Objective: To observe the effects of minimally invasive tangential excision in treating deep partial-thickness burn wounds on trunk and limbs in pediatric patients in the early stage post burn. Methods: Clinical data of 40 children with deep partial-thickness burn wounds on trunk and limbs, admitted to our burn ward from January 2016 to June 2017, conforming to the study criteria, were retrospectively analyzed. They were divided into conventional treatment group (CT, n=19) and minimally invasive tangential excision group (MITE, n=21) according to the different treatments. The patients in group CT were treated with eschar-reserving therapy firstly. When tangential excision was performed, the roller knife was used, and no necrotic tissue left on the wound bed was considered the proper depth of excision. Razor-thickness skin grafting was performed to cover the wound when adipose tissue exposed markedly after tangential excision. Dressing change was performed within 48 h after the operation and repeated every 2 days. Unhealed wounds were covered by razor-thickness skin grafting. The patients in group MITE were treated with tangential excision in the early stage post burn. The tangential excision was operated with electric dermatome, and the thickness was set at 0.1 mm to excise the surface of eschar until the sporadic punctate hemorrhage on wound surface was observed and some necrotic tissue was left on the wound bed. Porcine acellular dermal matrix was applied after tangential excision. The first dressing change was often performed about 1 week after the operation. Razor-thickness skin grafting was performed to cover the unhealed wounds. The length of wound healing, high fever, antibiotic usage, and hospital stay, times of later operation, and hospitalization expenses of patients in the 2 groups were recorded. The excisional eschar and wound bed tissue of patients in group MITE were harvested for pathological observation. Data were processed with t test and Fisher′s exact probability test. Results: (1) There were no statistically significant differences in length of high fever and length of hospital stay and hospitalization expenses between patients in the 2 groups (t=-1.67, -1.93, 0.31, P>0.05). The lengths of wound healing [(24.8±2.5) d] and antibiotic usage [(4.4±0.7) d] of patients in group MITE were significantly shorter than those in group CT [(33.3±2.5) and (7.0±0.7) d, t=-2.44, -2.44, P<0.05], and times of later operation of patients in group MITE [(0.29±0.14) times] were significantly less than those in group CT [(0.79±0.21) times, t=-2.03, P<0.05]. (2) The thickness of the excisional eschar of patients in group MITE was about 150 μm. The eschar has epidermis and upper dermis. Some necrotic tissue was left on the wound bed. Conclusions The treatment for pediatric deep partial-thickness burn wounds on trunk and limbs with minimally invasive tangential excision using electric dermatome in the early stage post burn can accelerate wound healing, shorten length of antibiotic usage, and reduce times of later operations.

Motor imagery electroencephalogram (EEG) signals are non-stationary time series with a low signal-to-noise ratio. Therefore, the single-channel EEG analysis method is difficult to effectively describe the interaction characteristics between multi-channel signals. This paper proposed a deep learning network model based on the multi-channel attention mechanism. First, we performed time-frequency sparse decomposition on the pre-processed data, which enhanced the difference of time-frequency characteristics of EEG signals. Then we used the attention module to map the data in time and space so that the model could make full use of the data characteristics of different channels of EEG signals. Finally, the improved time-convolution network (TCN) was used for feature fusion and classification. The BCI competition IV-2a data set was used to verify the proposed algorithm. The experimental results showed that the proposed algorithm could effectively improve the classification accuracy of motor imagination EEG signals, which achieved an average accuracy of 83.03% for 9 subjects. Compared with the existing methods, the classification accuracy of EEG signals was improved. With the enhanced difference features between different motor imagery EEG data, the proposed method is important for the study of improving classifier performance.
Algorithms ; Brain-Computer Interfaces ; Electroencephalography/methods* ; Humans ; Imagery, Psychotherapy ; Imagination