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The Italian Government planned to invest €15 billion of European funds on National Health Service digitalization and primary care enhancement. The critical burden brought by the pandemic upon hospital care mean these investments could no longer be delayed, considering the extraordinary backlogs of many treatments and the ordinary gaps of fragmented long-term care, in Italy and abroad. National guidelines have been published to standardize interventions across the Italian regions, and telemedicine is frequently mentioned as a key innovation to achieve both goals. The professional resources needed to run the facilities introduced in primary care are defined with great precision, but no details are given on how digitalization and remote care technologies must be implemented in this context. Building on this policy case, this paper focuses on what contribution digitalization and telemedicine can offer to specific primary care innovations, drawing from implemented technology-driven policies which may support the effective stratification, prevention and management of chronic patient needs, including anticipatory healthcare, population health management, adjusted clinical groups, chronic care management, quality and outcomes frameworks, patient-reported outcomes and patient-reported experience. All these policies can benefit significantly from digitalization and remote care technology, provided that some risks and limitations are considered by design. [ABSTRACT FROM AUTHOR]

Background: This paper aims to present open-source computer simulation programs developed to simulate, track, and estimate the COVID-19 outbreak. Methods: The programs included two separate parts, one set of programs built in Simulink with a block diagram display, and another one coded as a script in MATLAB R2020b. The mathematical model used in this package was the suspectable-infected-removed (SIR), suspectable-exposed-infected-removed (SEIR), and susceptible-exposed-infected-recovereddeceased (SEIRD) models represented by a set of differential-algebraic equations. It can be easily modified to develop new models for the problem. A generalized method was adopted to simulate worldwide outbreaks in an efficient, fast, and simple way. Results: To get a good tracking of the virus spread, a sum of sigmoid functions was proposed to capture any dynamic changes in the data. The parameters used for the input (infection and recovery rate functions) were computed using the parameter estimation tool in MATLAB. Several statistical methods were applied for the rate function, including linear, Mean±SD and root mean square (RMS). In addition, an adaptive neuro-fuzzy inference system (ANFIS) was employed and proposed to train the model and predict its output. Conclusion: This procedure is presented in such a way that it can be generalized and applied in other parts and applications of estimating the scenarios of an event, including the potential of several models, including suspectable-infected-removed (SIR), which is sensitive to pollution, etc. This program can be used as an educational tool or for research studies and this article promises some lasting contributions in the field of COVID-19. [ABSTRACT FROM AUTHOR]

The field of additive manufacturing, particularly 3D printing, has ushered in a significant transformation in the realm of joint arthritis treatment through prosthetic surgery. This innovative technology allows for the creation of bespoke prosthetic devices that are tailored to meet the specific needs of individual patients. These devices are constructed using high-performance materials, including titanium and cobalt-chrome alloys. Nevertheless, the routine physical activities of patients, such as walking, sitting, and running, can induce wear and tear on the materials comprising these prosthetic devices, subsequently diminishing their functionality and durability. In response to this challenge, this research has endeavored to leverage novel techniques. The primary focus of this study lies in the development of an algorithm designed to optimize hip replacement procedures via the mechanical design of the prosthesis. This optimization process exploits the capabilities of machine learning algorithms, multi-body dynamics, and finite element method (FEM) simulations. The paramount innovation in this methodology is the capacity to design a prosthetic system that intricately adapts to the distinctive characteristics of each patient (weight, height, gait cycle). The primary objective of this research is to enhance the performance and longevity of prosthetic devices by improving their fatigue strength. The evaluation of load distribution on the prosthetic device, facilitated by FEM simulations, anticipates a substantial augmentation in the useful life of the prosthetic system. This research holds promise as a notable advancement in prosthetic technology, offering a more efficacious treatment option for patients suffering from joint arthritis. The aim of this research is to make meaningful contributions to the enhancement of patient quality of life and the long-term performance of prosthetic devices. [ABSTRACT FROM AUTHOR]

Around the world, the occurrence of landslides has become one of the greatest threats to human life, property, infrastructure, and natural environments. Despite extensive research and discussions on the spatiotemporal dependence of landslide displacements, there is still a lack of understanding concerning the factors that appear to control displacement distribution in landslides because of their significant variations. This paper implements a Graph Convolutional Network (GCN) to predict displacement following the Moio della Civitella landslide in southern Italy and identify factors that may affect the distribution of movement following the landslide. An interferometric technique, known as permanent scatter interferometry (PSI), has been developed based on Synthetic Aperture Radar (SAR) satellite imagery to derive permanent scatter points that can be used to represent the deformation of landslides. This study utilized the GCN regression model applied to PSs points and data reflecting geological and geomorphological factors to extract the interdependency between paired data points, resulting in an adjacency matrix of the interval [0, 0,8). The proposed model outperforms conventional machine learning and deep learning algorithms such as linear regression (LR), K-nearest neighbors (KNN), Support vector regression (SVR), Decision tree, lasso, and artificial neural network (ANN). The absolute error between the actual and predicted deformation is used to evaluate the proposed model, which is less than 2 millimeters for most test set points. [ABSTRACT FROM AUTHOR]

The aim of this study was to develop and test an artificial intelligence (AI)-based algorithm for detecting common technical errors in canine thoracic radiography. The algorithm was trained using a database of thoracic radiographs from three veterinary clinics in Italy, which were evaluated for image quality by three experienced veterinary diagnostic imagers. The algorithm was designed to classify the images as correct or having one or more of the following errors: rotation, underexposure, overexposure, incorrect limb positioning, incorrect neck positioning, blurriness, cut-off, or the presence of foreign objects, or medical devices. The algorithm was able to correctly identify errors in thoracic radiographs with an overall accuracy of 81.5% in latero-lateral and 75.7% in sagittal images. The most accurately identified errors were limb mispositioning and underexposure both in latero-lateral and sagittal images. The accuracy of the developed model in the classification of technically correct radiographs was fair in latero-lateral and good in sagittal images. The authors conclude that their AI-based algorithm is a promising tool for improving the accuracy of radiographic interpretation by identifying technical errors in canine thoracic radiographs. [ABSTRACT FROM AUTHOR]

The aim of this study was to develop and test an artificial intelligence (AI)-based algorithm for detecting common technical errors in canine thoracic radiography. The algorithm was trained using a database of thoracic radiographs from three veterinary clinics in Italy, which were evaluated for image quality by three experienced veterinary diagnostic imagers. The algorithm was designed to classify the images as correct or having one or more of the following errors: rotation, underexposure, overexposure, incorrect limb positioning, incorrect neck positioning, blurriness, cut-off, or the presence of foreign objects, or medical devices. The algorithm was able to correctly identify errors in thoracic radiographs with an overall accuracy of 81.5% in latero-lateral and 75.7% in sagittal images. The most accurately identified errors were limb mispositioning and underexposure both in latero-lateral and sagittal images. The accuracy of the developed model in the classification of technically correct radiographs was fair in latero-lateral and good in sagittal images. The authors conclude that their AI-based algorithm is a promising tool for improving the accuracy of radiographic interpretation by identifying technical errors in canine thoracic radiographs. [ABSTRACT FROM AUTHOR]

Background: Resilience is defined as the ability to modify thoughts to cope with stressful events. Patients with schizophrenia (SCZ) having higher resilience (HR) levels show less severe symptoms and better real-life functioning. However, the clinical factors contributing to determine resilience levels in patients remain unclear. Thus, based on psychological, historical, clinical and environmental variables, we built a supervised machine learning algorithm to classify patients with HR or lower resilience (LR). Methods: SCZ from the Italian Network for Research on Psychoses (N = 598 in the Discovery sample, N = 298 in the Validation sample) underwent historical, clinical, psychological, environmental and resilience assessments. A Support Vector Machine algorithm (based on 85 variables extracted from the above-mentioned assessments) was built in the Discovery sample, and replicated in the Validation sample, to classify between HR and LR patients, within a nested, Leave-Site-Out Cross-Validation framework. We then investigated whether algorithm decision scores were associated with the cognitive and clinical characteristics of patients. Results: The algorithm classified patients as HR or LR with a Balanced Accuracy of 74.5% (p < 0.0001) in the Discovery sample, and 80.2% in the Validation sample. Higher self-esteem, larger social network and use of adaptive coping strategies were the variables most frequently chosen by the algorithm to generate decisions. Correlations between algorithm decision scores, socio-cognitive abilities, and symptom severity were significant (p FDR < 0.05). Conclusions: We identified an accurate, meaningful and generalizable clinical-psychological signature associated with resilience in SCZ. This study delivers relevant information regarding psychological and clinical factors that non-pharmacological interventions could target in schizophrenia. [ABSTRACT FROM AUTHOR]

Copyright of Revista Técnica Tributaria is the property of Asociacion Espanola de Asesores Fiscales and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A reliable prediction of the spatio-temporal drought variation can lead to a reduction in vulnerability and an improvement in the management of drought-dependent businesses. In this study, three clustering algorithms, K-mean, Hierarchical and Expectation–Maximization, were first used to divide Southern Italy into homogeneous drought regions, based on gridded data of the Standardized Precipitation Evapotranspiration Index forecasting with a 6 months' time scale (SPEI6). The Hierarchical algorithm identified five well-distinct clusters characterized by drought events of different duration and severity, considering the different morphoclimatic characteristics of the study area. Then, the mean SPEI6 time series were evaluated for each cluster and used to assess the evolutionary drought trends. In addition, two Machine Learning (ML) algorithms, M5P and Support Vector Regression (SVR), were also used to develop forecasting models for the SPEI6, without the need for additional exogenous inputs. Moreover, the Stacking ML technique was used to develop a hybrid model based on both individual M5P and SVR algorithms. The clustering-forecasting combination makes it possible to identify the evolutionary trends taking place in the various homogeneous areas in a concise but effective manner. The hybrid M5P-SVR model (R2 up to 0.91, minimum Root Mean Square Error RMSE = 0.38) outperformed both M5P (R2 up to 0.87, minimum RMSE = 0.42) and SVR (R2 up to 0.89, minimum RMSE = 0.39) models, showing to be particularly suitable for drought forecasting in areas with long and severe drought events. [ABSTRACT FROM AUTHOR]

A three-dimensional survey of natural caves is often a difficult task due to the roughness of the investigated area and the problems of accessibility. Traditional adopted techniques allow a simplified acquisition of the topography of caves characterized by an oversimplification of the geometry. Nowadays, the advent of LiDAR and Structure from Motion applications eased three-dimensional surveys in different environments. In this paper, we present a comparison between other three-dimensional survey systems, namely a Terrestrial Laser Scanner, a SLAM-based portable instrument, and a commercial photo camera, to test their possible deployment in natural caves survey. We presented a comparative test carried out in a tunnel stretch to calibrate the instrumentation on a benchmark site. The choice of the site is motivated by its regular geometry and easy accessibility. According to the result obtained in the calibration site, we presented a methodology, based on the Structure from Motion approach that resulted in the best compromise among accuracy, feasibility, and cost-effectiveness, that could be adopted for the three-dimensional survey of complex natural caves using a sequence of images and the structure from motion algorithm. The methods consider two different approaches to obtain a low resolution complete three-dimensional model of the cave and ultra-detailed models of most peculiar cave morphological elements. The proposed system was tested in the Gazzano Cave (Piemonte region, Northwestern Italy). The obtained result is a three-dimensional model of the cave at low resolution due to the site's extension and the remarkable amount of data. Additionally, a peculiar speleothem, i.e., a stalagmite, in the cave was surveyed at high resolution to test the proposed high-resolution approach on a single object. The benchmark and the cave trials allowed a better definition of the instrumentation choice for underground surveys regarding accuracy and feasibility. [ABSTRACT FROM AUTHOR]

Technologically advanced strategies in infrastructural maintenance are increasingly required in countries such as Italy, where recovery and rehabilitation interventions are preferred to new works. For this purpose, Interferometric Synthetic Aperture Radar (InSAR) techniques have been employed in recent years, achieving reliable outcomes in the identification of infrastructural instabilities. Nevertheless, using the InSAR survey exclusively, it is not feasible to recognize the reasons for such vulnerabilities, and further in-depth investigations are essential.The primary purpose of this paper is to predict infrastructural displacements connected to surface motion and the related causes by combining InSAR techniques and Machine Learning algorithms. The development and application of a Regression Tree-based algorithm have been carried out for estimating the displacement of road pavement structures detected by the Persistent Scatterer InSAR technique.The study area is located in the province of Pistoia, Tuscany, Italy. Sentinel-1 images from 2014 to 2019 were used for the interferometric process, and a set of 29 environmental parameters was collected in a GIS platform. The database is randomly split into a Training (70%) and Test sets (30%). With the Training set, through a 10-Fold Cross-Validation, the model is trained, validated, and the Goodness-of-Fit is evaluated. Also, with the Test set, the Predictive Performance of the model is assessed. Lastly, we applied the model onto a stretch of a two-lane rural road that crosses the area. Results show that the suggested procedure can be used for supporting decision-making processes on planning road maintenance by National Road Authorities. [ABSTRACT FROM AUTHOR]

In recent years, immediate breast reconstruction after mastectomy surgery has steadily increased in the treatment pathway of breast cancer (BC) patients due to its potential impact on both the morpho-functional and aesthetic type of the breast and the quality of life. Although recent studies have demonstrated how recent radiotherapy techniques have allowed a reduction of adverse events related to breast reconstruction, capsular contracture (CC) remains the main complication after post-mastectomy radio-therapy (PMRT). In this study, we evaluated the association of the occurrence of CC with some clinical, histological and therapeutic parameters related to BC patients. We firstly performed bivariate statistical tests and we then evaluated the prognostic predictive power of the collected data by using machine learning techniques. Out of a sample of 59 patients referred to our institute, 28 patients (i.e., 47%) showed contracture after PMRT. As a result, only estrogen receptor status (ER) and molecular subtypes were significantly associated with the occurrence of CC after PMRT. Different machine learning models were trained on a subset of clinical features selected by a feature importance approach. Experimental results have shown that collected features have a non-negligible predictive power. The extreme gradient boosting classifier achieved an area under the curve (AUC) value of 68% and accuracy, sensitivity, and specificity values of 68%, 64%, and 74%, respectively. Such a support tool, after further suitable optimization and validation, would allow clinicians to identify the best therapeutic strategy and reconstructive timing. [ABSTRACT FROM AUTHOR]

During the outbreak of a disease caused by a pathogen with unknown characteristics, the uncertainty of its progression parameters can be reduced by devising methods that, based on rational assumptions, exploit available information to provide actionable insights. In this study, performed a few (~6) weeks into the outbreak of COVID-19 (caused by SARS-CoV-2), one of the most important disease parameters, the average time-to-recovery, was calculated using data publicly available on the internet (daily reported cases of confirmed infections, deaths, and recoveries), and fed into an algorithm that matches confirmed cases with deaths and recoveries. Unmatched cases were adjusted based on the matched cases calculation. The mean time-to-recovery, calculated from all globally reported cases, was found to be 18.01 days (SD 3.31 days) for the matched cases and 18.29 days (SD 2.73 days) taking into consideration the adjusted unmatched cases as well. The proposed method used limited data and provided experimental results in the same region as clinical studies published several months later. This indicates that the proposed method, combined with expert knowledge and informed calculated assumptions, could provide a meaningful calculated average time-to-recovery figure, which can be used as an evidence-based estimation to support containment and mitigation policy decisions, even at the very early stages of an outbreak. [ABSTRACT FROM AUTHOR]

This paper investigates the possibility of the implementation of Genetic Programming (GP) algorithm on a publicly available COVID-19 data set, in order to obtain mathematical models which could be used for estimation of confirmed, deceased, and recovered cases and the estimation of epidemiology curve for specific countries, with a high number of cases, such as China, Italy, Spain, and USA and as well as on the global scale. The conducted investigation shows that the best mathematical models produced for estimating confirmed and deceased cases achieved R2 scores of 0.999, while the models developed for estimation of recovered cases achieved the R2 score of 0.998. The equations generated for confirmed, deceased, and recovered cases were combined in order to estimate the epidemiology curve of specific countries and on the global scale. The estimated epidemiology curve for each country obtained from these equations is almost identical to the real data contained within the data set [ABSTRACT FROM AUTHOR]

Copyright of Regional Science Policy & Practice is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper we report the observations of HD189733b, Kepler-41b, Kepler-42b, GJ 436b, WASP-77ab, HAT-P-32b and EPIC 211818569 as measured at the Osservatorio Polifunzionale del Chianti, a new astro-nomical site in Italy. Commissioning observing runs have been done in order to test capabilities, systematics and limits of the system and to improve its accuracy. For this purpose, a software algorithm has been developed to estimate the differential photometric error of any transit observation, so that the integration time can be chosen to reach optimal signal-to-noise ratios, and to obtain a picture of what kind of transits this setup can reveal. Currently, the system is able to reach an accuracy of about 1 mmag and so it is ready for the much needed exoplanetary transit follow-up. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to examine the processes involved in a guided self‐help (GSH) pre‐treatment intervention (RecoveryMANTRA) for patients with anorexia nervosa (AN), by measuring the levels of patient/mentor Language Style Matching (LSM). RecoveryMANTRA was supported by student mentors or peer mentors (recovered individuals) over six weekly chat‐based sessions. We examined whether LSM during RecoveryMANTRA predicted patients'working alliance with the clinic therapist, motivation, eating disorder (ED) and general psychopathology. A further aim was to examine differences in LSM between student mentors and peer mentors. Method: 87 AN adults received RecoveryMANTRA plus treatment as usual. The LSM algorithm was used to calculate verbal attunement between patient and mentor. Participants were assessed at baseline and at the end of the intervention. Results: Both early (1st session) and late (6th session) LSM predicted higher working alliance with the clinic therapist. Moreover, late LSM predicted lower EDs symptoms at the end of the RecoveryMANTRA intervention. Patient/peer mentor dyads showed higher late verbal attunement than patient/student mentor dyads. Conclusions: These findings suggests that in the early phase of treatment relational aspects can impact on engagement with treatment. Verbal attunement in a GSH for AN is associated with working alliance and better clinical outcome. Highlights: Language Style Matching (LSM) between patients and mentors in both the early and late phase of the RecoveryMANTRA intervention predicted higher working alliance with the clinic therapist.LSM in the last phase (6th session) was associated with lower eating disorders (EDs) symptoms at the end of the RecoveryMANTRA intervention.Patients/peer mentors dyads showed higher late LSM than patients/student mentors dyads. [ABSTRACT FROM AUTHOR]

Fibromyalgia (FM) is a chronic pain syndrome with an unclear etiology. In addition to pain, FM patients suffer from a diverse array of symptoms and comorbidities, encompassing fatigue, cognitive dysfunction, mood disorders, sleep deprivation, and dizziness. Due to the complexity of FM, the diagnosis and treatment of it are highly challenging. The aim of the present work was to investigate some clinical and psychological characteristics of FM patients, and to uncover possible correlations with pharmacological and non-pharmacological therapies. We conducted a cross-sectional, questionnaire-based study aimed at evaluating pain, psychological traits, and the self-perceived effectiveness of pharmacological and non-pharmacological treatments in an Italian population of FM patients. Descriptive statistics, correlation, and inference analyses were performed. We found a prevalence of a neuropathic/nociplastic type of pain, which correlated with psychological traits such as anxiety, low mood, psychophysical discomfort, and the inability to relax. The pain type and psychological traits proved to play a role in determining the self-perceived effectiveness of therapeutic interventions. Patients revealed a better response to non-pharmacological therapies, particularly dietary interventions, relaxation techniques, and psychotherapy rather than pharmacological interventions. The sum of our data indicates that for better outcomes, the type of pain and psychological traits should be considered for tailor-made treatments considering non-pharmacological protocols as a complement to the use of drugs. [ABSTRACT FROM AUTHOR]

With seismic catalogues becoming progressively larger, extracting information becomes challenging and calls upon using sophisticated statistical analysis. Data are typically clustered by machine learning algorithms to find patterns or identify regions of interest that require further exploration. Here, we investigate two density-based clustering algorithms, DBSCAN and OPTICS, for their capability to analyse the spatial distribution of seismicity and their effectiveness in discovering highly active seismic volumes of arbitrary shapes in large data sets. In particular, we study the influence of varying input parameters on the cluster solutions. By exploring the parameter space, we identify a crossover region with optimal solutions in between two phases with opposite behaviours (i.e. only clustered and only unclustered data points). Using a synthetic case with various geometric structures, we find that solutions in the crossover region consistently have the largest clusters and best represent the individual structures. For identifying strong anisotropic structures, we illustrate the usefulness of data rescaling. Applying the clustering algorithms to seismic catalogues of recent earthquake sequences (2016 Central Italy and 2016 Kumamoto) confirms that cluster solutions in the crossover region are the best candidates to identify 3-D features of tectonic structures that were activated in a seismic sequence. Finally, we propose a list of recipes that generalizes our analyses to obtain such solutions for other seismic sequences. [ABSTRACT FROM AUTHOR]

This paper explores the relationship between speaker normalization and dialectal identity in sociolinguistic data, examining a database of vowel formants collected from 88 monolingual American English speakers in Michigan's Upper Peninsula. Audio recordings of Finnish- and Italian-heritage American English speakers reading a passage and a word list were normalized using two normalization procedures. These algorithms are based on different concepts of normalization: Lobanov, which models normalization as based on experience with individual talkers, and Labov ANAE, which models normalization as based on experience with scale-factors inherent in acoustic resonators of all kinds. The two procedures yielded different results; while the Labov ANAE method reveals a cluster shifting of low and back vowels that correlated with heritage, the Lobanov procedure seems to eliminate this sociolinguistic variation. The difference between the two procedures lies in how they treat relations between formant changes, suggesting that dimensions of variation in the vowel space may be treated differently by different normalization procedures, raising the question of how anatomical variation and dialectal variation interact in the real world. The structure of the sociolinguistic effects found with the Labov ANAE normalized data, but not in the Lobanov normalized data, suggest that the Lobanov normalization does over-normalize formant measures and remove sociolinguistically relevant information. [ABSTRACT FROM AUTHOR]

The article discusses the intercomparison of ground-based ultraviolet (UV) radiometers conducted by the Environmental Protection Agency of Aosta Valley (ARPA Valle d'Aosta) in Saint-Christophe, Italy from June 8-23, 2010. It states that the downward global solar UV irradiance at the ground have been measured in a synchronized manner and the raw series has been processed by the respective operators. It also explores the development of an algorithm for comparing broadband spectra and data.

Introduction: The Respiratory Disturbance Index (RDI) computed by an implantable cardioverter defibrillator (ICD) algorithm accurately identifies severe sleep apnea (SA). In the present analysis, we tested the hypothesis that RDI could also predict atrial fibrillation (AF) burden. Methods: Patients with ejection fraction ≤35% implanted with an ICD were enrolled and followed up for 24 months. One month after implantation, patients underwent a polysomnographic study. The weekly mean RDI value was considered, as calculated during the entire follow‐up period and over a 1‐week period preceding the sleep study. The endpoints were as follows: daily AF burden of ≥5 min, ≥6 h, ≥23 h. Results: Here, 164 patients had usable RDI values during the entire follow‐up period. Severe SA (RDI ≥ 30 episodes/h) was diagnosed in 92 (56%) patients at the time of the sleep study. During follow‐up, AF burden ≥ 5 min/day was documented in 70 (43%), ≥6 h/day in 48 (29%), and ≥23 h/day in 33 (20%) patients. Device‐detected RDI ≥ 30 episodes/h at the time of the polygraphy, as well as the polygraphy‐measured apnea hypopnea index ≥ 30 episodes/h, were not associated with the occurrence of the endpoints, using a Cox regression model. However, using a time‐dependent model, continuously measured weekly mean RDI ≥ 30 episodes/h was independently associated with AF burden ≥ 5 min/day (hazard ratio [HR]: 2.13, 95% confidence interval [CI]: 1.24–3.65, p =.006), ≥6 h/day (HR: 2.75, 95% CI: 1.37–5.49, p =.004), and ≥23 h/day (HR: 2.26, 95% CI: 1.05–4.86, p =.037). Conclusions: In heart failure patients, ICD‐diagnosed severe SA on follow‐up data review identifies patients who are from two‐ to three‐fold more likely to experience an AF episode, according to various thresholds of daily AF burden. [ABSTRACT FROM AUTHOR]

Background The proportion of reported causes of death (CoDs) that are not underlying causes can be relevant even in high-income countries and seriously affect health planning. The Global Burden of Disease (GBD) study identifies these 'garbage codes' (GCs) and redistributes them to underlying causes using evidence-based algorithms. Planners relying on vital registration data will find discrepancies with GBD estimates. We analyse these discrepancies, through the analysis of GCs and their redistribution. Methods We explored the case of Italy, at national and regional level, and compared it to nine other Western European countries with similar population sizes. We analysed differences between official data and GBD 2019 estimates, for the period 1990–2017 for which we had vital registration data for most select countries. Results In Italy, in 2017, 33 000 deaths were attributed to unspecified type of stroke and 15 000 to unspecified type of diabetes, these making a fourth of the overall garbage. Significant heterogeneity exists on the overall proportion of GCs, type (unspecified or impossible underlying causes), and size of specific GCs among regions in Italy, and among the select countries. We found no pattern between level of garbage and relevance of specific GCs. Even locations performing below average show interesting lower levels for certain GCs if compared to better performing countries. Conclusions This systematic analysis suggests the heterogeneity in GC levels and causes, paired with a more detailed analysis of local practices, strengths and weaknesses, could be a positive element in a strategy for the reduction of GCs in Italy. [ABSTRACT FROM AUTHOR]

Background: Handwriting is an acquired complex cognitive and motor skill resulting from the activation of a widespread brain network. Handwriting therefore may provide biologically relevant information on health status. Also, handwriting can be collected easily in an ecological scenario, through safe, cheap, and largely available tools. Hence, objective handwriting analysis through artificial intelligence would represent an innovative strategy for telemedicine purposes in healthy subjects and people affected by neurological disorders. Materials and Methods: One-hundred and fifty-six healthy subjects (61 males; 49.6 ± 20.4 years) were enrolled and divided according to age into three subgroups: Younger adults (YA), middle-aged adults (MA), and older adults (OA). Participants performed an ecological handwriting task that was digitalized through smartphones. Data underwent the DBNet algorithm for measuring and comparing the average stroke sizes in the three groups. A convolutional neural network (CNN) was also used to classify handwriting samples. Lastly, receiver operating characteristic (ROC) curves and sensitivity, specificity, positive, negative predictive values (PPV, NPV), accuracy and area under the curve (AUC) were calculated to report the performance of the algorithm. Results: Stroke sizes were significantly smaller in OA than in MA and YA. The CNN classifier objectively discriminated YA vs. OA (sensitivity = 82%, specificity = 80%, PPV = 78%, NPV = 79%, accuracy = 77%, and AUC = 0.84), MA vs. OA (sensitivity = 84%, specificity = 56%, PPV = 78%, NPV = 73%, accuracy = 74%, and AUC = 0.7), and YA vs. MA (sensitivity = 75%, specificity = 82%, PPV = 79%, NPV = 83%, accuracy = 79%, and AUC = 0.83). Discussion: Handwriting progressively declines with human aging. The effect of physiological aging on handwriting abilities can be detected remotely and objectively by using machine learning algorithms. [ABSTRACT FROM AUTHOR]

The article presents a study which examines the impact of clouds on the MAX-DOAS observations during the measurement campaign in Milano, Italy in September 2003. The study applies a MAX-DOAS inversion algorithms for aerosols and trace gases with assumptions on the relative profile shapes, and compares the inversion results to independent measurements. The results show a reasonable agreement between the formaldehyde (HCHO) and aerosol profiles and the MAX-DOAS layer heights.

The COVID-19 pandemic has impacted on every human activity and, because of the urgency of finding the proper responses to such an unprecedented emergency, it generated a diffused societal debate. The online version of this discussion was not exempted by the presence of misinformation campaigns, but, differently from what already witnessed in other debates, the COVID-19 -intentional or not- flow of false information put at severe risk the public health, possibly reducing the efficacy of government countermeasures. In this manuscript, we study the effective impact of misinformation in the Italian societal debate on Twitter during the pandemic, focusing on the various discursive communities. In order to extract such communities, we start by focusing on verified users, i.e., accounts whose identity is officially certified by Twitter. We start by considering each couple of verified users and count how many unverified ones interacted with both of them via tweets or retweets: if this number is statically significant, i.e. so great that it cannot be explained only by their activity on the online social network, we can consider the two verified accounts as similar and put a link connecting them in a monopartite network of verified users. The discursive communities can then be found by running a community detection algorithm on this network. We observe that, despite being a mostly scientific subject, the COVID-19 discussion shows a clear division in what results to be different political groups. We filter the network of retweets from random noise and check the presence of messages displaying URLs. By using the well known browser extension NewsGuard, we assess the trustworthiness of the most recurrent news sites, among those tweeted by the political groups. The impact of low reputable posts reaches the 22.1% in the right and center-right wing community and its contribution is even stronger in absolute numbers, due to the activity of this group: 96% of all non reputable URLs shared by political groups come from this community. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has impacted on every human activity and, because of the urgency of finding the proper responses to such an unprecedented emergency, it generated a diffused societal debate. The online version of this discussion was not exempted by the presence of misinformation campaigns, but, differently from what already witnessed in other debates, the COVID-19 -intentional or not- flow of false information put at severe risk the public health, possibly reducing the efficacy of government countermeasures. In this manuscript, we study the effective impact of misinformation in the Italian societal debate on Twitter during the pandemic, focusing on the various discursive communities. In order to extract such communities, we start by focusing on verified users, i.e., accounts whose identity is officially certified by Twitter. We start by considering each couple of verified users and count how many unverified ones interacted with both of them via tweets or retweets: if this number is statically significant, i.e. so great that it cannot be explained only by their activity on the online social network, we can consider the two verified accounts as similar and put a link connecting them in a monopartite network of verified users. The discursive communities can then be found by running a community detection algorithm on this network. We observe that, despite being a mostly scientific subject, the COVID-19 discussion shows a clear division in what results to be different political groups. We filter the network of retweets from random noise and check the presence of messages displaying URLs. By using the well known browser extension NewsGuard, we assess the trustworthiness of the most recurrent news sites, among those tweeted by the political groups. The impact of low reputable posts reaches the 22.1% in the right and center-right wing community and its contribution is even stronger in absolute numbers, due to the activity of this group: 96% of all non reputable URLs shared by political groups come from this community. [ABSTRACT FROM AUTHOR]

Bed sills are a kind of grade control structure built at the same level as the river bed to protect it against erosion and to reduce the longitudinal slope of the channel. The aim of the present research was to determine the optimal dimensions of the structure by devising a new methodology to minimize cost and maximize stability, based on the optimization model of the multi-objective non-dominated sorting genetic algorithm (NSGA-II). This is achieved by calculating the width, height and distance between bed sills with these stability and cost objectives in mind. The NSGA-II algorithm is applied to solve the problem, and a set of Pareto points is obtained. The results are verified by considering the bed sills of the River Madarsoo in Iran and the River Plima in Italy, and comparing the results with those obtained using the Branch-and-Bound (B-and-B) algorithm. The results indicate that the cost and stability could respectively be improved by up to 18 and 20% in the River Madarsoo, and up to 18 and 26% in the River Plima, if the NSGA-II algorithm is used. [ABSTRACT FROM AUTHOR]

The HLA‐DPB1 locus has been demonstrated to have a significant role on patients' outcome after allogeneic HSCT, and the so‐called T‐cell epitope (TCE) algorithm has been incorporated in international guidelines for the selection of unrelated donors. The purpose of the present study is to measure, through a national survey conducted on behalf of the Associazione Italiana di Immunogenetica e Biologia dei Trapianti (AIBT), the extent of awareness and use of HLA‐DPB1 TCE‐based algorithms during the donor search. 89% of the HLA laboratories answered to a short questionnaire and the results showed a progressive increase of the laboratories typing DPB1 in patients and their potential donors during the search (from 44% to 79% during the 2010–2019 period) as well as the application of a TCE‐based algorithm for the donor choice whenever possible (from 24% to 65% during the same period). The DP‐permissiveness status is detailed in the official HLA typing report by 12%, 32% and 50% of laboratories in 2010, 2015 and 2019, respectively. The present data indicate an encouraging raise in the awareness of the HLA‐DPB1 role in unrelated donor selection; noteworthy, mentioning the TCE‐based permissiveness status in the HLA typing report of each potential unrelated donor represents a notable mean to raise awareness among transplant physicians and to support them in their task of choosing the best donor. Nonetheless, despite the compelling evidence of the predictive ability of TCE‐based algorithms, further efforts are still needed to extend its application to all transplant centers in Italy. [ABSTRACT FROM AUTHOR]

Background: Case-finding tools, such as the Identify Chronic Migraine (ID-CM) questionnaire, can improve detection of CM and alleviate its significant societal burden. We aimed to develop and validate the Italian version of the ID-CM (ID-EC) in paper and as a smart app version in a headache clinic-based setting.Methods: The study investigators translated and adapted to the Italian language the original ID-CM questionnaire (ID-EC) and further implemented it as a smart app. The ID-EC was tested in its paper and electronic version in consecutive patients referring to 9 Italian tertiary headache centers for their first in-person visit. The scoring algorithm of the ID-EC paper version was applied by the study investigators (case-finding) and by patients (self-diagnosis), while the smart app provided to patients automatically the diagnosis. Diagnostic accuracy of the ID-EC was assessed by matching the questionnaire results with the interview-based diagnoses performed by the headache specialists during the visit according to the criteria of International Classification of Headache Disorders, III edition, beta version.Results: We enrolled 531 patients in the test of the paper version of ID-EC and 427 in the validation study of the smart app. According to the clinical diagnosis 209 patients had CM in the paper version study and 202 had CM in the smart app study. 79.5% of patients returned valid paper questionnaires, while 100% of patients returned valid and complete smart app questionnaires. The paper questionnaire had a 81.5% sensitivity and a 81.1% specificity for case-finding and a 30.7% sensitivity and 90.7% specificity for self-diagnosis, while the smart app had a 64.9% sensitivity and 90.2% specificity.Conclusions: Our data suggest that the ID-EC, developed and validated in tertiary headache centers, is a valid case-finding tool for CM, with sensitivity and specificity values above 80% in paper form, while the ID-EC smart app is more useful to exclude CM diagnosis in case of a negative result. Further studies are warranted to assess the diagnostic accuracy of the ID-EC in general practice and population-based settings. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has impacted on every human activity and, because of the urgency of finding the proper responses to such an unprecedented emergency, it generated a diffused societal debate. The online version of this discussion was not exempted by the presence of misinformation campaigns, but, differently from what already witnessed in other debates, the COVID-19 -intentional or not- flow of false information put at severe risk the public health, possibly reducing the efficacy of government countermeasures. In this manuscript, we study the effective impact of misinformation in the Italian societal debate on Twitter during the pandemic, focusing on the various discursive communities. In order to extract such communities, we start by focusing on verified users, i.e., accounts whose identity is officially certified by Twitter. We start by considering each couple of verified users and count how many unverified ones interacted with both of them via tweets or retweets: if this number is statically significant, i.e. so great that it cannot be explained only by their activity on the online social network, we can consider the two verified accounts as similar and put a link connecting them in a monopartite network of verified users. The discursive communities can then be found by running a community detection algorithm on this network. We observe that, despite being a mostly scientific subject, the COVID-19 discussion shows a clear division in what results to be different political groups. We filter the network of retweets from random noise and check the presence of messages displaying URLs. By using the well known browser extension NewsGuard, we assess the trustworthiness of the most recurrent news sites, among those tweeted by the political groups. The impact of low reputable posts reaches the 22.1% in the right and center-right wing community and its contribution is even stronger in absolute numbers, due to the activity of this group: 96% of all non reputable URLs shared by political groups come from this community. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has impacted on every human activity and, because of the urgency of finding the proper responses to such an unprecedented emergency, it generated a diffused societal debate. The online version of this discussion was not exempted by the presence of misinformation campaigns, but, differently from what already witnessed in other debates, the COVID-19 -intentional or not- flow of false information put at severe risk the public health, possibly reducing the efficacy of government countermeasures. In this manuscript, we study the effective impact of misinformation in the Italian societal debate on Twitter during the pandemic, focusing on the various discursive communities. In order to extract such communities, we start by focusing on verified users, i.e., accounts whose identity is officially certified by Twitter. We start by considering each couple of verified users and count how many unverified ones interacted with both of them via tweets or retweets: if this number is statically significant, i.e. so great that it cannot be explained only by their activity on the online social network, we can consider the two verified accounts as similar and put a link connecting them in a monopartite network of verified users. The discursive communities can then be found by running a community detection algorithm on this network. We observe that, despite being a mostly scientific subject, the COVID-19 discussion shows a clear division in what results to be different political groups. We filter the network of retweets from random noise and check the presence of messages displaying URLs. By using the well known browser extension NewsGuard, we assess the trustworthiness of the most recurrent news sites, among those tweeted by the political groups. The impact of low reputable posts reaches the 22.1% in the right and center-right wing community and its contribution is even stronger in absolute numbers, due to the activity of this group: 96% of all non reputable URLs shared by political groups come from this community. [ABSTRACT FROM AUTHOR]

Although material requirement planning (MRP) systems are widely used in manufacturing companies, they exhibit significant drawbacks. Rev MRP algorithm has been developed in order to reduce the system nervousness, generate a more regular pattern of order released and lower inventory levels. The aim of this paper is to test the Rev MRP algorithm on the case of one of the largest Italian manufacturing companies of home appliances. The company provided three sets of real data, referring to three different runs of their MRP. Results showed that the Rev MRP outperformed the company’s MRP smoothing the production order releases, eliminating overshoots and drastically reducing the inventory levels along the supply chain, even in case of frequent forecasts updates. [ABSTRACT FROM AUTHOR]

A Brain Computer Interface (BCI) allows communication for impaired people unable to express their intention with common channels. Electroencephalography (EEG) represents an effective tool to allow the implementation of a BCI. The present paper describes a modular framework for the implementation of the graphic interface for binary BCIs based on the selection of symbols in a table. The proposed system is also designed to reduce the time required for writing text. This is made by including a motivational tool, necessary to improve the quality of the collected signals, and by containing a predictive module based on the frequency of occurrence of letters in a language, and of words in a dictionary. The proposed framework is described in a top-down approach through its modules: signal acquisition, analysis, classification, communication, visualization, and predictive engine. The framework, being modular, can be easily modified to personalize the graphic interface to the needs of the subject who has to use the BCI and it can be integrated with different classification strategies, communication paradigms, and dictionaries/languages. The implementation of a scenario and some experimental results on healthy subjects are also reported and discussed: the modules of the proposed scenario can be used as a starting point for further developments, and application on severely disabled people under the guide of specialized personnel. [ABSTRACT FROM AUTHOR]

The measure of the equitable and sustainable well-being (Bes) is of growing interest in the last years. The National Institute of Statistics (Istat) provides, for Italy, a wide set of indicators describing each domain of well-being that is, by definition, a multidimensional concept. In this study, we propose the use of Bayesian networks to deal with basic and composite Bes indicators. Its capability to model very complex multivariate dependence structures is useful to describe the relationships between indicators belonging to different domains and, being a probabilistic expert system, the estimated network could be also useful for probabilistic inference and what-if analysis. In this study, all the Bayesian networks structures have been estimated by means of the hill climbing algorithm based on bootstrap resampling and model averaging in order to prevent bias due to deviations from the normality assumption. [ABSTRACT FROM AUTHOR]

Objective: To validate the performance of a first-trimester simple risk score based on the ASPRE trial algorithm for pre-eclampsia.Design: Multicentre retrospective cohort analysis.Setting: Four Italian hospitals.Population: Unselected nulliparous women at 11-13 weeks of gestation from January 2014 through to January 2018.Methods: Model performance was evaluated based on discrimination and calibration.Main Outcome Measures: Delivery before 37 weeks of gestation with a diagnosis of pre-eclampsia.Results: Based on 73 preterm pre-eclampsia cases and 7546 controls (including 101 cases of late pre-eclampsia), the area under the receiver operating characteristics curve was 0.659 (95% CI 0.579-0.726). The sensitivity was 32.9% (95% CI 22.1-43.7) at a false-positive rate of 8.8%. The positive likelihood ratio was 3.74 (95% CI 2.67-5.23), the positive predictive value was 3.49% (95% CI 2.12-4.86%) and the negative predictive value was 99.3% (95% CI 99.1-99.5%). The sensitivity and positive likelihood ratio were approximately 40% lower than in the original study. The calibration analysis showed a good agreement between observed and expected risks (P = 0.037). Comparison with the Fetal Medicine Foundation (FMF) algorithm yielded a difference in the area under the curve of 0.084 (P = 0.007).Conclusions: In our Italian population, the simple risk score had a lower performance than expected for the prediction of preterm pre-eclampsia in nulliparous women. The FMF algorithm applied to the same data set resulted in a better prediction.Tweetable Abstract: Simple risk score predicts preterm pre-eclampsia in Italy. [ABSTRACT FROM AUTHOR]

A rockfall (RF) is a ubiquitous geohazard that is difficult to monitor or predict and poses a significant risk for people and transportation in several hilly and mountainous environments. The seismic signal generated by RF carries abundant physical and mechanical information. Thus, signals can be used by researchers to reconstruct the event location, onset time, volume and trajectory, and develop an efficient early warning system. Therefore, the precise automatic detection and classification of RF events are important objectives for scientists, especially in seismic monitoring arrays. An algorithm called DESTRO (DEtection and STorage of ROckfalls) aimed at combining seismic event automatic detection and classification was implemented ad hoc within the MATLAB environment. In event detection, the STA/LTA (short-time-average through long-time-average) method combined with other parameters, such as the minimum duration of an RF and the minimum interval time between two continuous seismic events is used. Furthermore, nine significant features based on the frequency, amplitude, seismic waveform, duration and multiple station attributes are newly proposed to classify seismic events in a RF environment. In particular, a three-step classification method is proposed for the discrimination of five different source types: RFs, earthquakes (EQs), tremors, multispike events (MSs) and subordinate MS events. Each component (vertical, east–west and north–south) at each station within the monitoring network is analysed, and a three-step classification is performed. At a given time, the event series detected from each component are integrated and reclassified component by component and station by station into a final event-type series as an output result. By this algorithm, a case study of the seven-month-long seismic monitoring of a former quarry in Central Italy was investigated by means of four triaxial velocimeters with continuous acquisition at a sampling rate of 200 Hz. During this monitoring period, a human-induced RF simulation was performed, releasing 95 blocks (in which 90 blocks validated) of different sizes from the benches of the quarry. Consequently, 64.9 per cent of EQs within 100 km were confirmed in a one-month monitoring period, 88 blocks in the RF simulation were classified correctly as RF events and 2 blocks were classified as MSs given their small energy. Finally, an ad hoc section of the algorithm was designed specifically for RF classification combined with EQ recognition. The algorithm could be applied in slope seismic monitoring to monitor the dynamic states of rock masses, as well as in slope instability forecasting and risk evaluation in EQ-prone areas. [ABSTRACT FROM AUTHOR]

The presence of road ice has always been a key issue during winter months. A reliable forecast system capable of predicting the Land Surface Temperature (LST) and, consequently, its formation is one of the best strategies to operate towards reducing both vehicles accidents and waste of chemical solvents used for prevention which have a significant economic and environmental impact. Hence, the Meteo Expert Centre (MEC) has developed an algorithm for LST forecasts able to issue ice risk warnings as well. This algorithm operationally works every day in real-time and it is here tested, first, on a paved area of the Pedemontana Lombarda motorway and the Milano Linate airport airstrip, and, afterwards, since the LST plays a crucial role in understanding phenomena of energy exchange between soil, vegetation, and atmosphere, its knowledge and prediction becomes relevant also for other purposes such as agricultural management and irrigation system control, further experiments are carried out over two agricultural fields, one in the North and the other in the South of Italy during the SIM (Smart Irrigation Management) project. All LST analyses showed encouraging results with reasonable high values of statistical scores, in both applications on asphalted and different vegetated terrains, demonstrating that the developed algorithm has a high versatility even on completely different types of surfaces, and it can be applied as a valid tool for road ice risk warnings too. [ABSTRACT FROM AUTHOR]

Background: We quantified, among diabetic adults, the frequency, costs, and factors associated with visits to the emergency department (EDs) and subsequent hospitalizations for acute hypoglycaemic and hyperglycaemic events. Methods: We included adults with diabetes residing in the Milan Agency for Health Protection between 2015 and 2017. From healthcare databases, we identified demographic variables, comorbidities, type of treatment, insulin treatment duration, previous ED attendances for acute glycaemic events, and two indicators of glycaemic monitoring. Using a validated ICD-9-CM coding algorithm, we identified all ED attendances for acute glycaemic events from the ED database and calculated their incidence. We computed the direct costs from health databases and presented them as average annual mean costs for those having had at least an ED attendance. The analysis of the association between the number of ED attendances and potential determinants was performed using zero-inflated negative binomial regression models. These two-part models concomitantly estimate two sets of parameters: the odds-ratios (ORs) of having no attendances and the incidence rate ratios (IRRs) of attendance. Results: The cohort included 168,285 subjects, 70% of subjects were older than 64 years, 56% were males, and 26% were treated with insulin. The incidence of acute glycaemic events for those attending the ED was 7.0 per 1000 patient-years, followed by hospitalization 26.0% of the time. The total annual direct cost for ED attendances due to acute glycaemic events was 174,000 €. Type of antidiabetic treatment had the strongest association with ED attendances for hypoglycaemia. Patients assuming insulin only had a lower probability of having no attendances (OR compared to those who assumed non-insulin antidiabetic drugs =0.01, 95% CI = 0.00–0.02). These patients also had the highest rate of hyperglycaemic episodes (IRR = 7.7, 95% CI = 5.1–11.7 for insulin only vs. non-insulin antidiabetic drugs). Subjects having had a previous episode of the same type leading to an ED visit had a higher rate of subsequent attendances (IRR for hypoglycaemia = 5.3, 95% CI = 3.9–7.3 and IRR for hyperglycaemia = 3.7, 95% CI = 1.3–10.2). Conclusion: Insulin treatment and having had a prior acute glycaemic event leading to an ED visit were major predictors of ED attendance for hyper and hypoglycaemia in a population of adults with diabetes. [ABSTRACT FROM AUTHOR]

In this paper, we propose an analysis on the joint effect of hyperspectral and light detection and ranging (LIDAR) data for the classification of complex forest areas. In greater detail, we present: 1) an advanced system for the joint use of hyperspectral and LIDAR data in complex classification problems; 2) an investigation on the effectiveness of the very promising support vector machines (SVMs) and Gaussian maximum likelihood with leave-one-out-covariance algorithm classifiers for the analysis of complex forest scenarios characterized from a high number of species in a multisource framework; and 3) an analysis on the effectiveness of different LIDAR returns and channels (elevation and intensity) for increasing the classification accuracy obtained with hyperspectral images, particularly in relation to the discrimination of very similar classes. Several experiments carried out on a complex forest area in Italy provide interesting conclusions on the effectiveness and potentialities of the joint use of hyperspectral and LIDAR data and on the accuracy of the different classification techniques analyzed in the proposed system. In particular, the elevation channel of the first LIDAR return was very effective for the separation of species with similar spectral signatures but different mean heights, and the SVM classifier proved to be very robust and accurate in the exploitation of the considered multisource data. [ABSTRACT FROM AUTHOR]

Idiopathic pulmonary fibrosis (IPF) is a chronic disease with unknown etiology and poor prognosis. Little is known about the epidemiology of this disease; most of the studies are limited by small and restricted cohort studies. We aim to investigate the epidemiology of IPF in the Italian primary care setting using the Health Search Database (HSD) between January 2002 and June 2017. In an attempt to define cases of IPF we adopted iterative combinations of International Classification of Diseases Ninth Revision (ICD-9-CM) and other clinical investigations according to three different operational Algorithms. Incidence and prevalence rate, according to the three Algorithms defining IPF, were calculated and the association with candidate determinants [sex, age, gastro-esophageal reflux (GERD) and smoking status] was evaluated. We identified 1,104,307 eligible patients. The prevalence rate of IPF varies between 2.6 to 24.3 per 100.000 person-year, using algorithm 1 and from 0.8 to 7 using algorithm 3. The incidence rate of IPF varies between 1.25 and 3.77 per 100.000 person-years, using algorithm 1 and from 0.10 to 1.61 using algorithm 3. The mean adjusted incidence rate ratio of IPF, using algorithm 1, is 2.33 (95% CI 2.11-2.57) per 100.000 person-years. Over the study years, the trend of prevalence was statistically significantly increasing while the incidence rate started to increase in the last 3 years. The analyses on candidate determinants showed that patients aged 61 years or older, those suffering from GERD, and former smokers were statistically significantly at greater risk of incurring IPF. To our knowledge, this is one of the first European IPF epidemiological studies conducted in primary care. The increase of the incidence rates is likely due to a growing awareness for IPF among General Practitioners, while the increase of prevalence rates may be due to an increase of survival, a result of recent advances in the diagnosis, management and therapies for the disease. [ABSTRACT FROM AUTHOR]

Human cytomegalovirus (HCMV) infections are among the most common complications arising in transplant patients, elevating the risk of various complications including loss of graft and death. HCMV infections are also responsible for more congenital infections worldwide than any other agent. Congenital HCMV (cCMV) infections are the leading nongenetic cause of sensorineural hearing loss and a source of significant neurological disabilities in children. While there is overlap in the clinical and laboratory approaches to diagnosis of HCMV infections in these settings, the management, follow-up, treatment, and diagnostic strategies differ considerably. As yet, no country has implemented a universal screening program for cCMV. Here, we summarize the issues, limitations, and application of diagnostic strategies for transplant recipients and congenital infection, including examples of screening programs for congenital HCMV that have been implemented at several centers in Japan, Italy, and the United States. [ABSTRACT FROM AUTHOR]

Background: Dependency in older ages is increasing. Many older persons receive care while living in the community. We aimed to identify the predictive value of four clinical measurements to predict home care intensity in older patients following discharge from hospital to home care over 90 days. Methods: We included 425 inpatients from the "Frailty Department–Local Palliative Care Network" of the local social health authority (ASST) Lecco, Italy (mean age 75.4 years, SD 14.5; female 75.5%). Changes in Health, End-stage disease, and Signs and Symptoms, light version (CHESS-Lite), activities of daily living (ADL), frailty, and the Service Urgency Algorithm. Receiver operative curves were used to calculate the area under the curve (AUC) for predicting Home Care Intensity coefficient (ratio of the number of days when any home care was provided 90 days post-discharge). The interRAI Contact Assessment Instrument was used to calculate these measures. Results: Analysis was stratified using six different home care intensity score cut-offs. CHESS-Lite had a higher AUC for predicting home care intensity at all cut-off levels but was best for predicting the highest level of home care intensity (≥ 0.8) where the AUC was 0.71 (0.64–0.79). The frailty index also had an acceptable AUC. ADL had the lowest AUC. Conclusions: Health instability measured with CHESS-Lite has a high predictive value for identifying home care intensity in geriatric patients after discharge from hospital to home, especially in persons with higher home care intensity scores. Geriatric patients with high health instability should be focused on at discharge to prioritize assessment and initiate timely services for home care support. [ABSTRACT FROM AUTHOR]

Purpose. To describe intravitreal anti-VEGF drug and dexamethasone use in four Italian regions. Methods. Four regional claims databases were used to measure drug prevalence, compare dosing intervals to those recommended in the summary of product characteristics (SPC), and identify switchers. Bilateral treatment and diabetic macular edema (DME) coding algorithms were validated, linking claims with a sample of prospectively collected ophthalmological data. Results. Overall, 41,836 patients received ≥1 study drug in 2010–2016 (4.8 per 10,000 persons). In 2016, anti-VEGF drug use ranged from 0.8 (Basilicata) to 5.7 (Lombardy) per 10,000 persons while intravitreal dexamethasone use ranged from 0.2 (Basilicata) to 1.4 (Lombardy) per 10,000 persons. Overall, 40,815 persons were incident users of study drugs. Among incident users with ≥1 year of follow-up (N = 30,745), 16.0% (N = 4,890) had only one pharmacy claim, especially for ranibizumab (60.9%). Switching occurred in 8.0% of users with ≥2 pharmacy claims (N = 33,637). The algorithms had an accuracy of 83.8 (95% CI: 79.7–87.3) concerning bilateral treatment and 72.3% (95% CI: 67.5–76.8) for DME. Conclusion. Study drug use increased over time in Lombardy, Basilicata, Calabria, and Sicily, despite a large heterogeneity in prevalence of use across regions. Drug treatment appeared to be partly in line with SPC, suggesting that improvement in clinical practice may be needed to maximize drug benefits. [ABSTRACT FROM AUTHOR]

Non‐deferrable medical conditions (NDMC) are clinical entities other than exacerbations of chronic diseases which are too frequently managed by emergency departments. The primary care setting would be indeed the ideal environment to proficiently treat them. Few studies have investigated the epidemiology of NDMC in primary care, especially in Italy. With the aim to better identify NDMC sufferers, we assessed the accuracy of the NDMC definition by means of four algorithms, featured by different specificities and sensitivities. Four algorithms, based on the hierarchical definition of NDMC, were developed to calculate the incidence rate of NDMC in 2014. Each was tested using a Cox univariate model adjusted for gender, comparing patients aged <45 years old with older patients. Algorithms 1 and 3 gave similar results (408.1 vs. 405.5 per 1,000 person‐years); Algorithms 2 and 4 reported sensibly lower rates (84.7 and 84.0 per 1,000 person‐years). Incidence rate of NDMC for female patients was higher than in males and for older age group (i.e. 75–84 age group) than younger patients, for both gender groups. Regarding the regression model, a higher risk of NDMC was estimated in patients aged 45 years or more and in females. This study allowed us to assess the incidence rate of NDMC in Italy which was unexpectedly higher among older patients. Given the crucial role of general practitioners (GPs) in the diagnosis and management of these conditions, as well as the healthcare system reforms imposed by the ongoing financial crisis, our findings may contribute to informing the capacity and strategic workload planning in group of GPs to improve service quality and profitably to reduce the excessive use of emergency departments. [ABSTRACT FROM AUTHOR]

Coastal tide gauges play a very important role in a tsunami warning system, since sea-level data are needed for a correct evaluation of the tsunami threat, and the tsunami arrival has to be recognized as early as possible. Real-time tsunami detection algorithms serve this purpose. For an efficient detection, they have to be calibrated and adapted to the specific local characteristics of the site where they are installed, which is easily done when the station has recorded a sufficiently large number of tsunamis. In this case the recorded database can be used to select the best set of parameters enhancing the discrimination power of the algorithm and minimizing the detection time. This chance is however rare, since most of the coastal tide-gauge stations, either historical or of new installation, have recorded only a few tsunamis in their lifetimes, if any. In this case calibration must be carried out by using synthetic tsunami signals, which poses the problem of how to generate them and how to use them. This paper investigates this issue and proposes a calibration approach by using as an example a specific case, which is the calibration of a real-time detection algorithm called TEDA (Tsunami Early Detection Algorithm) for two stations (namely Tremestieri and Catania) in eastern Sicily, Italy, which were recently installed in the frame of the Italian project TSUNET, aiming at improving the tsunami monitoring capacity in a region that is one of the most hazardous tsunami areas of Italy and of the Mediterranean. [ABSTRACT FROM AUTHOR]

Pattern informatics (PI) algorithm, which was introduced at the beginning of past decade, uses instrumental earthquake catalogs to investigate the time-dependent rate of seismicity in the study area and, based on the information from past events, calculates the probabilities for the occurrence of future large earthquakes. The main measure in this method is the number of events above a specified magnitude threshold M that is counted over a gridded area. PI has been applied in several regions of the world and different variants of the method have been developed over the past decade. Hence, the problem of formally evaluating and comparing the performances of the different PI variants needs to be addressed from an operational perspective, in order to identify the preferred application scheme and as well as to improve the performances of the method. In this study, PI is applied for the first time to the retrospective analysis of the earthquake catalogs of Iran and Italy, so as to check whether this method could forecast the past large events in these two regions with different level of data completeness and complex seismotectonic setting. The original PI algorithm and one of its modified variants, as well as the relative intensity (RI) model, are used to check the stability and statistical significance of the obtained results. In order to assess and compare the obtained results, the performances of the different PI variants are analyzed considering different evaluation strategies, which turn out to provide significantly different scores even for the same algorithm variant. We show that a critical point in the assessment of the obtained results is related with the definition and quantification of the space uncertainty of the issued forecasts, that is, with the extent of the territory where large earthquakes are to be expected. Accordingly, we emphasize the need for an appropriate definition of the evaluation strategies, clearly and unambiguously indicating the area where a large earthquake has to be expected. The study shows that, with respect to application in Iran and Italy, the performances of PI algorithm (both original and modified variants) are highly dependent on the selected evaluation strategy and do not provide better information than the simple RI model, which does not account for temporal properties of seismicity evolution. The overall performances can be improved by introducing specific thresholds that discard the less active cells; however, being based on some posterior optimization, a rigorous prospective testing is required to assess the forecasting capability of the method. In this paper, we aim to set up the rules for such testing, including advance definition of the evaluation strategy. [ABSTRACT FROM AUTHOR]

A simplified version of TOMGRO, a well-known model for tomato growth simulation, was delivered by its authors in 1999, with the aim to adapt the program to operational exigencies. This model version was chosen to integrate the functions of a decision support tool, oriented to support Italian tomato growers in soilless crop fertigation management. During the preliminary evaluation phase, its application on data collected in three greenhouse experiments conducted on tomato cv. Jama in Pisa (central Italy) in 2004 and 2005, showed an insufficient accuracy in the Leaf Area Index (LAI) estimation of tomato crops. Consequently, node number and LAI computational algorithms were modified. This paper discusses model modifications, analysing their effects on tomato growth simulation. In comparison with the observed data, the modified model showed a good enhancement of estimation accuracy of node and LAI, with significantly positive effects also on plant and fruit biomass estimation. [ABSTRACT FROM AUTHOR]