Ten simple rules on how to develop a stakeholder engagement plan.

To make research responsible and research outcomes meaningful, it is necessary to communicate our research and to involve as many relevant stakeholders as possible, especially in application-oriented-including information and communications technology (ICT)-research. Nowadays, stakeholder engagement is of fundamental importance to project success and achieving the expected impact and is often mandatory in a third-party funding context. Ultimately, research and development can only be successful if people react positively to the results and benefits generated by a project. For the wider acceptance of research outcomes, it is therefore essential that the public is made aware of and has an opportunity to discuss the results of research undertaken through two-way communication (interpersonal communication) with researchers. Responsible Research and Innovation (RRI), an approach that anticipates and assesses potential implications and societal expectations regarding research and innovation, aims to foster inclusive and sustainable research and innovation. Research and innovation processes need to become more responsive and adaptive to these grand challenges. This implies, among other things, the introduction of broader foresight and impact assessments for new technologies beyond their anticipated market benefits and risks. Therefore, this article provides a structured workflow that explains "how to develop a stakeholder engagement plan" step by step.

Whole-Exome and Transcriptome Sequencing Expands the Genotype of Majewski Osteodysplastic Primordial Dwarfism Type II.

Microcephalic Osteodysplastic Primordial Dwarfism type II (MOPDII) represents the most common form of primordial dwarfism. MOPD clinical features include severe prenatal and postnatal growth retardation, postnatal severe microcephaly, hypotonia, and an increased risk for cerebrovascular disease and insulin resistance. Autosomal recessive biallelic loss-of-function genomic variants in the centrosomal pericentrin (PCNT) gene on chromosome 21q22 cause MOPDII. Over the past decade, exome sequencing (ES) and massive RNA sequencing have been effectively employed for both the discovery of novel disease genes and to expand the genotypes of well-known diseases. In this paper we report the results both the RNA sequencing and ES of three patients affected by MOPDII with the aim of exploring whether differentially expressed genes and previously uncharacterized gene variants, in addition to PCNT pathogenic variants, could be associated with the complex phenotype of this disease. We discovered a downregulation of key factors involved in growth, such as IGF1R, IGF2R, and RAF1, in all three investigated patients. Moreover, ES identified a shortlist of genes associated with deleterious, rare variants in MOPDII patients. Our results suggest that Next Generation Sequencing (NGS) technologies can be successfully applied for the molecular characterization of the complex genotypic background of MOPDII.

Participation of nurses and allied health professionals in research activities: a survey in an academic tertiary pediatric hospital.

BACKGROUND: Involvement in research activities is complex in pediatric nursing and allied health professionals (AHPs). It is important to understand which individual factors are associated with it to inform policy makers in promoting research. METHODS: A cross-sectional observational study was conducted to describe the level of participation in research activities over the last ten years of nurses and AHPs working in a tertiary pediatric hospital. A large sample of nurses and AHPs working in an Italian academic tertiary pediatric hospital completed an online self-report questionnaire between June and December 2018. Three multivariate logistic regression analyses were performed to predict participation in research projects, speaking at conferences, and writing scientific articles. RESULTS: Overall, data from 921 health professionals were analyzed (response rate = 66%), of which about 21% (n = 196) reported participating in a research project, while 33% (n = 297) had attended a scientific conference as a speaker, and 11% (n = 94) had written at least one scientific paper. Having a Master or a Regional Advanced Course, working as an AHP or a ward manager, as well as regularly reading scientific journals and participation in an internal hospital research group or attendance in a specific course about research in the hospital, significantly predicted participation in research projects, speaking at conferences and writing scientific papers. It is important to foster research interest and competencies among health professionals to improve participation in research projects, speaking at conferences, and writing scientific papers. CONCLUSIONS: Overall, we found a good level of attendance at conferences as speakers (33%), a moderate level of participation in research (21%), and low levels for writing scientific papers (11%). Our study highlighted the need to support participation in research activities among nurses and AHPs. Policymakers should identify strategies to promote research among nurses and AHPs, such as protected rewarded time for research, specific education, strengthened collaboration with academics, and financial support. Moreover, hospital managers should promote the development of research culture among health professionals, to improve their research competencies and evidence-based practice.

Exploiting transfer learning for the reconstruction of the human gene regulatory network.

MOTIVATION: The reconstruction of gene regulatory networks (GRNs) from gene expression data has received increasing attention in recent years, due to its usefulness in the understanding of regulatory mechanisms involved in human diseases. Most of the existing methods reconstruct the network through machine learning approaches, by analyzing known examples of interactions. However, (i) they often produce poor results when the amount of labeled examples is limited, or when no negative example is available and (ii) they are not able to exploit information extracted from GRNs of other (better studied) related organisms, when this information is available. RESULTS: In this paper, we propose a novel machine learning method that overcomes these limitations, by exploiting the knowledge about the GRN of a source organism for the reconstruction of the GRN of the target organism, by means of a novel transfer learning technique. Moreover, the proposed method is natively able to work in the positive-unlabeled setting, where no negative example is available, by fruitfully exploiting a (possibly large) set of unlabeled examples. In our experiments, we reconstructed the human GRN, by exploiting the knowledge of the GRN of Mus musculus. Results showed that the proposed method outperforms state-of-the-art approaches and identifies previously unknown functional relationships among the analyzed genes. AVAILABILITY AND IMPLEMENTATION: http://www.di.uniba.it/∼mignone/systems/biosfer/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Ten simple rules on how to write a standard operating procedure.

Research publications and data nowadays should be publicly available on the internet and, theoretically, usable for everyone to develop further research, products, or services. The long-term accessibility of research data is, therefore, fundamental in the economy of the research production process. However, the availability of data is not sufficient by itself, but also their quality must be verifiable. Measures to ensure reuse and reproducibility need to include the entire research life cycle, from the experimental design to the generation of data, quality control, statistical analysis, interpretation, and validation of the results. Hence, high-quality records, particularly for providing a string of documents for the verifiable origin of data, are essential elements that can act as a certificate for potential users (customers). These records also improve the traceability and transparency of data and processes, therefore, improving the reliability of results. Standards for data acquisition, analysis, and documentation have been fostered in the last decade driven by grassroot initiatives of researchers and organizations such as the Research Data Alliance (RDA). Nevertheless, what is still largely missing in the life science academic research are agreed procedures for complex routine research workflows. Here, well-crafted documentation like standard operating procedures (SOPs) offer clear direction and instructions specifically designed to avoid deviations as an absolute necessity for reproducibility. Therefore, this paper provides a standardized workflow that explains step by step how to write an SOP to be used as a starting point for appropriate research documentation.

Prediction of new associations between ncRNAs and diseases exploiting multi-type hierarchical clustering.

BACKGROUND: The study of functional associations between ncRNAs and human diseases is a pivotal task of modern research to develop new and more effective therapeutic approaches. Nevertheless, it is not a trivial task since it involves entities of different types, such as microRNAs, lncRNAs or target genes whose expression also depends on endogenous or exogenous factors. Such a complexity can be faced by representing the involved biological entities and their relationships as a network and by exploiting network-based computational approaches able to identify new associations. However, existing methods are limited to homogeneous networks (i.e., consisting of only one type of objects and relationships) or can exploit only a small subset of the features of biological entities, such as the presence of a particular binding domain, enzymatic properties or their involvement in specific diseases. RESULTS: To overcome the limitations of existing approaches, we propose the system LP-HCLUS, which exploits a multi-type hierarchical clustering method to predict possibly unknown ncRNA-disease relationships. In particular, LP-HCLUS analyzes heterogeneous networks consisting of several types of objects and relationships, each possibly described by a set of features, and extracts multi-type clusters that are subsequently exploited to predict new ncRNA-disease associations. The extracted clusters are overlapping, hierarchically organized, involve entities of different types, and allow LP-HCLUS to catch multiple roles of ncRNAs in diseases at different levels of granularity. Our experimental evaluation, performed on heterogeneous attributed networks consisting of microRNAs, lncRNAs, diseases, genes and their known relationships, shows that LP-HCLUS is able to obtain better results with respect to existing approaches. The biological relevance of the obtained results was evaluated according to both quantitative (i.e., TPR@k, Areas Under the TPR@k, ROC and Precision-Recall curves) and qualitative (i.e., according to the consultation of the existing literature) criteria. CONCLUSIONS: The obtained results prove the utility of LP-HCLUS to conduct robust predictive studies on the biological role of ncRNAs in human diseases. The produced predictions can therefore be reliably considered as new, previously unknown, relationships among ncRNAs and diseases.

Arena-Idb: a platform to build human non-coding RNA interaction networks.

BACKGROUND: High throughput technologies have provided the scientific community an unprecedented opportunity for large-scale analysis of genomes. Non-coding RNAs (ncRNAs), for a long time believed to be non-functional, are emerging as one of the most important and large family of gene regulators and key elements for genome maintenance. Functional studies have been able to assign to ncRNAs a wide spectrum of functions in primary biological processes, and for this reason they are assuming a growing importance as a potential new family of cancer therapeutic targets. Nevertheless, the number of functionally characterized ncRNAs is still too poor if compared to the number of new discovered ncRNAs. Thus platforms able to merge information from available resources addressing data integration issues are necessary and still insufficient to elucidate ncRNAs biological roles. RESULTS: In this paper, we describe a platform called Arena-Idb for the retrieval of comprehensive and non-redundant annotated ncRNAs interactions. Arena-Idb provides a framework for network reconstruction of ncRNA heterogeneous interactions (i.e., with other type of molecules) and relationships with human diseases which guide the integration of data, extracted from different sources, via mapping of entities and minimization of ambiguity. CONCLUSIONS: Arena-Idb provides a schema and a visualization system to integrate ncRNA interactions that assists in discovering ncRNA functions through the extraction of heterogeneous interaction networks. The Arena-Idb is available at http://arenaidb.ba.itb.cnr.it.

The joint NETTAB/Integrative Bioinformatics 2015 Meeting: aims, topics and outcomes.

The 15th International NETTAB workshop and the 11th Integrative Bioinformatics Symposium were held together in Bari, on October 14-16, 2016, as Joint NETTAB/IB 2015 Meeting. A special topic for the meeting was "Bioinformatics for ncRNA", but the traditional topics of both meetings series were also included in the event.About 60 scientific contributions were presented, including six keynote lectures, one special guest lecture, and many oral communications and posters. A "Two-Day Hands-on Tutorial" event was organised before the workshop.Selected full papers from some of the best works presented in Bari were submitted either to the Journal of Integrative Bioinformatics or to a purpose Call for a Supplement of BMC Bioinformatics.Here, we provide an overview of meeting aims and scope. We also shortly introduce selected papers that have been either accepted for publication in this Supplement or published in the Journal of Integrative Bioinformatics, for a more complete presentation of the outcomes of the meeting.

ComiRNet: a web-based system for the analysis of miRNA-gene regulatory networks.

BACKGROUND: The understanding of mechanisms and functions of microRNAs (miRNAs) is fundamental for the study of many biological processes and for the elucidation of the pathogenesis of many human diseases. Technological advances represented by high-throughput technologies, such as microarray and next-generation sequencing, have significantly aided miRNA research in the last decade. Nevertheless, the identification of true miRNA targets and the complete elucidation of the rules governing their functional targeting remain nebulous. Computational tools have been proven to be fundamental for guiding experimental validations for the discovery of new miRNAs, for the identification of their targets and for the elucidation of their regulatory mechanisms. DESCRIPTION: ComiRNet (Co-clustered miRNA Regulatory Networks) is a web-based database specifically designed to provide biologists and clinicians with user-friendly and effective tools for the study of miRNA-gene target interaction data and for the discovery of miRNA functions and mechanisms. Data in ComiRNet are produced by a combined computational approach based on: 1) a semi-supervised ensemble-based classifier, which learns to combine miRNA-gene target interactions (MTIs) from several prediction algorithms, and 2) the biclustering algorithm HOCCLUS2, which exploits the large set of produced predictions, with the associated probabilities, to identify overlapping and hierarchically organized biclusters that represent miRNA-gene regulatory networks (MGRNs). CONCLUSIONS: ComiRNet represents a valuable resource for elucidating the miRNAs' role in complex biological processes by exploiting data on their putative function in the context of MGRNs. ComiRnet currently stores about 5 million predicted MTIs between 934 human miRNAs and 30,875 mRNAs, as well as 15 bicluster hierarchies, each of which represents MGRNs at different levels of granularity. The database can be freely accessed at: http://comirnet.di.uniba.it.

Integrating microRNA target predictions for the discovery of gene regulatory networks: a semi-supervised ensemble learning approach.

BACKGROUND: MicroRNAs (miRNAs) are small non-coding RNAs which play a key role in the post-transcriptional regulation of many genes. Elucidating miRNA-regulated gene networks is crucial for the understanding of mechanisms and functions of miRNAs in many biological processes, such as cell proliferation, development, differentiation and cell homeostasis, as well as in many types of human tumors. To this aim, we have recently presented the biclustering method HOCCLUS2, for the discovery of miRNA regulatory networks. Experiments on predicted interactions revealed that the statistical and biological consistency of the obtained networks is negatively affected by the poor reliability of the output of miRNA target prediction algorithms. Recently, some learning approaches have been proposed to learn to combine the outputs of distinct prediction algorithms and improve their accuracy. However, the application of classical supervised learning algorithms presents two challenges: i) the presence of only positive examples in datasets of experimentally verified interactions and ii) unbalanced number of labeled and unlabeled examples. RESULTS: We present a learning algorithm that learns to combine the score returned by several prediction algorithms, by exploiting information conveyed by (only positively labeled/) validated and unlabeled examples of interactions. To face the two related challenges, we resort to a semi-supervised ensemble learning setting. Results obtained using miRTarBase as the set of labeled (positive) interactions and mirDIP as the set of unlabeled interactions show a significant improvement, over competitive approaches, in the quality of the predictions. This solution also improves the effectiveness of HOCCLUS2 in discovering biologically realistic miRNA:mRNA regulatory networks from large-scale prediction data. Using the miR-17-92 gene cluster family as a reference system and comparing results with previous experiments, we find a large increase in the number of significantly enriched biclusters in pathways, consistent with miR-17-92 functions. CONCLUSION: The proposed approach proves to be fundamental for the computational discovery of miRNA regulatory networks from large-scale predictions. This paves the way to the systematic application of HOCCLUS2 for a comprehensive reconstruction of all the possible multiple interactions established by miRNAs in regulating the expression of gene networks, which would be otherwise impossible to reconstruct by considering only experimentally validated interactions.

Long-term outcome of home parenteral nutrition in patients with ultra-short bowel syndrome.

OBJECTIVE: The patients with ultra-short bowel syndrome (U-SBS) have been considered potential candidates for a preemptive/rehabilitative intestinal transplantation owing to the high risk of death from the underlying disease. We hypothesized that children with U-SBS, in the absence of intestinal failure-associated liver disease (IFALD), could also have a good rate of survival on home parenteral nutrition (HPN). METHODS: A prospective database from the "Bambino Gesù" Artificial Nutrition and Intestinal Failure Program was used to evaluate outcomes and morbidities of consecutive patients with ≤ 10 cm of small bowel enrolled since 2000. RESULTS: Eleven patients were identified with a median bowel length of 7.5 (3-9) cm. Eight patients developed IFALD, which reversed in 7 of them; the IFALD progressively worsened in 1 patient until death. One patient underwent isolated intestinal transplantation and 1 patient is no longer receiving parenteral nutrition (PN) and both are fully enterally fed. The other patients remained at least partially dependent on HPN. The number of days of inpatient care decreased in all of the patients except for the 1 who had repeated episodes of central line infections. CONCLUSIONS: The survival of patients with U-SBS receiving HPN was good. Although IFALD was frequent, it had been manageable in most of the patients, but in a single complex case, it led to death. The multidisciplinary management warranted to these patients to approach the school age, to grow, and to maintain the oral intake. Patients with U-SBS are rare, and to better understand their long-term survival, further studies, including more large patient populations, are required.

Non-interventional, 5-year retrospective data of home parenteral nutrition in patients with benign chronic intestinal failure: Analysis of an Italian nurse registry (SERECARE II).

OBJECTIVE: This study is an assessment of home parenteral nutrition service performance and safety and efficacy outcomes in patients with benign chronic intestinal failure. METHODS: This is a retrospective, non-interventional, and multicenter study. Data were collected by trained nurses and recorded in a dedicated registry (SERECARE). RESULTS: From January 1, 2013 to June 30, 2018, data from a total of 683 patients with benign chronic intestinal failure were entered in the registry. Patients included 208 pediatric (53.8% male; median age = 4.0 y) and 475 adult (47.6% male; median age = 59.0 y) participants. On average, patients were visited 5.4 ± 4.5 times and received 1.4 ± 0.8 training sessions. Retraining was not common and mostly due to change of therapy or change of caregiver. Of 939 complications, 40.9% were related to the central venous catheter and were mostly infectious (n = 182) and mechanical (n = 187). The rate of infectious and mechanical complications per 1000 catheter days decreased over 5 y (0.30-0.15 and 0.33 -0.19, respectively). The rate of complications per 1000 catheter days and the mean complications per patient were higher in pediatric than in adult patients. The hospitalization rate was 1.01 per patient throughout the study period. These data were similar to those registered in a previous study period (2002-2011) (n = 1.53 per patient). Changes over time in the efficacy variables were mostly small and non-significant. CONCLUSIONS: This study confirms the importance of setting up and maintaining structured registries to monitor and improve home parenteral nutrition care. Safety outcomes have improved over the years, most likely due to the underlying efficient nursing service.

A novel biclustering algorithm for the discovery of meaningful biological correlations between microRNAs and their target genes.

BACKGROUND: microRNAs (miRNAs) are a class of small non-coding RNAs which have been recognized as ubiquitous post-transcriptional regulators. The analysis of interactions between different miRNAs and their target genes is necessary for the understanding of miRNAs' role in the control of cell life and death. In this paper we propose a novel data mining algorithm, called HOCCLUS2, specifically designed to bicluster miRNAs and target messenger RNAs (mRNAs) on the basis of their experimentally-verified and/or predicted interactions. Indeed, existing biclustering approaches, typically used to analyze gene expression data, fail when applied to miRNA:mRNA interactions since they usually do not extract possibly overlapping biclusters (miRNAs and their target genes may have multiple roles), extract a huge amount of biclusters (difficult to browse and rank on the basis of their importance) and work on similarities of feature values (do not limit the analysis to reliable interactions). RESULTS: To overcome these limitations, HOCCLUS2 i) extracts possibly overlapping biclusters, to catch multiple roles of both miRNAs and their target genes; ii) extracts hierarchically organized biclusters, to facilitate bicluster browsing and to distinguish between universe and pathway-specific miRNAs; iii) extracts highly cohesive biclusters, to consider only reliable interactions; iv) ranks biclusters according to the functional similarities, computed on the basis of Gene Ontology, to facilitate bicluster analysis. CONCLUSIONS: Our results show that HOCCLUS2 is a valid tool to support biologists in the identification of context-specific miRNAs regulatory modules and in the detection of possibly unknown miRNAs target genes. Indeed, results prove that HOCCLUS2 is able to extract cohesiveness-preserving biclusters, when compared with competitive approaches, and statistically confirm (at a confidence level of 99%) that mRNAs which belong to the same biclusters are, on average, more functionally similar than mRNAs which belong to different biclusters. Finally, the hierarchy of biclusters provides useful insights to understand the intrinsic hierarchical organization of miRNAs and their potential multiple interactions on target genes.

A platform independent RNA-Seq protocol for the detection of transcriptome complexity.

BACKGROUND: Recent studies have demonstrated an unexpected complexity of transcription in eukaryotes. The majority of the genome is transcribed and only a little fraction of these transcripts is annotated as protein coding genes and their splice variants. Indeed, most transcripts are the result of antisense, overlapping and non-coding RNA expression. In this frame, one of the key aims of high throughput transcriptome sequencing is the detection of all RNA species present in the cell and the first crucial step for RNA-seq users is represented by the choice of the strategy for cDNA library construction. The protocols developed so far provide the utilization of the entire library for a single sequencing run with a specific platform. RESULTS: We set up a unique protocol to generate and amplify a strand-specific cDNA library representative of all RNA species that may be implemented with all major platforms currently available on the market (Roche 454, Illumina, ABI/SOLiD). Our method is reproducible, fast, easy-to-perform and even allows to start from low input total RNA. Furthermore, we provide a suitable bioinformatics tool for the analysis of the sequences produced following this protocol. CONCLUSION: We tested the efficiency of our strategy, showing that our method is platform-independent, thus allowing the simultaneous analysis of the same sample with different NGS technologies, and providing an accurate quantitative and qualitative portrait of complex whole transcriptomes.

Overview of data preprocessing for machine learning applications in human microbiome research.

Although metagenomic sequencing is now the preferred technique to study microbiome-host interactions, analyzing and interpreting microbiome sequencing data presents challenges primarily attributed to the statistical specificities of the data (e.g., sparse, over-dispersed, compositional, inter-variable dependency). This mini review explores preprocessing and transformation methods applied in recent human microbiome studies to address microbiome data analysis challenges. Our results indicate a limited adoption of transformation methods targeting the statistical characteristics of microbiome sequencing data. Instead, there is a prevalent usage of relative and normalization-based transformations that do not specifically account for the specific attributes of microbiome data. The information on preprocessing and transformations applied to the data before analysis was incomplete or missing in many publications, leading to reproducibility concerns, comparability issues, and questionable results. We hope this mini review will provide researchers and newcomers to the field of human microbiome research with an up-to-date point of reference for various data transformation tools and assist them in choosing the most suitable transformation method based on their research questions, objectives, and data characteristics.

Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action.

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.

Real food in enteral nutrition for chronically ill children: overview and practical clinical cases.

BACKGROUND: Many feeding strategies may be used in chronically ill children on enteral nutrition. Interest is currently growing in real food-based enteral nutrition. A new tube feeding formula with real food ingredients is currently commercially available in Europe. CASE REPORTS: By focusing on four clinical cases, this article illustrates the use of a tube feeding formula with real food ingredients in pediatric patients with various complex conditions. The formula contains a milk-based mixture of peas, green beans, peaches, carrots, and chicken, and provides 1.2 kcal/ml. It was offered under medical supervision and after full consideration of all feeding options. CONCLUSIONS: Formula choice appears to be based on clinical experience and must be individualized to patients' characteristics and needs. Real food-containing formulas seem to improve tolerance and feeding outcomes as well as promote family inclusion and mealtime engagement, but further studies are warranted.

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions.

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.

The Ketogenic Diet Increases In Vivo Glutathione Levels in Patients with Epilepsy.

The Ketogenic Diet (KD) is a high-fat, low-carbohydrate diet that has been utilized as the first line treatment for contrasting intractable epilepsy. It is responsible for the presence of ketone bodies in blood, whose neuroprotective effect has been widely shown in recent years but remains unclear. Since glutathione (GSH) is implicated in oxidation-reduction reactions, our aim was to monitor the effects of KD on GSH brain levels by means of magnetic resonance spectroscopy (MRS). MRS was acquired from 16 KD patients and seven age-matched Healthy Controls (HC). We estimated metabolite concentrations with linear combination model (LCModel), assessing differences between KD and HC with t-test. Pearson was used to investigate GHS correlations with blood serum 3-B-Hydroxybutyrate (3HB) concentrations and with number of weekly epileptic seizures. The results have shown higher levels of brain GSH for KD patients (2.5 ± 0.5 mM) compared to HC (2.0 ± 0.5 mM). Both blood serum 3HB and number of seizures did not correlate with GSH concentration. The present study showed a significant increase in GSH in the brain of epileptic children treated with KD, reproducing for the first time in humans what was previously observed in animal studies. Our results may suggest a pivotal role of GSH in the antioxidant neuroprotective effect of KD in the human brain.

Plant miRNAs Reduce Cancer Cell Proliferation by Targeting MALAT1 and NEAT1: A Beneficial Cross-Kingdom Interaction.

MicroRNAs (miRNAs) are ubiquitous regulators of gene expression, evolutionarily conserved in plants and mammals. In recent years, although a growing number of papers debate the role of plant miRNAs on human gene expression, the molecular mechanisms through which this effect is achieved are still not completely elucidated. Some evidence suggest that this interaction might be sequence specific, and in this work, we investigated this possibility by transcriptomic and bioinformatics approaches. Plant and human miRNA sequences from primary databases were collected and compared for their similarities (global or local alignments). Out of 2,588 human miRNAs, 1,606 showed a perfect match of their seed sequence with the 5' end of 3,172 plant miRNAs. Further selections were applied based on the role of the human target genes or of the miRNA in cell cycle regulation (as an oncogene, tumor suppressor, or a biomarker for prognosis, or diagnosis in cancer). Based on these criteria, 20 human miRNAs were selected as potential functional analogous of 7 plant miRNAs, which were in turn transfected in different cell lines to evaluate their effect on cell proliferation. A significant decrease was observed in colorectal carcinoma HCT116 cell line. RNA-Seq demonstrated that 446 genes were differentially expressed 72 h after transfection. Noteworthy, we demonstrated that the plant mtr-miR-5754 and gma-miR4995 directly target the tumor-associated long non-coding RNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) and nuclear paraspeckle assembly transcript 1 (NEAT1) in a sequence-specific manner. In conclusion, according to other recent discoveries, our study strengthens and expands the hypothesis that plant miRNAs can have a regulatory effect in mammals by targeting both protein-coding and non-coding RNA, thus suggesting new biotechnological applications.