Application of Supply Chain Management at Drugs Flow in an Italian Hospital District

The globalization has pushed to change the organization of every companies, even the hospitals. The principal phenomenon in that period and fundamental today again, has been the Supply Chain Management (SCM), with which the company is no longer seen as an isolated entity but active part in an extremely complex supply network. In fact, the only way to guarantee the competitiveness of businesses in the new world economy is through the cooperation and the integration between customers and suppliers. The present work analyses the drugs flow of three Italian hospital: the Cardarelli Hospital in Campobasso, the Veneziale located in Isernia and the San Timoteo site in Termoli. The data was provided by MOLISE DATA SPA that collected the information from all ASREM with particular interest in the already mentioned hospitals. Particularly, will be highlight, using simulation model, the benefits deriving from the implementation of a new Supply Chain, creating a collaboration along the entire logistics production chain. Thanks to a more efficient management of drugs will get a reduction of business costs and an improvement of the health services offered

Spectral Gap Superposition States

This work introduces a novel NISQ-friendly procedure for estimating spectral gaps in quantum systems. By leveraging Adiabatic Thermalization, we are able to create the Spectral Gap Superposition state, a newly defined quantum state exhibiting observable fluctuations in time that allow for the accurate estimation of any energy gap. Our method is tested by estimating the energy gap between the ground and the first excited state for the 1D and 2D Ising model, the Hydrogen molecule H2 and Helium molecule He2. Despite limiting our circuit design to have at most 40 Trotter steps, our numerical experiments of both noiseless and noisy devices for the presented systems give relative errors in the order of $10^{-2}$ and $10^{-1}$. Further experiments on the IonQ Aria device lead to spectral gap estimations with a relative error of $10^{-2}$ for a 4-site Ising chain, demonstrating the validity of the procedure for NISQ devices and charting a path towards a new way of calculating energy gaps

Unsupervised Algorithms for Microarray Sample Stratification

The amount of data made available by microarrays gives researchers the opportunity to delve into the complexity of biological systems. However, the noisy and extremely high-dimensional nature of this kind of data poses significant challenges. Microarrays allow for the parallel measurement of thousands of molecular objects spanning different layers of interactions. In order to be able to discover hidden patterns, the most disparate analytical techniques have been proposed. Here, we describe the basic methodologies to approach the analysis of microarray datasets that focus on the task of (sub)group discovery.Peer reviewe

Multiple regression model to analyze the total LOS for patients undergoing laparoscopic appendectomy

The rapid growth in the complexity of services and stringent quality requirements present a challenge to all healthcare facilities, especially from an economic perspective. The goal is to implement different strategies that allows to enhance and obtain health processes closer to standards. The Length Of Stay (LOS) is a very useful parameter for the management of services within the hospital and is an index evaluated for the management of costs. In fact, a patient's LOS can be affected by a number of factors, including their particular condition, medical history, or medical needs. To reduce and better manage the LOS it is necessary to be able to predict this value

Electrical release of dopamine and levodopa mediated by amphiphilic \u3b2-cyclodextrins immobilized on polycrystalline gold

Vesicles of cationic amphiphilic \u3b2-cyclodextrins have been immobilized on polycrystalline gold by exploiting the chemical affinity between their amino groups and Au atoms. The presence of cyclodextrins has been widely investigated by means of AFM, XPS, kelvin probe and electrochemical measurements. This multi-functional coating confers distinct electrochemical features such as pH-dependent behavior and partial/total blocking properties towards electro-active species. The host-guest properties of \u3b2-cyclodextrins have been successfully exploited in order to trap drugs, like dopamine and levodopa. The further release of these drugs was successfully achieved by providing specific electrical stimuli. This proof-of-concept led us to fabricate an electronic device (i.e. an organic transistor) capable of dispensing both dopamine and levodopa in aqueous solution

INfORM : Inference of NetwOrk Response Modules

The Summary: Detecting and interpreting responsive modules from gene expression data by using network-based approaches is a common but laborious task. It often requires the application of several computational methods implemented in different software packages, forcing biologists to compile complex analytical pipelines. Here we introduce INfORM (Inference of NetwOrk Response Modules), an R shiny application that enables non-expert users to detect, evaluate and select gene modules with high statistical and biological significance. INfORM is a comprehensive tool for the identification of biologically meaningful response modules from consensus gene networks inferred by using multiple algorithms. It is accessible through an intuitive graphical user interface allowing for a level of abstraction from the computational steps.Peer reviewe

Toxicogenomics analysis of dynamic dose-response in macrophages highlights molecular alterations relevant for multi-walled carbon nanotube-induced lung fibrosis

Toxicogenomics approaches are increasingly used to gain mechanistic insight into the toxicity of engineered nanomaterials (ENMs). These emerging technologies have been shown to aid the translation of in vitro experimentation into relevant information on real-life exposures. Furthermore, integrating multiple layers of molecular alteration can provide a broader understanding of the toxicological insult. While there is growing evidence of the immunotoxic effects of several ENMs, the mechanisms are less characterized, and the dynamics of the molecular adaptation of the immune cells are still largely unknown. Here, we hypothesized that a multi-omics investigation of dynamic dose-dependent (DDD) molecular alterations could be used to retrieve relevant information concerning possible long-term consequences of the exposure. To this end, we applied this approach on a model of human macrophages to investigate the effects of rigid multi-walled carbon nanotubes (rCNTs). THP-1 macrophages were exposed to increasing concentrations of rCNTs and the genome-wide transcription and gene promoter methylation were assessed at three consecutive time points. The results suggest dynamic molecular adaptation with a rapid response in the gene expression and contribution of DNA methylation in the long-term adaptation. Moreover, our analytical approach is able to highlight patterns of molecular alteration in vitro that are relevant for the pathogenesis of pulmonary fibrosis, a known long-term effect of rCNTs exposure in vivo.Peer reviewe

Microarray Data Preprocessing: From Experimental Design to Differential Analysis

DNA microarray data preprocessing is of utmost importance in the analytical path starting from the experimental design and leading to a reliable biological interpretation. In fact, when all relevant aspects regarding the experimental plan have been considered, the following steps from data quality check to differential analysis will lead to robust, trustworthy results. In this chapter, all the relevant aspects and considerations about microarray preprocessing will be discussed. Preprocessing steps are organized in an orderly manner, from experimental design to quality check and batch effect removal, including the most common visualization methods. Furthermore, we will discuss data representation and differential testing methods with a focus on the most common microarray technologies, such as gene expression and DNA methylation.Peer reviewe

Cross-cultural adaptation and psychometric evaluation of the Juvenile Arthritis Multidimensional Assessment Report (JAMAR) in 54 languages across 52 countries: review of the general methodology

The aim of this project was to cross-culturally adapt and validate the Juvenile Arthritis Multidimensional Assessment Report (JAMAR) questionnaire in 54 languages across 52 different countries that are members of the Paediatric Rheumatology International Trials Organisation (PRINTO). This effort was part of a wider project named Epidemiology and Outcome of Children with Arthritis (EPOCA) to obtain information on the frequency of juvenile idiopathic arthritis (JIA) categories in different geographic areas, the therapeutic approaches adopted, and the disease status of children with JIA currently followed worldwide. A total of 13,843 subjects were enrolled from the 49 countries that took part both in the cross-cultural adaptation phase and in the related validation and data collection: Algeria, Argentina, Belgium, Brazil, Bulgaria, Canada, Chile, Colombia, Croatia, Czech Republic, Denmark, Ecuador, Egypt, Estonia, Finland, France, Georgia, Germany, Greece, Hungary, India, Islamic Republic of Iran, Israel, Italy, Latvia, Libya, Lithuania, Mexico, Netherlands, Norway, Oman, Paraguay, Poland, Portugal, Romania, Russian Federation, Saudi Arabia, Serbia, Slovakia, Slovenia, South Africa, Spain, Sweden, Switzerland, Thailand, Turkey, Ukraine, United Kingdom and United States of America. 9021 patients had JIA (10.7% systemic arthritis, 41.9% oligoarthritis, 23.5% RF negative polyarthritis, 4.2% RF positive polyarthritis, 3.4% psoriatic arthritis, 10.6% enthesitis-related arthritis and 5.7% undifferentiated arthritis) while 4822 were healthy children. This introductory paper describes the overall methodology; results pertaining to each country are fully described in the accompanying manuscripts. In conclusion, the JAMAR translations were found to have satisfactory psychometric properties and it is thus a reliable and valid tool for the multidimensional assessment of children with JIA

Integrated Network Pharmacology Approach for Drug Combination Discovery : A Multi-Cancer Case Study

Simple Summary Current treatments for complex diseases, including cancer, are generally characterized by high toxicity due to their low selectivity for target cells. Moreover, patients often develop drug resistance, hence becoming less sensitive to the therapy. For this reason, novel, improved, and more specific pharmacological therapies are needed. The high cost and the time required to develop new drugs poses the attention on the development of computational methods for drug repositioning and combination therapy prediction. In this study, we developed an integrated network pharmacology framework that combines mechanistic and chemocentric approaches in order to predict potential drug combinations for cancer therapy. We applied our paradigm in five cancer types, which we used as case studies. Our strategy can be applied to the study of any complex disease by guiding the prioritization of drug combinations. Despite remarkable efforts of computational and predictive pharmacology to improve therapeutic strategies for complex diseases, only in a few cases have the predictions been eventually employed in the clinics. One of the reasons behind this drawback is that current predictive approaches are based only on the integration of molecular perturbation of a certain disease with drug sensitivity signatures, neglecting intrinsic properties of the drugs. Here we integrate mechanistic and chemocentric approaches to drug repositioning by developing an innovative network pharmacology strategy. We developed a multilayer network-based computational framework integrating perturbational signatures of the disease as well as intrinsic characteristics of the drugs, such as their mechanism of action and chemical structure. We present five case studies carried out on public data from The Cancer Genome Atlas, including invasive breast cancer, colon adenocarcinoma, lung squamous cell carcinoma, hepatocellular carcinoma and prostate adenocarcinoma. Our results highlight paclitaxel as a suitable drug for combination therapy for many of the considered cancer types. In addition, several non-cancer-related genes representing unusual drug targets were identified as potential candidates for pharmacological treatment of cancer.Peer reviewe