An efficient lung disease classification from X-ray images using hybrid Mask-RCNN and BiDLSTM

Lung diseases mainly affect the inner lining of the lungs causing complications in breathing, airway obstruction, and exhalation. Identifying lung diseases such as COVID-19, pneumonia, fibrosis, and tuberculosis at the earlier stage is a great challenge due to the availability of insufficient laboratory kits and image modalities. The rapid progression of the lung disease can be easily identified via Chest X-rays and this serves as a major boon for the terminally ill patients admitted to Intensive Care Units (ICU). To enhance the decision-making capability of the clinicians, a novel lung disease prediction framework is proposed using a hybrid bidirectional Long-Short-Term-Memory (BiDLSTM)-Mask Region-Based Convolutional Neural Network (Mask-RCNN) model. The Crystal algorithm is used to optimize the scalability and convergence issues in the Mask-RCNN model by hyperparameter tuning. The long-range dependencies for lung disease prediction are done using the BiDLSTM architecture which is connected to the fully connected layer of the Mask RCNN model. The efficiency of the proposed methodology is evaluated using three publicly accessible lung disease datasets namely the COVID-19 radiography dataset, Tuberculosis (TB) Chest X-ray Database, and National Institute of Health Chest X-ray Dataset which consists of the images of infected lung disease patients. The efficiency of the proposed technique is evaluated using different performance metrics such as Accuracy, Precision, Recall, F-measure, Specificity, confusion matrix, and sensitivity. The high accuracy obtained when comparing the proposed methodology with conventional techniques shows its efficiency of it in improving lung disease diagnosis. Copyright © 2022 Elsevier Ltd

Covid19 Vaccination and Lifestyle Counsel using Machine Learning algorithms

Machine Learning is a predominant area in Artificial Intelligence. It gets the ability to make predictions by learning the past observed values and information. This learning process is Machine Learning. A large amount of data is accessed and processed to gain more accurate results. Nowadays anyone around the world can use any Machine Learning algorithm to obtain competitive and accurate results. The main objective of this project is to recommend the Life style modification of the people after covid19 and to predict whether the particular person needs for the vaccination intake or not by accessing thousands of patient details. Hence the accuracy rate is very high compared to other predicting processes. These techniques are used to predict the current health conditions of the people. © 2022 IEEE.

Epistaxis management on COVID-19-positive patients: Our early case experience and treatment.

Epistaxis management on COVID-19 patients is concerning for otolaryngologists due to the highly virulence and increased concentration within respiratory droplets and nasal secretions. Authors suggest initial management with oxymetazoline nasal drops and local pressure before considering nasal packing with absorbable material to prevent COVID-19 transmission to surrounding healthcare workers.

Understanding the activating mechanism of the immune system against COVID-19 by Traditional Indian Medicine: Network pharmacology approach.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) transmissions are occurring rapidly; it is raising the alarm around the globe. Though vaccines are currently available, the evolution and mutations in the SARS-CoV-2 threaten available vaccines' significance. The drugs are still undergoing clinical trials, and certain medications are approved for "emergency use" or as an "off-label" drug during the pandemic. These drugs have been effective yet accommodating side effects, which also can be lethal. Complementary and alternative medicine is highly demanded since it embraces a holistic approach. Since ancient times, natural products have been used as drugs to treat various diseases in the medical field and are still widely practiced. Medicinal plants contain many active compounds that serve as the key to an effective drug design. The Kabasura kudineer and Nilavembu kudineer are the two most widely approved formulations to treat COVID-19. However, the mechanism of these formulations is not well known. The proposed study used a network pharmacology approach to understand the immune-boosting mechanism by the Kabasura kudineer, Nilavembu kudineer, and JACOM in treating COVID-19. The plants and phytochemical chemical compounds in the Kabasura kudineer, Nilavembu kudineer, and JACOM were obtained from the literature. The Swiss target prediction algorithm was used to predict the targets for these phytochemical compounds. The common genes for the COVID-19 infection and the drug targets were identified. The gene-gene interaction network was constructed to understand the interactions between these common genes and enrichment analyses to determine the biological process, molecular functions, cellular functions, pathways involved, etc. Finally, virtual screening and molecular docking studies were performed to identify the most potential targets and significant phytochemical compounds to treat the COVID-19. The present study identified potential targets as ACE, Cathepsin L, Cathepsin B, Cathepsin K, DPP4, EGFR, HDAC2, IL6, RIPK1, and VEGFA. Similarly, betulinic acid, 5″-(2⁗-Hydroxybenzyl) uvarinol, antofine, (S)-1'-methyloctyl caffeate, (Z)-3-phenyl-2-propenal, 7-oxo-10α-cucurbitadienol, and PLX-4720 collectively to be potential treatment agents for COVID-19.

Pulmonary disease diagnosis using African vulture optimized weighted support vector machine approach

Pulmonary disease is a kind of disease that affects the lungs and other parts of the respiratory system and is mainly caused by smoking, asbestos, secondhand smoke, and other forms of air pollutants. Several types of pulmonary diseases are emphysema, fibrosis, pneumothorax, asthma, lung cancer, chronic obstructive pulmonary disease (COPD), and so on. Pulmonary diseases are otherwise known as lung disorders and respiratory diseases. Pulmonary diseases are predicted by several methods using x-ray images and CT scan images. Some of the recent works predict only a specific disease, and optimal prediction is not yet achieved. Hence, we proposed a novel method known as African vulture optimization (AVO) algorithm-based weighted support vector machine approach (w-SVM). The proposed method in this article predicts emphysema, fibrosis, pneumothorax, and normal kinds from the NIH chest x-ray dataset. The X-images are preprocessed after data acquisition to obtain the desired size and to remove undesirable noise. The preprocessed images are then sent into the SVM for feature extraction, and the AVO is used to improve the SVM so that a kernel function may be obtained. The proposed w-SVM effectively predicts the emphysema, fibrosis, pneumothorax, and normal classes from the dataset. The experimental analyses are conducted and compared with existing works and concluded that the proposed work outperforms other approaches in terms of accuracy, sensitivity, specificity, and Matthews's correlation coefficient, prediction time, and modeling time.

Siddha Medicine and Computer Modeling: A Treasure for SARS-CoV-2 Treatment

The SARS-CoV-2 was identified in December 2019 and spread quickly around the globe. Around 218 countries with 61,468,916 cases have been diagnosed as of November 27, 2020. The epidemic quarantine and symptomatic care plan control are the first step of the treatment in the absence of medicines and vaccines. The need for treatments/therapeutics is in high demand. Pharma companies are working around the clock to develop these treatments/therapeutics. Several traditional medicines are also followed as the treatments/therapeutics across the globe. In India, the traditional medicine system ranks to be one of the topmost promising treatments/therapeutics for ages. In COVID-19, the medicinal products for treatment, prophylaxis, and convalescence were listed based on the Siddha Medicine advisory provided by the Ministry of AYUSH. These drugs are recommended for the treatment and prophylaxis of symptoms. In reality, however, these medications have been in vogue for infectious diseases like Dengue and Chikungunya for the past two decades. In parallel, the in silico studies are positively helping in the drug discovery and unravel the drug mechanisms. The computational protein modeling techniques also play a significant role in identifying all the reference genome's proteins. This chapter discussed Siddha and Quarantine's traditional insight in viral diseases, Virus-based drug repurposing for coronaviruses, and various treatment, including significant drug repurposing and BSAA combination therapy. We have also used computational modeling techniques to identify and model the individual protein structures from the whole genome of SARS CoV-2. Finally, this chapter will explain the steps taken to develop and repurpose Kabasura Kudineer as a drug to inhibit the COVID-19 pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Demystifying Excess Immune Response in COVID-19 to Reposition an Orphan Drug for Down-Regulation of NF-κB: A Systematic Review.

The immunological findings from autopsies, biopsies, and various studies in COVID-19 patients show that the major cause of morbidity and mortality in COVID-19 is excess immune response resulting in hyper-inflammation. With the objective to review various mechanisms of excess immune response in adult COVID-19 patients, Pubmed was searched for free full articles not related to therapeutics or co-morbid sub-groups, published in English until 27.10.2020, irrespective of type of article, country, or region. Joanna Briggs Institute's design-specific checklists were used to assess the risk of bias. Out of 122 records screened for eligibility, 42 articles were included in the final review. The review found that eventually, most mechanisms result in cytokine excess and up-regulation of Nuclear Factor-κB (NF-κB) signaling as a common pathway of excess immune response. Molecules blocking NF-κB or targeting downstream effectors like Tumour Necrosis Factor α (TNFα) are either undergoing clinical trials or lack specificity and cause unwanted side effects. Neutralization of upstream histamine by histamine-conjugated normal human immunoglobulin has been demonstrated to inhibit the nuclear translocation of NF-κB, thereby preventing the release of pro-inflammatory cytokines Interleukin (IL) 1ß, TNF-α, and IL-6 and IL-10 in a safer manner. The authors recommend repositioning it in COVID-19.

The Impact of Various Policy Factors Implemented for Controlling the Spread of COVID-19.

More than sixty million cases were affected by the novel corona virus around the world till date. The virus has reached more than 200 countries and more than seven lakh people have lost their lives globally so far. To control the spread of this virus many countries have taken extreme measures but still couldn't control the spread. The primary objective of this analysis is to classify the various policy factors adopted by the countries to manage the spread of Covid-19. Our study uses Oxford Covid-19 Government Response Tracker (OxCGRT) dataset and Autoregressive Integrated Moving Average (ARIMA) model as the model for forecasting. The representation is trained using day wise number of infected cases reported in each country from August'2020 to October'2020 and then forecasts the number of infections for five days from 15th November' 2020 to 19th November'2020. We have included 15 countries in our study and analysed 13 factors which includes 8 factors in Containment and Closure policies category, 2 factors in Economic policies category and 3 factors in Health System policies category. We analysed the impact of above factors by comparing the forecasted number of affected people with the actual total diseased cases reported in those five days. The study discovers the fact that out of thirteen policy factors, the countries which concentrated more on policies in economic category during the pandemic have helped in controlling the dissemination of covid-19.