Prognostic indicators associated with progresses of severe dengue.

BACKGROUND: Dengue usually progress abnormally, especially in the critical phase. The main causes of death were shock, severe bleeding and organ failure. The aim of our study was to evaluate prognostic indicators of severe dengue according to the phases of the disease progression. METHODS: A cross-sectional study was conducted from July to December 2017 at the National Hospital for Tropical Diseases and the Hospital for Tropical Diseases of Ho Chi Minh City. 326 patients, aged 6 years and over, including 99/326 patients with severe dengue and 227/326 patients with non-severe dengue, hospitalized in the first 3 days of illness, confirmed Dengue virus by the RT-PCR assay have been registered for the study. Clinical manifestations were monitored daily. The hematocrit, white blood cells, platelet, serum albumin, ALT, AST, bilirubin, prothrombin time (PT%, PTs), fibrinogen, aPTT, INR and creatinine were evaluated at two times: febrile phase and critical phase. RESULTS: Independent factors associated with severe dengue were identified on multivariate logistic regression models. During the first 3 days of the disease, the prognostic indicators were platelet count ≤ 100 G/L (OR = 2.2; 95%CI: 1.2-3.9), or serum albumin < 35 g/L (OR = 3.3; 95%CI: 1.8-6.1). From day 4-6, the indicator were AST > 400 U/L (OR = 3.0; 95%CI: 1.1-7.9), ALT > 400 U/L (OR = 6.6; 95%CI: 1.7-24.6), albumin < 35 g/L (OR = 3.0; 95%CI: 1.5-5.9), and bilirubin total >17 µmol/L (OR = 4.6; 95%CI: 2.0-10.4). CONCLUSION: To predict the risk of patients with severe dengue, prognostic laboratory indicators should be indicated consistent with the progression of the disease. During the first 3 days of illness, prognostic indicators should be platelet count, or serum albumin. From the 4th - 6th day of illness, prognostic indicators should be AST, ALT, albumin, or bilirubin total.

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

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.