Impact of vaccination on electrocardiograms of hospitalized patients for Covid-19.

Introduction: We sought to assess the impact of SarsCov-2 vaccination on admission12-lead electrocardiogram of hospitalized patients. Methods: We retrospectively analyzed and compared admission 12-lead electrocardiograms of all patients hospitalized in dedicated Internal Medicine Unit for Covid-19 both in pre-vaccination period (PV) and after vaccination (V). Results: 667 consecutive Covid-19 in-patients were enrolled in the study: PV hospitalized patients were older (68vs57 years, p < 0.01), had higher rates of atrial fibrillation/flutter (13%vs2.5%, p < 0.01), any arrhythmia (26%vs8%, p < 0.01), and ST-T abnormalities (22%vs7.4%, p < 0.01). Mortality rates in hospitalized Covid-19 patients were higher before vaccination period (20%vs4%, p < 0.01). Minimal vaccination coverage of population (V period) was inversely and independently associated with in-hospital mortality (odds ratio 0.09, 95%CI 0.01-0.68, p < 0.05). Conclusions: SarsCov-2 vaccination campaign and even partial coverage of local population was associated with less frequent abnormalities at admission ECG in hospitalized non-critically hill Covid-19 patients and lower mortality.

A machine learning algorithm to analyse the effects of vaccination on COVID-19 mortality.

The coronavirus disease 2019 (COVID-19), with new variants, continues to be a constant pandemic threat that is generating socio-economic and health issues in manifold countries. The principal goal of this study is to develop a machine learning experiment to assess the effects of vaccination on the fatality rate of the COVID-19 pandemic. Data from 192 countries are analysed to explain the phenomena under study. This new algorithm selected two targets: the number of deaths and the fatality rate. Results suggest that, based on the respective vaccination plan, the turnout in the participation in the vaccination campaign, and the doses administered, countries under study suddenly have a reduction in the fatality rate of COVID-19 precisely at the point where the cut effect is generated in the neural network. This result is significant for the international scientific community. It would demonstrate the effective impact of the vaccination campaign on the fatality rate of COVID-19, whatever the country considered. In fact, once the vaccination has started (for vaccines that require a booster, we refer to at least the first dose), the antibody response of people seems to prevent the probability of death related to COVID-19. In short, at a certain point, the fatality rate collapses with increasing doses administered. All these results here can help decisions of policymakers to prepare optimal strategies, based on effective vaccination plans, to lessen the negative effects of the COVID-19 pandemic crisis in socioeconomic and health systems.

Electrocardiographic findings and mortality in covid-19 patients hospitalized in different clinical settings.

BACKGROUND: Twelve-lead electrocardiogram (ECG) represents the first-line approach for cardiovascular assessment in patients with Covid-19. OBJECTIVES: We sought to describe and compare admission ECG findings in 3 different hospital settings: intensive-care unit (ICU) (invasive ventilatory support), respiratory care unit (RCU) (non-invasive ventilatory support) and Covid-19 dedicated internal-medicine unit (IMU) (oxygen supplement with or without high flow). We also aimed to assess the prognostic impact of admission ECG variables in Covid-19 patients. METHODS: We retrospectively analyzed the admission 12-lead ECGs of 1124 consecutive patients hospitalized for respiratory distress and Covid-19 in a single III-level hospital. Age, gender, main clinical data and in-hospital survival were recorded. RESULTS: 548 patients were hospitalized in IMU, 361 in RCU, 215 in ICU. Arrhythmias in general were less frequently found in RCU (16% vs 26%, p<0.001). Deaths occurred more frequently in ICU patients (43% vs 20-21%, p<0.001). After pooling predictors of mortality (age, intensity of care setting, heart rate, ST-elevation, QTc prolongation, Q-waves, right bundle branch block, and atrial fibrillation), the risk of in-hospital death can be estimated by using a derived score. Three zones of mortality risk can be identified: <5%, score <5 points; 5-50%, score 5-10, and >50%, score >10 points. The accuracy of the score assessed at ROC curve analysis was 0.791. CONCLUSIONS: ECG differences at admission can be found in Covid-19 patients according to different clinical settings and intensity of care. A simplified score derived from few clinical and ECG variables may be helpful in stratifying the risk of in-hospital mortality.

Arrhythmic myocarditis in an adolescent male: A unique presentation of multi-organ inflammatory syndrome (MIS-C).

Although children with Covid-19 generally present with mild symptoms or are often asymptomatic, there is increasing recognition of a delayed multi-organ inflammatory syndrome (MIS-C) following SARS-CoV-2 infection. We report the case of MIS-C associated arrhythmic myocarditis which recovered after anti-inflammatory therapy and immunoglobulin infusion.

Assessing a fossil fuels externality with a new neural networks and image optimisation algorithm: the case of atmospheric pollutants as confounders to COVID-19 lethality.

This paper demonstrates how the combustion of fossil fuels for transport purpose might cause health implications. Based on an original case study [i.e. the Hubei province in China, the epicentre of the coronavirus disease-2019 (COVID-19) pandemic], we collected data on atmospheric pollutants (PM2.5, PM10 and CO2) and economic growth (GDP), along with daily series on COVID-19 indicators (cases, resuscitations and deaths). Then, we adopted an innovative Machine Learning approach, applying a new image Neural Networks model to investigate the causal relationships among economic, atmospheric and COVID-19 indicators. Empirical findings emphasise that any change in economic activity is found to substantially affect the dynamic levels of PM2.5, PM10 and CO2 which, in turn, generates significant variations in the spread of the COVID-19 epidemic and its associated lethality. As a robustness check, the conduction of an optimisation algorithm further corroborates previous results.

Electrocardiographic findings and mortality in COVID-19 patients hospitalized in different clinical settings

Aims 12-lead electrocardiogram (ECG) still represents the first line approach for cardiovascular assessment even in patients with COVID-19. We therefore sought to describe and compare ECG findings in three different hospital settings: intensive care unit (ICU) (invasive ventilatory support), respiratory care unit (RCU) (non-invasive ventilatory support) and Covid-19 dedicated internal medicine unit (IMU) (oxygen supplement with or without high flow). Methods and results We retrospectively analysed the 12-lead ECGs of 1124 consecutive patients hospitalized for respiratory distress and COVID-19 in a single III level hospital. Age, gender, main clinical data and in-hospital survival were recorded. 548 patients were hospitalized in IMU, 361 in RCU, 215 in ICU. Arrhythmias in general were less frequently found in RCU (16% vs. 26%, P < 0.001). Deaths occurred more frequently in ICU patients (43% vs. 20–21%, P < 0.001). After pooling predictors of mortality (age, intensity of care setting, heart rate, ST-elevation, QTc prolongation, Q-waves, right bundle branch block, and atrial fibrillation), the risk of in-hospital death can be estimated by using a derived score. Three zones of mortality risk can be thus identified: <5%, score <5 points;5–50% score 5–10, and >50%, score >10 points. The accuracy of the score assessed at ROC curve analysis was 0.791. Conclusions ECG differences at admission con be found in COVID-19 patients according to different clinical settings and intensity of care. A simplified score derived from few clinical and ECG variables may predict in-hospital mortality with a good accuracy.

Nature and climate change effects on economic growth: an LSTM experiment on renewable energy resources.

Global energy demand increases overtime, especially in emerging market economies, producing potential negative environmental impacts, particularly on the long term, on nature and climate changes. Promoting renewables is a robust policy action in world energy-based economies. This study examines if an increase in renewables production has a positive effect on the Brazilian economy, partially offsetting the SARS-CoV2 outbreak recession. Using data on Brazilian economy, we test the contribution of renewables on the economy via a ML architecture (through a LSTM model). Empirical findings show that an ever-greater use of renewables may sustain the economic growth recovery, generating a better performing GDP acceleration vs. other energy variables.

The nexus between COVID-19 deaths, air pollution and economic growth in New York state: Evidence from Deep Machine Learning.

The aim of this paper is to assess the relationship between COVID-19-related deaths, economic growth, PM10, PM2.5, and NO2 concentrations in New York state using city-level daily data through two Machine Learning experiments. PM2.5 and NO2 are the most significant pollutant agents responsible for facilitating COVID-19 attributed death rates. Besides, we found only six out of many tested causal inferences to be significant and true within the AUPRC analysis. In line with the causal findings, a unidirectional causal effect is found from PM2.5 to Deaths, NO2 to Deaths, and economic growth to both PM2.5 and NO2. Corroborating the first experiment, the causal results confirmed the capability of polluting variables (PM2.5 to Deaths, NO2 to Deaths) to accelerate COVID-19 deaths. In contrast, we found evidence that unsustainable economic growth predicts the dynamics of air pollutants. This shows how unsustainable economic growth could increase environmental pollution by escalating emissions of pollutant agents (PM2.5 and NO2) in New York state.

The relationship between air pollution and COVID-19-related deaths: An application to three French cities.

Being heavily dependent to oil products (mainly gasoline and diesel), the French transport sector is the main emitter of Particulate Matter (PMs) whose critical levels induce harmful health effects for urban inhabitants. We selected three major French cities (Paris, Lyon, and Marseille) to investigate the relationship between the Coronavirus Disease 19 (COVID-19) outbreak and air pollution. Using Artificial Neural Networks (ANNs) experiments, we have determined the concentration of PM2.5 and PM10 linked to COVID-19-related deaths. Our focus is on the potential effects of Particulate Matter (PM) in spreading the epidemic. The underlying hypothesis is that a pre-determined particulate concentration can foster COVID-19 and make the respiratory system more susceptible to this infection. The empirical strategy used an innovative Machine Learning (ML) methodology. In particular, through the so-called cutting technique in ANNs, we found new threshold levels of PM2.5 and PM10 connected to COVID-19: 17.4 µg/m3 (PM2.5) and 29.6 µg/m3 (PM10) for Paris; 15.6 µg/m3 (PM2.5) and 20.6 µg/m3 (PM10) for Lyon; 14.3 µg/m3 (PM2.5) and 22.04 µg/m3 (PM10) for Marseille. Interestingly, all the threshold values identified by the ANNs are higher than the limits imposed by the European Parliament. Finally, a Causal Direction from Dependency (D2C) algorithm is applied to check the consistency of our findings.

The Relationship between Renewable Energy and Economic Growth in a Time of Covid-19: A Machine Learning Experiment on the Brazilian Economy

This paper examines the relationship between renewable energy consumption and economic growth in Brazil, in the Covid-19 pandemic. Using an Artificial Neural Networks (ANNs) experiment in Machine Learning, we tried to verify if a more intensive use of renewable energy could generate a positive GDP acceleration in Brazil. This acceleration could offset the harmful effects of the Covid-19 global pandemic. Empirical findings show that an ever-greater use of renewable energies may sustain the economic growth process. In fact, through a model of ANNs, we highlighted how an increasing consumption of renewable energies triggers an acceleration of the GDP compared to other energy variables considered in the model.

NO2 levels as a contributing factor to COVID-19 deaths: The first empirical estimate of threshold values.

This study represents the first empirical estimation of threshold values between nitrogen dioxide (NO2) concentrations and COVID-19-related deaths in France. The concentration of NO2 linked to COVID-19-related deaths in three major French cities were determined using Artificial Neural Networks experiments and a Causal Direction from Dependency (D2C) algorithm. The aim of the study was to evaluate the potential effects of NO2 in spreading the epidemic. The underlying hypothesis is that NO2, as a precursor to secondary particulate matter formation, can foster COVID-19 and make the respiratory system more susceptible to this infection. Three different neural networks for the cities of Paris, Lyon and Marseille were built in this work, followed by the application of an innovative tool of cutting the signal from the inputs to the selected target. The results show that the threshold levels of NO2 connected to COVID-19 range between 15.8 µg/m3 for Lyon, 21.8 µg/m3 for Marseille and 22.9 µg/m3 for Paris, which were significantly lower than the average annual concentration limit of 40 µg/m³ imposed by Directive 2008/50/EC of the European Parliament.

The Impact of the COVID-19 Pandemic on Surgical Management of Breast Cancer: Global Trends and Future Perspectives.

INTRODUCTION: The rapid spread of COVID-19 across the globe is forcing surgical oncologists to change their daily practice. We sought to evaluate how breast surgeons are adapting their surgical activity to limit viral spread and spare hospital resources. METHODS: A panel of 12 breast surgeons from the most affected regions of the world convened a virtual meeting on April 7, 2020, to discuss the changes in their local surgical practice during the COVID-19 pandemic. Similarly, a Web-based poll based was created to evaluate changes in surgical practice among breast surgeons from several countries. RESULTS: The virtual meeting showed that distinct countries and regions were experiencing different phases of the pandemic. Surgical priority was given to patients with aggressive disease not candidate for primary systemic therapy, those with progressive disease under neoadjuvant systemic therapy, and patients who have finished neoadjuvant therapy. One hundred breast surgeons filled out the poll. The trend showed reductions in operating room schedules, indications for surgery, and consultations, with an increasingly restrictive approach to elective surgery with worsening of the pandemic. CONCLUSION: The COVID-19 emergency should not compromise treatment of a potentially lethal disease such as breast cancer. Our results reveal that physicians are instinctively reluctant to abandon conventional standards of care when possible. However, as the situation deteriorates, alternative strategies of de-escalation are being adopted. IMPLICATIONS FOR PRACTICE: This study aimed to characterize how the COVID-19 pandemic is affecting breast cancer surgery and which strategies are being adopted to cope with the situation.

Pollution, economic growth, and COVID-19 deaths in India: a machine learning evidence.

This study uses two different approaches to explore the relationship between pollution emissions, economic growth, and COVID-19 deaths in India. Using a time series approach and annual data for the years from 1980 to 2018, stationarity and Toda-Yamamoto causality tests were performed. The results highlight unidirectional causality between economic growth and pollution. Then, a D2C algorithm on proportion-based causality is applied, implementing the Oryx 2.0.8 protocol in Apache. The underlying hypothesis is that a predetermined pollution concentration, caused by economic growth, could foster COVID-19 by making the respiratory system more susceptible to infection. We use data (from January 29 to May 18, 2020) on confirmed deaths (total and daily) and air pollution concentration levels for 25 major Indian cities. We verify a ML causal link between PM2.5, CO2, NO2, and COVID-19 deaths. The implications require careful policy design.