Comparing Different Data Partitioning Strategies for Segmenting Areas Affected by COVID-19 in CT Scans.

Background/objectives: According to the World Health Organization, the gold standard for diagnosing COVID-19 is the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. However, to confirm the diagnosis in patients who have negative results but still show symptoms, imaging tests, especially computed tomography (CT), are used. In this study, using convolutional neural networks, we compared the following topics using manual and automatic lung segmentation methods: (1) the performance of an automatic segmentation of COVID-19 areas using two strategies for data partitioning, CT scans, and slice strategies; (2) the performance of an automatic segmentation method of COVID-19 when there was interobserver agreement between two groups of radiologists; and (3) the performance of the area affected by COVID-19.

The protective effects of naringenin, a citrus flavonoid, non-complexed or complexed with hydroxypropyl-β-cyclodextrin against multiorgan damage caused by neonatal endotoxemia.

Background: Endotoxemia is a severe and dangerous clinical syndrome that results in elevated morbidity, especially in intensive care units. Neonates are particularly susceptible to endotoxemia due to their immature immune systems. There are few effective treatments for neonatal endotoxemia.