Retropharyngeal lipostructure in the treatment of velopharyngeal insufficiency: A prospective study and update.

INTRODUCTION: Retropharyngeal lipostructure is a recent procedure in velopharyngeal insufficiency (VPI), offering an effective alternative to heavier surgery. OBJECTIVES: To update and assess retropharyngeal lipostructure as a treatment for VPI in the University Hospital Center of Rouen (France). TYPE OF STUDY: Single-center prospective study, from May 2012 to May 2014. PATIENTS AND METHODS: Six patients (4 girls, 2 boys) presenting with VPI were treated by retropharyngeal lipostructure. Age at surgery ranged between 6 and 12 years. Four of the patients bore a 22q11 microdeletion. Treatment was indicated in case of Borel-Maisonny type 2b (n=2) or 2m (n=4) despite well-conducted speech therapy and of≥50% velopharyngeal sphincter closure on nasal endoscopy. Patients were assessed preoperatively and at 3 months, by a multidisciplinary team. Borel-Maisonny type was assessed by a speech therapist. Nasality was measured on assisted vocal evaluation (EVA®). Sphincter closure was assessed on dynamic MRI. RESULTS: Between 6 and 8cm3autologous fat was injected. At 3months, 4 children showed 1-grade improvement in Borel-Maisonny type. Nasality decreased systematically, from a mean 14.5% preoperatively to 10.5% postoperatively. MRI showed improvement in all cases, with complete closure in occlusive vowels in 3 children. CONCLUSION: EVA® and MRI provide precise objective assessment of VPI. Retropharyngeal lipostructure is a simple, relatively non-invasive, reproducible technique, providing good results in VPI.

A robust computational technique for model order reduction of two-time-scale discrete systems via genetic algorithms.

A robust computational technique for model order reduction (MOR) of multi-time-scale discrete systems (single input single output (SISO) and multi-input multioutput (MIMO)) is presented in this paper. This work is motivated by the singular perturbation of multi-time-scale systems where some specific dynamics may not have significant influence on the overall system behavior. The new approach is proposed using genetic algorithms (GA) with the advantage of obtaining a reduced order model, maintaining the exact dominant dynamics in the reduced order, and minimizing the steady state error. The reduction process is performed by obtaining an upper triangular transformed matrix of the system state matrix defined in state space representation along with the elements of B, C, and D matrices. The GA computational procedure is based on maximizing the fitness function corresponding to the response deviation between the full and reduced order models. The proposed computational intelligence MOR method is compared to recently published work on MOR techniques where simulation results show the potential and advantages of the new approach.