Temporal cytokine storm dynamics in dengue infection predicts severity

The immunopathogenesis of dengue severity is convoluted. The primary objective of the research was to examine the dynamics of cytokine storm and its correlation with disease development in individuals affected by DENV infection. Additionally, the study aimed to discover potential biomarkers that could indicate severe dengue infection and determine the most suitable timeframe for predicting the severity of these biomarkers during the acute stage of dengue infections. We conducted a temporal analysis of the daily viral load and cytokine levels in 60 hospitalized dengue patients until discharge. Our findings reveal a distinct cytokine profile (elevated IL-8, IL-10, IL-6, GM-CSF, MCP-1, IL-13, and IL-4 and decreased IL-12, MIP-1β) on the third day after symptom onset is predictive of severe dengue in secondary dengue infection. The imbalanced cytokine signature may inform clinical decision-making in treating severe dengue infections.

Measuring Historic and Longitudinal Social Vulnerability in Disaster-Prone Communities: A Modification to the Centers for Disease Control and Prevention Social Vulnerability Index (CDC-SVI)

Researchers have developed numerous indices to identify vulnerable sub-populations. The Centers for Disease Control and Prevention (CDC) Social Vulnerability Index (SVI) is the most common and highly functional, but it has some temporal limitations; constant transformations in societal composition change social vulnerability. The 15 variables categorized into four themes temporally limits the use of SVI to recent years because some variables used in calculating SVI were not available prior to 1980. We defined an alternative index that could serve as a surrogate for the CDC-SVI without the temporal limitations. An inventory analysis of the historical census data (1960-2018) was used to develop a Modified SVI that allows for historic analyses. This modified SVI can be used to generate historical maps, find temporal patterns, and inform a longitudinal SVI measure. Furthermore, to consider the chronical effect of social vulnerabilities, a longitudinal SVI was introduced to elucidate how a community's multidimensional experiences exacerbate social vulnerability to disaster events. We use Harris County, Texas to examine how the modified SVI performs against the original CDC-SVI. The results showed a good agreement among the developed indices and the CDC-SVI and satisfactory performance in identifying the areas that are most vulnerable to COVID-19 pandemic.

Early temporal prediction of Type 2 Diabetes Risk Condition from a General Practitioner Electronic Health Record: A Multiple Instance Boosting Approach

Early prediction of target patients at high risk of developing Type 2 diabetes (T2D) plays a significant role in preventing the onset of overt disease and its associated comorbidities. Although fundamental in early phases of T2D natural history, insulin resistance is not usually quantified by General Practitioners (GPs). Triglyceride-glucose (TyG) index has been proven useful in clinical studies for quantifying insulin resistance and for the early identification of individuals at T2D risk but still not applied by GPs for diagnostic purposes. The aim of this study is to propose a multiple instance learning boosting algorithm (MIL-Boost) for creating a predictive model capable of early prediction of worsening insulin resistance (low vs high T2D risk) in terms of TyG index. The MIL-Boost is applied to past electronic health record (EHR) patients' information stored by a single GP. The proposed MIL-Boost algorithm proved to be effective in dealing with this task, by performing better than the other state-of-the-art ML competitors (Recall from 0.70 and up to 0.83). The proposed MIL-based approach is able to extract hidden patterns from past EHR temporal data, even not directly exploiting triglycerides and glucose measurements. The major advantages of our method can be found in its ability to model the temporal evolution of longitudinal EHR data while dealing with small sample size and variability in the observations (e.g., a small variable number of prescriptions for non-hospitalized patients). The proposed algorithm may represent the main core of a clinical decision support system.

Spatial distribution of the relative risk of Zika virus disease in Colombia during the 2015-2016 epidemic from a Bayesian approach

Objective

To determine the spatial distribution of the risk of Zika virus disease in each region of Colombia during the 2015–2016 epidemic.

Methods

An ecological study was designed to estimate the risks for each Colombian region using first‐order neighbors, covariate effects, and three adjacent periods of time (beginning, development, and end of the epidemic) to analyze the spatial distribution of the disease based on a Bayesian hierarchical model.

Results

Spatial distribution of the estimated risks of Zika virus disease showed that it increased in a strip that crosses the central area of the country from west to east. Analysis of the three time periods showed greater risk of the disease in the central and southern zones—Arauca and Santander—where the increase in risk was four times higher during the peak phase compared with the initial phase of the outbreak.

Conclusion

In the identified high‐risk areas, integrated surveillance systems for Zika virus disease and its complications must be strengthened to provide up‐to‐date and accurate epidemiological information. This information would allow those involved in policy and decision making to identify new outbreaks and risk clusters, enabling more focused and accurate measures to target at‐risk populations.

The spatial distribution of the estimated risk of Zika virus disease in Colombia was four times higher during the epidemic phase of the outbreak.