Even in the current age of advanced medicine, the prognosis of malignant peritoneal mesothelioma (MPM) remains abysmal. Molecular mechanisms responsible for the initiation and progression of MPM are still largely not understood. Adopting an integrated bioinformatics approach, this study aims to identify the key genes and pathways responsible for MPM. Genes that are differentially expressed in MPM in comparison with the peritoneum of healthy controls have been identified by analyzing a microarray gene expression dataset. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses of these differentially expressed genes (DEG) were conducted to gain a better insight. A protein-protein interaction (PPI) network of the proteins encoded by the DEGs was constructed using STRING and hub genes were detected analyzing this network. Next, the transcription factors and miRNAs that have possible regulatory roles on the hub genes were detected. Finally, survival analyses based on the hub genes were conducted using the GEPIA2 web server. Six hundred six genes were found to be differentially expressed in MPM; 133 are upregulated and 473 are downregulated. Analyzing the STRING generated PPI network, six dense modules and 12 hub genes were identified. Fifteen transcription factors and 10 miRNAs were identified to have the most extensive regulatory functions on the DEGs. Through bioinformatics analyses, this work provides an insight into the potential genes and pathways involved in MPM.

Even in the current age of advanced medicine, the prognosis of malignant peritoneal mesothelioma (MPM) remains abysmal. Molecular mechanisms responsible for the initiation and progression of MPM are still largely not understood. Adopting an integrated bioinformatics approach, this study aims to identify the key genes and pathways responsible for MPM. Genes that are differentially expressed in MPM in comparison with the peritoneum of healthy controls have been identified by analyzing a microarray gene expression dataset. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses of these differentially expressed genes (DEG) were conducted to gain a better insight. A protein-protein interaction (PPI) network of the proteins encoded by the DEGs was constructed using STRING and hub genes were detected analyzing this network. Next, the transcription factors and miRNAs that have possible regulatory roles on the hub genes were detected. Finally, survival analyses based on the hub genes were conducted using the GEPIA2 web server. Six hundred six genes were found to be differentially expressed in MPM; 133 are upregulated and 473 are downregulated. Analyzing the STRING generated PPI network, six dense modules and 12 hub genes were identified. Fifteen transcription factors and 10 miRNAs were identified to have the most extensive regulatory functions on the DEGs. Through bioinformatics analyses, this work provides an insight into the potential genes and pathways involved in MPM.

BACKGROUND: Although the exact mechanism of insulin resistance (IR) has not yet been established, IR is the hallmark characteristic of type 2 diabetes mellitus (T2DM). The aim of this study was to examine the relationship between plasma ghrelin levels and IR in Saudi subjects with T2DM. METHODS: Patients with T2DM (n=107, cases) and non-diabetic apparently healthy subjects (n=101, controls) from Saudi Arabia were included in this study. The biochemical profiles and plasma insulin levels of all subjects were analyzed, and IR was estimated using the homeostatic model assessment of insulin resistance (HOMA-IR) index. Active ghrelin levels in plasma were measured using the radioimmunoassay technique. RESULTS: Only 46.7% (50 of 107) of the T2DM subjects had IR, including 26% (28 of 107) with severe IR (HOMA-IR ≥5), while 5.9% (six of 101) of the controls had moderate IR (3 ≤HOMA-IR <5). HOMA-IR values were not associated with age, disease duration, or gender. Importantly, T2DM itself and the co-occurrence of IR with T2DM were significantly associated with low plasma ghrelin levels. However, ghrelin levels were inversely correlated with the HOMA-IR index, body weight, and fasting plasma insulin levels, mainly in the control subjects, which was indicative of the breakdown of metabolic homeostasis in T2DM. CONCLUSION: The prevalence of IR was relatively low, and IR may be inversely associated with plasma ghrelin levels among Saudi patients with T2DM.
Body Weight ; Diabetes Mellitus ; Diabetes Mellitus, Type 2* ; Fasting ; Ghrelin* ; Healthy Volunteers ; Homeostasis ; Humans ; Insulin Resistance* ; Insulin* ; Plasma* ; Prevalence ; Radioimmunoassay ; Saudi Arabia

Objective: To carry out the genetic characterization and evolutionary analysis of three avian orthoavulavirus 1 (AOAV-1) isolates from poultry workers with respiratory symptoms. Methods: Using Illumina MiSeq, whole-genome sequencing was carried out to assess the evolutionary dynamics of three AOAV-1 isolates. A phylogenetic and comparative analysis of all coding genes was done using bioinformatics tools. Results: Phylogenetic analysis and genetic distance estimation suggested a close relationship among human- and avian-originated velogenic strains of genotype XIII, sub-genotype XIII.2.1. Several substitutions in the significant structural and biological motifs were exclusively identified in the human-originated strains. Conclusions: To our knowledge, this is the first report of a velogenic AOAV-1 isolate from natural infection of the human upper respiratory tract. Our findings highlight the evolution and zoonotic potential of velogenic AOAV-1 in a disease endemic setting.

Accurately delineating tumor boundaries is key to predicting survival rates of cancer patients and assessing response of tumor microenvironment to various therapeutic techniques such as chemotherapy and radiotherapy. This review discusses various strategies that have been deployed to accurately delineate tumor boundaries with particular emphasis on the potential of chemotherapeutic nanomaterials in tumor boundary delineation. It also compiles the types of tumors that have been successfully delineated by currently available strategies. Finally, the challenges that still abound in accurate tumor boundary delineation are presented alongside possible perspective strategies to either ameliorate or solve the problems. It is expected that the information communicated herein will form the first compendious baseline information on tumor boundary delineation with chemotherapeutic nanomaterials and provide useful insights into future possible paths to advancing current available tumor boundary delineation approaches to achieve efficacious tumor therapy.