AI identifies potent inducers of breast cancer stem cell differentiation based on adversarial learning from gene expression data.

Cancer stem cells (CSCs) are a subpopulation of cancer cells within tumors that exhibit stem-like properties and represent a potentially effective therapeutic target toward long-term remission by means of differentiation induction. By leveraging an artificial intelligence approach solely based on transcriptomics data, this study scored a large library of small molecules based on their predicted ability to induce differentiation in stem-like cells. In particular, a deep neural network model was trained using publicly available single-cell RNA-Seq data obtained from untreated human-induced pluripotent stem cells at various differentiation stages and subsequently utilized to screen drug-induced gene expression profiles from the Library of Integrated Network-based Cellular Signatures (LINCS) database. The challenge of adapting such different data domains was tackled by devising an adversarial learning approach that was able to effectively identify and remove domain-specific bias during the training phase. Experimental validation in MDA-MB-231 and MCF7 cells demonstrated the efficacy of five out of six tested molecules among those scored highest by the model. In particular, the efficacy of triptolide, OTS-167, quinacrine, granisetron and A-443654 offer a potential avenue for targeted therapies against breast CSCs.

Sudden Cardiac Death in the Young: State-of-the-Art Review in Molecular Autopsy.

Sudden cardiac death (SCD) is defined as unexpected death due to a cardiac cause that occurs rapidly. Despite the identification of prevention strategies, SCD remains a serious public health problem worldwide, accounting for 15-20% of all deaths, and is therefore a challenge for modern medicine, especially when it affects young people. Sudden cardiac death in young people affects the population aged ≤ 35 years, including athletes and non-athletes, and it is due to various hereditary and non-hereditary causes. After an autopsy, if the cause remains unknown, it is called sudden unexplained death, often attributable to genetic causes. In these cases, molecular autopsy-post-mortem genetic testing-is essential to facilitate diagnostic and therapeutic pathways and/or the monitoring of family members of the cases. This review aims to elaborate on cardiac disorders marked by genetic mutations, necessitating the post-mortem genetic investigation of the deceased for an accurate diagnosis in order to facilitate informed genetic counseling and to implement preventive strategies for family members of the cases.

Impact of computational approaches in the fight against COVID-19: an AI guided review of 17 000 studies.

SARS-CoV-2 caused the first severe pandemic of the digital era. Computational approaches have been ubiquitously used in an attempt to timely and effectively cope with the resulting global health crisis. In order to extensively assess such contribution, we collected, categorized and prioritized over 17 000 COVID-19-related research articles including both peer-reviewed and preprint publications that make a relevant use of computational approaches. Using machine learning methods, we identified six broad application areas i.e. Molecular Pharmacology and Biomarkers, Molecular Virology, Epidemiology, Healthcare, Clinical Medicine and Clinical Imaging. We then used our prioritization model as a guidance through an extensive, systematic review of the most relevant studies. We believe that the remarkable contribution provided by computational applications during the ongoing pandemic motivates additional efforts toward their further development and adoption, with the aim of enhancing preparedness and critical response for current and future emergencies.

Identification of therapeutic targets in osteoarthritis by combining heterogeneous transcriptional datasets, drug-induced expression profiles, and known drug-target interactions.

BACKGROUND: Osteoarthritis (OA) is a multifactorial, hypertrophic, and degenerative condition involving the whole joint and affecting a high percentage of middle-aged people. It is due to a combination of factors, although the pivotal mechanisms underlying the disease are still obscure. Moreover, current treatments are still poorly effective, and patients experience a painful and degenerative disease course. METHODS: We used an integrative approach that led us to extract a consensus signature from a meta-analysis of three different OA cohorts. We performed a network-based drug prioritization to detect the most relevant drugs targeting these genes and validated in vitro the most promising candidates. We also proposed a risk score based on a minimal set of genes to predict the OA clinical stage from RNA-Seq data. RESULTS: We derived a consensus signature of 44 genes that we validated on an independent dataset. Using network analysis, we identified Resveratrol, Tenoxicam, Benzbromarone, Pirinixic Acid, and Mesalazine as putative drugs of interest for therapeutics in OA for anti-inflammatory properties. We also derived a list of seven gene-targets validated with functional RT-qPCR assays, confirming the in silico predictions. Finally, we identified a predictive subset of genes composed of DNER, TNFSF11, THBS3, LOXL3, TSPAN2, DYSF, ASPN and HTRA1 to compute the patient's risk score. We validated this risk score on an independent dataset with a high AUC (0.875) and compared it with the same approach computed using the entire consensus signature (AUC 0.922). CONCLUSIONS: The consensus signature highlights crucial mechanisms for disease progression. Moreover, these genes were associated with several candidate drugs that could represent potential innovative therapeutics. Furthermore, the patient's risk scores can be used in clinical settings.

Altered centriolar cohesion by CEP250 and appendages impact outcome of patients with pancreatic cancer.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is one of the leading cause of cancer death worldwide. PDACs are characterized by centrosome aberrations, but whether centrosome-related genes influence patient outcomes has not been tested. METHODS: Publicly available RNA-sequencing data of patients diagnosed with PDAC were interrogated with unsupervised approaches to identify centrosome protein-encoding genes with prognostic relevance. Candidate genes were validated by immunohistochemistry and multiplex immunofluorescence in a set of clinical PDAC and normal pancreatic tissues. RESULTS: Results showed that two genes CEP250 and CEP170, involved in centrosome linker and centriolar subdistal appendages, were expressed at high levels in PDAC tissues and were correlated with prognosis of PDAC patients in independent databases. Large clustered γ-tubulin-labelled centrosomes were linked together by aberrant circular and planar-shaped CEP250 arrangements in CEP250-high expressing PDACs. Furthermore, PDACs displayed prominent centrosome separation and reduced CEP164-centrosomal labelling associated with acetylated-tubulin staining compared to normal pancreatic tissues. Interestingly, in a small validation cohort, CEP250-high expressing patients had shorter disease free- and overall-survival and almost none of those who received gemcitabine plus nab-paclitaxel first-line therapy achieved a clinical response. In contrast, weak CEP250 expression was associated with long-term survivors or responses to medical treatments. CONCLUSIONS: Alteration of the centriolar cohesion and appendages has effect on the survival of patients with PDAC.

In-depth immunophenotyping reveals significant alteration of lymphocytes in buffalo with brucellosis.

Water buffalo (Bubalus bubalis) has a prominent position in the livestock industry worldwide but still suffers from limited knowledge on the mechanisms regulating the immune against infections, including brucellosis (BRC), one of the most significant neglected zoonotic diseases of livestock. Seventy-three buffalo were recruited for the study. Thirty-five were naturally infected with Brucella spp. The aims of the study were to (i) verify the cross-reactivity of 16 monoclonal antibodies (mAbs) developed against human, bovine, and ovine antigens; (ii) evaluate lymphocyte subset alterations in BRC positive buffalo; (iii) evaluate the use of the canonical discriminant analysis (CDA), with flow cytometric data, to discriminate BRC positive from negative animals. A new set of eight mAbs (anti CD3e, CD16, CD18, CD45R0, CD79a; CD172a) were shown to cross-react with water buffalo orthologous molecules. BRC positive animals presented a significant (p < 0.0001) decrease in the percentage of PBMC (29.5 vs. 40.3), total, T and B lymphocytes (23.0 vs. 35.5, 19.2 vs. 28.9, 2.6 vs. 5.7, respectively). In contrast, they showed an increase in percentage of granulocytes (65.2 vs. 55.1; p < 0.0001) and B lymphocytes CD21neg (22.9 vs. 16.1; p = 0.0067), a higher T/B lymphocyte ratio (10.3 vs. 6.4; p = 0.0011) and CD3+ /CD21+ (14.7 vs. 8.3; p = 0.0005) ratio. The CDA, applied to 33 different flow cytometric traits, allowed the discrimination of all BRC positive from negative buffalo. Although this is a preliminary study, our results show that flow cytometry can be used in a wide range of applications in livestock diseases, including in support of uncertain BRC diagnoses.

Pharmacoproteomics pinpoints HSP70 interaction for correction of the most frequent Wilson disease-causing mutant of ATP7B.

Pathogenic mutations in the copper transporter ATP7B have been hypothesized to affect its protein interaction landscape contributing to loss of function and, thereby, to hepatic copper toxicosis in Wilson disease. Although targeting mutant interactomes was proposed as a therapeutic strategy, druggable interactors for rescue of ATP7B mutants remain elusive. Using proteomics, we found that the frequent H1069Q substitution promotes ATP7B interaction with HSP70, thus accelerating endoplasmic reticulum (ER) degradation of the mutant protein and consequent copper accumulation in hepatic cells. This prompted us to use an HSP70 inhibitor as bait in a bioinformatics search for structurally similar Food and Drug Administration-approved drugs. Among the hits, domperidone emerged as an effective corrector that recovered trafficking and function of ATP7B-H1069Q by impairing its exposure to the HSP70 proteostatic network. Our findings suggest that HSP70-mediated degradation can be safely targeted with domperidone to rescue ER-retained ATP7B mutants and, hence, to counter the onset of Wilson disease.

Computer-aided drug repurposing for cancer therapy: Approaches and opportunities to challenge anticancer targets.

Despite huge efforts made in academic and pharmaceutical worldwide research, current anticancer therapies achieve effective treatment in a limited number of neoplasia cases only. Oncology terms such as big killers - to identify tumours with yet a high mortality rate - or undruggable cancer targets, and chemoresistance, represent the current therapeutic debacle of cancer treatments. In addition, metastases, tumour microenvironments, tumour heterogeneity, metabolic adaptations, and immunotherapy resistance are essential features controlling tumour response to therapies, but still, lack effective therapeutics or modulators. In this scenario, where the pharmaceutical productivity and drug efficacy in oncology seem to have reached a plateau, the so-called drug repurposing - i.e. the use of old drugs, already in clinical use, for a different therapeutic indication - is an appealing strategy to improve cancer therapy. Opportunities for drug repurposing are often based on occasional observations or on time-consuming pre-clinical drug screenings that are often not hypothesis-driven. In contrast, in-silico drug repurposing is an emerging, hypothesis-driven approach that takes advantage of the use of big-data. Indeed, the extensive use of -omics technologies, improved data storage, data meaning, machine learning algorithms, and computational modeling all offer unprecedented knowledge of the biological mechanisms of cancers and drugs' modes of action, providing extensive availability for both disease-related data and drugs-related data. This offers the opportunity to generate, with time and cost-effective approaches, computational drug networks to predict, in-silico, the efficacy of approved drugs against relevant cancer targets, as well as to select better responder patients or disease' biomarkers. Here, we will review selected disease-related data together with computational tools to be exploited for the in-silico repurposing of drugs against validated targets in cancer therapies, focusing on the oncogenic signaling pathways activation in cancer. We will discuss how in-silico drug repurposing has the promise to shortly improve our arsenal of anticancer drugs and, likely, overcome certain limitations of modern cancer therapies against old and new therapeutic targets in oncology.

Do Pediatricians and Nurses Recommend Vaccines for Preterm Infants? A Survey in Italy.

OBJECTIVE: To assess the level of knowledge, attitudes, and behaviors regarding vaccination in preterm infants among primary care pediatricians (PCPs) and health care workers (HCWs) in neonatal intensive care units (NICUs). STUDY DESIGN: Data were collected from PCPs through a confidential questionnaire distributed by email, whereas the research team distributed a self-administered anonymous questionnaire to all HCWs working in the selected NICUs. RESULTS: Overall, 64.1% of HCWs consider vaccines in preterm infants to be very safe. The majority of HCWs (69.8%) stated that they always recommend that preterm infants' parents vaccinate their children following the same schedule as for term infants. This behavior was significantly more likely among those who know that the vaccination schedule for preterm infants is the same as for term infants, who consider vaccines in preterm infants very effective, and who strongly agree that preterm infants should be vaccinated on the same schedule as term infants. Moreover, PCPs were more likely than NICU pediatricians to always recommend that preterm infants' parents vaccinate their children following the immunization schedule of term infants, whereas this behavior was significantly less frequent among NICU nurses. CONCLUSIONS: There is a need for physicians and nurses in the NICU and in the community to counteract missed or delayed immunizations. Engagement of HCWs in healthcare quality improvement initiatives focused on the promotion of timely vaccinations in preterm infants should be encouraged.

Combined effects of CXCL8 (IL-8) and CXCR2 (IL-8R) gene polymorphisms on deregressed MACE EBV indexes of milk-related traits in Simmental bulls.

CXCL8 (also known as IL-8) is a member of the CXC subfamily of chemokines that binds two of the seven transmembrane G-protein-coupled receptors (GPCRs), CXCR1 and CXCR2, to mediate and regulate leucocyte accumulation and activation at sites of inflammation. They are known to play a critical role in both disease susceptibility and infection outcome. The aim of this study was to investigate the entire sequences of CXCL8 and CXCR2 genes in thirty-one Simmental sires to evaluate the effects of genomic variants on the indexes of the bulls for milk, fat and protein yields, and for somatic cell score (SCS). Five new single nucleotide polymorphisms (SNPs) were found in CXCR2 gene. The analysis of association indicated that one SNP in CXCL8 and two in CXCR2 influenced the considered traits. To evaluate the existence of functional haplotypic effects, combinations among the three genomic variants (SNP 1 in CXCL8, SNP 6 and SNP 7 in CXCR2) were investigated. Four different haplotypic alleles were identified in the experimental population, one of which at a high frequency (61%). Bulls with Hap 4 (G-C-G at SNP 1, SNP 6, and SNP 7 respectively) had more favourable indexes for SCS (P < 0.05). These results suggest that the SNPs in CXCL8 and CXCR2 may be potential genetic markers to improve udder health in the Simmental breed.

Antibiotic-Dispensing Practice in Community Pharmacies: Results of a Cross-Sectional Study in Italy.

Inappropriate use of antibiotics in the community contributes to the development of antibiotic resistance (ABR), one of the most concerning issues in modern medicine. The objectives of the study were to investigate the knowledge and attitudes regarding ABR and dispensing antibiotics without prescription (DAwP) and to assess the extent of the practice of DAwP among Italian community pharmacists (CPs). A nationwide cross-sectional study using an anonymous, structured, validated, and pilot-tested questionnaire was conducted. The five sections gathered data on demographic and professional characteristics, knowledge and attitudes toward ABR and DAwP, practices regarding dispensing antibiotics with or without prescription and their reasons, counselling on the potential antibiotic side effects and the importance of adherence to medication regimen, and the information sources used to update the knowledge about ABR. About 4 in 10 CPs (37.1%) reported being involved in DAwP, although 93.7% knew that it is illegal in Italy. The vast majority affirmed to have always/often asked clients about their drug allergies (95.5%) and about their medication history (82.5%). Two-thirds (66.2%) warned their clients about the potential side effects of the drugs, and 55% informed them about the importance of completing the full course of antibiotics. Complacency with clients who found it difficult to consult the physician was the most significant predictor of DAwP. A considerable proportion of DAwP was described, so it could be easy for patients to misuse these drugs. Future policies need to enhance the enforcement of existing prescription-only regulations and to develop monitoring strategies to ensure their establishment in real-life practices.

A Siamese neural network model for the prioritization of metabolic disorders by integrating real and simulated data.

MOTIVATION: Untargeted metabolomic approaches hold a great promise as a diagnostic tool for inborn errors of metabolisms (IEMs) in the near future. However, the complexity of the involved data makes its application difficult and time consuming. Computational approaches, such as metabolic network simulations and machine learning, could significantly help to exploit metabolomic data to aid the diagnostic process. While the former suffers from limited predictive accuracy, the latter is normally able to generalize only to IEMs for which sufficient data are available. Here, we propose a hybrid approach that exploits the best of both worlds by building a mapping between simulated and real metabolic data through a novel method based on Siamese neural networks (SNN). RESULTS: The proposed SNN model is able to perform disease prioritization for the metabolic profiles of IEM patients even for diseases that it was not trained to identify. To the best of our knowledge, this has not been attempted before. The developed model is able to significantly outperform a baseline model that relies on metabolic simulations only. The prioritization performances demonstrate the feasibility of the method, suggesting that the integration of metabolic models and data could significantly aid the IEM diagnosis process in the near future. AVAILABILITY AND IMPLEMENTATION: Metabolic datasets used in this study are publicly available from the cited sources. The original data produced in this study, including the trained models and the simulated metabolic profiles, are also publicly available (Messa et al., 2020).

Transcriptomic analysis identifies organ-specific metastasis genes and pathways across different primary sites.

BACKGROUND: Metastasis is the most devastating stage of cancer progression and often shows a preference for specific organs. METHODS: To reveal the mechanisms underlying organ-specific metastasis, we systematically analyzed gene expression profiles for three common metastasis sites across all available primary origins. A rank-based method was used to detect differentially expressed genes between metastatic tumor tissues and corresponding control tissues. For each metastasis site, the common differentially expressed genes across all primary origins were identified as organ-specific metastasis genes. RESULTS: Pathways enriched by these genes reveal an interplay between the molecular characteristics of the cancer cells and those of the target organ. Specifically, the neuroactive ligand-receptor interaction pathway and HIF-1 signaling pathway were found to have prominent roles in adapting to the target organ environment in brain and liver metastases, respectively. Finally, the identified organ-specific metastasis genes and pathways were validated using a primary breast tumor dataset. Survival and cluster analysis showed that organ-specific metastasis genes and pathways tended to be expressed uniquely by a subgroup of patients having metastasis to the target organ, and were associated with the clinical outcome. CONCLUSIONS: Elucidating the genes and pathways underlying organ-specific metastasis may help to identify drug targets and develop treatment strategies to benefit patients.

Novel SNPs and haplotypes identified in the CD4 gene and their influence on deregressed MACE EBV indexes of milk-related traits in Simmental breed.

Cluster of differentiation 4 (CD4) is the accessory protein non-covalently bound to the T cell receptor that recognizes an invariant region of MHC class II on antigen presenting cells. Its cytoplasmic tail, physically associated with a protein tyrosine kinase, is important in the activation of helper/inducer T lymphocytes. In Bos taurus, CD4 gene is located on chromosome 5 from which two isoforms are transcribed, with a different number of amino acids due to splicing of exon 7 and variation in the reading frame. The aim of this study was to investigate the sequence of the entire CD4 gene in Simmental sires to evaluate the effects of genomic variants on the indexes of the bulls for milk, fat and protein yields, as well as somatic cell score. The associations among genomic variants and indexes were analysed using the Allele and GLM procedures of SAS 9.4. The analysis indicated that only four of the thirty-one identified SNPs influenced the considered traits. Identified variants insist on coding zones and intronic sequences, where we revealed the presence of sites for transcription factors. To evaluate the existence of haplotypic effects, combinations among the four genomic variants (SNP 3, SNP 8, SNP 11 and SNP 19) were investigated. Six different haplotypic alleles were identified, but only four of them were frequent enough to allow for an evaluation of any haplotypic effect (at least six copies in the examined sample): Hap1, Hap2, Hap3 and Hap6. The analysis of associations between the selected haplotypes in the CD4 gene with milk related indexes showed that bulls with Hap2 (T-A-C-C) had better indexes for milk and protein yields (P < 0.05), whereas the presence of the Hap1 haplotype (A-G-A-T) caused a significant decrease of the index for protein yield (P < 0.05). Frequencies of the two alleles Hap1 and Hap2 (9 and 36% respectively) make them of interest for their possible inclusion in breeding schemes and support the hypothesis of considering this gene as a candidate for the improvement of milk-related traits in the Simmental breed.

Involvement of the Protein Ras Homolog Enriched in the Striatum, Rhes, in Dopaminergic Neurons' Degeneration: Link to Parkinson's Disease.

Rhes is one of the most interesting genes regulated by thyroid hormones that, through the inhibition of the striatal cAMP/PKA pathway, acts as a modulator of dopamine neurotransmission. Rhes mRNA is expressed at high levels in the dorsal striatum, with a medial-to-lateral expression gradient reflecting that of both dopamine D2 and adenosine A2A receptors. Rhes transcript is also present in the hippocampus, cerebral cortex, olfactory tubercle and bulb, substantia nigra pars compacta (SNc) and ventral tegmental area of the rodent brain. In line with Rhes-dependent regulation of dopaminergic transmission, data showed that lack of Rhes enhanced cocaine- and amphetamine-induced motor stimulation in mice. Previous studies showed that pharmacological depletion of dopamine significantly reduces Rhes mRNA levels in rodents, non-human primates and Parkinson's disease (PD) patients, suggesting a link between dopaminergic innervation and physiological Rhes mRNA expression. Rhes protein binds to and activates striatal mTORC1, and modulates L-DOPA-induced dyskinesia in PD rodent models. Finally, Rhes is involved in the survival of mouse midbrain dopaminergic neurons of SNc, thus pointing towards a Rhes-dependent modulation of autophagy and mitophagy processes, and encouraging further investigations about mechanisms underlying dysfunctions of the nigrostriatal system.

gene2drug: a computational tool for pathway-based rational drug repositioning.

Motivation: Drug repositioning has been proposed as an effective shortcut to drug discovery. The availability of large collections of transcriptional responses to drugs enables computational approaches to drug repositioning directly based on measured molecular effects. Results: We introduce a novel computational methodology for rational drug repositioning, which exploits the transcriptional responses following treatment with small molecule. Specifically, given a therapeutic target gene, a prioritization of potential effective drugs is obtained by assessing their impact on the transcription of genes in the pathway(s) including the target. We performed in silico validation and comparison with a state-of-art technique based on similar principles. We next performed experimental validation in two different real-case drug repositioning scenarios: (i) upregulation of the glutamate-pyruvate transaminase (GPT), which has been shown to induce reduction of oxalate levels in a mouse model of primary hyperoxaluria, and (ii) activation of the transcription factor TFEB, a master regulator of lysosomal biogenesis and autophagy, whose modulation may be beneficial in neurodegenerative disorders. Availability and implementation: A web tool for Gene2drug is freely available at http://gene2drug.tigem.it. An R package is under development and can be obtained from https://github.com/franapoli/gep2pep. Contact: dibernardo@tigem.it. Supplementary information: Supplementary data are available at Bioinformatics online.

Striatal spreading depolarization: Possible implication in levodopa-induced dyskinetic-like behavior.

OBJECTIVE: Spreading depolarization (SD) is a transient self-propagating wave of neuronal and glial depolarization coupled with large membrane ionic changes and a subsequent depression of neuronal activity. Spreading depolarization in the cortex is implicated in migraine, stroke, and epilepsy. Conversely, spreading depolarization in the striatum, a brain structure deeply involved in motor control and in Parkinson's disease (PD) pathophysiology, has been poorly investigated. METHODS: We characterized the participation of glutamatergic and dopaminergic transmission in the induction of striatal spreading depolarization by using a novel approach combining optical imaging, measurements of endogenous DA levels, and pharmacological and molecular analyses. RESULTS: We found that striatal spreading depolarization requires the concomitant activation of D1-like DA and N-methyl-d-aspartate receptors, and it is reduced in experimental PD. Chronic l-dopa treatment, inducing dyskinesia in the parkinsonian condition, increases the occurrence and speed of propagation of striatal spreading depolarization, which has a direct impact on one of the signaling pathways downstream from the activation of D1 receptors. CONCLUSION: Striatal spreading depolarization might contribute to abnormal basal ganglia activity in the dyskinetic condition and represents a possible therapeutic target. © 2019 International Parkinson and Movement Disorder Society.

Lack of Rhes Increases MDMA-Induced Neuroinflammation and Dopamine Neuron Degeneration: Role of Gender and Age.

Ras homolog enriched in striatum (Rhes) is a protein that exerts important physiological functions and modulates psychostimulant drug effects. On this basis, the object of this study was to assess 3,4-methylenedioxymethamphetamine (MDMA) effects on microglial (CD11b) and astroglial (GFAP) activation and on dopamine neuron degeneration (TH) in wild-type (WT) and Rhes knockout (KO) male and female mice of different ages. Motor activity was also evaluated. Adult (3 months) MDMA-treated mice displayed an increase in GFAP-positive cells in striatum (STR), whereas the substantia nigra pars compacta (SNc) was affected only in male mice. In these mice, the increase of CD11b was more extensive including STR, SNc, motor cortex (CTX), ventral tegmental area (VTA), and nucleus accumbens (NAc). MDMA administration also affected TH immunoreactivity in both STR and SNc of male but not female WT and Rhes KO mice. In middle-aged mice (12 months), MDMA administration further increased GFAP and CD11b and decreased TH immunoreactivity in STR and SNc of all mice. Finally, MDMA induced a higher increase of motor activity in adult Rhes KO male, but not female mice. The results show that Rhes protein plays an important role on MDMA-mediated neuroinflammation and neurodegeneration dependent on gender and age, and confirm the important role of Rhes protein in neuroinflammatory and neurodegenerative processes.

Evaluation of hospital readmissions for surgical site infections in Italy.

Background: The objectives of this investigation are to assess the prevalence of hospital readmissions for surgical site infections (SSIs) in patients aged ≥18 in Italy and to describe the clinical characteristics of these patients and evaluate the possible association with readmission for SSIs. Methods: A retrospective epidemiological study was conducted between January and May 2015 considering a sample of patients aged ≥18 years admitted to the surgical wards of two hospitals in Naples and undergoing surgery in the year 2014. Results: 3.8% of patients had been readmitted and 28.8% of them were readmitted to hospital due to SSIs. The multiple logistic regression model showed that readmissions for SSIs were significantly more common in smokers (odds ratio [OR] = 3.14; 95% confidence interval [CI] = 1.13-8.69), in patients with immunosuppression status (OR = 8.28; 95% CI = 1.76-38.87), in patients with low serum albumin (OR = 3.07; 95% CI = 1.05-9.01) and in patients who had undergone a surgical procedure classified as contaminated (OR = 10:44; 95% CI = 3.11-35.01) compared with those that had undergone a surgical procedure classified as clean. Conclusions: The results point to the need that hospital infection prevention strategies are implemented in order to reduce morbidity and mortality for patients. Moreover, the measures taken to prevent infections would lead to a reduction in health spending since almost one third of readmissions to the hospital in our study were due to SSIs.

repo: an R package for data-centered management of bioinformatic pipelines.

BACKGROUND: Reproducibility in Data Analysis research has long been a significant concern, particularly in the areas of Bioinformatics and Computational Biology. Towards the aim of developing reproducible and reusable processes, Data Analysis management tools can help giving structure and coherence to complex data flows. Nonetheless, improved software quality comes at the cost of additional design and planning effort, which may become impractical in rapidly changing development environments. I propose that an adjustment of focus from processes to data in the management of Bioinformatic pipelines may help improving reproducibility with minimal impact on preexisting development practices. RESULTS: In this paper I introduce the repo R package for bioinformatic analysis management. The tool supports a data-centered philosophy that aims at improving analysis reproducibility and reusability with minimal design overhead. The core of repo lies in its support for easy data storage, retrieval, distribution and annotation. In repo the data analysis flow is derived a posteriori from dependency annotations. CONCLUSIONS: The repo package constitutes an unobtrusive data and flow management extension of the R statistical language. Its adoption, together with good development practices, can help improving data analysis management, sharing and reproducibility, especially in the fields of Bioinformatics and Computational Biology.