2022
DOI: 10.7759/cureus.29619
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Crimean-Congo Hemorrhagic Fever Case Series: a Chronology of Biochemical and Hematological Parameters

Abstract: IntroductionCrimean-Congo hemorrhagic fever (CCHF) is a widespread tick-borne zoonotic disease. Sporadic outbreaks of CCHF occur in endemic regions, including Pakistan. The clinical spectrum of the illness varies from asymptomatic seroconversion to severe disease which may end in death. The treatment is supportive, including blood and blood products. There is multi-organ involvement in CCHF including acute hepatitis, thrombocytopenia, coagulopathy, acute kidney injury (AKI), and encephalopathy. Hematological a… Show more

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Cited by 3 publications
(3 citation statements)
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References 21 publications
(27 reference statements)
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“…The article presents a methodology for fault diagnosis in bearings by using Singular Value Decomposition for feature extraction and transfer learning for classification .Subsequent studies delves into the application of machine learning and deep learning algorithms for disease detection and diagnosis. For example, the articles [3] "Comparative Analysis of the Classification Performance of Machine Learning Classifiers and Deep Neural Network Classifier for Prediction of Parkinson Disease" and [4] "Identifying The Predictive Capability of Machine Learning Classifiers for Designing Heart Disease Detection System" analyze the potential of these algorithms for predicting Parkinson's disease and heart disease respectively. This theme of disease detection is expanded upon in the sixth article [6], "Performance Evaluation of Deep Neural Ensembles Toward Malaria Parasite Detection in Thin blood Smear Images", which evaluates the effectiveness of deep neural ensembles for detecting malaria parasites.…”
Section: Literature Surveymentioning
confidence: 99%
“…The article presents a methodology for fault diagnosis in bearings by using Singular Value Decomposition for feature extraction and transfer learning for classification .Subsequent studies delves into the application of machine learning and deep learning algorithms for disease detection and diagnosis. For example, the articles [3] "Comparative Analysis of the Classification Performance of Machine Learning Classifiers and Deep Neural Network Classifier for Prediction of Parkinson Disease" and [4] "Identifying The Predictive Capability of Machine Learning Classifiers for Designing Heart Disease Detection System" analyze the potential of these algorithms for predicting Parkinson's disease and heart disease respectively. This theme of disease detection is expanded upon in the sixth article [6], "Performance Evaluation of Deep Neural Ensembles Toward Malaria Parasite Detection in Thin blood Smear Images", which evaluates the effectiveness of deep neural ensembles for detecting malaria parasites.…”
Section: Literature Surveymentioning
confidence: 99%
“…In addition, 63% of the Pakistani population in 2022 living in the rural area mainly depends upon animal husbandry and farming. Thus, the wide‐geographical spread and occupational vulnerability urges for ongoing monitoring and surveillance at both national and provincial levels, covering human and zoonotic animal health with the necessary infrastructure 15–18 . However, limited diagnostic facilities are available in the country coupled with lack of whole‐genome sequencing capacity.…”
Section: Introductionmentioning
confidence: 99%
“…Thus, the wide-geographical spread and occupational vulnerability urges for ongoing monitoring and surveillance at both national and provincial levels, covering human and zoonotic animal health with the necessary infrastructure. [15][16][17][18] However, limited diagnostic facilities are available in the country coupled with lack of whole-genome sequencing capacity. Consequently, data on disease burden and prevalent strains from both endemic and non-endemic regions is scare.…”
mentioning
confidence: 99%