The World Health Organization (WHO) introduced the new dengue classification in 2009. We aimed to assess the association of clinical signs and symptoms with WHO severe dengue classification in clinical practice. A systematic literature search was performed using the databases of PubMed, Embase, and Scopus between 2009 and 2018 according to PRISMA guideline. Meta-analysis was performed with the RevMan software. A random or fixed-effect model was applied to pool odds ratios and 95% confidence intervals of important signs and symptoms across studies. Thirty nine articles from 1790 records were included in this review. In our meta-analysis, signs and symptoms associated with higher risk of severe dengue were comorbidity, vomiting, persistent vomiting, abdominal pain or tenderness, pleural effusion, ascites, epistaxis, gum bleeding, GI bleeding, skin bleeding, lethargy or restlessness, hepatomegaly (>2 cm), increased HCT with decreased platelets, shock, dyspnea, impaired consciousness, thrombocytopenia, elevated AST and ALT, gall bladder wall thickening and secondary infection. This review shows new factors comorbidity, epistaxis, GI and skin bleeding, dyspnea, gall bladder wall thickening and secondary infection may be useful to refine the 2009 classification to triage severe dengue patients.
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http://dx.doi.org/10.1080/22221751.2021.1935327 | DOI Listing |
Clin Exp Med
January 2025
Department of Clinical Oncology, Maria Sklodowska-Curie National Research Institute of Oncology, Krakow Branch, Poland.
Immune checkpoint inhibitors have improved the treatment of metastatic renal cell carcinoma (RCC), with the combination of nivolumab (NIVO) and ipilimumab (IPI) showing promising results. However, not all patients benefit from these therapies, emphasizing the need for reliable, easily assessable biomarkers. This multicenter study involved 116 advanced RCC patients treated with NIVO + IPI across nine oncology centers in Poland.
View Article and Find Full Text PDFExp Appl Acarol
January 2025
Laboratorio de Vectores y Enfermedades Transmitidas, Departamento de Ciencias Biológicas, CENUR Litoral Norte, Universidad de la República, Salto, Uruguay.
Babesia species (Piroplasmida) are hemoparasites that infect erythrocytes of mammals and birds and are mainly transmitted by hard ticks (Acari: Ixodidae). These hemoparasites are known to be the second most common parasites infecting mammals, after trypanosomes, and some species may cause malaria-like disease in humans. Diagnosis and understanding of Babesia diversity increasingly rely on genetic data obtained through molecular techniques.
View Article and Find Full Text PDFHeart failure with preserved ejection fraction (HFpEF) and atrial fibrillation (AF) are increasingly prevalent cardiovascular conditions, particularly among the elderly population. These two conditions share common risk factors and often coexist, leading to a complex interplay that alters the clinical course of each other. The pathophysiology of HFpEF is multifaceted and intricately linked, with atrial disease serving as a common pathophysiological pathway.
View Article and Find Full Text PDFClin Oral Investig
January 2025
Fujian Key Laboratory of Oral Diseases & Stomatological Key lab of Fujian College and University, School and Hospital of Stomatology, Fujian Medical University, Fuzhou, Fujian Province, 350002, China.
Objective: Both the Masquelet technique (MT) and concentrated growth factors (CGF) reduce early graft loss and improve bone regeneration. This study aims to explore the efficacy of combining MT with CGF for mandibular defect repair by characterizing the induced membrane and assessing in vivo osteogenesis.
Materials And Methods: Three experimental groups were compared: negative control (NC), MT, and Masquelet combined with CGF (MTC).
Neurosurg Rev
January 2025
Department of Neurosurgery, Mount Sinai Hospital, Icahn School of Medicine, New York City, NY, USA.
Currently, the World Health Organization (WHO) grade of meningiomas is determined based on the biopsy results. Therefore, accurate non-invasive preoperative grading could significantly improve treatment planning and patient outcomes. Considering recent advances in machine learning (ML) and deep learning (DL), this meta-analysis aimed to evaluate the performance of these models in predicting the WHO meningioma grade using imaging data.
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