Competition and firm recovery post-COVID-19

Plain English SummaryThe COVID-19 crisis had a profound impact on firms. Firms which were more productive pre-crisis fared relatively better, particularly in countries with a more competitive business environment. Using survey data for about 8000 firms, including both small and large firms, in 23 emerging and developing countries in Europe and Central Asia, the paper finds that during the COVID-19 crisis, smaller firms were hit harder, and economic activity was reallocated toward firms with higher pre-crisis labor productivity. Countries with a strong competition environment experienced more reallocation from less productive to more productive firms than countries with a weak competition environment. The evidence also suggests that reallocation from low- to high-productivity firms during the COVID-19 crisis was stronger compared with pre-crisis times. Finally, the analysis shows that government support measures implemented in response to the crisis went to less productive and larger firms, regardless of their pre-crisis innovation. Thus, government support measures during the COVID-19 crisis may have had adverse effects on competition and productivity growth. As economies enter the economic recovery phase, it will be important for policymakers to phase out support measures as soon as appropriate and focus on fostering a competitive business environment. This paper examines the impact of the COVID-19 crisis on the reallocation of economic activity across firms and whether this reallocation depends on the competition environment. The paper uses the World Bank's Enterprise Surveys COVID-19 Follow-up Surveys for about 8000 firms, including both small and large firms, in 23 emerging and developing countries in Europe and Central Asia, matched with 2019 Enterprise Surveys data. It finds that during the COVID-19 crisis, smaller firms were hit harder, and economic activity was reallocated toward firms with higher pre-crisis labor productivity. Countries with a strong competition environment experienced more reallocation from less productive to more productive firms than countries with a weak competition environment. The evidence also suggests that reallocation from low- to high-productivity firms during the COVID-19 crisis was stronger compared with pre-crisis times. Finally, the analysis shows that government support measures implemented in response to the crisis may have adverse effects on competition and productivity growth since support went to less productive and larger firms, regardless of their pre-crisis innovation.

COVID-19 from a cardiovascular perspective / COVID-19 desde una perspectiva cardiovascular

Currently, myocardial injury has been reported in patients hospitalized with coronavirus disease 2019 (COVID-19). The studies also show a correlation between cardiac events and severe forms of the disease. COVID-19 begins with an early infection phase in which the virus infiltrates the lung parenchyma and proliferates. It then progresses to the pulmonary phase, where the initial inflammatory process, characterized by vasodilation, vascular permeability, and leukocyte recruitment, leads to lung damage, hypoxemia, and cardiovascular stress. The renin angiotensin aldosterone system is important in the pathophysiology of severe acute respiratory syndrome coronavirus 2 infection and in the propagation of systemic inflammation. Within this system, the pathway mediated by angiotensin-converting enzyme 2 (ACE2) produces vasodilation, cardioprotection, anti-oxidation, and anti-inflammation. Furthermore, the free form of ECA2 prevents binding of the virus to host cells and reduces its damage to the lung.

Actualmente, se ha reportado injuria miocárdica en pacientes hospitalizados por enfermedad por coronavirus 2019 (COVID-19). Los estudios, además, demuestran una correlación entre los eventos cardiacos y formas severas de la enfermedad. La COVID-19 comienza con una fase de infección temprana en la que el virus infiltra el parénquima pulmonar y prolifera. Luego progresa a la fase pulmonar, donde el proceso inflamatorio inicial, caracterizado por vasodilatación, permeabilidad vascular y reclutamiento de leucocitos, lleva a daño pulmonar, hipoxemia y estrés cardiovascular. El sistema renina angiotensina aldosterona es importante en la fisiopatología de la infección por el coronavirus 2 del síndrome respiratorio agudo grave y en la propagación de la inflamación sistémica. Dentro de este sistema, la vía mediada por la enzima convertidora de angiotensina 2 (ECA2) produce vasodilatación, cardioprotección, antioxidación y antiinflamación. Además, la forma libre de la ECA2 previene la unión del virus a las células huésped y reduce su daño al pulmón.

Potential use of renin-angiotensin-aldosterone system inhibitors to reduce COVID-19 severity.

SARS-CoV-2 infection and its clinical manifestations (COVID-19) quickly evolved to a pandemic and a global public health emergency. The limited effectivity of available treatments aimed at reducing virus replication and the lessons learned from other coronavirus infections (SARS-CoV-1 or NL63) that share the internalization process of SARS-CoV-2, led us to revisit the COVID-19 pathogenesis and potential treatments. Virus protein S binds to the angiotensin-converting enzyme 2 (ACE2) initiating the internalization process. Endosome formation removes ACE2 from the cellular membrane preventing its counter-regulative effect mediated by the metabolism of angiotensin II to angiotensin (1-7). Internalized virus-ACE2 complexes have been identified for these coronaviruses. SARS-CoV-2 presents the highest affinity for ACE2 and produces the most severe symptoms. Assuming ACE2 internalization is the trigger for COVID-19 pathogenesis, accumulation of angiotensin II can be viewed as the potential cause of symptoms. Angiotensin II is a strong vasoconstrictor, but has also important roles in hypertrophy, inflammation, remodeling, and apoptosis. Higher levels of ACE2 in the lungs explain the acute respiratory distress syndrome as primary symptoms. Most of the described findings and clinical manifestations of COVID-19, including increased interleukin levels, endothelial inflammation, hypercoagulability, myocarditis, dysgeusia, inflammatory neuropathies, epileptic seizures and memory disorders can be explained by excessive angiotensin II levels. Several meta-analyses have demonstrated that previous use of angiotensin-converting enzyme inhibitors or angiotensin receptor blockers were associated with better prognosis for COVID-19. Therefore, pragmatic trials to assess the potential therapeutic benefits of renin-angiotensin-aldosterone system inhibitors should be urgently promoted by health authorities to widen the therapeutic options for COVID-19.

Interpretation of lung disease classification with light attention connected module.

Lung diseases lead to complications from obstructive diseases, and the COVID-19 pandemic has increased lung disease-related deaths. Medical practitioners use stethoscopes to diagnose lung disease. However, an artificial intelligence model capable of objective judgment is required since the experience and diagnosis of respiratory sounds differ. Therefore, in this study, we propose a lung disease classification model that uses an attention module and deep learning. Respiratory sounds were extracted using log-Mel spectrogram MFCC. Normal and five types of adventitious sounds were effectively classified by improving VGGish and adding a light attention connected module to which the efficient channel attention module (ECA-Net) was applied. The performance of the model was evaluated for accuracy, precision, sensitivity, specificity, f1-score, and balanced accuracy, which were 92.56%, 92.81%, 92.22%, 98.50%, 92.29%, and 95.4%, respectively. We confirmed high performance according to the attention effect. The classification causes of lung diseases were analyzed using gradient-weighted class activation mapping (Grad-CAM), and the performances of their models were compared using open lung sounds measured using a Littmann 3200 stethoscope. The experts' opinions were also included. Our results will contribute to the early diagnosis and interpretation of diseases in patients with lung disease by utilizing algorithms in smart medical stethoscopes.

CAE-UNet: An Effective Automatic Segmentation Model for CT Images of COVID-19

Since December 2019, COVID-19 has ravaged the world, severely affecting the quality of life and physical health of human society. Computed tomography (CT) imaging is an effective way to detect solid lung lesions as well as pulmonary ground-glass nodules and is an effective way to diagnose COVID-19. The automatic and accurate segmentation of COVID-19 lesion areas from CT images can determine the severity of the disease, which is essential for the diagnosis and treatment of COVID-19. A new model CAE-UNet(Combine-ASPP-ECA-UNet) is proposed in this paper for COVID-19 CT image segmentation based on UNet. The coding structure of UNet is replaced with the improved ResNet50 and incorporated with ECA attention module and atrous spatial pyramid pooling(ASPP). Fusing different sensory fields, global, local and spatial features to enhance the detail segmentation effect of the network. The experimental results on the CC-CCII show that the mIoU of the proposed CAE-UNet reaches 79.53%, which is better than some other mainstream methods. The proposed method achieves automatic and efficient segmentation of COVID-19 CT images. © 2022 IEEE.

CAE-UNet: An Effective Automatic Segmentation Model for CT Images of COVID-19

Since December 2019, COVID-19 has ravaged the world, severely affecting the quality of life and physical health of human society. Computed tomography (CT) imaging is an effective way to detect solid lung lesions as well as pulmonary ground-glass nodules and is an effective way to diagnose COVID-19. The automatic and accurate segmentation of COVID-19 lesion areas from CT images can determine the severity of the disease, which is essential for the diagnosis and treatment of COVID-19. A new model CAE-UNet(Combine-ASPP-ECA-UNet) is proposed in this paper for COVID-19 CT image segmentation based on UNet. The coding structure of UNet is replaced with the improved ResNet50 and incorporated with ECA attention module and atrous spatial pyramid pooling(ASPP). Fusing different sensory fields, global, local and spatial features to enhance the detail segmentation effect of the network. The experimental results on the CC-CCII show that the mIoU of the proposed CAE-UNet reaches 79.53%, which is better than some other mainstream methods. The proposed method achieves automatic and efficient segmentation of COVID-19 CT images. © 2022 IEEE.

Future assessment of the impact of the COVID-19 pandemic on the electricity market based on a stochastic socioeconomic model.

This paper proposes a time-series stochastic socioeconomic model for analyzing the impact of the pandemic on the regulated distribution electricity market. The proposed methodology combines the optimized tariff model (socioeconomic market model) and the random walk concept (risk assessment technique) to ensure robustness/accuracy. The model enables both a past and future analysis of the impact of the pandemic, which is essential to prepare regulatory agencies beforehand and allow enough time for the development of efficient public policies. By applying it to six Brazilian concession areas, results demonstrate that consumers have been/will be heavily affected in general, mainly due to the high electricity tariffs that took place with the pandemic, overcoming the natural trend of the market. In contrast, the model demonstrates that the pandemic did not/will not significantly harm power distribution companies in general, mainly due to the loan granted by the regulator agency, named COVID-account. Socioeconomic welfare losses averaging 500 (MR$/month) are estimated for the equivalent concession area, i.e., the sum of the six analyzed concession areas. Furthermore, this paper proposes a stochastic optimization problem to mitigate the impact of the pandemic on the electricity market over time, considering the interests of consumers, power distribution companies, and the government. Results demonstrate that it is successful as the tariffs provided by the algorithm compensate for the reduction in demand while increasing the socioeconomic welfare of the market.

Renin-angiotensin system: Basic and clinical aspects-A general perspective.

The renin-angiotensin system (RAS) is one of the most complex hormonal regulatory systems, involving several organs that interact to regulate multiple body functions. The study of this system initially focused on investigating its role in the regulation of both cardiovascular function and related pathologies. From this approach, pharmacological strategies were developed for the treatment of cardiovascular diseases. However, new findings in recent decades have suggested that the RAS is much more complex and comprises two subsystems, the classic RAS and an alternative RAS, with antagonistic effects that are usually in equilibrium. The classic system is involved in pathologies where inflammatory, hypertrophic and fibrotic phenomena are common and is related to the development of chronic diseases that affect various body systems. This understanding has been reinforced by the evidence that local renin-angiotensin systems exist in many tissue types and by the role of the RAS in the spread and severity of COVID-19 infection, where it was discovered that viral entry into cells of the respiratory system is accomplished through binding to angiotensin-converting enzyme 2, which is present in the alveolar epithelium and is overexpressed in patients with chronic cardiometabolic diseases. In this narrative review, preclinical and clinical aspects of the RAS are presented and topics for future research are discussed some aspects are raised that should be clarified in the future and that call for further investigation of this system.

A novel cluster detection of COVID-19 patients and medical disease conditions using improved evolutionary clustering algorithm star.

With the increasing number of samples, the manual clustering of COVID-19 and medical disease data samples becomes time-consuming and requires highly skilled labour. Recently, several algorithms have been used for clustering medical datasets deterministically; however, these definitions have not been effective in grouping and analysing medical diseases. The use of evolutionary clustering algorithms may help to effectively cluster these diseases. On this presumption, we improved the current evolutionary clustering algorithm star (ECA*), called iECA*, in three manners: (i) utilising the elbow method to find the correct number of clusters; (ii) cleaning and processing data as part of iECA* to apply it to multivariate and domain-theory datasets; (iii) using iECA* for real-world applications in clustering COVID-19 and medical disease datasets. Experiments were conducted to examine the performance of iECA* against state-of-the-art algorithms using performance and validation measures (validation measures, statistical benchmarking, and performance ranking framework). The results demonstrate three primary findings. First, iECA* was more effective than other algorithms in grouping the chosen medical disease datasets according to the cluster validation criteria. Second, iECA* exhibited the lower execution time and memory consumption for clustering all the datasets, compared to the current clustering methods analysed. Third, an operational framework was proposed to rate the effectiveness of iECA* against other algorithms in the datasets analysed, and the results indicated that iECA* exhibited the best performance in clustering all medical datasets. Further research is required on real-world multi-dimensional data containing complex knowledge fields for experimental verification of iECA* compared to evolutionary algorithms.

Overexpression of the Severe Acute Respiratory Syndrome Coronavirus-2 Receptor, Angiotensin-Converting Enzyme 2, in Diabetic Kidney Disease: Implications for Kidney Injury in Novel Coronavirus Disease 2019.

OBJECTIVES: Diabetes is associated with adverse outcomes, including death, after coronavirus disease 19 (COVID-19) infection. Beyond the lungs, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), the etiologic agent of the COVID-19 pandemic, can infect a range of other tissues, including the kidney, potentially contributing to acute kidney injury in those with severe disease. We hypothesized that the renal abundance of angiotensin-converting enzyme (ACE) 2, the cell surface receptor for SARS-CoV-2, may be modulated by diabetes and agents that block the renin-angiotensin-aldosterone system (RAAS). METHODS: The expression of ACE 2 was examined in 49 archival kidney biopsies from patients with diabetic kidney disease and from 12 healthy, potential living allograft donors using next-generation sequencing technology (RNA Seq). RESULTS: Mean ACE 2 messenger RNA was increased approximately 2-fold in diabetes when compared with healthy control subjects (mean ± SD, 13.2±7.9 vs 7.7±3.6 reads per million reads, respectively; p=0.001). No difference in transcript abundance was noted between recipients and nonrecipients of agents that block the RAAS (12.2±6.7 vs 16.2±10.7 reads per million reads, respectively; p=0.25). CONCLUSIONS: Increased ACE 2 messenger RNA in the diabetic kidney may increase the risk and/or severity of kidney infection with SARS-CoV-2 in the setting of COVID-19 disease. Further studies are needed to ascertain whether this diabetes-related overexpression is generalizable to other tissues, most notably the lungs.