Towards Future Internet: The Metaverse Perspective for Diverse Industrial Applications

The Metaverse allows the integration of physical and digital versions of users, processes, and environments where entities communicate, transact, and socialize. With the shift towards Extended Reality (XR) technologies, the Metaverse is envisioned to support a wide range of applicative verticals. It will support a seamless mix of physical and virtual worlds (realities) and, thus, will be a game changer for the Future Internet, built on the Semantic Web framework. The Metaverse will be ably assisted by the convergence of emerging wireless communication networks (such as Fifth-Generation and Beyond networks) or Sixth-Generation (6G) networks, Blockchain (BC), Web 3.0, Artificial Intelligence (AI), and Non-Fungible Tokens (NFTs). It has the potential for convergence in diverse industrial applications such as digital twins, telehealth care, connected vehicles, virtual education, social networks, and financial applications. Recent studies on the Metaverse have focused on explaining its key components, but a systematic study of the Metaverse in terms of industrial applications has not yet been performed. Owing to this gap, this survey presents the salient features and assistive Metaverse technologies. We discuss a high-level and generic Metaverse framework for modern industrial cyberspace and discuss the potential challenges and future directions of the Metaverse's realization. A case study on Metaverse-assisted Real Estate Management (REM) is presented, where the Metaverse governs a Buyer–Broker–Seller (BBS) architecture for land registrations. We discuss the performance evaluation of the current land registration ecosystem in terms of cost evaluation, trust probability, and mining cost on the BC network. The obtained results show the viability of the Metaverse in REM setups.

SanJeeVni: Secure UAV-Envisioned Massive Vaccine Distribution for COVID-19 Underlying 6G Network.

Recently, unmanned aerial vehicles (UAVs) are deployed in Novel Coronavirus Disease-2019 (COVID-19) vaccine distribution process. To address issues of fake vaccine distribution, real-time massive UAV monitoring and control at nodal centers (NCs), the authors propose SanJeeVni, a blockchain (BC)-assisted UAV vaccine distribution at the backdrop of sixth-generation (6G) enhanced ultra-reliable low latency communication (6G-eRLLC) communication. The scheme considers user registration, vaccine request, and distribution through a public Solana BC setup, which assures a scalable transaction rate. Based on vaccine requests at production setups, UAV swarms are triggered with vaccine delivery to NCs. An intelligent edge offloading scheme is proposed to support UAV coordinates and routing path setups. The scheme is compared against fifth-generation (5G) uRLLC communication. In the simulation, we achieve and 86% improvement in service latency, 12.2% energy reduction of UAV with 76.25% more UAV coverage in 6G-eRLLC, and a significant improvement of [Formula: see text]% in storage cost against the Ethereum network, which indicates the scheme efficacy in practical setups.

Fight against Future Pandemics: UAV-Based Data-Centric Social Distancing, Sanitizing, and Monitoring Scheme

The novel coronavirus disease-2019 (COVID-19) has transformed into a global health concern, which resulted in human containment and isolation to flatten the curve of mortality rates of infected patients. To leverage the massive containment strategy, fifth-generation (5G)-envisioned unmanned aerial vehicles (UAVs) are used to minimize human intervention with the key benefits of ultra-low latency, high bandwidth, and reliability. This allows phased treatment of infected patients via threefold functionalities (3FFs) such as social distancing, proper sanitization, and inspection and monitoring. However, UAVs have to send massive recorded data back to ground stations (GS), which requires a real-time device connection density of 107/km2, which forms huge bottlenecks on 5G ecosystems. A sixth-generation (6G) ecosystem can provide terahertz (THz) frequency bands with massive short beamforming cells, intelligent deep connectivity, and physical- and link-level protocol virtualization. The UAVs form a swarm network to assure 3FFs which requires high-end computations and are data-intensive;thus, these computational tasks can be offloaded to nearby edge servers, which employ local federated learning to train the global models. It synchronizes the UAV task formations and optimizes the network functions. Task optimization of UAV swarms in 6G-assisted channels allows better management and ubiquitous and energy-efficient seamless communication over ground, space, and underwater channels. Thus, a data-centric 3FF approach is essential to fight against future pandemics, with a 6G backdrop channel. The proposed scheme is compared with traditional fourth-generation (4G) and 5G-networks-based schemes to indicate its efficiency in traffic density, processing latency, spectral efficiency, UAV mobility, radio loss, and device connection density.

Genomic profile of SARS-CoV-2 Omicron variant and its correlation with disease severity in Rajasthan.

Background: Omicron, a new variant of Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2), was first detected in November 2021. This was believed to be highly transmissible and was reported to evade immunity. As a result, an urgent need was felt to screen all positive samples so as to rapidly identify Omicron cases and isolate them to prevent the spread of infection. Genomic surveillance of SARS-CoV-2 was planned to correlate disease severity with the genomic profile. Methods: All the SARS-CoV-2 positive cases detected in the state of Rajasthan were sent to our Lab. Samples received from 24 November 2021 to 4 January 2022 were selected for Next-Generation Sequencing (NGS). Processing was done as per protocol on the Ion Torrent S5 System for 1,210 samples and bioinformatics analysis was done. Results: Among the 1,210 samples tested, 762 (62.9%) were Delta/Delta-like and other lineages, 291 (24%) were Omicron, and 157 (12.9%) were invalid or repeat samples. Within a month, the proportion of Delta and other variants was reversed, 6% Omicron became 81%, and Delta and other variants became 19%, initially all Omicron cases were seen in international travelers and their contacts but soon community transmission was seen. The majority of patients with Omicron were asymptomatic (56.7%) or had mild disease (33%), 9.2% had moderate symptoms, and two (0.7%) had severe disease requiring hospitalization, of which one (0.3%) died and the rest were (99.7%) recovered. History of vaccination was seen in 81.1%, of the previous infection in 43.2% of cases. Among the Omicron cases, BA.1 (62.8%) was the predominant lineage followed by BA.2 (23.7%) and B.1.529 (13.4%), rising trends were seen initially for BA.1 and later for BA.2 also. Although 8.9% of patients with Delta lineage during that period were hospitalized, 7.2% required oxygen, and 0.9% died. To conclude, the community spread of Omicron occurred in a short time and became the predominant circulating variant; BA.1 was the predominant lineage detected. Most of the cases with Omicron were asymptomatic or had mild disease, and the mortality rate was very low as compared to Delta and other lineages.

Interval assessment using task- and procedure-based simulations: an attempt to supplement neurosurgical residency curriculum.

OBJECTIVE: The longer learning curve and smaller margin of error make nontraditional, or "out of operating room" simulation training, essential in neurosurgery. In this study, the authors propose an evaluation system for residents combining both task-based and procedure-based exercises and also present the perception of residents regarding its utility. METHODS: Residents were evaluated using a combination of task-based and virtual reality (VR)-based exercises. The results were analyzed in terms of the seniority of the residents as well as their laboratory credits. Questionnaire-based feedback was sought from the residents regarding the utility of this evaluation system incorporating the VR-based exercises. RESULTS: A total of 35 residents were included in this study and were divided into 3 groups according to seniority. There were 11 residents in groups 1 and 3 and 13 residents in group 2. On the overall assessment of microsuturing skills including both 4-0 and 10-0 microsuturing, the suturing skills of groups 2 and 3 were observed to be better than those of group 1 (p = 0.0014). Additionally, it was found that microsuturing scores improved significantly with the increasing laboratory credits (R2 = 0.72, p < 0.001), and this was found to be the most significant for group 1 residents (R2 = 0.85, p < 0.001). Group 3 residents performed significantly better than the other two groups in both straight (p = 0.02) and diagonal (p = 0.042) ring transfer tasks, but there was no significant difference between group 1 and group 2 residents (p = 0.35). Endoscopic evaluation points were also found to be positively correlated with previous laboratory training (p = 0.002); however, for the individual seniority groups, the correlation failed to reach statistical significance. The 3 seniority groups performed similarly in the cranial and spinal VR modules. Group 3 residents showed significant disagreement with the utility of the VR platform for improving surgical dexterity (p = 0.027) and improving the understanding of surgical procedures (p = 0.034). Similarly, there was greater disagreement for VR-based evaluation to identify target areas of improvement among the senior residents (groups 2 and 3), but it did not reach statistical significance (p = 0.194). CONCLUSIONS: The combination of task- and procedure-based assessment of trainees using physical and VR simulation models can supplement the existing neurosurgery curriculum. The currently available VR-based simulations are useful in the early years of training, but they need significant improvement to offer beneficial learning opportunities to senior trainees.

An AI-based disease detection and prevention scheme for COVID-19☆

The proliferating outbreak of COVID-19 raises global health concerns and has brought many countries to a standstill. Several restrain strategies are imposed to suppress and flatten the mortality curve, such as lockdowns, quarantines, etc. Artificial Intelligence (AI) techniques could be a promising solution to leverage these restraint strategies. However, real-time decision-making necessitates a cloud-oriented AI solution to control the pandemic. Though many cloud-oriented solutions exist, they have not been fully exploited for real-time data accessibility and high prediction accuracy. Motivated by these facts, this paper proposes a cloud-oriented AI-based scheme referred to as D-espy (i.e., Disease-espy) for disease detection and prevention. The proposed D-espy scheme performs a comparative analysis between Autoregressive Integrated Moving Average (ARIMA), Vanilla Long Short Term Memory (LSTM), and Stacked LSTM techniques, which signify the dominance of Stacked LSTM in terms of prediction accuracy. Then, a Medical Resource Distribution (MRD) mechanism is proposed for the optimal distribution of medical resources. Next, a three-phase analysis of the COVID-19 spread is presented, which can benefit the governing bodies in deciding lockdown relaxation. Results show the efficacy of the D-espy scheme concerning 96.2% of prediction accuracy compared to the existing approaches. Graphical

An AI-based disease detection and prevention scheme for COVID-19.

The proliferating outbreak of COVID-19 raises global health concerns and has brought many countries to a standstill. Several restrain strategies are imposed to suppress and flatten the mortality curve, such as lockdowns, quarantines, etc. Artificial Intelligence (AI) techniques could be a promising solution to leverage these restraint strategies. However, real-time decision-making necessitates a cloud-oriented AI solution to control the pandemic. Though many cloud-oriented solutions exist, they have not been fully exploited for real-time data accessibility and high prediction accuracy. Motivated by these facts, this paper proposes a cloud-oriented AI-based scheme referred to as D-espy (i.e., Disease-espy) for disease detection and prevention. The proposed D-espy scheme performs a comparative analysis between Autoregressive Integrated Moving Average (ARIMA), Vanilla Long Short Term Memory (LSTM), and Stacked LSTM techniques, which signify the dominance of Stacked LSTM in terms of prediction accuracy. Then, a Medical Resource Distribution (MRD) mechanism is proposed for the optimal distribution of medical resources. Next, a three-phase analysis of the COVID-19 spread is presented, which can benefit the governing bodies in deciding lockdown relaxation. Results show the efficacy of the D-espy scheme concerning 96.2% of prediction accuracy compared to the existing approaches.

Chronic Pain Management During a Pandemic: Evidence-Based Review.

Chronic pain is the leading cause of morbidity in the world and is strongly associated with physical and psychological disabilities. In this pandemic, most of the pain care centers are forced to shut their doors leaving patients in dismay and adding to their misery. A systematic review was performed following the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. All research articles from March 2020 to September 15, 2020, available on PubMed, Google scholar, and EmBase were included in this study. The keywords used for data search were "chronic pain," "coronavirus," "pain management," "COVID-19," "drugs usage in covid-19," "recommendation," and "guidelines". This review summarizes findings from the current literature available worldwide from different databases regarding guidelines to practice during chronic pain in coronavirus disease (COVID) crisis. This article acts as a specimen on how to handle future pandemics. We concluded that chronic pain management is a fundamental right and telemedicine is the silver lining that can be used for primary, follow-up consultation and to address mental health issues in chronic pain patients. Outpatient department visits should be scheduled using "forward triage." Pain Interventions should be carried out if urgent or semi-urgent with necessary precautions. Reopening of elective procedures with COVID testing can be planned, considering pain interventions to be usually percutaneous, of short duration, and involving office procedures with minimal aerosol generation. Drugs contrib- uting to immune suppression, for example, strong opioids and steroids, should be avoided. Regenerative therapy can be used instead during pain interventions. Physicians are expected to follow the recommended government guidelines before prescribing any drugs.

Challenges and prospects of neurosurgical teleconsultations in a developing country: a reality check.

OBJECTIVE: The adoption of telemedicine became a necessity during the COVID-19 pandemic because patients found commuting to be difficult owing to travel restrictions. Initially, audio-based teleconsultations were provided. Later, on the basis of the feedback of patients and caregivers, the authors started to provide video-based teleconsultations via WhatsApp. The authors subsequently surveyed the patients and caregivers to determine their satisfaction levels with telemedicine services. METHODS: An anonymized telephone survey of patients who had participated in teleconsultation was conducted with a structured questionnaire. The responses were analyzed and their correlations with the perceived benefits and limitations of audio and video teleconsultation were determined. RESULTS: Three hundred respondents were included in the first round of surveys, of whom 250 (83.3%) consented to video teleconsultation. Among the respondents who participated in both audio and video teleconsultations (n = 250), paired analysis showed that video teleconsultation was perceived as better in terms of providing easier access to healthcare services (p < 0.001), saving time (p < 0.001), and satisfaction with the way patient needs were conveyed to healthcare providers (p = 0.023), as well as in terms of adequacy of addressing healthcare needs (p < 0.001) and consequently providing a higher rate of overall satisfaction (p < 0.001). For both audio and video teleconsultation, overall patient satisfaction was significantly related to only previous exposure to WhatsApp. However, for video consultation, longer call duration (p = 0.023) was an important independent factor. Video teleconsultation was preferable to face-to-face consultation irrespective of educational status, but higher education was associated with preference for video teleconsultation. CONCLUSIONS: Both audio and video teleconsultation are viable cost-effective surrogates for in-person physical neurosurgical consultation. Although audio teleconsultation is more user-friendly and is not restricted by educational status, video teleconsultation trumps the former owing to a more efficient and satisfactory doctor-to-patient interface.

ABV-CoViD: An Ensemble Forecasting Model to Predict Availability of Beds and Ventilators for COVID-19 Like Pandemics.

Recently, healthcare stakeholders have orchestrated steps to strengthen and curb the COVID-19 wave. There has been a surge in vaccinations to curb the virus wave, but it is crucial to strengthen our healthcare resources to fight COVID-19 and like pandemics. Recent researchers have suggested effective forecasting models for COVID-19 transmission rate, spread, and the number of positive cases, but the focus on healthcare resources to meet the current spread is not discussed. Motivated from the gap, in this paper, we propose a scheme, ABV-CoViD (Availibility of Beds and Ventilators for COVID-19 patients), that forms an ensemble forecasting model to predict the availability of beds and ventilators (ABV) for the COVID-19 patients. The scheme considers a region-wise demarcation for the allotment of beds and ventilators (BV), termed resources, based on region-wise ABV and COVID-19 positive patients (inside the hospitals occupying the BV resource). We consider an integration of artificial neural network (ANN) and auto-regressive integrated neural network (ARIMA) model to address both the linear and non-linear dependencies. We also consider the effective wave spread of COVID-19 on external patients (not occupying the BV resources) through a [Formula: see text]- ARNN model, which gives us long-term complex dependencies of BV resources in the future time window. We have considered the COVID-19 healthcare dataset on 3 USA regions (Illinois, Michigan, and Indiana) for testing our ensemble forecasting scheme from January 2021 to May 2022. We evaluated our scheme in terms of statistical performance metrics and validated that ensemble methods have higher accuracy. In simulation, for linear modelling, we considered the [Formula: see text] model, and [Formula: see text] model for ANN modelling. We considered the [Formula: see text](12,6) forecasting. On a population of 2,93,90,897, the obtained mean absolute error (MAE) on average for 3 regions is 170.5514. The average root means square error (RMSE) of [Formula: see text]-ARNN is 333.18, with an accuracy of 98.876%, which shows the scheme's efficacy in ABV measurement over conventional and manual resource allocation schemes.

A Taxonomy on Smart Healthcare Technologies: Security Framework, Case Study, and Future Directions

There is a massive transformation in the traditional healthcare system from the specialist-centric approach to the patient-centric approach by adopting modern and intelligent healthcare solutions to build a smart healthcare system. It permits patients to directly share their medical data with the specialist for remote diagnosis without any human intervention. Furthermore, the remote monitoring of patients utilizing wearable sensors, Internet of Things (IoT) technologies, and artificial intelligence (AI) has made the treatment readily accessible and affordable. However, the advancement also brings several security and privacy concerns that poorly maneuvered the effective performance of the smart healthcare system. An attacker can exploit the IoT infrastructure, perform an adversarial attack on AI models, and proliferate resource starvation attacks in smart healthcare system. To overcome the aforementioned issues, in this survey, we extensively reviewed and created a comprehensive taxonomy of various smart healthcare technologies such as wearable devices, digital healthcare, and body area networks (BANs), along with their security aspects and solutions for the smart healthcare system. Moreover, we propose an AI-based architecture with the 6G network interface to secure the data exchange between patients and medical practitioners. We have examined our proposed architecture with the case study based on the COVID-19 pandemic by adopting unmanned aerial vehicles (UAVs) for data exchange. The performance of the proposed architecture is evaluated using various machine learning (ML) classification algorithms such as random forest (RF), naive Bayes (NB), logistic regression (LR), linear discriminant analysis (LDA), and perceptron. The RF classification algorithm outperforms the conventional algorithms in terms of accuracy, i.e., 98%. Finally, we present open issues and research challenges associated with smart healthcare technologies.

Managing Traumatic Brain Injury During the Coronavirus Disease 2019 Pandemic-A Case-Matched Controlled Analysis of Immediate Outcomes.

OBJECTIVE: The primary objective of this study was to evaluate the outcome of patients with traumatic brain injury (TBI) during the coronavirus disease 2019 (COVID-19) pandemic and to compare their outcome with case-matched controls from the prepandemic phase. METHODS: This is a retrospective case-control study in which all patients with TBI admitted during COVID-19 pandemic phase (Arm A) from March 24, 2020 to November 30, 2020 were matched with age and Glasgow Coma Scale score-matched controls from the patients admitted before March 2020 (Arm B). RESULTS: The total number of patients matched in each arm was 118. The length of hospital stay (8 days vs. 5 days; P < 0.001), transit time from emergency room to operation room (150 minutes vs. 97 minutes; P = 0.271), anesthesia induction time (75 minutes vs. 45 minutes; P = 0.002), and operative duration (275 minutes vs. 180 minutes; P = 0.002) were longer in arm A. Although the incidence of fever and pneumonia was significantly higher in arm A than in arm B (50% vs. 26.3%, P < 0.001 and 27.1% vs. 1.7%, P < 0.001, respectively), outcome (Glasgow Outcome Scale-Extended) and mortality (18.6% vs. 14.4% respectively; P = 0.42) were similar in both the groups. CONCLUSIONS: The outcome of the patients managed for TBI during the COVID-19 pandemic was similar to matched patients with TBI managed at our center before the onset of the COVID-19 pandemic. This finding suggests that the guidelines followed during the COVID-19 pandemic were effective in dealing with patients with TBI. This model can serve as a guide for any future pandemic waves for effective management of patients with TBI without compromising their outcome.

The digital transformation of higher education – “uni for nothin’, MOOCs for free”?: JITCAR

In 2017, the newly-elected, Labor-led government of New Zealand boldly declared access to higher-education to be a universal right and committed to a year’s “fees-free” studentship, with the promise of eventually extending it to an entire first-degree programme. Against such a backdrop, this article will examine the role of Massive Open Online Courses (MOOCs) as surrogates for “fees-free” higher education and whether the design of such a Higher Education 4.0 platform is even a credible proposition. More specifically, the research question addressed is: can higher education be made universal in terms of access and costs through the intermediation of MOOCs? The case attempts to provide a socio-technical view of such a “value proposition” and concludes that the charter of higher education extends beyond the distribution of knowledge and skills that may perhaps be better delivered with blended learning models than MOOC platforms. A university is more than a certification of core-competencies in that it also brings about socialization and participation. With the undercurrent of design ideals such as “tech for good”, the academic community must examine whether MOOCs are credible substitutes or at-best, complementary platforms. In this era of Industry 4.0, higher education should not be about the creative destruction of what we value in universities, but their digital transformation. Regretfully, the onset of the Covid-19 pandemic has revealed gaping holes in the sectors’ readiness for online learning. The article concludes with an agenda for large Randomized Controlled Trials (RCTs) driven by Action Design Research that could fulfil the aspirations of the key stakeholder groups – students, faculty and regulators. It is intended that the case will inform policy makers on the implementation of a Blended Learning platform which draws from the relative strengths of traditional and online delivery.

A Comprehensive Review of the Technological Solutions to Analyse the Effects of Pandemic Outbreak on Human Lives.

A coronavirus outbreak caused by a novel virus known as SARS-CoV-2 originated towards the latter half of 2019. COVID-19's abrupt emergence and unchecked global expansion highlight the inability of the current healthcare services to respond to public health emergencies promptly. This paper reviews the different aspects of human life comprehensively affected by COVID-19. It then discusses various tools and technologies from the leading domains and their integration into people's lives to overcome issues resulting from pandemics. This paper further focuses on providing a detailed review of existing and probable Artificial Intelligence (AI), Internet of Things (IoT), Augmented Reality (AR), Virtual Reality (VR), and Blockchain-based solutions. The COVID-19 pandemic brings several challenges from the viewpoint of the nation's healthcare, security, privacy, and economy. AI offers different predictive services and intelligent strategies for detecting coronavirus signs, promoting drug development, remote healthcare, classifying fake news detection, and security attacks. The incorporation of AI in the COVID-19 outbreak brings robust and reliable solutions to enhance the healthcare systems, increases user's life expectancy, and boosts the nation's economy. Furthermore, AR/VR helps in distance learning, factory automation, and setting up an environment of work from home. Blockchain helps in protecting consumer's privacy, and securing the medical supply chain operations. IoT is helpful in remote patient monitoring, distant sanitising via drones, managing social distancing (using IoT cameras), and many more in combating the pandemic. This study covers an up-to-date analysis on the use of blockchain technology, AI, AR/VR, and IoT for combating COVID-19 pandemic considering various applications. These technologies provide new emerging initiatives and use cases to deal with the COVID-19 pandemic. Finally, we discuss challenges and potential research paths that will promote further research into future pandemic outbreaks.

Detection of Serum Cross-Reactive Antibodies and Memory Response to SARS-CoV-2 in Prepandemic and Post-COVID-19 Convalescent Samples.

BACKGROUND: A notable feature of coronavirus disease 2019 (COVID-19) is that children are less susceptible to severe disease. Children are known to experience more infections with endemic human coronaviruses (HCoVs) compared to adults. Little is known whether HCoV infections lead to cross-reactive anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies. METHODS: We investigated the presence of cross-reactive anti-SARS-CoV-2 IgG antibodies to spike 1 (S1), S1-receptor-binding domain (S1-RBD), and nucleocapsid protein (NP) by enzyme-linked immunosorbent assays, and neutralizing activity by a SARS-CoV-2 pseudotyped virus neutralization assay, in prepandemic sera collected from children (n = 50) and adults (n = 45), and compared with serum samples from convalescent COVID-19 patients (n = 16). RESULTS: A significant proportion of children (up to 40%) had detectable cross-reactive antibodies to SARS-CoV-2 S1, S1-RBD, and NP antigens, and the anti-S1 and anti-S1-RBD antibody levels correlated with anti-HCoV-HKU1 and anti-HCoV-OC43 S1 antibody titers in prepandemic samples (P < .001). There were marked increases of anti-HCoV-HKU1 and - OC43 S1 (but not anti-NL63 and -229E S1-RBD) antibody titers in serum samples from convalescent COVID-19 patients (P < .001), indicating an activation of cross-reactive immunological memory to ß-coronavirus spike. CONCLUSIONS: We demonstrated cross-reactive anti-SARS-CoV-2 antibodies in prepandemic serum samples from children and young adults. Promoting this cross-reactive immunity and memory response derived from common HCoV may be an effective strategy against SARS-COV-2 and future novel coronaviruses.

Lessons for Treating Structural Heart Patients during the COVID-19 Pandemic and Beyond.

Background: We sought to compare characteristics and outcomes of structural heart disease (SHD) patients treated during the regional peak of the Coronavirus Disease 2019 (COVID-19) pandemic ("COVID era") compared with historical controls. During the COVID era, elective SHD procedures at Beth Israel Deaconess Medical Center were canceled but urgent cases were still performed. We enacted several practice changes in an effort to minimize complications, prevent COVID transmission, and decrease hospital stay during the pandemic. Methods: Baseline characteristics and outcomes were collected on all patients who underwent SHD procedures during the COVID era and compared with patients treated during the same time period in 2019. Results: Compared with SHD patients treated during 2019 (N = 259), those treated during the COVID era (N = 26) had higher left ventricular end diastolic pressure (LVEDP; 28 vs. 21 mmHg, p = 0.001), and were more likely New York Heart Association class IV (26.9% vs. 10.0%, p = 0.019), but had a lower rate of bleeding/vascular complications (0% vs. 16.2%, p = 0.013), a lower rate of permanent pacemaker implantation (0% vs. 17.4%, p = 0.019), and a greater proportion of patients were discharged on post-operative day 1 (POD#1; 68.2% vs. 22.2%, p < 0.001). Conclusion: Practice changes employed for patients treated during the COVID era were associated with fewer vascular complications, a greater proportion of patients discharged on POD#1, and a lower rate of pacemaker implantation despite more severe illness. As a result, we plan to continue these practices in the post-COVID era.

A comprehensive insight on the COVID-19 vaccine candidates.

The world is currently facing a pandemic triggered by the novel corona virus (SARS - CoV2), which causes a highly infectious infection that predominantly affects the lungs, resulting in a variety of clinical symptoms some cases may be asymptomatic while others may result in to severe respiratory disorder, if the infection is left unattended it may result in multi-organ failure and eventually death of the patient. The transmission of infection is by droplet and fomites of the infected person. The incubation period of virus is from 2 to 14 days. Most common symptoms resemble flu-like but later progress to pneumonia along with dyspnoea and worsening of oxygen saturation, thus requiring ventilator support. The diagnostic modalities include Reverse transcriptase real time PCR (Quantitative Reverse transcriptase polymerase chain reaction) which is recommended method used for diagnosis of the COVID-19 infection using oro-pharyngeal or nasopharyngeal swabs of the patients. Recently serological tests for antigen and antibody detection has been approved by ICMR. Till now, nine COVID-19 vaccines are granted emergency approval for prevention and for the management of infection symptomatic and supportive measures are being adopted. Globally major pharmaceutical firms are engrossed for development of a potent vaccine candidate. This review highlights on various vaccine candidates under clinical trials.

Identifying the natural polyphenol catechin as a multi-targeted agent against SARS-CoV-2 for the plausible therapy of COVID-19: an integrated computational approach.

The global pandemic crisis, coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has claimed the lives of millions of people across the world. Development and testing of anti-SARS-CoV-2 drugs or vaccines have not turned to be realistic within the timeframe needed to combat this pandemic. Here, we report a comprehensive computational approach to identify the multi-targeted drug molecules against the SARS-CoV-2 proteins, whichare crucially involved in the viral-host interaction, replication of the virus inside the host, disease progression and transmission of coronavirus infection. Virtual screening of 75 FDA-approved potential antiviral drugs against the target proteins, spike (S) glycoprotein, human angiotensin-converting enzyme 2 (hACE2), 3-chymotrypsin-like cysteine protease (3CLpro), cathepsin L (CTSL), nucleocapsid protein, RNA-dependent RNA polymerase (RdRp) and non-structural protein 6 (NSP6), resulted in the selection of seven drugs which preferentially bind to the target proteins. Further, the molecular interactions determined by molecular dynamics simulation revealed that among the 75 drug molecules, catechin can effectively bind to 3CLpro, CTSL, RBD of S protein, NSP6 and nucleocapsid protein. It is more conveniently involved in key molecular interactions, showing binding free energy (ΔGbind) in the range of -5.09 kcal/mol (CTSL) to -26.09 kcal/mol (NSP6). At the binding pocket, catechin is majorly stabilized by the hydrophobic interactions, displays ΔEvdW values: -7.59 to -37.39 kcal/mol. Thus, the structural insights of better binding affinity and favorable molecular interaction of catechin toward multiple target proteins signify that catechin can be potentially explored as a multi-targeted agent against COVID-19.

Management of Obstetric Analgesia in the Developing Countries during the Coronavirus Disease Pandemic: A Narrative Review.

Coronavirus disease (COVID), also known as COVID-19, has brought the immense challenges for the health-care system globally. All the branches of medicine are equally involved in managing these patients. During this pandemic, care of obstetric patients in terms of obstetric analgesia becomes crucial. Hence, the purpose of this review was to draft a basic model of strategies related to the provision of safe obstetric analgesia during this coronavirus pandemic, which will assist the health-care providers across the developing countries to formulate their own protocols depending upon the resource availability. All research articles related to obstetric analgesia during the COVID-19 pandemic from January 2020 to December 01, 2020 available on PubMed, Cochrane, Google scholar, and Embase are included in this study. The keywords used for data search were "obstetric analgesia during COVID-19," "coronavirus pandemic," "Labor pain," "obstetric pain management guidelines," and "regional anesthesia during COVID-19." Eventually, our review yielded the most recentmodel for the provision of safe and effective obstetric analgesia practices during the COVID-19 pandemic across the developing countries.

Analysis of Neurosurgical Cases Before and During the Coronavirus Disease 2019 Pandemic from a Tertiary-Care Centre in India.

OBJECTIVE: We present the unique administrative issues as well as specific patient-related and surgeon-related challenges and solutions implemented while treating neurosurgical patients during the coronavirus disease 2019 (COVID-19) pandemic vis-à-vis pre-COVID-19 times at our tertiary-care center. METHODS: This is a retrospective study comparing the outcome of the neurosurgical patients treated from the beginning of lockdown in India on March 25, 2020 to November 30, 2020 with that of same period in the previous year, 2019. RESULTS: There were 687 neurosurgery admissions during the study period compared with 2550 admissions in 2019. The total number of surgeries performed in neurosurgery also showed a similar trend, with only 654 surgeries in 2020 compared with 3165 surgeries in 2019. During COVID-19 times, 474 patients were operated on including both trauma and nontrauma cases. Of the 50 patients with suspected/indeterminate COVID-19 who were operated on, 5 turned out to be positive for COVID-19. Significant differences were seen in the mortality (P < 0.01) and morbidity (P < 0.01) among patients with trauma on comparing COVID and pre-COVID periods. Similarly, a significant difference was observed in the mortality (P < 0.001) and morbidity (P < 0.001) in patients who did not have trauma. CONCLUSIONS: The higher mortality and morbidity during the COVID pandemic is primarily attributable to poorer baseline clinical status. Our experience from this COVID period might not only help us in tackling subsequent waves but also help other institutions in the developing world to be better prepared for similar circumstances.