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The epigenetic abnormality is generally accepted as the key to cancer initiation. Epigenetics that ensure the somatic inheritance of differentiated state is defined as a crucial factor influencing malignant phenotype without altering genotype. Histone modification is one such alteration playing an essential role in tumor formation, progression, and resistance to treatment. Notably, changes in histone acetylation have been strongly linked to gene expression, cell cycle, and carcinogenesis. The balance of two types of enzyme, histone acetyltransferases (HATs) and histone deacetylases (HDACs), determines the stage of histone acetylation and then the architecture of chromatin. Changes in chromatin structure result in transcriptional dysregulation of genes that are involved in cell-cycle progression, differentiation, apoptosis, and so on. Recently, HDAC inhibitors (HDACis) are identified as novel agents to keep this balance, leading to numerous researches on it for more effective strategies against cancers, including glioblastoma (GBM). This review elaborated influences on gene expression and tumorigenesis by acetylation and the antitumor mechanism of HDACis. Besdes, we outlined the preclinical and clinical advancement of HDACis in GBM as monotherapies and combination therapies.

Introduction Atopic dermatitis (AD) is a chronic immuno-inflammatory skin disease. Crisaborole ointment, 2%, is a nonsteroidal phosphodiesterase 4 inhibitor approved for the treatment of mild to moderate AD. This post hoc analysis assesses the efficacy and safety of crisaborole in Chinese patients aged ≥ 2 years with mild to moderate AD. Methods We evaluated the efficacy and safety of crisaborole in Chinese patients from the vehicle-controlled, phase 3 CrisADe CLEAR study. Patients were randomly assigned 2:1 to receive crisaborole or vehicle twice daily, respectively, for 28 days. The primary endpoint was percent change from baseline in Eczema Area and Severity Index (EASI) total score at day 29. Key secondary endpoints were improvement in Investigator’s Static Global Assessment (ISGA), ISGA success, and change from baseline in weekly average Peak Pruritus Numerical Rating Scale (PP-NRS) score. Adverse events were documented. Results Of 391 patients in the overall study, 237 were from China, 157 assigned to crisaborole and 80 assigned to vehicle. A greater reduction in percent change from baseline in EASI total score at day 29 was shown in the crisaborole vs. vehicle group (least squares mean [LSM]: −66.34 [95% (confidence interval) CI −71.55 to −61.12] vs. −50.18 [95% CI −58.02 to −42.34]). Response rates for achievement of ISGA improvement (43.2% [95% CI 35.4–51.1] vs. 33.4% [95% CI 22.5–44.2]) and ISGA success (31.7% [95% CI 24.3–39.0] vs. 21.5% [95% CI 12.1–30.9]) at day 29 were higher in the crisaborole vs. vehicle group. A greater reduction in change from baseline in weekly average PP-NRS score at week 4 was observed in the crisaborole vs. vehicle group (LSM: −1.98 [95% CI −2.34 to −1.62] vs. −1.08 [95% CI −1.63 to −0.53]). No new safety signals were observed. Conclusion Crisaborole was effective and well tolerated in Chinese patients aged ≥ 2 years with mild to moderate AD. Trial Registration ClinicalTrials.gov, NCT04360187.

Glioblastoma is a common malignant tumor in the central nervous system with an extremely poor outcome; understanding the mechanisms of glioblastoma at the molecular level is essential for clinical treatment. In the present study, we used bioinformatics analysis to identify potential biomarkers associated with prognosis in glioblastoma and elucidate the underlying mechanisms. The result revealed that 552 common genes were differentially expressed between glioblastoma and normal tissues based on TCGA, GSE4290, and GSE 50161 datasets. Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and protein-protein interaction (PPI) network were carried out to gain insight into the actions of differentially expressed genes (DEGs). As a result, 20 genes (CALB1, CDC20, CDCA8, CDK1, CEP55, DLGAP5, KIF20A, KIF4A, NDC80, PBK, RRM2, SYN1, SYP, SYT1, TPX2, TTK, VEGFA, BDNF, GNG3, and TOP2A) were found as hub genes via CytoHubba in Cytoscape and functioned mainly by participating in cell cycle and p53 signaling pathway; among them, RRM2 and CEP55 were considered to have relationship with the prognosis of glioblastoma, especially RRM2. High expression of RRM2 was consistent with shorter overall survival time. In conclusion, our study displayed the bioinformatic analysis methods in screening potential oncogenes in glioblastoma and underlying mechanisms. What is more is that we successfully identified RRM2 as a novel biomarker linked with prognosis, which might be expected to be a promising target for the therapy of glioblastoma.

Based on both the lower and the upper triangular Cholesky decomposition algorithms, the (inverse) lower triangular Cholesky integer transformation and the (inverse) upper triangular Cholesky integer transformation are defined, and the (inverse) paired Cholesky integer transformation is proposed. Then, for the case of high-correlation ambiguity, a multi-time (inverse) paired Cholesky integer transformation is given. In addition, a simple and practical criterion is presented to solve the uniqueness problem of the integer transformation. It is verified by an example that (1) the (inverse) paired Cholesky integer transformation is very convenient and very efficient in practical computation; (2) the (inverse) paired Cholesky integer transformation is better than both the (inverse) lower triangular Cholesky integer transformation and the (inverse) upper triangular Cholesky integer transformation; and that (3) the inverse paired Cholesky integer transformation outperforms the paired Cholesky integer transformation slightly in the most cases.

Glioblastoma (GBM) is the most aggressive primary intracranial tumor in adults. Chemoradiotherapy resistance and recurrence after surgery are the main malignant progression factors, leading to a high mortality rate. Therefore, the exploration of novel biomarkers and molecular mechanisms of GBM is urgent. Differentially expressed genes (DEGs) of GBM were screened in a TCGA dataset. Homo sapiens ZW10 interacting kinetochore protein (ZWINT) was found to be upregulated in GBM, which was confirmed by immunohistochemical staining of a tissue microarray. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) database. A protein-protein interaction (PPI) network was established by the STRING database, and hub genes were visualized by Cytoscape. The correlation results were verified with the GSE15824 dataset. Bioinformatic analysis confirmed that ZWINT was significantly positively correlated with kinetochore protein NDC80 homolog (NDC80), serine/threonine-protein kinase PLK1 (PLK1) and spindle and kinetochore associated complex subunit 1 (SKA1) and together are involved in regulating mitosis and the cell cycle of GBM. ZWINT expression was knocked down in U251 and U87 MG GBM cells by lentiviral vectors carrying a small hairpin RNA (shRNA) targeting ZWINT. The effect of ZWINT silencing on cell proliferation, invasion and apoptosis was determined by the Celigo assay, MTT assay, Transwell assay, flow cytometry and caspase-3/7 assay in vitro. A subcutaneous xenograft tumor model was established to explore the influence of ZWINT knockdown on GBM growth in vivo. Our preliminary study demonstrated that ZWINT knockdown effectively inhibited proliferation and invasion and induced apoptosis of GBM cells and notably suppressed GBM growth in vivo. Therefore, we speculate that ZWINT may be a potential therapeutic biomarker for GBM, with NDC80 and PLK1 conjointly involved in regulating cell division and the mitotic cell cycle.

Glioblastoma (GBM) is the most common primary intracranial malignancy. GBM still exhibits high recurrence and mortality rates even following combined treatment with surgery, radiotherapy and chemotherapy, Therefore, the identification of novel therapeutic targets is urgent. Previous research has shown that nicotinamide phosphoribosyltransferase (NAMPT) plays a key role in cell metabolism and is closely related to the occurrence and development of many tumor types; yet, little is known concerning its relationship with GBM. Oncomine database analysis showed that the expression of NAMPT in GBM was higher than that in normal tissues; this finding was further confirmed by immunohistochemical staining of a tissue microarray. Data analysis with the R2 platform showed that patients with higher expression of NAMPT had worse prognoses than those with lower NAMPT expression. Using the GBM data in TCGA, four pathways enriched in the high NAMPT expression group were identified by gene set enrichment analysis (GSEA). NAMPT expression was knocked down in U87 and U251 GBM cells by lentiviral vectors carrying a small hairpin RNA (shRNA) targeting NAMPT. CCK-8, colony formation, wound healing, Transwell and apoptosis assays were carried out. The results showed that NAMPT knockdown decreased cell proliferation, migration, and invasion and promoted apoptosis. U87 GBM cells were used in a model of subcutaneous tumorigenesis in nude mice. The results showed that NAMPT knockdown slowed the growth of tumors in vivo. Therefore, we speculate that NAMPT may be a potential prognostic and therapeutic biomarker for glioblastoma.

It has been discovered that (a) the variance of all entries of the ambiguity vector transformed by a (inverse) paired Cholesky integer transformation is reduced relative to that of the corresponding entries of the original ambiguity vector; (b) the higher the dimension of the ambiguity vector, the more significantly the transformed variance will be decreased. The property of variance reduction is explained theoretically in detail. In order to better measure the property of variance reduction, an efficiency factor on variance reduction of ambiguities is defined. Since the (inverse) paired Cholesky integer transformation is generally performed many times for the GNSS high-dimensional ambiguity vector, the computation formula of the efficiency factor on the multi-time (inverse) paired Cholesky integer transformation is deduced. The computation results in the example show that (a) the (inverse) paired Cholesky integer transformation has a very good property of variance reduction, especially for the GNSS high-dimensional ambiguity vector; (b) this property of variance reduction can obviously improve the success rate of the transformed ambiguity vector.

Background: Diabetic kidney disease (DKD) lacks a simple and relatively accurate predictor. The Triglyceride-Glucose (TyG) Index is a proxy of insulin resistance, but the association between the TyG Index and DKD is less certain. We investigated if the TyG Index can predict DKD onset effectively. Materials and Methods: Cross-sectional and longitudinal analyses were undertaken. In total, 1432 type-2 diabetes mellitus (T2DM) patients were included in the cross-sectional analysis. The TyG Index (calculated by In [fasting triglycerides (mg/dL) * fasting glucose (mg/dL)/2]) was split into three tertiles. Associations of the TyG Index with microalbuminuria and estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 [m.sup.2] were calculated. Longitudinally, 424 patients without DKD at baseline were followed up for 21 (range, 12-24) months. The main outcome was DKD prevalence as defined with eGFR <60 mL/min/1.73 [m.sup.2] or continuously increased urinary microalbuminuria: creatinine ratio (>30 mg/mL) over 3 months. Cox regression was used to analyze the association between the TyG Index at baseline and DKD. Receiver operating characteristics curve (ROC) analysis was used to assess the sensitivity and specificity of the TyG Index in predicting DKD. Results: In cross-sectional analysis, patients with a higher TyG Index had a higher risk of microalbuminuria (OR = 2.342, 95% CI = 1.744-3.144, p < 0.001), and eGFR <60 mL/min/1.73 [m.sup.2] (1.696, 95% CI =1.096-2.625, p = 0.018). Longitudinally, 94 of 424 participants developed DKD. After confounder adjustment, patients in the high tertile of the TyG Index at baseline had a greater risk to developing DKD than those in the low tertile (HR = 1.727, 95% CI = 1.042-2.863, p = 0.034). The area under the ROC curve was 0.69 (0.63-0.76). Conclusion: The TyG Index is a potential predictor for DKD in T2DM patients. Clinical Trial: Clinical Trials identification number = NCT03692884. Keywords: diabetic kidney disease, triglyceride-glucose index, insulin resistance

This dissertation consists of two parts. The first part considers a semi-parametric two-component mixture model with one component completely known. Assuming the density of the unknown component to be log-concave, which contains a very broad family of densities, we develop a semi-parametric maximum likelihood estimator and propose an EM algorithm to compute it. Our new estimation method finds the mixing proportion and the distribution of the unknown component simultaneously. We establish the identifiability of the proposed semi-parametric mixture model and prove the existence and consistency of the proposed estimators. We further compare our estimator with several existing estimators through simulation studies and apply our method to two real data sets from biological sciences and astronomy. The second part of this dissertation considers the model g(x) = (1 − p)f0(x; θ) + pf(x), where θ represents the unknown parameters of a known distribution f0 , and f represents the distribution of possible outliers. We propose two innovative algorithms to estimate θ nonparametrically. The first method is called Minimum Search, which is based on identifiability of the mixture model. A strong sufficient condition is proposed for the model to be identifiable and a weaker condition is given for the model to be locally identifiable. The second estimator is the maximum likelihood estimator, which is obtained by EM algorithm assuming f is log-concave. Extensive simulation studies show that our methods give very promising performances.

In this study, the influence of glycoproteomic changes, specifically MUC16, on NK cell-mediated immunotherapy response in ovarian cancer is explored. Analysis of glycoprotein data from the CPTAC database identified significant upregulation of MUC16 in ovarian cancer tissues, associated with tumor invasiveness and immune evasion. Experimental findings showed that MUC16 knockdown increased NK cell cytotoxicity, decreased invasiveness, and boosted NK cell activation, while MUC16 overexpression resulted in the opposite effects. In vivo experiments demonstrated that MUC16 knockdown suppressed tumor growth, enhanced NK cell infiltration, and bolstered NK cell activation, underscoring the potential of MUC16 as a target for novel immunotherapy approaches in ovarian cancer treatment.