Search

Background Unpredictable outcomes with autologous fat grafting due to reabsorption processes present a major challenge for healthcare providers and patients. A higher number of viable adipocytes is considered to result in a higher volume being retained. Although various adverse factors have been extensively researched, other potential parameters have been less investigated or even neglected. Objective The aim of this study was to investigate the harvesting process of adipose tissue as the primary cause of cell damage and to determine the risk factors associated with low cell survival. Methods Thirty-nine male and female subjects undergoing planned elective liposuction or abdominoplasty were enrolled. Forty-seven lipoaspirates harvested by different liposuction techniques were analyzed. RNA isolation and real-time polymerase chain reaction was performed to elucidate differences in the expression of various adipocyte markers. Furthermore, scanning electron microscopy was performed on various samples to determine the cell damage caused by the different techniques. Results A statistically significant lower expression of peroxisome proliferator–activated receptor γ was detected in subjects with a higher BMI. A trend towards a lower expression of perilipin 1 in lipoaspirates harvested by a super wet + ultrasound technique, compared with dry and super wet techniques, was shown. The lowest level of cell damage determined from scanning electron microscopy images was in lipoaspirates harvested by the super wet + ultrasound technique, and this level was statistically significantly different from those obtained by the 2 other techniques. Conclusions Optimization of the outcome in autologous fat grafting may be feasible by targeting and optimizing the harvesting process as a main risk factor for impaired adipocyte viability. Ultrasound-assisted liposuction might be considered a suitable harvesting technique. Level of Evidence: 5

The COVID-19 related lockdown measures offer a unique opportunity to understand how changes in economic activity and traffic affect ambient air quality, and how much pollution reduction can the society offer through digitalization and mobility-limiting policies. In this work, we estimate pollution reduction over the lockdown period by using the measurements from ground air pollution monitoring networks, training long-term prediction models and comparing their predictions to measured values over the lockdown month. We show that our models achieve state-of-the-art performance and evaluate up to -29.4%, -28.1%, and -52.8%, change in NO2 in Eastern Switzerland, Beijing and Wuhan respectively. Our reduction estimates take local weather into account. What can we learn from pollution emissions during lockdown? The lockdown period was too short to train meaningful models from scratch. We therefore use transfer learning to update only mobility-dependent variables. We show that the obtained models are suitable for the analysis of the post-lockdown periods and capable of estimating the future air pollution reduction potential.