Objective To evaluate the therapeutic effect of bone marrow mesenchymal stem cells (BMSC) transplantation via renal artery in treating experimental rats with adriamycin-induced chronic nephro -pathy.Methods A total of 50 male Sprague-Dawley (SD) rats were used as experimental animals.Two rats were used for the isolation and culture of BMSC.Twelve rats were designed as blank control group (group N);in other 36 rats adriamycin was injected through caudal vein to establish rat models of chronic nephropathy,these 36 rats were randomly and equally divided into three groups with 12 rats in each group:control group (group C,n=12),BMSC transplantation via renal artery group (group A,n=12),and BMSC transplantation via caudal vein group (group V,n=12).For the rats of group N,the same amount of normal saline was injected through caudal vein.Results At each observation point,the levels of blood urea nitrogen,serum creatinine,24 h urinary protein and 24 h urinary microprotein in group A,V and C were remarkably higher than those in group N (P＜0.01).One and two weeks after BMSC transplantation,the 24 h urinary microprotein level in group A was significantly lower than that in group C (P＜0.01);the serum creatinine level in group A was significantly lower than that in group C and group V (P＜0.01).One week after BMSC transplantation,both the 24 h urinary protein level and 24 h urinary microprotein level in group A were strikingly lower than those in group V (P＜0.01),but two weeks after BMSC transplantation these differences between group A and group V became not statistically significant.Conclusion BMSC transplantation via renal artery can improve cell-homing efficiency and improve the repair of damaged tissue as well.

Objective To evaluate the clinical application of high quality for super-selective renal artery embolization (SRAE) in treating bleeding after percutaneous nephrolithotomy (PCNL).Methods 134 patients received percutaneous nephrolithotomy were divided into control group (67 patients) and observation group (67 patients).All of patients with serious bleeding after PCNL were given SRAE in the Second Affiliated Hospital of Kunming Medical College from June 2010 to June 2015.At the same time,we gave high quality nursing to observation group.The patients in control group received routine nursing.The effect of nursing was observed.Results The degree of hematuria disappear of the patients in observation group was higher than that in control group (P＜ 0.05).There were fewer complications in observation group.In the sixth month after discharge,none of them had obvious renal impairment.No recurrence of hematuria,pus kidney and urinary cyst was tested.All cases were satisfied with the treatment.Conclusion It's the key to prevent serious complications and cure successfully with effective and timely supervision and high quality nursing care during the perioperation of SRAE in treating bleeding after PCNL.

Objective To compare the repair effect on renal function between different times of bone marrow mesenchymal stem cells (BMSCs) transplant via renal artery route in experimental rats with adriamycininduced nephropathy.Methods Adriamycin-induced nephropathy model was established in 32 rats through injection of adriamycin though the caudal vein.Based on the scheduled times of BMSCs transplant,the experimental rats were randomly and equally divided into M0 group (zero time),M1 group (one time),M2group (2 times) and M3 group (3 times) with 8 rats in each group.Other 8 SD rats were used as normal control group (N group).Single dose of 0.5 rnl BMSC suspension (2×106 cells/ml) was transplanted to the rats of M0 group (zero time),M1 group (one time),M2 group (2 times) and M3 group (3 times),for the rats of the groups not receiving BMSC transplant a single dose of 0.5 ml L-DMEM culture medium,used as a placebo,was adopted to replace BMSC suspension.The transplant interval was one week.Before transplant as well as one and two weeks after last time of transplant,the serum urea nitrogen,serum creatinine,24 h urine protein and 24 h urine microprotein were tested,and one week after last time of transplant pathological sections were made for laser focusing microscope examination to observe renal pathological changes and the distribution of BMSC cells in the kidney.Results The values of serum urea nitrogen,serum creatinine,24 h urine protein and 24 h urine microprotein determined at each observation time point in M0 group,M1 group,M2 group and M3 group were significantly higher than those in N group (P＜0.001).The values of 24 h urine protein and 24 h urine microprotein determined at one week after last time of transplant in M2 group and M3 group were strikingly lower than those in M1 group (P＜0.05),but these differences between M2 group and M3 group were not statistically significant (P=0.063).Conclusion For the treatment of adriamycin-induced nephropathy in experimental rats,two times of using BMSCs transplant via renal artery route can achieve optimal curative effect.

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: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.

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.

Objective To evaluate the therapeutic effect of bone marrow mesenchymal stem cell (MSC) infusion transplantation via renal artery and via caudal vein in treating chronic kidney disease (CKD) in rats,and to compare the expressions of aquaporin1 (AQP1) and aquaporin2 (AQP2) between the two transplantation routes.Methods A total of 50 male SD rats were selected for this experiment.Two experimental rats were used to make preparation of bone marrow MSC.CKD model was established with infusion of adriamycin via caudal vein in 36 rats.The 36 CKD models were randomly divided into adriamycininduced renal failure model control group (A-C group,n=12),MSC transplantation through the right renal artery group (M-A group,n=12) and MSC transplantation through the caudal vein group (M-V group,n=12).The remaining 12 male SD rats were used as the blank control group (N group).One week after the last bone marrow MSC transplantation,the 24 h urine volume,24 h urinary protein content,serum sodium content and serum albumin level were measured,and AQP1 and AQP2 expressions in the kidney tissue were determined by immunohistochemistry.Results Compared with A-C group,the serum albumin level and 24h urine volume in both M-V group and M-A group were significantly increased (P＜0.05),while 24h urinary protein content and serum sodium content were remarkably decreased (P＜0.05).The 24h urinary protein content in the M-A group was obviously lower than that in the M-V group (P＜0.05).The AQP1 and AQP2 expressions in the kidney tissue in both M-V group and M-A group were strikingly lower than those in the A-C group (P＜ 0.05),but no statistically significant differences in AQP1 and AQP2 expressions existed between the M-V group and the M-A group (P＞0.05).Conclusion MSC transplantation can increase serum albumin,and lower urinary protein,serum sodium and the expressions of AQP1 and AQP2 in renal parenchymal cells,which has the effect on repairing renal injury of adriamycin-induced CKD rats.For a given period of time,the clinical curative effect of MSC transplantation via renal artery is better than that of MSC transplantation via peripheral vein,but the difference in curative effect between the two MSC transplantation pathways has no obvious correlation with AQP1 and AQP2 expressions.

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.