Global Impact of the COVID-19 Pandemic on Stroke Volumes and Cerebrovascular Events: One-Year Follow-up.

BACKGROUND AND OBJECTIVES: Declines in stroke admission, intravenous thrombolysis, and mechanical thrombectomy volumes were reported during the first wave of the COVID-19 pandemic. There is a paucity of data on the longer-term effect of the pandemic on stroke volumes over the course of a year and through the second wave of the pandemic. We sought to measure the impact of the COVID-19 pandemic on the volumes of stroke admissions, intracranial hemorrhage (ICH), intravenous thrombolysis (IVT), and mechanical thrombectomy over a one-year period at the onset of the pandemic (March 1, 2020, to February 28, 2021) compared with the immediately preceding year (March 1, 2019, to February 29, 2020). METHODS: We conducted a longitudinal retrospective study across 6 continents, 56 countries, and 275 stroke centers. We collected volume data for COVID-19 admissions and 4 stroke metrics: ischemic stroke admissions, ICH admissions, intravenous thrombolysis treatments, and mechanical thrombectomy procedures. Diagnoses were identified by their ICD-10 codes or classifications in stroke databases. RESULTS: There were 148,895 stroke admissions in the one-year immediately before compared to 138,453 admissions during the one-year pandemic, representing a 7% decline (95% confidence interval [95% CI 7.1, 6.9]; p<0.0001). ICH volumes declined from 29,585 to 28,156 (4.8%, [5.1, 4.6]; p<0.0001) and IVT volume from 24,584 to 23,077 (6.1%, [6.4, 5.8]; p<0.0001). Larger declines were observed at high volume compared to low volume centers (all p<0.0001). There was no significant change in mechanical thrombectomy volumes (0.7%, [0.6,0.9]; p=0.49). Stroke was diagnosed in 1.3% [1.31,1.38] of 406,792 COVID-19 hospitalizations. SARS-CoV-2 infection was present in 2.9% ([2.82,2.97], 5,656/195,539) of all stroke hospitalizations. DISCUSSION: There was a global decline and shift to lower volume centers of stroke admission volumes, ICH volumes, and IVT volumes during the 1st year of the COVID-19 pandemic compared to the prior year. Mechanical thrombectomy volumes were preserved. These results suggest preservation in the stroke care of higher severity of disease through the first pandemic year. TRIAL REGISTRATION INFORMATION: This study is registered under NCT04934020.

E-Learning Environment Based Intelligent Profiling System for Enhancing User Adaptation

Online learning systems have expanded significantly over the last couple of years. Massive Open Online Courses (MOOCs) have become a major trend on the internet. During the COVID-19 pandemic, the count of learner enrolment has increased in various MOOC platforms like Coursera, Udemy, Swayam, Udacity, FutureLearn, NPTEL, Khan Academy, EdX, SWAYAM, etc. These platforms offer multiple courses, and it is difficult for online learners to choose a suitable course as per their requirements. In order to improve this e-learning education environment and to reduce the drop-out ratio, online learners will need a system in which all the platform's offered courses are compared and recommended, according to the needs of the learner. So, there is a need to create a learner's profile to analyze so many platforms in order to fulfill the educational needs of the learners. To develop a profile of a learner or user, three input parameters are considered: personal details, educational details, and knowledge level. Along with these parameters, learners can also create their user profiles by uploading their CVs or LinkedIn. In this paper, the major innovation is to implement a user interface-based intelligent profiling system for enhancing user adaptation in which feedback will be received from a user and courses will be recommended according to user/learners' preferences.

IoT Fog-Enabled Multi-Node Centralized Ecosystem for Real Time Screening and Monitoring of Health Information

In today's technological and stressful world, when everyone is busy in their daily routines and places blind faith in pharmaceutical advancements to protect their health, the sudden, horrifying effects of the COVID-19 pandemic have resulted in serious emotional and psychological impacts in the general population. In spite of advanced vaccination campaigns, fear and hesitation have become a part of human life since there are a number of people who do not want to take these immunity boosting vaccinations. Such people may become carriers of infectious viruses, leading to a more rapid rate of spread;therefore, this class of spreaders needs to be screened at the earliest opportunity. In this context, there is a need for advanced health monitoring systems which can assist the pharmaceutical industry to monitor and record the health status of people. To address this need and reduce the uncertainty of the situation, this study has designed and tested an Internet of Things (IoT) and Fog computing-based multi-node architecture was for real-time initial screening and recording of such subjects. The proposed system was able to record current body temperature and location coordinates along with the facial images. Further, the proposed system was able to transmit data to a cloud database using internet-connected services. An implementation and reviews-based working environment analysis was conducted to determine the efficacy of the proposed system. It was observed from the statistical analysis that the proposed IoT Fog-enabled ecosystem could be utilized efficiently.

Impact of COVID-19 on transportation in urban India

In this chapter, first, it is found that with the rapid increase in COVID-19 during the wave-1 and wave-2 of the pandemic in India, the overall mobility for various purposes as well as using various modes had decreased, while with decrease in COVID-19, the overall mobility had registered rapid increase. Second, an analysis of the perceptions of public transport commuters with respect to quality of service pre and post COVID-19 highlighted the declining popularity of public transport amongst the commuters post COVID-19. Third, the mode choices of the commuters during COVID-19 and prior to it in some of the Indian cities were investigated. Fourth, various existing contactless technologies were studied considering their inherent advantages and disadvantages and how to overcome those demerits. In summary, the readers are presented with a comprehensive overview of the issues as well as the possible solution towards handling public transportation in urban India post COVID-19.

Intelligent Load-Balancing Framework for Fog-Enabled Communication in Healthcare

The present technological era significantly makes use of Internet-of-Things (IoT) devices for offering and implementing healthcare services. Post COVID-19, the future of the healthcare system is highly reliant upon the inculcation of Artificial-Intelligence (AI) mechanisms in its day-to-day procedures, and this is realized in its implementation using sensor-enabled smart and intelligent IoT devices for providing extensive care to patients relative to the symmetric concept. The offerings of such AI-enabled services include handling the huge amount of data processed and sensed by smart medical sensors without compromising the performance parameters, such as the response time, latency, availability, cost and processing time. This has resulted in a need to balance the load of the smart operational devices to avoid any failure of responsiveness. Thus, in this paper, a fog-based framework is proposed that can balance the load among fog nodes for handling the challenging communication and processing requirements of intelligent real-time applications.

A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images.

COVID-19 declared as a pandemic that has a faster rate of infection and has impacted the lives and the country's economy due to forced lockdowns. Its detection using RT-PCR is required long time and due to which its infection has grown exponentially. This creates havoc for the shortage of testing kits in many countries. This work has proposed a new image processing-based technique for the health care systems named "C19D-Net", to detect "COVID-19" infection from "Chest X-Ray" (XR) images, which can help radiologists to improve their accuracy of detection COVID-19. The proposed system extracts deep learning (DL) features by applying the InceptionV4 architecture and Multiclass SVM classifier to classify and detect COVID-19 infection into four different classes. The dataset of 1900 Chest XR images has been collected from two publicly accessible databases. Images are pre-processed with proper scaling and regular feeding to the proposed model for accuracy attainments. Extensive tests are conducted with the proposed model ("C19D-Net") and it has succeeded to achieve the highest COVID-19 detection accuracy as 96.24% for 4-classes, 95.51% for three-classes, and 98.1% for two-classes. The proposed method has outperformed well in expressions of "precision", "accuracy", "F1-score" and "recall" in comparison with most of the recent previously published methods. As a result, for the present situation of COVID-19, the proposed "C19D-Net" can be employed in places where test kits are in short supply, to help the radiologists to improve their accuracy of detection of COVID-19 patients through XR-Images.

Blockchain Enabled Transparent and Anti-Counterfeiting Supply of COVID-19 Vaccine Vials.

The COVID-19 pandemic has profoundly affected almost all facets of peoples' lives, various economic areas and regions of the world. In such a situation implementation of a vaccination can be viewed as essential but its success will be dependent on availability and transparency in the distribution process that will be shared among the stakeholders. Various distributed ledgers (DLTs) such as blockchain provide an open, public, immutable system that has numerous applications due the mentioned abilities. In this paper the authors have proposed a solution based on blockchain to increase the security and transparency in the tracing of COVID-19 vaccination vials. Smart contracts have been developed to monitor the supply, distribution of vaccination vials. The proposed solution will help to generate a tamper-proof and secure environment for the distribution of COVID-19 vaccination vials. Proof of delivery is used as a consensus mechanism for the proposed solution. A feedback feature is also implemented in order to track the vials lot in case of any side effect cause to the patient. The authors have implemented and tested the proposed solution using Ethereum test network, RinkeyBy, MetaMask, one clicks DApp. The proposed solution shows promising results in terms of throughput and scalability.

SARS-CoV-2 and Stroke Characteristics: A Report From the Multinational COVID-19 Stroke Study Group.

[Figure: see text].

Alarming downtrend in mechanical thrombectomy rates in African American patients during the COVID-19 pandemic-Insights from STAR.

BACKGROUND: The coronavirus disease (COVID-19) pandemic has affected stroke care globally. In this study, we aim to evaluate the impact of the current pandemic on racial disparities among stroke patients receiving mechanical thrombectomy (MT). METHODS: We used the prospectively collected data in the Stroke Thrombectomy and Aneurysm Registry from 12 thrombectomy-capable stroke centers in the US and Europe. We included acute stroke patients who underwent MT between January 2017 and May 2020. We compared baseline features, vascular risk factors, location of occlusion, procedural metrics, complications, and discharge outcomes between patients presenting before (before February 2020) and those who presented during the pandemic (February to May 2020). RESULTS: We identified 2083 stroke patients: of those 235 (11.3%) underwent MT during the COVID-19 pandemic. Compared with pre-pandemic, stroke patients who received MT during the pandemic had longer procedure duration (44 vs 38 min, P=0.006), longer length of hospitalization (6 vs 4 days, P<0.001), and higher in-hospital mortality (18.7% vs 11%, P<0.001). Importantly, there was a lower number of African American patients undergoing MT during the COVID-19 pandemic (609 (32.9%) vs 56 (23.8%); P=0.004). CONCLUSION: The COVID-19 pandemic has affected the care process for stroke patients receiving MT globally. There is a significant decline in the number of African American patients receiving MT, which mandates further investigation.

International experience of mechanical thrombectomy during the COVID-19 pandemic: insights from STAR and ENRG.

BACKGROUND: In response to the COVID-19 pandemic, many centers altered stroke triage protocols for the protection of their providers. However, the effect of workflow changes on stroke patients receiving mechanical thrombectomy (MT) has not been systematically studied. METHODS: A prospective international study was launched at the initiation of the COVID-19 pandemic. All included centers participated in the Stroke Thrombectomy and Aneurysm Registry (STAR) and Endovascular Neurosurgery Research Group (ENRG). Data was collected during the peak months of the COVID-19 surge at each site. Collected data included patient and disease characteristics. A generalized linear model with logit link function was used to estimate the effect of general anesthesia (GA) on in-hospital mortality and discharge outcome controlling for confounders. RESULTS: 458 patients and 28 centers were included from North America, South America, and Europe. Five centers were in high-COVID burden counties (HCC) in which 9/104 (8.7%) of patients were positive for COVID-19 compared with 4/354 (1.1%) in low-COVID burden counties (LCC) (P<0.001). 241 patients underwent pre-procedure GA. Compared with patients treated awake, GA patients had longer door to reperfusion time (138 vs 100 min, P=<0.001). On multivariate analysis, GA was associated with higher probability of in-hospital mortality (RR 1.871, P=0.029) and lower probability of functional independence at discharge (RR 0.53, P=0.015). CONCLUSION: We observed a low rate of COVID-19 infection among stroke patients undergoing MT in LCC. Overall, more than half of the patients underwent intubation prior to MT, leading to prolonged door to reperfusion time, higher in-hospital mortality, and lower likelihood of functional independence at discharge.

Risk of stroke in hospitalized SARS-CoV-2 infected patients: A multinational study.

BACKGROUND: There is an increased attention to stroke following SARS-CoV-2. The goal of this study was to better depict the short-term risk of stroke and its associated factors among SARS-CoV-2 hospitalized patients. METHODS: This multicentre, multinational observational study includes hospitalized SARS-CoV-2 patients from North and South America (United States, Canada, and Brazil), Europe (Greece, Italy, Finland, and Turkey), Asia (Lebanon, Iran, and India), and Oceania (New Zealand). The outcome was the risk of subsequent stroke. Centres were included by non-probability sampling. The counts and clinical characteristics including laboratory findings and imaging of the patients with and without a subsequent stroke were recorded according to a predefined protocol. Quality, risk of bias, and heterogeneity assessments were conducted according to ROBINS-E and Cochrane Q-test. The risk of subsequent stroke was estimated through meta-analyses with random effect models. Bivariate logistic regression was used to determine the parameters with predictive outcome value. The study was reported according to the STROBE, MOOSE, and EQUATOR guidelines. FINDINGS: We received data from 26,175 hospitalized SARS-CoV-2 patients from 99 tertiary centres in 65 regions of 11 countries until May 1st, 2020. A total of 17,799 patients were included in meta-analyses. Among them, 156(0.9%) patients had a stroke-123(79%) ischaemic stroke, 27(17%) intracerebral/subarachnoid hemorrhage, and 6(4%) cerebral sinus thrombosis. Subsequent stroke risks calculated with meta-analyses, under low to moderate heterogeneity, were 0.5% among all centres in all countries, and 0.7% among countries with higher health expenditures. The need for mechanical ventilation (OR: 1.9, 95% CI:1.1-3.5, p = 0.03) and the presence of ischaemic heart disease (OR: 2.5, 95% CI:1.4-4.7, p = 0.006) were predictive of stroke. INTERPRETATION: The results of this multi-national study on hospitalized patients with SARS-CoV-2 infection indicated an overall stroke risk of 0.5%(pooled risk: 0.9%). The need for mechanical ventilation and the history of ischaemic heart disease are the independent predictors of stroke among SARS-CoV-2 patients. FUNDING: None.