Effect of high-density fat combined with adipose stem cell glue on the success rate of facial filling and its clinical value.

Facial fat grafting is a popular cosmetic procedure, and experts are increasingly endorsing the use of high-density fat with adipose stem cell glue for better results. This study aims to explore the effect of high-density fat combined with adipose stem cell glue on the success rate of facial filling and its clinical value. We conducted a randomized trial with 100 patients who underwent facial fat transplantation between August 2020 and August 2022. They were divided into two groups: a control group receiving traditional Coleman fat transplantation and an observation group receiving high-density fat with adipose stem cells. In the observation and control groups, the excellent and good rate was 98.00 and 80.00%. After 3 months of treatment, the thickness of frontal subcutaneous fat and temporal subcutaneous fat in the observation group was higher (P < 0.05). Observation group retention of fat transplantation was noticeably higher 3 months after treatment (P < 0.05). Three months after treatment, the VISIA (facial imaging system) scores of facial color spots, facial pores and facial wrinkles in the observation group were lower (P < 0.05). After treatment, both groups indicated noticeable improvements in physiological functions, health status, social function, mental health, and somatic diseases compared to before treatment. Notably, the observation group had higher scores (P < 0.05). The observation group had a lower complication rate (4.00% vs. 22.00%) and higher satisfaction rate (98.00% vs. 86.00%) than the control group. Using high-density fat combined with adipose stem cell glue for facial fat grafting yields superior results, reduces complications, and boosts patient satisfaction compared to traditional methods. We have complied with all relevant ethical regulations with regard to the use of stem cells.

Prediction of Soil Water-Soluble Organic Matter by Continuous Use of Corn Biochar Using Three-Dimensional Fluorescence Spectra and Deep Learning.

The purpose is to study the soil's water-soluble organic matter and improve the utilization rate of the soil layer. This exploration is based on the theories of three-dimensional fluorescence spectroscopy, deep learning, and biochar. Chernozem in Harbin City, Heilongjiang Province, is taken as the research object. Three-dimensional fluorescence spectra and a deep learning model are used to analyze the content of water-soluble organic matter in the soil layer after continuous application of corn biochar for six years and to calculate different fluorescence indexes in the whole soil depth. Among them, the three-dimensional fluorescence spectrum theory provides the detection standard for the application effect detection of biochar, the deep learning theory provides the technical support for this exploration, and the biochar theory provides the specific research direction. The results show that the application of corn biochar for six consecutive years significantly reduces the average content of water-soluble organic matter in different soil layers. Among them, the highest average content of soil water-soluble organic matter is "nitrogen, potassium, phosphorous" (NPK) and the lowest is "boron, carbon" (BC). Comparing the soil with BC alone, in the topsoil, the second section (330-380 nm/200-250 nm) with BC + NPK increases by 13.3%, the third section (380-550 nm/220-250 nm) increases by 8.4%, and the fourth section (250-380 nm/250-600 nm) increases by 50.1%. The combination of nitrogen (N) + BC has a positive effect of 20.7%, 12.2%, and 28.4% on sections I, II, and IV, respectively. In addition, in the topsoil, the combination of NPK + BC significantly increases the content of acid-like substances compared with the application of BC alone. In the black soil, with or without fertilizer NPK, there is no significant difference in the level of fulvic acid-like components. The prediction of soil water-soluble organic matter after continuous application of corn biochar based on three-dimensional fluorescence spectra and deep learning is carried out, which has reference significance for the rapid identification and early prediction of subsequent soil activity.