Vaccine sentiment analysis using BERT + NBSVM and geo-spatial approaches.

Since the spread of the coronavirus flu in 2019 (hereafter referred to as COVID-19), millions of people worldwide have been affected by the pandemic, which has significantly impacted our habits in various ways. In order to eradicate the disease, a great help came from unprecedentedly fast vaccines development along with strict preventive measures adoption like lockdown. Thus, world wide provisioning of vaccines was crucial in order to achieve the maximum immunization of population. However, the fast development of vaccines, driven by the urge of limiting the pandemic caused skeptical reactions by a vast amount of population. More specifically, the people's hesitancy in getting vaccinated was an additional obstacle in fighting COVID-19. To ameliorate this scenario, it is important to understand people's sentiments about vaccines in order to take proper actions to better inform the population. As a matter of fact, people continuously update their feelings and sentiments on social media, thus a proper analysis of those opinions is an important challenge for providing proper information to avoid misinformation. More in detail, sentiment analysis (Wankhade et al. in Artif Intell Rev 55(7):5731-5780, 2022. 10.1007/s10462-022-10144-1) is a powerful technique in natural language processing that enables the identification and classification of people feelings (mainly) in text data. It involves the use of machine learning algorithms and other computational techniques to analyze large volumes of text and determine whether they express positive, negative or neutral sentiment. Sentiment analysis is widely used in industries such as marketing, customer service, and healthcare, among others, to gain actionable insights from customer feedback, social media posts, and other forms of unstructured textual data. In this paper, Sentiment Analysis will be used to elaborate on people reaction to COVID-19 vaccines in order to provide useful insights to improve the correct understanding of their correct usage and possible advantages. In this paper, a framework that leverages artificial intelligence (AI) methods is proposed for classifying tweets based on their polarity values. We analyzed Twitter data related to COVID-19 vaccines after the most appropriate pre-processing on them. More specifically, we identified the word-cloud of negative, positive, and neutral words using an artificial intelligence tool to determine the sentiment of tweets. After this pre-processing step, we performed classification using the BERT + NBSVM model to classify people's sentiments about vaccines. The reason for choosing to combine bidirectional encoder representations from transformers (BERT) and Naive Bayes and support vector machine (NBSVM ) can be understood by considering the limitation of BERT-based approaches, which only leverage encoder layers, resulting in lower performance on short texts like the ones used in our analysis. Such a limitation can be ameliorated by using Naive Bayes and Support Vector Machine approaches that are able to achieve higher performance in short text sentiment analysis. Thus, we took advantage of both BERT features and NBSVM features to define a flexible framework for our sentiment analysis goal related to vaccine sentiment identification. Moreover, we enrich our results with spatial analysis of the data by using geo-coding, visualization, and spatial correlation analysis to suggest the most suitable vaccination centers to users based on the sentiment analysis outcomes. In principle, we do not need to implement a distributed architecture to run our experiments as the available public data are not massive. However, we discuss a high-performance architecture that will be used if the collected data scales up dramatically. We compared our approach with the state-of-art methods by comparing most widely used metrics like Accuracy, Precision, Recall and F-measure. The proposed BERT + NBSVM outperformed alternative models by achieving 73% accuracy, 71% precision, 88% recall and 73% F-measure for classification of positive sentiments while 73% accuracy, 71% precision, 74% recall and 73% F-measure for classification of negative sentiments respectively. These promising results will be properly discussed in next sections. The use of artificial intelligence methods and social media analysis can lead to a better understanding of people's reactions and opinions about any trending topic. However, in the case of health-related topics like COVID-19 vaccines, proper sentiment identification could be crucial for implementing public health policies. More in detail, the availability of useful findings on user opinions about vaccines can help policymakers design proper strategies and implement ad-hoc vaccination protocols according to people's feelings, in order to provide better public service. To this end, we leveraged geospatial information to support effective recommendations for vaccination centers.

Anatomical Variations in Pulmonary Fissures on Computed Tomography (CT).

Objective To determine the frequency of anatomical variations in lung fissures using computed tomography (CT) at a tertiary care hospital in Karachi, Pakistan. Methods A cross-sectional study was conducted in the department of Radiology and Imaging Services at Memon Medical Institute Hospital, Karachi, between November 2021 to April 2022. Patients aged between 15 to 92 years with a completed high-resolution CT scan chest were included. Subjects with no significant structural lung disease that could alter the anatomy were analyzed. Baseline data was gathered using a pre-designed questionnaire, and two qualified radiologists assessed the CT chest images. Results A total of 382 subjects participated in this study, out of which 57.1% were males whilst 42.9% were females. The right horizontal fissure was absent in 10 (2.6%) cases. Accessory fissures were seen in 7.33%. The most common fissural variation was azygos fissure (14; 3.7%), followed by superior accessory fissure (six; 1.6%), inferior accessory fissures (four; 1%), and left horizontal fissure (four; 1%). These variations were more common in males. The significant difference was only seen in the superior accessory fissures with respect to gender (P-value<0.05). Conclusion This study showed the presence of accessory fissures in 7.33% of patients, the most common being the azygos fissure, irrespective of gender. The absence of normal right horizontal fissures was observed in 2.6% of cases.

Do green supply chain management practices improve organizational resilience during the COVID-19 crisis? A survival analysis of global firms.

This study investigates whether green supply chain management (GSCM) practices help companies to be resilient against the buffer effect in the context of COVID-19. Building on the instrumental version of stakeholder theory, companies implementing GSCM practices should build environmental skills and competitive advantage to cope with a crisis caused by supply chain disruptions. Our survival analysis, conducted on 5,696 firms headquartered in 35 countries, shows clear evidence that GSCM companies' market prices recover quickly from the shock. Considering mounting pressure on environmental issues, this study documents the new benefits of GSCM for companies confronted with a global financial shock. By applying a large sample, the study has originality and implications for stakeholders, including investors, governments, and policymakers, to push firms to become more eco-friendly and resilient.

Effect of Metarhizium anisopliae on the nutritional physiology of the fall armyworm, Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae)

Background: The fall armyworm, Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), was recorded for the first time in Pakistan in 2019, and now it has spread in several regions, i.e., Punjab and Sindh, affecting maize production. Farmers are using widely synthetic pesticides to control the pest. Further, the resistance development in insects and the non-target effect of chemicals on the environment and humans pose serious threats of using insecticides. The use of entomopathogenic fungi (EPF) is being considered an important tool in integrated pest management program. The main objective of this study was to check the impact of different conidial concentrations of Metarhizium anisopliae fungus on the nutritional physiology of S. frugiperda. Results: The dose-dependent effect of tested EPF was observed, and the highest concentration (1 × 109 conidia/ml) considerably affected nutritional parameters. Reduced relative consumption rate (21.7%), relative growth rate (19.5%), the efficiency of conversion of ingested food (24.2%), and approximate digestibility (16.3%) were observed in treated larvae compared to the untreated by using a higher concentration of EPF. Pupal weight was also found lower (77.9 mg and 84.2 mg, respectively), when larvae were treated by 1 × 109 and 1 × 108 conidia/ml concentrations of EPF. Further, 46.7% of larvae were found dead with this conidial concentration. Conclusions: This study suggests that the application of a higher concentration of M. anisopliae could be an effective option to control S. frugiperda. The EPF can enhance the integrated pest management programs and could be useful in reducing the environmental impact of synthetic insecticides. [ FROM AUTHOR] Copyright of Egyptian Journal of Biological Pest Control 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 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.)

Performance Evaluation of Soft Computing Approaches for Forecasting COVID-19 Pandemic Cases.

An unexpected outbreak of deadly Covid-19 in later part of 2019 not only endangered the economies of the world but also posed threats to the cultural, social and psychological barriers of mankind. As soon as the virus emerged, scientists and researchers from all over the world started investigating the dynamics of this disease. Despite extensive investments in research, no cure has been officially found to date. This uncertain situation rises severe threats to the survival of mankind. An ultimate need of the time is to investigate the course of disease transfer and suggest a future projection of the disease transfer to be enabled to effectively tackle the always evolving situations ahead. In the present study daily new cases of COVID-19 was predicted using different forecasting techniques; Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing/Error Trend Seasonality (ETS), Artificial Neural Network Models (ANN), Gene Expression Programming (GEP), and Long Short-Term Memory (LSTM) in four countries; Pakistan, USA, India and Brazil. The dataset of new daily confirmed cases of COVID-19 from the date on which first case was registered in the respective country to 30 November 2020 is analyzed through these five forecasting models to forecast the new daily cases up to 31st January 2020. The forecasting efficiency of each model was evaluated using well known statistical parameters R 2, RMSE, and NSE. A comparative analysis of all above-mentioned models was performed. Finally, the study concluded that Long Short-Term Memory (LSTM) neural network-based forecasting model projected the future cases of COVID-19 pandemic best in all the selected four stations. The accuracy of the model ranges from coefficient of determination value of 0.85 in Brazil to 0.96 in Pakistan. NSE value for the model in India is 0. 99, 0.98 in USA and Pakistan and 0.97 in Brazil. This high-accuracy forecast of COVID-19 cases enables the projection of possible peaks in near future in the aforementioned countries and, therefore, prove to be helpful in formulating strategies to get prepared for the potential hard times ahead.

ASSESSING THE CRITICAL CARE RESUSCITATION UNIT'S STROKE OPERATIONS DURING THE CORONAVIRUS PANDEMIC

B Results: b We analyzed 154 PP patients (6 patients/month) vs. 71 DP patients (5 patients/month). The Critical Care Resuscitation Unit (CCRU) at the University of Maryland Medical Center expedites interhospital transfers (IHT) for critically ill patients that need time-sensitive interventions, such as, patients with an ischemic stroke from a large vessel occlusion (IS-LVO) requiring endovascular mechanical thrombectomy (EVMT). Although ED LOS increased during the pandemic, though not statistically significant, CCRU operations and overall essential time intervals for stroke patient care at University of Maryland Medical Center improved during the COVID-19 pandemic. [Extracted from the article] Copyright of Critical Care Medicine is the property of Lippincott Williams & Wilkins 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.)

A review of COVID-19: Treatment strategies and CRISPR/Cas9 gene editing technology approaches to the coronavirus disease.

The new coronavirus SARS-CoV-2 pandemic has put the world on lockdown for the first time in decades. This has wreaked havoc on the global economy, put additional burden on local and global public health resources, and, most importantly, jeopardised human health. CRISPR stands for Clustered Regularly Interspaced Short Palindromic Repeats, and the CRISPR associated (Cas) protein (CRISPR/Cas) was identified to have structures in E. coli. The most modern of these systems is CRISPR/Cas. Editing the genomes of plants and animals took several years and cost hundreds of thousands of dollars until the CRISPR approach was discovered in 2012. As a result, CRISPR/Cas has piqued the scientific community's attention, particularly for disease diagnosis and treatment, because it is faster, less expensive, and more precise than previous genome editing technologies. Data from gene mutations in specific patients gathered using CRISPR/Cas can aid in the identification of the best treatment strategy for each patient, as well as other research domains such as coronavirus replication in cell culture, such as SARS-CoV2. The implications of the most prevalent driver mutations, on the other hand, are often unknown, making treatment interpretation difficult. For detecting a wide range of target genes, the CRISPR/Cas categories provide highly sensitive and selective tools. Genome-wide association studies are a relatively new strategy to discovering genes involved in human disease when it comes to the next steps in genomic research. Furthermore, CRISPR/Cas provides a method for modifying non-coding portions of the genome, which will help advance whole genome libraries by speeding up the analysis of these poorly defined parts of the genome.

From Molecular Pathology of COVID 19 to Nigella Sativum as a Treatment Option: Scientific Based Evidence of Its Myth or Reality.

COVID-19 virus is a causative agent of viral pandemic in human beings which specifically targets respiratory system of humans and causes viral pneumonia. This unusual viral pneumonia is rapidly spreading to all parts of the world, currently affecting about 105 million people with 2.3 million deaths. Current review described history, genomic characteristics, replication, and pathogenesis of COVID-19 with special emphasis on Nigella sativum (N. sativum) as a treatment option. N. sativum seeds are historically and religiously used over the centuries, both for prevention and treatment of different diseases. This review summarizes the potential role of N. sativum seeds against COVID-19 infection at levels of in silico, cell lines and animal models.

Phenolic and flavonoid contents in Malva sylvestris and exploration of active drugs as antioxidant and anti-COVID19 by quantum chemical and molecular docking studies

The exploration of natural sources of antioxidant phytochemicals for human use with little toxicity gained worldwide attention. The preliminary screening of Malva sylvestrisextracts revealed that its phytochemicals such as polyphenols, flavonoids and tannins, have high therapeutic potential. The total phenolic/flavonoids compounds ofMalva sylvestris were extracted and isolated using bioassay guidelines, as well as in-silico studies. The extracts radical scavenging activity was further investigated using 1-diphenyl-2-picrylhydrazyl (DPPH) and nitric oxide (NO) radical bioassays. The antioxidant potentials of various fractions were compared to standard antioxidants such as ascorbic acid and quercetin. The dichloromethane extracts of Malva sylvestris exhibited the antiradical activity against DPPH and NO with radical scavenging activities (RSA) of 88.52 and 91.05 % with IC50 values 22.11 and 19.01 µg/mL respectively. Bio guided isolation form the dichloromethane sub fractions that afforded twelve phytochemicals. Furthermore, the frontier molecular orbitals (FMO), several molecular descriptors, electron affinity, ionization potential and molecular electrostatic potential (MEP) have been discussed to probe the active sites of various phytochemicals. A systematic study of isolated drugs wasconducted, as well as docking, frontier molecular orbitals energies, active sites and molecular descriptors were compared with drugs currently used against COVID19 namely, dexamethasone, hydroxychloroquine, favipiravir and remdesivir. For the first time, through molecular docking approach, the inhibitions of these plant phytochemicals with NADPH were recorded to show antioxidant behavior and to explore anti-SARS-CoV-2 using core protease (6LU7) protein.

Effective Immune Functions of Micronutrients against SARS-CoV-2.

The third coronavirus outbreak in the last two decades has caused significant damage to the world's economy and community health. The highly contagious COVID-19 infection has affected millions of people to date and has led to hundreds of thousands of deaths worldwide. Aside from the highly infectious nature of SARS-CoV-2, the lack of a treatment or vaccine has been the main reason for its spread. Thus, it has become necessary to find alternative methods for controlling SARS-CoV-2. For the present review, we conducted an online search for different available nutrition-based therapies for previously known coronavirus infections and RNA-based virus infections as well as general antiviral therapies. These treatments have promise for combating COVID-19, as various nutrients and minerals play direct and indirect roles in the control and prevention of this newly emerged viral infection. The patients' nutritional status with COVID-19 must be analyzed before administering any treatment, and nutritional supplements should be given to the affected individuals along with routine treatment. We suggest a potential interventional role of nutrients to strengthen the immune system against the emerging infection caused by COVID-19.

Prevention and treatment of COVID-19: Focus on interferons, chloroquine/hydroxychloroquine, azithromycin, and vaccine.

The ongoing pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has drawn the attention of researchers and clinicians from several disciplines and sectors who are trying to find durable solutions both at preventive and treatment levels. To date, there is no approved effective treatment or vaccine available to control the coronavirus disease-2019 (COVID-19). The preliminary in vitro studies on viral infection models showed potential antiviral activities of type I and III interferons (IFNs), chloroquine (CQ)/hydroxychloroquine (HCQ), and azithromycin (AZM); however, the clinical studies on COVID-19 patients treated with CQ/HCQ and AZM led to controversies in different regions due to their adverse side effects, as well as their combined treatment could prolong the QT interval. Interestingly, the treatment with type I IFNs showed encouraging results. Moreover, the different preliminary reports of COVID-19 candidate vaccines showcase promising results by inducing the production of a high level of neutralizing antibodies (NAbs) and specific T cell-mediated immune response in almost all participants. The present review aims to summarize and analyze the recent progress evidence concerning the use of IFNs, CQ/HCQ, and AZM for the treatment of COVID-19. The available data on immunization options to prevent the COVID-19 are also analyzed with the aim to present the promising options which could be investigated in future for sustainable control of the pandemic.

Effective Immune Functions of Micronutrients against SARS-CoV-2

The third coronavirus outbreak in the last two decades has caused significant damage to the world"s economy and community health. The highly contagious COVID-19 infection has affected millions of people to date and has led to hundreds of thousands of deaths worldwide. Aside from the highly infectious nature of SARS-CoV-2, the lack of a treatment or vaccine has been the main reason for its spread. Thus, it has become necessary to find alternative methods for controlling SARS-CoV-2. For the present review, we conducted an online search for different available nutrition-based therapies for previously known coronavirus infections and RNA-based virus infections as well as general antiviral therapies. These treatments have promise for combating COVID-19, as various nutrients and minerals play direct and indirect roles in the control and prevention of this newly emerged viral infection. The patients"nutritional status with COVID-19 must be analyzed before administering any treatment, and nutritional supplements should be given to the affected individuals along with routine treatment. We suggest a potential interventional role of nutrients to strengthen the immune system against the emerging infection caused by COVID-19.