EVALUATION OF AUTOMATED QUANTIFICATION OF CT PATTERNS ASSOCIATED WITH COVID-19 FROM CHEST CT

Introduction: Computed Tomography (CT) is rapid and sensitive enough to identify COVID-19 pneumonia in its early stages. But because of the disease's high case load, it is difficult for the talented radiologists to report the cases. Therefore, using Artificial Intelligence (AI) to support radiologists' work will be crucial for producing prompt and precise results. Objective: To determine diagnostic effectiveness of AI in identifying different COVID-19 CT patterns and to correlate the AI findings with the findings appreciated by skilled Radiologists. Material and Methods: A prospective study consisting of 500 patients with RT-PCR positive COVID- 19 patients were evaluated, after obtaining informed consent. Data was analysed and represented in the form of frequencies and proportions. Collected data were analysed by Pearson's correlation coefficient (r), Intra Class Correlation (ICC) coefficient, Bland--Altman analysis. Results: AI can assess the severity of disease quickly and with good accuracy compared to manual analysis by decreasing the time taken to analyse the scan by 50%, and overall accuracy of approximately 90%. Conclusion: We conclude that as manual analysis of Chest CT in COVID-19 high case load scenario is comparatively more time-consuming, there is a need for a quick, accurate, and automated technique for identification and quantification of common findings in COVID-19. [ FROM AUTHOR] Copyright of Journal of Cardiovascular Disease Research (Journal of Cardiovascular Disease Research) is the property of Journal of Cardiovascular Disease Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Using Dynamic Models to Showcase Pandemic Prevention Empirical Covid-19

Coronavirus disease (COVID-19) is a viral contagious disease caused by a newly discovered coronavirus. The COVID-19 virus primarily spreads from an infected person through droplets of saliva or nasal discharge when the person coughs or sneezes, and most people who have been infected with the virus usually experience mild to severe respiratory illness, and they recover with minimal or no treatment. COVID-19 causes mild illness in the majority of patients although it can be fatal in rare cases. Our project focuses on using an SPO2 level monitor and thermal scanning to monitor patient health and take precautions to avoid constant transmission, as well as providing support to patients by assisting them with basic needs with the help of food delivery agencies and non-governmental organizations (NGOs) and assisting with prevention. We use an enhanced version of the SIR epidemic model, which is further explained in this work as an IoT-based system which is being used for automated health monitoring and surveillance, this work aims to reveal certain facts about the current situation that are not presented by data, as well as predict and forecast future situations. AI-assisted sensors can be of major help to foresee whether or not someone is tested positive for the virus supported on indicators like body temperature, coughing patterns, and blood oxygen levels. The ability to track people's locations is another helpful function. All these problems collectively checked will make an efficient model to curb the virus. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Antagonism and Evasion of Cellular Innate Immunity by SARS-CoV-2

The replication cycle of severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) shares many features with other human Coronaviruses such as SARS-CoV and Middle East respiratory syndrome Coronavirus (MERS-CoV). Recent studies have elucidated the viral strategies of antagonizing the host immune response, including a multitude of mechanisms by which SARS-CoV-2 can dampen the interferon-mediated innate immunity. Furthermore, an imbalance and delay in interferon production, and exaggerated secretion of pro-inflammatory cytokines contribute to the severe immunopathology of Coronavirus disease 2019 (COVID-19). This chapter summarizes our current understanding of the intimate relationship between SARS-CoV-2 and the host innate and adaptive immune responses. The strategies that the virus utilizes to exploit cellular resources and to evade the innate immune system are described. The chapter provides a detailed discussion of interferonmediated innate immunity, interferon evasion and antagonism by SARSCoV- 2 and human Coronaviruses. © 2023 by World Scientific Publishing Co. Pte. Ltd.

Clinical, Biochemical, and Radiological Presentation of RT-PCR-Positive vs RT-PCR-Negative SARS-CoV-2 Pneumonia Requiring ICU Care: An Observational, Cross-Sectional, Single-Center Study in Kalaburagi, Kalyana Karnataka.

Introduction Studies have reported similar clinical, biochemical, and radiological features between real-time polymerase chain reaction (RT-PCR)-positive and RT-PCR-negative patients. Therefore, the present study aims to assess differences in RT-PCR-positive versus RT-PCR-negative patients' characteristics. Methods We prospectively included 70 consecutive patients with typical coronavirus disease 2019 (COVID-19)-like clinical features who were either RT-PCR-positive or negative, requiring admission to the intensive care unit. The patients were classified into positive and negative RT-PCR groups and evaluated for clinical features, comorbidities, laboratory findings, and radiologic features. Results Fifty-seven point one percent (57.1%; 40/70) were RT-PCR positive, and 42.9% (30/70) were RT-PCR negative patients. The respiratory rate was higher among negative patients (P = 0.02), whereas the mean duration of fever was longer (3.34 vs 2.5; P = 0.022) among positive patients. At presentation, RT-PCR-negative patients had lower saturation of peripheral oxygen (SpO2) (near significant P = 0.058). Evaluation of co-morbidities revealed no differences. The neutrophil/lymphocyte ratio (NLR) (4.57 vs 6.52; P = 0.048), C-reactive protein (CRP) (9.97 vs 22.7; P = 0.007), and serum ferritin (158 vs 248.52; P = 0.010) were higher in patients who tested negative for RT-PCR. Thrombocytopenia (2.42 vs 1.76; P = 0.009), D-dimer levels (408.91 vs 123.06; P = 0.03), and interleukin (IL-6) levels (219.3 vs 80.81; P = 0.04) were significantly elevated among RT-PCR positive patients. The percentage of lung involvement in negative cases was 42.29+/-22.78 vs 36.21+/-21.8 in positive cases (P=0.23). The CT severity score was similar in both cohorts. Conclusion Both RT-PCR-positive and negative patients have similar clinical, biochemical, and radiological features. Considering that we are amidst a pandemic, it is advisable to have a similar approach irrespective of the RT-PCR report and triage and isolate accordingly. We recommend an RT-PCR-negative intensive care unit (ICU) ward and that the treating physician take a call on the management with a holistic approach driven clinically by the laboratory findings and helped by radiological findings. Stressing only on the RT-PCR report for management can be counterproductive.

A layman approach to adopt COVID-19 appropriate behaviour and vaccination: A narrative review

Everyone is aware of the continuing global health catastrophe caused by the advent of a new virus that causes coronavirus disease-2019 (COVID-19). A virus is known as severe acute respiratory syndrome coronavirus-2 is the cause of the viral disease COVID-19 (SARS-CoV-2). The virus was first discovered in bats in Wuhan, Hubei Province, China, in December 2019 and then spread to humans via an unknown intermediary host (animal). The virus can be passed directly from an infected person to a healthy person nearby or indirectly by contact with infected droplets. Fever, sore throat, cough, exhaustion, and dyspnea are the most common symptoms of the condition, while many patients remain asymptomatic. In most cases, the situation is moderate, but it can progress to pneumonia, acute respiratory distress syndrome, and multi-organ failure. Although the transmission rate is high, the fatality rate is 2–3%. The diagnostic method of the disease uses some molecular tests of the samples from an infected person. The preventive measures include using mask, maintaining social distance, home quarantine, and frequent handwashing with soap and sanitizer with a high percentage of alcohol. This review may assist each individual in raising awareness about COVID-19 and make them responsible for battling the pandemic on a personal level to maintain a healthy environment. To control the spreading of the virus, vaccination and the availability of vaccines play an important role. By the quest, various vaccines are in the market and some more are in the trial stage. Hence, different available vaccines are also highlighted. The present review discusses the details regarding the coronavirus's origin, epidemiology, diagnosis, treatment, and vaccination details.

Classification of COVID-19 Patients with their Severity Level from Chest CT Scans using Transfer Learning (preprint)

Background and Objective: During pandemics, the use of artificial intelligence (AI) approaches combined with biomedical science play a significant role in reducing the burden on the healthcare systems and physicians. The rapid increment in cases of COVID-19 has led to an increase in demand for hospital beds and other medical equipment. However, since medical facilities are limited, it is recommended to diagnose patients as per the severity of the infection. Keeping this in mind, we share our research in detecting COVID-19 as well as assessing its severity using chest-CT scans and Deep Learning pre-trained models. Dataset: We have collected a total of 1966 CT Scan images for three different class labels, namely, Non-COVID, Severe COVID, and Non-Severe COVID, out of which 714 CT images belong to the Non-COVID category, 713 CT images are for Non-Severe COVID category and 539 CT images are of Severe COVID category. Methods: All of the images are initially pre-processed using the Contrast Limited Histogram Equalization (CLAHE) approach. The pre-processed images are then fed into the VGG-16 network for extracting features. Finally, the retrieved characteristics are categorized and the accuracy is evaluated using a support vector machine (SVM) with 10-fold cross-validation (CV). Result and Conclusion: In our study, we have combined well-known strategies for pre-processing, feature extraction, and classification which brings us to a remarkable success rate of disease and its severity recognition with an accuracy of 96.05% (97.7% for Non-Severe COVID-19 images and 93% for Severe COVID-19 images). Our model can therefore help radiologists detect COVID-19 and the extent of its severity.

COVID-19 Severity Classification on Chest X-ray Images (preprint)

Biomedical imaging analysis combined with artificial intelligence (AI) methods has proven to be quite valuable in order to diagnose COVID-19. So far, various classification models have been used for diagnosing COVID-19. However, classification of patients based on their severity level is not yet analyzed. In this work, we classify covid images based on the severity of the infection. First, we pre-process the X-ray images using a median filter and histogram equalization. Enhanced X-ray images are then augmented using SMOTE technique for achieving a balanced dataset. Pre-trained Resnet50, VGG16 model and SVM classifier are then used for feature extraction and classification. The result of the classification model confirms that compared with the alternatives, with chest X-Ray images, the ResNet-50 model produced remarkable classification results in terms of accuracy (95%), recall (0.94), and F1-Score (0.92), and precision (0.91).

COVID-19 induced ischemic stroke and mechanisms of viral entry in brain and clot formation: a systematic review and current update.

Background: Coronavirus disease 2019, caused by SARS-CoV-2 (SCV-2) was stated as a pandemic on March 11 2020 by World Health Organization (WHO), and since then, it has become a major health issue worldwide. It mainly attacks the respiratory system with various accompanying complications, including cardiac injury, renal failure, encephalitis and Stroke.Materials and Methods: The current systematic review has been compiled to summarize the available literature on SCV-2 induced ischemic Stroke and its subtypes. Further, the mechanisms by which the virus crosses the blood-brain barrier (BBB) to enter the brain have also been explored. The role of CRP and D-dimer as potent prognostic markers was also explored. The literature search was carried out comprehensively on Google scholar, PubMed, SCOP US, Embase and Cochrane databases by following guidelines.Results: All the studies were reviewed thoroughly by authors and disagreements were resolved by consensus and help of the senior authors. The most common subtype of the IS was found to be large artery atherosclerosis in SCV-2 induced IS. Hypertension emerged as the most significant risk factor. The mechanism resulting in elevated levels of CRP and D-dimer have also been discussed. However, there is a scarcity of definitive evidence on how SCV-2 enters the human brain. The available literature based on various studies demonstrated that SCV-2 enters through the nasopharyngeal tract via olfactory cells to olfactory neurons, astrocytes and via choroid plexus through endothelial cells. Further, disruption of gut-brain axis has been also discussed.Conclusion: Data available in the literature is not adequate to come to a conclusion. Therefore, there is a need to carry out further studies to delineate the possible association between SCV-2 induced IS.

Combined Central Retinal Artery and Vein Occlusion with Ischemic Optic Neuropathy After COVID-19 Vaccination.

PURPOSE: To report a case of combined central retinal vein and artery occlusion that evolved into ischemic optic neuropathy following the Pfizer COVID-19 vaccination. METHODS: Patient was followed with optical coherence tomography (OCT), fluorescein angiography, and Humphrey visual field. RESULTS: Patient was able to recover vision from count fingers to 20/30 on a combination of aflibercept, steroidal and non-steroidal anti-inflammatories, a diuretic (acetazolamide), antiplatelet agents (aspirin and pentoxifylline), and an anticoagulant (apixaban). CONCLUSION: COVID-19 vaccination may be associated with a myriad of sight-threatening ocular thrombotic conditions, which may respond to a combination of anti-inflammatory and anticoagulant therapies.

Classification of COVID-19 on chest X-Ray images using Deep Learning model with Histogram Equalization and Lungs Segmentation (preprint)

Background and Objective: Artificial intelligence (AI) methods coupled with biomedical analysis has a critical role during pandemics as it helps to release the overwhelming pressure from healthcare systems and physicians. As the ongoing COVID-19 crisis worsens in countries having dense populations and inadequate testing kits like Brazil and India, radiological imaging can act as an important diagnostic tool to accurately classify covid-19 patients and prescribe the necessary treatment in due time. With this motivation, we present our study based on deep learning architecture for detecting covid-19 infected lungs using chest X-rays. Dataset: We collected a total of 2470 images for three different class labels, namely, healthy lungs, ordinary pneumonia, and covid-19 infected pneumonia, out of which 470 X-ray images belong to the covid-19 category. Methods: We first pre-process all the images using histogram equalization techniques and segment them using U-net architecture. VGG-16 network is then used for feature extraction from the pre-processed images which is further sampled by SMOTE oversampling technique to achieve a balanced dataset. Finally, the class-balanced features are classified using a support vector machine (SVM) classifier with 10-fold cross-validation and the accuracy is evaluated. Result and Conclusion: Our novel approach combining well-known pre-processing techniques, feature extraction methods, and dataset balancing method, lead us to an outstanding rate of recognition of 98% for COVID-19 images over a dataset of 2470 X-ray images. Our model is therefore fit to be utilized in healthcare facilities for screening purposes.

Implementation of stacking based ARIMA model for prediction of Covid-19 cases in India.

BACKGROUND: Time-series forecasting has a critical role during pandemics as it provides essential information that can lead to abstaining from the spread of the disease. The novel coronavirus disease, COVID-19, is spreading rapidly all over the world. The countries with dense populations, in particular, such as India, await imminent risk in tackling the epidemic. Different forecasting models are being used to predict future cases of COVID-19. The predicament for most of them is that they are not able to capture both the linear and nonlinear features of the data solely. METHODS: We propose an ensemble model integrating an autoregressive integrated moving average model (ARIMA) and a nonlinear autoregressive neural network (NAR). ARIMA models are used to extract the linear correlations and the NAR neural network for modeling the residuals of ARIMA containing nonlinear components of the data. Comparison: Single ARIMA model, ARIMA-NAR model and few other existing models which have been applied on the COVID-19 data in different countries are compared based on performance evaluation parameters. RESULT: The hybrid combination displayed significant reduction in RMSE (16.23%), MAE (37.89%) and MAPE (39.53%) values when compared with single ARIMA model for daily observed cases. Similar results with reduced error percentages were found for daily reported deaths and cases of recovery as well. RMSE value of our hybrid model was lesser in comparison to other models used for forecasting COVID-19 in different countries. CONCLUSION: Results suggested the effectiveness of the new hybrid model over a single ARIMA model in capturing the linear as well as nonlinear patterns of the COVID-19 data.

Identification of COVID-19 prognostic markers and therapeutic targets through meta-analysis and validation of Omics data from nasopharyngeal samples.

BACKGROUND: While our battle with the COVID-19 pandemic continues, a multitude of Omics data have been generated from patient samples in various studies. Translation of these data into clinical interventions against COVID-19 remains to be accomplished. Exploring host response to COVID-19 in the upper respiratory tract can unveil prognostic markers and therapeutic targets. METHODS: We conducted a meta-analysis of published transcriptome and proteome profiles of respiratory samples of COVID-19 patients to shortlist high confidence upregulated host factors. Subsequently, mRNA overexpression of selected genes was validated in nasal swabs from a cohort of COVID-19 positive/negative, symptomatic/asymptomatic individuals. Guided by this analysis, we sought to check for potential drug targets. An FDA-approved drug, Auranofin, was tested against SARS-CoV-2 replication in cell culture and Syrian hamster challenge model. FINDINGS: The meta-analysis and validation in the COVID-19 cohort revealed S100 family genes (S100A6, S100A8, S100A9, and S100P) as prognostic markers of severe COVID-19. Furthermore, Thioredoxin (TXN) was found to be consistently upregulated. Auranofin, which targets Thioredoxin reductase, was found to mitigate SARS-CoV-2 replication in vitro. Furthermore, oral administration of Auranofin in Syrian hamsters in therapeutic as well as prophylactic regimen reduced viral replication, IL-6 production, and inflammation in the lungs. INTERPRETATION: Elevated mRNA level of S100s in the nasal swabs indicate severe COVID-19 disease, and FDA-approved drug Auranofin mitigated SARS-CoV-2 replication in preclinical hamster model. FUNDING: This study was supported by the DBT-IISc partnership program (DBT (IED/4/2020-MED/DBT)), the Infosys Young Investigator award (YI/2019/1106), DBT-BIRAC grant (BT/CS0007/CS/02/20) and the DBT-Wellcome Trust India Alliance Intermediate Fellowship (IA/I/18/1/503613) to ST lab.

Understanding the SARS-CoV-2 virus to mitigate current and future pandemic(s).

Micro-organisms form the first pioneer community in the history of biological life, thought to be present in the primordial soup and evolving later with more complex life-forms. Among micro-organisms, viruses form a separate taxon of organisms. Viruses are obligate parasites, being inactive without a host and becoming active once in contact with specific hosts. Viruses, with an inherent ability to infect and hijack cellular structures, have been utilised as vectors to introduce foreign genetic material in a variety of biological species, e.g. adenoviral vectors. However, viruses have also been the root cause of many infectious diseases, most notable being HIV-AIDS, for its resistance to treatment and omnipresent occurrence. There are many families of viruses like retroviridae, picornaviridae and poxviridae. This review focuses on a specific member of the coronaviridae, the SARS-CoV-2. This virus is responsible for the current COVID-19 pandemic. This review summarises its transmission, molecular mechanism by which it causes disease, associated clinical symptoms and the strategies available to control it from sources like PubMed, Google Scholar, webservers of National Institute of Health (NIH), European Molecular Biology Laboratory (EMBL), World Health Organisation (WHO), United States Food and Drug Administration (USFDA) and Centers for Disease Control and Prevention (CDC) available as on 1st May 2021.

Comparison of Traditional and Hybrid Time Series Models for Forecasting COVID-19 Cases (preprint)

Time series forecasting methods play critical role in estimating the spread of an epidemic. The coronavirus outbreak of December 2019 has already infected millions all over the world and continues to spread on. Just when the curve of the outbreak had started to flatten, many countries have again started to witness a rise in cases which is now being referred as the 2nd wave of the pandemic. A thorough analysis of time-series forecasting models is therefore required to equip state authorities and health officials with immediate strategies for future times. This aims of the study are three-fold: (a) To model the overall trend of the spread; (b) To generate a short-term forecast of 10 days in countries with the highest incidence of confirmed cases (USA, India and Brazil); (c) To quantitatively determine the algorithm that is best suited for precise modelling of the linear and non-linear features of the time series. The comparison of forecasting models for the total cumulative cases of each country is carried out by comparing the reported data and the predicted value, and then ranking the algorithms (Prophet, Holt-Winters, LSTM, ARIMA, and ARIMA-NARNN) based on their RMSE, MAE and MAPE values. The hybrid combination of ARIMA and NARNN (Nonlinear Auto-Regression Neural Network) gave the best result among the selected models with a reduced RMSE, which proved to be almost 35.3% better than one of the most prevalent method of time-series prediction (ARIMA). The results demonstrated the efficacy of the hybrid implementation of the ARIMA-NARNN model over other forecasting methods such as Prophet, Holt Winters, LSTM, and the ARIMA model in encapsulating the linear as well as non-linear patterns of the epidemical datasets.

Comparison of Traditional and Hybrid Time Series Models for Forecasting COVID-19 Cases (preprint)

Background: Time series forecasting methods play a critical role in estimating the spread of an epidemic. The coronavirus outbreak of December 2019 has already infected millions all over the world and continues to spread. Just when the curve of the outbreak had started to flatten, many countries have again started to witness a rise in cases which is now being referred to as the 2nd wave of the pandemic. A thorough analysis of time-series forecasting models is therefore required to equip state authorities and health officials with immediate strategies for future times.Objective: The aims of the study are three-fold: (a) To model the overall trend of the spread; (b) To generate a short-term forecast of 10 days in countries with the highest incidence of confirmed cases (USA, India and Brazil); (c) To quantitatively determine the algorithm that is best suited for precise modelling of the linear and non-linear features of the time series.Comparison: The comparison of forecasting models for the total cumulative cases of each country is carried out by comparing the reported data and the predicted value, and then ranking the algorithms (Prophet, Holt-Winters, LSTM, ARIMA, and ARIMA-NARNN) based on their RMSE, MAE and MAPE values.Result: The hybrid combination of ARIMA and NARNN (Nonlinear Auto-Regression Neural Network) gave the best result among the selected models with a reduced RMSE, which proved to be almost 35.3% better than one of the most prevalent methods of time-series prediction (ARIMA).Conclusion: The results demonstrated the efficacy of the hybrid implementation of the ARIMA-NARNN model over other forecasting methods such as Prophet, Holt-Winters, LSTM, and the ARIMA model in encapsulating the linear as well as non-linear patterns of the epidemical datasets.

Prevalence of Elevated D-dimer Levels in Confirmed COVID-19 Cases in Intensive Care Unit of a Tertiary Care Centre of Western Nepal.

INTRODUCTION: D-dimer is currently the best available marker for COVID-19 associated hemostatic abnormalities. This study aims to find out the prevelance of elevated D-dimer levels in confirmed COVID-19 cases in intensive care unit of a tertiary care hospital of western Nepal. METHODS: This descriptive cross-sectional study was conducted among 95 patients admitted to COVID Intensive Care Unit of a teriary care centre from August 2020 to January 2021 after taking ethical clearence from Institutional Review Committee in order to determine the D-dimer levels in confirmed COVID-19 cases. D-dimer value was measured at the admission and the highest D-dimer value was recorded during the course of hospital stay with the risk of mortality in confirmed COVID-19 cases. The normal range of D-dimer was taken as <0.35 mg/dl as per our hospital laboratory standards. Convenience sampling method was used. Data entry and descriptive analysis were done in Statistical Package for the Social Sciences version 25.0, point estimate at 95% Confidence Interval was calculated along with frequency and proportion for binary data. RESULTS: Out of total 95 cases of COVID-19 included in this study, 25 (89.3%) patients with age ≥ 65 years and 42 (62.69%) patients aged <65 years had elevated D-dimer on admission. Data showed that 29 (67.4%) patients having elevated D-dimer at admission had mortality. CONCLUSIONS: Elevated D-dimer levels was frequently seen in patients admitted in Intensive Care Unit with COVID-19. Our study suggested that measurement of D-dimer may guide in clinical decision making.

High Resolution Computed Tomography of Thorax- Is it an Early Predictor of Hypoxaemia in COVID-19 Patients?

Introduction: Novel Coronavirus-2019 (nCoV-2019) is capable of human-to-human transmission and can lead to acute respiratory distress syndrome similar to Middle East Respiratory Syndrome (MERS) due to lung parenchyma destruction. Some patients with COVID-19 consistently demonstrated no hypoxaemia, however, some patients develop sense of difficulty in breathing due to increased airway resistance. Aim: To assess the potential of High Resolution Computed Tomography (HRCT) thorax as an early predictor of hypoxaemia in COVID-19 patients. Materials and Methods: A prospective longitudinal cohort study of 1000 Reverse Transcription Polymerase Chain Reaction (RT-PCR) confirmed COVID-19 and HRCT thorax positive patients, who were monitored simultaneously for SpO(2) levels, were undertaken. HRCT findings were graded into Computerised Tomography Severity Index (CTSI) and correlated with patient's SpO(2) levels, at the time of scan on admission. Patients, who had normal SpO(2) levels (>= 95%) at the time of initial scan, were monitored upto five days. Pearson's correlation test was used to find correlation between CTSI and SpO(2) levels. Results: In present study group there was male predominance (4:1). Fever was the most common clinical presentation followed by cough. HRCT thorax features were categorised as Typical 769 (76.9%), Indeterminate 176 (17.6%) and atypical 55 (5.5%). 371 (82.8%) patients with SpO(2) >95% were having CTSI between 0-7, similarly 189 (54.4%) patients with SpO(2) 90-94% were having CTSI between 8-15 and 133 (64.8%) patients with SpO(2) <90% were having CTSI between 16-25. So, the present study categorised the patients into three groups-Category 1 (CTSI 0-7), Category 2 (CTSI 8-15) and Category 3 (CTSI 16-25) for better and prompt identification of clinical severity and their management. Majority of patients in CTSI category 1, 2 and 3 were having SpO(2) levels >= 95%, 90-94% and <90%, respectively. Statistical correlation between CTSI and SpO(2) levels at the time of initial scan was significant (Pearson's correlation coefficient (r)=-0.261 and p-value <0.01). Number of patients who developed hypoxaemia (SpO(2) <95%) on follow-up in CTSI Category 1, 2 and 3 were 42 (11.32%), 10 15.87%) and 2 (14.28%), respectively. The association between CTSI and development of hypoxaemia based on follow-up SpO(2) levels was statistically found to be insignificant (chi-square value=1.21, degree of freedom (d.f.) 2 and p-value=0.570). Conclusion: In present study group, a negative correlation was established between CTSI and SpO(2) levels. The association between CTSI and development of hypoxaemia on follow-up SpO(2) monitoring was found to be non-significant statistically. So, HRCT thorax cannot be relied upon as an early predictor of hypoxaemia in COVID-19 patients.

Novel corona virus (COVID-19) pandemic: current status and possible strategies for detection and treatment of the disease.

INTRODUCTION: In December 2019, a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak occurred and caused the coronavirus disease of 2019 (COVID-19), which affected ~ 190 countries. The World Health Organization (WHO) has declared COVID-19 a pandemic on 11 March 2020. AREA COVERED: In the review, a comprehensive analysis of the recent developments of the COVID-19 pandemic has been provided, including the structural characterization of the virus, the current worldwide status of the disease, various detection strategies, drugs recommended for the effective treatment, and progress of vaccine development programs by different countries. This report was constructed by following a systematic literature search of bibliographic databases of published reports of relevance until 1 September 2020. EXPERT OPINION: Currently, the countries are opening businesses despite a spike in the number of COVID-19 cases. The pharmaceutical industries are developing clinical diagnostic kits, medicines, and vaccines. They target different approaches, including repurposing the already approved diagnosis and treatment options for similar CoVs. At present, over ~200 vaccine candidates are being developed against COVID-19. Future research may unravel the genetic variations or polymorphisms that dictate these differences in susceptibilities to the disease.

Implementation of Stacking Based ARIMA Model for Prediction of Covid-19 Cases in India (preprint)

Background: Time-series forecasting has a critical role during pandemics as it provides essential information that can lead to abstaining from the spread of the disease. The novel coronavirus disease, COVID-19, is spreading rapidly all over the world. The countries with dense populations, in particular, such as India, await imminent risk in tackling the epidemic. Different forecasting models are being used to predict future cases of COVID-19. The predicament for most of them is that they are not able to capture both the linear and nonlinear features of the data solely.Methods: We propose an ensemble model integrating an autoregressive integrated moving average model (ARIMA) and a nonlinear autoregressive neural network (NAR). ARIMA models are used to extract the linear correlations and the NAR neural network for modeling the residuals of ARIMA containing nonlinear components of the data.Comparison: Single ARIMA model, ARIMA-NAR model and few other existing models which have been applied on the COVID-19 data in different countries are compared based on performance evaluation parameters.Result:The hybrid combination displayed significant reduction in RMSE(16.23%), MAE(37.89%) and MAPE (39.53%) values when compared with single ARIMA model for daily observed cases. Similar results with reduced error percentages were found for daily reported deaths and cases of recovery as well. RMSE value of our hybrid model was lesser in comparison to other models used for forecasting COVID-19 in different countries.Conclusion: Results suggested the effectiveness of the new hybrid model over a single ARIMA model in capturing the linear as well as nonlinear patterns of the COVID-19 data.