A Soft-Voting Ensemble Classifier for Detecting Patients Affected by COVID-19

COVID-19 is an ongoing global pandemic of coronavirus disease 2019, which may cause severe acute respiratory syndrome. This disease highlighted the limitations of health systems worldwide regarding managing the pandemic. In particular, the lack of diagnostic tests that can quickly and reliably detect infected patients has contributed to the spread of the virus. Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) and antigen tests, which are the main diagnostic tests for COVID-19, showed their limitations during the pandemic. In fact, RT-PCR requires several hours to provide a diagnosis and is not properly accurate, thus generating a high number of false negatives. Unlike RT-PCR, antigen tests provide rapid diagnosis but are less accurate in detecting COVID-19 positive patients. Medical imaging is an alternative diagnostic test for COVID-19. In particular, chest computed tomography allows detecting lung infections related to the disease with high accuracy. However, visual analysis of a chest scan generated by computed tomography is a demanding activity for radiologists, making widespread use of this test unfeasible. Therefore, it is essential to lighten their work with automated tools able to provide accurate diagnosis in a short time. To deal with this challenge, in this work, an approach based on 3D Inception CNNs is proposed. Specifically, 3D Inception-V1 and Inception-V3 models have been built and compared. Then, soft-voting ensemble classifier models have been separately built on these models to boost the performance. As for the individual models, results showed that Inception-V1 outperformed Inception-V3 according to different measures. As for the ensemble classifier models, the outcome of experiments pointed out that the adopted voting strategy boosted the performance of individual models. The best results have been achieved enforcing soft voting on Inception-V1 models

Validation of the Predictive Model of the European Society of Cardiology for Early Mortality in Acute Pulmonary Embolism

Background \u2003Acute pulmonary embolism (PE) is burdened by high mortality, especially within 30 days from the diagnosis. The development and the validation of predictive models for the risk of early mortality allow to differentiate patients who can undergo home treatment from those who need admission into intensive care units. Methods \u2003To validate the prognostic model for early mortality after PE diagnosis proposed by the European Society of Cardiology (ESC) in 2014, we analyzed data of a cohort of 272 consecutive patients with acute PE, observed in our hospital during a 10-year period. Moreover, we evaluated the additional contribution of D-dimer, measured at PE diagnosis, in improving the prognostic ability of the model. All cases of PE were objectively diagnosed by angiography chest CT scan or perfusion lung scan. Results \u2003The overall mortality rate within 30 days from PE diagnosis was 10% (95% confidence interval [CI]: 6.4-13.5%). According to the ESC prognostic model, the risk of death increased 3.23 times in the intermediate-low-risk category, 5.55 times in the intermediate-high-risk category, and 23.78 times in the high-risk category, as compared with the low-risk category. The receiver operating characteristic analysis showed a good discriminatory power of the model (area under the curve [AUC]\u2009=\u20090.77 [95% CI: 0.67-0.87]), which further increased when D-dimer was added (AUC\u2009=\u20090.85 [95% CI: 0.73-0.96]). Conclusion \u2003This study represents a good validation of the ESC predictive model whose performance can be further improved by adding D-dimer plasma levels measured at PE diagnosis

Prevention of clostridium difficile infection and associated diarrhea: An unsolved problem

For many years, it has been known that Clostridium difficile (CD) is the primary cause of health-care-associated infectious diarrhea, afflicting approximately 1% of hospitalized patients. CD may be simply carried or lead to a mild disease, but in a relevant number of patients, it can cause a very severe, potentially fatal, disease. In this narrative review, the present possibilities of CD infection (CDI) prevention will be discussed. Interventions usually recommended for infection control and prevention can be effective in reducing CDI incidence. However, in order to overcome limitations of these measures and reduce the risk of new CDI episodes, novel strategies have been developed. As most of the cases of CDI follow antibiotic use, attempts to rationalize antibiotic prescriptions have been implemented. Moreover, to reconstitute normal gut microbiota composition and suppress CD colonization in patients given antimicrobial drugs, administration of probiotics has been suggested. Finally, active and passive immunization has been studied. Vaccines containing inactivated CD toxins or components of CD spores have been studied. Passive immunization with monoclonal antibodies against CD toxins or the administration of hyperimmune whey derived from colostrum or breast milk from immunized cows has been tried. However, most advanced methods have significant limitations as they cannot prevent colonization and development of primary CDI. Only the availability of vaccines able to face these problems can allow a resolutive approach to the total burden due to this pathogen

G-CNV: A GPU-based tool for preparing data to detect CNVs with read-depth methods

Copy number variations (CNVs) are the most prevalent types of structural variations (SVs) in the human genome and are involved in a wide range of common human diseases. Different computational methods have been devised to detect this type of SVs and to study how they are implicated in human diseases. Recently, computational methods based on high-throughput sequencing (HTS) are increasingly used. The majority of these methods focus on mapping short-read sequences generated from a donor against a reference genome to detect signatures distinctive of CNVs. In particular, read-depth based methods detect CNVs by analyzing genomic regions with significantly different read-depth from the other ones. The pipeline analysis of these methods consists of four main stages: (i) data preparation, (ii) data normalization, (iii) CNV regions identification, and (iv) copy number estimation. However, available tools do not support most of the operations required at the first two stages of this pipeline. Typically, they start the analysis by building the read-depth signal from pre-processed alignments. Therefore, third-party tools must be used to perform most of the preliminary operations required to build the read-depth signal. These data-intensive operations can be efficiently parallelized on graphics processing units (GPUs). In this article, we present G-CNV, a GPU-based tool devised to perform the common operations required at the first two stages of the analysis pipeline. G-CNV is able to filter low-quality read sequences, to mask low-quality nucleotides, to remove adapter sequences, to remove duplicated read sequences, to map the short-reads, to resolve multiple mapping ambiguities, to build the read-depth signal, and to normalize it. G-CNV can be efficiently used as a third-party tool able to prepare data for the subsequent read-depth signal generation and analysis. Moreover, it can also be integrated in CNV detection tools to generate read-depth signals

A HPC and Grid enabling framework for genetic linkage analysis of SNPs

Understanding the structure, function and development of the human genome is a key factor to improve the quality of life. In order to achieve this goal developing and using a modern ICT infrastructure is essential, and can exploit next generation High Performance Computing (HPC) systems beyond the Petaflop scale in a collaborative and efficient way. The genetic linkage analysis of Single Nucleotide Polymorphism (SNP) markers has recently become a very popular approach for genetic epidemiology and population studies, aiming to discover the genetic correlation in complex diseases. The high computational cost and memory requirements of the major algorithms proposed in the literature make analyses of medium/large data sets very hard on a single CPU. A Grid based facility has hence been set up upon a high-performance infrastructure, the EGEE Grid, in order to create a tool for achieving whole-genome linkage analysis

A HPC and Grid enabling framework for genetic linkage analysis of SNPs

Understanding the structure, function and development of the human genome is a key factor to improve the quality of life. In order to achieve this goal developing and using a modern ICT infrastructure is essential, and can exploit next generation High Performance Computing (HPC) systems beyond the Petaflop scale in a collaborative and efficient way. The genetic linkage analysis of Single Nucleotide Polymorphism (SNP) markers has recently become a very popular approach for genetic epidemiology and population studies, aiming to discover the genetic correlation in complex diseases. The high computational cost and memory requirements of the major algorithms proposed in the literature make analyses of medium/large data sets very hard on a single CPU. A Grid based facility has hence been set up upon a high-performance infrastructure, the EGEE Grid, in order to create a tool for achieving whole-genome linkage analysis

Therapeutic strategies against COVID-19

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus that mainly affects the upper and lower respiratory tract and is responsible for extremely different degrees of disease, ranging from flu-like symptoms to atypical pneumonia that may evolve to acute respiratory distress syndrome and, ultimately, death. No specific therapy for SARS-CoV-2 has yet been identified, but since the beginning of the outbreak, several pre-existing therapeutics have been reconsidered for the treatment of infected patients. The aim of this article is to discuss current therapeutics against SARS-CoV-2. A literature review was performed using PubMed, collecting data from English-language articles published until June 20th, 2020. Literature analysis showed that with the acquisition of more in-depth knowledge on the characteristics of SARS-CoV-2 and the pathogenesis of the different clinical manifestations, a more rationale use of available drugs has become possible. However, the road to defining which drugs are effective and which schedules of administration must be used to maximize efficacy and minimize adverse events is still very long. To date, it is only clear that no drug can alone cope with all the problems posed by SARS-CoV-2 infection and effective antivirals and inflammatory drugs must be given together to reduce COVID-19 clinical manifestations. Moreover, choice of therapy must always be tailored on clinical manifestations and, when they occur, drugs able to fight coagulopathy and venous thromboembolism that may contribute to respiratory deterioration must be prescribed. (www.actabiomedica.com)

History of mammography: analysis of breast imaging diagnostic achievements over the last century

Breast cancer is the most common forms of cancer and a leading cause of mortality in women. Early and correct diagnosis is, therefore, essential to save lives. The development of diagnostic imaging applied to the breast has been impressive in recent years and the most used diagnostic test in the world is mammography, a low-dose X-ray technique used for imaging the breast. In the first half of the 20th century, the diagnosis was in practice only clinical, with consequent diagnostic delay and an unfavorable prognosis in the short term. The rise of organized mammography screening has led to a remarkable reduction in mortality through the early detection of breast malignancies. This historical review aims to offer a complete panorama of the development of mammography and breast imaging during the last century. Through this study, we want to understand the foundations of the pillar of radiology applied to the breast through to the most modern applications such as contrast-enhanced mammography (CEM), artificial intelligence, and radiomics. Understanding the history of the development of diagnostic imaging applied to the breast can help us understand how to better direct our efforts toward an increasingly personalized and effective diagnostic approach. The ultimate goal of imaging applied to the detection of breast malignancies should be to reduce mortality from this type of disease as much as possible. With this paper, we want to provide detailed documentation of the main steps in the evolution of breast imaging for the diagnosis of breast neoplasms; we also want to open up new scenarios where the possible current and future applications of imaging are aimed at being more precise and personalized

ACYLATED AND UNACYLATED GHRELIN IMPAIR SKELETAL MUSCLE ATROPHY IN MICE.

Cachexia is a wasting syndrome associated with cancer, AIDS, and multiple sclerosis, and several other disease states. It is characterized by weight loss, fatigue, loss of appetite and skeletal muscle atrophy and is associated with poor patient prognosis, making it an important treatment target. Ghrelin is a peptide hormone that stimulates growth hormone (GH) release and positive energy balance through binding to the receptor GHSR-1a. Only acylated ghrelin (AG), but not the unacylated form (UnAG), can bind GHSR-1a; however, UnAG and AG share several GHSR-1aindependent biological activities. Here we investigated whether UnAG and AG could protect against skeletal muscle atrophy in a GHSR-1a-independent manner. We found that both AG and UnAG inhibited dexamethasone-induced skeletal muscle atrophy and atrogene expression through PI3K\u3b2-, mTORC2-, and p38-mediated pathways in myotubes. Up-regulation of circulating UnAG in mice impaired skeletal muscle atrophy induced by either fasting or denervation without stimulating muscle hypertrophy and GHSR-1a-mediated activation of the GH/IGF-1 axis. In Ghsrdeficient mice, both AG and UnAG induced phosphorylation of Akt in skeletal muscle and impaired fasting-induced atrophy. These results demonstrate that AG and UnAG act on a common, unidentified receptor to block skeletal muscle atrophy in a GH-independent manner

Uncoordinated Transcription and Compromised Muscle Function in the Lmna-Null Mouse Model of Emery-Dreifuss Muscular Dystrophy

LMNA encodes both lamin A and C: major components of the nuclear lamina. Mutations in LMNA underlie a range of tissue-specific degenerative diseases, including those that affect skeletal muscle, such as autosomal-Emery-Dreifuss muscular dystrophy (A-EDMD) and limb girdle muscular dystrophy 1B. Here, we examine the morphology and transcriptional activity of myonuclei, the structure of the myotendinous junction and the muscle contraction dynamics in the lmna-null mouse model of A-EDMD. We found that there were fewer myonuclei in lmna-null mice, of which ∼50% had morphological abnormalities. Assaying transcriptional activity by examining acetylated histone H3 and PABPN1 levels indicated that there was a lack of coordinated transcription between myonuclei lacking lamin A/C. Myonuclei with abnormal morphology and transcriptional activity were distributed along the length of the myofibre, but accumulated at the myotendinous junction. Indeed, in addition to the presence of abnormal myonuclei, the structure of the myotendinous junction was perturbed, with disorganised sarcomeres and reduced interdigitation with the tendon, together with lipid and collagen deposition. Functionally, muscle contraction became severely affected within weeks of birth, with specific force generation dropping as low as ∼65% and ∼27% of control values in the extensor digitorum longus and soleus muscles respectively. These observations illustrate the importance of lamin A/C for correct myonuclear function, which likely acts synergistically with myotendinous junction disorganisation in the development of A-EDMD, and the consequential reduction in force generation and muscle wasting