RESUMO
Objective:To introduce a surgical technique of implant-based immediate breast reconstruction using the "latent orifice" procedure after skin-sparing mastectomy and to investigate the clinical value of this procedure.Methods:Clinical data of 72 patients who underwent immediate breast reconstruction using the "latent orifice" procedure (we placed the silicone prosthesis in a "latent orifice" consisting of fascia and underlying muscles) from Jan. 2016 to Dec. 2019 were collected. Characteristics of surgical technique and the effect of the reconstruction surgery were analyzed.Results:70 patients underwent nipple-areola complex sparing mastectomy (NSM) . Two patients’ nipples were resected due to intraoperative frozen pathology suggesting carcinoma existing in the nipples. The nipple epidermal necrosis occurred in 3 patients. No seroma, incision infection or capsular contracture occurred. According to the evaluation scale, the overall aesthetic score was 9.39 points. The average score of each subscale was: 9.57 points for breast volume, 9.43 for breast contour, 9.84 for placement of implant, 9.38 for scars, 9.27 for lower pole projection and 9.21 for inframammary fold definition. BREAST-Q questionnaires were filled by patients. Scores of psychosocial well-being ranged from 62 to 93 points, with the median score of 77 points. Satisfaction with breasts scores ranged from 58 to 100 points, with the median score of 71 points.Conclusion:The "latent orifice" procedure is a simple, safe, practicable, aesthetic and satisfying type of immediate implant-based breast reconstruction, which is worth practicing and promoting.
RESUMO
BACKGROUND: Plant pests mainly refers to insects and mites that harm crops and products. There are a wide variety of plant pests, with wide distribution, fast reproduction and large quantity, which directly causes serious losses to crops. Therefore, pest recognition is very important for crops to grow healthily, and this in turn affects crop yields and quality. At present, it is a great challenge to realize accurate and reliable pest identification. RESULTS: In this study, we put forward a diagnostic system based on transfer learning for pest detection and recognition. This method is able to train and test ten types of pests and achieves an accuracy of 93.84%. We compared this transfer learning method with human experts and a traditional neural network model. Experimental results show that the performance of the proposed method is comparable to human experts and the traditional neural network. To verify the general adaptability of this model, we used our model to recognize two types of weeds: Sisymbrium sophia and Procumbent Speedwell, and achieved an accuracy of 98.92%. CONCLUSION: The proposed method can provide evidence for the control of pests and weeds and the precise spraying of pesticides. Thus, it provides reliable technical support for precision agriculture. © 2019 Society of Chemical Industry.
