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1.
A Handbook of Artificial Intelligence in Drug Delivery ; : 571-580, 2023.
Artigo em Inglês | Scopus | ID: covidwho-20233072

RESUMO

In 2020, COVID-19 changed how health care was approached both in the United States and globally. In the early phases, the vast majority of energy and attention was devoted to containing the pandemic and treating the infected. Toward the end of 2020, that attention expanded to vaccinating people across the globe. What was not being considered at the time were challenges to future health and clinical trials that power new treatments for COVID-19 and non-COVID-19 treatments. © 2023 Elsevier Inc. All rights reserved.

2.
1st International Conference on Machine Learning, Computer Systems and Security, MLCSS 2022 ; : 204-207, 2022.
Artigo em Inglês | Scopus | ID: covidwho-2300254

RESUMO

The COVID-19 outbreak turned the world upside down by infecting hundred million people, killing more than five million and disrupting everyday life across the planet. The Wuhan virus shattered the global economy and brought daily life to a grinding halt in much of the world. The second largest populated country India had no escape as well. Since the very beginning of 20th century, machine learning based methodologies have been largely applied in epidemiological data analysis in order to control diseases and other health issues. In this regard, researchers have come up with various predictor models to forecast the future impact of the Wuhan virus, so that further spreading of virus can be controlled by implementing precautionary measures. The purpose behind this work is to investigate the prediction capability of Legendre Polynomial Neural Network (LEPNN) trained using the very popular bio-inspired Flower Pollination Algorithm on the real data set of three categories of COVID cases in India as well as Odisha. The three types are the confirmed, deceased and recovery cases of daily basis. The prediction performance of the LEPNN-FPA model has been assessed with respect to the performance of two other models. © 2022 IEEE.

3.
Public Administration and Development ; 2023.
Artigo em Inglês | Scopus | ID: covidwho-2209173

RESUMO

Since the outbreak of the Covid 19 pandemic, governments across the world including India, a South-Asian country is busy ‘strategizing', ‘managing' ‘containing' the crisis to restrict its spread. But given the vastness and diversity of the Indian territory, one pan Indian model of is not possible and the states have been working in consonance with the centre in a matter of ‘cooperative federalism' and are implementing various micro models of Covid 19 governance. This paper explores the micro models of governance strategies taken by states in India namely, Kerala located in its Southern coast and Odisha in the Eastern coast which have been experiencing disasters be it health or natural calamities. Inspite of the differences in social development indicators between both the states, they have managed to keep the death rates lower in the initial phases of the outbreak in comparison to other states. This is reflected in the strategies they took in controlling the pandemic like "preparedness” "decentralisation”, "community participation”. However, inspite of deploying various governance models, the gradual unlocking led to the explosion of positive cases as a result of which the challenges to deal with the pandemic still looms large. © 2023 John Wiley & Sons Ltd.

4.
2nd Asian Conference on Innovation in Technology, ASIANCON 2022 ; 2022.
Artigo em Inglês | Scopus | ID: covidwho-2136098

RESUMO

The variations in the price of crude oil are very erratic, nonlinear, and dynamic with a high degree of uncertainty making it much more difficult to predict accurately. As a result, the opacity and intricacy in determining the crude oil price have been a significant topic of interest for researchers. This paper develops an efficient Genetic Algorithm(GA) based fine-tuned Support Vector Regression(SVR) model for predicting crude oil prices. The strategy utilizes key economic factors that ascertain the price per barrel, which serves as the input. The NASDAQ dataset used in this work encompasses ten years of daily data. The GA technique fine-tunes the parameters of the SVR model to boost the model's ability to foresee crude oil price fluctuations. The proposed model's performance is evaluated by employing various major criteria that compare our model to its counterparts, such as SVR and Long Short-Term Memory (LSTM) approaches. In light of these criteria, the findings of root mean square error (RMSE) and mean absolute percentage error (MAPE) indicate that this model surpasses others in predicting crude oil prices more accurately. Finally, this study also analyzes the impact of persistent uncertainness concerning the COVID-19 outbreak on crude oil price trends. © 2022 IEEE.

5.
International Journal of Health Sciences ; 6:3686-3700, 2022.
Artigo em Inglês | Scopus | ID: covidwho-1995072

RESUMO

COVID-19 has significantly affected the teaching-learning continuum in India. Most of the educational institutes had adopted online mode for the delivery of content and pedagogies enabled by digital technology, devices, and platforms. The pandemic has adversely affected English language learning in India, for learners used to learning ESL in a real-life situation through regular face-to-face mode experienced challenges in earning ESL through virtual mode. Learning English to develop the required language skills in virtual classrooms was anything but easy. Nonetheless, Indian students, as this study finds, took up the challenge by their stride and survived the altered learning conditions forced by the pandemic. This student-centric study aims to explore the impact of COVID-19 on language learning, the problems and challenges faced by student-learners, and the strategies to overcome them. An online survey was conducted to collect data from a group of students (n=400) using a survey questionnaire and mixed methods were used for data analysis and interpretation. The results indicate that the students experienced moderate to high-level difficulty in language learning and their coping strategies worked out. © 2022 International Journal of Health Sciences.

6.
INTERNATIONAL JOURNAL OF EARLY CHILDHOOD SPECIAL EDUCATION ; 14(4):1225-1235, 2022.
Artigo em Inglês | Web of Science | ID: covidwho-1966000

RESUMO

The man-made brainpower (AI) methods overall and convolutional brain organizations (CNNs) specifically have achieved victories in clinical picture examination and grouping. A profound CNN design partakesprojected into this research article for the analysis of OMICRONgroundedonto clinical radiography analysis (X-ray). As matter of the fact, thenon-availability in adequate scope and excellent X-ray picture database, a compelling and exact Convolutional NN (CNN) characterization remained anexamination. Managingthose intricacies, for example, accessibility with avery-little measured and contrastdatabaseof picture resolutionchallenges, the database has been pre-processed into various stages utilizing various strategies to accomplish a powerful preparation databaseof the appliedConvolutional NN (CNN)prototypical to achieve itsfinest presentation. Preprocessing phases in the database acted intoresearch incorporate database adjusting, clinical specialists' picture investigation, and information expansion. The exploratory outcomes reveal general precision up to 98.08% that exhibits its great capacity of the prototypicalConvolutional NN (CNN)systemof the ongoing application space. Convolutional NN (CNN)prototype has been tried into 2 (two) situations. The primary situation explains that it hasbeen tried utilizing the 7762 X-ray pictures as database,it accomplished a precision of 98.08 percent. To the subsequent situation, the prototypical hastried been utilizing the autonomous database of Omicron X-ray pictures from Kaggle. The execution intocurrentassessment the situation remained just about 98.08%. It additionally demonstrates that the prototypicalsystem beats different systems, asa similar examination has finished been thru a portion of AIcalculations. The proposed model has superseded every one of the models by and large and explicitly when the model testing was finished utilizing a free testing set.

7.
Journal of the American College of Cardiology ; 79(9):2046-2046, 2022.
Artigo em Inglês | Web of Science | ID: covidwho-1848861
8.
2nd International Conference on Intelligent and Cloud Computing, ICICC 2021 ; 286:455-462, 2022.
Artigo em Inglês | Scopus | ID: covidwho-1826298

RESUMO

COVID-19 turned into a critical health problem around the world. Since the start of its spreading, numerous Artificial Intelligence-based models have been created for foreseeing the conduct of the infection and recognizing its contamination. One of the efficient methods of deciding the COVID-19, pneumonia disease is through the chest X-ray images analysis. As there are lots of patients in emergency clinical conditions, it would be tedious and difficult to analyze loads of X-ray images physically. So, an automated, AI-based system can be helpful to predict the infection due to COVID-19 in less time. In this study, a Modified Convolution Neural Network (CNN) model is suggested to predict the COVID-19 infections from the chest X-ray images. Proposed model is designed based on the state-of-art models like GoogleNet, U-Net, VGGNet. The model is fine-tuned using less number of layers than the existing model to get acceptable accuracy. The model is implemented on 724 chest X-ray images from COVID-19 image data collection and is able to produce 93.5% accuracy, 93.0% precision, 93.5% recall, and 92.5% F1-Score, respectively. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

9.
Journal of Clinical Lipidology ; 16(1):e25, 2022.
Artigo em Inglês | EMBASE | ID: covidwho-1778237

RESUMO

Lead Author's Financial Disclosures: Nothing to disclose. Study Funding: Piper Biosciences. Background/Synopsis: The effectiveness of a plant sterol gummy supplement was studied in a South-Asian (SA) patient population with low to moderate cardiovascular disease (CVD) risk as defined by an Atherosclerotic Cardiovascular Disease (ASCVD) Risk Score of < 7.5%, and a low density lipoprotein (LDL)-C level of 120-189 mg/dl. Statin therapy is often not recommended to patients with ASCVD score < 7.5% even in the presence of risk accelerators such as SA ethnicity, to which the 2018 National Lipid Association (NLA) guidelines call attention. Objective/Purpose: Phytosterols are known to lower LDL-C and are included in NLA and other global guidelines. This study aimed to establish their impact on LDL-C levels in 'borderline' risk SAs. Methods: 50 SAs were recruited during the COVID 19 pandemic, mainly from a preventive cardiology clinic dedicated to reducing SA heart disease risk. Eligible subjects had a 10-year CV risk score (ASCVD) <7.5% and LDL-C level of 120-193mg/dl at study enrollment. Subjects intolerant of or refusing statins were also recruited. The study was administered with a fully decentralized design, leveraging mailed supplements, televisits, remote lab collection, and SMS-based communications. Upon completing baseline labs and surveys, subjects were provided a 90-day supply of 1400mg phytosterol gummy supplements in individual packets (Piper Biosciences, Los Altos, CA) to be ingested twice daily. Subjects were instructed to continue current lifestyle habits and report major dietary pattern deviations. The primary endpoint was LDL-C reduction at 3 months. Pre- and post-study surveys were administered to assess diet and lifestyle. Results: 33 of the 50 subjects successfully completed the protocol. A significant overall reduction in LDL-C of 5.8% was observed (p=0.03) (Table 1, Figure 1). Subgroup A (n=27) completed the protocol with no significant dietary variation, demonstrating a significant LDL-C reduction of 6.5% (p=0.002), as well as a total cholesterol (TC) reduction of 4.4% (p=0.01). There was no significant change in other metrics, including BMI, fasting glucose, or HbA1C. Patients who completed the protocol but reported worsening dietary habits (Subgroup B, n=6) showed an average increase in LDL-C of 6% (p=0.2) and in TC of 8% (p=0.002). Survey responses indicate that 94% of subjects would be interested in long-term supplementation if recommended by physician and 80% would prefer taking it proactively to manage cholesterol levels. Conclusions: Plant sterols are an effective and sustainable means to lower LDL-C in middle-aged SAs, whose CV risk is often underestimated. To our knowledge this study represents the first demonstration of phytosterol effectiveness in the highest coronary disease risk population globally.

10.
Journal of Advanced Biotechnology and Experimental Therapeutics ; 5(1):100-114, 2022.
Artigo em Inglês | Scopus | ID: covidwho-1742884

RESUMO

Neurodegenerative disorders, including Alzheimer’s and Parkinson’s, are the leading causes of dementia in the elderly. In the coming days, an alarming upsurge of dementia patients is expected with increasing life expectancy. This is the scenario not only in the developed world but also in the developing world, where older people live in vulnerable situations. Even in the COVID-19 (coronavirus disease-19) pandemic, the situation has worsened. Due to the limitations of conventional therapeutic strategies, it is necessary to explore integrated approaches consisting of both pharmacological and non-pharmaceutical interventions. As existing anti-dementia drugs pose many adverse effects on patients, pharmacological intervention through naturally occurring agents should be employed to explore targeted therapy. Alongside, non-pharmacological interventions such as cognitive and motor rehabilitation, occupational therapy, and psychological therapy need to be explored. From this perspective, multidisciplinary approaches need to be employed in order to develop a sustainable patient-friendly treatment strategy for the management of these emerging health issues with tremendous social burdens. © 2022, Bangladesh Society for Microbiology, Immunology and Advanced Biotechnology. All rights reserved.

11.
European Heart Journal ; 42(SUPPL 1):3177, 2021.
Artigo em Inglês | EMBASE | ID: covidwho-1554235

RESUMO

Background: The COVID-19 pandemic has curtailed clinical trial activity significantly. Decentralized clinical trial (DCT) designs may lower cost, expand trial access, and reduce exposure risk for patients and staff. Whether such designs can be used for large, pivotal drug trials is not known. Purpose: We performed a feasibility study to inform whether a large phase 3 Cardiovascular DCT can achieve high quality trial results and also withstand health crises such as the COVID-19 pandemic. The DeTAP (Decentralized Trial in Afib Patients) was a single-arm, observational study that integrated a suite of digital health technologies, including paired home sensor devices, into a 100% virtual trial experience for atrial fibrillation (AF) patients on anticoagulation. Methods: We recruited 100 AF patients over age 55 on oral anticoagulation (OAC) by traditional methods or by social media ads placed Californiawide. Subjects completed an online prescreening, uploaded their active OAC prescription, and completed an e-consent via SMS message link. Participants downloaded a customized study app to integrate surveys and data from study-supplied wireless blood pressure (BP) and 6 lead EKG sensors (Figure 1A). Participants completed pre- and post-study engagement surveys, weekly OAC adherence surveys, 4 study televisits, 4 ECG/BP readings, and 4 post-study surveys over a 6 month period. The primary endpoints were protocol engagement-based measurements that quantified percent completion of: 1) televisits 2) surveys, 3) ECG/BP readings requirements. Secondary endpoints were the % changes in: 1) OAC adherence (OACA), 2) nuisance bleeding (NB), 3) individual patient engagement surveys. Results: 100 participants were recruited in 26 days (traditional: 6 in 2 weeks;social media: 94 in 12 days) with a dramatic surge in enrollment driven by social media ads (Figure 1B, Table). A recruitment overflow occurred with >200 eligible candidates on a waitlist. All key study completion metrics showed high compliance: televisit (91%);surveys (85%);ECG/BP completion (90%). Overall OACA was unchanged, but for subjects who reported low initial OACA, there was significant improvement at 6 months (85±16% to 98±6%;p=0.002). 47 participants (57%) reported NB, which did not correlate with OACA. Participant engagement measure scores (PAM-13) trended higher (baseline, 70%;6 months, 74%, p=0.32). Lastly, study participants exhibited strong interest in participating in a larger experimental drug DCT study (90%) in the future. Conclusion: The DeTAP study, conducted fully during the COVID-19 pandemic, demonstrates that a decentralized CV medical intervention trial is feasible and can achieve rapid recruitment, high study retention, physiologic and adverse event reporting, and high study engagement via the proper integration of digital technologies and a dedicated DCT study coordination effort. These findings could be informative for virtualizing large pivotal clinical trials at scale.

12.
Minerva Biotechnology and Biomolecular Research ; 33(3):157-165, 2021.
Artigo em Inglês | EMBASE | ID: covidwho-1362807

RESUMO

The COVID-19 pandemic has conceived a monstrous threat among the population. Here we portray some of the recent studies on COVID-19 which may supply bases for future research. This review brings a spotlight on the mode of transmission, pathophysiology, genetic structure, mutants of SARS-CoV-2, progress on the vaccination and some recent peculiar disguisable facts about the novel coronavirus 2019. Major scrutiny has been prepared on the new strains of SARS-CoV-2 and also a critical brief review on the ongoing vaccine research strategy and vaccine progress has been added further to that. Being in research this collection of data could have some advantageous response in the development of vaccines and understanding the actual behavior of SARS-CoV-2 in the research territory.

13.
Circulation ; 143(SUPPL 1), 2021.
Artigo em Inglês | EMBASE | ID: covidwho-1325202

RESUMO

Introduction: In response to the COVID-19 pandemic, medical practices have expanded utilization of telehealth. Little is known about the operational impacts of transitioning from in-person to video visits in specialty clinics. In 2018, the Stanford South Asian Translational Heart Initiative (SSATHI), a preventive cardiology clinic focused on high-risk South Asian adults, introduced CardioClick, a program replacing in-person follow-up visits with video visits. Hypothesis: We hypothesized that implementation of video visits increased the efficiency of clinic operations. Methods: We extracted visit-level data from the EHR for 134 patients enrolled in CardioClick with video follow-up visits from June 14, 2018 to April 21, 2020 and a cohort of 276 patients enrolled in the in-person SSATHI prevention program with follow-up visits from September 11, 2014 to March 6, 2020. Results: Patients in CardioClick and the in-person cohort were similar in terms of age (mean 45 years), gender balance (23 vs 21% female), and cardiometabolic risk profiles. There were 181 video and 637 in-person follow-up visits. Video visits were shorter than in-person visits, both in terms of total clinic time [median 22 min (IQR 16, 29) vs 67 min (48, 100)] and provider time required [median22 min (IQR 16, 29) vs 30 min (12, 58)]. Video visits were more likely to end on time (71 vs 11%,p<0.001). The median video visit ended on time while the median in-person visit ended 32 min late(13, 70) (see Figure). Providers were also more likely to complete video visit documentation thesame day (56 vs 42%, p=0.001). Conclusions: In a preventive cardiology clinic, video follow-up visits required less clinic and provider time than in-person visits, were more likely to end on time, and were associated with increased same-day provider documentation completion. In conclusion, video visits offer benefitsbeyond their convenience and may increase the operational efficiency of specialty care practicesfocused on disease prevention, improving value in care delivery.

14.
Journal of Advanced Biotechnology and Experimental Therapeutics ; 3(Special Issue 4):57-67, 2020.
Artigo em Inglês | Scopus | ID: covidwho-1209252

RESUMO

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is the aetiological agent behind the current pandemic of coronavirus disease 2019 (COVID-19). SARS-CoV-2 main protease plays a dynamic role in mediating viral replication and transcription, which is one of the most probable drug targets against SARS-CoV-2. Ficus carica latex encompasses notable bioactive molecules with various biological properties, including antiviral activities. In this study, latex compounds of Ficus carica were screened to find out active phytochemicals against SARS-CoV-2 main protease through molecular docking, molecular dynamics simulation, and ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiling. A total of 21 compounds were screened, and the compounds, lupeol, α-amyrin, and luteolin, showed the highest binding affinity and intense interaction with the vital catalytic residue His 41 and Cys 145. The molecular dynamics simulation revealed that the amyrin is the most stable compound with higher binding free energy, suggesting that this compound can compete with the native ligands of the main protease. The ADMET analysis indicated that these phytochemicals have considerable physicochemical, pharmacokinetics, and drug-likeness properties and do not possess any considerable detrimental effects and can be considered as potential drug candidates against SARS-CoV-2. However, further in-vitro, in-vivo, and clinical trials are required to observe the exact efficiency of these compounds. © 2020, Bangladesh Society for Microbiology, Immunology and Advanced Biotechnology. All rights reserved.

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