Intraoperative Post Partum Hemorrhage in a Patient with Dengue Fever

A 33 year old obstetric patient with mild fever of undiagnosed etiology underwent emergency caesarean section under general anesthesia. She had platelet count of 98,000 per microliter and increased APTT of 37.8 s at the time of surgery. After uneventful anesthetic induction and delivery of fetus, slow and oozing type of bleeding led to massive hemorrhage. Patient remained vitally stable throughout perioperative phase and was extubated. Next day, patient’s dengue IgM antibody was reported positive. Neonate was well and his dengue test was negative. Pregnant women are at high risk of developing severe complications of dengue fever with unclear mechanisms related to impaired coagulation. Regional anesthesia may not have safe outcome due to dengue infection. doi: https://doi.org/10.12669/pjms.38.1.4519 How to cite this:Ahmed U, Aman A. Intraoperative Post Partum Hemorrhage in a Patient with Dengue Fever. Pak J Med Sci. 2022;38(1):---------. doi: https://doi.org/10.12669/pjms.38.1.4519 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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1405. Clinical and laboratory features of fatal dengue fever in children: a case-control study

Open Forum Infectious Diseases ◽

10.1093/ofid/ofaa439.1587 ◽

2020 ◽

Vol 7 (Supplement_1) ◽

pp. S710-S711

Author(s):

Dolores E Freire ◽

Jeniffer D Olaya ◽

Michael Hawkes

Keyword(s):

Dengue Fever ◽

Case Control Study ◽

Dengue Infection ◽

Healthcare Providers ◽

Case Control ◽

Warning Signs ◽

Total Leukocyte Count ◽

Retrospective Case ◽

Time To Death ◽

Control Study

Abstract Background Dengue fever (DF) is a mosquito-borne illness that causes significant morbidity and mortality in tropical climates. This study compared the clinical features of fatal DF cases to severe non-fatal, and non-severe controls in Ecuador. Methods Retrospective case-control study of children (1 month to 15 years) hospitalized with serologically-confirmed DF in Guayaquil, Ecuador from 2013 to 2017. Cases of severe, fatal (SF) DF were compared to two control groups: (1) severe DF survivors (SS); and (2) patients with dengue with warning signs (DWS), matched 3:1 to cases for age, sex, and admission date. Observational trial profile Results 1051 patients were admitted with suspected DF and 552 were IgM-positive. Patients were classified as SF (n=11), SS (n=30), or DWS (n=511) (Figure1). Among SF cases, median age was 9.6 years (IQR 5.5-11), 7 (64%) were male, and median time to death was 1.5 days (IQR 0.8-4.0). (Table 1) SF cases had a median of 3 (Range 0-5) encounters with healthcare providers prior to presentation, compared to 2 (Range 0-5, p=0.02) for SS and 2 (Range 0-3, p=0.02) for DWS. Physical findings more common in SF cases than controls included: higher weight, tachycardia, tachypnea, delayed capillary refill, and hepatomegaly (p< 0.05 for all comparisons). Neurological manifestations were more prevalent in the SF group: 9/11 (82%) patients compared to 15/30 (50%, p=0.09) in SS and 7/33 (21%, p< 0.01) in DWS. Total leukocyte count (7.8x103/µL versus 4.5x103/µL, p=0.03) and absolute neutrophil count (5.1x103/µL versus 2.1x103/µL, p=0.03) were higher in SF cases than DWS controls. Fewer SF patients received intravenous dextrose than SS controls (27% versus 70%, p=0.03) (Table 2). Admission characteristics of children with dengue fever Management and outcome Conclusion Delayed recognition by healthcare workers, higher weight, vital sign abnormalities, hepatomegaly, neurological symptoms, leukocytosis, neutrophilia, and lack of dextrose in intravenous solutions were associated with mortality in children with DF. These findings have implications for optimizing the diagnosis and management of severe pediatric dengue infection. Disclosures All Authors: No reported disclosures

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Multi-Tissue Transcriptomic-Informed In Silico Investigation of Drugs for the Treatment of Dengue Fever Disease

Viruses ◽

10.3390/v13081540 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1540

Author(s):

Beatriz Sierra ◽

Ana Cristina Magalhães ◽

Daniel Soares ◽

Bruno Cavadas ◽

Ana B. Perez ◽

...

Keyword(s):

Receptor Antagonist ◽

Dengue Fever ◽

In Silico ◽

Serotonin Receptor ◽

Neglected Tropical Diseases ◽

Dengue Infection ◽

Severe Dengue ◽

Functional Evaluation ◽

Atpase Inhibitor ◽

Serotonin Receptor Antagonist

Transcriptomics, proteomics and pathogen-host interactomics data are being explored for the in silico–informed selection of drugs, prior to their functional evaluation. The effectiveness of this kind of strategy has been put to the test in the current COVID-19 pandemic, and it has been paying off, leading to a few drugs being rapidly repurposed as treatment against SARS-CoV-2 infection. Several neglected tropical diseases, for which treatment remains unavailable, would benefit from informed in silico investigations of drugs, as performed in this work for Dengue fever disease. We analyzed transcriptomic data in the key tissues of liver, spleen and blood profiles and verified that despite transcriptomic differences due to tissue specialization, the common mechanisms of action, “Adrenergic receptor antagonist”, “ATPase inhibitor”, “NF-kB pathway inhibitor” and “Serotonin receptor antagonist”, were identified as druggable (e.g., oxprenolol, digoxin, auranofin and palonosetron, respectively) to oppose the effects of severe Dengue infection in these tissues. These are good candidates for future functional evaluation and clinical trials.

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Infectious Disease Relational Data Analysis Using String Grammar Non-Euclidean Relational Fuzzy C-Means

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18158153 ◽

2021 ◽

Vol 18 (15) ◽

pp. 8153

Author(s):

Apiwat Budwong ◽

Sansanee Auephanwiriyakul ◽

Nipon Theera-Umpon

Keyword(s):

High Risk ◽

Dengue Fever ◽

Policy Development ◽

Dengue Infection ◽

Prevention Policy ◽

Fuzzy C Means ◽

North East ◽

B Virus ◽

North And South ◽

Better Than

Statistical analysis in infectious diseases is becoming more important, especially in prevention policy development. To achieve that, the epidemiology, a study of the relationship between the occurrence and who/when/where, is needed. In this paper, we develop the string grammar non-Euclidean relational fuzzy C-means (sgNERF-CM) algorithm to determine a relationship inside the data from the age, career, and month viewpoint for all provinces in Thailand for the dengue fever, influenza, and Hepatitis B virus (HBV) infection. The Dunn’s index is used to select the best models because of its ability to identify the compact and well-separated clusters. We compare the results of the sgNERF-CM algorithm with the string grammar relational hard C-means (sgRHCM) algorithm. In addition, their numerical counterparts, i.e., relational hard C-means (RHCM) and non-Euclidean relational fuzzy C-means (NERF-CM) algorithms are also applied in the comparison. We found that the sgNERF-CM algorithm is far better than the numerical counterparts and better than the sgRHCM algorithm in most cases. From the results, we found that the month-based dataset does not help in relationship-finding since the diseases tend to happen all year round. People from different age ranges in different regions in Thailand have different numbers of dengue fever infections. The occupations that have a higher chance to have dengue fever are student and teacher groups from the central, north-east, north, and south regions. Additionally, students in all regions, except the central region, have a high risk of dengue infection. For the influenza dataset, we found that a group of people with the age of more than 1 year to 64 years old has higher number of influenza infections in every province. Most occupations in all regions have a higher risk of infecting the influenza. For the HBV dataset, people in all regions with an age between 10 to 65 years old have a high risk in infecting the disease. In addition, only farmer and general contractor groups in all regions have high chance of infecting HBV as well.

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