Sex-Specific Systemic Inflammatory Responses in Mice Infected with a SARS-CoV-2-like Virus and Femur Fracture (preprint)

This study investigates the systemic inflammatory response in mice infected with a murine coronavirus (MHV), which shares a common genus with SARS-CoV-2, and sustaining a fracture. The study reveals that the combined inflammatory incidents of MHV infection and fracture disrupt the systemic immune response in both female and male mice, likely leading to immune dysregulation, altered cell recruitment, and disruption of the typical inflammatory cascade. Notably, the study uncovers sex-specific responses that modulate circulating immune factors. Females exhibit elevated levels of inflammatory factors, whereas males demonstrate a diminished response. This divergence is mirrored in cell populations, suggesting that the quantity of immune factors released may contribute to these discrepancies. The findings suggest that an overproduction of proinflammatory cytokines may induce a dysregulated immune response, contributing to the observed poorer prognosis in comorbid cases. These insights could pave the way for therapeutic advancements and treatment strategies aimed at reducing mortality rates in COVID-19 patients with fractures.

The role of operations and supply chain management during epidemics and pandemics: Potential and future research opportunities

Epidemics have been posing significant challenges to health, existence, and continuity. From the emergence of an outbreak to its elimination, managing an epidemic/pandemic entails many operations and supply chain management decisions that can contribute to a lessening of its impact. With these decisions, epidemic-/pandemic-imposed challenges related to forecasting, planning, supply, manufacturing, storage, and transportation can be addressed in an effort to curtail and end the epidemic/pandemic. We have witnessed these disruptions first-hand during the COVID-19 pandemic, which has had a destructive effect on many well-established supply chains, threatening the existence of firms. The role of operations and supply chain management is thus pivotal for navigating epidemics/pandemics. Against this background, we present a systematic literature review on the role of operations and supply chain management during epidemics and pandemics, illustrating its potential and calling for future research. Leveraging bibliographic coupling analysis, we identify major research areas and contributions that serve as a foundation to propel these domains forward. We further critically review these research areas, identifying multiple themes of which many have been perennially relevant, while others have come to the fore only recently due to the COVID-19 pandemic. Our review provides an integrative view of the field, concurrently advancing theory, and offering ten distinct future research directions. Overall, this paper is meant to serve as a starting point for researchers in operations and supply chain management aiming to investigate this increasingly important domain.

Modified Convolutional Neural Network Architecture with XGBoost for Mucormycosis Detection and compare performance with XGBoost

Patients with coronavirus illness 2019, especially those in India, are more likely to see an increase in rhino-orbital mucormycosis. A well-known risk factor during COVID-19 infection and mucormycosis is diabetes mellitus (DM). With roughly 0.15 instances per 1000 people, mucormycosis is almost 82 times more common in India than it is in Western nations. Additionally, this fungus expanded quickly across numerous states, leading some of them to designate this illness an epidemic. Finding a solution for this potentially fatal fungal infection requires the aid of modern technologies, including artificial intelligence and data learning. In this paper, we combine a modified convolutional learning neural network with an XGBoost classifier to propose a unique black fungus detection method. Under the right circumstances, the CNNXGB model is made simpler by lowering the no of attributes since it is not essential to re-adjust the weight values throughout a back propagation cycle. On testing data, the mean classification performance is 98.26%, far better than current approaches. © 2023 IEEE.

Mucormycosis Detection using Hybrid Convolutional Neural Network with Support Vector Machine and Compare the performance with Support Vector Machine

Based on the patient's underlying condition, mucormycosis, often known as a black fungus illness, is an uncommon but severe disease with a high fatality rate. The large second wave of the COVID-19 epidemic has presented a challenge for the Indian healthcare system from this life-threatening powerful threat. The fungus family Mucorales causes mucormycosis, which affects numerous bodily organs. This fungal opportunistic illness spreads quickly. Recently, this unusual fungus has been infecting covid sufferers in India at greater rates than before. In India, the frequency of this black fungus illness amongst covid-19 as well as post-covid-19 patients is now on the rise. Finding a solution for this potentially fatal fungal infection requires the aid of modern technologies, including artificial intelligence and data learning. In this article, we present a unique hybrid model for black fungus identification that combines support vector machine classifier and convolutional learning network. Under the proper circumstances, the CNNSVM model is made simpler by minimizing the amount of variables because it is not important to constantly the weighting factors in a back propagation cycle. Additionally, it was shown that the SVM classifier was the best merging equivalent when the CNN was employed as a feature extractor, offering the highest accuracy-related synergy effect. On testing data, the mean classification performance was 99.3%, which is a significant improvement over current techniques. © 2023 IEEE.

A Novel Deep Learning based Black Fungus Detection using the Bagging Ensemble with K-Nearest Neighbor

Machine learning is widely employed, and broadly speaking, scientists consider applying it everywhere. Around the same period, we can see that India has been devastated by the second corona wave. In a single day, more than 4 lakh instances arrive. Meanwhile, reports of the arrival of a new, fatal fungus called Mucormycosis emerged (Black fungus). Additionally, this fungus expanded quickly throughout numerous states, leading some of them to designate this illness an epidemic. People with weak immunity functions, including those who have had the corona virus and some of whom are still recovering, are more likely to get a black fungus infection since their bodies can't successfully fight it off. Bagging Ensemble with K-Nearest Neighbor is a modified machine learning approach that will be developed in this study (BKNN). The traditional methods, including K-Nearest Neighbor ensemble with bagging classification, are the basis for the suggested methodology. After the image processing techniques, including pre-processing and segmentation, were reviewed, the accuracy score for this classifier was 96.4 percent, which would have been the highest of all the findings. This paper described how machine learning was beneficial during the Corona era, much as it would be beneficial during epidemics like mucormycosis. The last section of this essay presents accurate, graphical evidence for all items addressed, along with explicit specifications. © 2022 IEEE.

Black Fungus Classification using Adaboost with SVM-based classifier and Compare accuracy with Support Vector Machine

India recognize a severe public health issue in addition to the COVID-19 outbreak and the growing percentage of patients with related mucormycosis from 2021. An uncommon condition known as mucormycosis is brought on by fungus in the family Mucorales. Mucormycosis is a fairly uncommon illness that is caused by common environmental moulds that may be found in soil and decomposing organic materials. Spores develop into hyphae in a susceptible individual, which subsequently infect nearby tissue, including blood vessels, leading to hemorrhagic infarction. Doctors have offered many hypotheses on this. The issue is if black fungus is present in other countries given how uncontrolled it is growing in India. Patients in India with weakened immune systems are more susceptible to illnesses other than corona virus infection. The revised machine learning strategy which will be created in this work is Adaboost with an Support Vector Machine-based classifier (ASVM). Due of the difficulties in learning SVM and the differential in variety as well as efficiency over straightforward SVM classifiers, ASVM classifier is frequently believed to violate the Boosting principle. The Adaboost classifier used in the study gradually replaces SVM as the primary classifier when the weight value of the training sample changes. On testing data, the mean accuracy of the classification was 97.1%, which was much higher than that of SVM classifiers without Adaboost. © 2022 IEEE.

IoT Based Health Machine for COVID-19

The new Corona Virus (COVID-19) is a pandemic of unthinkable scope and magnitude that is posing a significant threat to the medical business worldwide in the twenty-first century. To a greater extent, it has fundamentally altered the texture of life. The growing number of people dying as a result of sickness has instilled fear in the minds of many who are hesitant to seek even basic medical help. And, in light of the recent COVID-19 scenario and the growing number of affected people, researchers began to focus on ways to communicate and monitor patient information remotely in order to reduce the risk of getting infected. The Internet of Things (IoT) is one of the booming technologies in the medical and industrial fields. Patients could benefit from the proposed device because it can monitor and diagnose their health status. This study describes a gadget that measures and records heart rate, body temperature, and CT imaging. These records will be measured and sent to the cloud server using an Arduino device with sensors. © 2022 IEEE.

Hybrid Data mining Based Breast Prediction of COVID-19

Coronavirus, Corona Virus Disease-2019, brought about by an original Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2). A compelling screening of this infection can empower speedy and proficient finding of COVID-19 can diminish the weight on the medical care framework. A nitty gritty examination gave dataset can assemble unique and different kinds of AI calculations, which their exhibition could be processed and further assessed. This paper proposed a mixture information mining method that coordinated Random Forest with SVM (Support Vector Machines). The accompanying case proposed model is to beat the wide range of various Machine Learning models like SVM, Decision Tree, KNN and Logistic Regression. © 2023 American Institute of Physics Inc.. All rights reserved.

An ultrasound service during COVID-19: A single tertiary centre's experience.

The COVID-19 pandemic is generating great change and challenge to unparalleled levels across the National Health Service, UK. With insufficient and still emerging evidence on this little known virus, recommendations and guidance are changing continually and still evolving. The authors outline some of the planning through the initial stages of the pandemic within a clinical radiology ultrasound service at one UK tertiary centre. Patient triaging, infection control, equipment, staff mental wellbeing, ongoing training and recovery are all subjects of focus. By sharing our experience and strategies, we anticipate that other similar departments may benefit.

Artificial Intelligence: A Next-Level Approach in Confronting the COVID-19 Pandemic.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which caused coronavirus diseases (COVID-19) in late 2019 in China created a devastating economical loss and loss of human lives. To date, 11 variants have been identified with minimum to maximum severity of infection and surges in cases. Bacterial co-infection/secondary infection is identified during viral respiratory infection, which is a vital reason for morbidity and mortality. The occurrence of secondary infections is an additional burden to the healthcare system; therefore, the quick diagnosis of both COVID-19 and secondary infections will reduce work pressure on healthcare workers. Therefore, well-established support from Artificial Intelligence (AI) could reduce the stress in healthcare and even help in creating novel products to defend against the coronavirus. AI is one of the rapidly growing fields with numerous applications for the healthcare sector. The present review aims to access the recent literature on the role of AI and how its subfamily machine learning (ML) and deep learning (DL) are used to curb the pandemic's effects. We discuss the role of AI in COVID-19 infections, the detection of secondary infections, technology-assisted protection from COVID-19, global laws and regulations on AI, and the impact of the pandemic on public life.

COVID-19-associated Neurological Manifestations and Complications: An Observational Study.

BACKGROUND: Coronavirus disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), led to one of the deadliest pandemics faced by mankind. The typical manifestation of COVID-19 infection includes respiratory distress. However, we know that the characteristic immunologic pathways of SARS-CoV-2 infection inflict systemic disorders and eventually multi-organ dysfunction in a subgroup of patients. The disease can affect both central and peripheral nervous systems. OBJECTIVE: The aim of our study was to describe the wide spectrum of neurological manifestations associated with SARS-CoV-2 infection and its clinical characteristics. MATERIALS AND METHODS: We conducted a retrospective, single-center, observational study to analyze neurologic manifestations associated with COVID-19 patients from May 2020 to September 2021 at a tertiary care hospital in Chennai, South India. RESULTS: A total of 80 COVID-19-confirmed patients with neurological disorders were included in our study. The most reported neurological manifestation was altered sensorium (29.6%). Twenty-nine (34.4%) patients were on noninvasive ventilation and a significant number of patients (22) (26.8%) needed invasive ventilation. The mortality rate was 34.1% and the large vessel involvement in stroke patients was 10%. CONCLUSION: Neurological issues in COVID-19 patients are relatively common and have the propensity to manifest later as post-acute COVID-19 syndrome.

Risk assessment in supply chains: a state-of-the-art review of methodologies and their applications.

The year 2020 can be earmarked as the year of global supply chain disruption owing to the outbreak of the coronavirus (COVID-19). It is however not only because of the pandemic that supply chain risk assessment (SCRA) has become more critical today than it has ever been. With the number of supply chain risks having increased significantly over the last decade, particularly during the last 5 years, there has been a flurry of literature on supply chain risk management (SCRM), illustrating the need for further classification so as to guide researchers to the most promising avenues and opportunities. We therefore conduct a bibliometric and network analysis of SCRA publications to identify research areas and underlying themes, leading to the identification of three major research clusters for which we provide interpretation and guidance for future work. In doing so we focus in particular on the variety of parameters, analytical approaches, and characteristics of multi-criteria decision-making techniques for assessing supply chain risks. This offers an invaluable synthesis of the SCRA literature, providing recommendations for future research opportunities. As such, this paper is a formidable starting point for operations researchers delving into this domain, which is expected to increase significantly also due to the current pandemic.

Green synthesis, Single-Crystal X-RD, Hirshfeld Analysis and Anti-Covid-19 Molecular Docking Investigation of Symmetrical Azines

The 1,2-bis(3,4-dimethoxybenzylidene)hydrazine (VAHD) and 1,2-bis(3-methoxy-4-hydroxybenzylidene)hydrazine (VNHD) are synthesised in a solvent free and catalyst free by greener method (MW). Both the compounds are characterized by FT-IR,1H NMR and 13C NMR spectral studies. Single crystal XRD analysis provides more information on the structure of the compounds VAHD and VNHD. The energy gap (Eg), frontier orbital energies (EHOMO, ELUMO) and reactivity parameters like chemical hardness and global hardness andMulliken charges are calculated using density functional theory with B3LYP/6-311++G(d,p) basis set. The experimental and theoretical calculated IR frequencies and NMR chemical shifts values are compared by DFT method. Hirshfeld surface analysis was conducted to study structure and molecular properties. Molecular docking of symmetrical azine at the active sites of SARS-COVID receptors was investigated. Furthermore, the swissADME online application was used to analyse the physicochemical and pharmacokinetic features of the compounds (VAHD and VNHD). © 2023 Wiley-VCH GmbH.

Customer expectations in the hotel industry during the COVID-19 pandemic: a global perspective using sentiment analysis

Hotel industry is the one which has confronted the unprecedented effect of the coronavirus disease 2019 (COVID-19) pandemic to significant social and economic risks. The COVID-19 pandemic has challenged the tourism across the globe and impacted hospitality in hotel industry severely. This study aims to assess customer satisfaction by carrying sentiment analysis and topic modelling over customer reviews on the hospitality provided by hotels in different continents during January to September 2020, i.e. the COVID-19 pandemic. We formulate an improved new scale of metrics to categorize customer satisfaction assessed by sentiment analysis in an elaborate way. Topic modelling was deployed to understand various topics most often discussed by customers. We find that North America and Europe could perform up to customer expectation. In Asia, Sri Lanka did well, Indonesia could maintain its customer satisfaction, while India consistently improved the satisfaction level. We identified 12 most discussed topics, and main reasons of dissatisfaction appear in staff, service, room, cleanliness, slow booking, and pandemic response by hotel. Findings of this study will help senior managers of hotels of developed as well as developing countries in providing new and effective services that can satisfy customers and restore their confidence. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

A systematic review of artificial intelligence-based COVID-19 modeling on multimodal genetic information.

This study systematically reviews the Artificial Intelligence (AI) methods developed to resolve the critical process of COVID-19 gene data analysis, including diagnosis, prognosis, biomarker discovery, drug responsiveness, and vaccine efficacy. This systematic review follows the guidelines of Preferred Reporting for Systematic Reviews and Meta-Analyses (PRISMA). We searched PubMed, Embase, Web of Science, and Scopus databases to identify the relevant articles from January 2020 to June 2022. It includes the published studies of AI-based COVID-19 gene modeling extracted through relevant keyword searches in academic databases. This study included 48 articles discussing AI-based genetic studies for several objectives. Ten articles confer about the COVID-19 gene modeling with computational tools, and five articles evaluated ML-based diagnosis with observed accuracy of 97% on SARS-CoV-2 classification. Gene-based prognosis study reviewed three articles and found host biomarkers detecting COVID-19 progression with 90% accuracy. Twelve manuscripts reviewed the prediction models with various genome analysis studies, nine articles examined the gene-based in silico drug discovery, and another nine investigated the AI-based vaccine development models. This study compiled the novel coronavirus gene biomarkers and targeted drugs identified through ML approaches from published clinical studies. This review provided sufficient evidence to delineate the potential of AI in analyzing complex gene information for COVID-19 modeling on multiple aspects like diagnosis, drug discovery, and disease dynamics. AI models entrenched a substantial positive impact by enhancing the efficiency of the healthcare system during the COVID-19 pandemic.

Impact of Diabetes Mellitus on Clinical Profile and Outcome of Covid-19 Patients in a Tertiary Care Hospital

Objective: A novel coronavirus infection (SARS-CoV-2) pneumonia (COVID-19) has been quickly spreading throughout China and the rest of the world since December 2019. Respiratory tract infections are frequently linked to diabetes mellitus (DM), a different risk factor. This study has reported the clinical presentation and therapeutic outcomes of COVID-19 with diabetes. Method(s): From medical records and histories provided by 72 Covid-19-infected patients with diabetes admitted to the KMCH institute of health sciences and research, Coimbatore, data on demographics, clinical, laboratory, and radiological characteristics as well as treatment outcomes were collected using data collection forms. Real-time reverse transcription polymerase chain reaction (RT-PCR) assay of 2019-CoV RNA was used to screen patients with Covid-19. Result(s): 72 diabetes patients who tested positive for Covid-19 were admitted for this study. SPSS software version 26 was used to evaluate the data that had been collected. Clinical profiles and outcomes of patients with and without diabetes underwent descriptive analysis. Controlled diabetics had a mean plasma glucose of 112.22+/-11.41, while uncontrolled diabetics had a mean plasma glucose of 154.2+/-23.22. Fever was the most prevalent symptom in both managed and uncontrolled diabetes patients (94% and 100%), followed by sore throat (84% and 88%). In patients with uncontrolled diabetes compared to those with controlled diabetes, breathlessness is considerably higher (p<0.05). In the CORADS scoring, 11 of the 34 diabetics with uncontrolled blood sugar levels had CORADS 6 (32.35%), compared to just 2 of the 38 diabetics with regulated blood sugar levels (5.26%), which is considerably higher (p<0.01). In uncontrolled diabetics, the length of hospital stay is much longer (p<0.001). Compared to diabetics with controlled blood sugar, uncontrolled patients SPO2 dramatically dropped (p<0.001). Those with uncontrolled diabetes are more likely to be admitted to the ICU than patients with controlled diabetes (p<0.05). In uncontrolled diabetes compared to controlled patients, the severity was considerably higher (p<0.05). One person who had uncontrolled diabetes died, although no one who had controlled diabetes died. Conclusion(s): Covid 19, persons with uncontrolled diabetes appear to be more likely to sustain lung damage, necessitating admission to the ICU, an extended stay in the hospital, and oxygen assistance throughout the duration of the illness. Copyright © 2023 The Authors. Published by Innovare Academic Sciences Pvt Ltd.

Possibilities of Prediction of COVID 19 using K-Nearest Neighbour Algorithm

Nowadays the entire world has been suffered by a virus called corona which creates panic to the entire world. Even though the world has reached out its advanced level in medical and all other techniques this unseen virus has created an impact to the entire world. This virus has been explored in Wuhan at china, then it spread the entire world and the effect is being very dangerous. In this regard although there is been many researchers have given different solution to predict the root causes of this disease still it is a challenging task. So, this article addressed about the possibility of prediction rate using KNN algorithm. This proposed method would produce 85% of prediction accuracy and 1.4% to 3.4% accuracy improvement when compared with other algorithm. When compared with all other algorithm K- Nearest neighbour algorithm has given better classification than other machine learning algorithm for predicting the COVID 19 possibilities also it diminishes the error rate of prediction accuracy. © 2022 IEEE.

Classification of COVID-19 with Belief Functions and Deep Neural Network.

At present, the entire world has suffered a lot due to the spike of COVID disease. Despite the world has been developed with so much of technology in the domain of medicine, this is a very huge challenge in all over the world. Though, there is a rapid development in medical field, those are not even sufficient to diagnose the symptoms of this COVID in earlier stage. Since the spread of this disease in all over the world, it affects the livelihood of the human. Computed Tomography (CT) images have given necessary data for the radio diagnostics to detect the COVID cases. Therefore, this paper addressed about the classification techniques to diagnose about the symptoms of this virus with the help of belief function with the support of convolution neural networks. This method initially extracts the features and correlates the features with the belief maps to decide about the classification. This research work would provide classification of more accuracy than the earlier research. Therefore, compared with the traditional deep learning method, this proposed procedure would be more efficient with desirable results achieved for accuracy as 0.87, an F1 of 0.88, and 0.95 as AUC.

Interfering with nucleotide excision by the coronavirus 3'-to-5' exoribonuclease.

Some of the most efficacious antiviral therapeutics are ribonucleos(t)ide analogs. The presence of a 3'-to-5' proofreading exoribonuclease (ExoN) in coronaviruses diminishes the potency of many ribonucleotide analogs. The ability to interfere with ExoN activity will create new possibilities for control of SARS-CoV-2 infection. ExoN is formed by a 1:1 complex of nsp14 and nsp10 proteins. We have purified and characterized ExoN using a robust, quantitative system that reveals determinants of specificity and efficiency of hydrolysis. Double-stranded RNA is preferred over single-stranded RNA. Nucleotide excision is distributive, with only one or two nucleotides hydrolyzed in a single binding event. The composition of the terminal basepair modulates excision. A stalled SARS-CoV-2 replicase in complex with either correctly or incorrectly terminated products prevents excision, suggesting that a mispaired end is insufficient to displace the replicase. Finally, we have discovered several modifications to the 3'-RNA terminus that interfere with or block ExoN-catalyzed excision. While a 3'-OH facilitates hydrolysis of a nucleotide with a normal ribose configuration, this substituent is not required for a nucleotide with a planar ribose configuration such as that present in the antiviral nucleotide produced by viperin. Design of ExoN-resistant, antiviral ribonucleotides should be feasible.

Experimental Analysis to Detect Corona COVID-19 Virus Symptoms in Male Patients through Breath Pattern Using Machine Learning Algorithms

In the fourth quarter of the year 2019, the planet became overwhelmed by the pandemic caused by the coronavirus disease (COVID-19). This virus imperiled human life and have affected a considerable percentage of the world population much before its early stage detection mechanisms were discovered and made available at the grassroots level. As there is no specific drug available to treat this infection, the vaccine was intended to serve as the ultimate weapon in the war against this species of coronavirus, but like other viruses, being an RNA virus, this virus also mutates continuously while it passes from one human to the other, making the development of highly potent vaccines even more challenging. This work is being sketched at the juncture when a huge percentage of the human population is already affected by this virus globally. In this work, we are proposing an idea to develop an app to detect coronavirus (COVID-19) symptoms at an early stage by self-diagnosis at home or at the clinical level. An experimental study has been performed on a dummy dataset with 11000 entries of various breadth patterns based on the spirometry analysis, lung volume analysis, and lung capacity analysis of normal male subjects and detailed breath patterns of infected male patients. A logistic regression model is trained after using SMOTE oversampling to balance the data and the predictive accuracy levels of 80%, 78%, and 90%. The results accomplished through this study and experiments may not only aid the clinicians in their medical practice but may also bestow a blue chip to the masterminds engaged in the biomedical research for inventing more evolved, sophisticated, user-friendly, miniaturized, portable, and economical medical app/devices in the future.