Recurrent hemoptysis in pediatric bronchial Dieulafoy's disease with inferior phrenic artery supply: A case report.

BACKGROUND: Bronchial Dieulafoy's disease (BDD) is characterized by the erosion of an anomalous artery in the submucosa of the bronchus. The etiology of pediatric BDD is mainly congenital dysplasia of bronchus and pulmonary arteries, which is different from chronic inflammatory injury of the airway in adult patients. The internal thoracic artery, subclavian artery, and intercostal artery are known to be involved in the blood supply to the BDD lesion in children. CASE SUMMARY: We report a case of BDD in a 4-year-old boy with recurrent hemoptysis for one year. Selective angiography showed a dilated right bronchial artery, and anastomosis of its branches with the right lower pulmonary vascular network. Bronchoscopy showed nodular protrusion of the bronchial mucosa with a local scar. Selective embolization of the bronchial artery was performed to stop bleeding. One month after the first intervention, the symptoms of hemoptysis recurred. A computed tomography angiogram (CTA) showed another tortuous and dilated feeding artery in the right lower lung, which was an abnormal ascending branch of the inferior phrenic artery (IPA). The results of angiography were consistent with the CTA findings. The IPA was found to be another main supplying artery, which was not considered during the first intervention. Finally, the IPA was also treated by microsphere embolization combined with coil interventional closure. During the one-year follow-up, the patient never experienced hemoptysis. CONCLUSION: The supplying arteries of the bleeding lesion in children with BDD may originate from multiple different aortopulmonary collateral arteries, and the IPA should be considered to reduce missed diagnosis. CTA is a noninvasive radiological examination for the screening of suspected vessels, which shows a high coincidence with angiography, and can serve as the first choice for the diagnosis of BDD.

Water Quality Indicator Interval Prediction in Wastewater Treatment Process Based on the Improved BES-LSSVM Algorithm.

This paper proposes a novel interval prediction method for effluent water quality indicators (including biochemical oxygen demand (BOD) and ammonia nitrogen (NH3-N)), which are key performance indices in the water quality monitoring and control of a wastewater treatment plant. Firstly, the effluent data regarding BOD/NH3-N and their necessary auxiliary variables are collected. After some basic data pre-processing techniques, the key indicators with high correlation degrees of BOD and NH3-N are analyzed and selected based on a gray correlation analysis algorithm. Next, an improved IBES-LSSVM algorithm is designed to predict the BOD/NH3-N effluent data of a wastewater treatment plant. This algorithm relies on an improved bald eagle search (IBES) optimization algorithm that is used to find the optimal parameters of least squares support vector machine (LSSVM). Then, an interval estimation method is used to analyze the uncertainty of the optimized LSSVM model. Finally, the experimental results demonstrate that the proposed approach can obtain high prediction accuracy, with reduced computational time and an easy calculation process, in predicting effluent water quality parameters compared with other existing algorithms.

A nomogram prediction model for early death in patients with persistent pulmonary hypertension of the newborn.

Background: Persistent pulmonary hypertension of the newborn (PPHN) is a major lethal disorder in neonates that leads to an extremely high mortality rate. Thus, the early identification of adverse outcomes in PPHN is critical for clinical practice. This research attempted to develop a nomogram prediction system for assessing the mortality of newborns with PPHN. Methods: Two hundred and three newborns with PPHN diagnosed from January 2015 to March 2022 were involved in the study. The clinical features of these newborns and pregnancy details were compared between newborns in the survival and lethal groups. Univariable and multivariate analyses were established in sequence to demonstrate the essential risk factors. The nomogram prediction model was built. Results: A total of 203 newborns were included in the analysis. 136 (67.0%) newborns represented the hospital survival group. Plasma pH value (OR = 0.606, p = 0.000, 95% CI 0.45715-0.80315), septicemia (OR = 3.544, p = 0.000, 95% CI 1.85160-6.78300), and abnormal pregnancy history (OR = 3.331, p = 0.008, 95% CI 1.37550-8.06680) were identified as independent risk factors for neonatal death in newborns associated with PPHN. Finally, the nomogram predictive model was established based on multivariate analysis results, indicating the efficacies of prediction and calibration. Conclusion: This study generated an applicable risk score formula using the plasma pH value, septicemia, and abnormal pregnancy history to recognize neonatal death in newborns with PPHN, presenting a sufficient predictive value and calibration.