Specific Biomarkers Associated With Neurological Complications and Congenital Central Nervous System Abnormalities From Zika Virus-Infected Patients in Brazil.

Background: Zika virus (ZIKV) infections have been linked to different levels of clinical outcomes, ranging from mild rash and fever to severe neurological complications and congenital malformations. Methods: We investigated the clinical and immunological response, focusing on the immune mediators profile in 95 acute ZIKV-infected adult patients from Campinas, Brazil. These patients included 6 pregnant women who later delivered during the course of this study. Clinical observations were recorded during hospitalization. Levels of 45 immune mediators were quantified using multiplex microbead-based immunoassays. Results: Whereas 11.6% of patients had neurological complications, 88.4% displayed mild disease of rash and fever. Several immune mediators were specifically higher in ZIKV-infected patients, and levels of interleukin 10, interferon gamma-induced protein 10 (IP-10), and hepatocyte growth factor differentiated between patients with or without neurological complications. Interestingly, higher levels of interleukin 22, monocyte chemoattractant protein 1, TNF-α, and IP-10 were observed in ZIKV-infected pregnant women carrying fetuses with fetal growth-associated malformations. Notably, infants with congenital central nervous system deformities had significantly higher levels of interleukin 18 and IP-10 but lower levels of hepatocyte growth factor than those without such abnormalities born to ZIKV-infected mothers. Conclusions: This study identified several key markers for the control of ZIKV pathogenesis. This will allow a better understanding of the molecular mechanisms of ZIKV infection in patients.

Efficient detection of Zika virus RNA in patients' blood from the 2016 outbreak in Campinas, Brazil.

Infection with Zika virus (ZIKV), a mosquito-borne flavivirus has been casually linked with increased congenital microcephaly in Brazil from 2015 through 2016. Sensitive and specific diagnosis of patients with Zika fever (ZIKF) remains critical for patient management. We developed a ZIKV NS5 qRT-PCR assay by combining primers described by Balm et al. and a new Taqman probe. The assay was evaluated and compared with another assay described by Lanciotti et al. (ZIKV 1107) using 51 blood and 42 urine samples from 54 suspected ZIKV patients. ZIKV NS5 performed better in terms of sensitivity with more samples detected as ZIKV-positive (n = 37) than ZIKV 1107 (n = 34) for urine, and ZIKV-positive (n = 29) than ZIKV 1107 (n = 26) for blood. Both assays displayed good overall agreement for urine (κappa = 0.770) and blood (κappa = 0.825) samples. Improved availability of validated diagnostic tests, such ZIKV NS5 qRT-PCR, will be critical to ensure adequate and accurate ZIKV diagnosis.

A Machine Learning Application Based in Random Forest for Integrating Mass Spectrometry-Based Metabolomic Data: A Simple Screening Method for Patients With Zika Virus.

Recent Zika outbreaks in South America, accompanied by unexpectedly severe clinical complications have brought much interest in fast and reliable screening methods for ZIKV (Zika virus) identification. Reverse-transcriptase polymerase chain reaction (RT-PCR) is currently the method of choice to detect ZIKV in biological samples. This approach, nonetheless, demands a considerable amount of time and resources such as kits and reagents that, in endemic areas, may result in a substantial financial burden over affected individuals and health services veering away from RT-PCR analysis. This study presents a powerful combination of high-resolution mass spectrometry and a machine-learning prediction model for data analysis to assess the existence of ZIKV infection across a series of patients that bear similar symptomatic conditions, but not necessarily are infected with the disease. By using mass spectrometric data that are inputted with the developed decision-making algorithm, we were able to provide a set of features that work as a "fingerprint" for this specific pathophysiological condition, even after the acute phase of infection. Since both mass spectrometry and machine learning approaches are well-established and have largely utilized tools within their respective fields, this combination of methods emerges as a distinct alternative for clinical applications, providing a diagnostic screening-faster and more accurate-with improved cost-effectiveness when compared to existing technologies.