A role for surgery in primary pancreatic B-cell lymphoma: a case report

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Evaluation of DNA ploidy in relation with established prognostic factors in patients with locally advanced (unresectable) or metastatic pancreatic adenocarcinoma: a retrospective analysis

<p>Abstract</p> <p>Background</p> <p>Most patients with ductal pancreatic adenocarcinoma are diagnosed with locally advanced (unresectable) or metastatic disease. The aim of this study was to evaluate the prognostic significance of DNA ploidy in relation with established clinical and laboratory variables in such patients.</p> <p>Methods</p> <p>Two hundred and twenty six patients were studied retrospectively. Twenty two potential prognostic variables (demographics, clinical parameters, biochemical markers, treatment modality) were examined.</p> <p>Results</p> <p>Mean survival time was 38.41 weeks (95% c.i.: 33.17–43.65), median survival 27.00 weeks (95% c.i.: 23.18–30.82). On multivariate analysis, 10 factors had an independent effect on survival: performance status, local extension of tumor, distant metastases, ploidy score, anemia under epoetin therapy, weight loss, pain, steatorrhoea, CEA, and palliative surgery and chemotherapy. Patients managed with palliative surgery and chemotherapy had 6.7 times lower probability of death in comparison with patients without any treatment. Patients with ploidy score > 3.6 had 5.0 times higher probability of death in comparison with patients with ploidy score < 2.2 and these with ploidy score 2.2–3.6 had 6.3 times higher probability of death in comparison with patients with ploidy score < 2.2.</p> <p>Conclusion</p> <p>According to the significance of the examined factor, survival was improved mainly by the combination of surgery and chemotherapy, and the presence of low DNA ploidy score.</p

Unraveling the functional dark matter through global metagenomics

30 pages, 4 figures, 1 table, supplementary information https://doi.org/10.1038/s41586-023-06583-7.-- Data availability: All of the analysed datasets along with their corresponding sequences are available from the IMG system (http://img.jgi.doe.gov/). A list of the datasets used in this study is provided in Supplementary Data 8. All data from the protein clusters, including sequences, multiple alignments, HMM profiles, 3D structure models, and taxonomic and ecosystem annotation, are available through NMPFamsDB, publicly accessible at www.nmpfamsdb.org. The 3D models are also available at ModelArchive under accession code ma-nmpfamsdb.-- Code availability: Sequence analysis was performed using Tantan (https://gitlab.com/mcfrith/tantan), BLAST (https://blast.ncbi.nlm.nih.gov/Blast.cgi), LAST (https://gitlab.com/mcfrith/last), HMMER (http://hmmer.org/) and HH-suite3 (https://github.com/soedinglab/hh-suite). Clustering was performed using HipMCL (https://bitbucket.org/azadcse/hipmcl/src/master/). Additional taxonomic annotation was performed using Whokaryote (https://github.com/LottePronk/whokaryote), EukRep (https://github.com/patrickwest/EukRep), DeepVirFinder (https://github.com/jessieren/DeepVirFinder) and MMseqs2 (https://github.com/soedinglab/MMseqs2). 3D modelling was performed using AlphaFold2 (https://github.com/deepmind/alphafold) and TrRosetta2 (https://github.com/RosettaCommons/trRosetta2). Structural alignments were performed using TMalign (https://zhanggroup.org/TM-align/) and MMalign (https://zhanggroup.org/MM-align/). All custom scripts used for the generation and analysis of the data are available at Zenodo (https://doi.org/10.5281/zenodo.8097349)Metagenomes encode an enormous diversity of proteins, reflecting a multiplicity of functions and activities1,2. Exploration of this vast sequence space has been limited to a comparative analysis against reference microbial genomes and protein families derived from those genomes. Here, to examine the scale of yet untapped functional diversity beyond what is currently possible through the lens of reference genomes, we develop a computational approach to generate reference-free protein families from the sequence space in metagenomes. We analyse 26,931 metagenomes and identify 1.17 billion protein sequences longer than 35 amino acids with no similarity to any sequences from 102,491 reference genomes or the Pfam database3. Using massively parallel graph-based clustering, we group these proteins into 106,198 novel sequence clusters with more than 100 members, doubling the number of protein families obtained from the reference genomes clustered using the same approach. We annotate these families on the basis of their taxonomic, habitat, geographical and gene neighbourhood distributions and, where sufficient sequence diversity is available, predict protein three-dimensional models, revealing novel structures. Overall, our results uncover an enormously diverse functional space, highlighting the importance of further exploring the microbial functional dark matterWith the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)Peer reviewe

Τεχνικές δικτυακών υπογραφών για τον προσδιορισμό φαρμάκων στην πνευμονική ίνωση

Fibrotic diseases cover a spectrum of systemic and organ specific diseases that affect a large portion of the population, currently without cure. Idiopathic Pulmonary Fibrosis (IPF) is an interstitial disease as well as one of the most common and studied fibrotic diseases which still remains an active research target. Understanding the underlying biological mechanisms and respective interactions of a disease remains an elusive, time consuming and costly task. Computational methodologies that propose communities and reveal respective relationships can be of great value as they can help expedite the process of identifying how perturbations in a single can affect other pathways. Drug or Drug Repositioning (DR) is a methodology where already existing drugs are tested against diseases outside their initial spectrum to reduce the high cost and long periods of new drug development. The 4 main objectives of this PhD thesis is to (i) identify key differentially expressed genes of fibrotic diseases, (ii) explore the respective perturbed biological pathways and (iii) suggest repurposed drugs as potential anti-fibrotic candidates for further testing and (iv) identify which fibrotic diseases resemble based on common terms, to potentially pursue common regimens. We analyze transcriptomics datasets containing fibrotic and normal samples to identify key genes that are implicated in fibrotic diseases. We use these genes as input in DR tools and then propose a novel drug re-ranking methodology via a scoring formula that consolidates standard scores with structural, functional and side effect scores. Following, we present a analysis and community detection methodology, based on Random Walk theory, where a walker crosses a pathway-to-pathway network under the guidance of a disease-related map. The latter is a gene network that we construct by integrating multi-source information regarding a specific disease. The most frequent trajectories highlight communities of pathways that are expected to be strongly related to the disease under study. By applying our pathway analysis methodology on 9 different fibrotic maladies, we identify various common highlighted pathways as well as unique entries for some of the diseases.Οι ινωτικές ασθένειες καλύπτουν ένα φάσμα ασθενειών, οι οποίες στοχεύουν συστήματα αλλά και συγκεκριμένα όργανα, επηρεάζουν μεγάλο μέρος του πληθυσμού και επί του παρόντος δεν έχουν θεραπεία. Η Ιδιοπαθής Πνευμονική Ίνωση (ΙΠΙ) είναι μια διάμεση πνευμονοπάθεια, καθώς και μία από τις πιο συχνές και μελετημένες ινωτικές ασθένειες και παραμένει ακόμη ενεργός ερευνητικός στόχος. Η κατανόηση των υποκείμενων βιολογικών μηχανισμών και των αντίστοιχων αλληλεπιδράσεων μιας ασθένειας, παραμένει μια χρονοβόρα και δαπανηρή εργασία. Οι υπολογιστικές μεθοδολογίες που προτείνουν κοινότητες βιολογικών μονοπατιών και αναδεικνύουν αντίστοιχες σχέσεις, έχουν μεγάλη αξία, καθώς μπορούν να επιταχύνουν τη διαδικασία εντοπισμού του τρόπου, με τον οποίο οι διαταραχές σε ένα μονοπάτι μπορούν να επηρεάσουν άλλα μονοπάτια. Ο Επαναπροσδιορισμός Φαρμάκων (ΕΦ) είναι μια μεθοδολογία όπου ήδη υπάρχοντα φάρμακα δοκιμάζονται εναντίον ασθενειών, εκτός του αρχικού τους φάσματος, ώστε να αποφευχθεί το υψηλό κόστος και οι μεγάλες περίοδοι ανάπτυξης νέων φαρμάκων. Οι 4 κύριοι στόχοι αυτής της διδακτορικής διατριβής είναι να (i) εντοπιστούν βασικά γονίδια ινωτικών παθήσεων με υψηλή διαφορική έκφραση, (ii) να διερευνηθούν τα αντίστοιχα διαταραγμένα βιολογικά μονοπάτια, (iii) να προταθούν φάρμακα ως πιθανοί αντι-ινωτικοί υποψήφιοι αναστολείς για περαιτέρω δοκιμές και (iv) να προσδιοριστούν οι ινωτικές ασθένειες που μοιάζουν με την ΙΠΙ βάσει κοινών όρων, για πιθανή επιδίωξη κοινών θεραπειών. Αναλύουμε σύνολα μεταγραφικών δεδομένων που περιέχουν ινωτικά και φυσιολογικά δείγματα, για να αναδείξουμε βασικά γονίδια που εμπλέκονται σε ινωτικές ασθένειες. Χρησιμοποιούμε αυτά τα γονίδια ως είσοδο σε εργαλεία ΕΦ και στη συνέχεια προτείνουμε μια νέα μεθοδολογία ανακατάταξης φαρμάκων, μέσω μιας πολύ-επίπεδης βαθμολόγησης, η οποία ενοποιεί βαθμολογίες επαναπροσδιορισμού, με δομικά, λειτουργικά και σκορς παρενεργειών. Στη συνέχεια, παρουσιάζουμε μια μεθοδολογία ανάλυσης μονοπατιών και ανίχνευσης κοινοτήτων, βασισμένη στη θεωρία των Τυχαίων Περιπάτων, όπου ένας περιπατητής διασχίζει ένα δίκτυο βιολογικών μονοπατιών, υπό την καθοδήγηση ενός σύνθετου χάρτη γονιδίων, που σχετίζεται με μία ασθένεια. Ο χάρτης είναι ένα γονιδιακό δίκτυο το οποίο κατασκευάζουμε ενσωματώνοντας πληροφορίες multi-omics δεδομένων, σχετικά με μια συγκεκριμένη ασθένεια. Οι πιο συχνές διαδρομές επισημαίνουν κοινότητες βιολογικών μονοπατιών που αναμένεται να σχετίζονται στενά με την υπό μελέτη ασθένεια. Εφαρμόζουμε τη μεθοδολογία ανάλυσης μονοπατιών σε 9 διαφορετικές ινωτικές ασθένειες και εντοπίζουμε διάφορα κοινά μονοπάτια καθώς και μοναδικές καταχωρήσεις για ορισμένες από τις ασθένειες

NORMA: The Network Makeup Artist - A Web Tool for Network Annotation Visualization.

The Network Makeup Artist (NORMA) is a web tool for interactive network annotation visualization and topological analysis, able to handle multiple networks and annotations simultaneously. Precalculated annotations (e.g., Gene Ontology, Pathway enrichment, community detection, or clustering results) can be uploaded and visualized in a network, either as colored pie-chart nodes or as color-filled areas in a 2D/3D Venn-diagram-like style. In the case where no annotation exists, algorithms for automated community detection are offered. Users can adjust the network views using standard layout algorithms or allow NORMA to slightly modify them for visually better group separation. Once a network view is set, users can interactively select and highlight any group of interest in order to generate publication-ready figures. Briefly, with NORMA, users can encode three types of information simultaneously. These are 1) the network, 2) the communities or annotations of interest, and 3) node categories or expression values. Finally, NORMA offers basic topological analysis and direct topological comparison across any of the selected networks. NORMA service is available at http://norma.pavlopouloslab.info, whereas the code is available at https://github.com/PavlopoulosLab/NORMA

NORMA: The Network Makeup Artist — A Web Tool for Network Annotation Visualization

The Network Makeup Artist (NORMA) is a web tool for interactive network annotation visualization and topological analysis, able to handle multiple networks and annotations simultaneously. Precalculated annotations (e.g., Gene Ontology, Pathway enrichment, community detection, or clustering results) can be uploaded and visualized in a network, either as colored pie-chart nodes or as color-filled areas in a 2D/3D Venn-diagram-like style. In the case where no annotation exists, algorithms for automated community detection are offered. Users can adjust the network views using standard layout algorithms or allow NORMA to slightly modify them for visually better group separation. Once a network view is set, users can interactively select and highlight any group of interest in order to generate publication-ready figures. Briefly, with NORMA, users can encode three types of information simultaneously. These are 1) the network, 2) the communities or annotations of interest, and 3) node categories or expression values. Finally, NORMA offers basic topological analysis and direct topological comparison across any of the selected networks. NORMA service is available at http://norma.pavlopouloslab.info, whereas the code is available at https://github.com/PavlopoulosLab/NORMA

A Guide to Conquer the Biological Network Era Using Graph Theory.

Networks are one of the most common ways to represent biological systems as complex sets of binary interactions or relations between different bioentities. In this article, we discuss the basic graph theory concepts and the various graph types, as well as the available data structures for storing and reading graphs. In addition, we describe several network properties and we highlight some of the widely used network topological features. We briefly mention the network patterns, motifs and models, and we further comment on the types of biological and biomedical networks along with their corresponding computer- and human-readable file formats. Finally, we discuss a variety of algorithms and metrics for network analyses regarding graph drawing, clustering, visualization, link prediction, perturbation, and network alignment as well as the current state-of-the-art tools. We expect this review to reach a very broad spectrum of readers varying from experts to beginners while encouraging them to enhance the field further

Exploring fibrotic disease networks to identify common molecular mechanisms with IPF

Fibrotic diseases constitute incurable maladies that affect a large portion of the population. Idiopathic Pulmonary Fibrosis is one of the most common, and thus studied, fibrotic diseases. Common ground among all fibrotic diseases is the uncontrollable fibrogenesis which is responsible for accumulated damage in the susceptible tissues. The plethora and complexity of the underlying mechanisms of fibrotic diseases account for the lack of regimens. Hence it is highly likely that a combination of drugs is required in order to counter every perturbation. In this study, we seek to identify common biological mechanisms and characteristics of fibrotic diseases, based on information acquired from biological databases, while we focus on Idiopathic Pulmonary Fibrosis. We also try to predict links between molecular data and their respective fibrotic phenotypes. We finally construct phenotypic and molecular networks, visualize them and apply a clustering algorithm on each network to identify fibrotic diseases that are close to Idiopathic Pulmonary Fibrosis

Analyzing and Improving the Performance of a Particulate Matter Low Cost Air Quality Monitoring Device

Air quality (AQ) in urban areas is deteriorating, thus having negative effects on people’s everyday lives. Official air quality monitoring stations provide the most reliable information, but do not always depict air pollution levels at scales reflecting human activities. They also have a high cost and therefore are limited in number. This issue can be addressed by deploying low cost AQ monitoring devices (LCAQMD), though their measurements are of far lower quality. In this paper we study the correlation of air pollution levels reported by such a device and by a reference station for particulate matter, ozone and nitrogen dioxide in Thessaloniki, Greece. On this basis, a corrective factor is modeled via seven machine learning algorithms in order to improve the quality of measurements for the LCAQMD against reference stations, thus leading to its on-field computational improvement. We show that our computational intelligence approach can improve the performance of such a device for PM10 under operational conditions