Pharmacotherapeutic and Computational Approaches for Biopharmaceutical Considerations towards Drug Development and Delivery against COVID-19

The novel coronavirus disease (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), affected millions of people worldwide at an alarming rate. Moreover, the development of vaccines is still hope, but its camouflage mutations during transmission are still a challenge. In the dire condition of this pandemic, drug repurposing with the exploitation of computational modeling has become the cynosure to repurpose the already existing drugs such as remdesivir, Favipiravir, dexamethasone, and other drugs at clinical levels. Furthermore, their safety and efficacy against COVID-19 remain a challenge in different age groups and populations with preexisting conditions like heart disease, hepatic and renal impairment, pregnancy, and immunocompromised states. Moreover, computational modeling allows studying physiological and biochemical parameters on drug transport, delivery, and therapeutic efficacy of dosage forms. This review explicitly provides a comprehensive account of the challenges and opportunities for developing physiologically based pharmacokinetic models (PBPK) and pharmacodynamic(PD) models to establish a therapeutic dosage regimen based on dose selection, safety, and efficacy. We also highlight the pharmacologic targeting strategies for ACE receptors, toxicity concerns, combination therapy, and drug-drug interactions for different repurposed drugs against COVID-19. In dreadful scenarios, PBPK and PD models hold promise for human PK and dose prediction in COVID-19, along with paving new horizons to improve the therapeutic as well as immuno-therapeutic efficacy using nano-drug delivery approaches, computer-aided drug design (CADD), and speed up clinical trials with a better understanding of quantitative in vitro to in vivo extrapolation (QIVIE) and established PK data. © 2022 by the authors.

An Optimal Model Selection for COVID 19 Disease Classification

In the current scenario, the pandemic created by coronavirus is on the boom, and that is why it becomes very critical to control and cure this disease. The currently available technique for coronavirus disease 2019 (COVID-19) testing, i.e., reverse transcription polymerase chain reaction (RT-PCR), turns out to be a lot of time-consuming and requires modern labs, equipment, and highly trained medical staff that are rare to get. Chest computerized tomography (CT) is however available with a lot of ease, and it will be fruitful if these machines are used for COVID-19 testing. During this pandemic, there is an absolute need for an efficient and readily accessible way for COVID-19 patients classification, and CT is one of the best ways to do so. Keeping that in fact, this chapter introduces a study for understanding which deep learning models give the best result when classifying COVID-19 patients using chest CT images. For this study, ResNet 50, ResNet 101, DenseNet 121, DenseNet 169, and DenseNet 201 are compared with each other on the basis of classification accuracy, and it has been observed that DenseNet 169 has the tendency to yield best results with the accuracy of 96%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

An Optimal Model Selection for COVID 19 Disease Classification

In the current scenario, the pandemic created by coronavirus is on the boom, and that is why it becomes very critical to control and cure this disease. The currently available technique for coronavirus disease 2019 (COVID-19) testing, i.e., reverse transcription polymerase chain reaction (RT-PCR), turns out to be a lot of time-consuming and requires modern labs, equipment, and highly trained medical staff that are rare to get. Chest computerized tomography (CT) is however available with a lot of ease, and it will be fruitful if these machines are used for COVID-19 testing. During this pandemic, there is an absolute need for an efficient and readily accessible way for COVID-19 patients classification, and CT is one of the best ways to do so. Keeping that in fact, this chapter introduces a study for understanding which deep learning models give the best result when classifying COVID-19 patients using chest CT images. For this study, ResNet 50, ResNet 101, DenseNet 121, DenseNet 169, and DenseNet 201 are compared with each other on the basis of classification accuracy, and it has been observed that DenseNet 169 has the tendency to yield best results with the accuracy of 96%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Clinical profile and outcomes of COVID 19 patients having acute kidney injury: an experience from a tertiary care centre Eastern India

Introduction: SARS COVID 19 is the third corona virus affecting all over the world in last 20 years following SARS COV and MERS COV. Multiple complications and death usually occurs in 6.4 % cases. In the beginning reports, incidence of AKI was negligible. Various data shows AKI occurs is >20% hospitalized patients and >50% of ICU patients. Methods: The study was a cross sectional study, carried out in the department of nephrology, of IMS and SUM Hospital, Bhubaneswar for a period of 1.2 years (April 2020-June 2021). The patients data was obtained from the hospital information system (HIS). It included the individuals of at least 18 years old, with a laboratory confirmed SARS-COV2 infection, and were hospitalised for the same. It excluded the known ESKD prior to admission and patients who were hospitalised for less than 48 hours. Results: It was observed that most patients were in their 6th decade, 40 % were Female. Patients with AKI, have low Hb%,high TLC count, low platelet count, high potassium level and low albumin, bicarbonate level,lymphocyte count, and higher creatinine values. Out of 3993 patients, 46% 1 developed AKI. In ICU 76% developed AKI. Peak Serum Creatinine was 2.2(IQR 1.5-3.6) in non dialysis patients and 8.2 in dialysis requiring patients (IQR 6.1-11). Conclusion: It is not uncommon to come across AKI patients in patients suffering from COVID-19 also associated with high death rate. Meticulous clinical observation and instillation of therapy helps to render better patient care.

COVID-19 detection with X-ray images by using transfer learning

Coronavirus is an infectious disease induced by extreme acute respiratory syndrome coronavirus 2. Novel coronaviruses can lead to mild to serious symptoms, like tiredness, nausea, fever, dry cough and breathlessness. Coronavirus symptoms are close to influenza, pneumonia and common cold. So Coronavirus can only be confirmed with a diagnostic test. 218 countries and territories worldwide have reported a total of 59.6 million active cases of the COVID-19 and 1.4 million deaths as of November 24, 2020. Rapid, accurate and early medical diagnosis of the disease is vital at this stage. Researchers analyzed the CT and X-ray findings from a large number of patients with coronavirus pneumonia to draw their conclusions. In this paper, we applied Support Vector Machine (SVM) classifier. After that we moved on to deep transfer learning models such as VGG16 and Xception which are implemented using Keras and Tensor flow to detect positive coronavirus patient using X-ray images. VGG16 and Xception show better performances as compared to SVM. In our work, Xception gained an accuracy of 97.46% with 98% f-score. © 2022 - IOS Press. All rights reserved.

The Impact and Challenges of Covid-19 Pandemic on E-Learning

COVID-19 has caused havoc on educational systems around the globe, impacting over 2 billion learners in over 200 countries. University, college, and other institutional facility cutbacks have impacted more than 94% of the world’s largest population of students. As a result, enormous changes have occurred in every aspect of human life. Social distance and mobility restrictions have significantly altered conventional educational procedures. Because numerous new standards and procedures have been adopted, restarting classrooms once the restrictions have been withdrawn seems to be another issue. Many scientists have published their findings on teaching and learning in various ways in the aftermath of the COVID-19 epidemic. Face-to-face instruction has been phased out in a number of schools, colleges, and universities. There is concern that the 2021 academic year, or perhaps more in the future, will be lost. Innovation and implementation of alternative educational systems and evaluation techniques are urgently needed. The COVID-19 epidemic has given us the chance to lay the groundwork for digital learning. The goal of this article is to provide a complete assessment of the COVID-19 pandemic’s impact on e-learning and learning multiple papers, as well as to suggest a course of action. This study also emphasises the importance of digital transformation in e-learning as well as the significant challenges it confronts, such as technological accessibility, poor internet connectivity, and challenging study environments. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

INVENTIONS OF INTERVENTIONS: DATA DRIVEN STRATEGIES IN PANDEMIC RESEARCH AND CONTROL

Faced with a rapidly evolving virus, inventors must seek to experiment, iterate and deploy both creative and effective solutions. Supported by empirical model-driven analysis, this paper delves into fundamental paradoxes and biases in the context of epidemic research, increasing awareness at every stage of the clinical trial;ranging from hypothesizing to sampling, and analyses to fake data detection. Critically, the paper also presents novel ideas that demonstrate how the paradoxes and biases covered play into technology development and deployment to combat the surging pandemic, COVID-19.

Psychological Resilience, Coping, and Distress in Admitted Patients With COVID-19 Infection.

Objective: To assess psychological resilience, coping, and related psychological distress in admitted COVID-19 patients. Predictors of subsequent development of posttraumatic stress symptoms (PTSS) and disability were also studied.Methods: Stable inpatients with COVID-19 (aged > 18 years with mild symptoms) admitted to a tertiary care hospital from April 2020 to December 2020 were recruited for the study. During admission, the patients were assessed for resilience, coping, and psychological distress using the 10-item Connor-Davidson Resilience Scale (CD-RISC-10), Brief COPE (Coping Orientation to Problems Experienced), and 4-item Patient Health Questionnaire (PHQ-4). Similarly, they were assessed at 4 weeks after discharge using the PTSD Checklist for DSM-5 and World Health Organization Disability Assessment Schedule.Results: A total of 176 patients were recruited for the study and assessed during their admission, and 102 were reassessed during follow-up. Of the patients, 17.6% during admission and 58.8% at follow-up had significant psychological distress (PHQ-4 score > 2). The mean ± SD CD-RISC-10 score was 27.94 ± 5.82. The most used coping strategies were emotional support, religion, and acceptance. Increased resilience was associated with better education (rs[100] = 0.265, P = .007), less psychological distress (r[100] = -0.596, P = .001), and healthy coping strategies. PHQ-4, PCL-5, and disability scores at follow-up were positively correlated (Pearson correlation). The multiple regression model statistically significantly predicted PTSS (F7, 94 = 2.660, P < .015, adjusted R2 = 0.103).Conclusions: COVID-19 patients with better resilience are associated with reduced psychological distress. Better resilient traits and reduced psychological distress may prevent ensuing PTSS and disability.

Face Mask Detection from Live-Stream Surveillance Video using Convolutional Neural Network

The coronavirus is a contagious disease and can spread very rapidly if the proper measures are not taken. Though the invention of the vaccines against coronavirus has given a sigh of relief, however, the complete eradication still looks a very long way to go. With the presence of the new variants of the coronavirus, the risk of the spread still remains. Among several guidelines given by the WHO and healthcare practitioners, facemasks have been one of the most effective ways to prevent the spread of the virus. However, some people usually ignore or forget to follow these guidelines especially in public places such as offices, shopping malls, etc. The number of people in such places is usually high and facemask is a factor to consider against the spread of the virus. Therefore, to hinder the spread of the virus, people with no facemask must be identified and notified. This research proposes a convolutional neural network-based deep learning model for detecting the people without facemasks using the frames captured from the livestream surveillance video. The research primarily focuses on the facemask detection module of the proposed system. The data for this study contains almost 1500 images for masked and without mask faces. The proposed model has been implemented using two different optimizers. The RMSprop optimizer-based model outperforms the Adam optimizer-based model. The accuracy achieved by RMSprop based model was 92.27% and the accuracy achieved by Adam optimizer-based model was 85.1%.

Sustainable utilization of natural resources for socio-environmental resilience and transformation in the mountains of Nepal

Mountains of Nepal are rich in natural resources as well as shelter for many ethnic group and indigenous community. Mountain people depend on natural resources and unique landscapes to survive, to preserve a unique sense of identity, and to provide livelihoods for centuries. The COVID-19 pandemic has brought unprecedented damage to the mountain economy given the immediate effect on ecotourism and remittance, which are the main source of income for communities in the mountains. People in mountains have been using indigenous and local knowledge for utilization of natural resources for their survival. Skilled manpower returning home from abroad can be beneficial and with the use of appropriate technology will be beneficial for those returning from the abroad due to various reasons. Sustainable harvesting of natural resources and various micro/small enterprises can be developed using the appropriate technology. These enterprises not only creates jobs but also can contribute to improve the local to national level economy. A new approach is required combining the science and engineering aspect with local communities contributing their local knowledge and practices, which can develop socio-environmental resilience-building and transformation in mountains of Nepal. © 2021 Elsevier Inc.

Zoonotic disease in the face of rapidly changing human-nature interactions in the Anthropocene

The landscape of zoonotic diseases has gained renewed interest due to COVID-19 that has gripped the world since the last 1 year. This chapter argues that in the age of humans, called “Anthropocene, " we may have to deal with the challenge of zoonotic diseases that can increase due to increases human managed ecosystems at the expense of intact and wild ecosystems. This expansion of human managed ecosystems is argued to reduce the resilience of these ecosystems, and one additional characteristic can be increased numbers of zoonotic diseases entering human society. In the landscape of scattered and under researched knowledge about entry of zoonotic diseases in the human society, this chapter brings the argument to stimulate debate and stress on more research regarding this issue. © 2021 Elsevier Inc.

SARS-CoV-2 Spike Glycoprotein and ACE2 Interaction Reveals Modulation of Viral Entry in Wild and Domestic Animals.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a viral pathogen causing life-threatening diseases in humans. Interaction between the spike protein of SARS-CoV-2 and angiotensin-converting enzyme 2 (ACE2) is a potential factor in the infectivity of a host. In this study, the interaction of SARS-CoV-2 spike protein with its receptor, ACE2, in different hosts was evaluated to predict the probability of viral entry. Phylogeny and alignment comparison of the ACE2 sequences did not lead to any meaningful conclusion on viral entry in different hosts. The binding ability between ACE2 and the spike protein was assessed to delineate several spike binding parameters of ACE2. A significant difference between the known infected and uninfected species was observed for six parameters. However, these parameters did not specifically categorize the Orders into infected or uninfected. Finally, a logistic regression model constructed using spike binding parameters of ACE2, revealed that in the mammalian class, most of the species of Carnivores, Artiodactyls, Perissodactyls, Pholidota, and Primates had a high probability of viral entry. However, among the Proboscidea, African elephants had a low probability of viral entry. Among rodents, hamsters were highly probable for viral entry with rats and mice having a medium to low probability. Rabbits have a high probability of viral entry. In Birds, ducks have a very low probability, while chickens seemed to have medium probability and turkey showed the highest probability of viral entry. The findings prompt us to closely follow certain species of animals for determining pathogenic insult by SARS-CoV-2 and for determining their ability to act as a carrier and/or disseminator.

Intelligent Big Data Analytics: A Perspective for IoHT and HealthCare

Currently, we are in the COVID-19 pandemic situation, where the healthcare sector plays a major role to prevent the loss of life across the globe. During this COVID-19 period, the remote healthcare assistance and monitor systems show their impotence and existence. Science and technology are always associated with the healthcare sector, and it helps to diagnose the cause of bad health and helps to improve by proper treatment. The analysis of the causes plays a major role in treatment. With the advance of science and technology for the complex clinical solution in healthcare, IoHT (internet of health care things) and intelligent analytics in big data can play a bigger role in cancer detection, brain tumor, etc. The main focus of our work is to discuss a framework of intelligent analytics for big data utility to such a complex clinical problem on the IoHT platform. The digital imaging of medical imaging is playing a critical role in the clinical operation of a human in healthcare. The treatment of cancer or the brain is a very complex multistage treatment process;here, use of intelligent big data analytics and IoHT can help the doctors to provide better decision making which helps in treatment and recovery stages. So the remote healthcare sector, which is showing its utility during the COVID-19 situation, where intelligent data analytics play a major role to provide better healthcare solutions and monitoring to the human society. The intelligent medical systems for healthcare need proper data acquisition health condition, which need to analyze, store and process for each individual for clinical treatment and future reference. The past information periodically and present health condition can store and analyzed for better healthcare, this leads to an intelligent database system, where Intelligent data analytics play a big role, with the utility of the advanced platform it becomes robust and can be the backbone of the healthcare sector. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Clinico-virological-epidemiological characteristics in Covid positive clusters among frontline Health care workers and non-health care workers – A Hospital based study

Background:Ample of studies have been carried out on the causative agent, pattern of illness, treatment options which mainly concern regarding the patients and general population affected from COVID -19, however few studies have fo- cused on its adverse effects on front line health care workers ( HCW ) and other employees of health care facilities. The present retrospective study was planned to analyse the clinico-viro-epidemiological profile of different covid clusters in HCWs and non-health care employees of AIIMS, Bhubaneswar. Methods:A hospital based retrospective study was carried out on the HCWs and other employees of AIIMS, Bhubanes- war, who tested positive SARS-CoV-2 infection by RT-PCR test. The clinical and demographic information were analysed with corresponding virological data of the patient. Results:Of the 671 employees of AIIMS, Bhubaneswar who tested positive for SARS-CoV-2, 92 were from eight clusters that could be traced. The eight clusters involved 4 clusters each from both the HCWs group containing 66 individuals and non-HCWs group with 32. Male to female ratio was 2.5:1. Maximum 55(59.7%) individuals belonged to 20 -30yrs age group followed by 30-40yrs 28(30.4%) and least 3(3.2%) in 50-60yrs. Asymptomatic COVID positive individuals were more as compared to symptomatic in all the age groups. All the individuals with cycle threshold value (CT) ≤ 20 were symptomatic;of the 21 persons with CT value 21-30, seven were symptomatic and 14 were asymptomatic. Majority with >30 CT value (35/44) were asymptomatic. Conclusions:Frontline HCWs are constantly at increased risk of getting infection, but the disease burden and post -covid stigma can be substantially decreased among non-HCWS if COVID appropriate behaviour are strictly implemented and followe

Inconclusives: The diagnostic dilemma of COVID-19

Background:COVID-19 RT-PCR kits of various manufacturers categorize certain samples as inconclusive and repeat testing or re-sampling is advised in those cases to ascertain positivity or a negative result. This is of paramount im- portance because a definite result helps in effective implementation of public health measures, leading to implicit con- tainment. Our study aims to ascertain criteria through which the inconclusive can be definitively categorized as either positive or negative. This will be of help in conserving manpower and resources which are utilized in re -testing of pa- tients with inconclusive RT-PCR result. Methods:Hundred samples which were inconclusive (IC) as per Q-Line Covid-19 RT-PCR kit from 1st September, 2020 to 31st October, 2020 were included in the study. These were classified into 4 groups based on Ct value of N gene;namely A (<25;3 samples), B (25-30.9;31), C (31-34;62) and D (>34;4) and were tested by NIV kit. RNA extracts of these sam- ples were run through ICMR-NIV rRT-PCR screening and confirmatory assay to ascertain a criteria with which inconclu- sives can be definitively reported as either positive or negative. Results:Majority (62%) of IC samples were in group C (Ct 31-34) followed by 31% in group B, 4% in D and 3% in group A (<25). Confirmed positivity by NIV kit was 100% in group A and 51.6%, 20.96% and 25% respectively in B, C & D groups.29% of group B and 24% group C samples remained inconclusive by NIV kit. Majority of confirmed negatives were found in group D (75%), followed by group C (54.83%). Conclusions:All inconclusive samples with Ct values of N gene less than 25 were positive with ICMR -NIV kit, whereas >50% of samples of Ct >30 became negative. Repeat sampling could be avoided in 76% cases by following strategy of repeat testing in NIV kit.

Clinical Profile of Covid-19 in Children: A single centre, retrospective study from Eastern India

Background: The burden of corona virus disease of 2019 (COVID-19) in children is less well studied compared to adults.This study is to provide Clinical Profile of children positive for COVID-19 admitted to a tertiary care hospital with focus on mode of presentation, severity of disease, associated co-morbidities, radiological and biochemical abnormali- ties and outcome. Methods: This retrospective study included 74 children positive for COVID-19 admitted to a tertiary care hospital in eastern India between March 24 and August 31, 2020. Symptomatic screening and testing for SARS -CoV-2 through RT PCR in the hospital was started on 16th March 2020 as per the prevalent national guidelines. Universal screening for COVID 19 of all the newly admitted patients to the hospital started in middle of June 2020. Results: Of the 74 children included in the study 45 (60.8%) were male, and the median age was 5 years (range: 5 days –14 years). 35 patients (47.3 %) had pre-existing comorbidities, 27 patients (36.5 %) were diagnosed incidentally and 47 patients (63.5 %) presented with respiratory symptoms. 8 patients (10.8 %) required supplemental oxygen support. The median length of hospital stay was 8 days (range: 5 days -20 days). Chest X ray was abnormal in 11/32 (34.4%) of chil- dren imaged. 21/73 (27.8 %) patients were from containment zones. In 31/54 (57.4%) patients, at least one family member tested positive. Hydroxychloroquine therapy was used in 10 patients (13.5 %), and azithromycin was used in 34 patients (45.9%). The case fatality rate was 2 (2.7%). Conclusions: In this single centre retrospective study, severe illness in children in terms of Intensive care unit admission and death was far less frequent. However, as the pandemic is still evolving, larger and more extensive studies with fol- low up are required for better understanding of Covid-19 in Children

Incidence of COVID-19 infection among Asymptomatic pre-operative/ pre-procedure patients: An Experience

Background:The Coronavirus disease 2019 (COVID 19) pandemic has resulted in reduced performance of non-emergent surgeries and procedures across the nation. About four-fifth of COVID-19 infections remain asymptomatic. With an incu- bation period of 7-14 days, patients can also remain in the pre-symptomatic stage. Hence, knowledge of the prevalence of disease among the asymptomatic is important to prevent spread of disease to the health care professional as well as for patient safety. The present study sought to assess the frequency of positivity of COVID 19 among pre -procedural/pre-operative patients. Methods:A retrospective study of all asymptomatic pre-procedural cases was conducted from 10th June 2020 till 10th November 2020. Nasal swabs were collected from patients 48 hours before procedures (including elective and emer- gency surgeries, patient scheduled to receive chemotherapy, radiotherapy, patients undergoing biopsy, endoscopy). RT - PCR test was done for all samples. Case-specific data, results of all PCR tests, and answers to screening questions (about symptoms, exposure, and travel) were obtained Results: 5320 tests were conducted during the study period. Of these, data could be analysed for 2117 tests done. 51.1% were male patients and the median age group of the cohort was 46 years. 254 of 2117 (11.9%) belonged to the paediatric age group. Of 2117, 35 samples were rejected because of sample leakage or improper labelling. Out of the remaining 2082 samples;338 were positive;1606 were negative, and 138 inconclusive. We received second sample for 117 of the 138 inconclusive samples of which 18 were positive, 93 were negative and 6 remained inconclusive. Thus, the total positivity was 17.1%. Conclusions:This study confirms the high proportion of asymptomatic patients with COVID -19 positivity;and suggests the value of screening by RT-PCR during COVID-19 pandemics.