Single-incision endoscopic-assisted temporoparietal fascia harvest for single stage auricular reconstruction.

INTRODUCTION: The temporoparietal fascia flaps (TPFF) have been widely used to cover the framework in auricular reconstructions. However, flap harvesting is mostly done by open surgery which may be easier but often results in bad scarring and hair loss. We would like to present a series of cases using endoscopic-assisted flap harvesting techniques with only one single cosmetic auricular incision. PATIENTS AND METHODS: Prospective studies from June 2018 to September 2021 on patients who underwent single-stage total auricular reconstruction using autologous costal cartilage and porous polyethylene (PPE) framework. Variables include age, gender, flap survivability as well as visual results and complications. RESULTS: A total of 61 TPFFs were harvested to cover 15 autologous costal cartilages and 46 PPE frameworks in 60 patients (one patient had operation on both sides). TPFF harvests are performed by endoscopic techniques with one single auricular incision. There was no flap necrosis, no bleeding and no cases required framework removal. Only 7/61 (11.5%) ears had small framework exposure which resolved on their own or only required local skin flap coverage and 1 ear had frontal nerve injury. CONCLUSION: Single-stage auricular reconstruction is a difficult surgery, yet greatly beneficial to young children. Through a single-incision endoscopic technique, we can obtain sufficiently large high-survivability TPFFs ensuring full coverage of the autologous costal cartilage or PPE framework. This method is reliable, and reproducible with advanced training. After reviewing the literature, we can state that our report probably includes the largest endoscopic-assisted TPFF harvesting series and the first to implement single-incision endoscopic technique in auricular reconstructions.

Epidemic Dynamics via Wavelet Theory and Machine Learning with Applications to Covid-19.

We introduce the concept of epidemic-fitted wavelets which comprise, in particular, as special cases the number I(t) of infectious individuals at time t in classical SIR models and their derivatives. We present a novel method for modelling epidemic dynamics by a model selection method using wavelet theory and, for its applications, machine learning-based curve fitting techniques. Our universal models are functions that are finite linear combinations of epidemic-fitted wavelets. We apply our method by modelling and forecasting, based on the Johns Hopkins University dataset, the spread of the current Covid-19 (SARS-CoV-2) epidemic in France, Germany, Italy and the Czech Republic, as well as in the US federal states New York and Florida.