Objective:To evaluate the imaging stability of a portable boom-type ophthalmic ultra-high-resolution optical coherence tomography (OCT) device in multiple application scenarios.Methods:The boom-type mode and handheld mode of the portable boom-type OCT and the desktop OCT were used to perform three-dimensional imaging tests on three healthy adults undergoing physical examinations at the Zhongshan Ophthalmic Center, Sun Yat-sen University as well as on OEMI-7 model eyes in a sitting position.The same two modes of the portable boom-type OCT were used to perform three-dimensional imaging on four awake non-sedated infants, two sedated infants and four healthy adults in the supine position.The obtained 3D imaging data were processed using a correlation analysis method between adjacent B-scans, and the offset of B-scan in the axial (z-axis) and the fast axis transverse (x-axis) were calculated.The procedures for in vivo human eye experiments followed the Declaration of Helsinki and were approved by the Ethics Committee of Zhongshan Ophthalmic Center, Sun Yat-sen University (No.2020 KYPJ154).All subjects and infant guardians signed the informed consent form. Results:Compared with the handheld imaging mode, the axial and fast axis lateral motion offsets of the model eye were significantly reduced in the boom-type imaging mode from (124.00±12.49)μm to (48.00±15.87)μm and from (24.00±1.00)μm to (2.67±0.57)μm, respectively ( t=2.932, 4.337; both P<0.001).In both human and model eyes, the axial and fast axis lateral motion offsets of the boom-type mode were significantly lower than in the traditional handheld operation mode (both P<0.001).The axial and lateral motion offsets between the boom-type mode and desk-top OCT imaging were comparable, without significant differences (both P>0.05).In both sedated and awake, non-sedated infants in the supine position, the axial offset of the portable boom-type OCT system was similar to that of the healthy adults, without significant difference in the overall comparison ( P=0.385), and the lateral offsets were higher than those of healthy adults, with statistically significant differences (both P=0.013).There was no significant difference in axial deviation between sedated and non-sedated infants ( P>0.05).The lateral deviation of non-sedated infants was higher than that of sedated infants, though the difference was not statistically significant ( P=0.247). Conclusions:The portable boom-type OCT system can maintain high-speed, high-resolution imaging performance while achieving imaging stability comparable to traditional desktop OCT systems.It is more suitable for bedside imaging of supine subjects, especially uncooperative infants, and has good clinical application prospects.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective:To evaluate the imaging stability of a portable boom-type ophthalmic ultra-high-resolution optical coherence tomography (OCT) device in multiple application scenarios.Methods:The boom-type mode and handheld mode of the portable boom-type OCT and the desktop OCT were used to perform three-dimensional imaging tests on three healthy adults undergoing physical examinations at the Zhongshan Ophthalmic Center, Sun Yat-sen University as well as on OEMI-7 model eyes in a sitting position.The same two modes of the portable boom-type OCT were used to perform three-dimensional imaging on four awake non-sedated infants, two sedated infants and four healthy adults in the supine position.The obtained 3D imaging data were processed using a correlation analysis method between adjacent B-scans, and the offset of B-scan in the axial (z-axis) and the fast axis transverse (x-axis) were calculated.The procedures for in vivo human eye experiments followed the Declaration of Helsinki and were approved by the Ethics Committee of Zhongshan Ophthalmic Center, Sun Yat-sen University (No.2020 KYPJ154).All subjects and infant guardians signed the informed consent form. Results:Compared with the handheld imaging mode, the axial and fast axis lateral motion offsets of the model eye were significantly reduced in the boom-type imaging mode from (124.00±12.49)μm to (48.00±15.87)μm and from (24.00±1.00)μm to (2.67±0.57)μm, respectively ( t=2.932, 4.337; both P<0.001).In both human and model eyes, the axial and fast axis lateral motion offsets of the boom-type mode were significantly lower than in the traditional handheld operation mode (both P<0.001).The axial and lateral motion offsets between the boom-type mode and desk-top OCT imaging were comparable, without significant differences (both P>0.05).In both sedated and awake, non-sedated infants in the supine position, the axial offset of the portable boom-type OCT system was similar to that of the healthy adults, without significant difference in the overall comparison ( P=0.385), and the lateral offsets were higher than those of healthy adults, with statistically significant differences (both P=0.013).There was no significant difference in axial deviation between sedated and non-sedated infants ( P>0.05).The lateral deviation of non-sedated infants was higher than that of sedated infants, though the difference was not statistically significant ( P=0.247). Conclusions:The portable boom-type OCT system can maintain high-speed, high-resolution imaging performance while achieving imaging stability comparable to traditional desktop OCT systems.It is more suitable for bedside imaging of supine subjects, especially uncooperative infants, and has good clinical application prospects.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

This paper summarized the clinical experience of Professor GE Linyi in treating ulcerative colitis （UC） by stages with the method of clearing. Professor GE believes that the core pathogenesis of UC is dampness and heat in the intestines， and by taking the method of clearing as the basis， he proposed four methods for treatment of UC including clearing and transforming， clearing and dispersing，clearing and moisterning， clearing and nourishing. The pathogenesis of UC in its active stage is dampness and heat in the intestines， congestion and stagnation of qi and blood， and accumulation of stasis toxins， for which the treatment method is to clear and transform， accompanied by clearing and dispersing method. In terms of the clearing and transforming method， Bai Tou Weng Decoction （白头翁汤） combined with Haungqin （Radix Scutellariae）， Machixian （Herba Portulacae） and Pugongying （Herba Taraxaci） is taken as the basic prescription to clear and transform dampness and heat， cool blood， resolve toxins and stop dysentery. For the clearing and dispersing method， medicinals to rectify qi such as Chaihu （Radix Bupleuri）， Cuxiangfu （Vingar Rhizoma Cyperi）， Muxiang （Radix Aucklandiae）， Zhiqiao （Fructus Aurantii）， and Binlang （Semen Arecae）， as well as those to regulate blood such as Danggui （Radix Angelicae Sinensis）， Cebaiye （Cacumen Platycladi） and Diyutan （Radix Sanguisorbae Carbonisatus） are recommended. The pathogenesis of the remission stage is healthy qi depletion and lingering pathogen of dampness and heat stasis toxin in the intestines， for which the method of clearing and nourishing， clearing and moistening can be used； the latter is mainly for people with yin fluids injury， and self-made Qingrun Yichang Decoction （清润益肠汤） is recommended， while the former is for those with spleen and stomach weakness， and self-made Qingyang Jianpi Decoction （清养健脾汤） can be used.

Objective:To investigate the clinical efficacy in one stage reconstruction of composite defects of Achilles tendon and surrounding soft tissues with a flap transfer combined with allogeneic tendon transplantation.Methods:From July 2018 to August 2022, a total of 12 patients, including 9 males and 3 females, with a mean age of 31.5(ranged 8 to 56) years old, had surgery with flap transfer combined with transplantation of allogeneic tendon in one stage reconstruction for compound defects of Achilles tendon and soft tissue at the Department of Orthopaedics of First Affiliated Hospital of Nanchang University. The defects of Achilles tendons ranged from 4.0 to 9.0 cm, and the soft tissue defects sized from 3.0 cm × 4.0 cm to 14.0 cm × 6.0 cm. Of the 12 patients, 6 received transfers of sural neurovascular flaps, 3 with peroneal perforator flaps and 3 with free anterolateral thigh flaps(ALTF). The flaps sized from 4.0 cm × 4.5 cm to 15.0 cm×7.0 cm, and in addition, allogeneic tendon grafts were used to reconstruct the defects of Achilles tendons in all patients. All the flap donor sites were either directly sutured or covered with skin grafts. Follow-up was carried out by visits of outpatient clinic or telephone or WeChat distant interviews. The flap survival and recovery of ankle function and Achilles tendon were observed.Results:During the 3 months to 2 years of follow-up, none of the patient showed obvious immunological rejection against the transplanted allogeneic tendon. All 12 flaps survived well with the colour and texture close to the surrounding skin. No ulceration occurred in both of the donor and recipient sites. There was no re-rupture of the transplanted allogeneic tendon. At the final follow-up, ankle movement was measured at 13.4°±2.6° in dorsal extension and 33.6°±3.2° in plantar flexion. According to American Orthopaedic Foot and Ankle Society (AOFAS) ankle and hind foot function score, a score of 88.7±5.6 was achieved with 7 patients in excellent, 4 in good and 1 was acceptable.Conclusion:In patients with a composite defect of Achilles tendon and surrrounding soft tissue, the application of a flap transfer combined with a homogeneous allograft tendon transplantation in an one stage surgery is a feasible surgical procedure. It can achieve a satisfactory outcome with less trauma and fewer complications.