Computational Models for Biomarker Discovery

Skolariki, Konstantina; Exarchos, Themis P.; Vlamos, Panayiotis

doi:10.1007/978-3-031-31982-2_33

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…Although these structures allow for unimpeded passage, there is a limited understanding of how the movement of both humans and vehicles interacts with air curtains [279]. To address this issue, small-scale water bath experiments have been conducted using fresh and saltwater solutions [280]. utilised a vertical cylinder to simulate human movement through an air curtain, which was created using a planar jet divided into 2 zones with different densities [5].…”

Section: Statistical Inferencementioning

confidence: 99%

Role of Fluid Dynamics in Infectious Disease Transmission: Insights from COVID-19 and Other Pathogens

Koley

2024

Trends Sci

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The spread of infectious diseases such as COVID-19 depends on complex fluid dynamics interactions between pathogens and fluid phases, including individual droplets and multiphase clouds. Understanding these interactions is crucial for predicting and controlling disease spread. This applies to human and animal exhalations, such as coughs and sneezes, as well as bursting bubbles that create micron-sized droplets in various indoor and outdoor environments. By exploring case studies in this regard, this study examines the emerging field of fluid dynamics in disease transmission, focusing on multiphase flows, interfacial flows, turbulence, pathogens, human traffic, aerosol transmission, ventilation, and breathing microenvironments. These results indicate that increased ventilation rates and local ventilation methods can effectively reduce the concentration of SARS-CoV-2-laden aerosols in the immediate breathing spaces between individuals. In a displacement-ventilated room, both neutral and unstable conditions were more effective in removing breathed SARS-CoV-2-laden aerosols from the air, regardless of the presence of test subjects. However, stable conditions may increase the risk of infection in individuals living in confined spaces. Thus, the findings of this study are useful for providing practical guidance for managing the spread of airborne infections. HIGHLIGHTS Fluid dynamics affect the transmission of infectious diseases such as COVID-19 This study explored multiphase fluid flow, aerosol dispersion, and respiratory zones Increased ventilation and local methods reduce SARS-CoV-2 aerosol concentration Displacement ventilation eliminates SARS-CoV-2 aerosols under unstable conditions Cramped and damp living environments can increase the risk of transmission of infection GRAPHICAL ABSTRACT

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Section: Statistical Inferencementioning

confidence: 99%

Role of Fluid Dynamics in Infectious Disease Transmission: Insights from COVID-19 and Other Pathogens

Koley

2024

Trends Sci

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Machine Learning driven by MRI for the classification of Alzheimer's disease progression: systematic review and meta-analysis (Preprint)

Battineni,

Chintalapudi,

Amenta

2024

Preprint

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BACKGROUND To diagnose Alzheimer's disease (AD), individuals are classified according to the severity of their cognitive impairment. There are currently no specific causes or conditions for this disease. OBJECTIVE The purpose of this systematic review and meta-analysis was to assess AD prevalence across different stages using machine learning (ML) approaches comprehensively. METHODS The selection of articles was conducted in three phases, as per PRISMA 2020 guidelines: identification, screening, and final inclusion. The final analysis included 24 articles that met the criteria. The selection of ML approaches for AD diagnosis was rigorously based on their relevance to the investigation. The prevalence of AD subjects at two, three, four, and six stages was illustrated through the use of forest plots. RESULTS The prevalence rate for both Cognitively Normal [CN] and AD across six studies was 49.28% (95% CI: 46.12-52.45%, p=0.32). The prevalence estimate for the three stages of cognitive impairment (CN, MCI, and AD) is 29.75% (95% CI: 25.11-34.84%, p<0.01). Among five studies with 14,839 subjects, the analysis of four stages (ND, MoD, MD, and AD) found an overall prevalence of 13.13 percent (95% CI: 3.75-36.66%, p<0.01). In addition, four studies involving 3,819 subjects estimated the prevalence of six stages (CN, SMC, EMCI, MCI, LMCI, and AD), yielding a prevalence of 23.75 % (95% CI: 12.22-41.12%, p<0.01). CONCLUSIONS The significant heterogeneity observed across studies reveals that demographic and setting characteristics are responsible for the impact on AD prevalence estimates. This study shows how ML approaches can be used to describe AD prevalence across different stages, which provides valuable insights for future research. CLINICALTRIAL

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Computational Models for Biomarker Discovery

Cited by 2 publications

References 24 publications

Role of Fluid Dynamics in Infectious Disease Transmission: Insights from COVID-19 and Other Pathogens

Role of Fluid Dynamics in Infectious Disease Transmission: Insights from COVID-19 and Other Pathogens

Machine Learning driven by MRI for the classification of Alzheimer's disease progression: systematic review and meta-analysis (Preprint)

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