A serum interleukin-6-based analysis of patients with nasopharyngeal swab reverse-transcriptase polymerase chain reaction negative for SARS-CoV-2 infection with CO-RADS 4 and 5 on computed tomography of the chest

Background: Serum interleukin 6 (IL-6) levels have been studied in the diagnostic evaluation of patients with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) disease (COVID-19). Methods: We studied the utility of treatment with tocilizumab in COVID-19 patients (n=19) with a negative nasopharyngeal swab real time reverse transcriptase polymerase chain reaction (RT-PCR) test for SARS-CoV-2 who had suggestive computed tomography (CT) findings, namely, COVID-19 Reporting and Data System (CO-RADS) 4,5. Results: Receiver operator characteristic (ROC) curve analysis showed that serum IL 6 at a cut-off of >56.9 pg/L was a predictor of mortality in nasopharyngeal swab RT-PCR negative patients with suggestive CT findings. Tocilizumab had no significant effect on the mortality. Conclusions: In nasopharyngeal swab RT-PCR negative patients with suggestive chest CT findings, elevated serum IL-6 levels > 56.9 pg/L predicted mortality. However, treatment with tocilizumab had no effect on mortality.

An Insight into Association Between COVID-19 Infection and ABO Blood Group

Coronavirus disease 2019 (COVID-19), a pandemic that is triggered by a novel coronavirus, named severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) or 2019-nCoV, causes primarily respiratory discomfort along with other mild symptoms/no symptoms, leading to severe illness and death, if proper care is not taken. At present, COVID-19 is the resilient reason for a large number of human casualties worldwide as well as a cause of crucial economic loss posturing global threat. There is a necessity of intensive research to elucidate the pathogenic mechanisms of COVID-19, which would assist in understating the susceptibility towards the infection as well as prompt development of effective prevention and treatment strategies. Over the years, clinical studies have indicated the risk of various pathogenic infections prejudiced due to preexisting chronic diseases as well as ABO blood group types to a larger extent. In line of this, current COVID-19 infection-associated clinical studies intensely endorse the relationship of blood group type of individual and risk of COVID-19 infection. In this chapter, various clinical studies from January 2020 to June 2020 have been summarized to highlight the eminence of ABO blood group and COVID-19 infection susceptibility in human population. These reports evidently support the fact that individuals with A histo blood group were found to be more vulnerable to COVID-19 infection whereas individuals with blood group O were less likely to get infected with virus. To get deeper insight in this fact, many more studies are desirable in order to further explicate the promising protective role of the blood group O and it will be supportive for designing and planning several additional countermeasures against COVID-19 infection. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2021.

Design and Implementation of a Microstrip Array for WiFi External Antenna

The wireless communication system very essential technology and have significant use after corona virus effect the world very badly. The Wi-Fi technology exhibits good wireless communication to provide internet facility but suffers with low antenna gain. This novel array proposed method with different dielectric material properties is used to enhancement the gain of the Wi-Fi antenna. The operating frequency of the proposed antenna is at 2. 5GHZ. This proposed method consist of Teflon dielectric material with dielectric constant of 2.02 has the gain of 8.4dbi, return loss of -30db and VSWR is 1.85, with loss tangent 0.0002. This proposed method compares with different dielectric material like kapton and fr-4 substrate but Teflon exhibit the good results. This proposed method work good for PCB antennas and flexible and wearable antennas with kapton substrate. © 2022 IEEE.

Marching Ahead through the Pandemic: Continuing Anesthesia Services in COVID Era-Our Experience from a Tertiary Care Cancer Centre

Introduction This paper aims to provide an overview of the administrative and clinical preparations done in a tertiary care cancer hospital in continuing operation theatre (OT) services through the COVID pandemic.Methods Retrospective data collection, data for the past 1.5 years (COVID period) March 2020 to August 2021 were compared to surgical output for a similar duration of time before the COVID era (September 2018-February 2020).Results A total of 1,022 surgeries were done under anesthesia in the COVID period as against 1,710 surgeries done in a similar time frame in the pre-COVID era. Overall, we saw a 40%drop in the total number of cases. Thorax, abdominal, and miscellaneous surgeries (soft tissue sarcomas, urology, and gyneconcology) saw a maximum fall in numbers;however, head and neck cases saw an increase in numbers during the pandemic. Surgical morbidity and mortality were similar in the COVID and pre-COVID era. No cases of severe COVID infection were reported among the healthcare staff working in OT.Discussion We could successfully continue our anesthesia services with minimal risk to healthcare staff throughout the pandemic by adopting major guidelines in a pragmatic and practical approach with minor changes to suit our setup.

QACM: Quality Aware Crowd Sensing in Mobile Computing

Mobile computing is one of the significant opportunities that can be used for various practical applications in numerous fields in real life. Due to inherent characteristics of ubiquitous computing, devices can gather numerous types of data that led to innovative applications in many fields with a unique emerging prototype known as Crowd sensing. Here, the involvement of people is one of the important features and their mobility provides an exclusive opportunity to collect and transmit the data over a substantial geographical area. Thus, we put forward novel idea about Quality of Information (QOI) with unique parameters with opportunistic uniqueness of people's mobility in terms of sensing and transmission. Additionally, we propose some of the viable improved ideas about the competent opportunistic data collection through efficient techniques. This work also considered some of the open issues mentioned by previous related works.

A Decision Support System using Machine Learning -IoT to tackle the Covid-19 pandemic in Uttarakhand

Covid-19 spread is worldwide;India is now at the second place where this epidemic is spreading with high rate. The state of Uttarakhand, a hilly state of India also has a significant impact of Covid-19. This paper suggests that machine learning techniques with IOT can equipped the doctors, and lab technicians to deal with this pandemic. Here, we also design a prediction system to help the doctors so that they can keep the records of infected patients. We used IoT, machine learning and ensemble methods for healthcare to store infected patient's data in the cloud database, and enable doctors/others to screen patient's data about their disease. We developed a decision support system to detect the diseases quickly and the treatment can be initiated immediately. © 2022 IEEE.

Leveraging health information helpline to sustain health-care delivery during the COVID pandemic: Experiences from a State in Eastern India.

Background: The unprecedented demands on health-care systems due to the COVID-19 pandemic made countries including India to switch toward alternative modes of health-care delivery. Objectives: The aim of this study was to describe the various COVID-related services delivered through 104 health information helpline (HIHL), located in Jharkhand during the pandemic. Methods: The de-identified secondary data from February 2020 to December 2021 related to COVID services delivered through HIHL were analyzed. Results: There was a significant increase in the COVID call volume during the first as well as the second wave. The HIHL has been able to guide the callers on testing, home isolation and follow-up, home-based teleconsultation, vaccination, timely referral, and support with logistic-related information. Conclusion: The 104 HIHL has played a critical role in sustaining health-care delivery during the pandemic, combating the "infodemic" and guiding the general public by providing authentic information.

Histologic Assessment of Thromboemboli Due to Plaque Rupture, Plaque Erosion, or COVID-19 Microthrombi.

Plaque rupture, plaque erosion, and COVID-19 infection can cause acute coronary syndromes (ACS). We illustrate case examples demonstrating the distinctive and characteristic pathologic findings underlying each of these various causes of acute myocardial infarction. A deeper understanding of the pathophysiology of ACS is necessary for the development of newer agents and techniques to improve outcomes after ACS. (Level of Difficulty: Advanced.).

Researching COVID to enhance recovery (RECOVER) autopsy study protocol: Rationale, objectives, and design (preprint)

Importance: SARS-CoV-2 infection can result in ongoing, relapsing, or new symptoms or organ dysfunction after the acute phase of infection, termed Post-Acute Sequelae of SARS-CoV-2 (PASC), or long COVID. The characteristics, prevalence, trajectory and mechanisms of PASC are poorly understood. The objectives of the Researching COVID to Enhance Recovery (RECOVER) Tissue Pathology Study (RECOVER-Pathology) are to: (1) characterize prevalence and types of organ injury/disease and pathology occurring with PASC; (2) characterize the association of pathologic findings with clinical and other characteristics; (3) define the pathophysiology and mechanisms of PASC, and possible mediation via viral persistence; and (4) establish a post-mortem tissue biobank and post-mortem brain imaging biorepository. Methods: RECOVER-Pathology is a cross-sectional study of decedents dying at least 15 days following initial SARS-CoV-2 infection. Eligible decedents must meet WHO criteria for suspected, probable, or confirmed infection and must be aged 18 years or more at the time of death. Enrollment occurs at 7 sites in four U.S. states and Washington, DC. Comprehensive autopsies are conducted according to a standardized protocol within 24 hours of death; tissue samples are sent to the PASC Biorepository for later analyses. Data on clinical history are collected from the medical records and/or next of kin. The primary study outcomes include an array of pathologic features organized by organ system. Casual inference methods will be employed to investigate associations between risk factors and pathologic outcomes. Discussion: RECOVER-Pathology is the largest autopsy study addressing PASC among US adults. Results of this study are intended to elucidate mechanisms of organ injury and disease and enhance our understanding of the pathophysiology of PASC.

A Static Machine Learning Based Evaluation Method for Usability and Security Analysis in E-Commerce website

Measurement of e-commerce usability based on static quantities variable is state-of-the-art because of the adoption of sequential tracing of the next phase in the categorical data. An offline static model is trained. A static model is trained offline. In other words, we train the model once and then use it for a set period of time. The global COVID-19 outbreak has completely disrupted society and drastically altered daily life. The concept refers to an electronic commerce network that appears with thorough, understandable conviction, demand, and rapid confirmation as a replacement for the economic market’s "brick-and-mortar" model, which replaces how we do everything, including business strategy, and provides a better understanding with the interpretation of e-commerce features. This study was supervised to analyses usability assessments using statistical methods, as well as security assessments using online e-commerce security scanner tools, in order to investigate e-business standards that take into account the caliber of e-services in e-commerce websites across Asian nations. The method was developed to optimize complex systems based on multiple criteria. The initial (supplied) weights are used to determine the compromise ranking list and compromise solution. This paper examines the usability of e-commerce in rural areas using a new data set from the Jharkhand region. On the e-commerce websites of Jharkhand, India, usability is commonly considered in conjunction with learnability, memorability, effectiveness, engagement, efficiency, and completeness. Using a user-oriented questionnaire testing method, this survey attempts to close the gaps mentioned above. Then, across each column, divide each value by the column-wise sum that is created using their corresponding value, whichever produces a new matrix B. Finally, determine the row-wise sum of matrix B that represents the (3 X 1) matrix. Using model trees and bagging, this study addresses classification-related issues. This regression technique is useful for problems involving classification. The model is trained using secondary data from the MBTI 16 personality factors affecting personality category. Author

Modern Mortuary Complex Plan in Covid-19 Era

The COVID-19 pandemicis the most burning health emergency worldwide now a days and all health professionals are called to give support in the diagnosis, treatment of patients and management of dead bodies affected by this disease. Different modes of transmission of corona virus is well established, however aerosol generating procedure has significant importance for mortuary during autopsy. The risk of infection can be minimized by using standard universal precautions for autopsy dissection procedure, applying high bio-safety levels, using negative pressure and laminar flow system for ventilation, UV irradiation for sterilization, virtual autopsy etc. The aim can be achieved by constructing the mortuary complex in more planned way like area division, ventilation planning, environmental disinfection, individual protection, autopsy procedure, virtual autopsy, preservation and transportation of dead body and waste disposal management. The present manuscript describes in detail about modern mortuary complex during the pandemic era of covid-19 © 2022, Journal of Indian Academy of Forensic Medicine.All Rights Reserved.

AACO: Aquila Anti-Coronavirus Optimization-Based Deep LSTM Network for Road Accident and Severity Detection

Globally, traffic accidents are of main concern because of more death rates and economic losses every year. Thus, road accident severity is the most important issue of concern, mainly in the undeveloped countries. Generally, traffic accidents result in severe human fatalities and large economic losses in real-world circumstances. Moreover, appropriate, precise prediction of traffic accidents has a high probability with regard to safeguarding public security as well as decreasing economic losses. Hence, the conventional accident prediction techniques are usually devised with statistical evaluations, which identify and evaluate the fundamental relationships among human variability, environmental aspects, traffic accidents and road geometry. However, the conventional approaches have major restrictions based on the assumptions regarding function kind and data distribution. In this paper, Aquila Anti-Coronavirus Optimization-based Deep Long Short-Term Memory (AACO-based Deep LSTM) is developed for road accident severity detection. Spearman's rank correlation coefficient and Deep Recurrent Neural Network (DRNN) are utilized for the feature fusion process. Data augmentation method is carried out to improve the detection performance. Deep LSTM detects the road accident and its severity, where Deep LSTM is trained by the designed AACO algorithm for better performance. The developed AACO-based Deep LSTM model outperformed other existing methods with the Mean Square Error (MSE), Root-Mean-Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) of 0.0145, 0.1204 and 0.075%, respectively. [ FROM AUTHOR] Copyright of International Journal of Pattern Recognition & Artificial Intelligence is the property of World Scientific Publishing Company 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.)

Design and Development of Polymer-Based Optical Fiber Sensor for GAIT Analysis

In the present scenario like COVID-19 pandemic, to maintain physical distance, the gait-based biometric is a must. Human gait identification is a very difficult process, but it is a suitable distance biometric that also gives good results at low resolution conditions even with face features that are not clear. This study describes the construction of a smart carpet that measures ground response force (GRF) and spatio-temporal gait parameters (STGP) using a polymer optical fiber sensor (POFS). The suggested carpet contains two light detection units for acquiring signals. Each unit obtains response from 10 nearby sensors. There are 20 intensity deviation sensors on a fiber. Light-emitting diodes (LED) are triggered successively, using the multiplexing approach that is being employed. Multiplexing is dependent on coupling among the LED and POFS sections. Results of walking experiments performed on the smart carpet suggested that certain parameters, including step length, stride length, cadence, and stance time, might be used to estimate the GRF and STGP. The results enable the detection of gait, including the swing phase, stance, stance length, and double supporting periods. The suggested carpet is dependable, reasonably priced equipment for gait acquisition in a variety of applications. Using the sensor data, gait recognition is performed using genetic algorithm (GA) and particle swarm optimization (PSO) technique. GA- and PSO-based gait template analyses are performed to extract the features with respect to the gait signals obtained from polymer optical gait sensors (POGS). The techniques used for classification of the obtained signals are random forest (RF) and support vector machine (SVM). The accuracy, sensitivity, and specificity results are obtained using SVM classifier and RF classifier. The results obtained using both classifiers are compared. © 2023 Mamidipaka Hema et al.

BBACTFM (Blockchain Based Accurate Contact Tracing Framework Model) for Tourism Industry

Contact tracing is, at its core, a straightforward and logical operation. The testing facility or healthcare facility that performed the test notifies the neighborhood public health department if a person tests positive for any communicable disease (such as COVID-19, omicron). The health department then contacts the sick person and asks them to list any contacts they had for a predetermined amount of time before to the test. The health department then gets in touch with these people to request a test. The process is repeated until all of the patients have tested negative if any of those patients have a positive result. Patients who test positive for a disease are typically informed of how long they must spend in quarantine and are publicly notified after they "recover.” Blockchain technology can anonymize patient records and offer higher degrees of protection to maintain the privacy of tourist health details. Since test results are connected to a visitor's public/private key rather than their Personal ID, their name or identity cannot be determined by simply browsing or querying the blockchain. In addition, since every blockchain entry is cryptographically signed, malicious parties are unable to alter a record without also invalidating all future records. Additional controls can be added using identity access management software to restrict what data a user can read on the blockchain. The shortcomings of conventional contact tracing applications would be resolved by our proposed framework, dubbed BBACTFM (Blockchain Based Accurate Contact Tracing Framework Model for Tourism Industry). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COVID-19 vaccines and homoeopathy - An integrative review

Background: The COVID-19 pandemic has brought millions of people at risk. Clinical trials of several known antiviral drugs and vaccines have been done. Homoeopathy is known to be effective in the prevention and treatment of infectious diseases as well as in overcoming some adverse effects of vaccination. Objectives: The objectives of this review were to identify the adverse effects of COVID-19 vaccination from the published literature and doing homoeopathic repertorisation to find out indicated remedies for alleviating the ill effects of COVID-19 vaccines. Methods: Published literature was searched for 'Covid-19', 'Vaccination' and 'Adverse events' in PubMed between January 2020 and July 2021. The related symptoms pertaining to known adverse effects were picked and repertorised with appropriate rubrics from the 'Vaccination' chapter of Murphy's clinical repertory and synthesis repertory. Results: Repertorial totality of identified symptoms was analysed. Thuja scored high when symptoms related to the adverse effects of COVID-19 vaccines were repertorised according to Murphy's and synthesis repertories. Indications of the common remedies for the ill-effects identified for COVID-19 vaccination are discussed. Conclusion: Vaccines against nCoV-19 are still not fully researched, especially for their long-term effects. Hence, whether they would prove to be friends or foes to humankind, in the long run, is the arena of future research. Meanwhile, homoeopathy can lend its services to overcome the ill effects of the vaccination. Studies with rigorous designs to find out the efficacy of homoeopathic medicines in relieving the adverse effects of vaccination are suggested. © 2022 The authors.

Understanding the Role of HDL during COVID-19 Infection

Background: Recently emerged COVID-19 pandemic has caused a large number of deaths with lacs of confirmed cases worldwide posturing a grim situation and severe threat to public health. There is an imperative necessity of analyzing emerging clinical and laboratory data of COVID-19 pa-tients, which may contribute to elucidate the pathogenic mechanism and development of effective prevention and treatment countermeasures. Method(s): Under this article, the emerging role of High-Density Lipoprotein (HDL) was analyzed by collecting recently published articles related to this field having clinical data of COVID-19 patients. Result(s): Based on the recently published reports of laboratory-confirmed COVID-19 infected hospitalized patients it was consistently observed that levels of HDL were low at the time of admission to hospi-tal and remained relatively low during the disease course i.e., treatment, recovery, and discharge stage. It was also reported critically that levels of HDL in the patients, those did not survive, decreased continu-ously until death. Conclusion(s): These clinical reports of patients have risen the concern about probable infection and worsen the clinical outcome of a healthy person having a compromised level of HDL for COVID-19 infection. Eventually, these findings stated that there is a strong association of low HDL levels with a higher risk of COVID-19 infection and further severity of the illness. Proper attention is needed to understand the significance of altered quantity and quality of HDL in COVID-19 patients compared to healthy controls, so that appropriate therapies could be given at the right time to combat severity and mortality due to this infection.Copyright © 2021 Bentham Science Publishers.

Identification of potential Akt activators: A ligand and structure-based computational approach (preprint)

The Akt pathway plays an important role in cell metabolism, growth, proliferation, and survival. Akt is the central protein whose phosphorylation controls many downstream pathways. In many diseases like Alzheimer’s, Parkinson, and diabetes, there is downregulation of the Akt pathway. It is proven that the binding of small molecules to the PH domain of Akt facilitates its phosphorylation in the cytoplasm. In the current study, to identify Akt activators, ligand-based approaches like fingerprint-based 2D-QSAR, shape, and pharmacophore-based screening were used, followed by structure-based approaches like docking, MM-GBSA, ADME prediction, and MD simulation. Using the 2D-QSAR activity of the Asinex gold platinum database was predicted, and the top twenty-five molecules found to be active using most models were selected for shape-based and pharmacophore-based screening. Later docking was performed using the PH domain of Akt1 (PDB: 1UNQ), and 197105, 261126, 253878, 256085, and 123435 were selected based on docking score and interaction with Lys 14, Arg 23, Arg 25, Asn 53, and Arg 86. The selected molecules were druggable and formed a stable protein-ligand complex. MD simulations of 261126 and 123435 showed better stability and interaction with key residues. To further investigate the SAR of 261126 and 123435, derivates were downloaded from PubChem, and structure-based approaches were employed. The MD simulation of derivates 12289533, 12785801, 83824832, 102479045, and 6972939 was performed in which 83824832 and 12289533 showed interaction with key residues for a longer duration of time. Therefore, 83824832 and 12289533 may act as Akt activators, and further in-vitro and in-vivo experiments must be performed to support the study.

Self-Supervised Contrastive Learning for Covid-19 Classification from Computed Tomography Images

Computer-aided diagnosis (CAD) emerges as an exhaustive diagnostic tool in the Covid-19 pandemic outbreak and is enormously investigated for automatic and more accurate detections. Artificial intelligence (AI) based radiographic images (Computed Tomography, X-Ray, Lung Ultrasound) interpretation improves the overall diagnosis efficiency of Covid-19 infections. In this paper, CAD based deep meta learning approach has been discussed for automatically quick analysis of chest computed tomography (CT) images regarding the early detection of corona virus (Covid-19) presence inside a subject. We incorporated a self-supervised contrastive-learning neural network for unbiased feature representation and classifications using fine-tuned pre-trained Inception module on 28203 chest CT images. This trainable multi-shot end-to-end deep learning architecture is validated on public dataset of normal and covid-19 CT images obtaining normalized accuracy of 0.9708. Results verify our model to be able enough to assist radiologists and specialists in screening and correct diagnosis of Covid-19 patients in less span of time. © 2022 IEEE.

Sustainability of Start-Ups during Pandemic: An Analytical Study in Reference of Indian Perspective

Every growing country needs a pattern of industry development with cumulative growth year on year, every government tries to establish the industries for the development of the country but it requires lots of initiative and efforts, for this they need the energetic mind of entrepreneurship thinking. As India is having a huge consumer market with great potential of industry development, Indian conditions are most appropriate for the start-ups with various factors. Indian start-ups entrepreneurship sector has shown a great impact on country economic growth since last few years, this contribution to the Indian economy is still sustained. Though Indian economy is a promising economy & Indian government is looking for 3 trillion economies in near future but what about the start-ups economic health after pandemic of COVID 19, Is it still promising or there is any effect. This research paper is focused on the analysis of economic health of start-ups before & after of COVID 19. The research objective is to identify the condition of existing start-ups & the initiation of new ventures. The Exploratory research method is used for in-depth research of said objectives. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.