Long-Term Sequelae of severe COVID-19: Outpatient assessment of radiological and pulmonary function tests

Objective: Identify lung sequelae of COVID-19 through radiological and pulmonary function assessment. Design(s): Prospective, longitudinal, cohort study from March 2020 to March 2021. Setting(s): Intensive Care Units (ICU) in a tertiary hospital in Portugal. Patient(s): 254 patients with COVID-19 admitted to ICU due to respiratory illness. Intervention(s): A chest computed tomography (CT) scan and pulmonary function tests (PFT) were performed at 3 to 6 months. Main variables of interest: CT-scan;PFT;decreased diffusion capacity of carbon monoxide (DLCO). Result(s): All CT scans revealed improvement in the follow-up, with 72% of patients still showing abnormalities, 58% with ground glass opacities and 62% with evidence of fibrosis. PFT had abnormalities in 94 patients (46%): thirteen patients (7%) had an obstructive pattern, 35 (18%) had a restrictive pattern, and 58 (30%) had decreased DLCO. There was a statistically significant association between abnormalities in the follow-up CT scan and older age, more extended hospital and ICU stay, higher SAPS II and APACHE scores and invasive ventilation. Mechanical ventilation, especially with no lung protective parameters, was associated with abnormalities in PFT. Multivariate regression showed more abnormalities in lung function with more extended ICU hospitalization, chronic obstructive pulmonary disease (COPD), chronic kidney disease, invasive mechanical ventilation, and ventilation with higher plateau pressure, and more abnormalities in CT-scan with older age, more extended ICU stay, organ solid transplants and ventilation with higher positive end-expiratory pressure (PEEP). Conclusion(s): Most patients with severe COVID-19 still exhibit abnormalities in CT scans or lung function tests three to six months after discharge.Copyright © 2023

A Novel Approach to the Two-Dimensional Cargo Load Problem

The transport and logistics sector, which include freight forwarders companies, constitutes a vast network of entities that are central to a good performance in services. With the COVID-19 pandemic and its effects on the global economy, there was a huge shortage in the number of containers available, thus creating the need to optimize the loading of available equipment to avoid waste and maximize profits from each export. The present work presents a novel approach where a set of restrictions were created that, applied in synergy with the Non-Linear GRG algorithm, aim to allocate the boxes in different consecutive lines until forming a wall, and, therefore, the walls complete the container, in order to maximize the occupancy on it. To validate the proposed approach a prototype was developed and studied in real-world problem where the solutions resulted in occupations around 80% to 90%. Thus, we can foresee the importance of the proposed approach in decision-making regarding container consolidation services. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A further deterioration of the Brazilian fiscal reaction in view of COVID-19

The fiscal reaction function measures how the government’s primary surplus reacts to the evolution of public debt. Campos and Cysne (2019b) observed that the reaction function has been almost steadily decreasing since 2012 and it has turned from positive to negative values in 2017 and 2018. In the subsequent period, the improvement of some economic indicators led the fiscal reaction to a recovery. Nevertheless, in 2020, with the advent of COVID-19, health spending and emergency aids caused a sharp fiscal deterioration, leading the fiscal reaction coefficient to assume, again, negative values. © 2022, UNIV SAOPAULO. All rights reserved.

Machine Learning Methods for Signal, Image and Speech Processing

The signal processing (SP) landscape has been enriched by recent advances in artificial intelligence (AI) and machine learning (ML), yielding new tools for signal estimation, classification, prediction, and manipulation. Layered signal representations, nonlinear function approximation and nonlinear signal prediction are now feasible at very large scale in both dimensionality and data size. These are leading to significant performance gains in a variety of long-standing problem domains like speech and Image analysis. As well as providing the ability to construct new classes of nonlinear functions (e.g., fusion, nonlinear filtering). This book will help academics, researchers, developers, graduate and undergraduate students to comprehend complex SP data across a wide range of topical application areas such as social multimedia data collected from social media networks, medical imaging data, data from Covid tests etc. This book focuses on AI utilization in the speech, image, communications and yirtual reality domains. © 2021 River Publishers. All rights reserved.

Remote Monitor System for Alzheimer Disease

Health Remote Monitoring Systems (HRMS) offer the ability to address health-care human resource concerns. In developing nations, where pervasive mobile networks and device access are linking people like never before, HRMS are of special relevance. A fundamental aim of this research work is the realization of technological-based solution to triage and follow-up people living with dementias so as to reduce pressure on busy staff while doing this from home so as to avoid all unnecessary visits to hospital facilities, increasingly perceived as dangerous due to COVID-19 but also raising nosocomial infections, raising alerts for abnormal values. Sensing approaches are complemented by advanced predictive models based on Machine Learning (ML) and Artificial Intelligence (AI), thus being able to explore novel ways of demonstrating patient-centered predictive measures. Low-cost IoT devices composing a network of sensors and actuators aggregated to create a digital experience that will be used and exposure to people to simultaneously conduct several tests and obtain health data that can allow screening of early onset dementia and to aid in the follow-up of selected cases. The best ML for predicting AD was logistic regression with an accuracy of 86.9%. This application as demonstrated to be essential for caregivers once they can monitor multiple patients in real-time and actuate when abnormal values occur. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Chest CT imaging features of COVID-19 pneumonia: First radiological insights from Porto, Portugal.

INTRODUCTION: The outbreak of a highly infectious respiratory disease - COVID-19 - has spread globally and a novel type of coronavirus (SARS-CoV-2) was identified as its cause. Chest CT findings have been described as an aid for COVID-19 diagnosis and management. We aimed to describe the CT imaging characteristics in a group of COVID-19 patients while we also intended to assess if any of these radiological features were associated with short-term prognosis. MATERIALS AND METHODS: CT examinations from 164 consecutive patients with at least one positive RT-PCR nucleic acid assay for SARS-CoV-2 were retrospectively analyzed. Numerous CT imaging features were recorded independently by two radiologists. Patients were grouped according to their status 14 days after the initial CT scan in either discharged/hospitalized in a non-ICU ward (favorable prognosis group) versus deceased/admitted to an intensive care unit (unfavorable prognosis group). RESULTS: Ground-glass opacities (89.0 %) and consolidations (73.2 %) with multilobar involvement were the predominant imaging findings, while a nodular pattern (3.7 %) and cavitation (1.2 %) were uncommon. Mean age was higher in the mortality/ICU group. Ground-glass opacities and consolidations were dominant in both groups, but distribution pattern of abnormalities was different, being more often diffuse in the mortality/ICU group. Linear opacities and opacities that were rounded in shape were more frequently observed in the favorable prognosis group. CT severity index was significantly higher in the mortality/ICU group. For assessing unfavorable prognosis, the best cut-off for CT severity index was 24 (sensitivity 78 %; specificity 59 %). Interobserver agreement for all CT findings was excellent. CONCLUSION: COVID-19 pneumonia in Porto, Portugal, manifests as multilobar ground-glass opacities and consolidations. Older age, diffuse distribution and increasing CT severity index are associated with worse short-term prognosis while linear opacities resembling organizing pneumonia and rounded opacities herald a more favorable prognosis.