Background Due to the lack of a sensitive, specific and rapid detection method, aetiological diagnosis of pneumonia caused by Mycoplasma pneumoniae (M. pneumoniae, MP) is a constantly challenging issue. This retrospective study aimed to compare the diagnostic methods for Mycoplasma pneumoniae in children and evaluate their values. Methods From November 2018 to June 2019, 830 children with community-acquired pneumonia were selected from the Department of Respiratory Medicine, Shanghai Children’s Medical Center. On the first day of hospitalization, sputum, throat swab and venous blood samples were collected to analyse MP-IgM (particle agglutination, PA), MP-IgM (immune colloidal gold technique, GICT), MP-DNA, MP-RNA (simultaneous amplification and testing, SAT) and MP-DNA (real-time polymerase chain reaction, RT-PCR). Results Among these 830 children, RT-PCR showed that the positive rate was 36.6% (304/830), in which the positive rate of macrolide resistance (A2063G mutation) accounted for 86.2% of cases (262/304). Using RT-PCR as the standard, MP-RNA (SAT) had the highest specificity (97.5%), and MP-IgM (PA) had the highest sensitivity (74.0%) and Youden index (53.7%). If MP-RNA (SAT) was combined with MP-IgM (PA), its Kappa value (0.602), sensitivity (84.2%), specificity (78.7%) and Youden index (62.9%) were higher than those of single M. pneumoniae detection. Conclusions Our research indicated that a combination of MP-RNA (SAT) plus MP-IgM (PA) might lead to reliable results as an early diagnostic method for children with clinical manifestations of Mycoplasma pneumoniae pneumonia.

Objective: Lung auscultation plays an important role in the diagnosis of pulmonary diseases in children. The objective of this study was to evaluate the use of an artificial intelligence (AI) algorithm for the detection of breath sounds in a real clinical environment among children with pulmonary diseases.Method: The auscultations of breath sounds were collected in the respiratory department of Shanghai Children's Medical Center (SCMC) by using an electronic stethoscope. The discrimination results for all chest locations with respect to a gold standard (GS) established by 2 experienced pediatric pulmonologists from SCMC and 6 general pediatricians were recorded. The accuracy, sensitivity, specificity, precision, and F1-score of the AI algorithm and general pediatricians with respect to the GS were evaluated. Meanwhile, the performance of the AI algorithm for different patient ages and recording locations was evaluated.Result: A total of 112 hospitalized children with pulmonary diseases were recruited for the study from May to December 2019. A total of 672 breath sounds were collected, and 627 (93.3%) breath sounds, including 159 crackles (23.1%), 264 wheeze (38.4%), and 264 normal breath sounds (38.4%), were fully analyzed by the AI algorithm. The accuracy of the detection of adventitious breath sounds by the AI algorithm and general pediatricians with respect to the GS were 77.7% and 59.9% (p < 0.001), respectively. The sensitivity, specificity, and F1-score in the detection of crackles and wheeze from the AI algorithm were higher than those from the general pediatricians (crackles 81.1 vs. 47.8%, 94.1 vs. 77.1%, and 80.9 vs. 42.74%, respectively; wheeze 86.4 vs. 82.2%, 83.0 vs. 72.1%, and 80.9 vs. 72.5%, respectively; p < 0.001). Performance varied according to the age of the patient, with patients younger than 12 months yielding the highest accuracy (81.3%, p < 0.001) among the age groups.Conclusion: In a real clinical environment, children's breath sounds were collected and transmitted remotely by an electronic stethoscope; these breath sounds could be recognized by both pediatricians and an AI algorithm. The ability of the AI algorithm to analyze adventitious breath sounds was better than that of the general pediatricians.

Wireless Body Area Network (WBAN) is a small-scaled sensor network consisting of a series of medical devices attributed to, around, or implanted in a human body, providing continuous monitoring by different sensors to collect vital signals or motion and GPS. This paper proposes an effective routing algorithm to balance the energy consumption within a WBAN in order to prolong the overall lifetime of the network, called dynamic routing algorithm (DRA) and its improved version based on a multipath choice mechanism. Theoretical analysis and simulation result are demonstrated to evaluate the performance of the algorithm and represent that energy consumption of sensors in the network is reasonably scheduled and life cycle of the network is significantly extended. The routing algorithm proposed in this paper may be potentially applied to significantly save energy of multiple types of sensors during vital signals aggregating and transmitting under the WBAN.