Role of Hypertension on the Severity of COVID-19: A Review

Journal of Cardiovascular Pharmacology, Journal Year: 2021, Volume and Issue: 78(5), P. e648 - e655

Published: July 29, 2021

Abstract: The novel coronavirus disease (COVID-19) caused by the severe acute respiratory syndrome 2 (SARS-CoV-2) has rapidly evolved into a global pandemic. substantial morbidity and mortality associated with infection prompted us to understand potential risk factors that can predict patient outcomes. Hypertension been identified as most prevalent cardiovascular comorbidity in patients infected COVID-19 demonstrably increases of hospitalization death. Initial studies implied renin–angiotensin–aldosterone system inhibitors might increase viral aggravate severity, thereby causing panic given high prevalence hypertension. Nonetheless, subsequent evidence supported administration antihypertensive drugs noted they do not severity hypertension, rather may have beneficial effect. To date, precise mechanism which hypertension predisposes unfavorable outcomes remains unknown. In this mini review, we elaborate on pathology SARS-CoV-2 coexisting summarize mechanisms, focusing dual roles angiotensin-converting enzyme disorders effects proinflammatory released because immune response gastrointestinal dysfunction are also discussed.

Language: Английский

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The role of artificial intelligence in pandemic responses: from epidemiological modeling to vaccine development

Molecular Biomedicine, Journal Year: 2025, Volume and Issue: 6(1)

Published: Jan. 3, 2025

Abstract Integrating Artificial Intelligence (AI) across numerous disciplines has transformed the worldwide landscape of pandemic response. This review investigates multidimensional role AI in pandemic, which arises as a global health crisis, and its preparedness responses, ranging from enhanced epidemiological modelling to acceleration vaccine development. The confluence technologies guided us new era data-driven decision-making, revolutionizing our ability anticipate, mitigate, treat infectious illnesses. begins by discussing impact on emerging countries worldwide, elaborating critical significance modelling, bringing enabling forecasting, mitigation response pandemic. In epidemiology, AI-driven models like SIR (Susceptible-Infectious-Recovered) SIS (Susceptible-Infectious-Susceptible) are applied predict spread disease, preventing outbreaks optimising distribution. also demonstrates how Machine Learning (ML) algorithms predictive analytics improve knowledge disease propagation patterns. collaborative aspect discovery clinical trials various vaccines is emphasised, focusing constructing AI-powered surveillance networks. Conclusively, presents comprehensive assessment impacts builds AI-enabled dynamic collaborating ML Deep (DL) techniques, develops implements trials. focuses screening, contact tracing monitoring virus-causing It advocates for sustained research, real-world implications, ethical application strategic integration strengthen collective face alleviate effects issues.

Language: Английский

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Conventional and Novel Diagnostic Tools for the Diagnosis of Emerging SARS-CoV-2 Variants

Vaccines, Journal Year: 2023, Volume and Issue: 11(2), P. 374 - 374

Published: Feb. 6, 2023

Accurate identification at an early stage of infection is critical for effective care any infectious disease. The “coronavirus disease 2019 (COVID-19)” outbreak, caused by the virus “Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)”, corresponds to current and global pandemic, characterized several developing variants, many which are classified as variants concern (VOCs) “World Health Organization (WHO, Geneva, Switzerland)”. primary diagnosis made using either molecular technique RT-PCR, detects parts viral genome’s RNA, or immunodiagnostic procedures, identify proteins antibodies generated host. As demand RT-PCR test grew fast, inexperienced producers joined market with innovative kits, increasing number laboratories diagnostic field, rendering results increasingly prone mistakes. It difficult determine how outcomes one unnoticed result could influence decisions about patient quarantine social isolation, particularly when patients themselves health providers. development point-of-care testing helps in rapid in-field disease, such can also be used a bedside monitor mapping progression patients. In this review, we have provided readers available techniques their pitfalls detecting emerging VOCs SARS-CoV-2, lastly, discussed AI-ML- nanotechnology-based smart SARS-CoV-2 detection.

Language: Английский

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AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods

Osong Public Health and Research Perspectives, Journal Year: 2024, Volume and Issue: 15(2), P. 115 - 136

Published: March 28, 2024

Objectives: The coronavirus disease 2019 (COVID-19) pandemic continues to pose significant challenges the public health sector, including that of United Arab Emirates (UAE). objective this study was assess efficiency and accuracy various deep-learning models in forecasting COVID-19 cases within UAE, thereby aiding nation’s authorities informed decision-making.Methods: This utilized a comprehensive dataset encompassing confirmed cases, demographic statistics, socioeconomic indicators. Several advanced deep learning models, long short-term memory (LSTM), bidirectional LSTM, convolutional neural network (CNN), CNN-LSTM, multilayer perceptron, recurrent (RNN) were trained evaluated. Bayesian optimization also implemented fine-tune these models.Results: evaluation framework revealed each model exhibited different levels predictive precision. Specifically, RNN outperformed other architectures even without optimization. Comprehensive perspective analytics conducted scrutinize dataset.Conclusion: transcends academic boundaries by offering critical insights enable UAE deploy targeted data-driven interventions. model, which identified as most reliable accurate for specific context, can significantly influence decisions. Moreover, broader implications research validate capability techniques handling complex datasets, thus transformative potential healthcare sectors.

Language: Английский

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The Crucial Role of Interdisciplinary Conferences in Advancing Explainable AI in Healthcare

BioMedInformatics, Journal Year: 2024, Volume and Issue: 4(2), P. 1363 - 1383

Published: May 17, 2024

As artificial intelligence (AI) integrates within the intersecting domains of healthcare and computational biology, developing interpretable models tailored to medical contexts is met with significant challenges. Explainable AI (XAI) vital for fostering trust enabling effective use in healthcare, particularly image-based specialties such as pathology radiology where adjunctive solutions diagnostic image analysis are increasingly utilized. Overcoming these challenges necessitates interdisciplinary collaboration, essential advancing XAI enhance patient care. This commentary underscores critical role conferences promoting necessary cross-disciplinary exchange innovation. A literature review was conducted identify key challenges, best practices, case studies related collaboration healthcare. The distinctive contributions specialized dialogue, driving innovation, influencing research directions were scrutinized. Best practices recommendations organizing conferences, achieving targeted adapted from literature. By crucial collaborative junctures that drive progress, integrate diverse insights produce new ideas, knowledge gaps, crystallize solutions, spur long-term partnerships generate high-impact research. Thoughtful structuring events, including sessions focused on theoretical foundations, real-world applications, standardized evaluation, along ample networking opportunities, directing varied expertise toward overcoming core Successful collaborations depend building mutual understanding respect, clear communication, defined roles, a shared commitment ethical development robust, models. Specialized shape future explainable contributing improved outcomes innovations. Recognizing catalytic power this model accelerating innovation implementation medicine.

Language: Английский

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Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(3), P. e0294289 - e0294289

Published: March 14, 2024

The COVID-19 pandemic has had a significant impact on both the United Arab Emirates (UAE) and Malaysia, emphasizing importance of developing accurate reliable forecasting mechanisms to guide public health responses policies. In this study, we compared several cutting-edge deep learning models, including Long Short-Term Memory (LSTM), bidirectional LSTM, Convolutional Neural Networks (CNN), hybrid CNN-LSTM, Multilayer Perceptron’s, Recurrent (RNN), project cases in aforementioned regions. These models were calibrated evaluated using comprehensive dataset that includes confirmed case counts, demographic data, relevant socioeconomic factors. To enhance performance these Bayesian optimization techniques employed. Subsequently, re-evaluated compare their effectiveness. Analytic approaches, predictive retrospective nature, used interpret data. Our primary objective was determine most effective model for predicting Malaysia. findings indicate selected algorithms proficient cases, although efficacy varied across different models. After thorough evaluation, architectures suitable specific conditions UAE Malaysia identified. study contributes significantly ongoing efforts combat pandemic, providing crucial insights into application sophisticated precise timely cases. hold substantial value shaping strategies, enabling authorities develop targeted evidence-based interventions manage virus spread its populations confirms usefulness methodologies efficiently processing complex datasets generating projections, skill great healthcare professional settings.

Language: Английский

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Application of artificial intelligence (AI) to control COVID-19 pandemic: Current status and future prospects

Heliyon, Journal Year: 2024, Volume and Issue: 10(4), P. e25754 - e25754

Published: Feb. 1, 2024

The impact of the coronavirus disease 2019 (COVID-19) pandemic on everyday livelihood people has been monumental and unparalleled. Although vastly affected global healthcare system, it also a platform to promote develop pioneering applications based autonomic artificial intelligence (AI) technology with therapeutic significance in combating pandemic. Artificial successfully demonstrated that can reduce probability human-to-human infectivity virus through evaluation, analysis, triangulation existing data spread virus. This review talks about modern robotic automated systems may assist spreading In addition, this study discusses intelligent wearable devices how they could be helpful throughout COVID-19

Language: Английский

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Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews

International Journal of Medical Informatics, Journal Year: 2022, Volume and Issue: 166, P. 104855 - 104855

Published: Aug. 17, 2022

Artificial intelligence is fueling a new revolution in medicine and the healthcare sector. Despite growing evidence on benefits of artificial there are several aspects that limit measure its impact people's health. It necessary to assess current status application AI towards improvement health domains defined by WHO's Thirteenth General Programme Work (GPW13) European (EPW), inform about trends, gaps, opportunities, challenges.

Language: Английский

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Machine learning model for predicting the length of stay in the intensive care unit for Covid-19 patients in the eastern province of Saudi Arabia

Informatics in Medicine Unlocked, Journal Year: 2022, Volume and Issue: 30, P. 100937 - 100937

Published: Jan. 1, 2022

The COVID-19 virus has spread rapidally throughout the world. Managing resources is one of biggest challenges that healthcare providers around world face during pandemic. Allocating Intensive Care Unit (ICU) beds' capacity important since a respiratory disease and some patients need to be admitted hospital with an urgent for oxygen support, ventilation, and/or intensive medical care. In battle against COVID-19, many governments utilized technology, especially Artificial Intelligence (AI), contain pandemic limit its hazardous effects. this paper, Machine Learning models (ML) were developed help in detecting patients' ICU estimated duration their stay. Four ML algorithms utilized: Random Forest (RF), Gradient Boosting (GB), Extreme (XGBoost), Ensemble trained validated on dataset 895 King Fahad University eastern province Saudi Arabia. conducted experiments show Length Stay (LoS) can predicted highest accuracy by applying RF model prediction, as achieved was 94.16%. terms contributor factors length stay ICU, correlation results showed age, C-Reactive Protein (CRP), nasal support days are top related factors. By searching literature, there no published work used Arabia predict number needed. This contribution hoped pave path hospitals manage more efficiently saving lives.

Language: Английский

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Applying artificial intelligence in healthcare: lessons from the COVID-19 pandemic

International Journal of Production Research, Journal Year: 2023, Volume and Issue: unknown, P. 1 - 34

Published: Oct. 3, 2023

The COVID-19 pandemic exposed vulnerabilities in global healthcare systems and highlighted the need for innovative, technology-driven solutions like Artificial Intelligence (AI). However, previous research on topic has been limited fragmented, leading to an incomplete understanding of ‘what’, ‘where’ ‘how’ its application, as well associated benefits challenges. This study proposes a comprehensive AI framework assesses effectiveness within UAE's sector. It provides valuable insights into applications stakeholders that range from molecular population level. covers different computational techniques employed, machine learning computer vision, various types data inputs fed these techniques, including clinical, epidemiological, locational, behavioural genomic data. Additionally, highlights AI's capacity enhance healthcare's operational, quality-related social outcomes, recognises regulatory policies, technological infrastructure, stakeholder cooperation innovation readiness key facilitators adoption. Lastly, we stress importance addressing challenges such privacy, security, generalisability algorithmic bias. Our findings are relevant beyond facilitating development AI-related policy interventions support mechanisms building resilient sector can withstand future

Language: Английский

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