Clinical and radiological criteria for the differential diagnosis between asbestosis and idiopathic pulmonary fibrosis: Application in two cases

Introduction: Idiopathic pulmonary fibrosis (IPF) and asbestosis are pulmonary interstitial diseases that may present overlapping clinical aspects in the full-blown phase of the disease. For both clinical entities the gold standard for diagnosis is histological examination, but its execution poses ethical problems, especially when performed for preventive or forensic purposes. Objective: To evaluate the application of internationally accepted clinical, anamnestic and radiological criteria for differential diagnosis between asbestosis and IPF, and to assess the ability to discriminate between the two diseases. Even if clinically similar, the two diseases present extremely different prognostic and therapeutic perspectives. Methods: Two clinical cases of IPF are reported, in which the differential diagnosis was made by studying occupational exposure to asbestos, the onset and progression of clinical symptoms, and the identification of specific radiological elements by means of chest High Resolution Computed Tomography (HRCT). Results: The diagnosis of IPF could be made on the basis of the absence of significant exposure to asbestos, the early onset and rapid progression of dyspnea and restrictive ventilatory defects, in association with a pulmonary radiological pattern characterized by peculiar elements such as honeycombing. Discussion: The diagnostic procedure adopted to make a differential diagnosis with asbestosis provides practical clinical elements facilitating the differentiation between the two forms of pulmonary fibrosis, a fundamental aspect of the activity of the occupational physician

Lung segmentation and characterization in covid-19 patients for assessing pulmonary thromboembolism: An approach based on deep learning and radiomics

The COVID-19 pandemic is inevitably changing the world in a dramatic way, and the role of computed tomography (CT) scans can be pivotal for the prognosis of COVID-19 patients. Since the start of the pandemic, great care has been given to the relationship between interstitial pneumonia caused by the infection and the onset of thromboembolic phenomena. In this preliminary study, we collected n = 20 CT scans from the Polyclinic of Bari, all from patients positive with COVID-19, nine of which developed pulmonary thromboembolism (PTE). For eight CT scans, we obtained masks of the lesions caused by the infection, annotated by expert radiologists; whereas for the other four CT scans, we obtained masks of the lungs (including both healthy parenchyma and lesions). We developed a deep learning-based segmentation model that utilizes convolutional neural networks (CNNs) in order to accurately segment the lung and lesions. By considering the images from publicly available datasets, we also realized a training set composed of 32 CT scans and a validation set of 10 CT scans. The results obtained from the segmentation task are promising, allowing to reach a Dice coefficient higher than 97%, posing the basis for analysis concerning the assessment of PTE onset. We characterized the segmented region in order to individuate radiomic features that can be useful for the prognosis of PTE. Out of 919 extracted radiomic features, we found that 109 present different distributions according to the Mann–Whitney U test with corrected p-values less than 0.01. Lastly, nine uncorrelated features were retained that can be exploited to realize a prognostic signature

Effects of photoperiod on epididymal and sperm morphology in a wild rodent, the viscacha (Lagostomus maximus maximus)

The viscacha (Lagostomus maximus maximus) is a seasonal South American wild rodent. The adult males exhibit an annual reproductive cycle with periods of maximum and minimum gonadal activity. Four segments have been identified in the epididymis of this species: initial, caput, corpus, and cauda. The main objective of this work was to relate the seasonal morphological changes observed in the epididymal duct with the data from epididymal sperm during periods of activity and gonadal regression using light and scanning electron microscopy (SEM). Under light and electron microscopy, epididymal corpus and cauda showed marked seasonal variations in structural parameters and in the distribution of different cellular populations of epithelium. Initial and caput segments showed mild morphological variations between the two periods. Changes in epididymal sperm morphology were observed in the periods analyzed and an increased number of abnormal gametes were found during the regression period. During this period, anomalies were found mainly in the head, midpiece, and neck, while in the activity period, defects were found only in the head. Our results confirm that the morphological characteristics of the epididymal segments, as well as sperm morphology, undergo significant changes during the reproductive cycle of Lagostomus.Fil: Cruceño, A. M.. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia. Cátedra de Histología; ArgentinaFil: De Rosas, J. C.. Consejo Nacional de Investigaciones Científicas y Tecnicas. Centro Cientifico Tecnologico Mendoza. Instituto Histologia y Embriologia de Mendoza "Dr. M. Burgos"; Argentina. Universidad Nacional de Cuyo. Facultad de Ciencias Médicas; ArgentinaFil: Foscolo, Mabel Rosa. Consejo Nacional de Investigaciones Científicas y Tecnicas. Centro Cientifico Tecnologico Mendoza. Instituto Histologia y Embriologia de Mendoza "Dr. M. Burgos"; ArgentinaFil: Chaves, E. M.. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia. Cátedra de Histología; ArgentinaFil: Scardapane, L.. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia. Cátedra de Histología; ArgentinaFil: Dominguez, S.. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia. Cátedra de Histología; ArgentinaFil: Aguilera Merlo, C.. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia. Cátedra de Histología; Argentin

Improving Randomized Learning of Feedforward Neural Networks by Appropriate Generation of Random Parameters

In this work, a method of random parameters generation for randomized learning of a single-hidden-layer feedforward neural network is proposed. The method firstly, randomly selects the slope angles of the hidden neurons activation functions from an interval adjusted to the target function, then randomly rotates the activation functions, and finally distributes them across the input space. For complex target functions the proposed method gives better results than the approach commonly used in practice, where the random parameters are selected from the fixed interval. This is because it introduces the steepest fragments of the activation functions into the input hypercube, avoiding their saturation fragments

Lung ultrasonography for long-term follow-up of COVID-19 survivors compared to chest CT scan

Background: While lung ultrasonography (LUS) has utility for the evaluation of the acute phase of COVID-19 related lung disease, its role in long-term follow-up of this condition has not been well described. The objective of this study is to compare LUS and chest computed tomography (CT) results in COVID-19 survivors with the intent of defining the utility of LUS for long-term follow-up of COVID-19 respiratory disease. Methods: Prospective observational study that enrolled consecutive survivors of COVID-19 with acute hypoxemic respiratory failure (HARF) admitted to the Respiratory Intensive Care Unit. Three months following hospital discharge, patients underwent LUS, chest CT, body plethysmography and laboratory testing, the comparison of which forms the basis of this report. Results: 38 patients were enrolled, with a total of 190 lobes analysed: men 27/38 (71.1%), mean age 60.6 y (SD 10.4). LUS findings and pulmonary function tests outcomes were compared between patients with and without ILD, showing a statistically significant difference in terms of LUS score (p: 0.0002), FEV1 (p: 0.0039) and FVC (p: 0.012). ROC curve both in lobe by lobe and in patient's overall analysis revealed an outstanding ILD discrimination ability of LUS (AUC: 0.94 and 0.95 respectively) with a substantial Cohen's coefficient (K: 0.74 and 0.69). Conclusions: LUS has an outstanding discrimination ability compared to CT in identifying an ILD of at least mild grade in the post COVID-19 follow-up. LUS should be considered as the first-line tool in follow-up programs, while chest CT could be performed based on LUS findings

A primer to common major gastrointestinal post-surgical anatomy on CT—a pictorial review

The post-operative abdomen can be challenging and knowledge of normal post-operative anatomy is important for diagnosing complications. The aim of this pictorial essay is to describe a few selected common, major gastrointestinal surgeries, their clinical indications and depict their normal post-operative computed tomography (CT) appearance. This essay provides some clues to identify the surgeries, which can be helpful especially when surgical history is lacking: recognition of the organ(s) involved, determination of what was resected and familiarity with the type of anastomoses used

The Role of Multiparametric Magnetic Resonance in Volumetric Modulated Arc Radiation Therapy Planning for Prostate Cancer Recurrence After Radical Prostatectomy: A Pilot Study

Background and Purpose: Volumetric modulated arc radiotherapy (RT) has become pivotal in the treatment of prostate cancer recurrence (RPC) to optimize dose distribution and minimize toxicity, thanks to the high-precision delineation of prostate bed contours and organs at risk (OARs) under multiparametric magnetic resonance (mpMRI) guidance. We aimed to assess the role of pre-treatment mpMRI in ensuring target volume coverage and normal tissue sparing. Material and Methods: Patients with post-prostatectomy RPC eligible for salvage RT were prospectively recruited to this pilot study. Image registration between planning CT scan and T2w pre-treatment mpMRI was performed. Two sets of volumes were outlined, and DWI images/ADC maps were used to facilitate precise gross tumor volume (GTV) delineation on morphological MRI scans. Two rival plans (mpMRI-based or not) were drawn up. Results: Ten patients with evidence of RPC after prostatectomy were eligible. Preliminary data showed lower mpMRI-based clinical target volumes than CT-based RT planning (p = 0.0003): median volume difference 17.5 cm3. There were no differences in the boost volume coverage nor the dose delivered to the femoral heads and penile bulb, but median rectal and bladder V70Gy was 4% less (p = 0.005 and p = 0.210, respectively) for mpMRI-based segmentation. Conclusions: mpMRI provides high-precision target delineation and improves the accuracy of RT planning for post-prostatectomy RPC, ensures better volume coverage with better OARs sparing and allows non-homogeneous dose distribution, with an aggressive dose escalation to the GTV. Randomized phase III trials and wider datasets are needed to fully assess the role of mpMRI in optimizing therapeutic strategies

Richness of Deep Echo State Network Dynamics

Reservoir Computing (RC) is a popular methodology for the efficient design of Recurrent Neural Networks (RNNs). Recently, the advantages of the RC approach have been extended to the context of multi-layered RNNs, with the introduction of the Deep Echo State Network (DeepESN) model. In this paper, we study the quality of state dynamics in progressively higher layers of DeepESNs, using tools from the areas of information theory and numerical analysis. Our experimental results on RC benchmark datasets reveal the fundamental role played by the strength of inter-reservoir connections to increasingly enrich the representations developed in higher layers. Our analysis also gives interesting insights into the possibility of effective exploitation of training algorithms based on stochastic gradient descent in the RC field.Comment: Preprint of the paper accepted at IWANN 201

Avalanche: An end-to-end library for continual learning

Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed and fast-growing interest in continual learning, especially within the deep learning community. However, algorithmic solutions are often difficult to re-implement, evaluate and port across different settings, where even results on standard benchmarks are hard to reproduce. In this work, we propose Avalanche, an open-source end-to-end library for continual learning research based on PyTorch. Avalanche is designed to provide a shared and collaborative codebase for fast prototyping, training, and reproducible evaluation of continual learning algorithms