• Childhood Kidney Diseases
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• v.15 no.1
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• pp.66-75
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• 2011

Purpose: Early diagnosis and treatment of febrile urinary tract infection (UTI) in children is important to prevent kidney damage. This study aims to evaluate the relationship between the presence of pyuria, the severity, and underlying genitourinary anomalies in patients with UTI. Methods: We retrospectively reviewed 293 patients with febrile UTI who were admitted to Korea University Guro Hospital during the period from June, 2007 until January, 2010. We divided the patients into two groups, one with the finding of pyuria at admission, and the other without, and compared the fever duration, white blood cell counts (WBC) and C-reactive protein (CRP) in peripheral bloods, hydronephrosis, cortical defects, vesicoureteral reflux and admission period. Results: Among the 293 patients with febrile UTI, 189 patients showed findings of pyuria whereas 104 patients did not. Patients with pyuria showed an increment of WBC ($14,694{\pm}485.2$ vs. $11,374{\pm}451.2/uL$, P <0.05) and CRP ($46.9{\pm}3.9$ vs $17.1{\pm}3.6$ mg/L, P <0.05) in peripheral blood sample. The presence of cortical defects (21.7 Vs 5.8%, P <0.05) and vesicoureteral reflux (15.9 Vs 6.7%, P <0.05) was also increased in patients with pyuria compared to patients without pyuria. There were no specific differences in fever duration, admission period, and hydronephrosis. Within the group with pyuria, CRP in peripheral blood sample increased proportionally with the increment of pyuria (P <0.05). Conclusion: In patients with febrile UTI, the increment of WBC in the urine sample can be a helpful predictor for increased CRP in peripheral blood and acute pyelonephritis.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1501-1512
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• 2024

Plasma disruption in tokamak experiments is a challenging issue that causes damage to the device. Reliable prediction methods are needed, but the lack of full understanding of plasma disruption limits the effectiveness of physics-driven methods. Data-driven methods based on supervised learning are commonly used, and they rely on labelled training data. However, manual labelling of disruption precursors is a time-consuming and challenging task, as some precursors are difficult to accurately identify. The mainstream labelling methods assume that the precursor onset occurs at a fixed time before disruption, which leads to mislabeled samples and suboptimal prediction performance. In this paper, we present disruption prediction methods based on anomaly detection to address these issues, demonstrating good prediction performance on J-TEXT and EAST. By evaluating precursor onset times using different anomaly detection algorithms, it is found that labelling methods can be improved since the onset times of different shots are not necessarily the same. The study optimizes precursor labelling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors. The results on J-TEXT and EAST show that the models trained on the optimized labels outperform those trained on fixed onset time labels.