Objective: To observe the safety and efficiency of face-lift combined with fat grafting in facial rejuvenation. Methods: We performed a retrospective study, which included 23 patients. SMAS suspension and multi-site suspension were combined to correct the nasolabial fold, mid-cheek aging and malar mounds. Structural fat grafting was performed to treat the volume loss in mid-face. Results: All patients demonstrated a significant improvement in midfacial appearance. No infection or nerve injury were found in this study. Only three patients did not get primary healing in temple region, which led to temporal hair loss from secondary healing. Conclusions This study demonstrates that fat grafting and multiple layers face-lift are efficient method for facial rejuvenation. These approaches appear to be very promising for facial anti-aging techniques.

Objective: To observe the outcome of breast reconstruction with autologous fat grafting in the patients following treatment for breast cancer. Methods: The clinical data of 22 patients after breast cancer modified radical mastectomy with fat grafting for breast reconstruction from January 2012 to March 2015 at Department of Body Contouring and Liposuction Center of Plastic Surgery, Hospital of Peking Union Medical College were analyzed retrospectively. The age of 22 patients (all female) was 28 to 54 years. Fifteen patients were performed breast modified radical mastectomy 5 to 16 year ago without radiotherapy, 7 patients were performed breast modified radical mastectomy following regular radiotherapy 2 years ago. Low negative pressure liposuction technical was applied to harvest fat tissue for 400 to 800 ml which was filtrated and purified by cotton pad method in low temperature environment. Fat grafting was performed with multi-level and multi-tunnel and in multi-point injection ways. All patients were followed up by regular imaging evaluation with MRI or ultrasonography after operation every 3 months. Results: All breast reconstruction were successfully performed in 22 patients, no severe complications occurred. Among 15 patients without radiotherapy, 12 patients were performed with autologous fat grafting for breast reconstruction, 3 patients with prosthetic implantation for breast augmentation after autologous fat grafting. Among 7 patients with radiotherapy, 6 patients were performed with autologous fat grafting for breast reconstruction, 1 patient with prosthetic implantation for breast augmentation after autologous fat grafting. The volume of fat grafting was 104 to 380 ml. It took 2.5 hours to finish the operation including 1.0 to 1.5 hours for liposuction and 40 minutes for fat grafting. Next fat grafting were performed after 3 months. The fat of the breast were survived well detecting by MRI, only 1 patient had a cystic nodule which had been resected during nipple reconstruction. Ultrasonography screened several cystic nodules with the major axis of 0.1 to 0.2 cm in the breast, which couldn′t be found by palpating in 18 patients. The patients were followed up for 18 to 36 months, the outcome were satisfactory. Conclusion Autologous fat grafting for breast reconstruction simplifies the operation program with satisfied results and avoids the complications of breast reconstruction with skin flap.

Objective:To observe the clinical effect, postoperative satisfaction and complications of liposuction combined with low-energy bipolar radiofrequency BodyTite in arm with dermatolysis.Methods:From June 2013 to December 2019, 66 female patients were included. Firstly, liposuction treatment was performed in the designed operation area, and then radio-frequency skin tightening treatment was performed with BodyTite equipment. All patients were followed up for 6 months.Results:All the 66 patients had completed the operation successfully; their age ranged from 22-53 (28.3±6.9) years. The average body mass index was (23.8±3.4) kg/m 2, the average liposuction volume of unilateral upper arm was (288.6±95.6) ml, the average radiofrequency energy of unilateral upper arm was (4.2±1.1) kJ and the average operation time was (75.1±18.7) min 6 months after operation. A total of 42 cases were followed up. By self-evaluation satisfactory rate of patients was 92.8%, and the satisfactory rate of third-party independent plastic surgeons was 88.1%. There were no hematoma, seroma or infection except one case of skin blister. There was no serious complication requiring further surgical intervention. Conclusions:Bipolar radiofrequency BodyTite is a safe and effective method for the treatment of fat accumulation of upper arm with flabby skin.

Objective:To evaluate the clinical efficacy of microfat on neck wrinkles and its structure and viability.Methods:A retrospective analysis was conducted to review the clinical data of 23 patients with neck wrinkles corrected by microfat injection from June 2018 to June 2021 at the Body Contouring and Fat Grafting Center, Plastic Surgery Hospital, Chinese Academy of Medical Sciences, aged (38.1±10.7) years old. A blunt cannula with multiple side holes of 0.8 mm was used to obtain fat particles under low negative pressure and to prepare the microfat by washing and soft centrifugation. A 26-gauge sharp needle was used to inject microfat with small amount and at multiple points into the intradermal and subdermal layer where the neck lines were linearly depressed. Regular follow-ups were carried out after the operation, and the efficacy of the operation was evaluated from routine neck examinations, third-party doctor′s grade scoring and patient′s scoring towards satisfaction. To clarify the structure and viability of microfat, scanning electron microscope was used to observe the structure of fat particles, Calcein-AM/propidium iodide (Calcein-AM/PI) staining to detect tissue viability and Musecell counting to count SVF cells.Results:All of the 23 patients successfully completed the operation without complications such as infection and fat liquefaction. After a follow-up of 3 to 30 months, the neck wrinkles were effectively improved. The postoperative grade scores of third-party doctor was significantly reduced ( P<0.05). And the satisfaction of patients was high, with 20 cases (87.0%) satisfied. Scanning electron microscopy showed that the fat granule cells were tightly arranged and the structure remained intact. Calcein-AM/PI staining showed that most cells in the granules survived. The number of viable SVF cells in fat particles was (9.34±2.68)×10 5/ml. Conclusions:Microfat is easy to obtain with high tissue activity, which is suitable for neck wrinkle filling. Good effects can be achieved with high patients′ satisfaction. It is worthy of clinical application.

Objective:To study the effect of various concentrations of Enterococcus faecalis (Ef) supernatants on human periodontal ligament cell (hPDLC) and the inflammatory response of hPDLC under static pressure. Methods:The method of methyl thiazolyl tetrazolium (MTT) was used to detect the effect of various concentrations of Ef supernatants on the proliferation of hPDLCs and the flow cytometry was used to detect the expression of Toll-like receptor 2 (TLR-2) on the surface of hPDLC after 24-hour-stimulation of Ef supernatant. Furthermore, the hPDLCs were divided into non inducing group without Ef supernatant and inducing group with 5% Ef supernatant, and hPDLCs in each group were loaded with 0, 49 and 196 Pa static pressures respectively. The expressions of tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β) mRNA and protein were detected by reverse transcription-PCR (RT-PCR) and enzyme linked immunosorbent assay (ELISA) after 24 hours.Results:MTT results showed that the supernatant of Ef with concentratio n≥5% could significantly inhibit the proliferation activity of hPDLCs at 48 hours of cell culture ( P<0.05). Flow cytometry showed that the positive cell rates of TLR-2 increased with increasing volume fractions of the Ef supernatants. The values were (2.12±0.07)%, (2.41±0.32)%, (2.65±0.27)%, (4.76±0.46)%, (9.91±0.92)% and (12.01±1.35)%, respectively. The differences were statistically significant when the concentrations≥5% ( P<0.05). There were no significant differences in the expressions of IL-1β and TNF-α mRNA between the non inducing group and the control group under the pressure of 49 Pa ( P>0.05). However, there were significant differences in the expressions of IL-1β and TNF-α mRNA between the non inducing group and the control group under the pressure of 196 Pa ( P<0.05), while the expressions of IL-1β and TNF-α in the inducing group were significantly lower than that in the control group under the pressures of 49 and 196 Pa ( P<0.05). Compared with the control group, the mRNA expression was significantly increased ( P<0.05). The result of ELISA was consistent with that of PCR. Conclusions:High concentration of Ef supernatant could inhibit the proliferation of hPDLC. Ef supernatant might promote the expression of TLR-2 on the surface of hPDLC. Excessive mechanical pressure induced the inflammatory response of hPDLC. The presence of inflammatory mediators could lead to the intolerance of hPDLC to pressures and small pressure could aggravate the inflammatory response.

Objective:To report our clinical experience in simultaneous implant exchange with fat (SIEF) for breast augmentation and reshaping.Methods:The medical records of 26 patients who received SIEF in our department from January 2013 to June 2019 were reviewed. All patients were female, aged (35.2±8.3) years. The prosthesis was removed through axillary approach, inframammary approach or radial incision at the junction of lateral breast and chest wall. Capsulectomy was not performed after implant removal. Fat was harvested using a blunt liposuction cannulas with the diameter of 2.5 mm and 3 side holes, with a negative pressure of -60 kPa. After washing with 4 degree physiological saline, the fat was processed with the cotton pad filtering technique, then a diameter 2.5 mm, 1-hole blunt cannula was used to place the fat. The evaluation of postoperative improvements included breast size, incidence of complications. Patient satisfaction and physician satisfaction were assessed by two independent surgeons (the full score ranged from 4 to 20).Results:All patients completed the study. The volume of implants in the left breast was (216.2±54.6) ml and that in the right breast was (217.9±53.1) ml. Grafted fat volume for the left breast was (256.8±55.8) ml each time and that for the right breast was (258.1±55.7) ml. The mean follow-up time was (13.5±5.7) months. Small nonpalpable nodules were detected in two patients (7.7%) by ultrasound. No other complications were documented during the study period. The average chest circumference was decreased by (1.1±0.8) cm which was less than one cup according to Chinese Industrial Standards. Patient satisfaction and physician satisfaction were (16.8±2.0) and (16.4±1.6), respectively.Conclusions:SIEF is an effective and safe method to restore breast volume after implant removal.

June 5, 2019, a 36-year-old female was diagnosed with septic shock caused by Streptococcus dysgalactiae infection eight hours after liposuction and fat grafting in Plastic Surgery Hospital, Chinese Academy of Medical Sciences. As the symptoms were identified early, the patient received immediate treatment and was transferred to Peking Union Medical College Hospital. After multi-disciplinary coordination of departments of emergency, plastic surgery, and ICU, the septic status was finally resolved and the patient was discharged after a 103-day hospital stay. The authors reviewed the course of the treatment in detail and our experience in dealing with the special kind of toxic septic shock.

Objective:This study aims to compare the application value of Autoregressive Integrated Moving Average model (ARIMA ) and deep learning model inprediction and analysis of liposuction operation data.Methods:The patients who met inclusion criteria and underwent liposuction surgery in the Plastic Surgery Hospital of Chinese Academy of Medical Sciences from January 2019 to September 2019 were enrolled in this study. For each patient, 250~400 s operation data including kinematics and mechanical data were collected by a senior plastic surgeon, usingthe liposuction operation recording system which consists of optical tracking and force sensing equipment. After pretreatment, the collected data were divided into one liposuction reciprocating cycle as one set of data. ARIMA model and deep learning model were used to analyze the collected datafor establishing prediction models of liposuction operation. Using Matlab2017, 30 couplesofliposuction data setwereextractedby simple random sampling, andthe DTW valueofeachcoupleofdatasetswascalculated as test standard. Then, the DTW values between 30 sets of predicted data and actual data based on the ARIMA model and the deep learning model were calculated respectively and compared with the test standard to verify the prediction result of the two models. Matlab2017 was used for statistical analysis. Independent sample t-test was used to compare the two groups, and P<0. 05 indicates that the difference is statistically significant.Results:18 patients were enrolled. All patients were females at 23-49 years old, with the mean age of 36. 6 years old. Liposuction was performed in the abdomen, thighs, and waist. A total of 16 800 sets of liposuction cycle data were obtained. The mean DTW value of test standard was 0. 048±0. 028. The meanDTW value between the ARIMA model predicted data and the actual data was 0. 660±0. 577, which was statistically significant compared with the test standard ( P< 0. 05) . The meanDTW value between the deep learning model predicted data and the actual data was 0. 052±0. 030, which was no significant difference compared with the test standard ( P> 0. 05 ). Conclusions:Compared with ARIMA model, deep learning model can predict liposuction operation data more accurately, and has better adaptability and real-time performance.

Objective:This study aims to compare the applicability value of autoregressive integrated moving average model(ARIMA) and deep learning model inprediction and analysis of liposuction operation data.Methods:The patients who met inclusion criteria and underwent liposuction surgery in the Plastic Surgery Hospital of Chinese Academy of Medical Sciences from January 2019 to September 2019 were enrolled in this study. For each patient, 250-400 s operation data including kinematics and mechanical data were collected by a senior plastic surgeon, using the liposuction operation recording system which consists of optical tracking and force sensing equipment. After pretreatment, the collected data were divided into one liposuction reciprocating cycle as one set of data. ARIMA model and deep learning model were used to analyze the collected data for establishing prediction models of liposuction operation. Using Matlab 2017, 30 couples of liposuction data set were extracted by simple random sampling, and the dynamic time warping (DTW) value of each couple of data sets was calculated as test standard. Then, the DTW values between 30 sets of predicted data and actual data based on the ARIMA model and the deep learning model were calculated respectively and compared with the test standard to verify the prediction results of the two models. Matlab 2017 was used for statistical analysis. Independent sample t-test was used to compare the two groups, and P<0.05 indicated statistically significant difference. Results:Eighteen patients were enrolled. All patients were females at 23-49 years old, with the mean age of 36.6 years old. Liposuction was performed in the abdomen, thighs, and waist. A total of 16 800 sets of liposuction cycle data were obtained. The mean DTW value of test standard was 0.048±0.028. The mean DTW value between the ARIMA model predicted data and the actual data was 0.660±0.577, which was statistically significant compared with the test standard ( P<0.05). The mean DTW value between the deep learning model predicted data and the actual data was 0.052±0.030, which was not significantly different compared to the test standard ( P>0.05). Conclusions:Compared with ARIMA model, deep learning model can predict liposuction operation data more accurately, and has better adaptability and real-time performance.