Study of Clinical Profile and Outcome among COVID-19 Patients during Hospitalization in a Tertiary Care Hospital in Central India

Background: The COVID-19 outbreak in 2019 has presented in the form of pneumonia of unknown etiology in Wuhan. The complete clinical profile including the prevalence of different clinical symptoms of COVID-19 infection among Indian patients who develop a severe disease is largely unknown. This study is aimed to provide a detailed clinical characterization of the cohort of patients who visited our institute with signs and symptoms of COVID-19. Material(s) and Method(s): This was for inpatient hospital (inpatient) based prospective cohort study involving 520 COVID-19 patients admitted to the hospital. The adverse outcome included death and mechanical ventilation. Result(s): Total 520 participants enrolled in the study, (6.9%) participants died, (8.3%) participants required ICU and (5.5%) participants required mechanical ventilation. only signs and symptoms suggestive of severe respiratory system involvement or widespread infection were associated with adverse outcomes, T presence of dyspnoea, cyanosis and hypoxia. The most common chronic disease among patients with adverse outcomes were diabetes, hypertension and pre-existing respiratory disease, personal habit both smoking, and alcoholism was also associated with adverse clinical outcome. Conclusion(s): The adverse clinical outcome among COVID-19 patients is determined by several factors including advanced age, multi-morbidities, and the presence of severe respiratory symptoms.Copyright © 2023, Dr Yashwant Research Labs Pvt Ltd. All rights reserved.

Radiological Features on HRCT Associated with Poor Outcomes among COVID-19 Patients

Background: Ever since the beginning of the COVID-19 pandemic, physicians started investigating the clinical features and lab markers that can assist in predicting the outcome among hospitalized COVID-19 patients. Aim(s): This study aimed to investigate the association between initial chest CT scan findings and adverse outcomes of COVID-19. Material(s) and Method(s): This was a single centre;hospital (inpatient) based prospective cohort study involving 497 COVID-19 patients admitted to the hospital. The adverse outcome included death and mechanical ventilation. We collected data about 14 identifiable parameters available for the HRCT scan. Result(s): Among 14 studied parameters, only 8 features differed significantly among the patients who had favourable and unfavourable outcomes. These features included number of lobes of lungs involved (3 versus 5, p = 0.008), CT Severity score (16 versus 20, p = 0.004), air bronchogram (p=0.003), crazy paving (p=0.029), consolidation (p=0.021), and pleural effusion (p=0.026). We observed that high CT scores coupled with the diffuse distribution of lung lesions were responsible for poor prognosis in most patients. Conclusion(s): Several features of HRCT when combined can accurately predict adverse outcomes among participants and help in triaging the patient for admission in ICU.Copyright © 2023, Dr Yashwant Research Labs Pvt Ltd. All rights reserved.

Patent scenario of COVID-19 vaccines: A promising analysis and review of innovation and development

Coronavirus disease 2019 (COVID-19), a highly contagious infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has had a devastating effect on world demographics and emerged as a significant global health emergency since the influenza pandemic of 1918. It emphasized the significance of international cooperation in battling SARS-CoV-2 efficiently ever since the discovery and publication of the virus's genome in January 2020. The world took significant steps to combat the disease, ranging from increasing personal protective equipment production and emphasizing the importance of social distancing/masking to the Emergency Use Authorization of remdesivir/therapeutic antibodies. Despite significant advances in clinical research that have led to a better understanding of SARS-CoV-2 and COVID-19 management, limiting the virus's and its variants' spread, has become a growing concern as SARS-CoV-2 continues to cause chaos around the world, with many countries experiencing a second or third wave of outbreaks attributed primarily due to the emergence of mutant virus variants. Considering the potential threat of this global outbreak, scientist and medics have rushed to identify possible treatment regimens and effective therapeutic drugs and vaccinations. As a matter of fact, several COVID-19 vaccines candidate have been researched, created, tested, and reviewed at a breakneck pace. Finding patents, examining relevant patents for current research activities and assessing them plays a key part for the best possible research and development before establishing and executing a trading strategy, especially with recent technology advancements. Therefore, to support current research and development we have evaluated patents relevant to various COVID-19 vaccine technology platforms. The aim of the present research work is to map the existing work through an analysis of patent literature in the field of Coronaviruses, particularly COVID-19 vaccines which will subsequently help the organization launch campaigns, as well as academics and research-driven institutions with the aid of patent literature information for a range of initiatives to combat this circulating demon.

A review on current epidemiology and molecular studies of lumpy skin disease virus-an emerging worldwide threat to domestic animals

In South Asia, cattle are afflicted by the expanding virulent condition known as Lumpy Skin Disease (LSD), and sheep pox and goat pox are caused by the Capri virus. These illnesses endanger worldwide trade. Due to inadequate immunisations and poverty in rural areas, Capricorn poxviruses are spreading. This is due to the economic repercussions of the COVID-19 epidemic, debilitating sanctions in endemic countries, a growth in the legal and criminal trade of live animals and animal products, and global climate change. Skin spores are the main route of infection;however, the virus is also excreted through bodily fluids and semen. As a result, the virus is transmitted to susceptible hosts by biting flies, mosquitoes, and other insects. Insects can be transstadial and transovarial. Lumpy skin disease lesions can swell and rupture after 7 to 14 days in experimental settings, but it usually takes 2 to 5 weeks in a normal infection. Lumpy skin disease is characterised by hard, constrictive, few (mild forms) to numerous (severe forms) skin nodules that may encompass respiratory, urogenital, and other organ mucous membranes. Consequently, milk output decreases, and in countries that raise cattle, there are more abortions, cases of temporary or permanent infertility, hide damage, and mortality, all of which result in a financial loss. The best method for limiting the spread and monetary impacts of lumpy skin disease is mass immunisation and other management measures. This review provides the latest information on lumpy skin disease's viral biology, transmission, clinical, and pathological aspects. © 2023 Journal of medical pharmaceutical and allied sciences. All rights reserved.

Object detection and activity recognition in video surveillance using neural networks

Purpose: This paper aims to implement and extend the You Only Live Once (YOLO) algorithm for detection of objects and activities. The advantage of YOLO is that it only runs a neural network once to detect the objects in an image, which is why it is powerful and fast. Cameras are found at many different crossroads and locations, but video processing of the feed through an object detection algorithm allows determining and tracking what is captured. Video Surveillance has many applications such as Car Tracking and tracking of people related to crime prevention. This paper provides exhaustive comparison between the existing methods and proposed method. Proposed method is found to have highest object detection accuracy. Design/methodology/approach: The goal of this research is to develop a deep learning framework to automate the task of analyzing video footage through object detection in images. This framework processes video feed or image frames from CCTV, webcam or a DroidCam, which allows the camera in a mobile phone to be used as a webcam for a laptop. The object detection algorithm, with its model trained on a large data set of images, is able to load in each image given as an input, process the image and determine the categories of the matching objects that it finds. As a proof of concept, this research demonstrates the algorithm on images of several different objects. This research implements and extends the YOLO algorithm for detection of objects and activities. The advantage of YOLO is that it only runs a neural network once to detect the objects in an image, which is why it is powerful and fast. Cameras are found at many different crossroads and locations, but video processing of the feed through an object detection algorithm allows determining and tracking what is captured. For video surveillance of traffic cameras, this has many applications, such as car tracking and person tracking for crime prevention. In this research, the implemented algorithm with the proposed methodology is compared against several different prior existing methods in literature. The proposed method was found to have the highest object detection accuracy for object detection and activity recognition, better than other existing methods. Findings: The results indicate that the proposed deep learning–based model can be implemented in real-time for object detection and activity recognition. The added features of car crash detection, fall detection and social distancing detection can be used to implement a real-time video surveillance system that can help save lives and protect people. Such a real-time video surveillance system could be installed at street and traffic cameras and in CCTV systems. When this system would detect a car crash or a fatal human or pedestrian fall with injury, it can be programmed to send automatic messages to the nearest local police, emergency and fire stations. When this system would detect a social distancing violation, it can be programmed to inform the local authorities or sound an alarm with a warning message to alert the public to maintain their distance and avoid spreading their aerosol particles that may cause the spread of viruses, including the COVID-19 virus. Originality/value: This paper proposes an improved and augmented version of the YOLOv3 model that has been extended to perform activity recognition, such as car crash detection, human fall detection and social distancing detection. The proposed model is based on a deep learning convolutional neural network model used to detect objects in images. The model is trained using the widely used and publicly available Common Objects in Context data set. The proposed model, being an extension of YOLO, can be implemented for real-time object and activity recognition. The proposed model had higher accuracies for both large-scale and all-scale object detection. This proposed model also exceeded all the other previous methods that were compared in extending and augmenting the object detection to activity recognition. The proposed model resulted in the highest accuracy for car crash detection, fall detection and social distancing detection. © 2023, Emerald Publishing Limited.

Comparing Deep and Machine Learning Models for Sentiment and Emotion Classification from Vaccine #sideffects

The accelerated development of Covid-19 vaccines offered tremendous promise and hope, yet stirred significant trepidation and fear. These conflicting emotions motivated many to turn to social media to share their experiences and side effects during the process of getting vaccinated. This paper analyzes sentiment and emotions from tweets collected using the hashtag #sideffects during the early roll out of the Covid-19 vaccine. Each tweet was labeled according to its sentiment polarity (positive vs. negative), and was assigned one of four emotion labels (joy, gratitude, apprehension, and sadness). Exploratory analysis of the tweets through word cloud visualizations revealed that the negativity of emotions intensified with the severity of side effects. Word and numerical features extracted from the text of the tweets and metadata were used to train conventional machine learning and deep learning models. These models resulted in an accuracy of 81% for binary sentiment classification, and 71 % for multi-label emotion identification. The proposed framework, which yielded competitive performance, may be employed to gain insights into people's thoughts and feelings from vaccine-related conversations. These insights can be helpful in devising communication and education strategies to mitigate vaccine hesitancy. © 2022 IEEE.

Fitness Industry Propelling on IoT

In the contemporary work-from-home period and the previous Covid-19 times, fitness or, to put it another way, obesity, has emerged as a significant issue. Technology usage has suddenly increased and become ingrained in our daily lives. For the development of such individuals, we are developing the fitness application FITWORLD, which supports individuals in achieving their objectives by offering customised training and dietary regimens. Our proposal is based on research into the workout habits of many individuals with various objectives and BMIs. These guidelines are simple to follow and help boost immunity, which further guards against Covid. We are leveraging a variety of technologies and tools, including: •Android Studio •Kotlin •XML •Draw.io •Figma •Star UML •Firebase As a consequence, we are striving to create Fitworld, an app, employing the tools and technologies indicated above. By assisting individuals in maintaining a healthy lifestyle via the use of our app, we want to make our nation healthy and fit in the future. © 2022 IEEE.

Pattern of ocular morbidities during COVID-19 pandemic in a tertiary eye care centre in central India

Purpose: The study was conducted to observe the pattern of ocular morbidities in patients attending the ophthalmology department during the COVID-19 pandemic. Methodology: A prospective observational study was conducted in a tertiary eye care centre in Central India from 1 January 2020 to 31 December 2020, among a total of 982 patients with ocular manifestations who attended the outpatient department or emergency department. A detailed history was taken and a complete anterior and posterior segment examination was done. The standard investigation and treatment protocol of the institution was followed in all cases. Result: A total of 982 patients were enrolled in our study with a mean age of 36.42±18.05 years. Male preponderance was noted with an M: F ratio of 2.43. A wide spectrum of ocular manifestations during COVID-19 was observed. The anterior segment was involved in 85.1% and the posterior segment was involved in 14.9% and most common ocular manifestations affecting the anterior segment were noted as computer vision syndrome observed in 18.1% cases, followed by cataracts in 11.4% cases. Conclusion: Various programs should be implemented to help in reducing the load of visual disability and blindness in the community which is increased after the COVID outbreak. With changing trends in geographical and socio-economical patterns of diseases, similar kinds of a pandemic may occur in the future. There should be formation of flexible government strategies for changing dynamics that can be timely implemented in the future for better management of curable/avoidable diseases. © 2022 Innovative Publication, All rights reserved.

Co(II), Ni(II), Cu(II) and Zn(II) complexes of Schiff base ligands: synthesis, characterization, DFT, in vitro antimicrobial activity and molecular docking studies

Three Schiff base ligands [H2L1-H2L3] containing nitrogen/oxygen donor atoms and their Co(II), Ni(II), Cu(II) and Zn(II) complexes were synthesized by stirring metal acetates with Schiff base ligands obtained from condensation reaction of 2-amino-6-chloro-4-nitrophenol with 5-chloro salicylaldehyde/3,5-dibromo salicylaldehyde/3-methoxy-5-nitro salicylaldehyde. The structural traits of the synthesized compounds were done by using elemental analysis, spectroscopic techniques (UV-Vis, H-1 and C-13 NMR, FT-IR), mass spectrometry and some physical studies (XRD, TGA). According to spectral data, ligands behave as a tridentate (ONO) and formed complexes with octahedral geometry. The thermogravimetric analysis revealed that metal complexes decay in multi-steps leaving metal oxide as an end product. Powder XRD study suggested crystalline nature of the compounds. The energy gap (HOMO-LUMO) and molecular electrostatic potential calculation were computed by using DFT/B3LYP/6-31G** basis set. Derived ligands and complexes were explored for in vitro antimicrobial potential toward two gram-positive bacteria, two gram-negative bacteria, i.e., S. aureus, B. subtilis, P. aeruginosa, E. coli, and two fungal strains, i.e., A. niger, C. albicans, through serial dilution method taking ciprofloxacin and fluconazole as standard. The investigated results showed that complexes are more potent than free Schiff base ligands. The Cu(L-2)(H2O)(3) (0.0115 mu mol/mL) and Zn(L-2)(H2O)(3) (0.0115 mu mol/mL) complexes were found to be more active among all the investigated compounds. Additionally, molecular docking studies were also performed for some compounds in the active site of DNA Gyrase enzyme (PDB code: 1AJ6), suggesting good hydrophobic interactions of compounds with the enzyme.

Major Cancer Surgery during Covid- 19 Pandemic - Experience of Central India

World faced a biggest challenge on health care system during Covid-19 pandemic and has become the focus of attention worldwide. The challenge faced by surgeon treating cancer patient is different, because most of the cancer surgeries are elective but cannot be delayed beyond a period of time due to biology of cancer and adverse effect on survival. A prospective database of elective cancer surgeries was analyzed from May 3rd 2020 to august 30th 2021 by group of surgeons in Jabalpur Madhya Pradesh. In symptomatic patient RT PCR testing was advised and HRCT chest was performed. During the study period 350 elective major cancer surgeries was performed. Median age of our cohort was 53 years and 52.5% patients were male. Head neck surgeries constituted 41.6 % followed by breast 22%, Gynae-oncology (10.2%) and gastrointestinal (10 %). In 8 patients the RT PCR test was positive. Additional 12 patients were advised quarantine in view of clinical suspicion even with a negative RTPCR report. None of the patients undergoing surgery had clinical suspicion for COVID-19 infection. 43% patients were having associated comorbid illness among them 11.7% of the patients were ASA class-3. There was no postoperative mortality in our cohort across all cancer sub sites. Our lower rate of complication and zero mortality over 8 weeks not only reflect our case selection policy, screening strategies, adopting best surgical practices, judicious use of personal protective equipment(PPE), best operating team members and using the basic protocol by using a triple layer/ N-95 mask with physical distancing and avoid overcrowding. Relevant clinical history and examination about COVID infection was the most critical factor before proceeding to surgery during pandemic. RT PCR should be done only in selective patients. Our result possibly represented the largest published series of central India on cancer surgery during COVID pandemic. Copyright © 2022 Necati Ozpinar

Preventive measurement and Prediction of Covid-19 in India through Business Intelligence Tools

From December 2019, a major outbreak called novel corona virus is infecting people all over the world now. It is believed to be a beta corona virus of SARS-CoV and MERS-CoV. Infected people are unable to detect this disease as they feel normal till 10-12 days. After that, the virus infects the whole body and starts to find another body to infect, multiplying it day by day. As per the media news and other sources, epidemic is spreading globally, especially in countries like China, Italy where its effect is at peak, killing thousands of people. Based on the data of infected Covid-19 people in India, we systematically discuss the outbreak of epidemic corona virus in India. Defining the structure of active cases day by day, we predict the future of Covid-19 in India. We also suggest important measures to help prevent the spread of Covid-19 in India. © 2022 IEEE.

Plants with potent antiviral properties

Medicinal plants have been extensively used for treating a variety of infectious diseases for a long time. Drug discovery from these plants involves a versatile approach combining phytochemical, botanical, and molecular techniques. A broad range of active phytochemicals, like alkaloids, flavonoids, proteins, extracted from herbal plants, and some volatile essential oils extracted from culinary herbs, herbal teas, and spices possess antiviral property. Medicinal plants have proven to be potent sources of antiviral agents with some main advantages over conventional drug therapy due to their broad healing potency and causing no side effects. This chapter presents research advances done for the search of suitable drugs from medicinal plants against viruses with special consideration of severe acute respiratory syndrome -coronovirus-2. © 2022 Elsevier Inc. All rights reserved.

Aerococcus viridans Bacteraemia in a COVID-19 Positive Patient: A Rare Case Report from Northern India

Aerococcus viridans is a rare Gram positive microorganism identified largely as environmental or skin contaminants. With the advent of an increase in the immunosuppressed population due to diabetes, the use of steroids and the Coronavirus Disease 2019 (COVID-19) pandemic, this bacteria caused a variety of infections like bacteraemia, urinary tract infections, and endocarditis. The use of Matrix-Assisted Laser Desorption Ionisation Time-of-Flight Mass-Spectrometry (MALDI-TOF MS), a unique technique of microorganism identification, has placed Aerococci among human pathogens, capable of causing infection among immunocompromised patients. The present case was of a 48-year-old female presented with dry cough, high-grade fever associated with chills and rigors, and generalised body ache and weakness for the past one week. She was a known case of bronchial asthma. She tested positive for COVID-19 and over the course of hospital stay, her BACTEC blood culture performed due to high fever which flagged positive indicated her as a case of Aerococcus viridans bacteraemia. Despite of all the efforts she developed respiratory distress followed by an episode of asystole following which she could not be revived. Copyright © 2022 Journal of Clinical and Diagnostic Research. All rights reserved.

Smart Door System for Maintaining Social Distancing and Controlling Spread of Infectious Disease

COVID-19, also known as coronavirus, has spread throughout the world and changed all facets of lives drastically. Though the vaccines are being distributed, the situation is still miserable in many countries. To slow down the spread of this virus, social distancing is required to be maintained. This paper proposes a smart door system that helps in fast temperature scanning of people and ensures social distancing at public places. The proposed system also automatically opens or closes the doors at public places and switches the appliances on the basis of the number of people present. The system design is based on Internet of things (IoT) and is embedded using Arduino Uno. This is an efficient and low-cost model to control the spread of infectious disease. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Patent Landscape of COVID-19 Innovations: A Comprehensive Review

The COVID-19 pandemic save a stringent upshot on the lives of human. The people are running in race to save themselves and the race is still on. COVID emergency has not only retarded the health of the society but also made it face the economic downfall in a severe manner. The scientists and the data analyst gave their predictions for the additional wave of infection, thus stating COVID appropriate behaviour as the medicine for the time. This health calamity provides an opportunity for cross-sector partnership of technology and science as to keep the battle fight strong, finding new roads leading to fresh solutions in health care and innovations. There is a requirement of asystematic approach for accessing the patent literature that is already available to form a research platform for further advancements. The study serves a general view of the search strategy and approach, categorization of search, database set used, websites, novelty, derivation of innovation, field of work, and the investigational dataset regarding the COVID-19 patent literature under the category of diagnosis, sanitization, personal protection and vaccine development available from December 2019 to June 2021. The patent literature provides us with the knowledge which innovation is riled fust for patent and] published documents which can be found in the database search, thus deriving the ideologies as a supplementary guidance for the advancement of innovations. Evidently, it can be concluded that our research and report can be helpful in drawing some innovative outlines with industrial applications or some others which require further interpretation with more concern. © 2022, National Institute of Science Communication and Information Resources. All rights reserved.

COVID-19 Detection Using Raw Chest X-Ray Images

COVID-19 has had a lasting effect on the human population around the globe. originating from Wuhan, China, in December 2019, the virus managed to spread worldwide in a short time. Huge waiting time between the detection of symptoms and clinical confirmation of the virus being present in the body has made the virus more fatal;thus, rapid screening of large numbers of suspected patients is essential. Due to inefficiency in pathological testing, alternate ways must be devised to combat these issues. Due to advancements in CAD, integrating radiological images with Artificial Intelligence (AI) can detect the disease accurately. This study proposes a deep learning model for automatic COVID-19 detection using raw Chest X-ray (CXR) images. With 17 convolutional layers, the proposed model is trained to diagnose COVID-19 with an 96.67% accuracy. The model can be used to help the world in numerous ways. © 2022 IEEE.

Covid-19 Detection based on Transfer Learning & LSTM Network using X-ray Images

COVID-19 is a virus which leads to infections in the upper respiratory system and lungs. On the scale of the global pandemic, cases and deaths are rising daily. X-ray is one test that can give a better picture of the severity of COVID-19. To monitor various lung diseases, chest X-ray imaging is helpful. This paper proposed techniques, viz. deep feature extraction and pre-trained neural networks (CNN) to distinguish COVID-19 and normal (healthy) chest X-ray images. For deep feature extraction, the pre-trained deep CNN model VGG-16 was used. An LSTM model is introduced in this study. The dataset contains 180 X-ray images of COVID 19 and 200 healthy ones used in the experimental analysis. The performance measurement of the research was based on categorizing accuracy. Experimental activities show that deep learning demonstrates the potentiality in detecting COVID-19 based upon chest X-ray images as examined;the introduced model accomplishes an average accuracy of 97.37%. Other strategies like Resnet50 give 82% accuracy, Inception gives 96% accuracy, and Xception provides 92% accuracy. This has shown deep mechanisms that work well compared to local descriptions of the method of curing COVID-19 based upon the chest X-ray images. These findings allow us to conclude that this article's proposed procedure may help clinicians determine COVID-19-related diagnoses. © 2022 IEEE.

Neural Architecture and Feature Search for Predicting the Ridership of Public Transportation Routes

Accurately predicting the ridership of public-transit routes provides substantial benefits to both transit agencies, who can dispatch additional vehicles proactively before the vehicles that serve a route become crowded, and to passengers, who can avoid crowded vehicles based on publicly available predictions. The spread of the coronavirus disease has further elevated the importance of ridership prediction as crowded vehicles now present not only an inconvenience but also a public-health risk. At the same time, accurately predicting ridership has become more challenging due to evolving ridership patterns, which may make all data except for the most recent records stale. One promising approach for improving prediction accuracy is to fine-tune the hyper-parameters of machine-learning models for each transit route based on the characteristics of the particular route, such as the number of records. However, manually designing a machine-learning model for each route is a labor-intensive process, which may require experts to spend a significant amount of their valuable time. To help experts with designing machine-learning models, we propose a neural-architecture and feature search approach, which optimizes the architecture and features of a deep neural network for predicting the ridership of a public-transit route. Our approach is based on a randomized local hyper-parameter search, which minimizes both prediction error as well as the complexity of the model. We evaluate our approach on real-world ridership data provided by the public transit agency of Chattanooga, TN, and we demonstrate that training neural networks whose architectures and features are optimized for each route provides significantly better performance than training neural networks whose architectures and features are generic. © 2022 IEEE.