Identification of ticks collected in two areas of Sardinia

In the present note the resu1ts of preliminary studies on tick distribution in two areas of Sardinia (Cagliari and Ogliastra) are reported

A case of total bilateral congenital ulnar artery absence detected with CT angiography

Here we report a case of bilateral ulnar artery absence discovered during the treatment of a deep wound in the forearm. Variations and anomalies in the anatomy of the upper limb arteries are frequent, but the one that we found, to our knowledge, has never been described in other clinical cases

Salivary Proteomic Analysis and Acute Graft-versus-Host Disease after Allogeneic Hematopoietic Stem Cell Transplantation

Abstract Graft-versus-host disease (GVHD) is the major life-threatening complication after allogeneic hematopoietic stem cell transplantation (allo-HSCT), developing in 35%-70% of all allo-HSCT recipients despite immunosuppressive prophylaxis. The recent application of proteomic tools that allow screening for differentially expressed or excreted proteins in body fluids could possibly identify specific biomarkers for GVHD. Whole saliva is highly attractive for noninvasive specimen collection. In the present study, we collected saliva specimens from 40 consecutives patients who underwent allo-HSCT between December 2008 and March 2011 at our institution. The specimens were analyzed by HPLC coupled to electrospray-ionization mass spectrometry. Variable expression of S100 protein family members (S100A8, S100A9, and S100A7) was detected. Fourteen of 23 patients with GVHD demonstrated the presence of S100A8, compared with only 2 patients without GVHD and 0 patients in the control group ( P = .001). S100A7 was detectable in 11 of the 23 patients with GVHD but was absent in the other 2 groups ( P = .0001). S100A9-short was detected in 20 patients with GVHD, in 9 patients without GVHD, and in 8 healthy volunteers ( P = .01) Further studies are needed to clarify the role of these proteins as a marker of GVHD or as an index of mucosal inflammation

Thymosin β 4 in colorectal cancer is localized predominantly at the invasion front in tumor cells undergoing epithelial mesenchymal transition.

Thymosin β 4 (Tβ(4)) is a ubiquitous peptide that plays pivotal roles in the cytoskeletal system and in cell differentiation during embryogenesis. Recently, a role for Tβ(4) has been proposed in experimental and human carcinogenesis. This study was aimed at evaluating the correlation between Tβ(4) immunoractivity and colorectal cancer, with particular attemption to tumor cells undergoing epithelial-mesenchymal transition.86 intestinal biopsies were retrospectively analyzed including 76 colorectal adenocarcinomas with evident features of epithelial-mesenchymal transition, and 10 samples of normal colorectal mucosa. Paraffin sections were immunostained for Tβ(4) and for E-cadherin. Total RNA was isolated from frozen specimens obtained, at surgery, from the normal colon mucosa, the deeper regions and the superficial tumor regions in four cases of colon cancer. Tβ(4) immunoreactivity was detected in the vast majority (59/76) of colon carcinomas, showing a patchy distribution, with well differentiated areas significantly more reactive than the less differentiated tumor zones. We also noted a zonal pattern in the majority of tumors, characterized by a progressive increase in immunostaining for Tβ(4) from the superficial toward the deepest tumor regions. The strongest expression for Tβ(4) was frequently detected in invading tumor cells with features of epithelial-mesenchymal transition. The increase in reactivity for Tβ(4) matched with a progressive decrease in E-cadherin expression in invading cancer cells. At mRNA level, the differences in Tβ(4) expression between the surrounding colon mucosa and the tumors samples were not significant.Our data show that Tβ(4) is expressed in the majority of colon cancers, with preferential immunoreactivity in deep tumor regions. The preferential expression of the peptide and the increase in intensity of the immunostaining at the invasion front suggests a possible link between the peptide and the process of epithelial mesenchymal transition, suggesting a role for Tβ(4) in colorectal cancer invasion and metastasis

Long-term home ventilation of children in Italy: A national survey.

BACKGROUND: Improved technology, as well as professional and parental awareness, enable many ventilator-dependent children to live at home. However, the profile of this growing population, the quality and adequacy of home care, and patients' needs still require thorough assessment. OBJECTIVES: To define the characteristics of Italian children receiving long-term home mechanical ventilation (HMV) in Italy. METHODS: A detailed questionnaire was sent to 302 National Health Service hospitals potentially involved in the care of HVM in children (aged <17 years). Information was collected on patient characteristics, type of ventilation, and home respiratory care. RESULTS: A total of 362 HMV children was identified. The prevalence was 4.2 per 100,000 (95% CI: 3.8-4.6), median age was 8 years (interquartile range 4-14), median age at starting mechanical ventilation was 4 years (1-11), and 56% were male. The most frequent diagnostic categories were neuromuscular disorders (49%), lung and upper respiratory tract diseases (18%), hypoxic (ischemic) encephalopathy (13%), and abnormal ventilation control (12%). Medical professionals with nurses (for 62% of children) and physiotherapists (20%) participated in the patients' discharge from hospital, though parents were the primary care giver, and in 47% of cases, the sole care giver. Invasive ventilation was used in 41% and was significantly related to young age, southern regional residence, longer time spent under mechanical ventilation, neuromuscular disorders, or hypoxic (ischemic) encephalopathy. CONCLUSIONS: Care and technical assistance of long-term HMV children need assessment, planning, and resources. A wide variability in pattern of HMV was found throughout Italy. An Italian national ventilation program, as well as a national registry, could be useful in improving the care of these often critically ill children

Withdrawal of mechanical ventilation in amyotrophic lateral sclerosis patients: a multicenter Italian survey

Background: Law 219/2017 was approved in Italy in December 2017, after a years-long debate on the autonomy of healthcare choices. This Law, for the first time in Italian legislation, guarantees the patient's right to request for withdrawal of life-sustaining treatments, including mechanical ventilation (MV). Objective: To investigate the current status of MV withdrawal in amyotrophic lateral sclerosis (ALS) patients in Italy and to assess the impact of Law 219/2017 on this practice. Methods: We conducted a Web-based survey, addressed to Italian neurologists with expertise in ALS care, and members of the Motor Neuron Disease Study Group of the Italian Society of Neurology. Results: Out of 40 ALS Italian centers, 34 (85.0%) responded to the survey. Law 219/2017 was followed by an increasing trend in MV withdrawals, and a significant increase of neurologists involved in this procedure (p 0.004). However, variations across Italian ALS centers were observed, regarding the inconsistent involvement of community health services and palliative care (PC) services, and the intervention and composition of the multidisciplinary team. Conclusions: Law 219/2017 has had a positive impact on the practice of MV withdrawal in ALS patients in Italy. The recent growing public attention on end-of-life care choices, along with the cultural and social changes in Italy, requires further regulatory frameworks that strengthen tools for self-determination, increased investment of resources in community and PC health services, and practical recommendations and guidelines for health workers involved

Intelligenza artificiale e sicurezza: opportunità, rischi e raccomandazioni

L'IA (o intelligenza artificiale) è una disciplina in forte espansione negli ultimi anni e lo sarà sempre più nel prossimo futuro: tuttavia è dal 1956 che l’IA studia l’emulazione dell’intelligenza da parte delle macchine, intese come software e in certi casi hardware. L’IA è nata dall’idea di costruire macchine che - ispirandosi ai processi legati all’intelligenza umana - siano in grado di risolvere problemi complessi, per i quali solitamente si ritiene che sia necessario un qualche tipo di ragionamento intelligente. La principale area di ricerca e applicazione attuale dell’IA è il machine learning (algoritmi che imparano e si adattano in base ai dati che ricevono), che negli ultimi anni ha trovato ampie applicazioni grazie alle reti neurali (modelli matematici composti da neuroni artificiali) che a loro volta hanno consentito la nascita del deep learning (reti neurali di maggiore complessità). Appartengono al mondo dell’IA anche i sistemi esperti, la visione artificiale, il riconoscimento vocale, l’elaborazione del linguaggio naturale, la robotica avanzata e alcune soluzioni di cybersecurity. Quando si parla di IA c'è chi ne è entusiasta pensando alle opportunità, altri sono preoccupati poiché temono tecnologie futuristiche di un mondo in cui i robot sostituiranno l'uomo, gli toglieranno il lavoro e decideranno al suo posto. In realtà l'IA è ampiamente utilizzata già oggi in molti campi, ad esempio nei cellulari, negli oggetti smart (IoT), nelle industry 4.0, per le smart city, nei sistemi di sicurezza informatica, nei sistemi di guida autonoma (drive o parking assistant), nei chat bot di vari siti web; questi sono solo alcuni esempi basati tutti su algoritmi tipici dell’intelligenza artificiale. Grazie all'IA le aziende possono avere svariati vantaggi nel fornire servizi avanzati, personalizzati, prevedere trend, anticipare le scelte degli utenti, ecc. Ma non è tutto oro quel che luccica: ci sono talvolta problemi tecnici, interrogativi etici, rischi di sicurezza, norme e legislazioni non del tutto chiare. Le organizzazioni che già adottano soluzioni basate sull’IA, o quelle che intendono farlo, potrebbero beneficiare di questa pubblicazione per approfondirne le opportunità, i rischi e le relative contromisure. La Community for Security del Clusit si augura che questa pubblicazione possa fornire ai lettori un utile quadro d’insieme di una realtà, come l’intelligenza artificiale, che ci accompagnerà sempre più nella vita personale, sociale e lavorativa.AI (or artificial intelligence) is a booming discipline in recent years and will be increasingly so in the near future.However, it is since 1956 that AI has been studying the emulation of intelligence by machines, understood as software and in some cases hardware. AI arose from the idea of building machines that-inspired by processes related to human intelligence-are able to solve complex problems, for which it is usually believed that some kind of intelligent reasoning is required. The main current area of AI research and application is machine learning (algorithms that learn and adapt based on the data they receive), which has found wide applications in recent years thanks to neural networks (mathematical models composed of artificial neurons), which in turn have enabled the emergence of deep learning (neural networks of greater complexity). Also belonging to the AI world are expert systems, computer vision, speech recognition, natural language processing, advanced robotics and some cybersecurity solutions. When it comes to AI there are those who are enthusiastic about it thinking of the opportunities, others are concerned as they fear futuristic technologies of a world where robots will replace humans, take away their jobs and make decisions for them. In reality, AI is already widely used in many fields, for example, in cell phones, smart objects (IoT), industries 4.0, for smart cities, cybersecurity systems, autonomous driving systems (drive or parking assistant), chat bots on various websites; these are just a few examples all based on typical artificial intelligence algorithms. Thanks to AI, companies can have a variety of advantages in providing advanced, personalized services, predicting trends, anticipating user choices, etc. But not all that glitters is gold: there are sometimes technical problems, ethical questions, security risks, and standards and legislation that are not entirely clear. Organizations already adopting AI-based solutions, or those planning to do so, could benefit from this publication to learn more about the opportunities, risks, and related countermeasures. Clusit's Community for Security hopes that this publication will provide readers with a useful overview of a reality, such as artificial intelligence, that will increasingly accompany us in our personal, social and working lives

Traces of trauma – a multivariate pattern analysis of childhood trauma, brain structure and clinical phenotypes

Background: Childhood trauma (CT) is a major yet elusive psychiatric risk factor, whose multidimensional conceptualization and heterogeneous effects on brain morphology might demand advanced mathematical modeling. Therefore, we present an unsupervised machine learning approach to characterize the clinical and neuroanatomical complexity of CT in a larger, transdiagnostic context. Methods: We used a multicenter European cohort of 1076 female and male individuals (discovery: n = 649; replication: n = 427) comprising young, minimally medicated patients with clinical high-risk states for psychosis; patients with recent-onset depression or psychosis; and healthy volunteers. We employed multivariate sparse partial least squares analysis to detect parsimonious associations between combinations of items from the Childhood Trauma Questionnaire and gray matter volume and tested their generalizability via nested cross-validation as well as via external validation. We investigated the associations of these CT signatures with state (functioning, depressivity, quality of life), trait (personality), and sociodemographic levels. Results: We discovered signatures of age-dependent sexual abuse and sex-dependent physical and sexual abuse, as well as emotional trauma, which projected onto gray matter volume patterns in prefronto-cerebellar, limbic, and sensory networks. These signatures were associated with predominantly impaired clinical state- and trait-level phenotypes, while pointing toward an interaction between sexual abuse, age, urbanicity, and education. We validated the clinical profiles for all three CT signatures in the replication sample. Conclusions: Our results suggest distinct multilayered associations between partially age- and sex-dependent patterns of CT, distributed neuroanatomical networks, and clinical profiles. Hence, our study highlights how machine learning approaches can shape future, more fine-grained CT research

Challenges for new models of Territorial Governance: learning from the experience of Italian LAGs

Governance is a complex and polyhedral concept focused on the polity dimension of political activity. In this scenario, "Territorial Governance" is not only a governance process applied to urban and territorial policies but also a complex process that has specific characteristics stemming from its objective and territory and helps achieve the broader goal of territorial development and cohesion. Thus, understanding "territorial" needs is relevant. This aspect has emerged in recent decades and is characterized by increasing involvement of territorial entities. Citizen panels, open meetings, and public assemblies, for example, engage citizens, organizations, and firms with the goal of promoting sustainable development and direct participation in territorial governance policies. In particular, recent decades have seen the spread of a specific way of involving private and public organizations in the decision-making process related to territorial policies: the creation of public-private partnerships. The purpose of this paper is to examine this topic in depth, focusing on the impact that public-private partnerships may have on territorial governance. Specifically, in reviewing the governance theory literature, our goal is to propose a new model of territorial governance with specific elements derived from the experience of public-private partnerships. To do this, we fix our attention on a specific type of public-private partnership engaged in territorial development and cohesion: the Local Action Group (LAG). In particular, using a mix of qualitative and quantitative approaches, this study uses a sample of 63 LAGs located in Italy to highlight the LAG's role in challenges that territorial governance faces. Based on the results and findings in this study, we introduce an innovative governance model called Partnership Governance