1,720,999 research outputs found
Identification of Cancer Biomarkers for Multi-class Diagnostics through Network Analysis of RNAseq Data of Tumor-Educated Platelets
No full textTumor-educated platelets (TEPs) are circulating blood cells with a distinct tumor-driven phenotype and act as carriers and protectors of metastases. To date, some studies have shown that the TEPs transcriptome can be used for cancer diagnostics. The objective of this study is to propose a procedure based on differential gene expression and differential gene co-expression analyses to identify a set of key genes for multi-class cancer diagnostics. To reach this aim, we analyzed RNA-seq data (57736 genes) of 130 subjects, of whom 40 patients with glioblastoma multiforme (GBM), 35 patients with pancreatic adenocarcinoma (PAAD), and 55 healthy donors (HC). We focused our analysis on the subset of differentially expressed genes (DEGs), and we used these genes to build and analyze the differential co-expression networks, identifying the hub genes. With this procedure, we obtained a restricted set of DEGs that maximize the accuracy in classifying patients according to their conditions (GBM, PAAD, or HC). Indeed, we validated our results by comparing the achieved classification accuracy with that resulting from random selections of DEGs and we obtained that genes selected by differential co-expression (DCE) network analysis have greater predictive power than any other set of differentially expressed genes, including using all of them
Multi-layer network modelling of genomic and transcriptomic data to investigate the response to checkpoint inhibitors in NSCLC
No full textRecent innovations and developments in molecular biology and biotechnology have made it possible to acquire and store large omics datasets. In particular genomics, transcriptomics and the study of their relationship represent the key elements to understand how genotype influences phenotype. In this work we investigate the potential of multi-layer network modeling in integrating genomic and transcriptomic data of 152 advanced non-small cell lung cancer (NSCLC) patients treated with anti-PD-(L)1 therapy. For the transcriptomic layer, we performed differential expression and differential co-expression analyses in order to identify a subset of key genes in differentiating responder patients from non-responders. Adding to the genomic-transcriptomic model other three layers related to immune, myeloid and curated immunotherapy-based literature signatures we obtained a 5-layer Patient Similarity Network. The application of Similarity Network Fusion algorithm revealed a statistically significant stratification of patients which allows the identification of two clusters characterized by patients responding and not responding to immunotherapy
Differential Co-expression Network Analysis to Investigate Sexual Dimorphism in Colon Cancer
No full textColorectal cancer is the third most diagnosed cancer in the world, but it has a higher mortality rate in men compared to women. However, we are not close to understanding how and why sex influences the outcome of the disease. This study focuses on mRNA expression profiles of colon cancer patients to look for molecular differences in the development of colon cancer between men and women. We used paired expression data (i.e., data collected in pairs of normal and cancer cells, by taking samples from the same individual), we identified differentially expressed genes (cancer vs normal) and computed co-expression and differential co-expression gene networks (men vs women). Doing so, we inferred the main changes and alterations happening in cancer tissues, and specifically how these changes were different among men and women. We found that the co-expression networks of women and men affected by colon cancer are quite different and we reported the genes that show the most differences in this comparison, checking if they could also be associated to sexual dimorphism or sexual hormones. Among these genes we found a interesting presence of genes associated to the Wnt signaling pathway which has been found to be regulated by estrogen and whose activation is strongly linked with colon cancer
Precision medicine: Beyond AI
Full text linkAdvancing our understanding of complex diseases requires an interdisciplinary dialogue beyond artificial intelligence (AI). Fostering collaboration and training among genetics, molecular biology, computational biology, and clinical research represents an imperative need to address precision medicine challenges. Bringing together expertise and data from different fields, like a collective work of art, can make the real revolution in medicine
An EEG study on civil pilots during flight simulation
No full textOBIETTIVI
Misurazioni neurofisiologiche e relative features possono essere usate ai fini di caratterizzare differenti stati mentali e di stimare l’attività del sistema nervoso centrale legata all’esecuzione di task di diversa natura. Lo scopo del presente studio è quello di usare i segnali elettroencefalografici (EEG) e metodi di stima della connettività cerebrale per investigare lo sforzo mentale richiesto durante diverse fasi di una simulazione di volo su piloti civili.
METODI
Le registrazioni EEG e la stima della connettività sono state eseguite su piloti dell’aviazione civile (Comandanti e Primi Ufficiali) durante un volo in un simulatore professionale. La connettività cerebrale è stata stimata per mezzo della Partial Directed Coherence e misure sintetiche sono state estratte dai pattern ottenuti al fine di descrivere l’attività cerebrale elicitata durante le diverse fasi di volo.
RISULTATI
Durante le fasi di decollo ed atterraggio, un denso pattern di comunicazione tra le aree cerebrali è stato rilevato sia per i Capitani che per i Primi Ufficiali. Durante la fase di crociera invece, queste reti cerebrali diventano più sparse e caratterizzate da una comunicazione meno efficiente.
CONCLUSIONI
I risultati ottenuti hanno mostrato che le registrazioni EEG e lo studio della connettività cerebrale sono potenti strumenti di indagine per la comprensione dei meccanismi sottostanti l’esecuzione di compiti come un volo simulato.OBJECTIVES
Neurophysiological measurements and their related features can be used to characterize different mental states and to estimate the activity of the central nervous system related to the execution of tasks of different nature. The aim of the present study is to use electroencephalographic (EEG) signals and brain connectivity estimation to investigate the mental effort required during different phases of a flight simulation on civil pilots.
METHODS
EEG recordings and connectivity estimation were performed on civil aviation pilots (Captains and First Officers) during a flight in a professional simulator. Brain connectivity was estimated by means of Partial Directed Coherence and synthetic measures were extracted from the achieved patterns to describe the brain activity elicited during different flight phases.
RESULTS
In the take-off and landing phases, a dense communication between cerebral areas was detected both for Captains and First Officers. In contrast, these patterns of connections become sparser during the cruise phase. Moreover, the network organization during cruise phase results less efficient than in the other flight phases.
CONCLUSIONS
The obtained results have shown that the EEG recordings and brain connectivity study are powerful tools of investigation for the understanding of the neural mechanisms underlying the execution of tasks like a flight simulation
Resting state effective connectivity in stroke patients. An EEG study
No full textScientific evidences suggest the possibility of obtaining significant information about the state and the cognitive performances of the brain by using only EEG activity during resting state. In this study graph theory was applied to functional brain networks in order to describe the topographic reorganization of the brain connectivity network related to the resting state condition in a population of 42 stroke patients, with the aim to evaluate deviation from healthy conditions and characterize patients on the basis of their clinical features. Brain connectivity was estimated by means of the spectral estimator Partial Directed Coherence and synthetic graph indices were extracted from the estimated networks. Results showed significant differences between the properties of resting state brain networks of stroke patients and those of healthy subjects. A significant effect of the lesion side on the reorganization after the stroke event was also shown
Network-based methods for psychometric data of eating disorders: A systematic review
No full textBACKGROUND: Network science represents a powerful and increasingly promising method for studying complex real-world problems. In the last decade, it has been applied to psychometric data in the attempt to explain psychopathologies as complex systems of causally interconnected symptoms. One category of mental disorders, relevant for their severity, incidence and multifaceted structure, is that of eating disorders (EDs), serious disturbances that negatively affect a person’s eating behavior. AIMS: We aimed to review the corpus of psychometric network analysis methods by scrutinizing a large sample of network-based studies that exploit psychometric data related to EDs. A particular focus is given to the description of the methodologies for network estimation, network description and network stability analysis providing also a review of the statistical software packages currently used to carry out each phase of the network estimation and analysis workflow. Moreover, we try to highlight aspects with potential clinical impact such as core symptoms, influences of external factors, comorbidities, and related changes in network structure and connectivity across both time and subpopulations. METHODS: A systematic search was conducted (February 2022) on three different literature databases to identify 57 relevant research articles. The exclusion criteria comprehended studies not based on psychometric data, studies not using network analysis, studies with different aims or not focused on ED, and review articles. RESULTS: Almost all the selected 57 papers employed the same analytical procedures implemented in a collection of R packages specifically designed for psychometric network analysis and are mostly based on cross-sectional data retrieved from structured psychometric questionnaires, with just few exemptions of panel data. Most of them used the same techniques for all phases of their analysis. In particular, a pervasive use of the Gaussian Graphical Model with LASSO regularization was registered for in network estimation step. Among the clinically relevant results, we can include the fact that all papers found strong symptom interconnections between specific and nonspecific ED symptoms, suggesting that both types should therefore be addressed by clinical treatment. CONCLUSIONS: We here presented the largest and most comprehensive review to date about psychometric network analysis methods. Although these methods still need solid validation in the clinical setting, they have already been able to show many strengths and important results, as well as great potentials and perspectives, which have been analyzed here to provide suggestions on their use and their possible improvement
Neural Networks and Connectivity among Brain Regions
Full text linkAs is widely understood, brain functioning depends on the interaction among several neural populations, which are linked via complex connectivity circuits and work together (in antagonistic or synergistic ways) to exchange information, synchronize their activity, adapt plastically to external stimuli or internal requirements, and more generally to participate in solving multifaceted cognitive tasks [...
Network-based validation of the psychometric questionnaire EDI-3 for the assessment of eating disorders
Full text linkAssessing the validity of a psychometric test is fundamental to ensure a reliable interpretation of its outcomes. Few attempts have been made recently to complement classical approaches (e.g., factor models) with a novel technique based on network analysis. The objective of the current study is to carry out a network-based validation of the Eating Disorder Inventory 3 (EDI-3), a questionnaire designed for the assessment of eating disorders. Exploiting a reliable, open source sample of 1206 patients diagnosed with an eating disorder, we set up a robust validation process encompassing detection and handling of redundant EDI-3 items, estimation of the cross-sample psychometric network, resampling bootstrap procedure and computation of the median network of the replica samples. We then employed a community detection algorithm to identify the topological clusters, evaluated their coherence with the EDI-3 subscales and replicated the full validation analysis on the subpopulations corresponding to patients diagnosed with either anorexia nervosa or bulimia nervosa. Results of the network-based analysis, and particularly the topological community structures, provided support for almost all the composite scores of the EDI-3 and for 2 single subscales: Bulimia and Maturity Fear. A moderate instability of some dimensions led to the identification of a few multidimensional items that should be better located in the intersection of multiple psychological scales. We also found that, besides symptoms typically attributed to eating disorders, such as drive for thinness, also non-specific symptoms like low self-esteem and interoceptive deficits play a central role in both the cross-sample and the diagnosis-specific networks. Our work adds insights into the complex and multidimensional structure of EDI-3 by providing support to its network-based validity on both mixed and diagnosis-specific samples. Moreover, we replicated previous results that reinforce the transdiagnostic theory of eating disorders
Time-varying effective connectivity for investigating the neurophysiological basis of cognitive processes
No full textThis chapter describes the methodological advancements developed during the last 20 years in the field of effective connectivity based on Granger causality and linear autoregressive modeling. At first we introduce the concept of Granger causality and its application to the connectivity field. Then, a detailed description of both stationary and time-varying versions of Partial Directed Coherence (PDC) estimator for effective connectivity will be given. The General Linear Kalman Filter (GLKF) approach is described an algorithm, recently introduced for estimating the temporal evolution of the parameters of adaptive multivariate model, able to overcome the limits of existing time-varying approaches. Then a detailed description of the graph theory approach and of possible indexes which could be defined is given. At the end, the potentiality of the described methodologies is demonstrated in an application aiming at investigating the neurophysiological basis of motor imagery processes
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