P75NTR+CD64+ neutrophils promote sepsis-induced acute lung injury.

Patients suffering from sepsis-induced acute lung injury (ALI) exhibit a high mortality rate, and their prognosis is closely associated with infiltration of neutrophils into the lungs. In this study, we found a significant elevation of CD64+ neutrophils, which highly expressed p75 neurotrophin receptor (p75NTR) in peripheral blood of mice and patients with sepsis-induced ALI. p75NTR+CD64+ neutrophils were also abundantly expressed in the lung of ALI mice induced by lipopolysaccharide. Conditional knock-out of the myeloid lineage's p75NTR gene improved the survival rates, attenuated lung tissue inflammation, reduced neutrophil infiltration and enhanced the phagocytic functions of CD64+ neutrophils. In vitro, p75NTR+CD64+ neutrophils exhibited an upregulation and compromised phagocytic activity in blood samples of ALI patients. Blocking p75NTR activity by soluble p75NTR extracellular domain peptide (p75ECD-Fc) boosted CD64+ neutrophils phagocytic activity and reduced inflammatory cytokine production via regulation of the NF-κB activity. The findings strongly indicate that p75NTR+CD64+ neutrophils are a novel pathogenic neutrophil subpopulation promoting sepsis-induced ALI.

Machine learning for the early prediction of acute respiratory distress syndrome (ARDS) in patients with sepsis in the ICU based on clinical data.

Background: Acute respiratory distress syndrome (ARDS) is a fatal outcome of severe sepsis. Machine learning models are helpful for accurately predicting ARDS in patients with sepsis at an early stage. Objective: We aim to develop a machine-learning model for predicting ARDS in patients with sepsis in the intensive care unit (ICU). Methods: The initial clinical data of patients with sepsis admitted to the hospital (including population characteristics, clinical diagnosis, complications, and laboratory tests) were used to predict ARDS, and screen out the crucial variables. After comparing eight different algorithms, namely, XG boost, logistic regression, light GBM, random forest, GaussianNB, complement NB, support vector machine (SVM), and K nearest neighbors (KNN), rebuilding a prediction model with the best one. When remodeling with the best algorithm, 10% was randomly selected to test, and the remaining was trained for cross-validation. Using the area under the curve (AUC), sensitivity, accuracy, specificity, positive and negative predictive value, F1 score, kappa value, and clinical decision curve to evaluate the model's performance. Eventually, the application in the model illustrated by the SHAP package. Results: Ten critical features were screened utilizing the lasso method, namely, PaO2/PAO2, A-aDO2, PO2(T), CRP, gender, PO2, RDW, MCH, SG, and chlorine. The prior ranking of variables demonstrated that PaO2/PAO2 was the most significant variable. Among the eight algorithms, the performance of the Gaussian NB algorithm was significantly better than that of the others. After remodeling with the best algorithm, the AUC in the training and validation sets were 0.777 and 0.770, respectively, and the algorithm performed well in the test set (AUC = 0.781, accuracy = 78.6%, sensitivity = 82.4%, F1 score = 0.824). A comparison of the overlap factors with those of previous models revealed that the model we developed performs better. Conclusion: Sepsis-associated ARDS can be accurately predicted early via a machine learning model based on existing clinical data. These findings are helpful for accurate identification and improvement of the prognosis in patients with sepsis-associated ARDS.

Subgrouping testicular germ cell tumors based on immunotherapy and chemotherapy associated lncRNAs.

Testicular germ cell tumors (TGCT) are the most common reproductive system malignancies in men aged 15-44 years, accounting for 95 % of all testicular tumors. Our previous studies have been shown that long non-coding RNAs (lncRNAs), such as LINC00313, TTTY14 and RFPL3S, were associated with development of TGCT. Subgrouping TGCT according to differential expressed lncRNAs and immunological characteristics is helpful to comprehensively describe the characteristics of TGCT and implement precise treatment. In this study, the TGCT transcriptome data in The Cancer Genome Atlas Program (TCGA) database was used to perform consensus clustering analysis to construct a prognostic model for TGCT. TGCT was divided into 3 subtypes C1, C2, and C3 based on the differentially expressed lncRNAs. C1 subtype was sensitive to chemotherapy drugs, while the C2 subtype was not sensitive to chemotherapy drugs, and C3 subtype may benefit from immunotherapy. We defined the C1 subtype as epidermal progression subtype, the C2 subtype as mesenchymal progression subtype, and the C3 subtype as T cell activation subtype. Subgrouping based on differentially expressed genes (DEGs) and immunological characteristics is helpful for the precise treatment of TGCT.

Promoting CO2 electroreduction to CO by a graphdiyne-stabilized Au nanoparticle catalyst.

The electrochemical CO2 reduction reaction (CO2RR) gives an ideal approach for producing valuable chemicals, offering dual benefits in terms of environmental preservation and carbon recycling. In this work, a strong synergistic effect is constructed by adopting electron-rich graphdiyne (GDY) as the supporting matrix, which significantly stabilizes the Au active sites and boosts the CO2RR process. The as-prepared GDY-supported Au nanoparticles (Au/GDY) exhibit excellent CO2RR performance, with an extremely high faradaic efficiency of 94.6% for CO as well as good stability with continuous electrolysis for 36 hours. The superior activity and stability of the Au/GDY catalyst can be attributed to the electronic interaction between Au nanoparticles and the GDY substrate, resulting in enhanced electron transfer rates and a stable network of catalytically active sites that ultimately promote the CO2RR.

Single-cell multi-omics analysis of human testicular germ cell tumor reveals its molecular features and microenvironment.

Seminoma is the most common malignant solid tumor in 14 to 44 year-old men. However, its molecular features and tumor microenvironment (TME) is largely unexplored. Here, we perform a series of studies via genomics profiling (single cell multi-omics and spatial transcriptomics) and functional examination using seminoma samples and a seminoma cell line. We identify key gene expression programs share between seminoma and primordial germ cells, and further characterize the functions of TFAP2C in promoting tumor invasion and migration. We also identify 15 immune cell subtypes in TME, and find that subtypes with exhaustion features were located closer to the tumor region through combined spatial transcriptome analysis. Furthermore, we identify key pathways and genes that may facilitate seminoma disseminating beyond the seminiferous tubules. These findings advance our knowledge of seminoma tumorigenesis and produce a multi-omics atlas of in situ human seminoma microenvironment, which could help discover potential therapy targets for seminoma.

Long non-coding RNA TTTY14 promotes cell proliferation and functions as a prognostic biomarker in testicular germ cell tumor.

Testicular germ cell tumors (TGCTs) commonly occur in males between the ages of 15 and 34, accounting for 98% of testicular malignancies. Long non-coding RNAs (LncRNAs) have been reported to play important roles in TGCT proliferation, invasion, and functioned as prognostic biomarkers. Testis-specific transcript, Y-linked 14 (TTTY14), a long non-coding RNA localized on Chr Y q11.222, has been found to be a potential prognostic biomarker for laryngeal squamous cell carcinoma, gastric cancer, and osteosarcoma. The biological role of TTTY14 in TGCT is not well understood. In this study, we aim to clarify the biological role of TTTY14 in TGCT, as well as its role in TGCT survival prognosis and immunotherapy efficacy prediction through the deep mining of public data combined with the verification of cell biological experiments. We found that high TTTY14 expression was a poor survival prognostic factor in TGCT patients and the expression of TTTY14 might be regulated by copy number variation and DNA methylation. TTTY14 knockdown significantly inhibited the proliferation of TGCT in vitro. TTTY14 expression was positively correlated with immune cell dysfunction, and significantly negatively correlated with B cells, CD8+ T cells, and macrophages, suggesting that TTTY14 may also affect the drug sensitivity by regulating the tumor immune microenvironment. In conclusion, we revealed that lncRNA TTTY14 was a novel oncogene and a biomarker in TGCT. TTTY14 may influence the drugs sensitivity through regulating the tumor immune microenvironment.

DNA hypomethylation and upregulated LINC00518 acts as a promoter and biomarker in head and neck squamous cell carcinoma.

Background: LINC00518 acts as an oncogene in several cancers, but its function in head and neck squamous cell carcinoma (HNSCC) remains unclear. Materials & methods: The expression and methylation status of LINC00518 were analyzed by reviewing public databases. The ceRNA network and the relationship with tumor immunity of LINC00518 were analyzed using online tools and in vitro studies. Results: Upregulated LINC00518 was associated with poor clinicopathological characteristics of HNSCC. Silencing LINC00518 significantly inhibited the migration of HNSCC cells. LINC00518 might positively regulate HMGA2 via the ceRNA mechanism. Additionally, LINC00518 was negatively correlated with various immune cells and immunotherapy markers. Moreover, the upregulation of LINC00518 in HNSCC may be due to DNA hypomethylation. Conclusion: LINC00518 may be a potential biomarker and therapeutic target for HNSCC.

The role of cuproptosis-related gene in the classification and prognosis of melanoma.

Background: Melanoma, as one of the most aggressive and malignant cancers, ranks first in the lethality rate of skin cancers. Cuproptosis has been shown to paly a role in tumorigenesis, However, the role of cuproptosis in melanoma metastasis are not clear. Studying the correlation beteen the molecular subtypes of cuproptosis-related genes (CRGs) and metastasis of melanoma may provide some guidance for the prognosis of melanoma. Methods: We collected 1085 melanoma samples in The Cancer Genome Atlas(TCGA) and Gene Expression Omnibus(GEO) databases, constructed CRGs molecular subtypes and gene subtypes according to clinical characteristics, and investigated the role of CRGs in melanoma metastasis. We randomly divide the samples into train set and validation set according to the ratio of 1:1. A prognostic model was constructed using data from the train set and then validated on the validation set. We performed tumor microenvironment analysis and drug sensitivity analyses for high and low risk groups based on the outcome of the prognostic model risk score. Finally, we established a metastatic model of melanoma. Results: According to the expression levels of 12 cuproptosis-related genes, we obtained three subtypes of A1, B1, and C1. Among them, C1 subtype had the best survival outcome. Based on the differentially expressed genes shared by A1, B1, and C1 genotypes, we obtained the results of three gene subtypes of A2, B2, and C2. Among them, the B2 group had the best survival outcome. Then, we constructed a prognostic model consisting of 6 key variable genes, which could more accurately predict the 1-, 3-, and 5-year overall survival rates of melanoma patients. Besides, 98 drugs were screened out. Finally, we explored the role of cuproptosis-related genes in melanoma metastasis and established a metastasis model using seven key genes. Conclusions: In conclusion, CRGs play a role in the metastasis and prognosis of melanoma, and also provide new insights into the underlying pathogenesis of melanoma.

Identification and Validation of the Diagnostic Characteristic Genes of Ovarian Cancer by Bioinformatics and Machine Learning.

Background: Ovarian cancer (OC) has a high mortality rate and poses a severe threat to women's health. However, abnormal gene expression underlying the tumorigenesis of OC has not been fully understood. This study aims to identify diagnostic characteristic genes involved in OC by bioinformatics and machine learning. Methods: We utilized five datasets retrieved from the Gene Expression Omnibus (GEO) database, The Cancer Genome Atlas (TCGA) database, and the Genotype-Tissue Expression (GTEx) Project database. GSE12470 and GSE18520 were combined as the training set, and GSE27651 was used as the validation set A. Also, we combined the TCGA database and GTEx database as validation set B. First, in the training set, differentially expressed genes (DEGs) between OC and non-ovarian cancer tissues (nOC) were identified. Next, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO) enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were performed for functional enrichment analysis of these DEGs. Then, two machine learning algorithms, Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were used to get the diagnostic genes. Subsequently, the obtained diagnostic-related DEGs were validated in the validation sets. Then, we used the computational approach (CIBERSORT) to analyze the association between immune cell infiltration and DEGs. Finally, we analyzed the prognostic role of several genes on the KM-plotter website and used the human protein atlas (HPA) online database to analyze the expression of these genes at the protein level. Results: 590 DEGs were identified, including 276 upregulated and 314 downregulated DEGs.The Enrichment analysis results indicated the DEGs were mainly involved in the nuclear division, cell cycle, and IL-17 signaling pathway. Besides, DEGs were also closely related to immune cell infiltration. Finally, we found that BUB1, FOLR1, and PSAT1 have prognostic roles and the protein-level expression of these six genes SFPR1, PSAT1, PDE8B, INAVA and TMEM139 in OC tissue and nOC tissue was consistent with our analysis. Conclusions: We screened nine diagnostic characteristic genes of OC, including SFRP1, PSAT1, BUB1B, FOLR1, ABCB1, PDE8B, INAVA, BUB1, TMEM139. Combining these genes may be useful for OC diagnosis and evaluating immune cell infiltration.

MiR-29a-3p Improves Acute Lung Injury by Reducing Alveolar Epithelial Cell PANoptosis.

Alveolar epithelial cell damage is an important determinant of the severity of acute lung injury/acute respiratory distress syndrome (ALI/ARDS). However, the molecular mechanisms of alveolar epithelial death during the development of ALI/ARDS remain unclear. In this study, we explore the role of miR-29a-3p in ALI/ARDS and its molecular mechanism. Plasma samples were collected from healthy controls and ARDS patients. Mice were intratracheally instilled with lipopolysaccharide (LPS) to establish acute lung injury. N6-adenosine (m6A) quantification, RNA-binding protein immunoprecipitation, cell viability assay, quantitative real-time polymerase chain reaction, and western blotting were performed. We found that miR-29a-3p was down-regulated in plasma of ARDS patients and lung tissue of ALI model mice, and miR-29a-3p agomir injection down-regulated the levels of the inflammatory factors, including tumor necrosis factor-α (TNF-α), interleukin-1ß (IL-1ß), and interleukin-6 (IL-6) in the lungs, reducing alveolar epithelial cell PANoptosis as evaluated by the downregulation of Z-DNA binding protein 1 (ZBP1), gasdermin D (GSDMD), caspase-3, caspase-8, and mixed lineage kinase domain-like protein (MLKL), ultimately improving lung injury in the ALI model mice. Mechanism studies demonstrated that the knockout of methyltransferase 3 (N6-adenosine-methyltransferase complex catalytic subunit) removed the m6A modification of miR-29a-3p and reduced miR-29a-3p expression. Our findings suggest that miR-29a-3p is a potential target that can be manipulated for ALI/ARDS.

Long Non-Coding RNA RFPL3S Functions as a Biomarker of Prognostic and Immunotherapeutic Prediction in Testicular Germ Cell Tumor.

The incidence of testicular germ cell tumor (TGCT) is currently on the rise worldwide, of which 15%-30% of patients have occur recurrence and metastasis. However, clinical methods for diagnosing TGCT and judging its prognosis remained inadequate. In this study, we aimed to explore the possibility of testis-specific long-chain non-coding RNA (lncRNA) Ret finger protein-like 3S (RFPL3S) as a biomarker for TGCT diagnosis, prognosis, and treatment response by reviewing the TGCT gene expression data in Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The cohort data and DNA methylation data of TGCT in TCGA were downloaded from TGCA, UCSC XENA, and GEO. The bioinformatic tools were used, including GEPIA2, Kaplan-Meier Plotter, LinkedOmics, UCSC XENA, Sangerbox Tools, GSCA, and Tumor Immune Dysfunction and Exclusion. Compared with normal testicular tissues, the RFPL3S expression was significantly reduced in TGCT, and was significantly negatively correlated with the patient's Tumor, Node, Metastasis stage. Hypermethylation and low copy number of RFPL3S were present in TGCT, and low RFPL3S was associated with short disease-free and progression-free intervals. Silencing RFPL3S significantly enhanced the invasion ability and proliferation ability of TGCT cells as evaluated by Transwell and CCK-8 experiments. Additionally, RFPL3S expression was positively correlated with the infiltration of immune-activating cells such as B cells, CD8+ T cells, cytotoxic T cells, and natural killer cells, and negatively correlated with the infiltration of immunosuppressive cells such as Th17 and Th2. Higher RFPL3S expression was present in patients with immunotherapy benefits. In conclusion, we determined that the testis-specific lncRNA RFPL3S functioned as a tumor suppressor in TGCT and could be used as a prognostic predictor of TGCT, as well as a marker to predict the effect of TGCT immunotherapy.

A dynamic analysis of the impact of FDI, on economic growth and carbon emission, evidence from China, India and Singapore.

This study dynamically examines the impact of foreign direct investment and other explanatory variables on economic growth and carbon emissions, and tests the validity of pollution haven hypothesis (PHH) and environmental Kuznets curve (EKC) hypothesis in China, India, and Singapore from 1980 to 2020. The results of Westerlund's (Oxford Bulletin of Economics and Statistics, 69(6):709-748, 2007) panel cointegration test illustrate long-run equilibrium relationships among the proposed set of panel variables in the model. The estimated parameters of the AMG, CCEMG, and MG estimators in each of the specified models show that renewable and non-renewable energy consumption, foreign direct investment, and capital accumulation all have significant and progressive effects on economic growth. However, the labor force is insignificant and carbon emissions have a significant negative impact on economic growth. Non-renewable energy consumption significantly stimulates and renewable energy consumption significantly reduces carbon emissions. Moreover, the moderating role of non-renewable energy in the impact of foreign direct investment on carbon emissions is significantly positive, thus validating the PHH. The moderating role of renewable energy consumption in the impact of foreign direct investment on carbon emissions is significantly negative. The study's analysis also clearly validated the inverted U-shaped EKC hypothesis in China, India, and Singapore. Policymakers in emerging economies must prioritize the maturity of renewable energy, which not only increases productivity but also protects the environment from damage by reducing carbon dioxide emissions. The governments of China, India, and Singapore should initiate direct foreign inflows based on advanced and clean technologies to avoid environmental degradation and drive higher growth in these economies.

BRD7 Stabilizes P53 via Dephosphorylation of MDM2 to Inhibit Tumor Growth in Breast Cancer Harboring Wild-type P53.

Bromodomain-containing protein 7 (BRD7) was found to be down-expressed in nasopharyngeal carcinoma as well as breast cancer and to function as a potential tumor suppressor. BRD7 interacts with p53 and is required for p53-dependent oncogene-induced senescence. However, the mechanism how BRD7 functions as tumor suppressor roles in breast cancer remains unclear. MTT, colony formation assay, cell cycle, cell apoptosis, and tumorigenicity assays were performed to evaluate the biological functions of BRD7 in breast cancer cells in vitro and in vivo. Real-time PCR, western blot, luciferase reporter gene assays, and co-immunoprecipitation were used to examine the gene expression, transcription activation and protein-protein interaction. We reported that BRD7 effectively suppressed cell proliferation and tumor growth in vitro and in vivo. In addition, BRD7 increased p53 protein stability through ubiquitin-dependent proteasome pathway and regulated the expression of p53 downstream target genes by activating its transcriptional activity in breast cancers harboring wild-type p53. Mechanistically, BRD7 decreased phosphorylation and activation of MDM2 via inactivating its upstream kinase AKT depending on the bromodomain of BRD7, therefore BRD7 significantly reduced the amounts of phosphorylated MDM2 binding with p53 eventually decreasing ubiquitination level of p53. Furthermore, silencing the expression of p53 at least partly reversed the inhibition effect of BRD7 on cell proliferation and tumor growth in vitro and in vivo. Our studies identify that BRD7 stabilizes p53 by inhibiting the phosphorylation of MDM2 via AKT pathway dependent on its bromodomain to function as a tumor suppressor in breast cancer harboring wild-type p53.

Single Nuclear RNA Sequencing Highlights Intra-Tumoral Heterogeneity and Tumor Microenvironment Complexity in Testicular Embryonic Rhabdomyosarcoma.

BACKGROUND: Testicular embryonic rhabdomyosarcoma (ERMS) is a rare soft tissue tumor in children featured with high intra-tumoral heterogeneity. In this study, we aimed to comprehensively delineate the testicular ERMS intra-tumoral heterogeneity and tumor microenvironment. METHODS: Cell types and the corresponding marker genes were identified by single-nuclear RNA sequencing (snRNA-seq). Functional states of different clusters were evaluated by uniform manifold approximation and projection and differentially expressed genes. Kaplan-Meier curve analysis was constructed according to the gene expression profile to determine the correlation between candidate marker genes and the overall survival and disease-free survival of patients with osteosarcoma from TCGA. RESULTS: A total of 8868 tumor cells and 10,147 normal cells were obtained from testicular ERMS tissues. The heterogeneous malignant subtype was composed of six subgroups (C1-C6) with differential proliferative and migratory potentials. Cell trajectory analysis revealed the C1 subgroup might be the starting cells of the tumor and transform into two different types of malignant cells, C2 and C5/6, during the development of RMS. The differentially expressed genes were closely related to cell adhesion and extracellular matrix signaling pathways. Furthermore, the interaction analysis between cell subgroups (macrophages and tumor cells, endothelial cells and tumor cells) demonstrated that collagen-related gene COL6A1 plays a key role from the initiation of ERMS to the entire process of malignant transformation. CONCLUSION: Our findings provide a new insight in the understanding of the initiation and progression of testicular ERMS and have potential value in the development of markers for the diagnosis and stratification of testicular ERMS.

A Novel Mutation Eliminates GATA-1 and RUNX1-Mediated Promoter Activity in Galactosyltransferase Gene.

Introduction: Mutations in the promoter region and exons of ABO gene may cause changes in the expression of blood group antigens, often showing a weak ABO phenotype. Here, we identified a novel weak ABO subgroup allele that caused Bel phenotype and explored its mechanisms. Methods: The ABO phenotype of subjects (Chinese Han nationality) was classified by serological method. The plasma activity of erythrocyte glycosyltransferase was detected by the phosphate coupling method. ABO subtype genotyping was performed by PCR-SSP and exon sequencing. The activity of the promoter was evaluated by a dual-luciferase reporter assay. Results: We identified a mutation exon 1 c.15_16insTGTTG of the B allele in a Bel subject. Genealogical investigation showed that the mutation was inherited from her mother. The mutation was located in the promoter region of the ABO gene. The dual-luciferase reporter assay showed that the mutation inactivated GATA-1 and RUNX1-mediated activity of the ABO gene promoter, leading to a decrease in the expression and activity of B glycosyltransferase. Conclusion: A novel Bvar ABO subgroup allele was identified. The novel mutation can reduce the promoter activity that activated by GATA-1 and RUNX1, subsequently causing the Bel phenotype.

Identification and Validation of the Pyroptosis-Related Molecular Subtypes of Lung Adenocarcinoma by Bioinformatics and Machine Learning.

Lung cancer remains the leading cause of cancer death globally, with lung adenocarcinoma (LUAD) being its most prevalent subtype. Due to the heterogeneity of LUAD, patients given the same treatment regimen may have different responses and clinical outcomes. Therefore, identifying new subtypes of LUAD is important for predicting prognosis and providing personalized treatment for patients. Pyroptosis-related genes play an essential role in anticancer, but there is limited research investigating pyroptosis in LUAD. In this study, 33 pyroptosis gene expression profiles and clinical information were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. By bioinformatics and machine learning analyses, we identified novel subtypes of LUAD based on 10 pyroptosis-related genes and further validated them in the GEO dataset, with machine learning models performing up to an AUC of 1 for classifying in GEO. A web-based tool was established for clinicians to use our clustering model (http://www.aimedicallab.com/tool/aiml-subphe-luad.html). LUAD patients were clustered into 3 subtypes (A, B, and C), and survival analysis showed that B had the best survival outcome and C had the worst survival outcome. The relationships between pyroptosis gene expression and clinical characteristics were further analyzed in the three molecular subtypes. Immune profiling revealed significant differences in immune cell infiltration among the three molecular subtypes. GO enrichment and KEGG pathway analyses were performed based on the differential genes of the three subtypes, indicating that differentially expressed genes (DEGs) were involved in multiple cellular and biological functions, including RNA catabolic process, mRNA catabolic process, and pathways of neurodegeneration-multiple diseases. Finally, we developed an 8-gene prognostic model that accurately predicted 1-, 3-, and 5-year overall survival. In conclusion, pyroptosis-related genes may play a critical role in LUAD, and provide new insights into the underlying mechanisms of LUAD.

A Novel Intercellular Communication-Associated Gene Signature for Prognostic Prediction and Clinical Value in Patients With Lung Adenocarcinoma.

BACKGROUND: Lung cancer remains the leading cause of cancer death globally, with lung adenocarcinoma (LUAD) being its most prevalent subtype. This study aimed to identify the key intercellular communication-associated genes (ICAGs) in LUAD. METHODS: Eight publicly available datasets were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The prognosis-related ICAGs were identified and a risk score was developed by using survival analysis. Machine learning models were trained to predict LUAD recurrence based on the selected ICAGs and clinical information. Comprehensive analyses on ICAGs and tumor microenvironment were performed. A single-cell RNA-sequencing dataset was assessed to further elucidate aberrant changes in intercellular communication. RESULTS: Eight ICAGs with prognostic potential were identified in the present study, and a risk score was derived accordingly. The best machine-learning model to predict relapse was developed based on clinical information and the expression levels of these eight ICAGs. This model achieved a remarkable area under receiver operator characteristic curves of 0.841. Patients were divided into high- and low-risk groups according to their risk scores. DNA replication and cell cycle were significantly enriched by the differentially expressed genes between the high- and the low-risk groups. Infiltrating immune cells, immune functions were significantly related to ICAGs expressions and risk scores. Additionally, the changes of intercellular communication were modeled by analyzing the single-cell sequencing dataset. CONCLUSION: The present study identified eight key ICAGs in LUAD, which could contribute to patient stratification and act as novel therapeutic targets.

Dissolving and Regeneration of meta-Aramid Paper: Converting Loose Structure into Consolidated Networks with Enhanced Mechanical and Insulation Properties.

Aramid paper has been widely used in high-voltage motors and transformers due to its excellent insulation property and thermal durability. However, the smoothness and chemical inertness of aramid fibers lead to a loose structure (voids) of aramid paper, which limits its potential applications in harsh environments, such as high-frequency and high-voltage circuits. This work reports a simple and efficient method to improve the mechanical and insulation properties of meta-aramid paper via controllable dissolving and regeneration of aramid fibers. To obtain a dense and robust structure, the pristine meta-aramid paper was immersed in a dimethyl sulfoxide/potassium hydroxide (DMSO/KOH) mixture to make aramid fibers swelled and dissolved, followed by regeneration in water vapor, eventually generating densified aramid paper with fewer voids and enhanced insulation and mechanical performance. Optimum conditions resulted in aramid paper with the best comprehensive performance, and the tensile strength, Young's modulus, and electrical breakdown strength of the consolidated aramid paper were 22.85 MPa, 0.72 GPa, and 15.3 kV/mm, respectively, which were significantly higher than those of the pristine aramid paper (12.53 MPa, 0.41 GPa, and 8.36 kV/mm). Meanwhile, such treatment did not cause any chemical structure change, and thus it still retained the excellent thermal resistance (Td > 430 °C) of aramid fibers. This simple method can effectively regulate the surface porosity and the mechanical and breakdown strength of aramid paper, as well as provide a generic method for postprocessing and enhancing aramid paper.

Machine Learning for the Prediction of Red Blood Cell Transfusion in Patients During or After Liver Transplantation Surgery.

Aim: This study aimed to use machine learning algorithms to identify critical preoperative variables and predict the red blood cell (RBC) transfusion during or after liver transplantation surgery. Study Design and Methods: A total of 1,193 patients undergoing liver transplantation in three large tertiary hospitals in China were examined. Twenty-four preoperative variables were collected, including essential population characteristics, diagnosis, symptoms, and laboratory parameters. The cohort was randomly split into a train set (70%) and a validation set (30%). The Recursive Feature Elimination and eXtreme Gradient Boosting algorithms (XGBOOST) were used to select variables and build machine learning prediction models, respectively. Besides, seven other machine learning models and logistic regression were developed. The area under the receiver operating characteristic (AUROC) was used to compare the prediction performance of different models. The SHapley Additive exPlanations package was applied to interpret the XGBOOST model. Data from 31 patients at one of the hospitals were prospectively collected for model validation. Results: In this study, 72.1% of patients in the training set and 73.2% in the validation set underwent RBC transfusion during or after the surgery. Nine vital preoperative variables were finally selected, including the presence of portal hypertension, age, hemoglobin, diagnosis, direct bilirubin, activated partial thromboplastin time, globulin, aspartate aminotransferase, and alanine aminotransferase. The XGBOOST model presented significantly better predictive performance (AUROC: 0.813) than other models and also performed well in the prospective dataset (accuracy: 76.9%). Discussion: A model for predicting RBC transfusion during or after liver transplantation was successfully developed using a machine learning algorithm based on nine preoperative variables, which could guide high-risk patients to take appropriate preventive measures.

Long Non-coding RNA GAS5 Maintains Insulin Secretion by Regulating Multiple miRNAs in INS-1 832/13 Cells.

Type-2 diabetes mellitus (T2DM) is a complex disease characterized by reduced pancreatic islets ß-cell mass and impaired insulin release from these cells. Non-coding RNAs, including microRNAs (miRNA) and long non-coding RNAs (lncRNAs), play a role in the progression of T2DM. Decreased serum lncRNA GAS5 levels were reported to be associated with T2DM. However, the role of GAS5 in regulating islet cell functions remain unknown. In this study, we found that the serum levels of GAS5 were significantly lower in patients with T2DM compared with healthy control subjects, and the low serum GAS5 levels were associated with high levels of HbAlc and fasting glucose in patients with T2DM. In addition, we found that serum GAS5 levels were negatively correlated with the serum levels of miR-29a-3p, miR-96-3p, and miR-208a-3p in patients with T2DM. Consequently, using INS-1 832/13 rat ß-cell line, we found that overexpression of GAS5 by lentivirus infection increased glucose-stimulated insulin secretion and insulin content compared with negative control, whereas knockdown of GAS5 expression reduced both them. Moreover, GAS5 interacted with miR-29a-3p, miR-96-3p, and miR-208a-3p in INS-1 832/13 cells, as judged by pull-down assay and dual luciferase reporter assay. GAS5 overexpression caused the decrease in expression of miR-29a-3p, miR-96-3p, and miR-208a-3p in INS-1 832/13 cells and conversely caused the increase in expression of insulin receptor, insulin receptor substrate, and phosphoinositide-3-kinase regulatory subunit 1. Thus, these results reveal a novel mechanism whereby GAS5 is involved in maintaining insulin secretion and may represent a novel therapeutic target for T2DM.