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Arboviral Encephalitis

Arboviral Encephalitis

Arboviral encephalitis refers to brain inflammation caused by arthropod-borne viruses, particularly those transmitted through mosquito vectors. Among these, West Nile virus (WNV), a member of the Flaviviridae family, is a significant public health concern. WNV is an enveloped, positive-sense, single-stranded RNA virus. Human infection typically begins when an infected mosquito introduces the virus into the dermis during feeding. The primary transmission cycle involves birds as amplifying hosts...

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Visualizing Dengue Virus through Alexa Fluor Labeling

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Using Computational Simulations Based on Fuzzy Cognitive Maps to Detect Dengue Complications.

William Hoyos^1,2, Kenia Hoyos³, Rander Ruíz⁴

¹Grupo de Investigación en Ingeniería Sostenible e Inteligente, Universidad Cooperativa de Colombia, Montería 230002, Colombia.

Diagnostics (Basel, Switzerland)

|March 13, 2024

Summary

This study introduces fuzzy cognitive maps (FCMs) to enhance early detection of dengue complications. Computational simulations show this approach can improve clinical management of dengue, a significant global health concern.

Keywords:

artificial intelligence complications computer-aided systems dengue fuzzy cognitive maps

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A Murine Model of Dengue Virus-induced Acute Viral Encephalitis-like Disease

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Published on: April 28, 2019

Area of Science:

Computational biology
Medical informatics
Epidemiology

Background:

Dengue is a widespread, life-threatening disease impacting millions globally.
Early and accurate detection of dengue complications is vital for patient outcomes and healthcare efficiency.

Purpose of the Study:

To explore fuzzy cognitive maps (FCMs) for improving the detection of dengue complications.
To develop a computational model simulating expert decision-making for dengue management.

Main Methods:

Utilized fuzzy cognitive maps (FCMs) to model the complexity and uncertainty inherent in dengue.
Integrated clinical data into a computational model for dengue complication detection.
Evaluated the FCM model using simulated scenarios across different dengue classifications.

Main Results:

FCMs effectively represent and process vague and fuzzy information in dengue.
Simulations demonstrated the potential of the FCM approach for detecting dengue complications.
The method identified relationships often missed by conventional diagnostic techniques.

Conclusions:

The proposed FCM-based computational simulation shows promise for enhancing dengue complication detection.
This innovative strategy could transform clinical dengue management.
This research provides a foundation for developing advanced detection methods for public health issues like dengue.