RESUMO
BACKGROUND: This study aimed to investigate existing evidence regarding the associations of obesity and diabetes with Plasmodium infection and severe malaria in adults. METHODS: We comprehensively searched relevant studies using EMBASE, MEDLINE, Global Health, and CINAHL. The primary exposures were obesity and diabetes. The primary outcomes were Plasmodium infection and severe malaria. We performed meta-analyses to pool unadjusted and adjusted odds ratios (ORs) using a random-effects model. RESULTS: We found 9 studies that met our inclusion criteria; all these studies were eligible for meta-analyses. None of the 9 studies investigated the potential link between obesity and Plasmodium infection. The meta-analysis results showed that there was no statistically significant relationship between obesity and severe malaria (two studies), diabetes and Plasmodium infection (five studies), or diabetes and severe malaria (three studies). CONCLUSION: Our study findings showed that obesity was not associated with severe malaria, and diabetes was not associated with neither Plasmodium infection nor severe malaria. Additional epidemiological studies should be conducted to elucidate the relationships between obesity, diabetes, and Plasmodium infection.
RESUMO
BACKGROUND: The aim of this study was to compare the predictive performance of three statistical models-logistic regression, classification tree, and structural equation model (SEM)-in predicting severe dengue illness. METHODS/FINDINGS: We adopted modified classification of dengue illness severity based on WHO 1997 guideline. Predictive models were constructed using demographic factors and laboratory indicators on the day of fever occurrence. We developed statistical predictive models using data from two hospital cohorts in Thailand, consisting of 257 Thai children. Different predictive models for each category of severe dengue illness were developed employing logistic regression, classification tree, and SEM. The probability of discrimination of each model for severe output of disease was analyzed with external validation data sets from 55 and 700 patients not used in model development. From external validation using predictors on the day of presentation to the hospital, the area under the receiver operating characteristic curve was between 0.65 and 0.84 for the regression model. It was between 0.73 and 0.85 for SEM models. Classification tree models showed good results of sensitivity, ranging from 0.95 to 0.99. However, they showed poor specificity ranging from 0.10 to 0.44. CONCLUSIONS: Our study showed that SEM is comparable to logistic regression or classification tree, which was widely used for more severe form of dengue prediction.
RESUMO
Understanding brain function is essential for advancing our comprehension of human cognition, behavior, and neurological disorders. Magnetic resonance imaging (MRI) stands out as a powerful tool for exploring brain function, providing detailed insights into its structure and physiology. Combining MRI technology with electrophysiological recording system can enhance the comprehension of brain functionality through synergistic effects. However, the integration of neural implants with MRI technology presents challenges because of its strong electromagnetic (EM) energy during MRI scans. Therefore, MRI-compatible neural implants should facilitate detailed investigation of neural activities and brain functions in real-time in high resolution, without compromising patient safety and imaging quality. Here, we introduce the fully MRI-compatible monolayer open-mesh pristine PEDOT:PSS neural interface. This approach addresses the challenges encountered while using traditional metal-based electrodes in the MRI environment such as induced heat or imaging artifacts. PEDOT:PSS has a diamagnetic property with low electrical conductivity and negative magnetic susceptibility similar to human tissues. Furthermore, by adopting the optimized open-mesh structure, the induced currents generated by EM energy are significantly diminished, leading to optimized MRI compatibility. Through simulations and experiments, our PEDOT:PSS-based open-mesh electrodes showed improved performance in reducing heat generation and eliminating imaging artifacts in an MRI environment. The electrophysiological recording capability was also validated by measuring the local field potential (LFP) from the somatosensory cortex with an in vivo experiment. The development of neural implants with maximized MRI compatibility indicates the possibility of potential tools for future neural diagnostics.