Exploring Preclinical Experiments with Different Fat Types for Autologous Fat Grafting.

BACKGROUND: Autologous fat transplantation has been a cornerstone of tissue regeneration for decades. However, there is no standardized selection system or criteria for fat graft selection, often relying heavily on the surgeon's experience. OBJECTIVES: This study aimed to investigate various types of fat derivatives, both in vitro and in vivo at the same condition. METHODS: We collected traditional fat granules of different sizes and SVF-gel, evaluating the viability of ADSCs isolated from them and their performance after grafting into mice. RESULTS: Large fat granules exhibited more complete adipocyte structures, and the isolated ADSCs demonstrated superior differentiation, proliferation, and secretion capacities. They also showed excellent volume retention after 12 weeks. In contrast, ADSCs isolated from SVF-gel displayed lower vitality. However, grafts from SVF-gel exhibited the highest volume maintenance rate among the four groups after 12 weeks, closely resembling normal adipose tissue and displaying significant vascularization. Compared to large fat granule and SVF-gel group, medium and small fat granule grafts exhibited lower volume retention and less angiogenesis. CONCLUSIONS: Through preclinical studies, the flexible clinical use of different fat grafts can be tailored to their unique characteristics. LEVEL OF EVIDENCE I: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Denoising Ocean Turbulence Microstructure Signals for Application in Estimating Turbulence Kinetic Energy Dissipation Rates Based on EMD-PCA.

When ocean turbulence signals are collected using turbulence observation instruments in real marine environments, the effective signals in the acquired data set are often polluted by noise. In order to eliminate the noise component contained in the non-stationary and nonlinear ocean turbulence signals, a new multi-scale turbulence signal denoising method is proposed by combining the empirical mode decomposition (EMD) and principle component analysis (PCA). First, the time series of turbulence signals are decomposed into a couple of components by EMD algorithm and approximately calculate the noise energy in each intrinsic mode function (IMF). Then, PCA is implemented on each IMF. The appropriate principal components are selected according to the decomposition characteristics of PCA and the noise energy proportion in IMF. Each IMF is reconstructed by the selected principle components. At last, the effective ocean turbulence signals are reconstructed by the corrected IMFs and the residue. Ocean turbulence signals collected in the South China Sea (SCS) are used to evaluate the effectiveness of the proposed method. The results show that the proposed method can effectively eliminate the noise and maintain the characteristics of the effective turbulence signals under high noise. Turbulence kinetic energy (TKE) is also estimated from the denoised signals, which provide a reliable data basis for the analysis of the turbulent characteristics in later stage.