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Engaging deep neural networks for textual sentiment analysis is an extensively practiced domain of research. Textual sentiment classification harnesses the full computational potential of deep learning models. Typically, these research works are carried either with a popular open-source data corpus, or self-extracted short phrase texts from Twitter, Reddit, or web-scrapped text data from other resources. Rarely do we see a large amount of data on a current ongoing event is being collected and cultured further. Also, an even more complex task would be to model the data from a currently ongoing event, not only for scaling the sentiment accuracy but also for making a predictive analysis for the same. In this paper, we propose a novel approach for achieving sentiment evaluation accuracy by using a deep neural network on live-streamed tweets on Coronavirus and future case growth prediction. We develop a large tweet corpus exclusively based on the Coronavirus tweets. We split the data into train and test sets, alongside we perform polarity classification and trend analysis. The refined outcome from the trend analysis helps to train the data to provide an incremental learning curvature for our neural network, and we obtain an accuracy of 90.67%. Finally, we provide a statistical-based future prediction for Coronavirus cases growth. Not only our model outperforms several previous state-of-art experiments in overall sentiment accuracy comparison for similar tasks, but it also maintains a throughout performance stability among all the test cases when tested with several popular open-source text corpora.

A series of novel quinolone-based 4-(methoxymethyl)-1,2,3-triazole derivatives were synthesized, and their structures were characterized by 1H, 13C NMR and mass spectroscopy. The compounds (IXa- l ) were screened in vitro antibacterial activity against five gram-positive and five gram-negative bacterial strains, viz. M. Tuberculosis, M. Luteus, MRSA, B. Subtilis, B. Cereus, P. Aerginosa, K. Pneumonia, E. Coli, P. Vulgaris and S. Typhi, used and compared with standard gentamycin. The combination of the pharmacologically active moieties in a single scaffold results in their synergistic effect and high antimicrobial activity against several bacterial strains. COVID-19 has spread rapidly around the globe since its first identification in Wuhan, China, in December 2019. Coronavirus Disease 2019 (COVID‐19 Mpro) has become a major health problem causing severe acute respiratory illness in humans. The causative virus is called severe acute respiratory syndrome coronavirus 2, and the World Health Organization named the new epidemic disease Coronavirus Disease (COVID-19). Also, docking studies demonstrated that all derivatives exhibit a good theoretical affinity with Autodock 4.2 software score in between − 9.89 and − 13.4 kCal/mol against the main protease of COVID‐19 Mpro that caused worldwide epidemics. We believe that newly synthesized quinolone-based 4-(methoxymethyl)-1,2,3-triazole derivatives can guide many future studies in organic synthesis, medicine and pharmaceutical applications. [ABSTRACT FROM AUTHOR] Copyright of Journal of the Iranian Chemical Society is the property of Springer Nature 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 abstract 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 abstract. (Copyright applies to all Abstracts.)

Based on our present knowledge on SARS-CoV-2 virus, which is collectively summarized in this work, it is shown that water plays a crucial role in stabilizing the virus, in such a way that its viability may reduce from a few days to a few seconds in the absence of water layer. Water not only provides a protective shell for the virus in which it is enveloped, but enhances its binding with the human cell. Therefore, one may conclude that the dehydration of the hydrated virus makes it much more vulnerable, due to the fact that if it is exposed to many chemicals it will be deactivated; even by particles in air, such as ozone, singlet oxygen and pollutants. Thermodynamically, the dehydration may be facilitated at higher temperature, low humidity, and exposure to deliquescent materials. We have concluded that if the contaminated air passes over or through some water absorbents, like calcium chloride, with enough time contact, the virus is expected to become dehydrated and deactivated shortly afterward. If the time contact is not enough for the deactivation, the air may be exposed to the blue or UVC lights as well. The conclusion is based on the hydrated–dehydrated equilibrium of the coronavirus introduced in this work.

We present new data analytics-based predictions results that can help governments to plan their future actions and also help medical services to be better prepared for the future. Our system can predict new corona cases with 99.82% accuracy using susceptible infected recovered (SIR) model. We have predicted the results of new COVID cases per day for dense and highly populated country i.e. India. We found that traditional statistical methods will not work efficiently as they do not consider the limited population in a particular country. Using the data analytics-based curve we predicted four most likely possibilities for the number of new cases in India. Hence, we expect that the results mentioned in the manuscript help people to better understand the progress of this disease. Supplementary Information The online version contains supplementary material available at 10.1007/s12065-021-00600-2.

Negative symptoms are core features of schizophrenia-spectrum disorders that are frequently observed across all phases of illness. By their nature, COVID-19 social isolation, physical distancing, and health precautions induce behavioural aspects of negative symptoms. However, it is unclear whether these prevention measures also lead to increases in experiential negative symptoms, whether such effects are equivalent across individual negative symptom domains, and if exacerbations occur equivalently across phases of illness. The current study compared negative symptom severity scores obtained during the pandemic to pre-pandemic assessments in two samples: (1) outpatients with chronic schizophrenia (SZ: n = 32) and matched healthy controls (CN: n = 31) and (2) individuals at clinical high risk for psychosis (CHR: n = 25) and matched CN (n = 30). Pre-pandemic ratings of negative symptoms were clinically elevated in SZ and CHR groups, which did not differ from each other in severity. In SZ, ratings obtained during the pandemic were significantly higher than pre-pandemic ratings for all 5 domains (alogia, blunted affect, anhedonia, avolition, and asociality) and item-level analyses indicated that exacerbations occurred on both experiential and behavioral symptoms of anhedonia, avolition, and asociality. In contrast, CHR only exhibited increases in anhedonia and avolition items during the pandemic compared to pre-ratings. Findings suggest that negative symptoms should be a critical treatment target during and after the pandemic in the schizophrenia spectrum given that they are worsening and critically related to risk for conversion, functional outcome, and recovery.

Coronavirus is a fatal disease that affects mammals and birds. Usually, this virus spreads in humans through aerial precipitation of any fluid secreted from the infected entity's body part. This type of virus is fatal than other unpremeditated viruses. Meanwhile, another class of coronavirus was developed in December 2019, named Novel Coronavirus (2019-nCoV), first seen in Wuhan, China. From January 23, 2020, the number of affected individuals from this virus rapidly increased in Wuhan and other countries. This research proposes a system for classifying and analyzing the predictions obtained from symptoms of this virus. The proposed system aims to determine those attributes that help in the early detection of Coronavirus Disease (COVID-19) using the Adaptive Neuro-Fuzzy Inference System (ANFIS). This work computes the accuracy of different machine learning classifiers and selects the best classifier for COVID-19 detection based on comparative analysis. ANFIS is used to model and control ill-defined and uncertain systems to predict this globally spread disease's risk factor. COVID-19 dataset is classified using Support Vector Machine (SVM) because it achieved the highest accuracy of 100% among all classifiers. Furthermore, the ANFIS model is implemented on this classified dataset, which results in an 80% risk prediction for COVID-19.

Aims The purpose of the present study is to analyse clinical data of a series of cases who developed nosocomial infection with SARS-CoV-2 in an orthopaedic and traumatology department. Patients and methods In this non-interventional retrospective study, carried out at a tertiary hospital within the Spanish National Health System, all adult patients who were admitted in the Orthopaedic Surgery and Traumatology Department between March 9th and May 4th, 2020, were included. Clinical, biological and radiological data, as well as mortality rates, were collected from hospital medical records. Results A total of 293 periods of hospitalization were analysed in 288 patients. Mean age was 66.1 years old and 57.3% were females. Nineteen patients (6.48%) met the inclusion criteria to be categorized as a nosocomial infection with SARS-CoV-2. In a comparison between patients with and without nosocomial infection, age, mortality and hospital length of stay were statistically significant (p

Background The COVID-19 (SARS-CoV-2) pandemic has significantly affected all aspects of healthcare, including orthopaedics. Due to the unique challenges presented by COVID-19 as well as the distinct timeframes that it will surge in different geographies, much can be learned from the experiences of orthopaedic professionals in many global settings. The goal of this project is to characterize the preparations, strategies, lessons, and personal experiences of orthopaedic trauma surgeons and departments across the world in combating COVID-19. In doing so, we will shed light on current practices and challenges, which may help us manage the current pandemic in addition to preparing for future global pandemics that may arise. Methods A 20-item questionnaire was sent out to 150 orthopaedic trauma surgeons representing 42 countries who were identified based on professional relationships and/or prior involvement in international meetings either led or participated by the senior author. Results The questionnaire was completed by 63 orthopaedic trauma surgeons representing 28 countries and 14 US states. The results of this study show that orthopaedic trauma departments across the world have been greatly impacted by COVID-19 with 91% of participating hospitals currently having a reduced case load compared with pre-COVID-19 and only 17% of respondents currently performing elective orthopaedic surgery. Furthermore, 30% of orthopaedic departments have deployed orthopaedic personnel to non-orthopaedic floors in order to help mitigate the increased patient load and 86% of respondents noted at least some shortage of PPE. Lastly 73% of participating orthopedic departments including those in LMICs, have incorporated telemedicine into their practice with a majority stating that it would most likely become a permanent change to their practice post-COVID-19. Conclusion To our knowledge, this is the largest data set characterizing global COVID-19 situations and responses of orthopaedic trauma practices around the world. There is much to be learned from each of the participants' responses in order to persevere during the current pandemic, as well as to prepare for future pandemics as it relates specifically to orthopaedic trauma practices.

Purpose We aimed to report the clinical characteristics of 194 cases coronavirus disease-19 (COVID-19) in Huanggang, Hubei and Taian, Shandong. Methods We retrospectively investigated the clinical, laboratory characteristics and CT imaging of confirmed cases of COVID-19 from January 22 to February 28, 2020 in Huanggang Central Hospital and The Second Affiliated Hospital of Shandong First Medical University. Real time PCR was used to detect the new coronavirus in respiratory samples. Immunohistochemical staining was used to detect the expressions of ACE2 in tissues. Results Among the 194 patients infected with COVID-19, 108 patients were male, with a median age of 48.3 years. The average preclinical period was 7.44 day. Except for 37 severe or critically ill patients, the rest of the 157 patients exhibited mild or moderate symptoms. 190 (97.94%) patients were confirmed during the three times nucleic acid test. The main clinical symptom of the patients were fever, sore throat and cough, which accounted for 146 cases (75.26%), 98 (50.52%) and 86 cases (44.33%), respectively. 30 patients (15.46%) showed liver dysfunction. Imaging examination showed that 141 patients (72.68%) showed abnormal density shadow, while 53 cases (27.32%) had no obvious abnormality in the parenchyma of both lungs. Up to now, 109 cases have been discharged from the hospital, and 9 patients died. The ACE2 expression levels were up-regulated in patients of severe type and critically ill type. Conclusion Clinical symptoms, laboratory tests and CT imaging should be combined for comprehensive analysis to diagnose COVID-19. ACE2 may be the receptor of COVID-19. Electronic supplementary material The online version of this article (10.1007/s15010-020-01440-5) contains supplementary material, which is available to authorized users.

Coronavirus infectious disease 2019 (COVID-19), a viral infection caused by a novel coronavirus (nCoV), continues to emerge as a serious threat to public health. This pandemic caused by SARS-CoV-2 (severe acute respiratory syndrome–coronavirus-2) has infected globally with 1,550,000 plus deaths to date, representing a high risk to public health. No effective drug or vaccine is available to curb down this deadly virus. The expedition for searching for a potential drug or vaccine against COVID-19 is of massive potential and favour to the community. This study is focused on finding an effective natural compound that can be processed further into a potential inhibitor to check the activity of SARS-CoV-2 with minimal side effects targeting NSP15 protein, which belongs to the EndoU enzyme family. The natural screening suggested two efficient compounds (PubChem ID: 95372568 and 1776037) with dihydroxyphenyl region of the compound, found to be important in the interaction with the viral protein showing promising activity which may act as a potent lead inhibitory molecule against the virus. In combination with virtual screening, modelling, drug likeliness, molecular docking, and 500 ns cumulative molecular dynamics simulations (100 ns for each complex) along with the decomposition analysis to calculate and confirm the stability and fold, we propose 95372568 and 1776037 as novel compounds of natural origin capable of getting developed into potent lead molecules against SARS-CoV-2 target protein NSP15. Supplementary Information The online version contains supplementary material available at 10.1007/s00894-021-04750-z.