NetMe 2.0: a web-based platform for extracting and modeling knowledge from biomedical literature as a labeled graph

MOTIVATION

The rapid increase of bio-medical literature makes it harder and harder for scientists to keep pace with the discoveries on which they build their studies. Therefore, computational tools have become more widespread, among which network analysis plays a crucial role in several life-science contexts. Nevertheless, building correct and complete networks about some user-defined biomedical topics on top of the available literature is still challenging.

RESULTS

We introduce NetMe 2.0, a web-based platform that automatically extracts relevant biomedical entities and their relations from a set of input texts-i.e. in the form of full-text or abstract of PubMed Central's papers, free texts, or PDFs uploaded by users-and models them as a BioMedical Knowledge Graph (BKG). NetMe 2.0 also implements an innovative Retrieval Augmented Generation module (Graph-RAG) that works on top of the relationships modeled by the BKG and allows the distilling of well-formed sentences that explain their content. The experimental results show that NetMe 2.0 can infer comprehensive and reliable biological networks with significant Precision-Recall metrics when compared to state-of-the-art approaches.

AVAILABILITY AND IMPLEMENTATION

https://netme.click/.

Serum and Tissue Biomarkers Associated With Composite of Relevant Endpoints for Sjögren Syndrome (CRESS) and Sjögren Tool for Assessing Response (STAR) to B Cell-Targeted Therapy in the Trial of Anti-B Cell Therapy in Patients With Primary Sjögren Syndrome (TRACTISS)

OBJECTIVE

This study aimed to identify peripheral and salivary gland (SG) biomarkers of response/resistance to B cell depletion based on the novel concise Composite of Relevant Endpoints for Sjögren Syndrome (cCRESS) and candidate Sjögren Tool for Assessing Response (STAR) composite endpoints.

METHODS

Longitudinal analysis of peripheral blood and SG biopsies was performed pre- and post-treatment from the Trial of Anti-B Cell Therapy in Patients With Primary Sjögren Syndrome (TRACTISS) combining flow cytometry immunophenotyping, serum cytokines, and SG bulk RNA sequencing.

RESULTS

Rituximab treatment prevented the worsening of SG inflammation observed in the placebo arm, by inhibiting the accumulation of class-switched memory B cells within the SG. Furthermore, rituximab significantly down-regulated genes involved in immune-cell recruitment, lymphoid organization alongside antigen presentation, and T cell co-stimulatory pathways. In the peripheral compartment, rituximab down-regulated immunoglobulins  and auto-antibodies together with pro-inflammatory cytokines and chemokines. Interestingly, patients classified as responders  according to STAR displayed significantly higher baseline levels of C-X-C motif chemokine ligand-13 (CXCL13), interleukin (IL)-22, IL-17A, IL-17F, and tumor necrosis factor-α (TNF-α), whereas a longitudinal analysis of serum T cell-related cytokines showed a selective reduction in both STAR and cCRESS responder patients. Conversely, cCRESS response was better associated with biomarkers of SG immunopathology, with cCRESS-responders showing a significant decrease in SG B cell infiltration and reduced expression of transcriptional gene modules related to T cell costimulation, complement activation, and Fcγ-receptor engagement. Finally, cCRESS and STAR response were associated with a significant improvement in SG exocrine function linked to transcriptional evidence of SG epithelial and metabolic restoration.

CONCLUSION

Rituximab modulates both peripheral and SG inflammation, preventing the deterioration of exocrine function with functional and metabolic restoration of the glandular epithelium. Response assessed by newly developed cCRESS and STAR criteria was associated with differential modulation of peripheral and SG biomarkers, emerging as novel tools for patient stratification.

Measuring cancer driving force of chromosomal aberrations through multi-layer Boolean implication networks

Multi-layer Complex networks are commonly used for modeling and analysing biological entities. This paper presents the advantage of using COMBO (Combining Multi Bio Omics) to suggest a new role of the chromosomal aberration as a cancer driver factor. Exploiting the heterogeneous multi-layer networks, COMBO integrates gene expression and DNA-methylation data in order to identify complex bilateral relationships between transcriptome and epigenome. We evaluated the multi-layer networks generated by COMBO on different TCGA cancer datasets (COAD, BLCA, BRCA, CESC, STAD) focusing on the effect of a specific chromosomal numerical aberration, broad gain in chromosome 20, on different cancer histotypes. In addition, the effect of chromosome 8q amplification was tested in the same TCGA cancer dataset. The results demonstrate the ability of COMBO to identify the chromosome 20 amplification cancer driver force in the different TCGA Pan Cancer project datasets.

TMBcalc: a computational pipeline for identifying pan-cancer Tumor Mutational Burden gene signatures

Background

In the precision medicine era, identifying predictive factors to select patients most likely to benefit from treatment with immunological agents is a crucial and open challenge in oncology.

Methods

This paper presents a pan-cancer analysis of Tumor Mutational Burden (TMB). We developed a novel computational pipeline, TMBcalc, to calculate the TMB. Our methodology can identify small and reliable gene signatures to estimate TMB from custom targeted-sequencing panels. For this purpose, our pipeline has been trained on top of 17 cancer types data obtained from TCGA.

Results

Our results show that TMB, computed through the identified signature, strongly correlates with TMB obtained from whole-exome sequencing (WES).

Conclusion

We have rigorously analyzed the effectiveness of our methodology on top of several independent datasets. In particular we conducted a comprehensive testing on: (i) 126 samples sourced from the TCGA database; few independent whole-exome sequencing (WES) datasets linked to colon, breast, and liver cancers, all acquired from the EGA and the ICGC Data Portal. This rigorous evaluation clearly highlights the robustness and practicality of our approach, positioning it as a promising avenue for driving substantial progress within the realm of clinical practice.

Drug resistant epilepsies: A multicentre case series of steroid therapy

PURPOSE

Our study aimed to evaluate the effectiveness of corticosteroids on seizure control in drug-resistant epilepsies (DREs). Our primary goal was to assess the response to steroids for various underlying etiologies, interictal electroencephalographic (EEG) patterns and electroclinical seizure descriptions. Our second goal was to compare steroid responsiveness to different treatment protocols.

METHODS

This is a retrospective multicentre cohort study conducted according to the STROBE guidelines (Strengthening the Reporting of Observational Studies in Epidemiology). The following data were collected for each patient: epilepsy etiology, interictal EEG pattern, seizure types and type of steroid treatment protocol administered.

RESULTS

Thirty patients with DRE were included in the study. After 6 months of therapy, 62.7 % of patients experienced reduced seizure frequency by 50 %, and 6.6 % of patients experienced complete seizure cessation. Findings associated with favourable response to steroids included structural/lesional etiology of epilepsy, immune/infectious etiology and focal interictal abnormalities on EEG. Comparing four different steroid treatment protocols, the most effective for seizure control was treatment with methylprednisolone at the dose of 30 mg/kg/day administered for 3 days, leading to greater than 50 % seizure reduction at 6 months in 85.7 % of patients. Treatment with dexamethasone 6 mg/day for 5 days decreased seizure frequency in 71.4 % of patients. Hydrocortisone 10 mg/kg administered for 3 months showed a good response to treatment in 71 %.

CONCLUSIONS

In our study, two-thirds of patients with DRE experienced a significant seizure reduction following treatment with steroids. We suggest considering steroids as a potential therapeutic option in children with epilepsy not responding to conventional antiseizure medicines (ASM).

tRFUniverse: A comprehensive resource for the interactive analyses of tRNA-derived ncRNAs in human cancer

tRNA-derived ncRNAs are a heterogeneous class of non-coding RNAs recently proposed to be active regulators of gene expression and be involved in many diseases, including cancer. Consequently, several online resources on tRNA-derived ncRNAs have been released. Although interesting, such resources present only basic features and do not adequately exploit the wealth of knowledge available about tRNA-derived ncRNAs. Therefore, we introduce tRFUniverse, a novel online resource for the analysis of tRNA-derived ncRNAs in human cancer. tRFUniverse presents an extensive collection of classes of tRNA-derived ncRNAs analyzed across all the TCGA and TARGET tumor cohorts, NCI-60 cell lines, and biological fluids. Moreover, public AGO CLASH/CLIP-Seq data were analyzed to identify the molecular interactions between tRNA-derived ncRNAs and other transcripts. Importantly, tRFUniverse combines in a single resource a comprehensive set of features that we believe may be helpful to investigate the involvement of tRNA-derived ncRNAs in cancer biology.

Efficacy of High-dose Liraglutide 3.0 mg in Patients with Poor Response to Bariatric Surgery: Real-world Experience and Updated Meta-analysis

PURPOSE

Poor response to bariatric surgery, characterized by insufficient weight loss (IWL) or weight regain (WR), poses a significant challenge in obesity treatment. This study aims to assess the effectiveness of liraglutide in addressing this issue.

MATERIALS AND METHODS

A retrospective, multicenter cohort study investigated the impact of liraglutide 3 mg on weight loss in adults with suboptimal responses or weight regain after bariatric surgery (BS). Additionally, a systematic review and meta-analysis were conducted for a comprehensive evaluation.

RESULTS

A total of 119 patients (mean age 41.03 ± 11.2 years, 71.4% female) who experienced IWL or WR after BS received pharmacologic therapy with liraglutide 3 mg. Mean percent weight loss in the entire cohort was 5.6 ± 2.6% at 12 weeks and 9.3 ± 3.6% at 24 weeks with a significant reduction in waist circumference (p < 0.0001). No serious side effects were reported. A meta-analysis, utilizing the fixed effect model with the metafor package in R, included 6 and 5 papers for the change in body weight and BMI after liraglutide treatment, respectively. The analysis demonstrated a considerable reduction in body weight (7.9; CI - 10.4; - 5.4, p < 0.0001) and BMI (3.09; CI 3.89; - 2.28, p < 0.0001).

CONCLUSION

Liraglutide 3 mg emerges as a viable option for significant weight loss in patients experiencing IWL or WR after BS. Its inclusion in a multimodal, sequential obesity treatment approach proves promising.

Expression signature of human endogenous retroviruses in chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL) is one of the most diagnosed forms of leukemia worldwide and it is usually classified into two forms: indolent and aggressive. These two forms are characterized by distinct molecular features that drive different responses to treatment and clinical outcomes. In this context, a better understanding of the molecular landscape of the CLL forms may potentially lead to the development of new drugs or the identification of novel biomarkers. Human endogenous retroviruses (HERVs) are a class of transposable elements that have been associated with the development of different human cancers, including different forms of leukemias. However, no studies about HERVs in CLL have ever been reported so far. Here, we present the first locus-specific profiling of HERV expression in both the aggressive and indolent forms of CLL. Our analyses revealed several dysregulations in HERV expression occurring in CLL and some of them were specific for either the aggressive or indolent form of CLL. Such results were also validated by analyzing an external cohort of CLL patients and by RT-qPCR. Moreover, in silico analyses have shown relevant signaling pathways associated with them suggesting a potential involvement of the dysregulated HERVs in these pathways and consequently in CLL development.

High-dose liraglutide improves metabolic syndrome in poor responders to bariatric surgery

Background

Bariatric surgery (BS) represents the most effective therapy for obesity class III, or class II with at least one weight-related comorbidity. However, some patients have insufficient weight loss or clinically relevant weight regain after a successful primary procedure. This study aimed to assess the efficacy of liraglutide treatment on weight loss, body composition and improvement of metabolic syndrome (MS) in patients defined as poor responders after BS.

Methods

The study involved 59 non-diabetic adults with obesity (M/F: 17/42, age: 38.6 ± 11.8 years, BMI 38.3 ± 5.5 kg/m2) who had been treated with BS and experienced a poor response, categorized as either IWL (insufficient weight loss) or WR (weight regain). All patients were prescribed pharmacological therapy with liraglutide and attended nutritional counseling. Anthropometric and clinical measurements, body composition and the presence of MS defined according to the ATP-III classification were evaluated before starting liraglutide and after 24 weeks of treatment.

Results

After 24 weeks of treatment with liraglutide, the mean weight loss was 8.4% ± 3.6% with no difference between gender, bariatric procedure, or type of poor response (IWL or WR). A significant decrease in fat mass, free-fat mass and total body water was documented. After 24 weeks, patients presented significantly lowered fasting glucose, total cholesterol, triglycerides, AST and ALT. The prevalence of MS was reduced from 35% at baseline to 1.6% after 24 weeks. No patients discontinued the treatment during the study.

Conclusion

In patients who experience poor response after BS, liraglutide is well tolerated and promotes significant weight loss, ameliorates cardiometabolic comorbidities, and reduces the prevalence of MS.

XRF analysis searching for fingerprint elemental profile in south-eastern Sicily tomatoes

The implementation of analytical techniques able to certify food quality and origin in a fast and non-destructive way is becoming a widespread need in the agri-food sector. Among the physical non-destructive techniques, X-ray fluorescence (XRF) spectrometry is often used to analyze the elemental composition of biological samples. In this study, X-ray fluorescence (XRF) elemental profiles were measured on tomato samples belonging to different geographical areas in Sicily (Italy). The purpose of this investigation was aiming to establish a protocol for in-situ measurement and analysis able to provide quality assessment and traceability of PGI agri-food products, specifically sustaining health safety and self qualifying bio-chemical signature. In detail, sampling was performed in one of the most tomato productive area of south-eastern Sicily (Pachino district), characterised by a relative higher amount of Organic Carbon and Cation Exchange Capacity, and compared with samples from other growing areas of Sicily, falling in Ragusa province and Mt. Etna region. Experimental data were analyzed in the framework of multivariate analysis by using principal component analysis and further validated by discriminant analysis. The results show the presence of specific elemental signatures associated to several characterizing elements. This methodology establishes the possibility to disentangle a clear fingerprint pattern associated to the geographical origin of an agri-food product.

RAGE inhibition blunts insulin-induced oncogenic signals in breast cancer

The receptor for advanced glycation end products (RAGE) is implicated in diabetes and obesity complications, as well as in breast cancer (BC). Herein, we evaluated whether RAGE contributes to the oncogenic actions of Insulin, which plays a key role in BC progression particularly in obese and diabetic patients. Analysis of the publicly available METABRIC study, which collects gene expression and clinical data from a large cohort (n = 1904) of BC patients, revealed that RAGE and the Insulin Receptor (IR) are co-expressed and associated with negative prognostic parameters. In MCF-7, ZR75 and 4T1 BC cells, as well as in patient-derived Cancer-Associated Fibroblasts, the pharmacological inhibition of RAGE as well as its genetic depletion interfered with Insulin-induced activation of the oncogenic pathway IR/IRS1/AKT/CD1. Mechanistically, IR and RAGE directly interacted upon Insulin stimulation, as shown by in situ proximity ligation assays and coimmunoprecipitation studies. Of note, RAGE inhibition halted the activation of both IR and insulin like growth factor 1 receptor (IGF-1R), as demonstrated in MCF-7 cells KO for the IR and the IGF-1R gene via CRISPR-cas9 technology. An unbiased label-free proteomic analysis uncovered proteins and predicted pathways affected by RAGE inhibition in Insulin-stimulated BC cells. Biologically, RAGE inhibition reduced cell proliferation, migration, and patient-derived mammosphere formation triggered by Insulin. In vivo, the pharmacological inhibition of RAGE halted Insulin-induced tumor growth, without affecting blood glucose homeostasis. Together, our findings suggest that targeting RAGE may represent an appealing opportunity to blunt Insulin-induced oncogenic signaling in BC.

MultiplexSAGE: A Multiplex Embedding Algorithm for Inter-Layer Link Prediction

Research on graph representation learning has received great attention in recent years. However, most of the studies so far have focused on the embedding of single-layer graphs. The few studies dealing with the problem of representation learning of multilayer structures rely on the strong hypothesis that the inter-layer links are known, and this limits the range of possible applications. Here we propose MultiplexSAGE, a generalization of the GraphSAGE algorithm that allows embedding multiplex networks. We show that MultiplexSAGE is capable to reconstruct both the intra-layer and the inter-layer connectivity, outperforming competing methods. Next, through a comprehensive experimental analysis, we shed light also on the performance of the embedding, both in simple and multiplex networks, showing that both the density of the graph and the randomness of the links strongly influences the quality of the embedding.

DEGGs: an R package with shiny app for the identification of Differentially Expressed Gene-Gene interactions in high-throughput sequencing data

SUMMARY

The discovery of differential gene-gene correlations across phenotypical groups can help identify the activation/deactivation of critical biological processes underlying specific conditions. The presented R package, provided with a count and design matrix, extract networks of group-specific interactions that can be interactively explored through a shiny user-friendly interface. For each gene-gene link, differential statistical significance is provided through robust linear regression with an interaction term.

AVAILABILITY

DEGGs is implemented in R and available on GitHub at https://github.com/elisabettasciacca/DEGGs. The package is also under submission on Bioconductor.

Application of the PHENotype SIMulator for rapid identification of potential candidates in effective COVID-19 drug repurposing

The current, rapidly diversifying pandemic has accelerated the need for efficient and effective identification of potential drug candidates for COVID-19. Knowledge on host-immune response to SARS-CoV-2 infection, however, remains limited with few drugs approved to date. Viable strategies and tools are rapidly arising to address this, especially with repurposing of existing drugs offering significant promise. Here we introduce a systems biology tool, the PHENotype SIMulator, which -by leveraging available transcriptomic and proteomic databases-allows modeling of SARS-CoV-2 infection in host cells in silico to i) determine with high sensitivity and specificity (both>96%) the viral effects on cellular host-immune response, resulting in specific cellular SARS-CoV-2 signatures and ii) utilize these cell-specific signatures to identify promising repurposable therapeutics. Powered by this tool, coupled with domain expertise, we identify several potential COVID-19 drugs including methylprednisolone and metformin, and further discern key cellular SARS-CoV-2-affected pathways as potential druggable targets in COVID-19 pathogenesis.

Feasibility study of tomato fruit characterization by fast XRF analysis for quality assessment and food traceability

Food product nutritional and sensory characteristics are often deeply linked to its territory of origin; therefore, its authentication by means of elemental composition becomes crucial for traceability and fighting food fraud. This study aims to establish a fast and reproducible procedure for origin and quality assessment of Sicilian tomato fruits, including PGI "Pomodoro di Pachino", by using the X-ray fluorescence (XRF) technique. Measurements were performed on different parts of PGI Pachino tomatoes belonging to the same production lot. Principal Component and Cluster Analyses show that the samples cluster accordingly with the production lot, disentangling the different parts of the fruit. This procedure, which uses XRF yield elemental pattern and statistical analysis, establishes a solid basis for characterizing elemental profiles by a fast XRF in-situ campaign, supporting the traceability system. The reliability of XRF results was confirmed by comparing elemental concentrations with ICP-MS measurements, performed for comparison, and tomato literature values.

Network analysis of synovial RNA sequencing identifies gene-gene interactions predictive of response in rheumatoid arthritis

BACKGROUND

To determine whether gene-gene interaction network analysis of RNA sequencing (RNA-Seq) of synovial biopsies in early rheumatoid arthritis (RA) can inform our understanding of RA pathogenesis and yield improved treatment response prediction models.

METHODS

We utilized four well curated pathway repositories obtaining 10,537 experimentally evaluated gene-gene interactions. We extracted specific gene-gene interaction networks in synovial RNA-Seq to characterize histologically defined pathotypes in early RA and leverage these synovial specific gene-gene networks to predict response to methotrexate-based disease-modifying anti-rheumatic drug (DMARD) therapy in the Pathobiology of Early Arthritis Cohort (PEAC). Differential interactions identified within each network were statistically evaluated through robust linear regression models. Ability to predict response to DMARD treatment was evaluated by receiver operating characteristic (ROC) curve analysis.

RESULTS

Analysis comparing different histological pathotypes showed a coherent molecular signature matching the histological changes and highlighting novel pathotype-specific gene interactions and mechanisms. Analysis of responders vs non-responders revealed higher expression of apoptosis regulating gene-gene interactions in patients with good response to conventional synthetic DMARD. Detailed analysis of interactions between pairs of network-linked genes identified the SOCS2/STAT2 ratio as predictive of treatment success, improving ROC area under curve (AUC) from 0.62 to 0.78. We identified a key role for angiogenesis, observing significant statistical interactions between NOS3 (eNOS) and both CAMK1 and eNOS activator AKT3 when comparing responders and non-responders. The ratio of CAMKD2/NOS3 enhanced a prediction model of response improving ROC AUC from 0.63 to 0.73.

CONCLUSIONS

We demonstrate a novel, powerful method which harnesses gene interaction networks for leveraging biologically relevant gene-gene interactions leading to improved models for predicting treatment response.

Psychological well-being and family distress of Italian caregivers during the COVID-19 outbreak

The present study aimed to investigate the personal well-being and family distress of Italian caregivers during the lockdown. Five hundred sixty-five family caregivers and 638 age- and sex-matched noncaregivers completed a web-based survey. The following scales were administered to all participants: General Health Questionnaire-12 items (GHQ-12), Insomnia Severity Index (ISI), Brief Resilient Coping Scale (BRCS), and Family Distress Index (FDI). Caregivers were also asked to provide information about their family members with disabilities. Individual and family distress, as well as insomnia, were significantly higher in caregivers than controls. Contrariwise, caregivers reported lower resilience levels. Multiple linear regression showed that distress was higher in caregivers living in Central and Southern Italy. Individual well-being was negatively predicted by low independence measured by the activities of daily living (ADL). Family distress was higher in households of psychiatric patients. Finally, low resilience levels appeared as the strongest predictors of both individual and family distress. The lockdown caused severe distress among caregivers and families of people with disabilities. Support networks for people with disabilities and their families are fundamental to prevent severe consequences from a psychological, social, and economical point of view.

Virus finding tools: current solutions and limitations

MOTIVATION

The study of the Human Virome remains challenging nowadays. Viral metagenomics, through high-throughput sequencing data, is the best choice for virus discovery. The metagenomics approach is culture-independent and sequence-independent, helping search for either known or novel viruses. Though it is estimated that more than 40% of the viruses found in metagenomics analysis are not recognizable, we decided to analyze several tools to identify and discover viruses in RNA-seq samples.

RESULTS

We have analyzed eight Virus Tools for the identification of viruses in RNA-seq data. These tools were compared using a synthetic dataset of 30 viruses and a real one. Our analysis shows that no tool succeeds in recognizing all the viruses in the datasets. So we can conclude that each of these tools has pros and cons, and their choice depends on the application domain.

AVAILABILITY

Synthetic data used through the review and raw results of their analysis can be found at https://zenodo.org/record/6426147. FASTQ files of real data can be found in GEO (https://www.ncbi.nlm.nih.gov/gds) or ENA (https://www.ebi.ac.uk/ena/browser/home). Raw results of their analysis can be downloaded from https://zenodo.org/record/6425917.

Potential Clinical Applicability of The PHENotype SIMulator for In Silico Viral Co-Infection Studies in COVID-19

The PHENotype SIMulator, is a systems biology tool, which leverages available transcriptomic and proteomic databases to model of SARS-CoV-2 host-infection in silico. PHENSIM can determine the viral effects on cellular host-immune responses, which we recently applied to in silico drug repurposing for COVID-19. There is a clear importance of previously imprinted viral infections on the host-immune response against SARS-CoV2. In this study we explore the potential clinical applicability of PHENSIM in addressing co-infection of SARS-CoV2 (SCoV2) and human rhinovirus (HRV), or SCoV2 and Influenza A Virus (IAV).

We leveraged PHENSIM to simulate HRV-infection of A549 lung alveolar cells at 24hours in silico, yielding comparable cellular transcriptomic signatures as previously published for in vitro HRV-infection, including antiviral and inflammatory gene transcriptomics. Next, we simulated viral co-infection of HRV/SCoV2 or IAV/SCoV2 using PHENSIM, and assessed co-infection effects in silico.

Similar to recent in vitro studies, our in silico results indicated HRV-infection prior to SCoV2 exposure induced ISG responses and downregulated SCoV2 transcriptomic activity. Further analysis revealed several key SCoV2 affected pathways, including IFN Type-I & -III and MHC-II pathways, to be negatively affected by HRV-infection. In contrast, co-infection with IAV and SCoV2 resulted in overall increased SCoV2 transcriptomic activity.

Our PHENSIM results confirm HRV-imprinted downmodulation of SARS-CoV2 activity and the importance of timely IFN pathway activation for effective immune response and viral clearance. Identified anti-correlating pathways are of interest for therapeutic targeting and effective drug development.

N.I.M. was funded in part by a fellowship award from the Netherlands-Caribbean Foundation for Clinical Higher Education (NASKHO). S.A., A.F. and A.P. have been partially supported by the MIUR PON research project BILIGeCT “Liquid Biopsies for Cancer Clinical Management”. National Cancer Institute Physical Sciences-Oncology Center Grant U54 CA193313-01 (to B.M.), and US Army grant W911NF1810427 (to B.M.)

aEEG vs cEEG's sensivity for seizure detection in the setting of neonatal intensive care units: A systematic review and meta-analysis

AIM

Amplitude-integrated electroencephalography (aEEG)'s accuracy compared to conventional electroencephalography (cEEG) has not been fully established. The aim of our study was to conduct a systematic review on the sensitivity of the aEEG for neonatal seizure detection.

METHODS

Studies from PubMed and Google Scholar databases comparing recordings of cEEG and aEEG in newborns were included according to the PRISMA method. A quality assessment using the QUADAS-2 tool was provided. A random-effect model was used to account for different sources of variations among studies. Publication biases were represented by a funnel plot, and funnel plot symmetry was assessed.

RESULTS

Fourteen studies were reported; sensitivity of each diagnostic tool used (single-channel aEEG, two-channel aEEG, two-channel aEEG plus raw trace EEG) was compared to that of the gold-standard cEEG and to those of the other methods used. Overall sensitivity of the aEEG ranged from 31.25% to 90%.

CONCLUSION

Our study provides evidence that sensitivity of aEEG varies significantly and that seizure detection rate is lower than that of cEEG. The two-channel aEEG with raw trace EEG shows a high sensitivity and might represent a valid alternative to the cEEG in the setting of neonatal intensive care units (NICUs).

BioTAGME: A Comprehensive Platform for Biological Knowledge Network Analysis

The inference of novel knowledge and new hypotheses from the current literature analysis is crucial in making new scientific discoveries. In bio-medicine, given the enormous amount of literature and knowledge bases available, the automatic gain of knowledge concerning relationships among biological elements, in the form of semantically related terms (or entities), is rising novel research challenges and corresponding applications. In this regard, we propose BioTAGME, a system that combines an entity-annotation framework based on Wikipedia corpus (i.e., TAGME tool) with a network-based inference methodology (i.e., DT-Hybrid). This integration aims to create an extensive Knowledge Graph modeling relations among biological terms and phrases extracted from titles and abstracts of papers available in PubMed. The framework consists of a back-end and a front-end. The back-end is entirely implemented in Scala and runs on top of a Spark cluster that distributes the computing effort among several machines. The front-end is released through the Laravel framework, connected with the Neo4j graph database to store the knowledge graph.

MODIT: MOtif DIscovery in Temporal Networks

Temporal networks are graphs where each edge is linked with a timestamp, denoting when an interaction between two nodes happens. According to the most recently proposed definitions of the problem, motif search in temporal networks consists in finding and counting all connected temporal graphs Q (called motifs) occurring in a larger temporal network T, such that matched target edges follow the same chronological order imposed by edges in Q. In the last few years, several algorithms have been proposed to solve motif search, but most of them are limited to very small or specific motifs due to the computational complexity of the problem. In this paper, we present MODIT (MOtif DIscovery in Temporal Networks), an algorithm for counting motifs of any size in temporal networks, inspired by a very recent algorithm for subgraph isomorphism in temporal networks, called TemporalRI. Experiments show that for big motifs (more than 3 nodes and 3 edges) MODIT can efficiently retrieve them in reasonable time (up to few hours) in many networks of medium and large size and outperforms state-of-the art algorithms.

NETME: on-the-fly knowledge network construction from biomedical literature

BACKGROUND

The rapidly increasing biological literature is a key resource to automatically extract and gain knowledge concerning biological elements and their relations. Knowledge Networks are helpful tools in the context of biological knowledge discovery and modeling.

RESULTS

We introduce a novel system called NETME, which, starting from a set of full-texts obtained from PubMed, through an easy-to-use web interface, interactively extracts biological elements from ontological databases and then synthesizes a network inferring relations among such elements. The results clearly show that our tool is capable of inferring comprehensive and reliable biological networks.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s41109-021-00435-x.

Neuroblastic tumors and neurofibromatosis type 1: A retrospective multicenter study in Italy and systematic review of the literature

BACKGROUND

Neuroblastic tumors (NBTs) are the most common extra-cranial solid tumors of childhood. Neurofibromatosis type 1 (NF1) is the most common neurocutaneous disorder with a predisposition to tumors. The co-occurrence of NBTs in the setting of NF1 has been occasionally reported, suggesting a non-casual association and likely configuring a spectrum of neural crest-derived disorders.

AIM OF THE STUDY

To explore the occurrence of NBTs within NF1 and to report on its natural history, therapeutic strategies, and outcomes in an Italian cohort of children with NF1 and in the literature.

SUBJECTS AND METHODS

Study (a): a retrospective analysis of questionnaire-based data [years 1979-2017] derived from the databases of the Italian Registry for Neuroblastoma (RINB) of the Italian Society of Pediatric Onco-Haematology (AIEOP); and Study (b): a systematic review search on NF1/NB co-occurrence.

RESULTS

Study (a) identified eight children with NBTs, 0.2% of patients registered in the RINB, fulfilling the diagnostic criteria for NF1. The primary site of NBTs was abdominal in six patients. The NBTs were neuroblastoma (NB) in five patients, ganglioneuroblastoma (GNB) in one, patient, and ganglioneuroma (GN) in two. Metastatic diffusion occurred in three out of eight children. MYCN gene testing, performed in the tumors of five patients, resulted not-amplified. The major features of NF1 included the following: NF1 family history in four patients, café-au-lait spots in all, freckling in six, Lisch nodules in three, and neurofibromas in three. With regard to the outcome, four children survived three of these for the progression of NB and one for a second tumor. Study (b) identified 12 patients with NF1/NB from the years 1966-2017, and the median age at diagnosis was 27 months (range = 0-168 months). The primary site of NB was thoracic. The prevalent histotype was NB in nine patients, GNB in two, and GN in one. Eight/nine NBs were metastatic. The MYCN gene was amplified in the only studied case. The NF1 features included NF1 family history in seven patients; the major NF1 features were café-au-lait spots in nine patients, freckling in one, Lisch nodules in none, and neurofibromas in six. The outcome was good for only two children, while eight children died of neuroblastoma, at a median age of 49.5 months (range = 2.4-174 months), with a median survival time of 21.75 months after diagnosis.

CONCLUSIONS

To our knowledge, this represents the first systematic study on the occurrence of NBTs in NF1. This confirms that NBs are rare per se in the setting of NF1 (0.2% of all NBs) and even if compared to the overall frequency of malignancies in NF1 (i.e., 14.7%). The male:female ratio in study (a) (0.6) was different from what was recorded in study (b) (1.5) and in line with the overall increased frequency of malignancies in females with NF1. The median ages at diagnosis of NB in either study (a) or (b) were concordant with what occurred in the NB population. In study (a) versus study (b), the frequency of metastatic diffusion was lower, likely indicating less awareness on work-ups for malignancies in old NF1 series in the literature. The outcome was much better in study (a) than in study (b), indicating that multidisciplinary treatment for NB is highly recommended.

Novel Mechanisms of Tumor Promotion by the Insulin Receptor Isoform A in Triple-Negative Breast Cancer Cells

The insulin receptor isoform A (IR-A) plays an increasingly recognized role in fetal growth and tumor biology in response to circulating insulin and/or locally produced IGF2. This role seems not to be shared by the IR isoform B (IR-B). We aimed to dissect the specific impact of IR isoforms in modulating insulin signaling in triple negative breast cancer (TNBC) cells. We generated murine 4T1 TNBC cells deleted from the endogenous insulin receptor (INSR) gene and expressing comparable levels of either human IR-A or IR-B. We then measured IR isoform-specific in vitro and in vivo biological effects and transcriptome in response to insulin. Overall, the IR-A was more potent than the IR-B in mediating cell migration, invasion, and in vivo tumor growth. Transcriptome analysis showed that approximately 89% of insulin-stimulated transcripts depended solely on the expression of the specific isoform. Notably, in cells overexpressing IR-A, insulin strongly induced genes involved in tumor progression and immune evasion including chemokines and genes related to innate immunity. Conversely, in IR-B overexpressing cells, insulin predominantly induced the expression of genes primarily involved in the regulation of metabolic pathways and, to a lesser extent, tumor growth and angiogenesis.

Intravitreal anti-vascular endothelial growth factors, panretinal photocoagulation and combined treatment for proliferative diabetic retinopathy: a systematic review and network meta-analysis

PURPOSE

To conduct a systematic review with network meta-analysis (NMA) of randomized clinical trials (RCTs) comparing panretinal photocoagulation (PRP) versus anti-vascular endothelial growth factor (VEGF) treatment alone or in combination with PRP, for proliferative diabetic retinopathy (PDR).

METHODS

PubMed, Medline and Embase databases were searched for RCTs comparing PRP versus intravitreal anti-VEGF therapy and/or combined PRP and intravitreal anti-VEGF for PDR. The primary outcome measures were the mean best corrected visual acuity (BCVA) change and the regression of neovascularization. Mean change of central macular thickness (CMT), the subgroup analyses of patients without diabetic macular oedema (DME) and the rate of vitreous haemorrhage and vitrectomy were secondary outcomes. Frequentist NMAs were performed.

RESULTS

Twelve RCTs were included. For the 12-month mean BCVA change, NMA showed a better visual outcome in both the anti-VEGF group and combined group compared to PRP [anti-VEGF vs PRP, mean difference (MD) = 3.42; standard error (SE) = 1.5; combined vs PRP, MD = 3.92; SE = 1.65], with no difference between combined group and anti-VEGF (MD = -0.50; SE = 1.87). No difference in neovascularization regression was found between PRP and anti-VEGF alone or in combination with PRP, but there was significant inconsistency (p = 0.016). Subgroup analyses in patients without DME yielded no difference for the 12-month visual outcome between the three interventions, but with significant inconsistency (p = 0.005).

CONCLUSION

This NMA showed limited evidence of comparable efficacy in terms of neovascularization regression between PRP and anti-VEGF therapy alone or in combination with PRP, but better visual outcomes were associated with anti-VEGF use. Intravitreal anti-VEGF therapy could be a valid therapeutic option in association with PRP.

Long-term outcomes after pancreatoduodenectomy for ampullary cancer: The influence of the histological subtypes and comparison with the other periampullary neoplasms

BACKGROUND

Ampullary carcinoma (AC) is histologically classified as intestinal (In-AC), pancreaticobiliary (Pb-AC) or mixed-AC. The prognostic role of AC subtypes has been debated and remains unclear. The aims of this study were to evaluate outcomes after pancreatoduodenectomy (PD) for each subtype of AC and to compare these with pancreatic ductal adenocarcinoma [PDAC] and distal cholangiocarcinoma [DCC].

METHODS

PDs performed for AC between 2010 and 2018 were retrospectively evaluated. Histological subtype was obtained for all patients. One-year, 3-year and 5-year disease-free-survival (DFS) and overall survival (OS) rates were calculated. Kaplan-Meier survival analysis was performed to compare Pb-AC, In-AC and mixed-AC. Comparison with PDs performed for PDAC and DCC during the same period was also performed.

RESULTS

A total of 97 patients undergoing PD for AC were evaluated: 34 (35.1%) In-AC, 54 (55.7%) Pb-AC and 9 mixed-AC (9.3%). DFS and OS rates for Pb-AC were significantly lower compared to In-AC (p < 0.05 and p < 0.01), but similar to mixed-AC (p = 0.3 and p = 0.4). Adjuvant therapy was not associated with increased survival, regardless of the histological subtype (p > 0.05). During the same period, 337 and 53 PDs for PDAC and DCC, respectively, were performed. In-AC was associated with significantly better outcomes compared to PDAC and DCC (p < 0.001); DFS and OS rates for Pb-AC and mixed AC were significantly higher compared to PDAC (p < 0.001), but similar to DCC (p > 0.05).

CONCLUSIONS

Pb-AC has significantly worse survival compared to In-AC. Moreover, mixed-AC should be considered as Pb-AC. Pb-AC and mixed-AC seem to have better prognosis compared to PDAC, but similar to DCC.

Psychopathological outcomes and defence mechanisms in clinically healed adults with a paediatric cancer history: an exploratory study

Background

The incidence of paediatric cancers has increased in recent years; however, with advances in the treatment of paediatric cancer, almost 80% of children and adolescents who receive a diagnosis of cancer become long-term survivors. Given the high stress levels associated with cancer, it becomes important to ascertain the risk and likelihood of psychiatric disorders in adult paediatric cancer survivors.

Aims

This study aims to investigate the relationship between defence styles and predisposition to psychiatric diseases in adults with a history of paediatric cancer.

Methods

We performed an explorative study on a sample of 66 clinically healed adults with a history of paediatric cancer (survivors) during follow-up visits at the University Hospital ‘Policlinico G Rodolico’ of Catania (Italy) and 98 healthy controls among medicine students. We administered the Defence Mechanism Inventory (DMI) to assess defence styles. The Symptom Checklist-90-Revised (SCL-90-R) and the Davidson Trauma Scale (DTS) were administered to assess psychopathological indices. We conducted comprehensive statistical analysis based on correlation analysis and mediation analysis to investigate the relationship between defence styles and psychopathological outcomes in survivors compared with controls.

Results

The survivors obtained statistically significant lower values in TAO, PRO and TAS defence styles and a higher value in REV. Both groups showed non-pathlogical mean scores in DTS and SCL-90-R (with an exception of the obsessive-compulsive subscale), with lower mean values among survivors. The results of mediation analysis showed that TAS had mediation effects on interpersonal sensitivity, anxiety, PSDI, GSI and avoidance, while TAO had mediation effects on DTS total score and intrusivity. Thus, for these psychopathological indices, the effect of the oncological pathology was indirect and mediated by TAO or TAS. Our analysis exlcluded mediation effects between the remaining variables and defence styles.

Conclusion

Integrating data from mediation and correlation analysis, we found how the decreasing of TAS utilization in survivors as the consequence of cancer history, has decreased interpersonal sensitivity, anxiety and GSI score in these subjects compared with controls. Similary, the decrease of TAO utilization played a role in lower values of DTS total score and intrusivity subscale. Unexpectedly, our analysis excluded relationships between cancer history, other defence styles and psycopathological scores as we initially assumed.

VECTOR: An Integrated Correlation Network Database for the Identification of CeRNA Axes in Uveal Melanoma

Uveal melanoma (UM) is the most common primary intraocular malignant tumor in adults and, although its genetic background has been extensively studied, little is known about the contribution of non-coding RNAs (ncRNAs) to its pathogenesis. Indeed, its competitive endogenous RNA (ceRNA) regulatory network comprising microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and mRNAs has been insufficiently explored. Thanks to UM findings from The Cancer Genome Atlas (TCGA), it is now possible to statistically elaborate these data to identify the expression relationships among RNAs and correlative interaction data. In the present work, we propose the VECTOR (uVeal mElanoma Correlation NeTwORk) database, an interactive tool that identifies and visualizes the relationships among RNA molecules, based on the ceRNA model. The VECTOR database contains: (i) the TCGA-derived expression correlation values of miRNA-mRNA, miRNA-lncRNA and lncRNA-mRNA pairs combined with predicted or validated RNA-RNA interactions; (ii) data of sense-antisense sequence overlapping; (iii) correlation values of Transcription Factor (TF)-miRNA, TF-lncRNA, and TF-mRNA pairs associated with ChiPseq data; (iv) expression data of miRNAs, lncRNAs and mRNAs both in UM and physiological tissues. The VECTOR web interface can be queried, by inputting the gene name, to retrieve all the information about RNA signaling and visualize this as a graph. Finally, VECTOR provides a very detailed picture of ceRNA networks in UM and could be a very useful tool for researchers studying RNA signaling in UM. The web version of Vector is freely available at the URL reported at the end of the Introduction.

Risk of Death Associated With Intravitreal Anti-Vascular Endothelial Growth Factor Therapy: A Systematic Review and Meta-analysis

Importance

Although intravitreal anti-vascular endothelial growth factor (VEGF) treatment represents the first-line therapy for many retinal diseases, the issue of their systemic safety is debatable.

Objectives

To assess whether intravitreal anti-VEGF therapy might be associated with increased risk of mortality and which variables are associated with the increase.

Data Sources

PubMed, MEDLINE, and Embase databases, the Cochrane Library, and ClinicalTrials.gov were systematically searched from inception to May 6, 2019.

Study Selection

Randomized clinical trials comparing intravitreal anti-VEGF treatment with control groups and with follow-up of at least 6 months were selected.

Data Extraction and Synthesis

Data were independently collected by 2 investigators. Meta-analyses were conducted using the frequentist and Bayesian methods. For the frequentist approach, random- and fixed-effects models were used, with random-effects models considered the primary technique. Odds ratios (ORs) with 95% CIs were computed. For the bayesian approach, uninformative and informative priors were used. Odds ratios with 95% credible intervals (CrIs) were computed. Meta-regression analyses were based on random-effects models.

Main Outcomes and Measures

The primary outcome measure was the all-cause death rate. Secondary outcomes included meta-regression analyses on the following variables: type of drug, number of injections, follow-up time, diagnosis, and cardiovascular risk.

Results

Of 2336 studies identified, 34 unique studies with 8887 unique participants were included in the present meta-analysis. For the frequentist analysis, fixed- and random-effects models yielded similar estimates (ORs, 1.34 [95% CI, 0.95-2.07; P = .09] and 1.34 [95% CI, 0.89-2.01; P = .17], respectively). For the Bayesian approach, noninformative and informative priors yielded similar results (ORs, 1.34 [95% CrI, 0.79-2.34; 0.13 probability of OR≤1.00] and 1.40 [95% CrI, 0.82-2.32; 0.11 probability of OR≤1.00], respectively). Meta-regression analyses showed the following risk for 1 injection more: frequentist OR of 1.12 (95% CI, 1.04-1.22; P = .005) and Bayesian OR of 1.06 (95% CrI, 0.98-1.15; 0.06 probability of OR≤1.00).

Conclusions and Relevance

In this study, no difference was found in the mortality rate between intravitreal anti-VEGF treatment and control groups. Additional data seem warranted to determine whether the mortality rate is increased in patients receiving a greater number of injections.

PHENSIM: Phenotype Simulator

Despite the unprecedented growth in our understanding of cell biology, it still remains challenging to connect it to experimental data obtained with cells and tissues’ physiopathological status under precise circumstances. This knowledge gap often results in difficulties in designing validation experiments, which are usually labor-intensive, expensive to perform, and hard to interpret. Here we propose PHENSIM, a computational tool using a systems biology approach to simulate how cell phenotypes are affected by the activation/inhibition of one or multiple biomolecules, and it does so by exploiting signaling pathways. Our tool’s applications include predicting the outcome of drug administration, knockdown experiments, gene transduction, and exposure to exosomal cargo. Importantly, PHENSIM enables the user to make inferences on well-defined cell lines and includes pathway maps from three different model organisms. To assess our approach’s reliability, we built a benchmark from transcriptomics data gathered from NCBI GEO and performed four case studies on known biological experiments. Our results show high prediction accuracy, thus highlighting the capabilities of this methodology. PHENSIM standalone Java application is available at https://github.com/alaimos/phensim, along with all data and source codes for benchmarking. A web-based user interface is accessible at https://phensim.tech/.

Despite the unprecedented growth in our understanding of cell biology, it still remains challenging to connect it to experimental data obtained with cells and tissues’ physiopathological status under precise circumstances. This knowledge gap often results in difficulties in designing validation experiments, which are usually labor-intensive, expensive to perform, and hard to interpret. In this context, ’in silico’ simulations can be extensively applied in massive scales, testing thousands of hypotheses under various conditions, which is usually experimentally infeasible. At present, many simulation models have become available. However, complex biological networks might pose challenges to their performance.

We propose PHENSIM, a computational tool using a systems biology approach to simulate how cell phenotypes are affected by the activation/inhibition of one or multiple biomolecules, and it does so by exploiting signaling pathways. We implemented our tool as a freely accessible web application, hoping to allow ’in silico’ simulations to play a more central role in the modeling and understanding of biological phenomena.

RNAdetector: a free user-friendly stand-alone and cloud-based system for RNA-Seq data analysis

Background

RNA-Seq is a well-established technology extensively used for transcriptome profiling, allowing the analysis of coding and non-coding RNA molecules. However, this technology produces a vast amount of data requiring sophisticated computational approaches for their analysis than other traditional technologies such as Real-Time PCR or microarrays, strongly discouraging non-expert users. For this reason, dozens of pipelines have been deployed for the analysis of RNA-Seq data. Although interesting, these present several limitations and their usage require a technical background, which may be uncommon in small research laboratories. Therefore, the application of these technologies in such contexts is still limited and causes a clear bottleneck in knowledge advancement.

Results

Motivated by these considerations, we have developed RNAdetector, a new free cross-platform and user-friendly RNA-Seq data analysis software that can be used locally or in cloud environments through an easy-to-use Graphical User Interface allowing the analysis of coding and non-coding RNAs from RNA-Seq datasets of any sequenced biological species.

Conclusions

RNAdetector is a new software that fills an essential gap between the needs of biomedical and research labs to process RNA-Seq data and their common lack of technical background in performing such analysis, which usually relies on outsourcing such steps to third party bioinformatics facilities or using expensive commercial software.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04211-7.

Rapid Identification of Druggable Targets and the Power of the PHENotype SIMulator for Effective Drug Repurposing in COVID-19

The current, rapidly diversifying pandemic has accelerated the need for efficient and effective identification of potential drug candidates for COVID-19. Knowledge on host-immune response to SARS-CoV-2 infection, however, remains limited with very few drugs approved to date. Viable strategies and tools are rapidly arising to address this, especially with repurposing of existing drugs offering significant promise. Here we introduce a systems biology tool, the PHENotype SIMulator, which - by leveraging available transcriptomic and proteomic databases - allows modeling of SARS-CoV-2 infection in host cells in silico to i) determine with high sensitivity and specificity (both > 96%) the viral effects on cellular host-immune response, resulting in a specific cellular SARS-CoV-2 signature and ii) utilize this specific signature to narrow down promising repurposable therapeutic strategies. Powered by this tool, coupled with domain expertise, we have identified several potential COVID-19 drugs including methylprednisolone and metformin, and further discern key cellular SARS-CoV-2-affected pathways as potential new druggable targets in COVID-19 pathogenesis.

"Diagnosis on the Dock" project: A proactive screening program for diagnosing pulmonary tuberculosis in disembarking refugees and new SEI model

OBJECTIVE

From 2011 to 2017, the total number of refugees arriving in Europe, particularly in Italy, climbed dramatically. Our aim was to diagnose pulmonary TB in migrants coming from the African coast using a clinical-based port of arrival (PoA) screening program.

METHODS

From 2016 to 2018, migrants coming via the Mediterranean Route were screened for body temperature and the presence of cough directly on the dock: if they were feverish with productive cough, their sputum was examined with NAAT; with a dry cough, they underwent Chest-X-ray (CXR). Those migrants with positive NAAT or CXR suggestive for TB were admitted to our ward. In addition, we plotted an SEI simulation of our project to evaluate the epidemiological impact of our screening.

RESULTS

Out of 33.676 disembarking migrants, 314 (0.9%) had fever and cough: 80 (25.47%) with productive cough underwent NAAT in sputum, and 16 were positive for TB; 234 (74.52%) with dry cough had a CXR examination, and 39 were suggestive of TB, later confirmed by mycobacterial culture. The SEI-new model analysis demonstrated that our screening program significantly reduced TB spreading all over the country.

CONCLUSIONS

For possible future high migrant flows, PoA screening for TB has to be considered feasible and effective in decreasing TB spreading.

Treat and extend versus fixed regimen in neovascular age related macular degeneration: A systematic review and meta-analysis

PURPOSE

To compare efficacy of treat and extend (T&E) versus fixed regimen treatment protocols in neovascular age-related macular degeneration (nAMD).

METHODS

Randomized clinical trials (RCTs) comparing T&E versus fixed regimen protocols for nAMD were systematically searched. Primary outcome was to compare the mean best corrected visual acuity (BCVA) change in T&E regimen versus fixed regimen. Secondary outcomes were change in the mean optical coherence tomography (OCT) central retinal thickness (CRT) and mean number of injections. Standardized mean difference (SMD) along with 95% confidence intervals (CIs) were calculated. Random-effect models were used for meta-analyses.

RESULTS

Four RCTs were included, with a total of 649 and 621 eyes in the T&E and fixed regimen cohort at 12 months, and 267 and 249 eyes at 24 months. Pooled analysis of mean BCVA change included all four RCTs at 12 months and two RCTs at 24 months, showing no difference between the two groups (12-month: SMD = 0.08, 95% CI: -0.20 to 0.35, p = 0.55; 24-month: SMD = 0.04, 95% CI: -0.13 to 0.21, p = 0.64). Pooled analysis of OCT CRT change at 12 months included three studies, showing no difference between the two groups (SMD = 0.03, 95% CI: -0.46 to 0.51, p = 0.91). Pooled analysis of mean injection number included all four RCTs at 12 months and two RCTs at 24 months, showing significant difference between the two groups (12-month: SMD = -1.11, 95% CI: -1.67 to -0.56, p < 0.001; 24-month: SMD = -1.34, 95% CI: -1.54 to -1.15, p < 0.001).

CONCLUSION

A T&E regimen proved as effective as a fixed dosage regimen throughout a 24-month follow-up and with a lower number of injections.

Immune infiltrating cells in duodenal cancers

Background

Duodenal adenocarcinoma (DA) is a rare yet aggressive malignancy, with increasing incidence in the last decades. Its low frequency has hampered a thorough understanding of the pathogenesis of the disease and of its biology, limiting the identification of tailored therapeutic options. A large body of evidence has clearly shown the clinical relevance of immune cells in solid tumors, correlating immune features with post-surgical prognosis. The aim of this study was to analyze the immune contexture in a cohort of duodenal adenocarcinomas surgically resected at our Institution and define its correlation with clinical variables.

Methods

Tissue slides from paraffin-embedded tumor specimens of 15 consecutive DA and 3 adenomas that underwent a pancreaticoduodenectomy in our center between 2010 to 2018 were immunohistochemically stained. The density (percentage of immune reactive area, IRA%) of immune markers CD45RO, CD8, CD20, IL-17, PD-1, CD68 was quantified by computer-assisted image analysis. Demographic, clinical, histopathological data were collected.

Results

In our population, median IRA % (IQR) of immune subsets was respectively CD45RO-TILs 2.19 (2.14), CD8-TIL 0.42 (0.81), CD20-TILs 0.22 (0.51), CD20-TLT 2.84 (4.64), CD68-TAM 2.19 (1.56), IL17⁺ cells 0.39 (0.39), PD1-TILs 0.19 (0.41). The median follow-up was 47.5 (22.4–63.3) months. At statistical analysis, the density of CD8-TILs inversely correlated with lymph node ratio (p = 0.013), number of metastatic lymph nodes (p = 0.019), and was lower in N+ adenocarcinomas compared to N0 (1.07 vs 0.29; p = 0.093), albeit not significantly. Stratifying patients for the N status, the density of CD8-TILs decreased with the increasing of the N stage (p = 0.065) and was lower in patients who experienced recurrence and died for the disease (0.276 vs 0.641; p = 0.044). Notably, also CD68-TAM distribution was different in patients who had recurrence versus patients who did not (1.028 vs 2.276; p = 0.036).

Conclusions

Immune cells showed variable expression in correlation with common prognostic factors, suggesting T cell infiltration may play a protective role towards lymphatic spread of disease and nodal metastatization. Furthermore, T cell density and macrophage infiltration were associated to a lower risk of recurrence and disease related death. A multicentric approach may be indicated to allow analysis of larger cohorts of patients, potentially increasing the power of our observations.

P22 Micro-RNA enriched pathway impact analysis applied to synovial RNA-seq in early rheumatoid arthritis identifies response prediction pathways

Background

We use a new pathway analysis tool MITHrIL (Mirna enriched paTHway Impact anaLysis) to analyse RNA-seq patterns in synovial biopsies from patients with rheumatoid arthritis from the Pathobiology of Early Arthritis Cohort (PEAC). MITHrIL augments pathways with missing regulatory elements, such as microRNAs, and their interactions with genes to enhance quantification of immunological pathway activation in bulk RNA-seq. MITHrIL pathways were compared with three major pathotypes (lympho-myeloid, diffuse myeloid and pauci-immune fibroid) in early treatment-naïve RA patients and responders vs non-responders to methotrexate-based DMARD regimens.

Methods

A differential gene expression analysis is performed on the PEAC observational cohort. The Log-Fold Changes retrieved from the pairwise comparisons between responders/non responders and across different pathotypes are used as input to the MITHrIL software. Using the KEGG biological pathways, MITHrIL proportionally spreads the known differential perturbation of a gene to the downstream nodes in the pathway. In so doing, a perturbation factor is assigned to each gene and, as a result, a list of differentially altered pathways is returned together with the corresponding statistical significance (p-values). Particular attention is given to immunological pathways. We show a list of differentially altered pathways for each comparison and provide specific insight to those more characterising pathways.

Results

The results show coherence with the previous findings providing greater granularity on how the gene level alterations can propagate through biological pathways. In particular, we found different gene expression levels in the pairwise comparisons across pathotypes highlighting different perturbations in the following pathways: chemokine signalling (adjusted P = 3.3E-13), Jak-STAT signalling (adjusted P = 6.5E-04), Leukocyte transendothelial migration (adjusted P = 3.7E-02), PI3K-Akt signalling (adjusted P = 4.6E-04), T cell receptor signalling (adjusted P = 3.7E-02), Antigen processing and presentation (adjusted P = 1.1E-08) and NF-kappa B signalling (adjusted P = 4.1E-02).

We also found confirmation of changes in lymphoid and myeloid subtypes over time, while fibroid RA patients present no significant alteration in their expression levels after the methotrexate-based DMARD therapy. Finally, we found different levels of activation in MAPK and AMPK signalling pathways for patients that do not respond to the DMARD therapy in contrast to those who do.

Conclusion

A novel pathway analysis approach is used to show the most differentially active biological pathways between different RA pathotypes and responder-resistant patients to methotrexate-based DMARD therapy. The results identify responder-resistant gene expression pathway patterns in early RA which may help to stratify patients to biologic therapy at an earlier stage.

Disclosures

E. Sciacca None. S. Alaimo None. A. Pulvirenti None. V. Latora None. F. Humby None. A. Ferro None. M.J. Lewis None. C. Pitzalis None.

Endogenous and artificial miRNAs explore a rich variety of conformations: a potential relationship between secondary structure and biological functionality

Mature microRNAs are short non-coding RNA sequences which upon incorporation into the RISC ribonucleoprotein complex, play a crucial role in regulation of gene expression. However, miRNAs can exist within the cell also as free molecules fulfilling their biological activity. Therefore, it is emerging that in addition to sequence even the structure adopted by mature miRNAs might play an important role to reach the target. Indeed, we analysed by several spectroscopic techniques the secondary structures of two artificial miRNAs selected by computational tool (miR-Synth) as best candidates to silence c-MET and EGFR genes and of two endogenous miRNAs (miR-15a and miR-15b) having the same seed region, but different biological activity. Our results demonstrate that both endogenous and artificial miRNAs can arrange in several 3D-structures which affect their activity and selectivity toward the targets.

A benchmarking of pipelines for detecting ncRNAs from RNA-Seq data

Next-Generation Sequencing (NGS) is a high-throughput technology widely applied to genome sequencing and transcriptome profiling. RNA-Seq uses NGS to reveal RNA identities and quantities in a given sample. However, it produces a huge amount of raw data that need to be preprocessed with fast and effective computational methods. RNA-Seq can look at different populations of RNAs, including ncRNAs. Indeed, in the last few years, several ncRNAs pipelines have been developed for ncRNAs analysis from RNA-Seq experiments. In this paper, we analyze eight recent pipelines (iSmaRT, iSRAP, miARma-Seq, Oasis 2, SPORTS1.0, sRNAnalyzer, sRNApipe, sRNA workbench) which allows the analysis not only of single specific classes of ncRNAs but also of more than one ncRNA classes. Our systematic performance evaluation aims at guiding users to select the appropriate pipeline for processing each ncRNA class, focusing on three key points: (i) accuracy in ncRNAs identification, (ii) accuracy in read count estimation and (iii) deployment and ease of use.

TACITuS: transcriptomic data collector, integrator, and selector on big data platform

BACKGROUND

Several large public repositories of microarray datasets and RNA-seq data are available. Two prominent examples include ArrayExpress and NCBI GEO. Unfortunately, there is no easy way to import and manipulate data from such resources, because the data is stored in large files, requiring large bandwidth to download and special purpose data manipulation tools to extract subsets relevant for the specific analysis.

RESULTS

TACITuS is a web-based system that supports rapid query access to high-throughput microarray and NGS repositories. The system is equipped with modules capable of managing large files, storing them in a cloud environment and extracting subsets of data in an easy and efficient way. The system also supports the ability to import data into Galaxy for further analysis.

CONCLUSIONS

TACITuS automates most of the pre-processing needed to analyze high-throughput microarray and NGS data from large publicly-available repositories. The system implements several modules to manage large files in an easy and efficient way. Furthermore, it is capable deal with Galaxy environment allowing users to analyze data through a user-friendly interface.

Risk of acute stroke in patients with retinal artery occlusion: a systematic review and meta-analysis

OBJECTIVE

To estimate the incidence of acute cerebral ischaemia detected by magnetic resonance imaging (MRI) in acute central retinal artery occlusion (CRAO), branch retinal artery occlusion (BRAO) and transient monocular vision loss (TMVL).

METHODS

Studies reporting the incidence of acute cerebral ischaemia, detected by MRI, within 7 days from diagnosis of acute CRAO, BRAO and TMVL up to January 2019 were systematically searched for on Pubmed, Medline and Cochrane Library. Meta-analysis was performed using random effects model. The primary outcome was the pooled estimate of incidence of acute cerebral ischaemia in CRAO, BRAO and TMVL cohorts including both neurologically symptomatic and asymptomatic patients, expressed as a proportion along with 95% confidence intervals (CIs). The pooled estimate of incidence of asymptomatic acute cerebral ischaemia represented a secondary outcome measure.

RESULTS

For the primary outcome, the pooled proportion of acute cerebral ischaemia was 0.30 (CI 0.24-0.36) in the CRAO cohort, and 0.25 (CI 0.16-0.37) in the BRAO cohort, without statistical heterogeneity. The rate of acute cerebral ischaemia was 11.8% in the TMVL cohort. For the secondary outcome, the pooled proportion of asymptomatic acute cerebral ischaemia was 0.22 (CI 0.16-0.28) in the CRAO cohort, 0.29 (CI 0.20-0.41) in the BRAO cohort and 0.08 (CI 0.05-0.15) in the TMVL cohort, with no statistical heterogeneity.

CONCLUSIONS

30% of patients with acute CRAO and 25% of patients with acute BRAO presented an acute cerebral ischaemia on MRI. Such high rates support a care pathway of prompt referral of such patients for neurological evaluation and brain imaging.

miRandola 2017: a curated knowledge base of non-invasive biomarkers

miRandola (http://mirandola.iit.cnr.it/) is a database of extracellular non-coding RNAs (ncRNAs) that was initially published in 2012, foreseeing the relevance of ncRNAs as non-invasive biomarkers. An increasing amount of experimental evidence shows that ncRNAs are frequently dysregulated in diseases. Further, ncRNAs have been discovered in different extracellular forms, such as exosomes, which circulate in human body fluids. Thus, miRandola 2017 is an effort to update and collect the accumulating information on extracellular ncRNAs that is spread across scientific publications and different databases. Data are manually curated from 314 articles that describe miRNAs, long non-coding RNAs and circular RNAs. Fourteen organisms are now included in the database, and associations of ncRNAs with 25 drugs, 47 sample types and 197 diseases. miRandola also classifies extracellular RNAs based on their extracellular form: Argonaute2 protein, exosome, microvesicle, microparticle, membrane vesicle, high density lipoprotein and circulating. We also implemented a new web interface to improve the user experience.

Perioperative outcomes and long-term quality of life after total pancreatectomy

BACKGROUND

Total pancreatectomy is required to treat diseases involving the entire pancreas, and is characterized by high morbidity rates and impaired long-term quality of life (QoL). To date, risk factors associated with perioperative and long-term outcomes have not been determined fully.

METHODS

Data from patients undergoing total pancreatectomy between 2000 and 2014 at two high-volume centres were analysed retrospectively to assess risk factors for major surgical complications. Short Form (SF) 36, European Organisation for Research and Treatment of Cancer QLQ-PAN26 and Audit of Diabetes Dependent questionnaires, as well as an original survey were used to investigate factors influencing QoL.

RESULTS

A total of 329 consecutive patients underwent total pancreatectomy in the two centres. Overall, total pancreatectomy was associated with a morbidity rate of 59·3 per cent and a 30-day mortality rate of 2·1 per cent. Age over 65 years and long duration of surgery (more than 420 min) were independently associated with major complications (at least Clavien-Dindo grade III). QoL analysis was available for 94 patients (28·6 per cent) with a median follow-up of 63 (i.q.r. 20-109) months; the most common indication for total pancreatectomy in these patients was intraductal papillary mucinous neoplasms (46 per cent). Both physical (PCS) and mental (MCS) component summary scores of SF-36® were lower after total pancreatectomy compared with scores for a normative population (P = 0·020 and P < 0·001 respectively). Linear regression analysis showed that young age, abdominal pain and worse perception of body image were negatively associated with the PCS, whereas diabetes, sexual satisfaction and perception of body image affected MCS.

CONCLUSION

Total pancreatectomy can be performed with acceptable morbidity and mortality rates. Older patients had a higher risk of postoperative complications but reported better QoL than younger patients.

Network-Based Drug Repositioning: Approaches, Resources, and Research Directions

The wealth of knowledge and omic data available in drug research allowed the rising of several computational methods in drug discovery field yielding a novel and exciting application called drug repositioning. Several computational methods try to make a high-level integration of all the knowledge in order to discover unknown mechanisms. In this chapter we present an in-depth review of data resources and computational models for drug repositioning.

Identification of tRNA-derived ncRNAs in TCGA and NCI-60 panel cell lines and development of the public database tRFexplorer

Next-generation sequencing is increasing our understanding and knowledge of non-coding RNAs (ncRNAs), elucidating their roles in molecular mechanisms and processes such as cell growth and development. Within such a class, tRNA-derived ncRNAs have been recently associated with gene expression regulation in cancer progression. In this paper, we characterize, for the first time, tRNA-derived ncRNAs in NCI-60. Furthermore, we assess their expression profile in The Cancer Genome Atlas (TCGA). Our comprehensive analysis allowed us to report 322 distinct tRNA-derived ncRNAs in NCI-60, categorized in tRNA-derived fragments (11 tRF-5s, 55 tRF-3s), tRNA-derived small RNAs (107 tsRNAs) and tRNA 5' leader RNAs (149 sequences identified). In TCGA, we were able to identify 232 distinct tRNA-derived ncRNAs categorized in 53 tRF-5s, 58 tRF-3s, 63 tsRNAs and 58 5' leader RNAs. This latter group represents an additional evidence of tRNA-derived ncRNAs originating from the 5' leader region of precursor tRNA. We developed a public database, tRFexplorer, which provides users with the expression profile of each tRNA-derived ncRNAs in every cell line in NCI-60 as well as for each TCGA tumor type. Moreover, the system allows us to perform differential expression analyses of such fragments in TCGA, as well as correlation analyses of tRNA-derived ncRNAs expression in TCGA and NCI-60 with gene and miRNA expression in TCGA samples, in association with all omics and compound activities data available on CellMiner. Hence, the tool provides an important opportunity to investigate their potential biological roles in absence of any direct experimental evidence. Database URL: https://trfexplorer.cloud/.

Computational Methods to Investigate the Impact of miRNAs on Pathways

Pathway analysis is a wide class of methods allowing to determine the alteration of functional processes in complex diseases. However, biological pathways are still partial, and knowledge coming from posttranscriptional regulators has started to be considered in a systematic way only recently. Here we will give a global and updated view of the main pathway and subpathway analysis methodologies, focusing on the improvements obtained through the recent introduction of microRNAs as regulatory elements in these frameworks.

A Subset of Patients With Autism Spectrum Disorders Show a Distinctive Metabolic Profile by Dried Blood Spot Analyses

Autism spectrum disorder (ASD) is currently diagnosed according to behavioral criteria. Biomarkers that identify children with ASD could lead to more accurate and early diagnosis. ASD is a complex disorder with multifactorial and heterogeneous etiology supporting recognition of biomarkers that identify patient subsets. We investigated an easily testable blood metabolic profile associated with ASD diagnosis using high throughput analyses of samples extracted from dried blood spots (DBS). A targeted panel of 45 ASD analytes including acyl-carnitines and amino acids extracted from DBS was examined in 83 children with ASD (60 males; age 6.06 ± 3.58, range: 2-10 years) and 79 matched, neurotypical (NT) control children (57 males; age 6.8 ± 4.11 years, range 2.5-11 years). Based on their chronological ages, participants were divided in two groups: younger or older than 5 years. Two-sided T-tests were used to identify significant differences in measured metabolite levels between groups. Näive Bayes algorithm trained on the identified metabolites was used to profile children with ASD vs. NT controls. Of the 45 analyzed metabolites, nine (20%) were significantly increased in ASD patients including the amino acid citrulline and acyl-carnitines C2, C4DC/C5OH, C10, C12, C14:2, C16, C16:1, C18:1 (P: < 0.001). Näive Bayes algorithm using acyl-carnitine metabolites which were identified as significantly abnormal showed the highest performances for classifying ASD in children younger than 5 years (n: 42; mean age 3.26 ± 0.89) with 72.3% sensitivity (95% CI: 71.3;73.9), 72.1% specificity (95% CI: 71.2;72.9) and a diagnostic odds ratio 11.25 (95% CI: 9.47;17.7). Re-test analyses as a measure of validity showed an accuracy of 73% in children with ASD aged ≤ 5 years. This easily testable, non-invasive profile in DBS may support recognition of metabolic ASD individuals aged ≤ 5 years and represents a potential complementary tool to improve diagnosis at earlier stages of ASD development.