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BACKGROUND: Il fallimento accademico interessa fino al 69,4% degli studenti di Infermieristica. Questo fenomeno ha costi sociali e formativi rilevanti, aumenta la carenza infermieristica e costituisce un indicatore di inefficienza dell’università. È noto che la motivazione intrinseca, l’autoefficacia percepita, il tempo di studio e la frequenza assidua alle attività didattiche influenzino il risultato accademico ma rimane da determinare quali fattori influenzino (a) quanto tempo gli studenti dedicano allo studio e (b) la frequenza alle attività didattiche. OBIETTIVO: Identificare i predittori precoci e modificabili del rendimento accademico degli studenti e individuare i fattori su cui è possibile intervenire per prevenire l’insuccesso evitabile. IPOTESI: Il rendimento accademico può essere predetto da (a) frequenza alle lezioni, (b) ore di studio e (c) tendenza ad iscriversi e sostenere gli esami. Questi fattori sono influenzati (a) dalla difficoltà percepita ad affrontare gli impegni didattici, (b) dall’importanza attribuita a diventare infermieri e dall’aspettativa di riuscirci. METODOLOGIA: Studio longitudinale multicentrico quantitativo: le analisi verificheranno se la difficoltà percepita predica, secondo un andamento quadratico, (a) la frequenza alle lezioni e (b) l’approccio agli esami, e se l’importanza attribuita a diventare infermieri predica in modo positivo e lineare il tempo di studio giornaliero. Inoltre, se la frequenza alle attività didattiche, l’approccio agli esami e il tempo di studio giornaliero predicano secondo una relazione positiva il rendimento accademico. RISULTATI ATTESI E IMPLICAZIONI PER LA PRATICA: Creare un modello predittivo per identificare precocemente gli studenti a rischio elevato d’insuccesso e identificare i fattori modificabili su cui intervenire per ridurre il numero di studenti ripetenti o rinunciatari.

Abstract Background Gene expression is the result of the balance between transcription and degradation. Recent experimental findings have shown fine and specific regulation of RNA degradation and the presence of various molecular machinery purposely devoted to this task, such as RNA binding proteins, non-coding RNAs, etc. A biological process can be studied by measuring time-courses of RNA abundance in response of internal and/or external stimuli, using recent technologies, such as the microarrays or the Next Generation Sequencing devices. Unfortunately, the picture provided by looking only at the transcriptome abundance may not gain insight into its dynamic regulation. By contrast, independent simultaneous measurement of RNA expression and half-lives could provide such valuable additional insight. A computational approach to the estimation of RNAs half-lives from RNA expression time profiles data, can be a low-cost alternative to its experimental measurement which may be also affected by various artifacts. Results Here we present a computational methodology, called StaRTrEK (STAbility Rates ThRough Expression Kinetics), able to estimate half-life values basing only on genome-wide gene expression time series without transcriptional inhibition. The StaRTrEK algorithm makes use of a simple first order kinetic model and of a $$l_1$$ l 1 -norm regularized least square optimization approach to find its parameter values. Estimates provided by StaRTrEK are validated using simulated data and three independent experimental datasets of two short (6 samples) and one long (48 samples) time-courses. Conclusions We believe that our algorithm can be used as a fast valuable computational complement to time-course experimental gene expression studies by adding a relevant kinetic property, i.e. the RNA half-life, with a strong biological interpretation, thus providing a dynamic picture of what is going in a cell during the biological process under study.

Abstract Despite advances in modern medicine that led to improvements in cardiovascular outcomes, cardiovascular disease (CVD) remains the leading cause of mortality and morbidity globally. Thus, there is an urgent need for new approaches to improve CVD drug treatments. As the development time and cost of drug discovery to clinical application are excessive, alternate strategies for drug development are warranted. Among these are included computational approaches based on omics data for drug repositioning, which have attracted increasing attention. In this work, we developed an adjusted similarity measure implemented by the algorithm SAveRUNNER to reposition drugs for cardiovascular diseases while, at the same time, considering the side effects of drug candidates. We analyzed nine cardiovascular disorders and two side effects. We formulated both disease disorders and side effects as network modules in the human interactome, and considered those drug candidates that are proximal to disease modules but far from side-effects modules as ideal. Our method provides a list of drug candidates for cardiovascular diseases that are unlikely to produce common, adverse side-effects. This approach incorporating side effects is applicable to other diseases, as well.

Hormonal contraceptives (HCs) have been shown to be safe and effective when used correctly and consistently, however, as other classes of drugs, they are also associated with adverse health outcomes. In this study, we aim to explain the occurrence of common and unexpected HCs side effects (SEs) integrating drug-target, drug-SE and protein-protein interaction (PPI) public databases. We created a tripartite network that includes three types of vertices: SEs, drugs, and targets. The three layers are linked by means of the inter-layer associations drug-target and drug-SE, whereas only the target layer is characterized also by intra-layer links (PPIs). We exploited the drug-mediated association SE-target to identify the side effect modules defined as a network connected component composed of target proteins plus the proteins needed to connect them. We found that module proteins are associated with diseases/phenotypes and/or KEGG pathways related to the SEs. In particular, in many cases, targets are not enriched in SE features, whereas investigating their neighborhood (here defined as the proteins that allow the targets' connection) we found SE-related pathways. These results show that HCs action can perturb the targets’ neighborhood inducing unwanted reaction and that the proposed approach can help to understand how, and through which molecular mechanisms, side effects can occur. The approach is general in its nature: it can be applied to other drugs categories providing a support in identifying a subject-specific therapy that takes into account comorbidities and lifestyle to reduce or avoid the most undesired side effects.

Abstract Cancer stem-like cells (CSCs) have self-renewal abilities responsible for cancer progression, therapy resistance, and metastatic growth. The glioblastoma stem-like cells are the most studied among CSC populations. A recent study identified four transcription factors (SOX2, SALL2, OLIG2, and POU3F2) as the minimal core sufficient to reprogram differentiated glioblastoma (GBM) cells into stem-like cells. Transcriptomic data of GBM tissues and cell lines from two different datasets were then analyzed by the SWItch Miner (SWIM), a network-based software, and FOSL1 was identified as a putative regulator of the previously identified minimal core. Herein, we selected NTERA-2 and HEK293T cells to perform an in vitro study to investigate the role of FOSL1 in the reprogramming mechanisms. We transfected the two cell lines with a constitutive FOSL1 cDNA plasmid. We demonstrated that FOSL1 directly regulates the four transcription factors binding their promoter regions, is involved in the deregulation of several stemness markers, and reduces the cells’ ability to generate aggregates increasing the extracellular matrix component FN1. Although further experiments are necessary, our data suggest that FOSL1 reprograms the stemness by regulating the core of the four transcription factors.

Systems precision medicine is an interdisciplinary approach that recognises the complexity of diseases and emphasises the integration of clinical knowledge, multi-omics data, analytical models, and the expertise of physicians and data analysts to personalise the care pathway in complex diseases, such as cancer or diabetes. The aim is to gain a comprehensive understanding of diseases by analysing individual components and identifying relevant aspects for therapy and diagnosis. Key components, their interactions and emerging patterns can be studied using statistical, mathematical and computational tools. The combination of data analysis and clinical evaluation is crucial to effective decision-making, emphasising the need for an integrative approach rather than relying on data alone. Therefore, the crucial point discussed in this paper is that the “computational” part and the “artistic” part (i.e., the physician’s intuition) cannot be separated, and therefore, systems precision medicine can be configured as a collective work of art, involving not only different medical professionals but also, and above all, professional data analysts. The work is “artistic” because data and mathematics alone, without medical knowledge of the context, are not enough. But the work is also “collective” in the sense that it must be the place of cultural integration between the professional intuition of the physician, which cannot be translated into mathematical formulas, and the ability to extract information from multi-omics data of the data analysts, who instead use formal and computational mathematical methods. However, to drive the medical revolution and reassemble a patient’s parts, data analysts need to be involved in the hospital context, and precision medicine physicians should embrace data analytical perspectives. This will require ongoing dialogue, new languages of communication, and education that promotes continuous learning and collaboration between professions, fostering a new level of interdisciplinary collaboration for personalised care.

Abstract In this study, we integrate the outcomes of co-expression network analysis with the human interactome network to predict novel putative disease genes and modules. We first apply the SWItch Miner (SWIM) methodology, which predicts important (switch) genes within the co-expression network that regulate disease state transitions, then map them to the human protein–protein interaction network (PPI, or interactome) to predict novel disease–disease relationships (i.e., a SWIM-informed diseasome). Although the relevance of switch genes to an observed phenotype has been recently assessed, their performance at the system or network level constitutes a new, potentially fascinating territory yet to be explored. Quantifying the interplay between switch genes and human diseases in the interactome network, we found that switch genes associated with specific disorders are closer to each other than to other nodes in the network, and tend to form localized connected subnetworks. These subnetworks overlap between similar diseases and are situated in different neighborhoods for pathologically distinct phenotypes, consistent with the well-known topological proximity property of disease genes. These findings allow us to demonstrate how SWIM-based correlation network analysis can serve as a useful tool for efficient screening of potentially new disease gene associations. When integrated with an interactome-based network analysis, it not only identifies novel candidate disease genes, but also may offer testable hypotheses by which to elucidate the molecular underpinnings of human disease and reveal commonalities between seemingly unrelated diseases.

The MRI of the prostate is the gold standard for the detection of clinically significant prostate cancer (csPCa). Nonetheless, MRI still misses around 11% of clinically significant disease. The aim was to comprehensively integrate tissue and circulating microRNA profiling, MRI biomarkers and clinical data to implement PCa early detection. In this prospective cohort study, 76 biopsy naïve patients underwent MRI and MRI directed biopsy. A sentinel sample of 15 patients was selected for a pilot molecular analysis. Weighted gene coexpression network analysis was applied to identify the microRNAs drivers of csPCa. MicroRNA–target gene interaction maps were constructed, and enrichment analysis performed. The ANOVA on ranks test and ROC analysis were performed for statistics. Disease status was associated with the underexpression of the miRNA profiled; a correlation was found with ADC (r = −0.51, p = 0.02) and normalized ADC values (r = −0.64, p = 0.002). The overexpression of miRNAs from plasma was associated with csPCa (r = 0.72; p = 0.02), and with PI-RADS assessment score (r = 0.73; p = 0.02); a linear correlation was found with biomarkers of diffusion and perfusion. Among the 800 profiled microRNA, eleven were identified as correlating with PCa, among which miR-548a-3p, miR-138-5p and miR-520d-3p were confirmed using the RT-qPCR approach on an additional cohort of ten subjects. ROC analysis showed an accuracy of >90%. Provided an additional validation set of the identified miRNAs on a larger cohort, we propose a diagnostic paradigm shift that sees molecular data and MRI biomarkers as the prebiopsy triage of patients at risk for PCa. This approach will allow for accurate patient allocation to biopsy, and for stratification into risk group categories, reducing overdiagnosis and overtreatment.

Recent findings have identified competing endogenous RNAs (ceRNAs) as the drivers in many disease conditions, including cancers. The ceRNAs indirectly regulate each other by reducing the amount of microRNAs (miRNAs) available to target messenger RNAs (mRNAs). The ceRNA interactions mediated by miRNAs are modulated by a titration mechanism, i.e. large changes in the ceRNA expression levels either overcome, or relieve, the miRNA repression on competing RNAs; similarly, a very large miRNA overexpression may abolish competition. The ceRNAs are also called miRNA "decoys" or miRNA "sponges" and encompass different RNAs competing with each other to attract miRNAs for interactions: mRNA, long non-coding RNAs (lncRNAs), pseudogenes, or circular RNAs. Recently, we developed a computational method for identifying ceRNA-ceRNA interactions in breast invasive carcinoma. We were interested in unveiling which lncRNAs could exert the ceRNA activity. We found a drastic rewiring in the cross-talks between ceRNAs from the physiological to the pathological condition. The main actor of this dysregulated lncRNA-associated ceRNA network was the lncRNA PVT1, which revealed a net biding preference towards the miR-200 family members in normal breast tissues. Despite its up-regulation in breast cancer tissues, mimicked by the miR-200 family members, PVT1 stops working as ceRNA in the cancerous state. The specific conditions required for a ceRNA landscape to occur are still far from being determined. Here, we emphasized the importance of the relative concentration of the ceRNAs, and their related miRNAs. In particular, we focused on the withdrawal in breast cancer tissues of the PVT1 ceRNA activity and performed a gene expression and sequence analysis of its multiple isoforms. We found that the PVT1 isoform harbouring the binding site for a representative miRNA of the miR-200 family shows a drastic decrease in its relative concentration with respect to the miRNA abundance in breast cancer tissues, providing a plausibility argument to the breakdown of the sponge program orchestrated by the oncogene PVT1.