1,807 research outputs found
Maximum endurance for battery-powered rotary-wing aircraft
No full textThe paper presents an analytical framework for addressing the hovering time prediction of rotary-wing aircraft, with a particular focus on multi-rotor platforms. By imposing the balance between required and available power, the endurance expression is derived as a function of airframe features, rotor parameters, and battery capacity. The best endurance condition is also obtained in terms of optimum capacity and hovering time, by means of two approximate closed-form solutions. The proposed methodology was validated by means of numerical simulation and flight testing. Results show the effectiveness of the proposed approac
Gaining new insights on the Hsp90 regulatory network
No full textThe heat shock protein Hsp90 is a molecular chaperon that uses ATP and interacts with various co-chaperone proteins, acting as adapters, in order to carry out the maturation of its target proteins. In physiological conditions, the heat shock proteins (HSPs) favour post-translational modification, protein folding and sub-cellular transport of their "client" proteins. In stress conditions, many misfolded proteins accumulate exposing their hydrophobic residues and these are recognized by HSPs which prevent the aggregation and favour the correct folding. In case this is no longer possible, HSPs mediate elimination of such misfolded proteins, mainly by ubiquitin-proteasome system
A Comparison of Tools That Identify Tumor Cells by Inferring Copy Number Variations from Single-Cell Experiments in Pancreatic Ductal Adenocarcinoma
No full textSingle-cell RNA sequencing (scRNA-seq) technique has enabled detailed analysis of gene expression at the single cell level, enhancing the understanding of subtle mechanisms that underly pathologies and drug resistance. To derive such biological meaning from sequencing data in oncology, some critical processing must be performed, including identification of the tumor cells by markers and algorithms that infer copy number variations (CNVs). We compared the performance of sciCNV, InferCNV, CopyKAT and SCEVAN tools that identify tumor cells by inferring CNVs from scRNA-seq data. Sequencing data from Pancreatic Ductal Adenocarcinoma (PDAC) patients, adjacent and healthy tissues were analyzed, and the predicted tumor cells were compared to those identified by well-assessed PDAC markers. Results from InferCNV, CopyKAT and SCEVAN overlapped by less than 30% with InferCNV showing the highest sensitivity (0.72) and SCEVAN the highest specificity (0.75). We show that the predictions are highly dependent on the sample and the software used, and that they return so many false positives hence are of little use in verifying or filtering predictions made via tumor biomarkers. We highlight how critical this processing can be, warn against the blind use of these software and point out the great need for more reliable algorithms
Copy Number Variations in Pancreatic Cancer: From Biological Significance to Clinical Utility
No full textPancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer, characterized by high tumor heterogeneity and a poor prognosis. Inter- and intra-tumoral heterogeneity in PDAC is a major obstacle to effective PDAC treatment; therefore, it is highly desirable to explore the tumor heterogeneity and underlying mechanisms for the improvement of PDAC prognosis. Gene copy number variations (CNVs) are increasingly recognized as a common and heritable source of inter-individual variation in genomic sequence. In this review, we outline the origin, main characteristics, and pathological aspects of CNVs. We then describe the occurrence of CNVs in PDAC, including those that have been clearly shown to have a pathogenic role, and further highlight some key examples of their involvement in tumor development and progression. The ability to efficiently identify and analyze CNVs in tumor samples is important to support translational research and foster precision oncology, as copy number variants can be utilized to guide clinical decisions. We provide insights into understanding the CNV landscapes and the role of both somatic and germline CNVs in PDAC, which could lead to significant advances in diagnosis, prognosis, and treatment. Although there has been significant progress in this field, understanding the full contribution of CNVs to the genetic basis of PDAC will require further research, with more accurate CNV assays such as single-cell techniques and larger cohorts than have been performed to date
Osteocrin/musclin and the natriuretic peptides system: A novel focus in metabolism and cardiovascular prevention
No full textCross-link immunoprecipitation data to detect polymorphisms lying in splicing regulatory motifs: a method to refine single nucleotide polymorphism selection in association studies
No full textIdentification of candidate miRNA biomarkers for pancreatic ductal adenocarcinoma by weighted gene co-expression network analysis
No full textPURPOSE: Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with a dismal prognosis which is, among others, due to a lack of suitable biomarkers and therapeutic targets. Previously, basic gene expression analysis methods have been used for their identification, but recently new algorithms have been developed allowing more comprehensive data analyses. Among them, weighted gene co-expression network analysis (WGCNA) has already been applied to several cancer types with promising results.
METHODS: We applied WGCNA to miRNA expression data from PDAC patients. Specifically, we processed microarray-based expression data of 2555 miRNAs in serum from 100 PDAC patients and 150 healthy subjects. We identified network modules of co-expressed miRNAs in the healthy subject dataset and verified their preservation in the PDAC dataset. In the non-preserved modules, we selected key miRNAs and carried out functional enrichment analyses of their experimentally known target genes. Finally, we tested their prognostic significance using overall survival analyses.
RESULTS: Through WGCNA we identified several miRNAs that discriminate healthy subjects from PDAC patients and that, therefore, may play critical roles in PDAC development. At a functional level, we found that they regulate p53, FoxO and ErbB associated cellular signalling pathways, as well as cell cycle progression and various genes known to be involved in PDAC development. Some miRNAs were also found to serve as novel prognostic biomarkers, whereas others have previously already been proposed as such, thereby validating the WGCNA approach. In addition, we found that these novel data may explain at least some of our previous PDAC gene expression analysis results.
CONCLUSIONS: We identified several miRNAs critical for PDAC development using WGCNA. These miRNAs may serve as biomarkers for PDAC diagnosis/prognosis and patient stratification, and as putative novel therapeutic targets
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