Dengue overview: An updated systemic review

Khan, Muhammad Bilal; Yang, Zih‐Syuan; Lin, C. C.; Hsu, Ming-Cheng; Urbina, Aspiro Nayim; Assavalapsakul, Wanchai; Wang, Wen‐Hung; Chen, Yen-Hsu; Wang, Sheng-Fan

doi:10.1016/j.jiph.2023.08.001

Cited by 29 publications

(15 citation statements)

References 191 publications

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“…In the last decade, dengue cases have increased worldwide, posing a serious public health problem. Currently, the progression of each case of the disease to a severe state remains unpredictable [ 9 ]. Based on this premise, we have developed a model to assess patients’ conditions and to provide an early notification to medical staff of the onset of life-threatening complications.…”

Section: Discussionmentioning

confidence: 99%

“…Several methods are currently used for the diagnosis of the disease, such as serological tests that allow the detection of IgG and IgM antibodies by ELISA (useful for identifying past or active infections); the detection of viral antigens such as NS1; molecular methods, such as reverse transcriptase polymerase chain reaction (RT-PCR); isothermal amplification; and viral isolation [ 7 , 8 ]. However, diagnosing dengue can prove challenging due to the array of nonspecific clinical symptoms exhibited by the patient, the differential diagnosis with other febrile diseases, and the inexperience of some doctors [ 9 , 10 ]. In addition, a limited ability of the WHO guideline to predict the risk of severe outcomes in dengue patients has been reported [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

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

Hoyos,

Ruíz

2024

Diagnostics

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Hoyos,

Ruíz

2024

Diagnostics

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“…Dengue is the most common arboviral illness reported globally, endemic to most tropical and sub-tropical regions of the world (1). The Dengue avivirus is transmitted by the A aegypti mosquito with an incubation period of one to two weeks.…”

Section: Introductionmentioning

confidence: 99%

A B C Dengue: a case of multifactorial shock due to major trauma and dengue infection

Hoang,

Tang,

Phan

et al. 2024

Preprint

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Objective: We report on a case of severe dengue diagnosed in a patient with major trauma and illustrate some of the potential challenges and considerations in the clinical management of such cases. Method: Case report from Hanoi, Vietnam Results: A 49-year-old female presented following road trauma incident requiring urgent laparotomy. Her recovery in Intensive Care Unit was complicated by the development of Dengue Shock Syndrome. Conclusions: Dengue Shock Syndrome may complicate fluid management and bleeding control in major trauma cases.

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“…Sin embargo un porcentaje bajo de pacientes experimentan la forma grave, con impacto a nivel cardiovascular con arritmias y miocarditis. (1) Según Sud y cols. la prevalencia de miocarditis por Dengue no grave sin signos de alarma es del 9,7 %, en el dengue no grave pero con signos de alarma es del 21 % y para el dengue grave del 46,6 %.…”

Section: Introductionunclassified