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

ABSTRACT: Dengue remains a globally prevalent and potentially fatal disease, affecting millions of people worldwide each year. Early and accurate detection of dengue complications is crucial to improving clinical outcomes and reducing the burden on healthcare systems. In this study, we explore the use of computational simulations based on fuzzy cognitive maps (FCMs) to improve the detection of dengue complications. We propose an innovative approach that integrates clinical data into a computational model that mimics the decision-making process of a medical expert. Our method uses FCMs to model complexity and uncertainty in dengue. The model was evaluated in simulated scenarios with each of the dengue classifications. These maps allow us to represent and process vague and fuzzy information effectively, capturing relationships that often go unnoticed in conventional approaches. The results of the simulations show the potential of our approach to detecting dengue complications. This innovative strategy has the potential to transform the way clinical management of dengue is approached. This research is a starting point for further development of complication detection approaches for events of public health concern, such as dengue.

SUBMITTER: Hoyos W

PROVIDER: S-EPMC10931136 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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

Hoyos William W Hoyos Kenia K Ruíz Rander R

Diagnostics (Basel, Switzerland) 20240302 5

Dengue remains a globally prevalent and potentially fatal disease, affecting millions of people worldwide each year. Early and accurate detection of dengue complications is crucial to improving clinical outcomes and reducing the burden on healthcare systems. In this study, we explore the use of computational simulations based on fuzzy cognitive maps (FCMs) to improve the detection of dengue complications. We propose an innovative approach that integrates clinical data into a computational model ...[more]

PMID: 38473004

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Project description:Here; we have described and tested a microarray based-method for the screening of dengue virus (DENV) serotypes. This DNA microarray assay is specific and sensitive and can detect dual infections with two dengue virus serotypes and single-serotype infections. Other methodologies may underestimate samples containing more than one serotype. This technology can be used to discriminate between the four DENV serotypes. Single-stranded DNA targets were covalently attached to glass slides and hybridised with specific labelled probes. DENV isolates and dengue samples were used to evaluate microarray performance. Our results demonstrate that the probes hybridized specifically to DENV serotypes; with no detection of unspecific signals. This finding provides evidence that specific probes can effectively identify single and double infections in DENV samples. Background Dengue is a mosquito-borne viral infection causing a major public health problem globally. Dengue virus (DENV) is the causative agent of dengue fever (DF) and dengue hemorrhagic fever (DHF) and includes four distinct serotypes (DENV-1, DENV-2, DENV-3, and DENV-4). DENV-2 and DENV-3 have been associated with severe dengue disease, consequently, laboratory testing for DENV is needed to confirm the diagnosis of DENV infection, serotype and to differentiate dengue from other febrile tropical illnesses. In addition, surveillance of mosquitoes infected with DENV is needed to monitor the infection rates within vector mosquito populations harboring specific serotype to provide an early warning sign to predict epidemics. Results In this work we have applied microarray analysis to simultaneously determine the serotype of multiple RNA samples from human or mosquitoes. The proposed microarray method can be used for i) rapid and reliable dengue diagnosis; ii) serotyping and iii) surveillance of mosquitoes infected with dengue. These microarrays were useful to confirm the presence of DENV-2 in 94 serum samples, DENV-3 in three samples from Juchitan, Oaxaca and one case from Juchitan, Oaxaca contained DENV-2 and -3. Moreover by using these microarrays we also determined DENV in pools of gravid females mosquitoes collected in several sites of nineteen Mexican states in 2005. Mosquito pools from 31 cities in the states of Yucatan, Campeche, Tabasco, Chiapas, Veracruz, Oaxaca, Guerrero, Tamaulipas and Colima were infected with DENV-2, six cities in Yucatán, Tabasco, Morelos, Tamaulipas, Colima, and Nayarit with DENV-1, three from Tabasco, Veracruz and Oaxaca with DENV 3 and two with two serotypes simultaneously (Ciudad Mante with DENV-1 and DENV-2, and Tavela with DENV-2 and DENV-3). Conclusion Here we show the success of applying microarrays assay to provide a consistently robust qualitative detection of dengue serotypes (DENV-1, DENV-2, DENV-3 and DENV-4) in serum samples from patients or in pools of gravid female mosquitoes collected in the field of nineteen Mexican states. Interestingly, we did not detect any mosquito or serum sample containing DENV-4.

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

Publications

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

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