ABSTRACT
Background: Infectious bronchitis (IB) is an acute and highly contagious viral disease of poultry affecting chicken of all ages. The causative agent IB virus (IBV) is a Gammacoronavirus within the family Coronaviridae. Viral genetic mutations and recombination events particularly in the spike protein (S1) of IBV constantly give rise to emerging IBV variants. Vaccination is considered as the most reliable approach for IBV control, but current vaccines have been found to be ineffective due to constant emergence of new variant viruses. Objective(s): The objective of our study was to detect IBV genotypes prevalent in Assam, India. Material(s) and Method(s): Oro-pharyngeal swabs and tissue samples from unvaccinated broiler chickens showing respiratory symptoms were tested using RT-PCR targeting the N gene of IBV. The virus was isolated from infected swab/tissue samples in 9 days old specific pathogen free embryonated chicken eggs through allantoic cavity route. Phylogenetic studies were done based on the S1 gene of IBV. Results and Conclusion(s): Clinically, the birds showed gasping and tracheal rales. Necropsy revealed distended ureters. Virus was isolated and identified by curling and dwarfing of the dead embryos and further confirmed by RT-PCR. Positive PCR amplicons were sequenced and phylogenetic analysis clustered the IBV isolate from Assam with genotype I lineage 1 IBV prototype sequence belonging to Beaudette and Mass 41 strains but the isolate exhibited a relatively high degree of sequence divergence with reference strains. Our findings suggest that the IBV isolate might have emerged from recombination with the local circulating virus or vaccine strains. This will have important implications for IB prevention strategies.
ABSTRACT
Machine learning and deep learning technologies are reshaping the global medical industry at a breakneck pace. Image classification is one of its rapidly expanding fields. It is incorporated into nearly all technologies aimed at achieving intelligent smart health systems. The current paper implements and applies two image classification models based on convolutional neural network (CNN) versions to various image classification datasets. The current work makes use of the significant lungs X-ray images from COVID-19 medical datasets. It analyses the models' accuracy by adjusting their parameters such as layer count and activation function in order to identify the ideal parameters for CNN that provide the highest accuracy while classifying images. It evaluated the models' performance on the desired dataset and calculated the F-score, specificity and sensitivity matrices to validate the suggested models, as well as analysing their behaviour as a function of the image type. It achieves an accuracy of 90% for lungs X-rays in the COVID-19 dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Machine learning and deep learning technologies are reshaping the global medical industry at a breakneck pace. Image classification is one of its rapidly expanding fields. It is incorporated into nearly all technologies aimed at achieving intelligent smart health systems. The current paper implements and applies two image classification models based on convolutional neural network (CNN) versions to various image classification datasets. The current work makes use of the significant lungs X-ray images from COVID-19 medical datasets. It analyses the models’ accuracy by adjusting their parameters such as layer count and activation function in order to identify the ideal parameters for CNN that provide the highest accuracy while classifying images. It evaluated the models’ performance on the desired dataset and calculated the F-score, specificity and sensitivity matrices to validate the suggested models, as well as analysing their behaviour as a function of the image type. It achieves an accuracy of 90% for lungs X-rays in the COVID-19 dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Post reporting of deadly virus infecting mankind in city of Wuhan (China) major changes in socio-economic conditions have been encountered. Being reported on 31st December 2019, later named Covid-19 disease has been declared global pandemic on March 11th, 2020 by WHO. Assuming 40-60% people of entire world might get affected due to this virus, lockdown was imposed as an immediate action. This was to curtail transmission of virus through physical contact. This lockdown has shown significant impact on air pollution on a global scale which needs to be analysed for further requirements. It is a known fact that air pollution impacts human respiratory system. Hence analysis of particulate matter and air pollutants post-lockdown and pre-lockdown during Covid pandemic may yield significant results. Even though treatment and prevention of Covid-19 is a big challenge right now, role of nanotechnology should not be ignored. Since nanotechnology is a multidisciplinary and focused field, it is capable of pivoting solutions for problems posed due to this virus and could relieve the excess strained hospitals. Since Covid-19 work on a nanoscale idea of using nanotechnology may offer significant results in the biomedical field that include both diagnostic and therapeutic approaches. In this context an attempt was made to review some of the published results related to the nature of virus and role of nano and microparticles on Covid-19 as well as to analyse particulate matter and air pollutants for a coastal, urban, industrial station in Visakhapatnam India. © 2022 - Kalpana Corporation.
ABSTRACT
Introduction: Coronavirus Disease 2019 (COVID-19), the new contagious novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), pandemic in 2020-21 has had a devastating impact on human race. The most common cause of death among hospitalised patient was COVID-19 pneumonia or lung injury. Various studies have shown diurnal variation in human mortality due to all causes with or without intervention. Aim: To identify existence of diurnal variations for mortality among the hospitalised patients with COVID-19 pneumonia. Materials and Methods: This hospital-record based, retrospective study was conducted in a tertiary referral centre of north east India (Assam Medical College, Dibrugarh, Assam, India) which was a dedicated COVID-19 hospital during the pandemic. The study was conducted from September 2021 to December 2021 and the data was collected and recorded from the Cadaver slips issued to families of patient dying of COVID-19 pneumonia during the period January 2021 to August 2021. The data were generated by plotting the number of deaths of COVID-19 cases for each two hour interval as a percent of the mean number of deaths per two-hour interval and as a percentage of cumulative deaths per two-hour interval on a 24 hour scale. The deaths were sub grouped according to gender, age, and reported co-morbid causes of death along with pneumonia. Comparisons of data i.e., mean deaths/2 hour interval (mean±SD) were performed by one-way Analysis of Variance (ANOVA), followed by Bartlett's test for equal variances. The p-value <0.05 was considered as statistically significant. Results: Total 743 deaths, with 537 males and 206 females were included in the study. Mean age of the deaths was 56.39 years. There was rise of deaths during 4 PM to 6 PM (16:00 to 18:00) interval for all deaths due to COVID-19 pneumonia. The increase in deaths during this period was mainly due to deaths among males equal or above 65 years and females below age 65 years. However, the deaths of females equal or above the age of 65 years did not show significant diurnal variation. Only 26.51% (n=197) of pneumonia deaths were without co-morbidity. Conclusion: There exists a diurnal variation in mortality among COVID-19 pneumonia patients with evening rise of deaths. Diurnal variation is significantly more among males rather than females above 65 years.
ABSTRACT
COVID-19 pandemic is spreading continuously causing serious health problems. Wearing face mask is one of the prominent precautions people can easily follow. In this paper, we have built a model for face-mask detection system using deep learning technique that uses Histogram of Oriented Gradients (HOG) based features for face detection and Convolutional Neural Network (CNN) for detecting whether the person is wearing face mask or not. The model has also the capability of detecting whether the wearer is wearing the face mask properly or not. This model has been trained with 3650 images using python script in Google Colab environment applying Keras and TensorFlow. After a number of trials we have found that our model gives best result with 50 epochs. We have found training and validation accuracy 94.59% and 98.51% respectively. The model has been tested with real time inputs. From the experimental results it has been found that the proposed model is capable of detection faces with-mask and without-mask with 97% accuracy. © 2021 IEEE.
ABSTRACT
Collocated particulate matter concentration data of three stations Vi-sakhapatnam, Amaravathi and Tirupathi belonging to state of Andhra Pradesh in India was analysed for the period 2018 − 2020. These stations were selected based on their geographical, demographical and industrial conditions. Regression analysis was done by taking PM2.5 and PM10 as dependent and N O, CO, SO2, O3, T (Temperature) and RH(Relative Humidity) as independent variables for all three stations along with analysis of seasonal variation. The observed average values of PM2.5 and PM10 concentrations are 84.84 µg/m3 and 106.52 µg/m3 for Visakhapatnam followed by 34.99 µg/m3 and 71.98 µg/m3 for Amaravathi follwed by 24.96 µg/m3 and l57.01 µg/m3 for Tirupathi between January 2018 to Sep-tember 2020. The observed mean values of PM2.5 and PM10 concentrations for Visakhapatnam are 73.37 µg/m3 and 146.52 µg/m3 during winter, 29.7 µg/m3 and 90.48 µg/m3 during summer and l35.11 $µg/m3 and 93.00 µg/m3 during monsoon. Their values for Amaravthi are 61.42 µg/m3 and 108.28 µg/m3 during winter, 20.07 µg/m3 and 63.19 µg/m3 during summer and 18.37 µg/m3 and 45.55 µg/m3 during monsoon. Similarly for Tirupathi these values are 39.65 µg/m3 and 73.07 µg/m3 during winter, 29.22 µg/m3 and 63.08 µg/m3 during summer and 15.11 µg/m3 and 47.07 µg/m3 during monsoon. These observa-tions indicate higher particulate matter concentration during winter season. Summer concentrations should be minimum but slightly more than monsoon which might be due to COVID-19 lock-down during 2020 summer. © 2020, Research Publication. All rights reserved.