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Background and Aims: Alcohol-related hepatitis (AH) is the most severe form of acute alcohol-related liver disease. Maddrey's discriminant function >= 32 defines the severe form of AH, which is associated with a high mortality. Steroid therapy represents the main medical treatment that may reduce short-term mortality. Lille score at day 7 assesses the therapeutic response to steroid therapy. At present, no parameters able to predict the response to steroid therapy have been highlighted. The aim of the present study was to evaluate if baseline prothrombin time (BPT) could predict the response to steroid in severe AH (sAH).Methods: Patients consecutively admitted in two Italian Liver Units, from 2017 to 2022, suffering from sAH were included. Data were collected prospectively. In order to evaluate if BPT could predict steroid response, we assessed the correlation between BPT using the Lille score at day 7.Results: A total of 52 patients received steroid treatment were enrolled in the study. The response to therapy was assessed by Lille score at day 7. Responders were 34 patients (65%), non-responders 18 patients (34%). BPT significantly predicted the steroid response (p < .001). The likelihood of not responding to the steroid therapy was significantly higher in patients with higher BPT (OR = 2.954).Conclusions: BPT value predicted steroid response in patients with sAH. BPT could quickly identify non-responder patients to steroid therapy, reducing the risk of infections and it could allow the early evaluation for liver transplantation.

The paper investigates how the history of the colonial period was narrated in parascholastic materials that complemented school textbooks after the Second World War. The author focuses on the Enciclopedia del Ragazzo Italiano published in Milan by Giuseppe Latronico (1896-1981) with Labor publisher between 1938 and 1940. By analysing the paragraphs dealing with colonialism, the author delineates continuities and changes between the first edition, which appeared during the period of State-sponsored racism, and the 1949 one. The paper shows how Italian colonial imagery continued after the Second World War when Italians avoided any public reflection on African independence movements. The lack of public debate on colonialism resulted in the persistence of long-lasting cultural constructions that dated back to 19th-century military operations conducted in Africa.

The purpose of this research is to assess the impact of intrinsic and extrinsic motivations to share knowledge on knowledge sabotage, in order to shape a motivational model designed to reduce sabotage incidents in practice. It contributes to knowledge management literature broadening our understanding of knowledge sabotage, which has been highlighted as the most extreme counterproductive knowledge behaviour due to its deliberate nature of harming others for personal gain. In fact, even though knowledge sabotage has been widely identified in organizations, we still know too little about such a potentially dangerous phenomenon. In our empirical investigation, data collection took place through online questionnaires addressed to 329 employees and managers of heterogeneous companies from Europe. Data has been analysed employing a structural equation modelling (SEM) technique, whose results confirmed the relevance of this phenomenon and identified a negative relationship between intrinsic motivations to share knowledge and the phenomenon of knowledge sabotage. In the end, our conclusions can be useful to expand researchers' and practitioners' awareness of the most extreme counterproductive workplace behaviour that threatens the process of intra-organizational knowledge sharing.

Digital Transformation is upending businesses everywhere. While there is ample research on this topic, there is a clear gap when it comes to understanding the changing talent management role of senior executives in digital transformation processes and the demands of individual employees. This article relies on 23 in-depth interviews with senior managers who are leading all or some of the digital transformation efforts of their respective organizations. Our research, using a grounded theory approach, identifies four key activities (and 37 sub-activities or themes) stemming from the new managerial needs and talent management practices arising from DT processes. We branded these key activities “Drive business change”, “Master fluid & loose organizational structures”, “Master Talent Complexity”, and “Prioritize learning”. This paper aims to provide an overview into the thinking and managerial practices of senior executives in a digital transformation context and complements the limited number of studies that examine the intersection between managerial actions and digital transformation. It also provides a conceptual framework that captures the key managerial demands arising from digital transformation processes and identifies key actions made by senior executives as part of these processes, which can be leveraged by both scholars and practitioners alike.

The thousand-year-old history of Canossa castle is still unclear in many aspects. In fact, despite the fame of the Rocca, the systematic studies that took into consideration also the data of archives related to it have been few. The renewed interest in archaeological investigations at the Castle has led in recent years to a research project that would fill this gap by systematizing past data. The preliminary results have been encouraging and much new it is possible to say about the research and valorisation activities carried out by the archaeologist Don Gaetano Chierici.

La presente ricerca, focalizzata sull’evoluzione di Piacenza tra età antica e altomedioevo, si mette nel solco del dibattito sull’archeologia urbana sviluppatosi in Italia a partire dagli anni Ottanta del Novecento. Dopo la raccolta dei dati di scavo negli archivi della Soprintendenza e delle testimonianze delle fonti scritte è stato creato un geodatabase che potesse gestirli in maniera integrata. Lo studio è stato svolto per temi trasversali che prendessero in esame lo sviluppo dei diversi aspetti di Piacenza dalla fondazione al IX secolo, per poi restituire in fase di conclusione dei quadri sincronici relativi alla città romana, tardoantica e altomedievale. Il quadro che si delinea dallo studio condotto è quello di una città che, seppure interessata dalle vicende storiche che hanno coinvolto buona parte del nord della penisola, tra la tarda Antichità e l’alto Medioevo, mostra sia fenomeni che la accomunano alle altre realtà urbane regionali e extraregionali, sia caratteristiche specifiche scarsamente riscontrate altrove. L’età carolingia è il periodo nel quale il caso piacentino si presenta maggiormente denso di spunti per il tema dell’urbanesimo. Su questo Piacenza, soprattutto per il tramite delle fonti scritte, offre uno spaccato che poche altre città possono dare e che sarebbe ulteriormente implementabile con nuove e mirate ricerche archeologiche., The present research, focused on the evolution of Piacenza between the Roman age and early Middle Ages, takes the moves by the debate on urban archaeology developed in Italy since the eighties of the twentieth century. The study was carried out for transversal themes that examined the development of the different aspects of Piacenza from its foundation to the ninth century A.D. A picture emerges of the city that shows characteristics in common with other regional and extra-regional urban realities and specificities rarely found elsewhere. The Carolingian age is the period in which the case of Piacenza is more dense with ideas for the theme of urbanism. Especially through written sources, the city offers a cross section that few other towns can give and that would be further implemented with new and targeted archaeological research.

Backgrounds Validated tools for predicting individual in-hospital mortality of COVID-19 are lacking. We aimed to develop and to validate a simple clinical prediction rule for early identification of in-hospital mortality of patients with COVID-19. Methods and findings We enrolled 2191 consecutive hospitalized patients with COVID-19 from three Italian dedicated units (derivation cohort: 1810 consecutive patients from Bergamo and Pavia units; validation cohort: 381 consecutive patients from Rome unit). The outcome was in-hospital mortality. Fine and Gray competing risks multivariate model (with discharge as a competing event) was used to develop a prediction rule for in-hospital mortality. Discrimination and calibration were assessed by the area under the receiver operating characteristic curve (AUC) and by Brier score in both the derivation and validation cohorts. Seven variables were independent risk factors for in-hospital mortality: age (Hazard Ratio [HR] 1.08, 95% Confidence Interval [CI] 1.07–1.09), male sex (HR 1.62, 95%CI 1.30–2.00), duration of symptoms before hospital admission <10 days (HR 1.72, 95%CI 1.39–2.12), diabetes (HR 1.21, 95% CI 1.02–1.45), coronary heart disease (HR 1.40 95% CI 1.09–1.80), chronic liver disease (HR 1.78, 95%CI 1.16–2.72), and lactate dehydrogenase levels at admission (HR 1.0003, 95%CI 1.0002–1.0005). The AUC was 0.822 (95%CI 0.722–0.922) in the derivation cohort and 0.820 (95%CI 0.724–0.920) in the validation cohort with good calibration. The prediction rule is freely available as a web-app (COVID-CALC: https://sites.google.com/community. unipa.it/covid-19riskpredictions/c19-rp). Conclusions A validated simple clinical prediction rule can promptly and accurately assess the risk for in-hospital mortality, improving triage and the management of patients with COVID-19.

Objective: To investigate if cerebroplacental ratio (CPR) adds to the predictive value of umbilical artery pulsatility index (UA PI) alone – standard of practice – for adverse perinatal outcome in singleton pregnancies. Design and setting: Meta-analysis based on individual participant data (IPD). Population or sample: Ten centres provided 17 data sets for 21 661 participants, 18 731 of which could be included. Sample sizes per data set ranged from 207 to 9215 individuals. Patient populations varied from uncomplicated to complicated pregnancies. Methods: In a collaborative, pooled analysis, we compared the prognostic value of combining CPR with UA PI, versus UA PI only and CPR only, with a one-stage IPD approach. After multiple imputation of missing values, we used multilevel multivariable logistic regression to develop prediction models. We evaluated the classification performance of all models with receiver operating characteristics analysis. We performed subgroup analyses according to gestational age, birthweight centile and estimated fetal weight centile. Main outcome measures: Composite adverse perinatal outcome, defined as perinatal death, caesarean section for fetal distress or neonatal unit admission. Results: Adverse outcomes occurred in 3423 (18%) participants. The model with UA PI alone resulted in an area under the curve (AUC) of 0.775 (95% CI 0.709–0.828) and with CPR alone in an AUC of 0.778 (95% CI 0.715–0.831). Addition of CPR to the UA PI model resulted in an increase in the AUC of 0.003 points (0.778, 95% CI 0.714–0.831). These results were consistent across all subgroups. Conclusions: Cerebroplacental ratio added no predictive value for adverse perinatal outcome beyond UA PI, when assessing singleton pregnancies, irrespective of gestational age or fetal size. Tweetable abstract: Doppler measurement of cerebroplacental ratio in clinical practice has limited added predictive value to umbilical artery alone.

Background: The objective evaluation of the olfactory function of coronavirus disease 2019 patients is difficult because of logistical and operator-safety problems. For this reason, in the literature, the data obtained from psychophysical tests are few and based on small case series. Methods: A multicenter, cohort study conducted in seven European hospitals between March 22 and August 20, 2020. The Sniffin-Sticks test and the Connecticut Chemosensory Clinical Research Center orthonasal olfaction test were used to objectively evaluate the olfactory function. Results: This study included 774 patients, of these 481 (62.1%) presented olfactory dysfunction (OD): 280 were hyposmic and 201 were anosmic. There was a significant difference between self-reported anosmia/hyposmia and psychophysical test results (p = 0.006). Patients with gastroesophageal disorders reported a significantly higher probability of presenting hyposmia (OR 1.86; p = 0.015) and anosmia (OR 2.425; p < 0.001). Fever, chest pain, and phlegm significantly increased the likelihood of having hyposmia but not anosmia or an olfactory disturbance. In contrast, patients with dyspnea, dysphonia, and severe-to-critical COVID-19 were significantly more likely to have no anosmia, while these symptoms had no effect on the risk of developing hyposmia or an OD. Conclusions: Psychophysical assessment represents a significantly more accurate assessment tool for olfactory function than patient self-reported clinical outcomes. Olfactory disturbances appear to be largely independent from the epidemiological and clinical characteristics of the patients. The non-association with rhinitis symptoms and the high prevalence as a presenting symptom make olfactory disturbances an important symptom in the differential diagnosis between COVID-19 and common flu., SCOPUS: ar.j, info:eu-repo/semantics/published

We discuss in detail the quantum rotationally inelastic dynamics of an important anion often discussed as a possible constituent of the interstellar medium (ISM) and in different environments of circumstellar envelopes: the C2 - molecular ion. Its interaction forces with one of the most abundant atoms of the ISM, the neutral helium atom, are obtained for the first time using ab initio quantum chemistry methods. The overall angular anisotropy of the potential energy surface is analysed in order to link its features with the efficiency of transferring energy from the abundant He atoms to the internal rotational levels of this molecular anion. Calculations of the corresponding rotational state-to-state inelastic cross sections, for both excitation and de-excitation paths are obtained by using a multichannel quantum method. The corresponding inelastic rates at the temperatures of interest are determined and their role in distributing molecular states over the different populations of the rotational levels at the temperatures of that environment is discussed. These computed rates are also linked to the dynamical behaviour of the title molecule when confined in cold ion traps and made to interact with He as the common buffer gas, in preparation for state-selective photo-detachment experiments.

Splanchnic vein thrombosis (SVT) is a possible complication of acute pancreatitis (AP). There are no precise guidelines on the use of anticoagulant therapy (AT) in these patients. The aim of the study was to determine the safety and the efficacy of AT in AP-associated SVT. Two hundred twenty-one patients were retrospectively and consecutively enrolled from the Pancreatic Outpatient Clinic of the “A. Gemelli” hospital. Patients had a diagnosis of AP and a diagnostic imaging to evaluate whether they had or not SVT. Twenty-seven out of 221 AP patients had SVT (12.21%) and AT therapy was administered to 16 patients (59.3%), for 5.2 ± 2.2 months. A therapeutic dose of low molecular weight heparin was administered (100 UI/kg b.i.d.) at the diagnosis, with fondaparinux 7.5 mg/day, or vitamin K antagonist, or the novel direct oral anti-coagulants, upon discharge. The presence of SVT resulted significantly associated to male sex (p = 0.002). The recanalization rates were 11/16 (68.7%) in patients who received AT, and 3/11 (27.3%) in patients who did not receive it. There was a significant difference between the recanalization rates with and without AT (p = 0.03, OR 5.87). No SVT recurrence was registered during follow-up. No treated patient developed haemorrhagic complications after AT. No deaths were recorded, either in the group undergoing AT or in the one that was not. In conclusion, AT in AP-associated SVT appears to be safe and effective; yet prospective clinical trials are needed to confirm our results.

Background: : Ultrasound (US) is frequently the first line imaging technique used in patients with abdominal pain and clinical suspicion of chronic pancreatitis (CP), but its role in the diagnosis and follow-up of CP is still controversial. Aims: : We aimed to develop a dedicated score for the US staging of CP and to evaluate the agreement of this score with standard imaging techniques. Methods: : Ninety consecutive patients with a diagnosis of CP referred to the pancreatic outpatient clinic of A. Gemelli Hospital between June and September 2018 were recruited in the study. Patients underwent pancreatic US to evaluate different morphological parameters to develop an US based score system, called the Gemelli UltraSound Chronic Pancreatitis (USCP) score. Results: : The Gemelli USCP score significantly increased according to the Cambridge score for both mean value (p<0.0001) and each parameter evaluated (p<0.0001). Moreover, we found a significant correlation between the score and laboratory parameters related to pancreatic exocrine insufficiency such as vitamin D, B9, and B12 deficiency and fecal elastase values (p<0.0001). Conclusions: : The development of a dedicated US score could be useful in the follow up of patients with CP as alternative non-invasive technique to standard radiological imaging.

Background: Respiratory alemtuzumab-related adverse events are clinically heterogeneous and include respiratory infections, infusion-related dyspnea, hypoxia and secondary autoimmune disorders. Case report: Here we report three cases of drug-induced lung disease following treatment with alemtuzumab in multiple sclerosis patients. First case was diagnosed as a non-specified intestitial pneumonitis associated with organizing pneumonia with subacute onset, second case was an acute respiratory distress syndrome with onset during second cycle, third case was a diffuse acute alveolar hemorrhage during first cycle infusion. All patients developed acute respiratory failure, reversible after steroid therapy. Conclusions: Clinicians should be aware to early recognize acute and subacute respiratory adverse events for a promptly management. In these patients re-treatment is challenging.

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Wernicke-Korsakoff encephalopathy (WKE) is a neurologic disease due to a severe thiamine deficiency. This vitamin, an essential cofactor for cellular metabolism, is not produced by the human organism, so its total supply comes from diet, being absorbed from the duodenum. Wernicke described its acute onset in 1881; its classic form is characterized by the triad of ataxia, abnormal mental state, and ocular abnormalities (especially nystagmus). Although it is most commonly associated with chronic alcohol misuse (90% of all cases in USA), other medical conditions resulting in inadequate thiamine intake, such as gastrointestinal disease (vomiting, diarrhea), hyperemesis gravidarum, hemodialysis, sepsis, GI cancer, Crohn’s disease, psychiatric disorders, HIV infection, and malnutrition can determinate the onset of the disease. Another well-established cause of WKE is bariatric surgery. However, in most bariatric surgery-related cases, there were no specific neurological symptoms and no definitive neuroimaging markers were established. Moreover, a recent review upon the most common bariatric procedures showed that the large majority of cases develop in a range of onset between 4 and 12 weeks post-surgery (12 days up to 18 months). No significant difference in the time of onset in the different surgical procedures was observed. Roux-en-Y gastric bypass was the most related procedure (52%), followed by sleeve gastrectomy (21%); biliopancreatic diversion was associated with a small percentage of cases of WKE (3%). Biliopancreatic diversion is a single surgical procedure combining a sleeve gastrectomy and gut bypass, connecting the reduced stomach (about the 20% of the former organ) and the second tract of duodenum with the last part of the small intestine, with the aim to reduce calorie and nutrient absorption. Precipitating factors were persistent vomiting, diarrhea, rapid weight loss, anorexia, minimal food intake or glucose-containing intravenous feeding, alcohol misuse, a

Acute pancreatitis (AP) is the most common gastrointestinal disorder requiring hospitalization, with a high rate of morbidity and mortality. Severe AP is characterized by the presence of persistent organ failure involving single or multiple organs. Clinical evolution, laboratory and radiological assessment are necessary to evaluate the prognosis and inform the management of AP. The onset of severe AP may be classified in two principal phases. The early phase, during the first week, is characterized by the activation of the auto-inflammatory cascade, gut dysbiosis, bacterial translocation, and the down-regulation of immune responses. The late phase is characterized by the development of local and systemic complications. Several old paradigms have been amended in the management of AP patients, such as the indication of nutrition, the use of antibiotic therapy, pain control strategies, and even the use of surgery. Real world evidence has shown that in the majority of cases a step-up approach is most effective. In this review, we discuss the clinical assessment and improvements to the management of patients with severe AP in a high volume center where a multi-disciplinary approach is performed.

Background: Multiple sclerosis (MS) is a chronic, immune-mediated, inflammatory, neurodegenerative disorder. Many studies are investigating the potential role of body fluid biomarkers as prognostic factors for early identification of patients presenting with clinical isolated syndrome (CIS) at high risk for conversion to MS or to recognize RRMS patients at high risk for progression. Objectives: To evaluate the correlation between levels of BAFF, chitinase 3-like 1 (CHI3L1), sCD163, Osteopontin (OPN), both on serum and cerebral spinal fluid (CSF), and the disease activity and progression. We also want to explore a possible relationship between serological and CSF biomarker's levels. Patients and methods: We enrolled 82 patients between June 2014 and June 2016. Seventy-one received a diagnosis of demyelinating disease of CNS (46 RRMS and 25 CIS), while 11 were affected by other neurological diseases. All patients underwent a neural axis MRI, lumbar puncture and blood samples. Levels of BAFF, CHI3L1, sCD163, OPN on serum and CSF were analyzed by Luminex xMAP system, with a kit 11-plex ad hoc. Results: The CSF CHI3L1, sCD163 and OPN levels were significantly higher in MS patients than in controls. We did not find significant differences in serum CHI3L1, sCD163 and OPN levels, nor CSF or serum BAFF levels between patient and control groups. We found significantly higher CSF level of sCD163 and CHI3L1 in all patients’ subgroups compared with controls, while OPN was higher in CIS and RR subgroups. We did not find significant differences for serum and CSF levels of all the markers between patients with or without clinical or radiological disease activity. CSF sCD163 and CHI3L1 levels was significant higher in CIS patients who converted to MS (p < 0.05). Using ROC curve analysis, CSF sCD163 resulted the best predictive factor. CSF CHI3L1 and OPN levels resulted useful independent predictors too. Combined ROCs of those three analytes demonstrated a better predictive value

Background: Acute disseminated encephalomyelitis (ADEM) is a monophasic post-infectious demyelinating disease, clinically defined by the acute onset of polyfocal neurological deficits including encephalopathy. A subset of ADEM patients will subsequently be diagnosed with relapsing disorders, including recurrent DEM (RDEM), multiphasic DEM (MDEM), neuromyelitis optica spectrum disorders (NMOSD) and multiple sclerosis (MS). Here we describe the case of an adult patient, who presented two ADEM-like episodes after a very long (8 years) symptoms-free period. Clinical case: A 48 years old man presented a first case of sub-acute onset of encephalopathy and dysarthria with MRI findings suggestive for ADEM for which he underwent an intravenous and oral steroid treatment followed by a complete clinical remission. After 8 years he presented a new sub-acute onset of encephalopathy and balance disorders with the onset of new lesions at the MRI. The search for oligoclonal band (OCB) showed a single CSF-restricted IgG band. Suspecting a new ADEM episode he was treated with intravenous steroids without benefit and 3 apheresis sessions with clinical improvement followed by an oral steroid treatment. After 2 months he experienced a paroxysmal episode of dysarthria, upper and lower left limbs impairment and urge incontinence with a stable new brain and spinal cord MRI. The search for anti-aquaporin-4 and anti-MOG (cell-based assay) antibodies was repeated twice within a 6 months span and resulted in both cases negative. The patient was treated with Rituximab (1g followed by 1g after 15 days, followed by 1g after 6 months) with stability of the neurological and radiological examinations at the last follow-up. Conclusions: To the best of our knowledge, this is the first case of MDEM in which the two episodes of ADEM occurred 8 years apart. Although this case fulfills the diagnostic criteria for MDEM, the time elapsed between the two episodes is very long. Therefore, we cannot exclude th

Nutritional support is a crucial issue in Acute Pancreatitis (AP) management. Recommendations on nutrition in AP are still not completely translated in the clinical practice. We aimed to compare and evaluate the effects of parenteral nutrition (PN) vs oral/enteral nutrition (EN) on several clinical and economic outcomes in AP. This is a retrospective monocentric study conducted in a tertiary care center for pancreatic diseases. The primary outcomes were length of hospital stay (LOS) and associated costs. The secondary outcomes were the use and cost of antibiotics and fluid therapy, and the complication's rates. One hundred seventy-one patients were included from January 2015 to January 2018. Patients were 69 (40.4%) in PN group and 102 (59.6%) in EN group. There was a significant reduction in LOS in EN vs PN group in both mild AP (p < 0.0001), and moderate-severe AP (p < 0.005). There was a significant reduction in the total hospitalization costs in EN group vs PN group in both mild AP (p < 0.0001), and moderate-severe AP (p < 0.005). There was a significant reduction in the total costs of antibiotics and pain therapy in EN vs PN group (p < 0.0001 and p = 0.05, respectively). Finally, a significant reduction in the infected peri-pancreatic fluid collections rate (p = 0.04) was observed in EN vs PN group. The use of EN in AP is associated with substantial clinical and economic benefits. Thus, the application of the standard of care in nutrition and following AP guidelines is the best way to cure patients and improve healthcare system costs.

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Introduction Brain-Derived Neurotrophic Factor (BDNF) and its most common polymorphism Val66Met are known to have a role in Multiple Sclerosis (MS) pathogenesis. Evidence is accumulating that there is an involvement of DNA methylation in the regulation of BDNF expression. The aim of this study was to assess in blood samples of MS patients the correlation between the methylation status of the CpG site near BDNF-Val66Met polymorphism and the severity of the disease. Methods We recruited 209 MS patients that were genotyped for the BDNF Val66Met polymorphism. For each patient we quantitatively measured the methylation level of cytosine included in the exonic CpG site that can be created or abolished by the Val66Met BDNF polymorphism. Furthermore, we analyzed the clinical history of each patient and determined the time elapsed since the onset of the disease and an EDSS score of 6.0. Results The genetic analysis identified 122 (58.4%) subjects carrying the Val/Val genotype, 81 (38.8%) with Val/Met genotype, and 6 (2.8%) carrying the Met/Met genotype. When the endpoint of an EDSS score of 6 was taken into account by means of a survival analysis, 52 failures (i.e., reaching an EDSS score of 6) were reported. When the sample was stratified according to the percentage of the BDNF methylation, subjects falling below the median (median methylation = 81%) were at higher risk of failure (IRD = 0.016; 95%CI = 0.0050- 0.0279; p = 0.004). Conclusions In patients with a high disease progression the hypomethylation of the BDNF gene could increase the secretion of the protective neurotrophin, so epigenetic modifications could be the organism response to limit a brain functional reserve loss. Our study suggests that the percentage of methylation of the BDNF gene could be used as a prognostic factor for disease progression toward a high disability in MS patient.

Objectives: High-dose pulsed methylprednisolone-related liver injury cases have been reported in the literature, but a prospective study in patients with multiple sclerosis (MS) has never been performed. The aim of this study was to evaluate the prevalence and severity of liver injury in patients with MS after pulsed methylprednisolone therapy. Methods: We performed a prospective observational single-center study on patients with MS treated with i.v. methylprednisolone 1,000 mg/day for 5 days. We tested the liver functionality before and 2 weeks after the treatment. In case of severe liver injury, defined according to "Hy's law," a comprehensive hepatologic workup was performed. Results: During a 12-month observation period, we collected data on 251 cycles of i.v. steroid treatment of 175 patients with MS. After excluding eight cycles presenting a basal alteration of the biochemical liver tests, we observed a prevalence of 8.6% of liver injury in MS patients treated with pulsed methylprednisolone for clinical and neuroradiological relapses. In 2.5% of the patients, the liver injury was severe according to Hy's law; after a comprehensive hepatologic workup, three of them received a diagnosis of drug-induced liver injury and the other three of autoimmune hepatitis. Conclusions: Liver injury after pulsed methylprednisolone therapy in patients with MS is not infrequent, and a close monitoring of aminotransferase level before treatment and 2 weeks later seems advisable.

We introduce a first proofs-as-parallel-programs correspondence for classical logic. We define a parallel and more powerful extension of the simply typed lambda calculus corresponding to an analytic natural deduction based on the excluded middle law. The resulting functional language features a natural higher-order communication mechanism between processes, which also supports broadcasting. The normalization procedure makes use of reductions that implement novel techniques for handling and transmitting process closures., Comment: In Proceedings GandALF 2018, arXiv:1809.02416. arXiv admin note: text overlap with arXiv:1607.05120

Serious games and gamification to engage consumers in pro-environmental behaviours for energy efficiency are in their infancy. To date, despite growing interest and some initial attempts, their potential to engage consumers in energy efficiency behaviours has been understudied. To provide an overview of serious games and gamification to engage individuals in pro-environmental behaviours for energy efficiency, a computer-based search for relevant publications was performed in several databases. After applying the inclusion criteria and removing duplicates, 10 papers were included in this systematic review. Results showed that serious games and gamification have been used in three different areas related to energy efficiency: environmental education, consumption awareness, and pro-environmental behaviours. This review also showed that applied gaming interventions can be used in more than one of these three areas (comprehensive interventions). The main observation to be drawn from this review is that both serious games and gamification can foster energy-saving behaviours and vary widely in terms of type of games and of features that might be appealing and motivating

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Introduction Fatigue and sleep disorders are frequently reported in patients affected by Multiple Sclerosis (MS) but the causes and the relationship are not yet fully understood. This study aimed at evaluating their prevalence, at determining the relationships between clinical findings of MS and the occurrence of sleep disorders and at investigating the relations between sleep disorders and fatigue. Methods One hundred and two MS patients were enrolled in the study. They were analyzed on both their clinical features (type of MS, disease duration, clinical severity, type of treatment, presence of spinal demyelinating lesions) and specific scales scores (Expanded Disability Status Scale, Modified Fatigue Impact Scale - MFIS, Self-Administered Anxiety Scale - SAS, Beck's Depression Inventory - BDI, Pittsburgh Sleep Quality Index - PSQI, Epworth Sleepiness Scale - ESS, and the Berlin's questionnaire for Obstruction Sleep Apnea Syndrome - OSAS). Results Patients with poor sleep quality are more frequently fatigued (p = 0.001), have higher MFIS global scores (p < 0.001), higher prevalence of RLS symptoms (p = 0.049), and show higher scores at BDI (p = 0.017) and SAS (p ≤ 0.001). Conversely patients with fatigue show older age (p = 0.005), higher prevalence of sleepiness (p = 0.021), higher prevalence of RLS symptoms (p = 0.030), higher prevalence of poor sleep quality (p < 0.001) with higher PSQI scores (p < 0.001), higher scores on the BDI (p < 0.001) and SAS (p ≤ 0.001). Conclusion This study shows that MS is associated with a high prevalence of sleep complaints, including subjectively poor sleep quality, excessive daytime sleepiness, RLS and symptoms of OSAS. Further, it demonstrated a strict relation between fatigue and sleep disorders. Finally, it underlines their relationship with anxiety and depression in MS patients.

Introduction Dimethyl-fumarate is a recently approved drug for relapsing-remitting Multiple Sclerosis in Italy. Clinical case A 55-year-old woman started therapy with dimethyl-fumarate on June 2014; it was well-tolerated aside from moderate flushing. Starting September 2014 she noticed a progressive hair loss, that neither the dermatological examination nor clinical and medical history nor blood investigations could explain. The hair loss slowed down after two months and was followed by a hair growth back. Discussion Transient hair loss is not a reported side effect of dimethyl-fumarate therapy but by excluding any known cause we attributed it to the beginning of the new therapy.

We present a web document classification system based on the assumption that the images of a web page are those elements which mainly attract the attention of the user. This assumption implies that the text contained in the visual block in which an image is located, called semantic image-block, should contain relevant information about the page contents. In this paper we propose a new metric, called the Inverse Term Relevance Metric, aimed at assigning higher weighs to relevant terms contained into relevant image-blocks identified by performing a visual layout analysis. The traditional TFxIDF model is modified accordingly and used in the classification task. The effectiveness of this new metric has been validated using different classification algorithms, both supervised and unsupervised.

This paper extends previous research to analytically identify leaks within a water distribution network (WDN), by combining hydraulic simulation and network science based data analysis techniques. The WDNmodel is used to run several 'leakage scenarios', by varying leak location (pipe) and severity, and to build a datasetwith corresponding variations in pressure and flow, induced by the leak. All junctions and pipes are considered for potential pressure and flow sensors deployment; a clustering procedure on these locations identifies the most relevant nodes and pipes, and cost-effectiveness was considered. A graph is then generated from the dataset, having scenarios as nodes and edges weighted by the similarity between each pair of nodes (scenarios), in terms of pressure and flowvariation due to the leak. Spectral clustering groups together similar scenarios in the eigen-space spanned by the most relevant eigen-vectors of the Laplacian matrix of the graph. This method uses superior traditional techniques. Finally, support vectormachines classification learning is used to learn the relation between variations in pressure and flow at the deployed meters and the most probable set of pipes affected by the leak.

Background: The complexity of biological data related to the genetic origins of tumour cells, originates significant challenges to glean valuable knowledge that can be used to predict therapeutic responses. In order to discover a link between gene expression profiles and drug responses, a computational framework based on Consensus p-Median clustering is proposed. The main goal is to simultaneously predict (in silico) anticancer responses by extracting common patterns among tumour cell lines, selecting genes that could potentially explain the therapy outcome and finally learning a probabilistic model able to predict the therapeutic responses. Results: The experimental investigation performed on the NCI60 dataset highlights three main findings: (1) Consensus p-Median is able to create groups of cell lines that are highly correlated both in terms of gene expression and drug response; (2) from a biological point of view, the proposed approach enables the selection of genes that are strongly involved in several cancer processes; (3) the final prediction of drug responses, built upon Consensus p-Median and the selected genes, represents a promising step for predicting potential useful drugs. Conclusion: The proposed learning framework represents a promising approach predicting drug response in tumour cells.

Support Vector Machines (SVMs) classification learning is a powerful paradigm to investigate inverse input-output relationship of a specific problem according to some available and representative dataset. In particular, SVMs are able to identify even non-linear relationship by mapping non-linearly separable data into potentially linearly separable one through families of spatial transformation (kernel trick). With respect to this, several parameters (i.e., kernel function and its internal parameters) have to be tuned; however the optimal configuration is usually difficult to be defined a-priori. In this paper, a hyper-solution framework for SVM classification is presented. The main idea is to perform, simultaneously, three different SVM-based classification learning tasks: Model Selection, Multiple Kernel Learning and Ensemble Learning. The meta-heuristic known as Genetic Algorithms (GA) has been proposed to search for the most reliable final hyper-classifier (an SVM with a basic kernel, an SVM with a combination of kernel, or an ensemble of different SVMs, respectively), and the corresponding optimal configuration. We have applied the proposed framework on a critical and quite complex problem: the on-line assessment of structural health of aircraft fuselage panels, a crucial task both in military and civilian settings. In particular, the framework has been used to implement a diagnosis task, that is detecting a possible damage and identifying the structural component involved, according to the strain field measured through a monitoring sensor network deployed on the helicopter fuselage panels. Finite Elements (FE) simulation has been configured to simulate the response of a real panel to different damages. The resulting simulated strain fields have been used to build a dataset. Results obtained through 3 folds-cross validation proved the framework is reliable. Finally, when compared to results obtained by the authors in a previous work based on Artificial Neural Ne

A graph based analysis is proposed to improve leakage management in water distribution networks. Starting from the model of the network, leakage scenarios, created through hydraulic simulation (EPANET), are considered as nodes in a graph, whose edges are weighted by the similarity between each pair of nodes (scenarios), in terms of pressure and flow variation due to the leak. The graph is then analyzed in the eigenspace of its Normalized Laplacian matrix and specifically into the eigensubspace spanned by the most relevant eigenvectors, allowing Spectral Clustering, which is more effective than traditional techniques but with much higher computational requirements, to be applied also to large scale problems. The results obtained in the eigenspace are eventually mapped back into the physical space where the capability of leakage localization may be further improved through the fusion with leak severity estimation. © 2013 The Authors.

This article attempts to demonstrate that the senses are the the primary approach necessary for knowledge. The utility of this approach can be documented with two examples. Firstly, from the writings of St. Agustine emerges the importance of the senses in relation to the Risen Lord, also easily applicable to the rest of the reality. Secondly, from the writings of Cartesio it results that he refuses the senses as the first moment of knowing; hence, the reason does not know the reality fully. The most significant results of this work are: of not forgetting the hidden treasure of the earth; of recognizing the unity of sensibility and of the interior disposition of a human person., Il tentativo dell’articolo è quello di mostrare che i sensi sono il primo approccio necessario alla conoscenza. Con due esempi si documenta l’utilità di questo approccio. Il primo è tratto da alcuni brani di s. Agostino in cui emerge in modo l’importanza dei sensi nel rapporto con Gesù risorto, facilmente applicabile al resto della realtà. Il secondo esempio è tratto da alcuni brani di Cartesio in cui il filosofo rifiuta i sensi come primo momento della conoscenza. In questo modo ha portato la ragione a non conoscere la realtà. I frutti più significativi del lavoro sono quelli di non dimenticare il tesoro nascosto nella terra e riconoscere l’unità di sensibilità e di atteggiamento interiore nella persona umana.

A machine learning investigation on Oral Anticoagulation Therapy (OAT) in elderly patients to improve the dynamic network modelization of the bioclinical process of coagulation cascade. We compared and tested techniques of supervised Machine Learning for classification and regression analysis, in order to develop an innovative model, compared to those currently available in the literature. The results of this investigation can provide adequate support for dual purpose: give an advantage in managing the therapy in patients with an increased risk of adverse drug events (ADRs), as elderly patients are; define pharmacodynamics characteristics of the drug employed in the oral anticoagulant therapy to finely configure a mechanistic model of involved pathway, providing further insights in therapy management. Therefore, simultaneously, we investigated and tested techniques of modeling and simulation in order to analyze the system in terms of both structure and dynamics to specify the qualitative and quantitative properties of the system. Such an approach would make it possible to profile a manageable model of the coagulation cascade which is able to adapt to the need for integration of recent pharmacological and genetic data, distinctive of modern pharmacogenomic characterization of anticoagulant therapy. Among the different mathematical modeling frameworks that have evaluated, the Temporal Extension of Petri Nets (Timed Petri Net), in particular the Stochastic Petri Net (SPN), seem to offer the most suitable characteristics. They permit an approach to modelization, combining an intuitive graphical representation to a valid solid mathematical, which give rise to a model numerically solving, able to represent the relational components and the quantitative time-dependent behaviors, including randomness, of a complex biological system such as coagulation cascade. The employment of such a framework, compared to the most popular models to differential equations provides a tool t

Data driven on-line assessment of structural health of aircraft fuselage panels is crucial both in military and civilian settings. This paper shows how Support Vector Machines (SVM) and Genetic Algorithm (GA) enable to analyze the strain values acquired through a monitoring sensor network and improve the diagnostic steps: 1) detecting a damage 2) identifying the specific component affected 3) characterizing the damage in terms of centre and size. The first two steps are performed through the SVM while the 3rd step is based on an Artificial Neural Network (ANN). Finally, the remaining useful life is estimated by using ANNs to predict the values of two parameters of the NASGRO equation which is used to estimate the damage propagation. © 2014 IEEE.

This paper presents the approaches proposed in the Italian project TAM-TAM to support a smarter, personalized and sustainable urban mobility, by taking into the loop the users of the transportation services, in particular citizens, tourists and commuters in Milan. A computational module has been defined and developed in order to collect and analyze relevant tweets posted by users as well transport operator. The main goals are two: identifying events (e.g. accidents, sudden traffic jams, service interruptions, etc.) and evaluating the overall sentiment about the service as well as mobility options. Detected events are used by other computational modules of TAM-TAM in order to support a more effective travel planning; on the other hand, the perceived quality of service is provided both to users, enabling more personalized choices, and to transport company, supporting them in the management of mobility supply.

Big Data opportunities arise from high rate data streams acquired through smart sensors and smart meters, which, even for small water utilities, may produce a huge amount of data to be stored. This data enables the application of new data analytics to infer reliable predictive functionalities, with implications ranging from reducing No Revenue Water (NRW) to optimizing the water-energy nexus, meeting ever more pressing budgetary constraints. This paper presents the approach proposed in the EU-FP7-ICT project ICeWater, combining time series clustering, for the identification of typical daily urban water demand patterns, and Support Vector Regression for performing a short term forecast. Promising results obtained on the Water Distribution Network (WDN) in Milan are presented. The approach has been designed to also be applied on smart metering data related to individual customers, addressing Big Data analytics issues. © 2014 WIT Press.

Smart-Maintenance system integrates ICT technologies and mobile devices in order to support maintenance crew in any environment to ensure the availability of up-to-date technical information and provide more competitive services with higher levels of reliability. This allows maintenance management based on off-the-shelf tablets with user authentication based on digital certificates, role definitions, smart tasks assignment and object recognition through image analysis algorithms. © 2014 IEEE.

Worldwide growing water demand has been forcing utilities to successfully manage their costs. Contemporarily, within an era of tight budgets in most economic and social sectors, it affects also Water Distribution Networks (WDN). So, an efficient urban water management is needed to get a balance between consumer satisfaction and infrastructural assets inherent to WDN. Particular case is referred to pipe networks which suffer for frequent leaks, failures and service disruptions. The ensuing costs due to inspection, repair and replacement, are a significant part of operational expenses and give rise to difficult decision making. Recently, the goal regarding the improvement of the traditional leakage management process through the development of analytical leakage localization tools has been brought to the forefront leading to the proposal of several approaches. The basis of all methods relies on the fact that leaks can be detected correlating changes in flow to the output of a simulation model whose parameters are related to both location and severity of the leak. This paper, starting from a previous work of the authors, shows how the critical phases of leak localization can be accomplished through a combination of hydraulic simulation and clustering. The research deals with the benefits provided by Spectral Clustering which is usually adopted for network analysis tasks (e.g., community or sub-network discovery). A transformation from a data points dataset, consisting of leakage scenarios simulated through a hydraulic simulation model, to a similarity graph is presented. Spectral Clustering is then applied on the similarity graph and results are compared with those provided by traditional clustering techniques on the original data points dataset. The proposed spectral approach proved to be more effective with respect to traditional clustering, having a better performance to analytically localize leaks in a water distribution network and, consequently, reducing costs fo

During the last decade the world has witnessed a number of terroristic attacks and security incidents exposing numerous vulnerabilities of the urban environment. Hence the ability to proactively detect and even predict potential threats related to terrorist attacks is crucial for supporting government agencies in order to timely react (pro-act) to potentially alarming terrorist attacks. The work described in this paper is part of an overall larger effort in developing a framework for early identification and prediction of terrorist actions (the PROACTIVE project-http://www.fp7-proactive.eu/). The paper focuses on a near real-time reasoning layer designed around a number of reasoning capabilities for transforming raw and symbolic events into meaningful alerts. The reasoning layer was designed to process information sources at different abstraction levels (e.g. sensor information, police patrol inputs, external semantic crafted data sources) and simulates various expert user roles indicated as crucial in the intelligence analyst work flow (i.e. operational, tactical and strategic user roles). Additionally a special focus was given to support functional requirements of the overall terrorist attack prediction system, as producing near real-time detection of threat events by relying on reliable models regarding terrorist actions and predicting sensitive threat events. Hence the overall designed builds on top of approaches as event driven architecture, complex event processing systems, and machine learning techniques. A prototype implementation of layer is presented in a simulated validation scenario. The prototype allows an expert user to monitor threat probabilities for different physical environments, and influence the sensitivity of these environments in real-time as well as and provide feedback for adapting the machine learning models.

RATIONALE: Oxygen is commonly administered after extubation. Although several devices are available, data about their clinical efficacy are scarce. OBJECTIVES: To compare the effects of the Venturi mask and the nasal high-flow (NHF) therapy on PaO2/FiO2SET ratio after extubation. Secondary endpoints were to assess effects on patient discomfort, adverse events, and clinical outcomes. METHODS: Randomized, controlled, open-label trial on 105 patients with a PaO2/FiO2 ratio less than or equal to 300 immediately before extubation. The Venturi mask (n = 52) or NHF (n = 53) were applied for 48 hours postextubation. MEASUREMENTS AND MAIN RESULTS: PaO2/FiO2SET, patient discomfort caused by the interface and by symptoms of airways dryness (on a 10-point numerical rating scale), interface displacements, oxygen desaturations, need for ventilator support, and reintubation were assessed up to 48 hours after extubation. From the 24th hour, PaO2/FiO2SET was higher with the NHF (287 ± 74 vs. 247 ± 81 at 24 h; P = 0.03). Discomfort related both to the interface and to airways dryness was better with NHF (respectively, 2.6 ± 2.2 vs. 5.1 ± 3.3 at 24 h, P = 0.006; 2.2 ± 1.8 vs. 3.7 ± 2.4 at 24 h, P = 0.002). Fewer patients had interface displacements (32% vs. 56%; P = 0.01), oxygen desaturations (40% vs. 75%; P < 0.001), required reintubation (4% vs. 21%; P = 0.01), or any form of ventilator support (7% vs. 35%; P < 0.001) in the NHF group. CONCLUSIONS: Compared with the Venturi mask, NHF results in better oxygenation for the same set FiO2 after extubation. Use of NHF is associated with better comfort, fewer desaturations and interface displacements, and a lower reintubation rate. Clinical trial registered with www.clinicaltrials.gov (NCT 01575353). KEYWORDS: extubation; high-flow oxygen therapy; oxygen therapy; patient comfort; weaning PMID: 25003980 DOI: 10.1164/rccm.201402-0364OC

In this paper authors perform a variability analysis of a Stochastic Petri Net (SPN) model of the Tissue Factor induced coagulation cascade, one of the most complex biochemical networks. This pathway has been widely analyzed in literature mostly with ordinary differential equations, outlining the general behaviour but without pointing out the intrinsic variability of the system. The SPN formalism can introduce uncertainty to capture this variability and, through computer simulation allows to generate analyzable time series, over a broad range of conditions, to characterize the trend of the main system molecules. We provide a useful tool for the development and management of several observational studies, potentially customizable for each patient. The SPN has been simulated using Tau-Leaping Stochastic Simulation Algorithm, and in order to simulate a large number of models, to test different scenarios, we perform them using High Performance Computing. We analyze different settings for model representing the cases of "healthy" and different " unhealthy" subjects, comparing and testing their variability in order to gain valuable biological insights. © 2013 Springer-Verlag.

The progressive deployment of ICT technologies in the courtroom, jointly with the requirement for paperless judicial folders pushed by e-justice plans, are quickly transforming the traditional judicial folder into an integrated multimedia folder, where documents, audio recordings and video recordings can be accessed via a web-based platform. Most of the available ICT toolesets are aimed at the deployment of case management systems and ICT equipment infrastructure at different organisational levels (court or district). In this paper we present the JUMAS system, stemmed from the homonymous EU project, that instead takes up the challenge of exploiting semantics and machine learning techniques towards a better usability of the multimedia judicial folders. JUMAS provides not only a streamlined content creation and management support for acquiring and sharing the knowledge embedded into judicial folders but also a semantic enrichment of multimedia data for advanced information retrieval tasks.

This paper presents a further innovation of the collaborative decision support network LENVIS, developed in the homonymous European FP7 project (Localized ENVIronmental Services for all, www.lenvis.eu. The system is aimed at supporting water and air quality management and assessing the impact on health computed as emergency hospital admissions. LENVIS has been designed according to the Internet of Services paradigm: several web services, such as sensors data integrators, geographical information systems, predictive and simulation models, and data analytics tools, have been suitably combined to provide complex applications to different stakeholders. LENVIS has been already integrated with popular social networking platforms as Facebook, Twitter and Linkedin ensuring that its information can be assessed seamlessly during a user's regular internet browsing activities and that the user generated contents can be processed by LENVIS analytics. The experiences referred to in this paper are from Milan, a city in Northern Italy with severe pollution problems, mostly due to Particulate Matters (PM), and three cities in Portugal, where the quality of bathing water affects the way in which population uses water resources. © 2013 WIT Press.

The dynamic deployment of Information and Communication Technologies in the judicial field, together with the dematerialization of proceedings pushed by e-justice plans, is encouraging the introduction of novel litigation support systems. In this paper we present two innovative systems, JUMAS and eJRM, which take up the challenge of exploiting semantics and machine learning techniques for managing in-court and out-of-court proceedings respectively. JUMAS stems from the homonymous EU research project ended in 2011. It provides not only a streamlined content creation and management support for acquiring and sharing the knowledge embedded into judicial folders, but also a semantic enrichment of multimedia data towards a better usability of judicial folders. eJRM arises from the related ongoing research project funded in the framework PON “Ricerca e Competitività 2007-2013”. It exploits semantic representation and machine learning reasoning mechanisms towards a support system for online mediation to encourage the resolution of out-of-court disputes and consequently to increase access to justice.

Online assessment of the structural health of aircrafts is crucial both in military and civilian settings. In this paper, Artificial Neural Networks (ANNs) are exploited to obtain a reliable system performing two tasks: diagnosis and prognosis. Diagnosis is devoted to (a) detect a crack, (b) identify the component of the panel involved (bay or stringer) and (c) estimate crack centre and size. Prognosis aims at estimating the evolution of the crack and the Remaining Useful Life (RUL). Training of the ANNs is performed on data sets built through finite elements simulation. Two different ANN hierarchies are presented for diagnosis. Crack evolution is performed for cracks on bay and stringer, separately. Two ANNs are used to estimate the parameters of a crack propagation model (NASGRO equation) for RUL prediction. Copyright © 2013 Inderscience Enterprises Ltd.