Objective To explore the feasibility and reference value of allogeneic lung transplantation and postoperative monitoring in miniature pigs for lung transplantation research. Methods Two miniature pigs (R1 and R2) underwent left lung allogeneic transplantation. Complement-dependent cytotoxicity tests and blood cross-matching were performed before surgery. The main operative times and partial pressure of arterial oxygen (PaO₂) after opening the pulmonary artery were recorded during surgery. Postoperatively, routine blood tests, biochemical blood indicators and inflammatory factors were detected, and pathological examinations of multiple organs were conducted. Results The complement-dependent cytotoxicity test showed that the survival rate of lymphocytes between donors and recipients was 42.5%-47.3%, and no agglutination reaction occurred in the cross-matching. The first warm ischemia times of D1 and D2 were 17 min and 10 min, respectively, and the cold ischemia times were 246 min and 216 min, respectively. Ultimately, R1 and R2 survived for 1.5 h and 104 h, respectively. Postoperatively, in R1, albumin (ALB) and globulin (GLB) decreased, and alanine aminotransferase increased; in R2, ALB, GLB and aspartate aminotransferase all increased. Urea nitrogen and serum creatinine increased in both recipients. Pathological results showed that in R1, the transplanted lung had partial consolidation with inflammatory cell infiltration, and multiple organs were congested and damaged. In R2, the transplanted lung had severe necrosis with fibrosis, and multiple organs had mild to moderate damage. The expression levels of interleukin-1β and interleukin-6 increased in the transplanted lungs. Conclusions The allogeneic lung transplantation model in miniature pigs may systematically evaluate immunological compatibility, intraoperative function and postoperative organ damage. The data obtained may provide technical references for subsequent lung transplantation research.

OBJECTIVE: To investigate the anthropometric parameters that can accurately predict the diameter of the hamstring tendon graft, and to examine the correlation between disease etiology, duration, and graft diameter. METHODS: A retrospective analysis was conducted on data from 166 patients who underwent anterior cruciate ligament reconstruction using hamstring tendon autografts (semitendinosus and gracilis tendon) between January 2013 and December 2023. The cohort conprised 135 males and 31 females;the age ranged from 14 to 62 years old with an average of (28.87±10.46) years old. Pearson correlation coefficients, partial correlation coefficients, and stepwise multiple linear regression analysis were utilized to elucidate the relationship between the outcome variable (diameter of hamstring tendon grafts) and predictive variables (e.g., height). RESULTS: Correlation analysis revealed significant associations between the diameter of hamstring tendon grafts and height (r=0.379, P<0.001), weight (r=0.225, P=0.002), male gender (r=0.302, P<0.001), age(r=-0.218, P=0.002), and sports injury etiology (r=0.167, P=0.016). No significant correlations were found with surgical side, body mass index (BMI), or injury duration (P>0.05). Stepwise multiple linear regression analysis using a backward elimination method indicated that height was the sole significant predictive variable (R²=0.144, P<0.001), with the optimal predictive equation being:Graft size =2.636+0.029×Height (cm). Partial correlation analysis, after controlling for height, showed no significant association between age, gender, weight, and injury etiology with graft diameter. CONCLUSION Height is an effective predictive factor for the diameter of autologous hamstring tendon grafts. Factors such as gender, age, surgical side, body weight, and BMI are not influential to the diameter of the hamstring tendon grafts. Under the condition of the same height, there is no significant difference in the influence of these factors on the graft diameter. Preoperative physical activity level and the duration of injury do not significantly affect the diameter of the hamstring tendon grafts obtained during surgery. It is recommended to use the formula 'Graft Diameter=2.636 + 0.029 × Height (cm)' for preoperative prediction.
Humans ; Male ; Female ; Adult ; Middle Aged ; Retrospective Studies ; Adolescent ; Young Adult ; Transplantation, Autologous ; Anthropometry ; Anterior Cruciate Ligament Reconstruction ; Tendons/anatomy & histology* ; Autografts ; Hamstring Tendons/transplantation* ; Quadriceps Muscle/surgery*

Objective To investigate the postoperative efficacy of laparoscopic hiatal hernia repair(LHHR)combined with Toupet or Dor fundoplication for the treatment of esophageal hiatal hernia(HH).Methods A retrospective analysis was conducted on the case data of HH patients who underwent LHHR combined with Toupet(Toupet group,n=53)and Dor(Dor group,n=53)fundoplication between December 2018 and December 2022 in Department of General Surgery of Gansu Provincial Hospital.Intraoperative and postoperative recovery outcomes of both groups were observed.We analyzed and compared the incidence of dysphagia and gastroesophageal reflux disease questionnaire(GERD-Q)scores at preoperative and postoperative intervals of 1 month,6 months,and 1 year.The incidence of postoperative complications and the 1-year recurrence rate were compared between the two groups.Additionally,factors influencing postoperative dysphagia within the first month were examined.Results The differences between the two groups in operation time,intraoperative bleeding,postoperative ventilation time,postoperative extubation time and hospitalization time were not statistically significant(P>0.05).There was no significant difference in the incidence of immediate postoperative dysphagia in two groups(P>0.05).Furthermore,the differences between the two groups in the incidence of postoperative complications,such as bloating,abdominal pain and diarrhea,were not statistically significant(P>0.05).The incidence of dysphagia in Toupet group was higher than that in Dor group at 1 month postoperatively,and the difference was statistically significant(P=0.017);but the difference in the incidence of dysphagia between the two groups at 6 months and 1 year postoperatively was not statistically significant(P=0.767,1.000).The results of binary logistic regression analysis showed that both surgical procedure(OR=2.613,95%CI 1.141-5.983,P=0.023)and esophageal contractile reserve function(OR=2.921,95%CI 1.203-7.095,P=0.018)were independent risk factors for the incidence of dysphagia in patients with HH at 1 month after surgery.Compared with the preoperative period,the GERD-Q symptom scores were lower in both groups at 1 month,6 months,and 1 year postoperatively,and the difference was statistically significant(P<0.05);but there was no statistically significant difference between the groups at the same time point(Fintergroup=0.334,P=0.565).The difference between the two groups in 1-year postoperative recurrence rates was not statistically significant(P>0.05).Conclusions LHHR combined with Toupet or Dor fundoplication are both safe and effective surgical procedures for the treatment of HH,with excellent reflux control,fewer complications and lower recurrence rates,but Toupet fundoplication is more likely to have postoperative short-term dysphagia than Dor fundoplication.

Objective To construct a model combining with traditional Chinese medicine(TCM)syndrome for predicting the risk of disease progression in patients with idiopathic membranous nephropathy(IMN)by machine learning methods,thus to quantitatively evaluating the value of TCM syndrome in the prediction of the risk of disease progression in IMN.Methods Monofactor analysis,recursive feature elimination(RFE)and multivariate binary Logistic regression analysis were used to screen the independent related factors affecting the risk of disease progression of IMN,and then a risk prediction model was constructed.A total of 102 patients with IMN were randomly assigned to the training set and the test set in a ratio of 65∶35,and then the comparison was conducted in the performance indicators of accuracy,sensitivity,specificity,F1 value,and area under the receiver operating characteristic(ROC)area under the curve(AUC)of the risk prediction model with or without the inclusion of the TCM syndrome information.Results Before the inclusion of TCM syndrome information,12 clinical characteristic variables for patients with MN were obtained after monofactor analysis combined with RFE screening,and they were age,hemoglobin quantification,urinary occult blood,24-hour urine protein quantification,urine protein-creatinine ratio,estimated glomerular filtration rate(eGFR),creatinine,uric acid,alanine transaminase,anti-phospholipase A2 receptor antibody(PLA2R-Ab),total cholesterol,and low-density lipoprotein cholesterd.A risk cholesterol prediction model containing the above variables was constructed.The multivariate binary Logistic regression analysis showed that the differences of the clinical variables mentioned above between the training-set group and test-set group were statistically significant,and the risk prediction model presented good sensitivity and predictability.Monofactor analysis combined with RFE screening was performed again after the inclusion of TCM syndrome information,and then 14 variables were obtained,which included blood stasis syndrome and dampness obstruction syndrome.The sensitivity and specificity of the model with the inclusion of the TCM syndrome information were significantly improved when compared with those without the inclusion of TCM syndrome information.Conclusion The results of the study initially indicate that TCM syndrome can be used as an important supplementary variable for predicting the risk of disease progression in IMN,and will provide a reference for intelligent diagnosis through the integration of traditional Chinese and western medicine information,and will supply the guidance for the treatment of IMN with TCM.

Objective To propose a novel identification algorithm based on ensemble learning for assessing the severity of acute respiratory distress syndrome(ARDS)to achieve continuous monitoring of the disease severity.Methods Firstly,leve-raging the open-source MIMIC-Ⅳ database,a variety of non-invasive physiological parameters of patients were extracted and subjected to preliminary preprocessing.A multivariate feature selection algorithm was employed to rank these parameters and calculate feature importance scores through weighted computation.Secondly,based on the feature importance scores,a subset search algorithm was utilized to identify the subset of features that could yield optimal performance across four machine learning algorithms:neural networks,logistic regression,AdaBoost and XGBoost.Finally,a soft voting ensemble method was designed using a generalized linear regression model to integrate the results of each single machine learning algorithm,and a multivariate ensemble learning algorithm was proposed by combining the optimal feature subsets.The algorithm proposed when used to identify the severity of ADRS was evaluated with MIMIC-Ⅳ database,and compared with the traditional algorithms.Results The sensitivity,specificity,accuracy and AUC of the algorithm were 87.15％,89.23％,88.34％and 0.923 4,respectively,all of which outperformed those of the traditional algorithms.Conclusion The ARDS severity identification algorithm based on ensemble learning is capable of achieving continuous and real-time monitoring of the severity of ARDS,thereby offering robust support for the early identification and warning of ARDS in patients.[Chinese Medical Equipment Journal,2025,46(2):1-9]

Objective:To explore the latent class of childhood abuse and recent life stress in adolescent depression patients, and its effect on intermittent theta-burst stimulation (iTBS) treatment efficacy.Methods:From August 2023 to February 2024, a total of 60 adolescent patients with depression were enrolled. The Chinese version of the maltreatment and abuse chronology of exposure scale (MACE), adolescent self-rating life events check list (ASLEC), Montgomery-Asberg depression rating scale (MADRS)and suicidal ideation scale 5 (SSI-5)were used to assess all subjects. Using latent class analysis, subjects were divided into different subgroups according to 16 factors of MACE and ASLEC. Brain network analysis was conducted using the Dosenbach brain atlas with 160 regions of interest, which were divided into seven large-scale brain subnetworks: visual network (VN), somatosensory motor network (SMN), dorsal attention network (DAN), ventral attention network (VAN), subcortical network (SCN), frontoparietal network (FPN), and default mode network (DMN). Brain network functional connectivity analysis was performed using the DPABI plugin on MATLAB R2022b platform to compare differences in functional connectivity at both edge and network levels between subjects. Data analysis was conducted using SPSS 27.0 software, employing Shapiro-Wilk test, chi-square test, independent samples t-test, and Mann-Whitney U test. Results:Based on latent class analysis, subjects were divided into high abuse-high stress group ( n=38) and low abuse-high stress group ( n=22). At baseline, the high abuse-high stress group showed significantly higher scores in SSI-5 (6.39±2.26 vs 3.45(0, 5.25)), Z=-3.628, P<0.001) and MADRS item 10 (3.00(2.00, 5.00) vs 2.00(0.75, 3.25), Z=-2.794, P=0.005) compared to the low abuse-high stress group, while there was no statistically significant difference in the total score of MADRS between the two groups (27.77±4.19, 26.59±4.74, t=1.972, P=0.053). There was no statistically significant difference in the D-value of MADRS score before and after treatment between the two groups (8.45±6.03, 7.27±5.99, t=0.729, P=0.469) and the D-value of MADRS item 10 score before and after treatment between the two groups (1.00 (0, 3.00), 1.00 (0, 1.00), Z=-1.189, P=0.234). The high abuse-high group showed significantly weakened functional connectivity at the edge level compared to low abuse-high stress group at baseline ( P<0.05, NBS corrected). In the high abuse-high stress group, the mean FC values in the differential brain regions at baseline showed significant negative correlation with baseline suicidal ideation scores ( r=-0.475, P=0.003). Conclusion:The different patterns of childhood abuse and recent life stress affect the baseline clinical presentation and brain network functional connectivity of adolescent depression patients. However, the high abuse-high stress group and low abuse-high stress group showed similar responses to iTBS treatment.

Objective:To explore the latent class of childhood abuse and recent life stress in adolescent depression patients, and its effect on intermittent theta-burst stimulation (iTBS) treatment efficacy.Methods:From August 2023 to February 2024, a total of 60 adolescent patients with depression were enrolled. The Chinese version of the maltreatment and abuse chronology of exposure scale (MACE), adolescent self-rating life events check list (ASLEC), Montgomery-Asberg depression rating scale (MADRS)and suicidal ideation scale 5 (SSI-5)were used to assess all subjects. Using latent class analysis, subjects were divided into different subgroups according to 16 factors of MACE and ASLEC. Brain network analysis was conducted using the Dosenbach brain atlas with 160 regions of interest, which were divided into seven large-scale brain subnetworks: visual network (VN), somatosensory motor network (SMN), dorsal attention network (DAN), ventral attention network (VAN), subcortical network (SCN), frontoparietal network (FPN), and default mode network (DMN). Brain network functional connectivity analysis was performed using the DPABI plugin on MATLAB R2022b platform to compare differences in functional connectivity at both edge and network levels between subjects. Data analysis was conducted using SPSS 27.0 software, employing Shapiro-Wilk test, chi-square test, independent samples t-test, and Mann-Whitney U test. Results:Based on latent class analysis, subjects were divided into high abuse-high stress group ( n=38) and low abuse-high stress group ( n=22). At baseline, the high abuse-high stress group showed significantly higher scores in SSI-5 (6.39±2.26 vs 3.45(0, 5.25)), Z=-3.628, P<0.001) and MADRS item 10 (3.00(2.00, 5.00) vs 2.00(0.75, 3.25), Z=-2.794, P=0.005) compared to the low abuse-high stress group, while there was no statistically significant difference in the total score of MADRS between the two groups (27.77±4.19, 26.59±4.74, t=1.972, P=0.053). There was no statistically significant difference in the D-value of MADRS score before and after treatment between the two groups (8.45±6.03, 7.27±5.99, t=0.729, P=0.469) and the D-value of MADRS item 10 score before and after treatment between the two groups (1.00 (0, 3.00), 1.00 (0, 1.00), Z=-1.189, P=0.234). The high abuse-high group showed significantly weakened functional connectivity at the edge level compared to low abuse-high stress group at baseline ( P<0.05, NBS corrected). In the high abuse-high stress group, the mean FC values in the differential brain regions at baseline showed significant negative correlation with baseline suicidal ideation scores ( r=-0.475, P=0.003). Conclusion:The different patterns of childhood abuse and recent life stress affect the baseline clinical presentation and brain network functional connectivity of adolescent depression patients. However, the high abuse-high stress group and low abuse-high stress group showed similar responses to iTBS treatment.

Objective To propose a novel identification algorithm based on ensemble learning for assessing the severity of acute respiratory distress syndrome(ARDS)to achieve continuous monitoring of the disease severity.Methods Firstly,leve-raging the open-source MIMIC-Ⅳ database,a variety of non-invasive physiological parameters of patients were extracted and subjected to preliminary preprocessing.A multivariate feature selection algorithm was employed to rank these parameters and calculate feature importance scores through weighted computation.Secondly,based on the feature importance scores,a subset search algorithm was utilized to identify the subset of features that could yield optimal performance across four machine learning algorithms:neural networks,logistic regression,AdaBoost and XGBoost.Finally,a soft voting ensemble method was designed using a generalized linear regression model to integrate the results of each single machine learning algorithm,and a multivariate ensemble learning algorithm was proposed by combining the optimal feature subsets.The algorithm proposed when used to identify the severity of ADRS was evaluated with MIMIC-Ⅳ database,and compared with the traditional algorithms.Results The sensitivity,specificity,accuracy and AUC of the algorithm were 87.15％,89.23％,88.34％and 0.923 4,respectively,all of which outperformed those of the traditional algorithms.Conclusion The ARDS severity identification algorithm based on ensemble learning is capable of achieving continuous and real-time monitoring of the severity of ARDS,thereby offering robust support for the early identification and warning of ARDS in patients.[Chinese Medical Equipment Journal,2025,46(2):1-9]

AIM: To explore the dynamic expression of high mobility group box 1(HMGB1)in scar tissues after glaucoma drainage valve implantation, and to further reveal the role and possible mechanism of HMGB1 in scarring after glaucoma surgery.METHODS: A total of 60 New Zealand white rabbits were randomly divided into control group(n=20), model group(n=20, silicone implantation under conjunctival sac)and model with drug administration group(n=20, silicone implantation under conjunctival sac combined with 5-fluorouracil injection). The conjunctival tissues were collected at 4 and 8 wk after surgery. HE staining and Masson staining were used to detect the proliferation and distribution of fibroblasts and collagen fibers in conjunctival tissues. Immunohistochemistry was utilized to detect the distribution and changes of HMGB1, transforming growth factor(TGF)-β1, Smad3 and α-smooth muscle actin(SMA)in conjunctival tissues. RT-PCR and Western blot were adopted to detect the mRNA and protein expression of HMGB1, TGF-β1, Smad3 and α-SMA in conjunctival tissues.RESULTS: HE staining and Masson staining showed that the proliferation of inflammatory cells, fibroblasts and collagen fibers in the model group was significantly higher than that in the control group at both 4 and 8 wk. Meanwhile, the proliferation of fibroblasts and collagen fibers in the model with drug administration group was significantly lower than that in the model group. Immunohistochemical staining showed that the expression of HMGB1, TGF-β1, Smad3 and α-SMA protein was observed in the conjunctival tissues of the model group both 4 and 8 wk, with brown and significantly deeper staining of the model group at 8 wk. Meanwhile, the positive staining in the model with drug administration group at both 4 and 8 wk was significantly lower than that in the model group. There was positive correlations between the number of fibroblasts stained with HE and the expression of HMGB1 in the conjunctival tissue of the model group at both 4 and 8 wk(r=0.602, 0.703, all P<0.05). RT-PCR and Western blot revealed that the mRNA and protein expression levels of HMGB1, TGF-β1, Smad3 and α-SMA in the model group were significantly higher than those in the control group at both 4 and 8 wk(all P<0.05). Meanwhile, the mRNA and protein expression levels of HMGB1, TGF-β1, Smad3 and α-SMA in the model with drug administration group were significantly lower than those in the model group(all P<0.05). There was positive correlations between mRNA expressions of HMGB1 and TGF-β1, Smad3 in the model group and the model with drug administration group(all P<0.05).CONCLUSION: The expression of HMGB1 increased at a time-dependent manner after glaucoma valve implantation. HMGB1 acts an indispensable role in the initiation and progression of scar formation after glaucoma surgery, which may be involved in the regulation of TGF-β/Smad signaling pathway.

Objective: This study aimed to evaluate the effects of 2 endoscopic spine surgeries on the biomechanical properties of normal and osteoporotic spines. Methods: Based on computed tomography images of a healthy adult volunteer, 6 finite element models were created. After validating the normal intact model, a concentrated force of 400 N and a moment of 7.5 Nm were exerted on the upper surface of L3 to simulate 6 physiological activities of the spine. Five types of indices were used to assess the biomechanical properties of the 6 models, range of motion (ROM), maximum displacement value, intervertebral disc stress, maximum stress value, and articular protrusion stress, and by combining them with finite element stress cloud. Results: In normal and osteoporotic spines, there was no meaningful change in ROM or disc stress in the 2 surgical models for the 6 motion states. Model N1 (osteoporotic percutaneous transforaminal endoscopic discectomy model) showed a decrease in maximum displacement value of 20.28% in right lateral bending. Model M2 (unilateral biportal endoscopic model) increased maximum displacement values of 16.88% and 17.82% during left and right lateral bending, respectively. The maximum stress value of L4–5 increased by 11.72% for model M2 during left rotation. In addition, using the same surgical approach, ROM, maximum displacement values, disc stress, and maximum stress values were more significant in the osteoporotic model than in the normal model. Conclusion In both normal and osteoporotic spines, both surgical approaches were less disruptive to the physiologic structure of the spine. Furthermore, using the same endoscopic spine surgery, normal spine biomechanical properties are superior to osteoporotic spines.