Diagnostic Performance of Procalcitonin and C-Reactive Protein in Pediatric Acute Pyelonephritis: A Hospitalbased Study

Background and Aim: Urinary Tract Infection (UTI) is a common bacterial infection in children causing permanent renal damage. Differentiation between Acute Pyelonephritis (APN) and lower UTI is vital due to the involvement of renal parenchyma in APN. This study aimed to assess the efficiency of Procalcitonin (PCT) with C-Reactive Protein (CRP) to predict APN in children with UTI in a tertiary care hospital. Methods: This analytical cross-sectional study was conducted in a tertiary care hospital between March 2013 and July 2014. Children aged 1 month to 16 years with febrile UTI were included in the study Sensitivity, specificity, positive predictive value, negative predictive value, and receiver operating characteristic (ROC) curve were used to assess quantitative variables for diagnosing APN. Results: The Mean±SD age values in the APN group were 73.11±52.29 months, while it was 76.25±47.23 months in the lower UTI group. The Area Under the Curve (AUC) for fever, White Blood Cell (WBC), CRP, and PCT of the respondent showed that CRP was at the cut-off point of 5.0 mg/L, resulting in a sensitivity of 82.4% and a specificity of 80.0%, respectively. PCT was at the cut-off point of 1300 pg/mL, resulting in a sensitivity of 76.5% and a specificity of 100.0%, respectively. By comparing the Receiver Operating Characteristic (ROC) curve, PCT had a significantly higher Area Under the Curve (AUC) than CRP for differentiating APN and lower UTI. Conclusion: Serum CRP and PCT are good markers for diagnosing APN in febrile UTI in children. However, the study showed that PCT is a better marker to differentiate APN and lower UTI compare to CRP

Depression Detection Through Smartphone Sensing: A Federated Learning Approach

Depression is one of the most common mental health disorders which affects thousands of lives worldwide. The variation of depressive symptoms among individuals makes it difficult to detect and diagnose early. Moreover, the diagnosing procedure relies heavily on human intervention, making it prone to mistakes. Previous research shows that smartphone sensor data correlates to the users’ mental conditions. By applying machine learning algorithms to sensor data, the mental health status of a person can be predicted. However, traditional machine learning faces privacy challenges as it involves gathering patient data for training. Newly, federated learning has emerged as an effective solution for addressing the privacy issues of classical machine learning. In this study, we apply federated learning to predict depression severity using smartphone sensing capabilities. We develop a deep neural network model and measure its performance in centralized and federated learning settings. The results are quite promising, which validates the potential of federated learning as an alternative to traditional machine learning, with the added benefit of data privacy.  &nbsp

Investigating safety and cost-effectiveness of cable median barriers in Louisiana

Cable median barriers (CMBs) are installed on freeway medians to prevent cross-median crashes and reduce the severity of median-related crashes. Though CMBs are effective in preventing cross-median crashes, they are also known to increase the number of property damage–only (PDO) crashes. The higher frequency of PDO crashes could result in increased CMB maintenance and repair expenses. The aim of this study is to evaluate the safety impact and economic justification of CMBs in Louisiana. Initially, a flowchart was developed using Louisiana crash data to identify targeted crashes, such as median-related and cross-median crashes. This was followed by a 3-year observational before-and-after crash analysis with an emphasis on head-on collisions and crashes involving large trucks. Using a 4-step improved prediction method, crash modification factors were then developed to quantitatively assess the impact of CMBs on crash outcomes, accounting for and adjusting to changes in the annual average daily traffic (AADT) and relevant crash frequencies before and after CMB implementation. Finally, an exhaustive benefit–cost analysis was conducted to determine the cost-effectiveness of CMBs. The results revealed that CMBs significantly reduced cross-median crashes of all severities. However, an increase in PDO crashes was observed in both total and median-related crashes. Large truck cross-median crashes and head-on collisions also decreased significantly after CMB implementation. Testing Level 4 (TL-4) CMBs were found to be more effective in preventing vehicles from crossing the median and in reducing crashes of higher severity levels. The benefit–cost ratios, calculated using economic crash unit costs for both total and targeted crashes, were greater than 1. Notably, the estimated benefit–cost ratios were considerably higher, demonstrating that CMBs are cost-effective countermeasures for enhancing traffic safety. This study contributes to the understanding of CMB performance from both traffic safety and economic perspectives. The findings may assist transportation agencies in making decisions regarding the management of CMB systems. Based on the comprehensive analysis of CMBs on Louisiana freeways, this project has revealed that CMBs are an effective and economically justified crash countermeasure. Thus, further implementation of CMBs is recommended until better alternatives are available.</p