Diagnostic Role of Platelet to Lymphocyte Ratio and Platelet Parameters in COVID-19 Disease

Backgrounds: This study aimed to analyze the applicability of platelet parameters in assessing the severity of COVID-19 disease. Material(s) and Method(s): Patients with RT-PCR confirmed COVID-19 in the Pathology department of a tertiary care hospital in south India from June to December 2020 were included in this study. Clinical details and laboratory parameters of these patients were obtained. The difference between the studied variables in two groups was assessed using independent t-test. The optimum cut-off value of platelet to lymphocyte ratio (PLR) to differentiate between the tested groups was estimated using ROC (receiver operator curve) analysis. Finding(s): This study was conducted on 218 COVID-19 patients, of whom 17.9% showed thrombocytopenia at the time of admission. Among the hematological parameters, PLR, absolute lymphocyte count (ALC), platelet distribution width (PDW), D-dimer, and erythrocyte sedimentation rate (ESR) were significantly different between the ICU (intensive care unit) and non-ICU groups. Increased PLR values were associated with the disease severity. Conclusion(s): PLR could be used as an additional biomarker in assessing the severity of COVID-19 disease, and a cut-off value of 210.27 is optimal to differentiate severe COVID-19 disease from its mild and moderate forms with 79% specificity.Copyright © 2023, TMU Press.

Performance of Resnet-16 and Inception-V4 Architecture to Identify Covid-19 from X-Ray Images

Covid-19 has become a big challenge across the world and there has been an urgent need for breakthroughs in clinical research, vaccine discoveries/trial and pharmaceutical technologies. Symptom identification with the use of machine learning frameworks and strategies can greatly pave way for rapid control and assessments that eventually can help to contain virus outbreaks. We compare performance of two convolutional neural networks namely ResNet-16 and Inception-v4 for classification of X-ray images as Covid-19 or non-Covid-19. Results inferred the model performance is around 83% with Inception-v4, which is considerably a deeper network than ResNet-16 © 2022 IEEE.

Computational Deep Learning Models for Detection of COVID-19 Using Chest X-Ray Images

Traditional deep learning architectures after the AlexNet have added more layers to achieve higher accuracy. However, with increasing number of layers, we are likely to encounter vanishing/exploding gradient problems in these architectures which significantly impact the training performance. This was solved by the introduction of residual networks which make use of "skip connections” by adding the output from the previous layer to the layer ahead. ResNets are often combined with the Inception v4 model and was first used by Google researchers as Inception-ResNet. Inception v4 aimed to reduce the complexity of Inception v3 model which gave the state-of-the-art accuracy on ILSVRC 2015 challenge. The initial set of layers before the Inception block in Inception v4, referred to as "stem of the architecture,” was modified to make it more uniform. This model can be trained without partition of replicas unlike the previous versions of inceptions which required different replica in order to fit in memory. This architecture uses memory optimization on back propagation to reduce the memory requirement. In this paper, we propose two approaches for detection of COVID-19 using chest X-ray images by implementing ResNet16 and Inception v4 and providing a comparison of their performances. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Comparison Between ResNet 16 and Inception V4 Network for COVID-19 Prediction

COVID-19 claimed 5 million lives worldwide so far, and the count is continuing. It also affected socio-economic life of almost everybody in the world. Due to COVID-19, mortality and morbidity are continuing, and it is necessary to find new methods and techniques to contain the infection. Every government is trying hard to implement a new strategy to minimize the spread of the virus. COVID-19 infection occurs due to the virus strain SARS-COV-2. Generally, death occurs due to COVID-19 because of suppurative pulmonary infection and subsequent septic shock or multiorgan failure. In the literature, there are some computational techniques which use deep learning models and reported fairly good performance. This paper proposes a new deep learning architecture inception v4 to automatically detect COVID-19 using the chart X-ray images. The proposed methodology provided improved performance of 98.7 and 94.8% of training and validation accuracy. The developed technology can be used to detect COVID-19 with a high performance;the same may be deployed by the various governments in the detection and the management of COVID-19 in an efficient manner. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Revisiting 'The Plague' by Camus: Shaping the 'social absurdity' of the COVID-19 Pandemic.

COVID-19 has emerged as a global health threat. The catastrophic reaction to a pandemic in spite of knowing the deadly outcomes, has been referred to as the 'social absurdity'. Such reaction creates a negativistic outlook with regard to the infection, thus contributing to chaos and preventing containment. In this article, the current pandemic of COVID-19 is revisited through the lens of Camus' 'La Peste, 1947'. The philosophical roots of social 'absurdity' during a pandemic are critically discussed in the context of death anxiety. Subsequently, ways of reshaping it are highlighted, borrowing from the theories of existentialism and positive psychology.

A Systematic Literature Review in Diagnosis and Severity Estimation of COVID-19 Disease using Machine Learning

In identification of severe acute respiratory syndrome corona virus 2(SARS-CoV-2), the novel corona virus responsible for COVID-19, professionals related to medical domain have been entered to implement various novel technical solutions and patient diagnosis techniques. The COVID-19 pandemic has accelerated enforcement of machine learning (ML) technology, and various other such organizational groups have been eager to embrace and adjust these ML techniques to the outbreak concerns. We have carried out a tremendous analysis based on the literature available till now. The complete assessment carried related to the use of machine learning models to fight against COVID-19, emphasis on various aspects like disease effects, it's diagnosis, percentage of severity estimation, drug and treatment analysis, effective feature selection, and also post-Covid context related. A systematic search of online research repositories which are Google Scholar, Web of Science and PubMed was undertaken in corresponding to the "Preferred Reporting items for Meta-Analysis and Systematic Reviews" criteria to find all related published papers during 2020 and 2022 years. The search process was created by integrating COVID-19-typical terms with the word "machine learning.". Copyright © 2022, Anka Publishers. All rights reserved.

Bambusa arundinacea leaves extract-derived Ag NPs: evaluation of the photocatalytic, antioxidant, antibacterial, and anticancer activities.

Bio-fabrication has become a safe approach for silver nanoparticles (Ag NPs). The plant-mediated biosynthesized Ag NPs have emerged as a potential substitute for conventional chemical formation. The biosynthesized Ag NPs were analyzed in terms of crystalline nature, morphology, chemical composition, particle size, stability, size, and shape of the particles. The XRD, FTIR, and TEM analysis indicate the presence of the bioactive secondary metabolites compounds. The bamboo-mediated Ag NPs demonstrated a notable antibacterial efficacy against Gram-positive and Gram-negative pathogenic microorganisms and showed significant antioxidant activity against DPPH free radicals. The degradation of methylene blue at various intervals under solar light irradiation was used to evaluate the photocatalytic performance of Ag NPs. Further, Ag NPs conveyed potent anticancer activity against MCF-7 cell lines with a significant value IC50. The bamboo leaves-mediated Ag NPs synthesized Ag NPs signified strong antibacterial, antioxidant, and anticancer activity; hence, it can be used in various biomedical applications and face mask coating to prevent the coronavirus after successful clinical trials in research laboratories.

A Study of Relationship between Internet Usage and Emotional Maturity at Higher Secondary Level

Internet trend is growing day by day and the education system is going to be online sooner or later. In developing countries, like India, technological changes are rapidly taking place irrespective of age and gender. We have witnessed COVID 19, which brought technological adaptations in the education system. There was a clear shift of teaching-learning process from traditional to online. Technology is spreading out in 360 degree angle without which one cannot survive even. When it comes to the higher secondary education, how internet and other technologies can be utilized in an effective way? It is often observed adolescents are getting emotional when they watch online brutal advertisements. It is very difficult to balance their emotions once they are trolled or embarrassed in social networking sites. Such adolescents often isolate themselves from online or stop using social networking cites for some time. If we see the availability of internet, there are huge demographic differences. The students who have availability of internet are likely to access and visit online frequently compare to others. Thisstudy will help teachers to identify students with internet addiction and their emotional stability in class. The present study has gone in this direction in order to explore the relationship between availability of internet usage and emotional maturity in the students of higher secondary. A survey was conducted on 200 participants randomly and correlation was established. The findings were interesting and results were discussed.

Prediction of COVID-19 Through Chest X-Ray Images Employing Various Machine Learning Techniques

The COVID-19 is the most infectious disease which is recently discovered. The COVID-19 pandemic has led to excruciating loss to human life and it also caused mild to severe respiratory illness, including death. Detecting the infected patients and taking special care is crucial in fighting covid-19. Radiography and Radiology images are used to diagnose the patients. These are the fastest ways to identify COVID-19 disease. It is observed that patients affected with COVID-19 have specific abnormalities in their chest radiograms. Initially there were few limited set of CT images are available publicly in performing research. Board-certified radiologist role is to perform identification of images exhibiting COVID-19 disease. Chest CT scans are helpful to diagnose COVID-19 disease in individuals. COVID-19 directly shows impact on lungs and it damages and the tiny air sacs. In this paper we have used deep transfer learning models Residual Network (ResNet50) and VGG19 (Visual Geometry Group) to predict the disease at earlier stages. These models obtained a specificity rate of 90% and achieved a highest sensitivity rate of 98 %. In addition to sensitivity and specificity rates ROC curve, average prediction and confusion matrix of each model are presented in the papers. While this achieved performance is very encouraging if we have large set of COVID-19 images then it may give more reliable estimation of accuracy rates. © 2022 IEEE.

Pneumonia_Net Model: Deep Learning Based Intelligent System forPneumonia Classification using Chest X-ray Images

Radiologists are faced with a challenging problem whenever they have to classify the anomalies shown on chest x-rays. Because of this, throughout the course of the last few decades, computer aided diagnostic (CAD) systems have been created to extract meaningful information from X-rays in order to assist medical professionals in gaining a quantitative understanding of an X-ray.Because radiology is such an important field, most of the time the analysis of radiologist images is carried out by trained medical professionals. This is due to the fact that patients seek the highest possible level of treatment in addition to the highest possible quality, regardless of how much it costs.However, its complexity and the subjective nature of the visuals limit its usefulness. There is a great deal of diversity between different translators and a great deal of exhaustion in human professional image processing. Our main goal is to classify lung disorders utilizing diagnostic X-ray images analysed using deep learning and images exploited using Pandas, Keras, Open CV, Tensor Flow, etc. Chest radiographs are still diagnosed by doctors and radiologists using manual and visual methods. As a result, a system capable of diagnosing chest X-rays must be developed that is both smart and automated. The goal of this study is to classify chest X-ray images into normal and pathological using a deep neural network model called Pneumonia Net. It is trained and evaluated using chest X-rays taken from publicly available databases that include both normal and pathological radiographs. Due to their capacity to automatically extract high-level representations from large data sets, CNN-based deep learning categorization approaches outperform existing picture classification methods in this regard. Three different network models are compared depending on their performance. In experiments, it was found that the Pneumonia Net model had a good generalisation capacity in identifying unseen chest X-rays as normal or anomalous, and that its performance was better than that of other network models.

An Innovative Review Study on Using Pulmonary Vaccination in COVID 19 Pandemic

The current state of pulmonary vaccine delivery will be discussed in this review. The prospects for lung immunization using dry powder generation technologies and specialized medicinal formulations are discussed. In terms of vaccine durability and antigenicity, dry powder vaccine generation technologies may be advantageous. The non-invasive, reasonably safe, and low-cost nature of pulmonary delivery could help the public health vaccination significantly. The vaccines, which are all given intramuscularly, produce systemic antibodies in the blood but not antibodies in the pulmonary mucosal lining. Inhalation vaccines provide a number of potential benefits over injectable vaccines, including ease of delivery, and even self-administration. To create a dry powder inhalation formulation that is breathable and mediates robust transfection in the lung, a safe and effective mRNA delivery vector as well as a suitable particle engineering approach is needed.

Deep Learning Approaches for COVID-19 Diagnosis

Coronavirus disease (Covid19) is a pandemic communicable disease that has a serious risk of speedy transmission. Identifying and isolating the affected person is the initiative mark to counter this virus. In regard to this matter, chest radiology images have been manifested to be a powerful screening approach of Covid19 positive patients. Many Artificial Intelligence based solutions have evolved for fast screening of radiological images and more precise in detecting Coronavirus disease. To make the proposed model more powerful, labeled chest X-ray datasets comprising two categories Covid19 and Non-Covid from kaggle uci repository data set are used in this work. To perform feature extraction, effective CNN structures, namely EfficientNet, VGG-16 and Densenet-121 with ImageNet pre-training weights are applied. The features produced are moved to custom fine-tuned top layers which are then followed by a group of model snapshots. In this study, the main objectives are to create database of Covid19 patients and to develop different Deep learning model for analysis of Covid19 pneumonia and then to train the deep learning models to get desired accuracy. A deep learning-based approach using Densenet-121 with ReLu activation function is proposed to effectively detect Covidl9 patients X-ray images. The model is trained on Covidl9 dataset which consisted of 2159 labelled X-ray images (576 images are of confirmed Covid19 patients and 1583 are of non-covid patients) and achieved overall accuracy of 95.04% in classifying the X-ray images and tested this model on Covid dataset containing 25 unidentified chest X-ray images. As a final step, we performed two-class classification of unidentified X-ray images as Covid and Normal using the proposed deep learning model.

Phage therapy in the Covid-19 era: Advantages over antibiotics.

Today, the entire world is battling to contain the spread of COVID-19. Massive efforts are being made to find a therapeutic solution in the shortest possible time. However, the research community is becoming increasingly concerned about taking a shortsighted strategy without contemplating the long-term consequences. For example, It has been reported that only 8.4% of total COVID-19 patients develop a secondary bacterial infection. In comparison, 74.6% of them are administered with antibiotics as prophylactic treatment. We contend that overuse of broad-spectrum antibiotics increases the likelihood of AMR development and negatively affects the patient's recovery due to the prevalence of the "gut-lung axis.". Consequently, the use of antibiotics to treat COVID-19 patients must be rationalized, or an alternative treatment must be sought that does not risk contributing to AMR development and positively impacts the treatment outcomes. Phage therapy, a century-old concept, is one of the most promising approaches that can be adapted to serve this purpose. This review emphasizes the negative impact of excessive antibiotic use in COVID-19 treatment and provides an overview of how phage therapy can be used as an alternative treatment option. We have argued that targeted killing (narrow spectrum) and anti-inflammatory (which can target the primary cause of mortality in COVID-19) properties of phages can be an effective alternative to antibiotics.

Diagnostic Utility of Platelet-Lymphocyte Ratio (PLR)and Platelet Parameters in COVID 19 Disease

Introduction: COVID-19 is an infectious disease caused by novelcoronavirus and changes in haematological parameters are recorded.Among which platelet parameters play an indirect role in assessingthe coagulation status of the patients.Aims &Objectives: The aim of this study is to (1) compare theplatelet parameters and PLR among ICU and non-ICU COVID-19patients (2) evaluate the role of platelet parameters in assessing theseverity in COVID-19 patients.Materials &Methods: This was a prospective observational studyfrom June 2020 to December 2020 (during the first wave of pandemic). The clinical data of patients were recruited from hospitalmedical records to stratify the disease severity. Parameters such asAbsolute lymphocyte count (ALC), Platelet count (PLT), MeanPlatelet Volume (MPV), Platelet Distribution Width (PDW), D-dimer,Prothrombin Time (PT) and activated Partial Thromboplastin Time(aPTT) were included. Values of Erythrocyte Sedimentation Rate(ESR) and Interlukein-6 (IL6) were also included wherever available.Serial platelet count values were noted for ICU patients for assessingthe platelet trend. From ALC and PLT count, platelet to lymphocyteratio (PLR) was calculated using the formula: PLR = ALC/PLT 9100. Non-parametric independent 't' test was used to obtain thedifference between the study variables. ROC analysis was done tofind the optimum cut-off value of PLR between the tested groups.Result: A total of 218 RT-PCR proven COVID-19 patients wereincluded. Of these 145 (66.5%) patients were treated in ICU and73(33.5%) patients were treated in wards (non-ICU group). 39%patients showed thrombocytopenia at the time of admission. PLR,ALC and PDW showed statistically significant difference betweenICU and non-ICU group. Table 1 depicts the results.Conclusions: Haematological parameters provide vital clues aboutdisease severity. The most significant of them observed in our studyare ALC, PLR, PDW, D-dimer and ESR. Low values of ALC and high values of PLR, PDW, D-dimer andESR were associated with severe COVID-19 disease requiringICU care. PLR, an easily derivable parameter at a cut-off of 210.27 wasuseful to differentiate severe COVID disease from mild/moderateCOVID disease with 79% specificity.

ROS-Based Robot for Health Care Monitoring System

Hospitals play an important role in the development of the country. Every hospital staff plays an essential role in their work. If hospitals are overcrowded, patients feel so difficult to meet doctors and all staff facing the same issue. If it satisfies, the hospital facing another of hygiene. It is very important to concentrate everything to develop our nation. ROS and path planning-based robot is used in the hospital environment for all the staff members, especially nurses to complete their tasks quickly. A mini type robot is developed for monitoring the patient physical condition and deliver the tablets to the patients in time. Robots collect the whole data by themselves and send the data to doctors and nurses. A server is maintained for collecting the data. A doctor can access the data even doctors are unavailable. Data are registered in a common platform to view anyone according to that doctors can follow treatment for their patients. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

An Efficient Method for Coronavirus Detection Through X-rays Using Deep Neural Network.

BACKGROUND: Coronavirus (COVID-19) is a group of infectious diseases caused by related viruses called coronaviruses. In humans, the seriousness of infection caused by a coronavirus in the respiratory tract can vary from mild to lethal. A serious illness can be developed in old people and those with underlying medical problems like diabetes, cardiovascular disease, cancer, and chronic respiratory disease. For the diagnosis of coronavirus disease, due to the growing number of cases, a limited number of test kits for COVID-19 are available in the hospitals. Hence, it is important to implement an automated system as an immediate alternative diagnostic option to pause the spread of COVID-19 in the population. OBJECTIVE: This paper proposes a deep learning model for the classification of coronavirus infected patient detection using chest X-ray radiographs. METHODS: A fully connected convolutional neural network model is developed to classify healthy and diseased X-ray radiographs. The proposed neural network model consists of seven convolutional layers with the rectified linear unit, softmax (last layer) activation functions, and max-pooling layers which were trained using the publicly available COVID-19 dataset. RESULTS AND CONCLUSION: For validation of the proposed model, the publicly available chest X-ray radiograph dataset consisting of COVID-19 and normal patient's images were used. Considering the performance of the results that are evaluated based on various evaluation metrics such as precision, recall, MSE, RMSE and accuracy, it is seen that the accuracy of the proposed CNN model is 98.07%.

Effects of SARS‑CoV‑2 on testicular function

Objective: To describe the clinical characteristics of the male reproductive system of COVID‑19 patients and to explore the presence of SARS‑CoV‑2 in semen. Methods: Case series of 112 male patients with confirmed COVID‑19 who were admitted to Renmin Hospital of Wuhan University from January to March, 2020. Demographic data, symptoms and signs related to the male reproductive system, throat swabs and semen samples were collected and analyzed. SARS‑CoV‑2 RNA levels were measured in throat swab and semen samples. The organ distribution of ACE2 mRNA and protein in human tissue on HPA database were investigated. Results: The HPA dataset revealed relatively high levels of ACE2 protein and RNA expression in testis. A total of 3 severe COVID‑19 patients (2.7%) presented with orchidoptosis, while no patient experienced other symptoms or signs related to the male reproductive system. The analysis of SARS‑CoV‑2 RNA in semen included 17 patients with fertility needs. In the semen SARS‑CoV‑2 analysis, all 17 patients were negative for the N gene and ORF1ab gene. Conclusion: The online datasets indicated the potential impairment of the testicular function by SARS‑CoV‑2. However, this study suggestes that male patients have few reproductive symptoms and signs, and SARS‑CoV‑2 was not present in the semen of patients with confirmed COVID‑19. In view of the potential impairment, the long‑term follow‑up for male COVID‑19 patients with fertility needs is of great significance. © 2022, Editorial Board of Medical Journal of Wuhan University. All right reserved.

Recommendations for Pediatric Dermatology Practice during COVID-19 Pandemic.

The current scenario of the coronavirus disease (COVID-19) pandemic has resulted in a huge disease burden worldwide affecting people across all age groups. Although children get infected by coronavirus, they are less commonly affected. Only 2% of cases are being reported among patients aged less than 20 years of age and childhood cases constitute around 1-5% of them. Moreover, they are less likely to be seriously affected when compared to adults, with more than 90% of them being either asymptomatic or having mild to moderate disease. This could be attributed to less exposure or sensitivity to COVID-19, varying immune response mechanisms, differences in the expression/function of the Angiotensin Converting Enzyme 2 receptors or higher antibody levels to viruses owing to exposures to multiple respiratory infections, protective role of measles and BCG vaccine, and few associated comorbidities. However, children with certain underlying medical conditions like cardiac or respiratory disease, diabetes, immunodeficiency disorders, cancer or on immunosuppressants may be at a higher risk for developing severe disease.

Aesthetically Acceptable, Breath Friendly Triboelectric Face Masks: Design, Fabrication, and Its Efficacy

Since the beginning of the COVID-19 pandemic, several attempts have been made worldwide to control the spread of the virus. It is widely accepted that wearing face masks in public and workplaces suppresses the transmission of the virus. Highly effective face masks, e.g. N95, have a high filtration efficiency but with a large pressure drop, which does not allow one to wear the mask comfortably for long hours. A larger population wearing a moderate efficiency mask can also cut the transmission at large. Herein, mask panels from readily available fabrics are developed, that can generate triboelectricity, which enhances the filtration efficiency by around 18% without compromising the pressure drop-allowing one to wear the mask for an extended period. The unique cup-shaped design of the mask provides a snug fit with no speech distortion or fogging on the glasses.