Showing papers by "Marche Polytechnic University published in 2020"

Cholangiocarcinoma 2020: the next horizon in mechanisms and management

Abstract: Cholangiocarcinoma (CCA) includes a cluster of highly heterogeneous biliary malignant tumours that can arise at any point of the biliary tree Their incidence is increasing globally, currently accounting for ~15% of all primary liver cancers and ~3% of gastrointestinal malignancies The silent presentation of these tumours combined with their highly aggressive nature and refractoriness to chemotherapy contribute to their alarming mortality, representing ~2% of all cancer-related deaths worldwide yearly The current diagnosis of CCA by non-invasive approaches is not accurate enough, and histological confirmation is necessary Furthermore, the high heterogeneity of CCAs at the genomic, epigenetic and molecular levels severely compromises the efficacy of the available therapies In the past decade, increasing efforts have been made to understand the complexity of these tumours and to develop new diagnostic tools and therapies that might help to improve patient outcomes In this expert Consensus Statement, which is endorsed by the European Network for the Study of Cholangiocarcinoma, we aim to summarize and critically discuss the latest advances in CCA, mostly focusing on classification, cells of origin, genetic and epigenetic abnormalities, molecular alterations, biomarker discovery and treatments Furthermore, the horizon of CCA for the next decade from 2020 onwards is highlighted

COVID-19 in patients with thoracic malignancies (TERAVOLT): first results of an international, registry-based, cohort study.

Abstract: Summary Background Early reports on patients with cancer and COVID-19 have suggested a high mortality rate compared with the general population. Patients with thoracic malignancies are thought to be particularly susceptible to COVID-19 given their older age, smoking habits, and pre-existing cardiopulmonary comorbidities, in addition to cancer treatments. We aimed to study the effect of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection on patients with thoracic malignancies. Methods The Thoracic Cancers International COVID-19 Collaboration (TERAVOLT) registry is a multicentre observational study composed of a cross-sectional component and a longitudinal cohort component. Eligibility criteria were the presence of any thoracic cancer (non-small-cell lung cancer [NSCLC], small-cell lung cancer, mesothelioma, thymic epithelial tumours, and other pulmonary neuroendocrine neoplasms) and a COVID-19 diagnosis, either laboratory confirmed with RT-PCR, suspected with symptoms and contacts, or radiologically suspected cases with lung imaging features consistent with COVID-19 pneumonia and symptoms. Patients of any age, sex, histology, or stage were considered eligible, including those in active treatment and clinical follow-up. Clinical data were extracted from medical records of consecutive patients from Jan 1, 2020, and will be collected until the end of pandemic declared by WHO. Data on demographics, oncological history and comorbidities, COVID-19 diagnosis, and course of illness and clinical outcomes were collected. Associations between demographic or clinical characteristics and outcomes were measured with odds ratios (ORs) with 95% CIs using univariable and multivariable logistic regression, with sex, age, smoking status, hypertension, and chronic obstructive pulmonary disease included in multivariable analysis. This is a preliminary analysis of the first 200 patients. The registry continues to accept new sites and patient data. Findings Between March 26 and April 12, 2020, 200 patients with COVID-19 and thoracic cancers from eight countries were identified and included in the TERAVOLT registry; median age was 68·0 years (61·8–75·0) and the majority had an Eastern Cooperative Oncology Group performance status of 0–1 (142 [72%] of 196 patients), were current or former smokers (159 [81%] of 196), had non-small-cell lung cancer (151 [76%] of 200), and were on therapy at the time of COVID-19 diagnosis (147 [74%] of 199), with 112 (57%) of 197 on first-line treatment. 152 (76%) patients were hospitalised and 66 (33%) died. 13 (10%) of 134 patients who met criteria for ICU admission were admitted to ICU; the remaining 121 were hospitalised, but were not admitted to ICU. Univariable analyses revealed that being older than 65 years (OR 1·88, 95% 1·00–3·62), being a current or former smoker (4·24, 1·70–12·95), receiving treatment with chemotherapy alone (2·54, 1·09–6·11), and the presence of any comorbidities (2·65, 1·09–7·46) were associated with increased risk of death. However, in multivariable analysis, only smoking history (OR 3·18, 95% CI 1·11–9·06) was associated with increased risk of death. Interpretation With an ongoing global pandemic of COVID-19, our data suggest high mortality and low admission to intensive care in patients with thoracic cancer. Whether mortality could be reduced with treatment in intensive care remains to be determined. With improved cancer therapeutic options, access to intensive care should be discussed in a multidisciplinary setting based on cancer specific mortality and patients' preference. Funding None.

The Italian coronavirus disease 2019 outbreak: recommendations from clinical practice.

Abstract: Novel coronavirus 2019 is a single-stranded, ribonucleic acid virus that has led to an international pandemic of coronavirus disease 2019. Clinical data from the Chinese outbreak have been reported, but experiences and recommendations from clinical practice during the Italian outbreak have not. We report the impact of the coronavirus disease 2019 outbreak on regional and national healthcare infrastructure. We also report on recommendations based on clinical experiences of managing patients throughout Italy. In particular, we describe key elements of clinical management, including: safe oxygen therapy; airway management; personal protective equipment; and non-technical aspects of caring for patients diagnosed with coronavirus disease 2019. Only through planning, training and team working will clinicians and healthcare systems be best placed to deal with the many complex implications of this new pandemic.

snRNA-seq reveals a subpopulation of adipocytes that regulates thermogenesis.

Abstract: Adipose tissue is usually classified on the basis of its function as white, brown or beige (brite)1. It is an important regulator of systemic metabolism, as shown by the fact that dysfunctional adipose tissue in obesity leads to a variety of secondary metabolic complications2,3. In addition, adipose tissue functions as a signalling hub that regulates systemic metabolism through paracrine and endocrine signals4. Here we use single-nucleus RNA-sequencing (snRNA-seq) analysis in mice and humans to characterize adipocyte heterogeneity. We identify a rare subpopulation of adipocytes in mice that increases in abundance at higher temperatures, and we show that this subpopulation regulates the activity of neighbouring adipocytes through acetate-mediated modulation of their thermogenic capacity. Human adipose tissue contains higher numbers of cells of this subpopulation, which could explain the lower thermogenic activity of human compared to mouse adipose tissue and suggests that targeting this pathway could be used to restore thermogenic activity.

The identification and management of interstitial lung disease in systemic sclerosis: evidence-based European consensus statements

Abstract: Summary Background Systemic sclerosis-associated interstitial lung disease (ILD) carries a high mortality risk; expert guidance is required to aid early recognition and treatment. We aimed to develop the first expert consensus and define an algorithm for the identification and management of the condition through application of well established methods. Methods Evidence-based consensus statements for systemic sclerosis-associated ILD management were established for six domains (ie, risk factors, screening, diagnosis and severity assessment, treatment initiation and options, disease progression, and treatment escalation) using a modified Delphi process based on a systematic literature analysis. A panel of 27 Europe-based pulmonologists, rheumatologists, and internists with expertise in systemic sclerosis-associated ILD participated in three rounds of online surveys, a face-to-face discussion, and a WebEx meeting, followed by two supplemental Delphi rounds, to establish consensus and define a management algorithm. Consensus was considered achieved if at least 80% of panellists indicated agreement or disagreement. Findings Between July 1, 2018, and Aug 27, 2019, consensus agreement was reached for 52 primary statements and six supplemental statements across six domains of management, and an algorithm was defined for clinical practice use. The agreed statements most important for clinical use included: all patients with systemic sclerosis should be screened for systemic sclerosis-associated ILD using high-resolution CT; high-resolution CT is the primary tool for diagnosing ILD in systemic sclerosis; pulmonary function tests support screening and diagnosis; systemic sclerosis-associated ILD severity should be measured with more than one indicator; it is appropriate to treat all severe cases; no pharmacological treatment is an option for some patients; follow-up assessments enable identification of disease progression; progression pace, alongside disease severity, drives decisions to escalate treatment. Interpretation Through a robust modified Delphi process developed by a diverse panel of experts, the first evidence-based consensus statements were established on guidance for the identification and medical management of systemic sclerosis-associated ILD. Funding An unrestricted grant from Boehringer Ingelheim International.

The impact of quarantine and physical distancing following covid-19 on mental health: Study protocol of a multicentric italian population trial

Abstract: The COVID-19 pandemic and its related containment measures-mainly physical distancing and isolation-are having detrimental consequences on the mental health of the general population worldwide. In particular, frustration, loneliness, and worries about the future are common reactions and represent well-known risk factors for several mental disorders, including anxiety, affective, and post-traumatic stress disorders. The vast majority of available studies have been conducted in China, where the pandemic started. Italy has been severely hit by the pandemic, and the socio-cultural context is completely different from Eastern countries. Therefore, there is the need for methodologically rigorous studies aiming to evaluate the impact of COVID-19 and quarantine measures on the mental health of the Italian population. In fact, our results will help us to develop appropriate interventions for managing the psychosocial consequences of pandemic. The "COVID-IT-mental health trial" is a no-profit, not-funded, national, multicentric, cross-sectional population-based trial which has the following aims: a) to evaluate the impact of COVID-19 pandemic and its containment measures on mental health of the Italian population; b) to identify the main areas to be targeted by supportive long-term interventions for the different categories of people exposed to the pandemic. Data will be collected through a web-platform using validated assessment tools. Participants will be subdivided into four groups: a) Group 1-COVID-19 quarantine group. This group includes the general population which are quarantined but not isolated, i.e., those not directly exposed to contagion nor in contact with COVID-19+ individuals; b) Group 2-COVID-19+ group, which includes isolated people directly/indirectly exposed to the virus; c) Group 3-COVID-19 healthcare staff group, which includes first- and second-line healthcare professionals; d) Group 4-COVID-19 mental health, which includes users of mental health services and all those who had already been diagnosed with a mental disorder. Mental health services worldwide are not prepared yet to manage the short- and long-term consequences of the pandemic. It is necessary to have a clear picture of the impact that this new stressor will have on mental health and well-being in order to develop and disseminate appropriate interventions for the general population and for the other at-risk groups.

Coronavirus Disease 2019 and Stroke: Clinical Manifestations and Pathophysiological Insights.

Abstract: Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global health threat. Some COVID-19 patients have exhibited widespread neurological manifestations including stroke. Acute ischemic stroke, intracerebral hemorrhage, and cerebral venous sinus thrombosis have been reported in patients with COVID-19. COVID-19-associated coagulopathy is increasingly recognized as a result of acute infection and is likely caused by inflammation, including inflammatory cytokine storm. Recent studies suggest that axonal transport of SARS-CoV-2 to the brain can occur via the cribriform plate adjacent to the olfactory bulb that may lead to symptomatic anosmia. The internalization of SARS-CoV-2 is mediated by the binding of the spike glycoprotein of the virus to the angiotensin-converting enzyme 2 (ACE2) on cellular membranes. ACE2 is expressed in several tissues including lung alveolar cells, gastrointestinal tissue, and brain. The aim of this review is to provide insights into the clinical manifestations and pathophysiological mechanisms of stroke in COVID-19 patients. SARS-CoV-2 can down-regulate ACE2 and, in turn, overactivate the classical renin-angiotensin system (RAS) axis and decrease the activation of the alternative RAS pathway in the brain. The consequent imbalance in vasodilation, neuroinflammation, oxidative stress, and thrombotic response may contribute to the pathophysiology of stroke during SARS-CoV-2 infection.

Attention is All You Need in Speech Separation

Abstract: Recurrent Neural Networks (RNNs) have long been the dominant architecture in sequence-to-sequence learning. RNNs, however, are inherently sequential models that do not allow parallelization of their computations. Transformers are emerging as a natural alternative to standard RNNs, replacing recurrent computations with a multi-head attention mechanism. In this paper, we propose the SepFormer, a novel RNN-free Transformer-based neural network for speech separation. The SepFormer learns short and long-term dependencies with a multi-scale approach that employs transformers. The proposed model achieves state-of-the-art (SOTA) performance on the standard WSJ0-2/3mix datasets. It reaches an SI-SNRi of 22.3 dB on WSJ0-2mix and an SI-SNRi of 19.5 dB on WSJ0-3mix. The SepFormer inherits the parallelization advantages of Transformers and achieves a competitive performance even when downsampling the encoded representation by a factor of 8. It is thus significantly faster and it is less memory-demanding than the latest speech separation systems with comparable performance.

Occurrence of microplastics in commercial seafood under the perspective of the human food chain. A review

Abstract: The occurrence of microplastics in the marine ecosystem and aquatic organisms, their trophic transfer along the food web, and the identification of seafood species as suitable indicators have become a research priority. Despite the high quantity of research in this field, a comparison between the available data and an appropriate risk assessment remains difficult. In this perspective, as an innovative approach, the association of the feeding strategies of commercial seafood and the microplastic level was considered. Further research to assess the occurrence of microplastics in the marine food web, the long-term effects on animals and humans, and the health implications is needed.

LibriMix: An Open-Source Dataset for Generalizable Speech Separation

Abstract: In recent years, wsj0-2mix has become the reference dataset for single-channel speech separation. Most deep learning-based speech separation models today are benchmarked on it. However, recent studies have shown important performance drops when models trained on wsj0-2mix are evaluated on other, similar datasets. To address this generalization issue, we created LibriMix, an open-source alternative to wsj0-2mix, and to its noisy extension, WHAM!. Based on LibriSpeech, LibriMix consists of two- or three-speaker mixtures combined with ambient noise samples from WHAM!. Using Conv-TasNet, we achieve competitive performance on all LibriMix versions. In order to fairly evaluate across datasets, we introduce a third test set based on VCTK for speech and WHAM! for noise. Our experiments show that the generalization error is smaller for models trained with LibriMix than with WHAM!, in both clean and noisy conditions. Aiming towards evaluation in more realistic, conversation-like scenarios, we also release a sparsely overlapping version of LibriMix's test set.

Transfer Learning for Non-Intrusive Load Monitoring

Abstract: Non-intrusive load monitoring (NILM) is a technique to recover source appliances from only the recorded mains in a household. NILM is unidentifiable and thus a challenge problem because the inferred power value of an appliance given only the mains could not be unique. To mitigate the unidentifiable problem, various methods incorporating domain knowledge into NILM have been proposed and shown effective experimentally. Recently, among these methods, deep neural networks are shown performing best. Arguably, the recently proposed sequence-to-point (seq2point) learning is promising for NILM. However, the results were only carried out on the same data domain. It is not clear if the method could be generalised or transferred to different domains, e.g., the test data were drawn from a different country comparing to the training data. We address this issue in the paper, and two transfer learning schemes are proposed, i.e., appliance transfer learning (ATL) and cross-domain transfer learning (CTL). For ATL, our results show that the latent features learnt by a ‘complex’ appliance, e.g., washing machine, can be transferred to a ‘simple’ appliance, e.g., kettle. For CTL, our conclusion is that the seq2point learning is transferable. Precisely, when the training and test data are in a similar domain, seq2point learning can be directly applied to the test data without fine tuning; when the training and test data are in different domains, seq2point learning needs fine tuning before applying to the test data. Interestingly, we show that only the fully connected layers need fine tuning for transfer learning. Source code can be found at https://github.com/MingjunZhong/transferNILM .

Safety, pitfalls, and misunderstandings about the use of ozone therapy as a regenerative medicine tool. A narrative review.

Abstract: Despite various opinions and healthy controversy on Ozone Therapy (OT), the practices of this therapy have increased worldwide. Main areas of study with consistent scientific outcomes are the topical treatment of both disk herniation and periodontal disease. On the other hand, there is a net dissociation of the scientific resonance concerning systemic oxygen/ozone treatments. It is our intention to discuss in logical terms the numerous papers that commendably reported adverse reactions attributable to OT, focusing our attention mainly to the techniques of administration and not to the simple contact of ozone with biological material. The case reports on OT treatments safety concerns discussed on international journals, make it possible to state that most safety issues are secondary to infections or traumatic reactions due to malpractice. Commonly, the molecule of ozone itself is not responsible of severe reactions at the therapeutic modalities. The millions of patients treated so far from the thousands of physicians correctly practicing OT world widely in the last 40 years demonstrate the safety of this simple and cost-effective regenerative medicine tool. The promising therapeutic implications also for the current COVID-19 emergency are a further stimulus to the standardization of this therapeutic resource with multiple application specificities.

Asteroid: The PyTorch-Based Audio Source Separation Toolkit for Researchers.

Abstract: This paper describes Asteroid, the PyTorch-based audio source separation toolkit for researchers. Inspired by the most successful neural source separation systems, it provides all neu-ral building blocks required to build such a system. To improve reproducibility, Kaldi-style recipes on common audio source separation datasets are also provided. This paper describes the software architecture of Asteroid and its most important features. By showing experimental results obtained with Asteroid's recipes, we show that our implementations are at least on par with most results reported in reference papers. The toolkit is publicly available at github.com/mpariente/asteroid.

COVID-19 infection and glucocorticoids: update from the Italian Society of Endocrinology Expert Opinion on steroid replacement in adrenal insufficiency.

Abstract: In November 2019, the Italian Society of Endocrinology (SIE) has published a consensus statement on the tailoring of glucocorticoid replacement in adrenal insufficiency [1]. A few months later, a novel severe acute respiratory syndrome coronavirus (SARS-CoV2) has been recognized as responsible for COVID-19. The outbreak has now reached pandemic level, with a high global mortality rate [2]. From February on, Italy has experienced an exponential rise in the infected which is estimated to reach 200,000 people, with an overall lethality of approximately 10% [3]. A recent Chinese series of nearly 50,000 patients with confirmed COVID-19 infection found that approximately one-out-of-five (19%) evolve towards severe (14%) or critical (5%) pneumonia [4]. Several clinical trials are now testing the therapeutic options to treat lung and extra-respiratory complications of SARSCoV2 infection. While awaiting for a specific treatment strategy, the SIE task force met again to address the tailoring of corticosteroid replacement in adrenal insufficient patients coping with the stress related to COVID-19 infection.