliferator-activated receptor-γ (PPARγ) and down-regulated nuclear factor-kappa B (NF-κB). In summary, these results demonstrated that YCHT attenuated SAP progress by inducing apoptosis, repressing inflammation, alleviating oxidative stress and regulating lipid metabolism partially via regulation of the NF-κB/PPARγ signal pathway.

BACKGROUND: The development of multidrug resistance is a major problem in the treatment of pathogenic microorganisms by distinct antimicrobial agents. Characterizing the genetic variation among plasmids from different bacterial species or strains is a key step towards understanding the mechanism of virulence and their evolution. RESULTS: We applied a deep sequencing approach to 206 clinical strains of Klebsiella pneumoniae collected from 2002 to 2008 to understand the genetic variation of multidrug resistance plasmids, and to reveal the dynamic change of drug resistance over time. First, we sequenced three plasmids (70 Kb, 94 Kb, and 147 Kb) from a clonal strain of K. pneumoniae using Sanger sequencing. Using the Illumina sequencing technology, we obtained more than 17 million of short reads from two pooled plasmid samples. We mapped these short reads to the three reference plasmid sequences, and identified a large number of single nucleotide polymorphisms (SNPs) in these pooled plasmids. Many of these SNPs are present in drug-resistance genes. We also found that a significant fraction of short reads could not be mapped to the reference sequences, indicating a high degree of genetic variation among the collection of K. pneumoniae isolates. Moreover, we identified that plasmid conjugative transfer genes and antibiotic resistance genes are more likely to suffer from positive selection, as indicated by the elevated rates of nonsynonymous substitution. CONCLUSION: These data represent the first large-scale study of genetic variation in multidrug resistance plasmids and provide insight into the mechanisms of plasmid diversification and the genetic basis of antibiotic resistance.

Background: Interferon-g (IFN-g) is a key cytokine with diverse biological functions, including antiviral defense, antitumor activity, immune regulation, and modulation of cellular processes. Nonetheless, its role in pancreatic cancer (PC) therapy remains debated. Therefore, it is worthwhile to explore the role of Interferon-g related genes (IFN-gGs) in the progression of PC development. Methodology: Transcriptomic data from 930 PC were sourced from TCGA, GEO, ICGC, and ArrayExpress, and 93 IFN-gGs were obtained from the MSigDB. We researched the characteristics of IFN-gGs in pan-cancer. Subsequently, the cohort of 930 PC was stratified into two distinct subgroups using the NMF algorithm. We then examined disparities in the activation of cancer-associated pathways within these subpopulations through GSVA analysis. We scrutinized immune infiltration in both subsets and probed classical molecular target drug sensitivity variations. Finally, we devised and validated a novel IFN-g related prediction model using LASSO and Cox regression analyses. Furthermore, we conducted RT-qPCR and immunohistochemistry assays to validate the expression of seven target genes included in the prediction model. Results: We demonstrated the CNV, SNV, methylation, expression levels, and prognostic characteristics of IFN-gGs in pan-cancers. Notably, Cluster 2 demonstrated superior prognostic outcomes and heightened immune cell infiltration compared to Clusters 1. We also assessed the IC50 values of classical molecular targeted drugs to establish links between IFN-gGs expression levels and drug responsiveness. Additionally, by applying our prediction model, we segregated PC patients into high-risk and low-risk groups, identifying potential benefits of cisplatin, docetaxel, pazopanib midostaurin, epothilone.B, thapsigargin, bryostatin.1, and AICAR for high-risk PC patients, and metformin, roscovitine, salubrinal, and cyclopamine for those in the low-risk group. The expression levels of these model genes were further verified through HPA website data and qRT-PCR assays in PC cell lines and tissues. Conclusion: This study unveils IFN-gGs related molecular subsets in pancreatic cancer for the first time, shedding light on the pivotal role of IFN-gGs in the progression of PC. Furthermore, we establish an IFN-gGs related prognostic model for predicting the survival of PC, offering a theoretical foundation for exploring the precise mechanisms of IFN-gGs in PC. [ABSTRACT FROM AUTHOR]

Extracellular protein coronas (exPCs), which have been identified in various biofluids, are recognized for their pivotal role in mediating the interaction between nanoparticles and the cytomembrane. However, it is still unclear whether various exPCs can induce different levels of intracellular proteostasis, which is of utmost importance in preserving cellular function, and eliciting distinct intracellular biological behaviors. To investigate this, two types of exPC-coated iron oxide nanoparticles (IONPs) are prepared and used to investigate the influence of exPCs on extracellular and intracellular biological outcomes. The results demonstrate that the formation of exPCs promotes the colloidal stability of IONPs, and the discrepancies in the components of the two exPCs, including opsonin, dysopsonin, and lipoprotein, are responsible for the disparities in cellular uptake and endocytic pathways. Moreover, the differential evolution of the two exPCs during cellular internalization leads to distinct autophagy and glycolysis activities, which can be attributed to the altered depletion of angiopoietin 1 during the formation of intracellular protein coronas, which ultimately impacts the PI3K/AKT-mTOR signaling. These findings offer valuable insights into the dynamic characteristics of exPCs during cellular internalization, and their consequential implications for cellular internalization and intracellular metabolism activity, which may facilitate the comprehension of PCs on biological effects of NPs and expedite the design and application of biomedical nanoparticles.

Abstract Accurate identification and localization of multiple abnormalities are crucial steps in the interpretation of chest X-rays (CXRs); however, the lack of a large CXR dataset with bounding boxes severely constrains accurate localization research based on deep learning. We created a large CXR dataset named CXR-AL14, containing 165,988 CXRs and 253,844 bounding boxes. On the basis of this dataset, a deep-learning-based framework was developed to identify and localize 14 common abnormalities and calculate the cardiothoracic ratio (CTR) simultaneously. The mean average precision values obtained by the model for 14 abnormalities reached 0.572-0.631 with an intersection-over-union threshold of 0.5, and the intraclass correlation coefficient of the CTR algorithm exceeded 0.95 on the held-out, multicentre and prospective test datasets. This framework shows an excellent performance, good generalization ability and strong clinical applicability, which is superior to senior radiologists and suitable for routine clinical settings.

We propose a cost-effective and memory-aware end-to-end learning scheme utilizing bi-directional gated recurrent unit (bi-GRU) for geometric constellation shaping (GCS) under the first-order regular perturbation (FRP) auxiliary channel. The performance of the proposed system has been numerically verified at a 32 GBd 5-channel wavelength division multiplexing (WDM) 64 quadrature amplitude modulation (QAM) 800 km optical coherent communication system. Results show that the proposed bi-GRU based GCS scheme can achieve a performance gain over square 64QAM in mutual information (MI) with 0.12 bits/symbol and a Q-factor gain of 0.4 dB at optimal launched optical power. When transmission distance is extended to 1280 km, a generalized mutual information (GMI) gain of 0.136 bits/symbol is observed. Additionally, compared with the bi-directional long short-term memory (bi-LSTM) based GCS, the proposed bi-GRU scheme has lower computation complexity with similar system performance.