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Acute exacerbation of pediatric asthma (AEPA) has always been one of the most common reasons for children to visit the emergency department, whereas unified diagnostic criteria in the clinic are lacking. The purpose of this study was to determine potential biomarkers, and provide a basis for predictive and diagnostics AEPA. Urine samples were collected from 40 pediatric patients, including 19 patients with AEPA (PA) and 21 healthy controls (HCs). The samples were analyzed by high-performance liquid chromatography-quadrupole orbitrap mass spectrometry (HPLC-Q-Orbitrap-MS), and the data were statistically analyzed by principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA). Differential metabolites were selected by VIP (variable importance for the projection) > 1, and a p value ≤ 0.05 was used as the standard. The corresponding metabolic pathways of differential metabolites were subjected to analysis by the KEGG database, and further analysis and characterization of differential metabolites were conducted through the HMDB database. A total of 26 potential biomarkers were selected, of which 17 were found to be associated with respiratory diseases. Nine metabolites with obvious fluctuations in patients with AEPA, such as 13-L-hydroperoxylinoleic acid, gentisate aldehyde, L-3-phenyllactic acid, hydrocinnamic acid, and gentisic acid, could be used as potential biomarkers to further explore the prediction and diagnosis of AEPA for the first time. The contents of 3 potential biomarkers showed a positive correlation. Abnormalities in seven metabolic pathways, such as phenylalanine metabolism, tyrosine metabolism and beta-alanine metabolism, are also related to AEPA. This study further confirmed the reliability of this method to detect differences in urine metabolites of patients with AEPA. By monitoring the content of these 26 potential biomarkers and their related metabolic pathways, it provides a basis for further effective prediction and diagnosis of AEPA to avoid further development of this disease.

Unmanned Aerial Vehicle (UAV) is playing a gradually enhanced role in fields of recon and information acquisition, however restricted departure condition of fixed wing aircrafts, take-off preparation time of multirotor aircrafts etc. have limited its further applications. This research aims to combine advantage of hovering and self-deployable departure to reconcile the shortcomings, meanwhile adapts to drone swarm trend. A catapulted self-deployable quadrotor is designed using 3D modelling software, and later a compatible self-deployable control algorithm is developed using STM32F103 microcontroller, along with its circuitry. Eventually, a prototype of the design is 3D printed, assembled and tested. This design shows merits of easy to carry, low requirements for take-off conditions and good hovering performance and is compatible for multi-UAVs cooperation tendency.