BackgroundWe aimed to report our successful use of frontalis muscle flap suspension for the correction of congenital blepharoptosis in early age children.MethodsThis retrospective study included 61 early age children (41 boys, 20 girls) with an average age of 6 years (range, 3–10 years) with congenital blepharoptosis who received surgery during the period from March 2007 to January 2011. There were 39 cases of unilateral blepharoptosis and 22 cases of bilateral blepharoptosis, thus a total of 83 eyes were affected. If patient had bilateral blepharoptosis, both eyes were operated on in the same surgery. Patients were followed for 3 months to 5 years. The procedure was performed without complications in all cases.ResultsThe postoperative healing grade was good in 81 eyes (97.6%); the correction of blepharoptosis was satisfactory, the double eyelid folds were natural and aesthetic, the eyelid position and the curvature were ideal, and the eyes were bilaterally symmetrical. The postoperative healing grade was fair in 2 eyes (2.4%); blepharoptosis was improved compared with that before surgery. At discharge, lagophthalmos was noted in 10 eyes of which 4 cases resolved by the last follow-up. The remaining 6 cases were mild. Eleven eyes received reoperation for residual ptosis after the first surgery. The curvature of the palpebral margin was not natural in 4 eyes. These unnatural curvature possibly was caused by an excessively low lateral fixation point or postoperative avulsion.ConclusionFrontalis muscle flap suspension under general anesthesia for the correction of congenital blepharoptosis in early age children can achieve good surgical results.

No abstract

Studies have found differences in the concentration of volatile organic compounds in the breath of diabetics and healthy people, prompting attention to the use of devices such as electronic noses to detect diabetes. In this study, we explored the design of a non-invasive diabetes preliminary screening system that uses a homemade electronic nose sensor array to detect respiratory gas markers. In the algorithm part, two feature extraction methods were adopted, gradient boosting method was used to select promising feature subset, and then Particle Swarm Optimization (PSO) algorithm was introduced to extract 24 most effective features, which reduces the number of sensors by 56% and saves the system cost. Respiratory samples were collected from 120 healthy subjects and 120 diabetic subjects to assess the system performance. Random Forest (RF) algorithm was used to classify and predict electronic nose data, and the accuracy can reach 93.33%. Experimental results show that on the premise of ensuring accuracy, the system has low cost and small size after the number of sensors is optimized, and it is easy to install on in-car. It provides a more feasible method for the preliminary screening of diabetes on in-car, and can be used as an assistant to the existing detection methods.