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In recent years, hospitals increasingly faced with a growing proportion of their inpatient admissions coming from the fluctuating demand of emergency admissions. The opportunity to move emergency patients, with a decision to admit, out of an Emergency Department (ED) is linked to the ability of the hospital to actually receive them. Indeed, the growing concern on public budget constraints implies reducing the number of inpatient ward beds making crucial to improve the bed capacity planning. The attention must be focused on avoiding system bottlenecks such as the boarding in the ED of emergent patients waiting to be admitted into inpatient hospital wards. Bed management is considered a critical function in managing bed capacity and smoothing elective and emergent patient flows. In order to support the bed management function the clustering and provisional analysis of patient flows data are needed. In this work, we use an unsupervised neural network technique, namely Self Organizing Maps (SOMs), to explore input data and to extract significant patterns. A large quantity of data records has been collected over a yearly period to obtain information related to the arrivals of emergent patients in a medium-sized ED located in the city of Genova. The aim of the paper is twofold. Our first goal is to develop a new framework based on SOMs for the analysis of healthcare data that include heterogeneous information. Second, we give a seasonal connotation to the analysed data, as the SOM can discover clusters and patient profiles that can be used to support bed capacity planning

Understanding individual capability to adjust to protracted confinement and isolation may inform adaptive plasticity and disease vulnerability/resilience, and may have long-term implications for operations requiring prolonged presence in distant and restricted environments. Individual coping depends on many different factors encompassing psychological dispositional traits, endocrine reactivity and their underlying molecular mechanisms (e.g. gene expression). A positive view of self and others (secure attachment style) has been proposed to promote individual resilience under extreme environmental conditions. Here, we tested this hypothesis and investigated the underlying molecular mechanisms in 13 healthy volunteers confined and isolated for 12 months in a research station located 1670 km away from the south geographic pole on the Antarctic Plateau at 3233 m above sea level. Study participants, stratified for attachment style, were characterised longitudinally (before, during and after confinement) for their psychological appraisal of the stressful nature of the expedition, diurnal fluctuations in endocrine stress reactivity, and gene expression profiling (transcriptomics). Predictably, a secure attachment style was associated with reduced psychological distress and endocrine vulnerability to stress. In addition, while prolonged confinement and isolation remarkably altered overall patterns of gene expression, such alteration was largely reduced in individuals characterised by a secure attachment style. Furthermore, increased resilience was associated with a reduced expression of genes involved in energy metabolism (mitochondrial function and oxidative phosphorylation). Ultimately, our data indicate that a secure attachment style may favour individual resilience in extreme environments and that such resilience can be mapped onto identifiable molecular substrates.

The classical microbiological method for detection of Listeria monocytogenes requires around 7 days for final confirmation, and due to perishable nature of RTE food products, there is a clear need for an alternative methodology for detection of this pathogen. This study presents an international (at European level) ISO 16140-based validation trial of a non-proprietary real-time PCR-based methodology that can generate final results in the following day of the analysis. This methodology is based on an ISO compatible enrichment coupled to a bacterial DNA extraction and a consolidated real-time PCR assay. Twelve laboratories from six European countries participated in this trial, and soft cheese was selected as food model since it can represent a difficult matrix for the bacterial DNA extraction and real-time PCR amplification. The limit of detection observed was down to 10 CFU per 25 of sample, showing excellent concordance and accordance values between samples and laboratories (>75%). In addition, excellent values were obtained for relative accuracy, specificity and sensitivity (82.75%, 96.70% and 97.62%, respectively) when the results obtained for the real-time PCR-based methods were compared to those of the ISO 11290-1 standard method. An interesting observation was that the L. monocytogenes detection by the real-time PCR method was less affected in the presence of Listeria innocua in the contaminated samples, proving therefore to be more reliable than the reference method. The results of this international trial demonstrate that the evaluated real-time PCR-based method represents an excellent alterative to the ISO standard since it shows a higher performance as well as reduce the extent of the analytical process, and can be easily implemented routinely by the competent authorities and food industry laboratories.

The recent technological advances have led to the availability of a huge amount of biomedical data, that are heterogeneous, complex, and distributed in biological databases. In particular, in neurodegenerative diseases area, last years have witnessed the increasing of specialized databases such as Alzheimer's Disease Neuroimaging Initiative (ADNI) [1], which covers psychometric tests, biospecimen, imaging, and laboratory results. Analyzing these data is a challenging task and machine learning (ML) may offer methods and tools for knowledge discovery from them. However, ADNI suffers from a scarce conceptualization behind the collected data, which prevents a fully intuitive access to the data themselves and a direct analysis through ML methods. This fact suggests that a better semantic description of the domain must be proposed. Therefore, in order to take advantage of this big data repository, we are working on two directions: 1) develop a detailed ontology to give a more conceptual organization to the data, to ease data access and interpretation, and to facilitate data integration approaches with other data sources; 2) apply logic data mining methodologies to extract knowledge and generate probabilistic diagnostic models from the ontology, in order to classify patients into disease categories.

The recent advances in biotechnology and IT have led to an ever-increasing availability of public biomedical data distributed in large databases. Analyzing this huge volume of data is a challenging task because of its complexity, high heterogeneity and its multiple and numerous correlated factors. In the framework of neurodegenerative diseases, the last years have witnessed the creation of specialized databases such as the international projects ADNI (Alzheimer's Disease Neuroimaging Initiative). The main problems to fully exploit this database are related to the querying, integration, and analysis of data themselves. Here, we aim to develop a detailed ontology for clinical multidimensional datasets from ADNI repository in order to simplify the data access and to obtain new diagnostic knowledge about Alzheimer's Disease.

Low sensitivity and specificity of current diagnostic methodologies lead to frequent misdiagnosis of Alzheimer’s and other dementia, causing an extra economic and social burden. We aim to compare real word data with the largest public databases, to extract new diagnostic models for an earlier and more accurate diagnosis of cognitive impairment. We analyzed both neuropsychological, neurological, physical assessments and transcriptomic data from biosamples. We used Machine Learning approaches and biostatistical methods to analyze the transcriptome from the large-scale ADNI and AddNeuroMed international projects: we selected some genes as potential transcriptomic biomarkers and highlighted affected cellular processes. Furthermore the analysis, by machine learning, of real-world data provided by European clinical dementia centres, resulted in a small subset of comorbidities able to discriminate diagnostic classes with a good classifier performance.

This paper deals with the problem of patient boarding in Emergency Department (ED) due to the lack of availability of stay beds in inpatient hospital wards. The boarding of emergent patients is a major reason of ED overcrowding, which in turn creates long waiting times in ED, as well as patient and staff dissatisfaction. In this paper a hybrid simulation framework is proposed to describe how emergent and elective patients flows interact each other inside the hospital. The framework combines System Dynamics (SD) and Discrete Event Simulation (DES). Flows are generated at macro level by the SD model, while in DES model they are disentangled into single entities following a detailed process-oriented pathway. The hybrid model has been then applied and validated to data from a medium-size public hospital located in Genova (Italy). Furthermore, some metrics (such as waiting times to be admitted in hospital, number of trolleys in ED, inpatient bed occupancy rates and elective patients delayed) are proposed as key indicators to assess the system performance.

This paper introduces a tool to assess the impact of organizational strategies that are intended to allocate inpatient beds amongst emergent and elective flows inside a hospital. The tool, based on a System Dynamics model, is able to reproduce the entire system and the relationship between the various flows. In the absence of corrective strategies, an exogenous increase in the rate of arrivals at the Emergency Department (e.g., in winter) can trigger a reinforcing loop increasing elective waits and further overcrowding emergency rooms. The model can be used to discover the best strategies aimed at managing bed capacity between emergent and elective flows. Some preliminary results are given in the context of a public hospital located in Genova (Italy).

The classical microbiological method for detection of Salmonella spp. requires more than five days for final confirmation, and consequently there is a need for an alternative methodology for detection of this pathogen particularly in those food categories with a short shelf-life. This study presents an international (at European level) ISO 16140-based validation study of a non-proprietary Real-Time PCR-based method that can generate final results the day following sample analysis. It is based on an ISO compatible enrichment coupled to an easy and inexpensive DNA extraction and a consolidated Real-Time PCR assay. Thirteen laboratories from seven European Countries participated to this trial, and pork meat was selected as food model. The limit of detection observed was down to 10 CFU per 25 g of sample, showing excellent concordance and accordance values between samples and laboratories (100%). In addition, excellent values were obtained for relative accuracy, specificity and sensitivity (100%) when the results obtained for the Real-Time PCR-based methods were compared to those of the ISO 6579:2002 standard method. The results of this international trial demonstrate that the evaluated Real-Time PCR-based method represents an excellent alternative to the ISO standard. In fact, it shows an equal and solid performance as well as it reduces dramatically the extent of the analytical process, and can be easily implemented routinely by the Competent Authorities and Food Industry laboratories.

The business process analysis is currently the most diffused approach to improve efficiency and reduce costs in firms. Manufacturing industries in particular widely adopt business-process definition, engineering and measurement to improve cost savings and the effectiveness of their business.

In recent years, a growing proportion of patients flowing through inpatient hospital wards come from Emergency Departments (EDs). Because of ED overcrowding and the reduction of hospital beds, it is becoming crucial to improve the management of emergent patient flows to be admitted into inpatient wards. This study evaluates the impact and potential of introducing the so-called Bed Management function in a large city’s health district. Thanks to the collaboration with the Local Health Authority of the Liguria region, an observational analysis was conducted based on data collected over a 1-year period to develop a discrete event simulation model. The model has been utilised to evaluate several bed management strategies. Two scenarios at a tactical level, i.e. the opening of a discharge room and blocking elective arrivals, have also been simulated. The effects of such scenarios have been compared with respect to a set of performance metrics, such as waiting times, misallocated patients, trolleys in EDs, and inpatient bed occupancy rates.

Helderman and Peelen (2004), described how public decision- makers were evaluating the possibility of introducing deductibles in the social health system, to provide a scheme of regulated competition among insurers and providers. Testi et al. (2012) showed how deductibles deeply modify the actual co-payment rules and therefore the prices paid by patients. Indeed, demand depends on elasticity and therefore price increases reduce demand. The impact of co-payment over the public budget and over different citizen groups should be then carefully evaluated.

The aim of this study was to optimize a Real-Time PCR protocol for a rapid detection of Listeria monocytogenes in pork meat, using reduced volumes of primary selective enrichment broth and times of incubation to decrease the cost and time for analysis. Forty-five samples of pork meat were artificially contaminated with two different levels of L. monocytogenes (1–10 CFU per sample and 10–100 CFU per sample), homogenized in three different volumes of Half Fraser Broth (1:3; 1:5 and 1:10) and incubated at 30 °C ± 1 °C for 5 h, 8 h and 24 h. The detection was conducted in parallel by Real-Time PCR and the ISO standard 11290-1 methods. L. monocytogenes was detected in all the samples after 24 h by Real-Time PCR method, also using reduced volumes of Half Fraser Broth. This represents a clear advantage as the time to final detection and the inherent costs were significantly reduced compared to the ISO reference method. All samples artificially contaminated were correctly detected also after 8 of incubation at 30 °C ± 1 °C in Half Fraser Broth and 24 h in Fraser Broth at 37 °C ± 1 °C using cultural method.

Following a Listeria monocytogenes detection in a mozzarella cheese sampled at a dairy plant in Lazio Region, further investigations have been conducted both by the competent Authority and the food business operatordairy factory (as a part of dairy factory HACCP control). In total, 90 dairy products, 7 brine and 64 environmental samples have been tested. The prevalence of Listeria monocytogenes was 24.4% in mozzarella cheese, and 9.4% in environmental samples, while brines were all negatives. Forty-seven strains of L. monocytogenes have been isolated, all belonging to 4b/4e serotype. In 12 of these, the macrorestriction profile has been determined by means of pulsed field gel electrophoresis. The profiles obtained with AscI enzyme showed a 100% similarity while those obtained with ApaI a 96.78% similarity. These characteristics of the isolated strains jointly with the production process of mozzarella cheese has allowed to hypothesise an environmental contamination.

In recent years, due to the overcrowding of Emergency Department (ED) and the growing concern in reducing the number of inpatient ward beds, it has become crucial to improve the capacity planning and control activities, which manage the patient flows from EDs to hospital wards. Bed Management has a key role in this context. This study starts by a collaboration with the Local Health Government (LHG) of the Liguria region aimed at studying the impact of supporting bed management with some operational strategies without increasing the bed capacity. A large amount of data was collected over a one-year period at public hospital in Genova and a preliminary observational analysis was conducted to get the main information about the flow of emergency and elective patients from ED to inpatient wards. A Discrete Event Simulation (DES) model has been then developed in order to represent the real system. A scenarios analysis is proposed to assess the best strategy to improve the system performance without increasing bed capacity, by simply synchronizing bed supply and demand. The model can be used as a decision support tool to optimise the use of the available resources as well as to improve the quality of the patient pathway inside the hospital.

We present JAS 2, a new Java platform which aims at providing a unique simulation tool for discrete-event simulations, including agent-based and dynamic microsimulation models. JAS 2 is not released as a self-contained stand-alone application for model development. With the aim to develop large-scale, computationally resource-intensive models, the main architectural choice of JAS 2 is to use whenever possible standard, open-source tools aready available in the software development community. This decision entails a longer learning curve for the user than a dedicated, language-specific software package, but it allows for a more professional approach to simulation modeling compared to specific proprietary environments. There are two main advantages of using an open architecture where external libraries can be added and utilized: one is the possibility to easily integrate external functions that can be added as plug-ins; the other is the possibility to freely extend and modify the functions available on the platform, possibly within a cooperative effort of the community of users. Moreover, an open source application makes it easier to share and test the models. Building on the vast number of software solutions available, JAS 2 allows the user to separate data representation and management from the implementation of processes and behavioral algorithms. It also allows the user, within an object-oriented paradigm, to model the real-life system naturally and intuitively: each category of individuals or objects that populate the model is represented by a specific class, with its own variables and methods. The main value added of the platform lies in the potential for integration with RDBMS (relational database management) tools through ad-hoc microsimulation Java libraries. The management of input data persistence layers and simulation results is performed using standard database management tools, and the platform takes care of the automatic translation of the relational model (which is typical of a database) into the object-oriented simulation model.

The aim of a biological model is to understand the integrated structure and behavior of complex biological systems as a function of the underlying molecular networks to achieve simulation and forecast of their operation. Although several approaches have been introduced to take into account structural and environment related features, relatively little attention has been given to represent the behavior of biological systems. The Abstract Biological Process (ABP) model illustrated in this paper is an object-oriented model based on UML (the standard object-oriented language). Its main objective is to bring into focus the functional aspects of the biological system under analysis., {"references":["Stein, L. \"Integrating biological databases\" Nat. Rev. Genet., 4, 337-\n345, 2003.","P. E. Midford \"Ontologies for Behavior\" BIOINFORMATICS, Vol. 20\nno. 18, 2004, pp. 3700-3701.","Rumbaugh, J., Jacobson, I. and Booch, G.: \"The Unified Modeling\nLanguage Reference Manual\"; Addison Wesley, 1999.","D.Shegogue and W.J. Zheng \"Object-oriented modeling of the SARS\ncoronavirus\" BIOINFORMATICS, Vol. 21 no. 10, 2005, pages 2502-\n2509.","B.P. Zeigler, H. Praehofer, T.G. Kim Theory of Modeling and\nSimulation, 2nd Ed., Academic press, 2000.","Chang, Liang-Cheng and Yi-Ming Tu \"Attempt to Integrate System\nDynamics and UML in Business Process Modeling\" Proceedings\nThe 23rd International Conference of the System Dynamics Society\nJuly 17-21, 2005 Boston.","Sterman, J. Business Dynamics: Systems Thinking and Modeling for a\nComplex World, McGraw Hill, 2000."]}

This paper shows how agent-based modelling technique is a suitable approach for social scientists to model complex adaptive systems, using computer as experimental environment. Unfortunately advanced tools are lacking as well as an unified language supporting its development. We present JAS, a new agent-based simulation tool, developed with the aim to improve AB models designing. We give a brief methodological introduction to its use and a short description of its architecture.

PARADIGMA (PARticipative Approach to DIsease Global Management) is a pilot project which aims to develop and demonstrate an Internet based reference framework to share scientific resources and findings in the treatment of major diseases. PARADIGMA defines and disseminates a common methodology and optimised protocols (Clinical Pathways) to support service functions directed to patients and individuals on matters like prevention, posthospitalisation support and awareness. PARADIGMA will provide a platform of information services - user oriented and optimised against social, cultural and technological constraints - supporting the Health Care Global System of the Euro-Mediterranean Community in a continuous improvement process.

BACKGROUND: In developed countries invasive listeriosis is an infection of great concern to public health to due its clinical severity and high fatality rate, despite its low incidence. In Europe, statistically significant increasing trends in listeriosis notification rates from 2005 to 2009 were noted in Austria, Denmark, Hungary, Italy, Spain and Sweden. MATERIALS AND METHODS: The standardized techniques based on phenotype to typing Listeria monocytogenes is the serotyping. In Europe, as elsewhere in the world, about 95% of L. monocytogenes strains isolated from clinical and food samples belongs to serovars 1/2a, 1/2b, 1/2c and 4b. RESULTS: The target of this work is to draw attention to this important and atypical foodborne disease, reporting epidemiological data and serotypes distribution of 251 human L. monocytogenes isolates reported during 2000-2010 to Veterinary Public Health and Food Safety Department of Istituto Superiore di Sanità, focusing on epidemiological trend of invasive listeriosis in Lombardia, a North Italian Region. The serotypes most frequently identified are 1/2a, 4b, 1/2b (in total 92%), but the detection of uncommon serotypes is not missing (1/2c, 3a, 3b, 4d). CONCLUSIONS: In Italy the surveillance laboratory network, as well as the foodborne disease network (ENTER-NET), has revealed in the last 11 years an increase trend of listeriosis cases reported likewise with results of Notificable National Infectious Disease surveillance System. This is probably due to a real increase of listeriosis, even if there is a greater sensitivity of the network in some regions.

SUMMARYIn 2012 a US multistate outbreak of listeriosis was linked to ricotta salata imported from Italy, made from pasteurized sheep's milk. Sampling activities were conducted in Italy to trace the source ofListeria monocytogenescontamination. The cheese that caused the outbreak was produced in a plant in Apulia that processed semi-finished cheeses supplied by five plants in Sardinia. During an ‘emergency sampling’, 179 (23·6%) out of 758 end-products tested positive forL. monocytogenes, with concentrations from 6c.f.u./g. Positive processing environment samples were found in two out of four processing plants. A ‘follow-up sampling’ was conducted 8 months later, when environmental samples from three out of six plants tested positive forL. monocytogenesand forListeriaspp. PFGE subtyping showed 100% similarity between US clinical strains and isolates from ricotta salata, confirming the origin of the outbreak. The persistence of strains in environmental niches of processing plants was demonstrated, and is probably the cause of product contamination. Two PFGE profiles from clinical cases of listeriosis in Italy in 2011, stored in the MSS-TESSy database, were found to have 100% similarity to one PFGE profile from a US clinical case associated with the consumption of ricotta salata, according to the US epidemiological investigation (sample C, pulsotype 17). However, they had 87% similarity to the only PFGE profile found both in the US clinical case and in 14 ricotta cheese samples collected during the emergency sampling (sample B, pulsotype 1). Sharing of molecular data and availability of common characterization protocols were key elements that connected the detection of the US outbreak to the investigation of the food source in Italy. Simultaneous surveillance systems at both food and human levels are a necessity for the efficient rapid discovery of the source of an outbreak ofL. monocytogenes.