COVID-19 Automatic Indoor Safety Control through Mask, Temperature and Distance Monitoring

The COVID-19 pandemic has affected a variety of aspects of our everyday life. Most activities like entertainment, healthcare, education and businesses have been reshaped due to the safety guidelines. Proper monitoring in indoor areas is essential to limit the spread of COVID-19. This paper presents a low-cost prototype system that addresses the indoor safety issue by combining a mask detector and temperature measurement system with a smart wearable band which alerts people to maintain social distance in close vicinity. The focus is on ensuring safe distance, wearing a mask, and no entry for people with high temperatures. Firstly, the mask and temperature system has an Arduino NANO that works as the primary device. The Arduino is connected with an ESP32-Cam that sends the image to a client where we have trained and developed a machine learning model using thousands of masked and unmasked pictures. Following, the model uses an image classification algorithm with the tensorflow.js model and gives us the result with an accuracy percentage. Secondly, the temperature is measured with the help of an MLX90614 non-contact sensor. The temperature of a person is also shown on the monitor at of. Finally, a wearable device is presented with a NodeMCU 8266 Wi-Fi module. It uses Received Signal Strength Indicator (RSSI) value to detect another similar device and alerts through a vibrator and buzzer if the social distance rules are violated. We evaluated the system in real-life scenarios, and the mask detection system gives an average accuracy of 98.7%. We have presented an in-depth analysis of the Mask Detection System, showing different mask types, the accuracy of the machine learning algorithm, temperature measurements and results. Similarly, the distance measurement system is presented with several factors. © 2022 IEEE.

Adverse Effects of COVID-19 Vaccination: Machine Learning and Statistical Approach to Identify and Classify Incidences of Morbidity and Postvaccination Reactogenicity.

Good vaccine safety and reliability are essential for successfully countering infectious disease spread. A small but significant number of adverse reactions to COVID-19 vaccines have been reported. Here, we aim to identify possible common factors in such adverse reactions to enable strategies that reduce the incidence of such reactions by using patient data to classify and characterise those at risk. We examined patient medical histories and data documenting postvaccination effects and outcomes. The data analyses were conducted using a range of statistical approaches followed by a series of machine learning classification algorithms. In most cases, a group of similar features was significantly associated with poor patient reactions. These included patient prior illnesses, admission to hospitals and SARS-CoV-2 reinfection. The analyses indicated that patient age, gender, taking other medications, type-2 diabetes, hypertension, allergic history and heart disease are the most significant pre-existing factors associated with the risk of poor outcome. In addition, long duration of hospital treatments, dyspnoea, various kinds of pain, headache, cough, asthenia, and physical disability were the most significant clinical predictors. The machine learning classifiers that are trained with medical history were also able to predict patients with complication-free vaccination and have an accuracy score above 90%. Our study identifies profiles of individuals that may need extra monitoring and care (e.g., vaccination at a location with access to comprehensive clinical support) to reduce negative outcomes through classification approaches.

COVID-19: Vaccine Hesitancy in Africa and the way Forward

COVID-19 pandemic took the world by storm in late 2019, scientists and health authorities across the globe struggle to contain the deadly virus. Socio-economic activities across the globe were partly halted as countries around the world introduce various forms of restrictions to contain the spread of the COVID-19 virus. Most developing countries' economies, especially in Africa, slid into recession, unemployment among Africa countries skyrocketed to an all-time high, and famine and starvation were beginning to knock harder on poorer nations around the world. The race to develop a vaccine was pressing harder;developed countries continue to pump more money to help develop a vaccine within the shortest period of time, as that seems the only viable solution to the economic downturn of the global world. Finally, vaccines were developed and proved to have high efficacy. This has helped reverse the negative trend of the global economy caused by the COVID-19 pandemic. This vaccine faced a lot of global scrutinies;people have refused to get vaccinated and have also rejected the idea of making COVID-19 vaccination compulsory for citizens worldwide. This study analyzes the challenges posed by this ugly trend of COVID-19 vaccine hesitancy in African countries, its socio-economic consequences and the way forward.

Volatility and correlation of Islamic and conventional indices during crises

We examine the correlation and volatility of Islamic indices and their conventional counterparts during the 2008–2009 Global Financial Crisis as well as the Covid-19 pandemic. We provide evidence that the volatility of Islamic indices is relatively lower than that of conventional peers during turmoil periods. Consistent with the decoupling hypothesis, our results indicate that the volatility of Islamic and the volatility of conventional indices tend to move in tandem in tranquil times but diverge in times of crises. Our results also indicate that the correlation between Islamic and conventional indices is a priced risk factor for Islamic index returns. © 2022 Elsevier Inc.

SARS-CoV-2 Antibody Response in Patients with Co-morbidities in Kashmir's Ethnic Population: An Observational Cohort Study

Introduction: Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) infection risks in co-morbid patients are still unknown two years after the pandemic began. The prevalence of antibodies against SARS-CoV-2 infection is crucial for determining disease preventive and mitigation strategies. Obesity, type 2 diabetes, and chronic cardiovascular disease can raise the risk of Coronavirus Disease-2019 (COVID-19), which has a greater morbidity and fatality rate. Aim(s): To determine the seroprevalence of SARS-CoV-2 (COVID-19) antibodies and their relationship to co-morbidities in Kashmir's ethnic population. Material(s) and Method(s): The present observational cohort study was done in the Department of Pulmonary Medicine at Chest Disease Hospital Srinagar, Jammu and Kashmir, India, from September 2020 to September 2021 and 1,846 co-morbid unvaccinated patients were chosen for the study. As per standard methodology, a cohort study was undertaken, a questionnaire was prepared, and demographic and associated parameters were recorded. All participants had their immune profiles tested, and the existence of Immunoglobulin G (IgG) antibodies for SARS-CoV-2 was determined using the chemiluminisence immunoassay technique. Chi-square and Fischer exact test were used for stastical analyses and p-value <0.05 were taken as statistically significant. Result(s): As per the present study estimates, demographic and socio-economic characteristic affected test attendants. The SARS-CoV-2 IgG antibody response among co-morbid patients were found to be 54.3%. The hypertension and diabetes were most prevalent co-morbidity found in the individuals (p<0.001). Conclusion(s): Co-morbidities including hypertension and diabetes in an individual are more likely have COVID-19 which can lead to death. COVID-appropriate conduct is required to limit infection transmission in the community, and immunisation is of paramount importance for all individuals. More research is needed to determine the risk of co-morbidities among Kashmir's ethnic community. Copyright © 2022 Journal of Clinical and Diagnostic Research. All rights reserved.

Antibody Responses to an Additional Dose of SARS-CoV-2 mRNA Vaccine in Solid Organ Transplant Recipients with and without HIV

Purpose: Solid organ transplant (SOT) recipients mount suboptimal immune responses to a two-dose SARS-CoV-2 mRNA vaccine series. Data regarding antibody responses in HIV and SOT remains limited. We characterized spike binding antibody responses before and after an additional mRNA vaccine dose in SOT recipients, including in people with HIV (PWH). Method(s): Spike binding antibody titers were assessed before and one month after an additional vaccine dose using a quantitative ELISA. An additional vaccine dose was defined as a third dose of a mRNA vaccine primary series, as recommended by the CDC. Result(s): Antibody titers were assessed in 64 SOT recipients (58% kidney, 34% liver, 8% other). Participants had a median age of 57 and 47% were women. PWH comprised 14% of the cohort (9/64, 78% kidney). 70% (45/64) of SOT recipients developed antibodies after a two-dose vaccine series (62% kidney, 33% liver). The additional dose was given a median of 169 days (IQR 144.75-185.75 days) after the second vaccine dose, and 72% received three doses of BNT162b2 (Pfizer-BioNTech) while 28% received three doses of mRNA-1273 vaccine (Moderna). The median time between transplantation and an additional vaccine dose was 2.8 years (IQR, 0.6-8.9). 32% (6/19) of SOT recipients who had no detectable antibody seroconverted after receiving an additional vaccine dose. The 45 participants who were seropositive prior to the third dose displayed a median 4.4-fold increase in antibody titers. SOT recipients with HIV had comparable antibody responses to those without HIV. Conclusion(s): Our data indicate that SOT recipients benefit from an additional SARS-CoV-2 mRNA vaccine dose. SOT recipients with and without HIV appear to mount comparable antibody responses upon vaccination, although larger numbers are needed.

Prediction of Type II Diabetes Using Machine Learning Approaches

Diabetes is a major threat all over the world. It is rapidly getting worse day by day. It is found that about 90% of people are affected by type 2 diabetes. Now, in this COVID-19 epidemic there is a terrible situation worldwide. In this situation, it is very risk to go hospital and check diabetes properly. In this era of technology, several machine learning techniques are utilized to evolve the software to predict diabetes more accurately so that doctors can give patients proper advice and medicine in time, which can decrease the risk of death. In this work, we tried to find an efficient model based on symptoms so that people can easily understand that they have diabetes or not and they can follow a proper food habit which can reduce the risk of health. Here, we implement four different machine learning algorithms: Decision tree, Naïve Bayes, Random Forest, and K-Nearest Neighbor. After comparing the performance by using different parameter, the experimental results showed that Naïve Bayes algorithm performed better than other algorithms. We find the highest 90.27% accuracy from Naïve Bayes algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

WE-Net: An Ensemble Deep Learning Model for Covid-19 Detection in Chest X-ray Images Using Segmentation and Classification

Amidst the increasing surge of Covid-19 infections worldwide, chest X-ray (CXR) imaging data have been found incredibly helpful for the fast screening of COVID-19 patients. This has been particularly helpful in resolving the overcapacity situation in the urgent care center and emergency department. An accurate Covid-19 detection algorithm can further aid this effort to reduce the disease burden. As part of this study, we put forward WE-Net, an ensemble deep learning (DL) framework for detecting pulmonary manifestations of COVID-19 from CXRs. We incorporated lung segmentation using U-Net to identify the thoracic Region of Interest (RoI), which was further utilized to train DL models to learn from relevant features. ImageNet based pre-trained DL models were fine-tuned, trained, and evaluated on the publicly available CXR collections. Ensemble methods like stacked generalization, voting, averaging, and the weighted average were used to combine predictions from best-performing models. The purpose of incorporating ensemble techniques is to overcome some of the challenges, such as generalization errors encountered due to noise and training on a small number of data sets. Experimental evaluations concluded on significant improvement in performance using the deep fusion neural network, i.e., the WE-Net model, which led to 99.02% accuracy and 0.989 area under the curve (AUC) in detecting COVID-19 from CXRs. The combined use of image segmentation, pre-trained DL models, and ensemble learning (EL) boosted the prediction results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

An Observational Study to Evaluate Single Deepest Vertical Pocket (SDP) and Color Doppler Indices (CDI) with Severity of Covid in Pregnant Women with COVID-19

Aim: The present study was done for the assessment of the single deepest vertical pocket (SDP) and color Doppler indices (CDI) among COVID positive pregnant women to decide upon further management. Materials and method: A total of 102 patients were enrolled. The data collection was done by a single examiner following the protocol for the study. The study included confirmed COVID-19 positive pregnant patients. The inclusion standards were singleton pregnancy along with gestational age 28 weeks onward. Patients were divided on the basis of symptoms into asymptomatic and symptomatic subjects. Symptomatic subjects were further divided into mild, moderate, and severely symptomatic on the basis of established COVID guidelines. For the comparison of categorical variables, the analysis of Chi-square has been used. Results: The computation of Chi-square exhibited that the distribution of single deepest vertical pocket (SDP) score did not differ between asymptomatic and symptomatic subjects. Abnormal Doppler findings in the umbilical artery were found to be significantly more among subjects with severe COVID-19 symptoms compared to women with mild to moderate symptoms. Among women with abnormal Doppler, there were 6.3% vaginal and 93.7% cesarean deliveries. Mean APGAR at 1 minute and 5 minutes after birth was found to be 5.05 ± 1.09 and 3.16 ± 0.92 in asymptomatic and 5.24 ± 1.24 and 3.40 ± 1.02 in symptomatic patients, respectively. In symptomatic subjects, NICU admission was significantly more (54.0%) compared to asymptomatic subjects (16.0%). Conclusion: As the research has indicated that COVID-19-infected pregnant women may experience rapid and increasing placental insufficiency, it appears that a comprehensive assessment and management of the mother is required. © The Author(s). 2022 Open Access.

Anxiety, Depression, and Their Contributing Factors among Nurses Infected with COVID-19 in Iran: A Cross-sectional Study

Background: The coronavirus disease 2019 (COVID-19) epidemic is a great challenge among healthcare workers, especially nurses, due to their more frequent and closer contact with patients. Objectives: This study aimed to evaluate anxiety, depression, and their causes among nurses with COVID-19 infection in different wards of a hospital. Methods: This cross-sectional study was carried out between February 1st to October 30th, 2020, among all nurses with COVID-19 infection in different wards of Imam Khomeini Hospital (university hospital), Tehran, Iran. The nurses were contacted by phone, and data were collected using a self-administered, valid, structured questionnaire consisting of sociodemographic characteristics and clinical symptoms. Psychological complications, including anxiety and depression levels, were assessed and their association with other sociodemographic variables was also evaluated. Results: A total of 158 nurses were entered into the study, out of which 112 (70.2%) cases were females. Among all participants, 72.8% and 42.4% of the subjects reported anxiety and depression, respectively. The frequency of moderate to severe anxiety was significantly greater in women than in men (P<0.001). The infected nurses who worked in low-risk departments experienced a greater proportion of moderate to severe depression (P=0.004). In addition, the most prevalent reason for anxiety and depression was found to be the fear of infecting family members. Conclusion: Nurses bear a significant psychological burden during the COVID-19 pandemic, markedly when they get infected and experience clinical symptoms. Therefore, the government and other staff should provide some facilities and supportive administrative work for reducing anxiety and depression and improving nurses' psychological health.

Psychological Assessment of Health-Care Workers Working during COVID-19 Pandemic Condition in Bangladesh

The study was aimed to assess the psychological aspects and relevant factors of the health-care workers (HCWs) working in COVID 19 pandemic condition in Bangladesh. This online cross-sectional survey was conducted from different tertiary, secondary and primary hospitals in Bangladesh. Eligible 638 HCWs who were directly involved in the caring of confirmed or suspected COVID-19 patients were recruited in this study. The mental health was assessed by the Patient Health Questionnare-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7) and Athens Insomnia Scale (AIS). High frequency of depression 536(84.0%), anxiety 386(60.5%) and insomnia 302(47.3%) was found among the HCWs, which were significantly higher in physicians (p<0.001) than nurses. Moderate to severe depression was significantly higher in female, whereas minimal to mild depression was significant in male HCWs (p=0.014). Symptoms of depression (p<0.001), anxiety (p<0.001) and insomnia (p=0.004) were significantly higher among the HCWs of primary and secondary compared to the tertiary level. The HCWs developed psychological trauma due to family health (45.3%) and contagious disease property (66.6%). After adjusting confounders, multivariable logistic regression analysis showed that physicians and HCWs of secondary hospital had significant symptoms of severe depression (OR=2.95, 95% CI=0.50-17.24;p<0.001), anxiety (OR=2.64, 95% CI=0.80-8.72;p<0.001) and insomnia (OR=2.67, 95% CI=1.23-5.84;p=0.018);whereas female HCWs had more risk of developing symptoms of severe insomnia (OR= 1.84;95% CI=1.23-2.75;p=0.003). High rate of depression, anxiety and insomnia was found among HCWs working in the COVID-19 pandemic condition in this survey.

Anticoagulation is the answer in treating noncritical COVID-19 patients.

All autopsy studies demonstrated widespread thrombosis and alveolar-capillary microthrombi as the cause of death among patients with COVID-19. The autopsy studies are the gold-standard for diagnostic accuracy and therapeutic strategies for any clinical scenarios. The author initially observed that patients already taking therapeutic dose of oral direct factor Xa inhibitors for an unrelated reason, have significantly better survival rates than those not taking any anticoagulants. This influenced the author to conduct a retrospective chart review of the hospitalized patients in Jackson Hospital (Alabama) to evaluate the effect of variable doses of anticoagulation among COVID-19 patients. The study found that serum inflammatory bio-marker D-dimer trends are associated with changes in oxygen requirement among patients with COVID-19, if patients present at an early stage, and titration of Enoxaparin (anticoagulation) dose based on D-dimer trends leads to increased patient survival.

Effects of Bacille Calmette Guerin (BCG) vaccination during COVID-19 infection.

The coronavirus disease 2019 (COVID-19) is caused by the infection of highly contagious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as the novel coronavirus. In most countries, the containment of this virus spread is not controlled, which is driving the pandemic towards a more difficult phase. In this study, we investigated the impact of the Bacille Calmette Guerin (BCG) vaccination on the severity and mortality of COVID-19 by performing transcriptomic analyses of SARS-CoV-2 infected and BCG vaccinated samples in peripheral blood mononuclear cells (PBMC). A set of common differentially expressed genes (DEGs) were identified and seeded into their functional enrichment analyses via Gene Ontology (GO)-based functional terms and pre-annotated molecular pathways databases, and their Protein-Protein Interaction (PPI) network analysis. We further analysed the regulatory elements, possible comorbidities and putative drug candidates for COVID-19 patients who have not been BCG-vaccinated. Differential expression analyses of both BCG-vaccinated and COVID-19 infected samples identified 62 shared DEGs indicating their discordant expression pattern in their respected conditions compared to control. Next, PPI analysis of those DEGs revealed 10 hub genes, namely ITGB2, CXCL8, CXCL1, CCR2, IFNG, CCL4, PTGS2, ADORA3, TLR5 and CD33. Functional enrichment analyses found significantly enriched pathways/GO terms including cytokine activities, lysosome, IL-17 signalling pathway, TNF-signalling pathways. Moreover, a set of identified TFs, miRNAs and potential drug molecules were further investigated to assess their biological involvements in COVID-19 and their therapeutic possibilities. Findings showed significant genetic interactions between BCG vaccination and SARS-CoV-2 infection, suggesting an interesting prospect of the BCG vaccine in relation to the COVID-19 pandemic. We hope it may potentially trigger further research on this critical phenomenon to combat COVID-19 spread.

No Rumours Please! A Multi-Indic-Lingual Approach for COVID Fake-Tweet Detection

The sudden widespread menace created by the present global pandemic COVID-19 has had an unprecedented effect on our lives. Man-kind is going through humongous fear and dependence on social media like never before. Fear inevitably leads to panic, speculations, and spread of misinformation. Many governments have taken measures to curb the spread of such misinformation for public well being. Besides global measures, to have effective outreach, systems for demographically local languages have an important role to play in this effort. Towards this, we propose an approach to detect fake news about COVID-19 early on from social media, such as tweets, for multiple Indic-Languages besides English. In addition, we also create an annotated dataset of Hindi and Bengali tweet for fake news detection. We propose a BERT based model augmented with additional relevant features extracted from Twitter to identify fake tweets. To expand our approach to multiple Indic languages, we resort to mBERT based model which is fine tuned over created dataset in Hindi and Bengali. We also propose a zero shot learning approach to alleviate the data scarcity issue for such low resource languages. Through rigorous experiments, we show that our approach reaches around 89% F-Score in fake tweet detection which supercedes the state-of-the-art (SOTA) results. Moreover, we establish the first benchmark for two Indic-Languages, Hindi and Bengali. Using our annotated data, our model achieves about 79% F-Score in Hindi and 81% F-Score for Bengali Tweets. Our zero shot model achieves about 81% F-Score in Hindi and 78% F-Score for Bengali Tweets without any annotated data, which clearly indicates the efficacy of our approach. © 2021 IEEE.

COVID-19 Prevention: Role of Activated Carbon

Recently, Coronavirus Disease 2019 (COVID-19) has brought the whole world into a pandemic condition, where the number of infected cases and deaths is exponentially high. A number of vaccines are available for this novel virus, but these are in the preliminary stage and are also not available to everyone. As the virus is very contagious, protection and prevention are the best way to survive and get rid of this disease. The virus affects the human body by entering through the nose, mouth, and eyes, so face protection with an appropriate mask is highly advisable. Combined masks made with activated carbon (AC) can effectively adsorb the virus because of its high surface area and broad functional groups. Such combined masks can also control coronavirus transmission by capturing harmful gases and smoke as they help in decreasing the spread of the virus.

Integrative resource for network-based investigation of COVID-19 combinatorial drug repositioning and mechanism of action.

An effective monotherapy to target the complex and multifactorial pathology of SARS-CoV-2 infection poses a challenge to drug repositioning, which can be improved by combination therapy. We developed an online network pharmacology-based drug repositioning platform, COVID-CDR (http://vafaeelab.com/COVID19repositioning.html), that enables a visual and quantitative investigation of the interplay between the primary drug targets and the SARS-CoV-2-host interactome in the human protein-protein interaction network. COVID-CDR prioritizes drug combinations with potential to act synergistically through different, yet potentially complementary, pathways. It provides the options for understanding multi-evidence drug-pair similarity scores along with several other relevant information on individual drugs or drug pairs. Overall, COVID-CDR is a first-of-its-kind online platform that provides a systematic approach for pre-clinical in silico investigation of combination therapies for treating COVID-19 at the fingertips of the clinicians and researchers.

Machine Learning Approaches to Identify Patient Comorbidities and Symptoms That Increased Risk of Mortality in COVID-19.

Providing appropriate care for people suffering from COVID-19, the disease caused by the pandemic SARS-CoV-2 virus, is a significant global challenge. Many individuals who become infected may have pre-existing conditions that may interact with COVID-19 to increase symptom severity and mortality risk. COVID-19 patient comorbidities are likely to be informative regarding the individual risk of severe illness and mortality. Determining the degree to which comorbidities are associated with severe symptoms and mortality would thus greatly assist in COVID-19 care planning and provision. To assess this we performed a meta-analysis of published global literature, and machine learning predictive analysis using an aggregated COVID-19 global dataset. Our meta-analysis suggested that chronic obstructive pulmonary disease (COPD), cerebrovascular disease (CEVD), cardiovascular disease (CVD), type 2 diabetes, malignancy, and hypertension as most significantly associated with COVID-19 severity in the current published literature. Machine learning classification using novel aggregated cohort data similarly found COPD, CVD, CKD, type 2 diabetes, malignancy, and hypertension, as well as asthma, as the most significant features for classifying those deceased versus those who survived COVID-19. While age and gender were the most significant predictors of mortality, in terms of symptom-comorbidity combinations, it was observed that Pneumonia-Hypertension, Pneumonia-Diabetes, and Acute Respiratory Distress Syndrome (ARDS)-Hypertension showed the most significant associations with COVID-19 mortality. These results highlight the patient cohorts most likely to be at risk of COVID-19-related severe morbidity and mortality, which have implications for prioritization of hospital resources.

COVID-19 era: challenges and solutions in dental education

Coronavirus infecious disease 2019 (COVID-19) pandemic has brought a myriad of challenges to the dental education. Amidst the quarantine and lockdown measures, face-to-face education is disrupted around the globe. Dental educators have come up with innovative solutions to resume dental education remotely. Different online platforms are being utilised for didactic teaching and learning as well as for students' assessment and examination. Clinical learning has also shifted to virtual learning. Manikins and virtual reality/augmented reality (VR/AR)-based simulation devices along with haptic technology can be very helpful for skills training. However, some sorts of blended learning and virtual curriculum may be incorporated in dental education in the future. For this narrative review, a thorough in-depth review of the available literature, relevant to our field, was carried out. In this article, impact of COVID-19 on dental education has been discussed along with some solutions to these challenges.

Bioinformatics and system biology approaches to identify the diseasome and comorbidities complexities of SARS-CoV-2 infection with the digestive tract disorders.

Coronavirus Disease 2019 (COVID-19), although most commonly demonstrates respiratory symptoms, but there is a growing set of evidence reporting its correlation with the digestive tract and faeces. Interestingly, recent studies have shown the association of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection with gastrointestinal symptoms in infected patients but any sign of respiratory issues. Moreover, some studies have also shown that the presence of live SARS-CoV-2 virus in the faeces of patients with COVID-19. Therefore, the pathophysiology of digestive symptoms associated with COVID-19 has raised a critical need for comprehensive investigative efforts. To address this issue we have developed a bioinformatics pipeline involving a system biological framework to identify the effects of SARS-CoV-2 messenger RNA expression on deciphering its association with digestive symptoms in COVID-19 positive patients. Using two RNA-seq datasets derived from COVID-19 positive patients with celiac (CEL), Crohn's (CRO) and ulcerative colitis (ULC) as digestive disorders, we have found a significant overlap between the sets of differentially expressed genes from SARS-CoV-2 exposed tissue and digestive tract disordered tissues, reporting 7, 22 and 13 such overlapping genes, respectively. Moreover, gene set enrichment analysis, comprehensive analyses of protein-protein interaction network, gene regulatory network, protein-chemical agent interaction network revealed some critical association between SARS-CoV-2 infection and the presence of digestive disorders. The infectome, diseasome and comorbidity analyses also discover the influences of the identified signature genes in other risk factors of SARS-CoV-2 infection to human health. We hope the findings from this pathogenetic analysis may reveal important insights in deciphering the complex interplay between COVID-19 and digestive disorders and underpins its significance in therapeutic development strategy to combat against COVID-19 pandemic.

A Web-Based System for Efficient Contact Tracing Query in a Large Spatio-Temporal Database

In this demonstration, we present a web based system for the novel contact tracing query (CTQ) that finds users who have come into direct contact with the query user or indirect contact via the already contacted users from a large spatio-temporal database. The CTQ is of paramount importance in the era of new COVID-19 pandemic world for identifying people who came into close spatial and temporal proximity with persons carrying an infectious disease. We demonstrate a multi-level index named QzR-tree, that considers the space coverage and the co-visiting patterns of the trajectories to group users who are likely to meet. More specifically, we use a quadtree to partition user movement traces along with a linear ordering and use the space-time mapping to group users with an R-tree. We develop a web-based demo system to show the effectiveness of the QzR-tree for the CTQ. The web-based system essentially uses a PostgreSQL database to store user trajectories, and indexes these trajectories using the QzR-tree, and finally uses a web interface to take user query and display the results in a map. © 2020 Owner/Author.