Acute Bronchiolitis in Term Newborns Following Relaxation in COVID-19 Appropriate Behavior

Objective Neonatal bronchiolitis is not well characterized. We studied the profile of acute bronchiolitis in term newborns during a respiratory syncytial virus (RSV) surge following relaxation in coronavirus disease 2019 (COVID-19) appropriate behavior.Methods This was a retrospective descriptive study performed in the neonatology division of a tertiary care pediatric hospital at Srinagar, Jammu and Kashmir, India. Term neonates (born at =37 completed gestational weeks) from 7 up to 28 days of life admitted with bronchiolitis over a 1-month period (November 2021) were included.Results Out of total 480 neonatal admissions over a month, 35 (7%) had acute bronchiolitis. Eight neonates were excluded. Out of 27 included neonates, 13 were males. Mean age at presentation was 20 days. All neonates were born at term (=37 completed gestational weeks). Cough (26), rapid breathing (20), and lower chest indrawing (20) were the predominant presenting features. Median SPO2 was 87% (interquartile range 85-92%). Fourteen (52%) neonates needed admission to neonatal intensive care unit. Respiratory support was needed in the form of oxygen through nasal prongs in 24 (89%) newborns. Heated humidified high-flow nasal cannula (HHHFNC) and bubble continuous positive airway pressure were used in five neonates each. Two neonates were mechanically ventilated. The mean duration of the hospital stay was 6.2 days. All neonates survived.Conclusion A series of 27 term neonates with bronchiolitis during an RSV surge is reported in the aftermath of lifting of COVID-19 restrictions. Many of these neonates were sick enough to require significant respiratory support. The outcome was good in all neonates.

Successful ingredients of effective Collaborative Care programs in low- and middle-income countries: A rapid review

Integrating mental health care in primary healthcare settings is a compelling strategy to address the mental health treatment gap in low- and middle-income countries (LMICs). Collaborative Care is the integrated care model with the most evidence supporting its effectiveness, but most research has been conducted in high-income countries. Efforts to implement this complex multi-component model at scale in LMICs will be enhanced by understanding the model components that have been effective in LMIC settings. Following Cochrane Rapid Reviews Methods Group recommendations, we conducted a rapid review to identify studies of the effectiveness of Collaborative Care for priority adult mental disorders of mhGAP (mood and anxiety disorders, psychosis, substance use disorders and epilepsy) in outpatient medical settings in LMICs. Article screening and data extraction were performed using Covidence software. Data extraction by two authors utilized a checklist of key components of effective interventions. Information was aggregated to examine how frequently the components were applied. Our search yielded 25 articles describing 20 Collaborative Care models that treated depression, anxiety, schizophrenia, alcohol use disorder or epilepsy in nine different LMICs. Fourteen of these models demonstrated statistically significantly improved clinical outcomes compared to comparison groups. Successful models shared key structural and process-of-care elements: a multi-disciplinary care team with structured communication;standardized protocols for evidence-based treatments;systematic identification of mental disorders, and a stepped-care approach to treatment intensification. There was substantial heterogeneity across studies with respect to the specifics of model components, and clear evidence of the importance of tailoring the model to the local context. This review provides evidence that Collaborative Care is effective across a range of mental disorders in LMICs. More work is needed to demonstrate population-level and longer-term outcomes, and to identify strategies that will support successful and sustained implementation in routine clinical settings. Copyright © The Author(s), 2023. Published by Cambridge University Press.

Roles of online social media platforms and artificial intelligence in diffusing the impact of COVID-19 as scientists find a cure

Nations worldwide are currently fighting the pandemic of coronavirus disease 2019 (COVID-19) and are facing challenges in disseminating accurate and credible information to the public. During such conditions, people are seeking help from social media and social networking platforms, owing to their speed and reach, for the latest updates on the pandemic. Such alarming situations test the potential of social media and their role in providing assistance to the healthcare community. Current chapter broadly discusses the wide range of contributions made by social media platforms in acting as an information disseminating tool, a tracking tool, and also providing psychological aid to the public to elevate the positive attitude during pandemic. Spread of fake news and misinformation is a drawback faced by these platforms and they are continuously updating their technology to identify and solve this glitch. This chapter throws a brief light on how social media influencers are propitious in conveying information to the society. The chapter aims at encouraging the proper and validated use of social media and social networks in conditions of pandemic like COVID-19. © 2022 Elsevier Inc. All rights reserved.

Prediction of COVID 19 Using Chest X-Ray Images through CNN optimised using Genetic Algorithm

It's been over two years since the novel Coronavirus first appeared and with the constantly evolving new variants found around the world, the havoc of n-Coronavirus seems to be unstoppable. With more than 264 million people being affected by the n-coronavirus as of 2nd December 2021 and 5.2 million deaths around the world, there is a dire need to increase the COVID-19 testing to stop the virus from spreading further. With COVID - 19 devastating the economic situation of various countries across the globe, it has become necessary to come up with a fast, efficient, and inexpensive way to test the presence of the n-Coronavirus in people. However, the methods currently being used to test COVID 19 are rather very expensive and unavailable to a large section of society. One of the most feasible solutions to this problem is through radiological detection i.e., with Chest X - ray images. Contrary to the prevalent testing methods, Chest X - ray scans are much lesser in cost and are readily available. One major problem that arises is that COVID and pneumonia have very similar X-RAY results, so having a binary classification (COVID and NOT COVID) isn't enough. In this paper, we have put forward a model based on Convolutional NN for detection of Pneumonia, COVID - 19, and Normal patients using X - ray photos of Chest. We achieved an AUC score of 90% in our results while classifying the X-Ray Images. Besides Accuracy, we have also made the ROC Curve, confusion matrix, and classification report for our model. To keep our model lightweight, we have used a Genetic Algorithm to get the best hyperparameters possible for the model. © 2022 IEEE.

In silico Analysis of ACE2 Receptor to Find Potential Herbal Drugs in COVID-19 Associated Neurological Dysfunctions

COVID-19 mainly causes the collapse of the pulmonary system thereby causing a dearth of oxygen in the human body. Patients infected with this viral disease have been reported to experience various signs and symptoms associated with brain dysfunction, from the feeling of vagueness to loss of smell and taste to severe strokes. These neurological problems have been reported by younger COVID-19 infected patients mainly in their thirties and forties. Various researchers from around the globe have discerned numerous other brain dysfunctions, such as headache, dizziness, numbness, major depressive disorder, anosmia, encephalitis, febrile seizures, and Guillain-Barre syndrome. The involvement of the CNS by this viral infection has been predicted to be for a longer period of time, even if the patient recovers from COVID-19. The neuronal cell damage caused by COVID-19 is a potent factor responsible for cognitive, behavioral, and psychological problems among its sufferers. The hypoxic conditions can also trigger the formation of beta-amyloid plaques and tau-tangles and thus the virus can even induce Alzheimer's in patients in the near future. The virus affects the brain directly, thereby causing encephalitis. This pandemic has also been shown to have a negative psychological toll on people. This research aims to highlight the brain dysfunction associated with the ACE2 receptor that is known to be a crucial player in the COVID-19 pandemic using genetic networking approaches. Furthermore, we have identified herbal drug candidates that bind to the ACE2 receptor in order to identify potential treatments for the neurological manifestations of COVID-19.

A Survey on Deep Learning Techniques for COVID-19 Detection using Medical Imaging

Covid-19 has disrupted lives throughout the world. It has spread all over the world and detection of the virus is an imperative step in beating the virus. Methods such as the RTPCR and Rapid antigen tests are not only time consuming but also complex and expensive. Since the virus attacks the lungs, the Xray images of the chest can be used for the detection of coronavirus. This paper summarizes as well as gives a detailed study of the research and various techniques used for this subject. Methods used for COVID-19 detection using medical imaging using Chest X-Ray (CXR) and CT scan images as well as role and usage of GANs in tackling this problem have been summarized. © 2022 IEEE.

Bluetooth Camera based Online Examination System with Deep Learning

In recent years with the rising popularity of MOOCs and due to the coronavirus outbreak, the need and demand of online examination solutions has increased tremendously. Online examination posed various challenges of security and fairness. Despite a wide range of research, there exist multiple loopholes in the existing implementation for online based examination systems. This paper aims to implement a reliable mobile based at-home examination system utilising deep learning for cheating detection via object detection models. This paper aims to implement a bluetooth camera device that captures full body images and sends them over HTTPS for cheating detection. © 2022 IEEE.

IMPACT OF SARS-COV-2 PANDEMIC ON THE DIAGNOSIS AND SURGICAL INTERVENTION OF COMMON UROLOGICAL CONDITIONS: RESULTS FROM A MULTI-INSTITUTIONAL DATABASE FROM THE UNITED STATES

INTRODUCTION AND OBJECTIVE: The SARS-CoV-2 (COVID) pandemic threatened access to healthcare, raising concerns that patients were going underdiagnosed and undertreated. The aim of our study was to understand the impact of the COVID pandemic on diagnosis and surgical management of common urological conditions. METHODS: Using a large multi-center electronic health record network (TRINETx) consisting of 46 healthcare organizations, we conducted an epidemiological study investigating the number of patients newly diagnosed with common urological conditions and those undergoing urologic surgeries at yearly intervals from March 1st, 2016 to March 1st, 2021. Relevant international classification of diseases (ICD) codes used to identify urologic conditions are elaborated on in Table 1. Current procedural terminology (CPT) codes used to identify surgeries are detailed in Figure 1. We then determined the percentage of newly diagnosed patients who underwent surgery for each specific year. RESULTS: We saw a decrease in number of all urologic surgeries being performed during the initial year of the pandemic (Figure 1). From March 2020-2021, there was a >20% decrease in surgical case load for benign prostatic hyperplasia procedures (-29.5%), prostate biopsies (-30.1%), incontinence procedures (-33.6%), and vasectomies (-22.8%), compared to the preceding year. Radical cystectomies and orchiectomies saw the lowest decrease, -5.9% and -8.6%, respectively. A similar trend was seen in the number of individuals newly diagnosed with urologic conditions and percentage of patients undergoing surgical intervention. The lowest drops were seen with ureteral stent placements (-5.0%) and prostate biopsies (-3.1%). CONCLUSIONS: The number of people receiving urologic diagnoses and surgical case load for urologic procedures significantly reduced during the first year of the COVID pandemic. Providers should be aware of this healthcare disparity, and greater efforts made to identify these missed patients moving forward.

Construction of a volatility index from exchange-traded dollar-rupee options

Purpose This paper aims to propose the implied volatility index for the US dollar-Indian rupee pair (INRVIX). The study seeks to examine whether INRVIX truly reflects future USDINR (US Dollar-Indian rupee) volatility and signals profitable currency trading strategies. Design/methodology/approach Two measures of INRVIX are constructed and compared: a model-free version based on the methodology adopted by the Chicago Board of Options Exchange (CBOE) and a model-dependent version constructed from Black-Scholes-Merton-implied volatility. The proposed INRVIX is computed by tweaking some parameters of the CBOE methodology to ensure compatibility with the microstructure of the Indian currency derivatives market. The volatility forecasting ability of INRVIX is compared to that of a generalized autoregressive conditional heteroscedasticity (1,1) model. Ordinary least squares regression is used to examine the relationship between n-day-ahead USDINR returns and different quantiles of INRVIX. Findings Results indicate that INRVIX based on the model-free approach reflects ex post volatility in a better manner than its model-dependent counterpart, although neither measure is found to be an unbiased and efficient forecast. Subsample analysis across tranquil and turbulent periods corroborates the results. The volatility forecasting performance of INRVIX is found to be better than that of forecasts based on historical time-series. These results are consistent with similar studies of developed market currencies. The study does not find any significant relationship between extreme levels of INRVIX and the profitability of trading strategies based on such levels, which is contrary to results from the equity options market. Practical implications Foreign exchange volatility affects the costs of international trade and the external sector competitiveness of Indian multinationals. It is a significant risk factor for financial institutions and traders in the financial markets. An implied VIX for the USDINR could serve as an indicator of expected foreign exchange risk. It could thus provide a signal for a possible intervention in the forex market by the regulator. Regulators could introduce volatility derivative contracts based on the INRVIX. Such contracts would enable hedging of the pure volatility risk of dollar-rupee exposure. Thus, the study has practical implications for investors, hedgers, regulators and academicians alike. Originality/value To the author's knowledge, this is one of a few studies to construct an implied VIX for an emerging currency like the rupee. The study is based on up-to-date sample data that includes the recent COVID-19 market crash. A novel contribution of this paper is that in addition to examining whether INRVIX contains information about future USDINR volatility, and it also examines the signalling power of INRVIX for currency trading strategies.

A Lightweight COVID-19 predictive model with Synthetic CT images using Conditional GAN Knowledge Distillation

With the increase in the cases of COVID-19, the necessity of improving testing and treatment is increasing rapidly. Many techniques are currently being used by the medical fraternity for detection of COVID-19 in a patient such as RT-PCR, Chest CT Scan Images, Chest X-Ray scans, etc. Among these techniques, a Chest CT scan has proven to be highly accurate for screening of the novel coronavirus. But a trained professional like a radiologist is needed to analyze the CT scan and determine whether the patient is positive or not. Due to the sudden spike in the number of infections, there is a shortage of such professionals. A machine learning based system can be highly effective in assisting the doctors if it can accurately predict COVID-19 from a chest CT scan. However, the number of chest CT scan images available are very less in order to build an accurate machine learning based predictive model. We present a generative model for data augmentation of COVID-19 positive and negative Chest CT images. We use Conditional DCGAN for generating nearly 1502 COVID-19 positive and 1510 negative images thus extending a publicly available dataset. We also build predictive models using pre-trained models like VGG and ResNet to detect COVID-19, achieving an accuracy upto 87.7%. We also apply the technique of knowledge distillation to build a lightweight and computationally cheap predictive model that has an accuracy of 86.2% and is nearly 11 times smaller than the best model available on the dataset. © 2022 IEEE.

Temporal Unpredictability and Probabilistic Uncertainty Induced Anxiety in the Times of COVID-19 Pandemic

Human beings prefer a predictable and certain environment over an unpredictable and uncertain environment. No one predicted the coronavirus pandemic and it is not certain when the pandemic will come to an end. Pandemics not only affect the physical health but also the mental health of the public. An increase in anxiety and suicide rates has been reported during the previous pandemics and the same trend is also being observed in this current pandemic as well. Pandemics inevitably result in unpredictable and uncertain environments. Many studies have proven that unpredictability and uncertainty increase the levels of stress and anxiety in animals and humans. Coronavirus pandemic has caused many unpredictable and uncertain events resulting in increased confusion, frustration, stress and anxiety among the public. For instance, the institution of lockdowns was unpredictable and it was uncertain when the lockdowns will be unlocked. Similarly, events that unfolded around the initial touting of chloroquine and hydroxychloroquine as a possible therapeutic agent and later lack of evidence in support of these drugs resulted in extreme confusion and frustration. The uncertainty around if a vaccine for coronavirus will be developed and when it will become available for public use has also caused stress and anxiety. Current studies indicate that coronavirus is not just a respiratory virus but it also affects the kidneys, brain, heart and blood vessels. This unpredictable nature of the virus has caused further confusion and frustration. These unpredictable and uncertain events around the current pandemic might have increased the levels of stress and anxiety among the public.

Automated Detection of COVID-19 from X-ray Images using Deep Convolutional Neural Networks

Coronavirus, also known as COVID19, is a dangerous disease that has put many people's lives in jeopardy around the world by damaging the lungs directly. The detection of coronavirus is a challenging medical procedure due to its increasing cases. Currently, the use of x-ray images for coronavirus diagnosis is commonly used. Recently, various deep learning based models have been used for image classification. These models have generated competitive results in terms of feature selection and classification. In this article, we proposed a set of seven pretrained neural network models (VGG16, VGG19, InceptionV3, ResNet50, Xception, DenseNet121 and InceptionResNetV2) for the detection of coronavirus infection using chest X-ray images collected from an open source. It was observed that out of these models, pretrained DenseNet121 yielded highest classification accuracy of 97% for the particular dataset. © 2021 IEEE.

A Survey on COVID-19 Detection from X-ray Images using Deep Learning

Currently, the detection of coronavirus is one of the main challenges in the world. Recent statistics have shown that the total number of cases are increasing exponentially. Existing high-precision diagnostic technologies such as RT-PCRs are expensive and complex. In order to obtain a quick and precise medical diagnosis, X-ray images are commonly used. Detecting positive cases of COVID-19 from x-ray images is really difficult, challenging and susceptible to human error. Various deep learning networks have been used in recent studies for X-ray image classification and have generated competitive results, because stages like feature selection, feature extraction and classification are performed spontaneously in deep learning techniques. This article presents a detailed study of some of recent works for detecting coronavirus from X-ray images with the help of deep learning, a comparative analysis of the methodologies used by them, a comparison of available datasets, and scope of future exploration in this field. © 2021 IEEE.

A Conceptual Enhancement of LSTM Using Knowledge Distillation for Hate Speech Detection

Hate speech is by no means always on the rise due to the high rate of remote service usage such as communication, online studies, meeting, dating, etc. With the recent outbreak of COVID-19, there has been an increase in the number of users on different social media platforms. This increase in number has brought about an increase in issues such as hate speech, among others. This paper aims to provide a detailed process of improving LSTM used for hate speech detection using knowledge distillation. The knowledge transfer is done from the more extensive network (teacher) to the smaller student network. The teacher has trained for five entire epochs to output accuracy of 76.8%, the student network trained from the teacher network for three whole epochs attained an accuracy of 82.6%. Another student model cloned and trained from scratch for three entire epochs instead of the teacher network achieves an accuracy of 75.4%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Generation of COVID-19 Chest CT Scan Images using Generative Adversarial Networks

SARS-CoV-2, also known as COVID-19 or Coronavirus, is a viral contagious disease that is infected by a novel coronavirus, and has been rapidly spreading across the globe. It is very important to test and isolate people to reduce spread, and from here comes the need to do this quickly and efficiently. According to some studies, Chest-CT outperforms RT-PCR lab testing, which is the current standard, when diagnosing COVID-19 patients. Due to this, computer vision researchers have developed various deep learning systems that can predict COVID-19 using a Chest-CT scan correctly to a certain degree. The accuracy of these systems is limited since deep learning neural networks such as CNNs (Convolutional Neural Networks) need a significantly large quantity of data for training in order to produce good quality results. Since the disease is relatively recent and more focus has been on CXR (Chest XRay) images, the available chest CT Scan image dataset is much less. We propose a method, by utilizing GANs, to generate synthetic chest CT images of both positive and negative COVID-19 patients. Using a pre-built predictive model, we concluded that around 40% of the generated images are correctly predicted as COVID-19 positive. The dataset thus generated can be used to train a CNN-based classifier which can help determine COVID-19 in a patient with greater accuracy. © 2021 IEEE.

An analysis on sarcasm detection over twitter during COVID-19

The 2019 Coronavirus (COVID-19) significantly affected our society, the country, and the world. During the Corona time, people mostly spent their time on the internet and actively connected with other people through online social media (OSM) or game chatboxes. Due to extensive use of the internet and social media, the sharing of offensive content increases. OSM provides a platform where people freely express their opinion, emotions, and thoughts. Sometimes people share their feelings and thoughts sarcastically, wherein it signifies the opposite of what it states. Sarcastic content shared by people can vary in many forms, such as videos, images, podcasts, audio, and text. This research mainly focuses on Twitter text data extracted by the Twitter API during COVID-19 and investigates sarcastic content with negative sentiments during COVID-19. We extracted the data with some specific keywords like hashtag-related sarcastic information, sarcasm, irony, etc., and performed an offensive and aggressive nature analysis of people at this stage. We have used the linear support vector classifier (libSVM), Naïve Bayes, and Decision Tree for this analysis. The Decision Tree achieved the highest accuracy as compared to libSVM and Naïve Bayes. It can detect sarcastic content with up to 90% accuracy. © 2021 IEEE.

Clinical and biomarker profile of covid-19 patients admitted in a tertiary care hospital

Introduction and Aim: A novel beta-coronavirus emerged in Wuhan, China during the early December 2019 and spread globally. The clinical signs and symptoms and the disease severity in people infected with COVID-19 can be varied. The present study was conducted to study the biomarker profile and their association with disease severity in COVID-19. Materials and Methods: This was a single-centre Cohort study of data regarding epidemiological, clinical and biomarker parameters, and outcome of COVID-19 patients admitted in a tertiary care hospital in South India. CDC guidelines were followed for assessing disease severity. Results: A total of 336 COVID-19 patients were admitted during the study period. Of these 16 were excluded and 320 cases were analysed. Mean age of patients was 44.82 years. A male predominance was observed. Diabetes mellitus was the most common co-morbidity. Asymptomatic, Mild, moderate, severe and critical disease was seen in 15%, 52.5%, 20.3%, 6.3% and 5.9% patients respectively. ICU care was required in 15.3%. Overall mortality was 5.3%. The mean NLR, ALC, CRP, LCR, LDH, Ferritin and D-dimer in the severe group vs non-severe group were 19.03 vs 4.2, 1025cells/cu mm vs 1740cells/cu mm, 185.8mg/L vs 31.7mg/L, 17.1 vs 996.3, 552.8IU/L vs 252.7IU/L, 2531.9ng/ml vs 414.1ng/ml and 2245.5ng/ml vs 339.4ng/ml respectively. Conclusion: An increased NLR, CRP, LDH, Ferritin and D-dimer and a reduced ALC and LCR are significantly associated with disease severity, need for ICU and mortality. These biomarkers will be useful adjunct to clinical assessment in better categorising and management of COVID-19 patients.

The “super green pathway’’;what have we learned so far? The experience and outcomes of elective urgent and cancer surgery in a district general hospital in the united kingdom during covid-19 pandemic

Background The coronavirus disease (COVID-19) had so far claimed more than 600 000 lives worldwide. Many urgent and elective surgeries were postponed to cope with the pandemic, with the latest data found a substantial postoperative mortality risk (25.6%, 18.9%) after an emergency and elective surgery, respectively. Our institution was one of the first few in the country to offer essential elective surgery using a “COVID-free” designated site during the start of the pandemic. This study aims to analyze the clinical outcomes of patients who underwent essential elective procedures during the virus outbreak in the UK. Methods Retrospective analysis of outcomes of all patients who had undergone urgent elective and cancer surgery, from 30th March 2020 to 21st May 2020, using an implemented “Super Green Pathway.” The primary endpoints were 30 days mortality and COVID-related morbidities, and the secondary end-points were surgically related complications and oncological outcomes. Results A total of 92 patients (Male: 45%;Female: 55%) across 5 surgical specialties were identified. There was no record of mortality in our cohort. Only 1 patient was tested positive for SARS-CoV-2, 18 days after the initial operation without any pulmonary complications. There were 7 postoperative surgical complications managed at the acute hospital site. The waiting time for surgery ranges from 6 to 191 days, mean of 30 days, and a median of 23 days. Conclusion It is possible to mitigate the high mortality risk of post-operative complications associated with COVID-19, with no delay to essential surgeries for cancer patients, thus delivering safe practice during the pandemic.