AIM: To investigate the effects of Conbercept on various optical coherence tomography(OCT)biomarkers in patients with retinal vein occlusion-related macular edema(RVO-ME), and to analyze the correlation of these biomarker changes with visual prognosis.METHODS: Retrospective study. A total of 57 patients(57 eyes)with RVO-ME, including 25 patients(25 eyes)with central retinal vein occlusion(CRVO)and 32 patients(32 eyes)with branch retinal vein occlusion(BRVO), were enrolled in this study. All the patients received intravitreal injection of conbercept once a month, three times in total. The preoperative and postoperative best-corrected visual acuity(BCVA), and changes in OCT biomarkers, including central macular thickness(CMT), the length of disorganization of the retinal inner layers(DRIL), the number of hyperreflective dots(HRD), the area of intraretinal fluid(IRF), the area of subretinal fluid(SRF), and the length of ellipsoid zone(EZ)disruption were compared. Furthermore, the relationship of these changes with BCVA was analyzed.RESULTS:Compared with the baseline, at 3 mo post-treatment, BCVA(LogMAR)was improved, CMT was decreased, the length of DRIL was shortened, the number of HRD was reduced, the area of IRF was decreased, the area of SRF was reduced, and the length of EZ disruption was shortened(all P<0.05). Spearman correlation analysis showed that there was no correlation between the changes in CMT, the length of DRIL, the number of HRD, the area of IRF, the area of SRF and the change in BCVA before and after treatment(P>0.05). However, the change in the length of EZ disruption was positively correlated with the change in BCVA(rs=0.34, P=0.011), and the R2 value of the fitting curve between the change in the length of EZ disruption and the change in BCVA was 0.113(P=0.011). When comparing the pre- and post-treatment changes in BCVA, the length of DRIL, the number of HRD, the area of IRF, the area of SRF, and the length of EZ disruption between patients in the CRVO group and BRVO group, no significant differences were observed(all P>0.05). In contrast, a significant difference was found in the change in CMT between the two groups(P=0.002).CONCLUSION:Conbercept effectively improves multiple OCT biomarkers in patients with RVO-ME. Repair of EZ disruption is a key driver of visual recovery, and its stability may serve as a novel indicator for personalized decision-making in anti-vascular endothelial growth factor therapy.

AIM: To observe the characteristics of best corrected visual acuity(BCVA), microperimetry(MP), multifocal electroretinogram(mfERG), and optical coherence tomography angiography(OCTA)parameters in patients with idiopathic epiretinal membrane(IERM), and conduct a comparative study and correlation analysis on these parameters.METHODS:This was a cross-sectional study. A total of 56 patients(56 eyes)diagnosed with IERM who visited our hospital between February 2021 and November 2024 were collected as IERM group, and 33 healthy individuals(33 eyes)undergoing physical examinations were included as control group. Parameters were compared between the IERM group and the control group, as well as among IERM subgroups at different stages. Additionally, correlations among visual function parameters and between these visual function parameters and vascular structural OCTA parameters were analyzed.RESULTS: The general data of patients in the control group and IERM group were comparable. In the IERM group, BCVA, retinal sensitivity(RS), P1 wave amplitude in ring 1, superficial capillary plexus parafoveal vessel density(SCPpfvd), deep capillary plexus parafoveal vessel density(DCPpfvd), and the foveal avascular zone(FAZ)area were significantly lower than the control group(all P<0.01). In contrast, central retinal thickness(CRT), superficial capillary plexus foveal vessel density(SCPfvd), and deep capillary plexus foveal vessel density(DCPfvd)were significantly increased(all P<0.001). When comparing different stages of IERM, significant differences were observed in BCVA, CRT, RS, SCPfvd, and FAZ(all P<0.01). In eyes affected by IERM, BCVA(LogMAR)was negatively correlated with RS; P1 wave amplitude in ring 1 positively correlated with P1 wave implicit time in ring 1; SCPfvd positively correlated with BCVA(LogMAR)and negatively correlated with RS; DCPfvd negatively correlated with P1 wave implicit time in ring 1; and DCPpfvd positively correlated with RS(all P<0.05).CONCLUSION: Eyes with IERM exhibit abnormalities in visual function parameters and vascular structure, with varying degrees of alteration in BCVA, CRT, RS, SCPfvd, and FAZ across different stages. Comprehensive evaluation of BCVA, MP, mfERG, and OCTA contributes to a deeper understanding of the nature of IERM and aids in formulating appropriate diagnosis and treatment plans.

Objective:To investigate the early and mid-term outcomes of aortic endovascular remodeling device (AERD) for Stanford type A aortic dissection (TAAD) in type Ⅱhybrid surgery, and to evaluate its clinical efficacy.Methods:46 patients with TAAD, including 14 females and 32 males, participated in the single-center clinical trial of West China Hospital of Sichuan University and underwent type II hybrid surgery (Bentall / ascending aorta replacement + AERD implantation) from February 2021 to October 2023. The safety and efficacy of AERD in type Ⅱ hybrid surgery for TAAD were estimated by clinical indicators (postoperative mortality, cardiovascular and cerebrovascular accidents, paraplegia, ischemia), and blood flow condition (volume of the true and false lumen, and suprachial branches).Results:Three patients (6.52%) died during the follow-up period, and the operation-related mortality was 4.35% (2/46). The remaining 43 patients were followed up for an average of (25.53±9.60) months. There were two cases (4.35%) of stroke after the operation, and paraplegia, acute renal insufficiency, and other severe complications were not noticed. The blood flow of the superior branch of the aortic arch was unobstructed, and there was no significant difference in the blood flow of the branch before the operation and at each follow-up time point. Compared to the pre-operation, the true lumen volume of the stent part increased by 59.0% and the false lumen volume decreased by 82.4%.Conclusion:AERD is a safe and effective alternative in type II hybrid surgery for acute TAAD, which is helpful in improving perioperative and short- and long-term survival rates and clinical outcomes.

Objective:To analyzed the 3-year follow-up results in a single center to evaluate the mid-term clinical efficacy of aortic endovascular remodeling device(AERD).Methods:From January 2019 to June 2019, 18 patients with residual aortic dissection after proximal repair of acute aortic dissection were treated with AERD in our heart center of West China Hospital of Sichuan University. They were followed up for 3 years after surgery and underwent vascular enhanced CT review. The primary outcome of our study included all-cause mortality and stent-related mortality. Secondary outcome included branch patency rate, reoperation rate and the incidence of serious adverse events. Morphological measures assessed the effectiveness of AERD in treating residual distal dissection.Results:17 patients completed the 3-year follow-up, and 1 was lost to follow-up. There was no stent-related death, branch artery occlusion, or new serious adverse events. 12 patients completed vascular enhanced CT review, the true lumen was significantly expanded and the false lumen was reduced considerably at 3-year follow-up, true lumen volume, (52.39±22.32)cm 3 vs. (74.34±14.64) cm 3( P<0.01), false lumen volume(50.42±25.44) cm 3 vs. (32.32±31.75)cm 3( P<0.01). Increased true lumen diameter and area ( P<0.001) and decreased false lumen diameter( P<0.001) were significantly different from those before operation, especially in the level below the renal artery and 5 cm below the renal artery. Conclusion:The mid-term effect of AERD in treating distal residual dissection is satisfactory, and it promoted positive distal aorta remodeling with safety and effectiveness.

Objective:To compare the cost-effectiveness between open surgery and minimally invasive rotational surgery in patients with benign breast tumor, and to provide a theoretical support for the choice of surgical approach.Methods:The clinical data of 1 389 benign breast tumor patients underwent surgery from January 2017 to January 2020 in Beijing Friendship Hospital, Capital Medical University were retrospectively analyzed. Among them, 374 patients were treated with open surgery (open group), and 1 015 patients were treated with minimally invasive rotational surgery (minimally invasive group). The surgery-related and cost-effectiveness analysis indexes were compared between two groups.Results:The operation time, incidence of hematoma/subcutaneous bruising and cosmetic result satisfactory rate in minimally invasive group were significantly higher than those in open group: (37.37 ± 6.66) min vs. (34.58 ± 8.95) min, 10.54% (107/1 015) vs. 5.35% (20/374) and 98.72% (1 002/1 015) vs. 95.99% (359/374); while the incision length, length of hospital stay and pain score were significantly less than those in open group: (5.00 ± 0.00) mm vs. (26.55 ± 4.73) mm, (1.03 ± 0.36) d vs. (2.85 ± 1.99) d, (1.76 ± 1.56) scores vs. (2.72 ± 1.27) scores, and there were statistical differences ( P<0.01). The patients were followed up until May 2022. There were no incision infection, recurrence and residual lesions in the two groups. The total cost and cost-effectiveness ratio in minimally invasive group were significantly lower than those in open group: (6 553 ± 1 150) yuan vs. (7 965 ± 3 323) yuan and 71.10 ± 13.61 vs. 88.96 ± 37.48, the benefit score was significantly higher than that in open group: (96.50 ± 3.89) scores vs. (88.92 ± 6.39) scores, and there were statistical differences ( P<0.01). Conclusions:Compared with open surgery, minimally invasive rotational surgery is less costly and more benefits, so minimally invasive surgery should be the preferred surgical procedure for benign breast tumor.

As for the treatment of microtia, the status quo is as follows. The ear reconstruction with autologous costal cartilage has been generally accepted as the therapeutic strategy for type Ⅲ/Ⅳ patients with only small ear lobe remaining or complete absence of auricle, and the outcomes are improved steadily with the advances in technology; but for type Ⅰ/Ⅱ patients with larger remnant ear, there is insufficient evidence to be able to support any specific treatment methods as the potential unified approach for surgeons to choose from. Non-surgical treatment with appliance wearing may play an auxiliary role in the treatment of microtia, which is worthy of further research. The authors proposed a serialized treatment system based on their clinical practice and literature review. It is expected to be helpful for the treatment of microtia.

As for the treatment of microtia, the status quo is as follows. The ear reconstruction with autologous costal cartilage has been generally accepted as the therapeutic strategy for type Ⅲ/Ⅳ patients with only small ear lobe remaining or complete absence of auricle, and the outcomes are improved steadily with the advances in technology; but for type Ⅰ/Ⅱ patients with larger remnant ear, there is insufficient evidence to be able to support any specific treatment methods as the potential unified approach for surgeons to choose from. Non-surgical treatment with appliance wearing may play an auxiliary role in the treatment of microtia, which is worthy of further research. The authors proposed a serialized treatment system based on their clinical practice and literature review. It is expected to be helpful for the treatment of microtia.

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.