A radiomics-based model for the outcome prediction in COVID-19 positive patients through deep learning with both longitudinal chest x-ray and chest computed tomography images

The coronavirus disease 2019 (COVID-19) pandemic had a major impact on global health and was associated with millions of deaths worldwide. During the pandemic, imaging characteristics of chest X-ray (CXR) and chest computed tomography (CT) played an important role in the screening, diagnosis and monitoring the disease progression. Various studies suggested that quantitative image analysis methods including artificial intelligence and radiomics can greatly boost the value of imaging in the management of COVID-19. However, few studies have explored the use of longitudinal multi-modal medical images with varying visit intervals for outcome prediction in COVID-19 patients. This study aims to explore the potential of longitudinal multimodal radiomics in predicting the outcome of COVID-19 patients by integrating both CXR and CT images with variable visit intervals through deep learning. 2274 patients who underwent CXR and/or CT scans during disease progression were selected for this study. Of these, 946 patients were treated at the University of Pennsylvania Health System (UPHS) and the remaining 1328 patients were acquired at Stony Brook University (SBU) and curated by the Medical Imaging and Data Resource Center (MIDRC). 532 radiomic features were extracted with the Cancer Imaging Phenomics Toolkit (CaPTk) from the lung regions in CXR and CT images at all visits. We employed two commonly used deep learning algorithms to analyze the longitudinal multimodal features, and evaluated the prediction results based on the area under the receiver operating characteristic curve (AUC). Our models achieved testing AUC scores of 0.816 and 0.836, respectively, for the prediction of mortality. © 2023 SPIE.

The effect of the emergency shift to virtual instruction on student team dynamics

In spite of the sudden onset of the COVID-19 pandemic, many instructors who used team-based pedagogies shifted them online rather than suspending them entirely, but with limited time and resources. To examine the difference in team dynamics and outcomes for courses in Spring 2019 and Spring 2020 of over 1500 first-year engineering students per semester, Wilcoxon signed-rank tests and random forests method were used. Results show that students reported less improvement in team-member effectiveness, lower psychological safety, and less satisfaction in the semester with the emergency transition. However, students also reported lower conflict. The most important factor predicting project grades shifted from 'Interacting with teammates' to 'Having relevant knowledge, skills, and abilities' amid the emergency shift, accompanied by a reduction in team interdependence. In spite of the collection of data during an emergency transition, the foundation of face-to-face interaction before moving to virtual cooperation represents a useful contribution to research that has focused exclusively on virtual learning circumstances.

Characterization of Adaptive Immune Responses to SARS-CoV-2 Updated Bivalent Booster

Bivalent COVID-19 vaccines that contain two mRNAs encoding Wuhan-1 and Omicron BA.4/5 spike proteins are successful in preventing infection from the original strain and Omicron variants, but the quality of adaptive immune responses is still not well documented. This study aims at characterizing adaptive immune responses to the bivalent booster vaccination in 46 healthy participants. Plasma and PBMC were collected prior and three weeks after bivalent booster. We measured anti-N, anti-S, and RBD IgM, IgA, IgG plasma titers against original, Omicron BA.1, and BA.5 variants (pending) as well as total anti-S IgG titers and surrogate Virus Neutralization capacity against the Alpha, Delta, and BA.1 variant. With spectral flow-cytometry we identified peripheral blood B-cells specific for the RBD of the S-protein of the original and BA.1 variants. T-cell-specific responses were assessed by cytokine release assay after stimulation with SARS-CoV-2 peptides from the original, BA.1, BA.4, and BA.5 variants (pending). Finally, we performed TRB and IGH repertoire studies on sorted CD4+, CD8+, CD19+ lymphocytes, to study breadth of SARS-CoV-2 specific clonotypes (pending). 27/46 participants were analyzed;9 had SARS-CoV-2 infection (COVID+), while 18 are infection naive (COVID-). In both groups, median time since last dose of SARS-CoV-2 vaccine (3rd or 4th) was 11 months. All subjects were positive for anti-S IgG prior to bivalent booster. The COVID + group displayed anti-S IgG pre-booster levels and neutralization against BA.1 higher than the COVID- group. Significant increase post-boost of total anti-S IgG and BA.1 neutralizing activity was detected in the COVID- but not in the COVID+ group;however, no difference in neutralization activity post-boost was detected between the two groups. Furthermore, the COVIDgroup showed significant increase in the frequency of CD19+ and CD27+ switched memory B-cells specific for BA.1 RBD in post-boost compared to pre-boost samples. However, post-boost frequencies of the same B-cells were higher in the COVID+ compared to the COVID- group. These preliminary findings confirm that among individual immunized with the original COVID-19 mRNAvaccine, prior COVID infection provides increased protection against SARS-CoV-2 variants. They also demonstrate that booster immunization with the bivalent vaccine induces robust adaptive immune responses against Omicron variant.[Formula presented][Formula presented]Copyright © 2023 Elsevier Inc.

DEEP LEARNING BASED AN EFFICIENT HYBRID PREDICTION MODEL FOR COVID-19 CROSS-COUNTRY SPREAD AMONG E7 AND G7 COUNTRIES

The COVID-19 pandemic has caused the death of many people around the world and has also caused economic problems for all countries in the world. In the literature, there are many studies to analyze and predict the spread of COVID-19 in cities and countries. However, there is no study to predict and analyze the cross-country spread in the world. In this study, a deep learning based hybrid model was developed to predict and analysis of COVID-19 cross-country spread and a case study was carried out for Emerging Seven (E7) and Group of Seven (G7) countries. It is aimed to reduce the workload of healthcare professionals and to make health plans by predicting the daily number of COVID-19 cases and deaths. Developed model was tested extensively using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R Squared (R2). The experimental results showed that the developed model was more successful to predict and analysis of COVID-19 cross-country spread in E7 and G7 countries than Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). The developed model has R2 value close to 0.9 in predicting the number of daily cases and deaths in the majority of E7 and G7 countries. © 2023 by the authors.

Serum glial-derived neurotrophic factor and nerve growth factor levels in patients with COVID-19

Introduction and objective: The new type of coronavirus (COVID-19) causes high fever, fatigue, cough, respiratory distress, diarrhea, headache in some patients, cerebrovascular diseases, unconsciousness, encephalopathy, encephalitis, peripheral nervous system damage, etc. It is a viral respiratory disease that manifests itself with neurological findings. In our study, glial-derived neurotrophic factor (GDNF) and nerve growth factor (NGF) levels of neurotrophic factors (NF), which ensure the survival, growth, maturation and differentiation of neurons were investigated in COVID-19 patients, including their relationship with the severity of the disease. Materials and methods: Out of a total of 70 participants, 20 participants are in the healthy control group (CG) and 50 participants are in the group of patients with COVID-19 according to PCR test (uncomplicated group [NCG], moderately severe group [MG], severe group [SG]). Serum NGF and GDNF levels in all groups were evaluated spectrophotometrically using ELISA kits. The results were compared both between the patient groups and between the patient and healthy control groups. Results: Serum NGF concentration was significantly higher in the MG group than in the NCG and the SG group (P = 0.042). No statistically significant difference was found in serum GDNF levels in COVID-19 patients and CG. Conclusion: There was no difference in serum NGF and serum GDNF levels in COVID-19 patients compared to the healthy control group.

IRF2BP2 mutation with autoimmune enteropathy, immune deficiency, and respiratory failure

Case history: We present the case of a 31-year-old Hispanic male with history of recurrent bronchiectasis, invasive aspergillosis, and severe persistent asthma, who is now status post lung transplant for end-stage lung disease. He initially presented at 7 years of age with diarrhea, failure to thrive, and nearly absent immunoglobulin levels (IgG < 33 mg/dL, IgA < 7 mg/dL, IgM = 11 mg/dL, IgE = 4 IU/dL) necessitating IVIG treatment. Small intestinal biopsy showed villous atrophy consistent with autoimmune enteropathy. Sweat chloride was reported as indeterminate (44 me/dL). Initial WBC, platelet, and T- and NK-cell counts were within normal range, and B-cell count and percentage were borderline low. Most recently, he was found to have increased immature B-cell count (CD21low), decreased memory B-cells, and poor pneumococcal vaccine antibody response. Patient has been hospitalized numerous times with increasingly severe bronchiectasis, pneumonitis, and COVID-19 infections twice despite vaccination, leading to respiratory failure and lung transplantation. Family history is negative for immune deficiency and lung diseases. Discussion(s): Of these 3 VUSs (see the table), the one in IRF2BP2 has the most pathogenic potential due to its autosomal dominant inheritance, its location in a conserved domain (Ring), and previous case reports of pathogenic variants at the same or adjacent alleles 1-3. Baxter et al reported a de novo truncating mutation in IRF2BP2 at codon 536 (c.1606CinsTTT), which is similar to our patient's mutation. This patient was noted to have an IPEX-like presentation, with chronic diarrhea, hypogammaglobulinemia, and recurrent infections. Variant Functional Prediction Score for our variant predicts a potentially high damage effect. There are 2 other case reports of heterozygous mutations in loci adjacent to this allele;one (c.1652G>A)2 with a similar clinical phenotype to our patient and the other (C.625-665 del)3 with primarily inflammatory features and few infections. Impact: This case highlights a variant in IRF2BP2 associated with severe hypogammaglobulinemia, recurrent pulmonary infections, and autoimmune enteropathy. [Table presented]Copyright © 2023 Elsevier Inc.

Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging

The transition to Electric Vehicles (EV) in place of traditional internal combustion engines is increasing societal demand for electricity. The ability to integrate the additional demand from EV charging into forecasting electricity demand is critical for maintaining the reliability of electricity generation and distribution. Load forecasting studies typically exclude households with home EV charging, focusing on offices, schools, and public charging stations. Moreover, they provide point forecasts which do not offer information about prediction uncertainty. Consequently, this paper proposes the Long Short-Term Memory Bayesian Neural Networks (LSTM-BNNs) for household load forecasting in presence of EV charging. The approach takes advantage of the LSTM model to capture the time dependencies and uses the dropout layer with Bayesian inference to generate prediction intervals. Results show that the proposed LSTM-BNNs achieve accuracy similar to point forecasts with the advantage of prediction intervals. Moreover, the impact of lockdowns related to the COVID-19 pandemic on the load forecasting model is examined, and the analysis shows that there is no major change in the model performance as, for the considered households, the randomness of the EV charging outweighs the change due to pandemic.

Informational inefficiency on bitcoin futures

This paper investigates the dynamics and drivers of informational inefficiency in the Bitcoin futures market. To quantify the adaptive pattern of informational inefficiency, we leverage two groups of statistics which measure long memory and fractal dimension to construct a global-local market inefficiency index. Our findings validate the adaptive market hypothesis, and the global and local inefficiency exhibits different patterns and contributions. Regarding the driving factors of the time-varying inefficiency, our results suggest that trading activity of retailers (hedgers) increases (decreases) informational inefficiency. Compared to hedgers and retailers, the role played by speculators is more likely to be affected by the COVID-19 crisis. Extremely bullish and bearish investor sentiment has more significant impact on the local inefficiency. Arbitrage potential, funding liquidity, and the pandemic exert impacts on the global and local inefficiency differently. No significant evidence is found for market liquidity and policy uncertainty related to cryptocurrency.

Roots and resiliency for native American young people

Aim: The presentation shares traditional Native American knowledge about wellbeing and caring for a person's body, heart, connection to the Creator, ancestors, and the land Methods: Drawing upon community narratives and traditional ancestral knowledge themes pertinent to the topic will be presented. Narrative Review Results: Ancestral knowledge is essential to access and practice in? community care and healing. This knowledge is sacred to the lives wellbeing, and continuation of traditional ways for Confederated Tribes of Warm Springs (CTWS) people. CTWS young people play an important role in these practices for their community and elders. The practice of taking CTWS children from families was a pivotal moment that pushed forward the concept of mental health for the CTWS. The threat of climate change, and the COVID-19 pandemic's activation of memories of imposed isolation between our people and from traditional ways continues to impact our young people. The process of healing from historical and present-day traumas includes grieving those losses and healing from addictions, as well as physical and sexual abuse Conclusion(s): Rebuilding and strengthening connections to the land Chuush (water in Sahaptin language), food gathering, and being with each other, is central to our young people's, and community's, healing The path of returning to our traditional understanding of the knowledge of what the Creator has provided for the CTWS people will be shared. This knowledge is useful for the care of young people Native and non-Native alike.

COVID-19 Associated Brain Fog and Neurocognitive Assessment

BACKGROUND/AIMS: In this study, we aimed to make detailed neurocognitive assessments of patients who presented with brain fog after coronavirus disease-2019 (COVID-19) infection and to investigate their complaints after one-year of follow-up. MATERIALS AND METHODS: Patients who had COVID-19, which was not severe enough to require intensive care, and who subsequently applied to neurology due to cognitive complaints were included in this study. A neurocognitive test battery was applied to those patients who agreed to detailed examination (n=16). This battery consisted of the following tests: mini-mental test, enhanced cued recall test, phonemic fluency, categorical fluency, digit span, counting the months backwards, clock-drawing, arithmetic operations, trail-making, cube copying, intersecting pentagons, and the interpretation of proverbs and similes. At one year, the patients were called by phone and questioned as to whether their cognitive complaints had persisted. Those patients with ongoing complaints were invited to the hospital and re-evaluated via cognitive tests. The results are presented in comparison with age-matched healthy controls (n=15). RESULTS: Almost all of the patients' scores were within the "normal" range. The Spontaneous recall of the patients was statistically significantly lower than the controls (p=0.03). Although there were decreases in executive functions and central processing speed (trail making-A, trail making-B and reciting the months backwards tests) in the patient group, these differences were not statistically significant (p=0.07;p=0.14 and p=0.22, respectively) compared to the controls. We observed that the cognitive complaints of the patients had disappeared by the one-year follow-up. CONCLUSION: In our patients with brain fog, most of whom had mild COVID-19, we observed that among all cognitive functions, memory domain was most affected compared to the controls. At the one-year follow-up, COVID-related brain fog had disappeared.

Tit for tat? EU risk-sharing and experienced reciprocity

As with previous crises, EU-wide risk-sharing has also been demanded during the Covid-19 pandemic. Yet, this crisis did not unfold in a political vacuum. Instead, public backing for EU-wide risk-sharing might have been informed by past crises experiences. Building on the idea of experienced reciprocal risk-sharing, we assume that the willingness to share risks is greater when a crisis-ridden country has also shown solidarity before, whereas readiness to cooperate may be mitigated by non-solidarity-oriented behaviour in the past. We test this assumption based on a survey experiment carried out in eleven EU countries in 2020. Our findings suggest that, when people are given information about whether another country has acted in solidarity in the past, this influences their willingness to support risk-sharing in the present. However, we also find evidence that respondents' preferences outside the experimental setting do not always match their country's recent history of reciprocal risk-sharing.

Impact of Electronic Word-Of-Mouth on Consumer-Based Brand Equity and Purchase Intention: Hotel Industry in Thailand

Electronic word-of-mouth is a new form of informal communication where messages are disseminated to others using social media and other electronic platforms. This research investigates eWOM to determine its impact on the perception of brand equity and the intentions of consumers to purchase hotel services in Thailand. Using a quantitative approach and a non-probability sampling method, 410 Thai respondents aged 18 and above with relevant hotel experiences participated in this study. Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) were used to analyze the model fit and the validity and reliability of the variables. In addition, in order to investigate the relationship between the constructs, first-order and second-order approaches were used, in which eWOM was the second-order construct in the study, while its credibility, valence, and volume, were first-order constructs. The findings indicated that eWOM positively affects all brand equity dimensions and purchase intentions, showing the strongest significant positive effect on brand awareness. Additionally, brand equity dimensions were shown to mediate the effect of eWOM on purchase intentions. Details of the analyses and discussions are included in the latter part of this paper.

Time series prediction of the COVID-19 outbreak in India using LSTM based deep learning models

The epidemic caused by COVID-19 presents a significant risk to the continuation of human civilisation and has already done irreparable damage to society. In this paper, forecasting of Coronavirus outbreak in India is performed by LSTM and CovnLSTM deep neural network techniques. COVID-19 data of confirmed cases of India is used. It was taken from John Hopkins University. The loss rate of ConvLSTM is lower than LSTM and RMSE of ConvLSTM is lower than LSTM. For training Covn-LSTM shows 0.069% and testing ConvLSTM shows 0.32% improvement over LSTM model. Therefore, ConvLSTM outperformed over LSTM model. Further wise selection of hyper-parameters could increase the accuracy of the models. © 2023 IEEE.

Discursive epidemic: considerations from a discourse analysis perspective and communication studies

In this article, of an essayistic nature, we aim to present theoretical-conceptual foundations in the field of discourse and communication that allow us to reflect on epidemics as discursive realities (Maingueneau, 2020). For this, we revisit research developed by Bessa (1997;2002), Treichler (1987), Daniel and Parker (2018) and Santos Filho (2020) on the HIV/aids epidemic and tension the contemporary experience of the COVID-19 pandemic in a mediatization scenario. From our reflection, we propose, as a result, the expansion of the concept of discursive epidemic as a set of imaginaries created and shared discursively by different social spheres, in a context of mediatization and excess of statements on a certain theme, expressing certain dominant worldviews in a given society and at a given moment, but which, through proliferation and lasting reiteration without mutability, are consolidated in the collective memory in such a powerful way as to be frequently remembered.

Forecasting COVID-19 Pandemic using Prophet, LSTM, hybrid GRU-LSTM, CNN-LSTM, Bi-LSTM and Stacked-LSTM for India

The COVID-19 Pandemic has been around for four years and remains a health concern for everyone. Although things are somewhat returning to normal, increased incidence of COVID-19 cases in some regions of the world (such as China, Japan, France, South Korea, etc.) has bred worry and anxiety in world, including India. The scientific community, which includes governmental organizations and healthcare facilities, was eager to learn how the COVID-19 Pandemic would develop. The current work makes an attempt to address this question by employing cutting-edge machine learning and Deep Learning algorithms to anticipate the daily incidence of COVID-19 for India over the course of the next six months. For the purpose famous timeseries algorithms were implemented including LSTM, Bi-Directional LSTM and Stacked LSTM and Prophet. Owing to success of hybrid algorithms in specific problem domains- the present study also focuses on such algorithms like GRU-LSTM, CNN-LSTM and LSTM with Attention. All these models have been trained on timeseries dataset of COVID-19 for India and performance metrics are recorded. Of all the models, the simplistic algorithms have performed better than complex and hybrid ones. Owing to this best result was obtained with Prophet, Bidirectional LSTM and Vanilla LSTM. The forecast reveals flat nature of COVID-19 case load for India in future six months. . © 2023 IEEE.

COVID-19-Related Encephalitis in a Child with PIK3CD Defect

Introduction: COVID-19 related encephalitis has been reported in pediatric patients;however, there are no reports in patients with inborn errors of immunity (IEI). Activated PI3K Delta Syndrome (APDS) is a disease of immune dysregulation with immunodeficiency, autoimmunity, and abnormal lymphoproliferation resulting from autosomal dominant gain-offunction variants in PIK3CD or PIK3R1 genes. We investigate a family with APDS, one mother and three children, one of whom developed COVID-19 related encephalitis. Method(s): Patients were consented to an IRB-approved protocol at our institution. Medical records and detailed immunophenotyping were reviewed. Family members were sequenced for IEI with a targeted gene panel. Result(s): The index case is a 10-year-old female with a known pathogenic variant in PIK3CD (c.3061 G > A, p.Glu1021Lys), who contracted SARS-COV-2 despite one COVID-19 vaccination in the series. Her disease course included COVID-related encephalitis with cerebellitis and compression of the pons, resulting in lasting truncal ataxia and cerebellar mutism. At that time, the patient was not on immunoglobulin replacement therapy (IgRT), but was receiving Sirolimus. Besides the index case, 3 family members (2 brothers, 1 mother) also share the same PIK3CD variant with variable clinical and immunological phenotypes. All children exhibited high transitional B-cells, consistent with developmental block to follicular B cell stage. Increased non-class switched IgM+ memory B cells and skewing towards CD21lo B cell subset, which is considered autoreactive-like, was observed in all patients. Of note, the patient had low plasmablasts, but normal immunoglobulins. Of her family members, only one was receiving both sirolimus and IgRT. Conclusion(s): We describe a rare case of COVID-19-related encephalitis in a patient with inborn error of immunity while not on IgRT. This may indicate infection susceptibility because of a lack of sufficient immunity to SARS-CoV-2, unlike the rest of her family with the same PIK3CD variant.Copyright © 2023 Elsevier Inc.

COVID-19: AFTER TWO YEARS OF IATROGENIC AND BIOLOGIC IMMUNIZATION/ VACCINATION DO WE ACTUALLY NEED FURTHER BOOSTER IMMUNIZATION/ VACCINATION?

BackgroundThe Covid19 pandemic started in late 2019 and went through different phases by spreading from China around the whole globe. During the pandemic different mutation types got predominant from original Wuhan type through Alpha, Delta and Omicron variate BA 1/2 to BA 4/5 with different infectiousity and different potential to harm people´s health status. Immunization/ vaccination program started late 2020, first booster phase started midst of 2021, second booster phase in late 2021/ beginning of 2022 and Omicron specific booster phase midst of 2022.ObjectivesIs there a need of further iatrogenic (booster) immunization/ vaccination after 2 years of immunization/ vaccination program from efficacy driven analysis and safety issues standpoint?MethodsAnalysis of Covid-19 antibody development every three months since August 2021 with comparison of infection rates and assessment of safety parameters by assessing D-Dimers as potential endothelium damage marker in 725 patients (600 female, 125 male, age mean: 62,2 years) of a German rheumatological practice to improve the medical care.ResultsIn 99 % of the patients longstanding immune memory could be shown by analyzing the antibody curves in different exemplary shown biologic and iatrogenic immunization pathways after 2 years of immunization/ vaccination program and biologic immunization, mainly by Delta variate since late 2021 and Omicron variate since beginning of 2022. In 38.5 % of the patients the safety concerns of potential endothelium damage by analysing D-Dimers every 3 months showed a side effect potential of at least 8 months after every MRNA/ Vector immunization, but not after protein based vaccination and even not after infections in that amount.ConclusionOut of the obligation "nil nocere” no further iatrogenic Covid-19 immunization/ vaccination is of need in nearly all (99 %) already immunized people. At present only adult people with very low antibody levels (at least below 64 BAU/ml) (considering the infection or iatrogenic immunization/ vaccination status and time since last spike protein contact) and not yet immunized adult people should be forseen for iatrogenic immunization/ vaccination with protein based or attenuated viral vaccines or in rare cases one Omicron specific MRNA immunization drug. In that case D-Dimer controls for up to 8 months should be obligatory to detect endothelial damage side effect of MRNA (or Vector) technique. Intense cardiovascular monitoring (small vessels) of MRNA/ Vector immunized people in the next 10 – 20 years is necessary.Figure 1.References[1] Pohl C;SAFETY AND EFFICACY ASSESSMENT OF COVID-19 IMMUNIZATIONS/ VACCINATIONS IN PATIENTS OF A GERMAN GENERAL RHEUMATOLOGICAL PRACTICE;EULAR 2022 Poster POS1213;https://doi.org/10.1136/annrheumdis-2022-eular.1389[2] McConeghy KW et al. Effectiveness of a Second COVID-19 Vaccine Booster Dose Against Infection, Hospitalization, or Death Among Nursing Home Residents - 19 States, March 29-July 25, 2022. MMWR Morb Mortal Wkly Rep. 2022 Sep 30;71(39):1235-1238. doi: 10.15585/mmwr.mm7139a2. PMID: 36173757;PMCID: PMC9533729.[3] Bowe, B. Et al. Acute and postacute sequelae associated with SARS-CoV-2 reinfection. Nat Med 28, 2398–2405 (2022). https://doi.org/10.1038/s41591-022-02051-3[4] Hui-Lee Wong et al. Surveillance of COVID-19 vaccine safety among elderly persons aged 65 years and older, Vaccine, Volume 41, Issue 2, 2023, Pages 532-539, ISSN 0264-410X, https://doi.org/10.1016/j.vaccine.2022.11.069.[5] Maher AK et al. Transcriptional reprogramming from innate immune functions to a pro-thrombotic signature by monocytes in COVID-19. Nat Commun. 2022 Dec 26;13(1):7947. doi: 10.1038/s41467-022-35638-y. PMID: 36572683;PMCID: PMC9791976.[6] Erich Freisleben;Sie wollten alles richtig machen – Dokumentation eines verschwiegenen Leidens – Bericht eines Hausarztes über die Nebenwirkungen der Corona Impfungen;Nov 11, 2022;Cajus Verlag[7] Positive Testrate Germany – https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Testzahl.htmlAcknowledgementsThanks to my fami y, all my patients and my collegues for supporting me in my research to improve my personal patient care.Disclosure of InterestsNone Declared.

COVID-19 Prognosis from Chest X-ray Images by using Deep Learning Approaches: A Next Generation Diagnostic Tool

Global public health is overwhelmed due to the ongoing Corona Virus Disease (COVID-19). As of October 2022, the causative virus SARS-CoV-2 and its multiple variants have infected more than 600 million confirmed cases and nearly 6.5 million fatalities globally. The main objective of this reported study is to understand the COVID-19 infection better from the chest X-ray (CXR) image database of COVID-19 cases from the dataset of CXR of normal, pneumonia and COVID-19 patients. Deep learning approaches like VGG-16 and LSTM models were used to classify images as normal, pneumonia and COVID-19 impacted by extracting the features. It has been observed during the COVID-19 pandemic peaks that large number of patients could not avail medical beds and were seen stranded outdoors. To address such health emergency situations with limited available bed and scarcity of expert physicians, computer-aided analysis could save precious lives through early screening and appropriate care. Such computer-based deep-learning strategy could help during future pandemics, especially when the available health resources and the need for preventive measures to take do not match the burden of a disease. [ FROM AUTHOR] Copyright of Journal of Pure & Applied Microbiology is the property of Dr. M. N. Khan and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Text Mining and Emotion Classification on Monkeypox Twitter Dataset: A Deep Learning-Natural Language Processing (NLP) Approach

Emotion classification has become a valuable tool in analyzing text and emotions people express in response to events or crises, particularly on social media and other online platforms. The recent news about monkeypox highlighted various emotions individuals felt during the outbreak. People’s opinions and concerns have been very different based on their awareness and understanding of the disease. Although there have been studies on monkeypox, emotion classification related to this virus has not been considered. As a result, this study aims to analyze the emotions individual expressed on social media posts related to the monkeypox disease. Our goal is to provide real-time information and identify critical concerns about the disease. To conduct our analysis, first, we extract and preprocess 800,000 datasets and then use NRCLexicon, a Python library, to predict and measure the emotional significance of each text. Secondly, we develop deep learning models based on Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and the combination of Convolutional Neural Networks and Long Short-Term Memory (CLSTM) for emotion classification. We use SMOTE (Synthetic Minority Oversampling Technique) and Random Undersampling techniques to address the class imbalance in our training dataset. The results of our study revealed that the CNN model achieved the highest performance with an accuracy of 96%. Overall, emotion classification on the monkeypox dataset can be a powerful tool for improving our understanding of the disease. The findings of this study will help develop effective interventions and improve public health. Author

CovidMis20: COVID-19 Misinformation Detection System on Twitter Tweets Using Deep Learning Models

Online news and information sources are convenient and accessible ways to learn about current issues. For instance, more than 300 million people engage with posts on Twitter globally, which provides the possibility to disseminate misleading information. There are numerous cases where violent crimes have been committed due to fake news. This research presents the CovidMis20 dataset (COVID-19 Misinformation 2020 dataset), which consists of 1,375,592 tweets collected from February to July 2020. CovidMis20 can be automatically updated to fetch the latest news and is publicly available at: https://github.com/everythingguy/CovidMis20. This research was conducted using Bi-LSTM deep learning and an ensemble CNN+Bi-GRU for fake news detection. The results showed that, with testing accuracy of 92.23% and 90.56%, respectively, the ensemble CNN+Bi-GRU model consistently provided higher accuracy than the Bi-LSTM model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.