BACKGROUND:Chondrocyte apoptosis is an important factor in the development of osteoarthritis,and Complanatoside A has a flavonoid effect,which can inhibit apoptosis of various cells,but its effect on chondrocyte apoptosis and the mechanism of action are not clear. OBJECTIVE:To investigate the intrinsic association and mechanism of Complanatoside A in chondrocyte apoptosis based on the Wnt/β-catenin signaling pathway. METHODS:(1)The cartilage tissues of the femur and tibia transected during knee arthroplasty were collected,and chondrocytes were isolated,cultured in vitro,and identified.(2)Cell counting kit-8 was used to detect the optimal intervention concentration of Complanatoside A in the concentration range of 0-160 μmol/L.(3)Chondrocytes were divided into blank group,sodium nitroprusside(1.5 mmol/L)-induced group,and sodium nitroprusside(1.5 mmol/L)+Complanatoside A(5 μmol/L)group.The viability and apoptosis rate of the cells in each group were detected by cell counting kit-8 and flow cytometry.The expression of type Ⅱ collagen and SOX9 was detected by immunofluorescence staining.The expression of apoptosis-related proteins and Wnt/β-catenin pathway proteins was detected by western blot assay. RESULTS AND CONCLUSION:The cells extracted in vitro were cultured and stained,and were clearly identified as chondrocytes.Complanatoside A had no obvious cytotoxicity to chondrocytes in the concentration range of 0-80 μmol/L,and significantly improved the chondrocyte viability in the concentration range of 2.5-10 μmol/L,especially when the concentration was 5 μmol/L.The apoptotic rate of chondrocytes was higher in the sodium nitroprusside-induced group than the blank control group,while the apoptotic rate was lower in the sodium nitroprusside+Complanatoside A group than the sodium nitroprusside-induced group.The fluorescence intensity of type Ⅱ collagen and SOX9 in chondrocytes was weaker in the sodium nitroprusside-induced group than the blank control group,while the fluorescence intensity of type Ⅱ collagen and SOX9 in the sodium nitroprusside+Complanatoside A group was higher than that of the sodium nitroprusside-induced group.In the sodium nitroprusside-induced group,the protein expression of Bax,Caspase-3,matrix metalloproteinase 13,Wnt3a,Wnt5a and β-catenin was higher than that of the blank control group,while the protein expression of Bcl-2 was lower than that of the blank control group.In the sodium nitroprusside+Complanatoside A group,except for the protein expression of Bcl-2 which was higher than that of the sodium nitroprusside-induced group,the expression of the other aforementioned proteins was lower than that of the sodium nitroprusside-induced group.To conclude,Complanatoside A has a certain inhibitory effect on chondrocyte apoptosis,which could regulate apoptosis-related proteins and promote the expression of chondrocyte regulatory factors,and presumably might play a role through inhibiting the Wnt/β-catenin signaling pathway.

Objective To explore the effectiveness of"near peer learning"(NPL)in the electromyo-graphy(EMG)teaching module for neurology residents.Methods The Department of Neurology,Peking Union Medical College Hospital implemented an NPL instructional design for a course on EMG for residents from November 2020 to March 2024.This teaching session was held annually,in which senior residents in-structed juniors who were 1 or 2 years earlier in their training.The residents participated in the pre-course/post-course tests and completed a feedback survey at the end of the session.This evaluation method was used to un-derstand the effectiveness of the NPL intervention in EMG teaching.Results Over four years,a total of 83 residents participated.Among them,there were 24 postdoctoral students,52 postgraduates and 7 junior resi-dents.The results showed that the post-course test scores were significantly improved compared with pre-course test scores(74.33±2.43 vs.70.11±2.49,P=0.005),with the most remarkable improvements seen for"tu-tees"(73.84±20.53 vs.70.29±21.46,P=0.020),postgraduates(74.04±22.51 vs.68.97±21.40,P=0.009),first-year residents(70.19±4.02 vs.63.59±3.59,P=0.040)and first-time participating resi-dents(65.23±3.24 vs.60.97±3.21,P=0.030).The post-program feedback showed that both tutors and tu-tees thought highly of NPL,believing that it enabled them to gain knowledge and helped them to improve teaching skills.Conclusions The NPL intervention is suitable for the teaching of EMG,because of its contri-bution to knowledge acquisition and basic clinical skills improvement.The NPL is worth replicating in other teaching and learning programs.

Objective:To analyze and compare the interfractional setup errors between two body posi-tioning fixation methods(lithotomy position with carbon fiber full-body fixation frame vs.conventional carbon fiber body fixation frame combined with thermoplastic membrane)in radical radiotherapy for pros-tate cancer,and to calculate the clinical target volume(CTV)to planning target volume(PTV)margin(MPTV)for both methods to optimize immobilization techniques and radiotherapy workflows.Methods:A retrospective analysis was conducted on 37 consecutive patients who underwent radical prostate radio-therapy at Peking University First Hospital between August 2021 and March 2023.The patients were di-vided into two groups based on the immobilization method:Group A(18 patients,450 CBCT image sets)used a carbon fiber whole-body fixator in the lithotomy position,while Group B(19 patients,461 CBCT image sets)used a conventional carbon fiber fixator combined with a thermoplastic mask.All the patients underwent daily cone-beam computed tomography(CBCT)image guidance.Bone registration combined with manual registration was used to obtain the setup error data in the left-right(X),cranio-caudal(Y)and anterior-posterior(Z)directions.The positioning errors of the two groups were compared by using the independent sample t-test,the Mann-Whitney U test and the chi-square test.The average positioning error,systematic positioning error(Σ)and random positioning error(δ)were calculated,and the CTV-PTV extension distance was calculated by using the(MPTV=2.5Σ+0.7δ).Results:The analysis of the setup errors in the three-dimensional direction showed significant differences between the two groups(all P＜0.01).Specifically,the median(quartile)absolute values of the errors in the X,Y,and Z di-rections of group A were[0.40(0.20,0.70)cm,0.50(0.30,0.80)cm,and 0.35(0.20,0.60)cm],respectively.In group B,the corresponding values were significantly reduced to[0.20(0.10,0.40)cm,0.40(0.20,0.70)cm and 0.20(0.10,0.40)cm].The results of Mann-Whitney U test showed that the differences in each direction were highly statistically significant(X:z=-6.86;Y:z=-2.76;Z:z=-5.71).The cumulative distribution ratio of the setup error displacement within 0.5 cm in the X,Y,and Z directions in group A and group B were 297(66.0％)and 408(88.5％)(P＜0.01),250(55.6％)and 285(61.8％)(P=0.055),308(68.4％)and 391(84.8％)(P＜0.01),re-spectively.The CTV-PTV margins in three directions were X 0.66 cm in group A and 0.35 cm in group B;Y0.67 cm and 0.45 cm;Z 0.54 cm and 0.42 cm.Conclusion:Conventional carbon fiber human body fixator combined with thermoplastic film can significantly reduce the setup error.However,the car-bon fiber whole-body fixator in the lithotomy position demonstrated larger setup errors in the X,Y,and Z directions compared with the conventional fixator combined with a thermoplastic mask,indicating the need for further workflow optimization.Given that the lithotomy position is essential for transperineal ultrasound-guided techniques,further research and improvements are required to achieve higher positioning accuracy.

Objective:To explore the distribution of Traditional Chinese Medicine constitution among patients with impaired awareness of hypoglycemia (IAH) and identify risk factors for IAH in patients with diabetes mellitus, to develop a risk prediction model. The aim is to validate the models′ predictive accuracy to facilitate early prevention and treatment of IAH.Methods:A case control study employing convenience sampling model was conducted on 1351 hospitalized patients with diabetes mellitus in the endocrinology departments of Changshu Hospital Affiliated to Nanjing University of Chinese Medicine and Affiliated Hospital of Jiangsu University, between August 2021 and December 2023. Traditional Chinese medicine constitution types were determined using the Traditional Chinese Medicine Constitution Classification and Judgment (ZYYXH/T157-2009). Data were divided into training and test sets at a ratio of 7∶3. Two prediction models were developed: Model 1, a conventional IAH prediction model for patients with diabetes mellitus, and Model 2, an IAH prediction model for patients with diabetes mellitus incorporating traditional Chinese medicine constitution. Nomograms were drawn for both models. The Hosmer-Lemeshow goodness-of-fit test, calibration curve, receiver operating characteristic (ROC) curve, and area under the curve (AUC) were calculated to evaluate the effectiveness of models 1 and 2. The improvement in prediction performance between Models 1 and 2 was assessed using Delong test, AUC, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA).Results:The study included 1 283 patients with diabetes mellitus, including 578 males and 705 females, aged (59.61 ± 14.09) years. The incidence of IAH among patients with diabetes mellitus was 20.50% (263/1283), with yang deficiency constitution being the most prevalent traditional Chinese medicine constitution type, at 47.53% (125/263). Multivariate analysis revealed that age, body mass index, course of diabetes, neurological hypoglycemia symptoms, hypoglycemia symptoms and severe hypoglycemia history were the influencing factors of Model 1 (all P<0.05); age, body mass index, neurological hypoglycemic symptoms, hypoglycemic symptoms, history of severe hypoglycemia, and traditional Chinese medicine constitution were the influencing factors of Model 2 (all P<0.05). The Hosmer-Lemeshow goodness-of-fit test showed a good fit of Model 2 [training set ( χ2=8.48, P>0.05), test set ( χ2=3.92, P>0.05)]. The Delong test results showed that the AUC for Model 2 was 0.96 for both the training and test sets, significantly higher than the AUCs of the 0.90 and 0.91 for Model 1 ( Z=-7.27, -3.70, both P<0.01). Furthermore, NRI was 0.66 ( 95%CI 0.53-0.79, P<0.01) and IDI was 0.02 (95% CI 0.01-0.03, P<0.05) for Model 2. Comparative analysis of clinical utility demonstrated that the net benefit of Model 2 for predicting IAH in patients with diabetes mellitus surpassed that of Model 1 across threshold probabilities ranging from 5% to 100%. Conclusions:The study constructed a nomogram prediction model included traditional Chinese medicine constitution with good predictive performance for IAH in patients with diabetes mellitus, and is of significant clinical value for identifying high-risk IAH populations.IAH patients mainly have a biased constitution, indicating that medical staff can reduce the incidence of IAH by improving the patients′ constitution.

Objective:To analyze and compare the interfractional setup errors between two body posi-tioning fixation methods(lithotomy position with carbon fiber full-body fixation frame vs.conventional carbon fiber body fixation frame combined with thermoplastic membrane)in radical radiotherapy for pros-tate cancer,and to calculate the clinical target volume(CTV)to planning target volume(PTV)margin(MPTV)for both methods to optimize immobilization techniques and radiotherapy workflows.Methods:A retrospective analysis was conducted on 37 consecutive patients who underwent radical prostate radio-therapy at Peking University First Hospital between August 2021 and March 2023.The patients were di-vided into two groups based on the immobilization method:Group A(18 patients,450 CBCT image sets)used a carbon fiber whole-body fixator in the lithotomy position,while Group B(19 patients,461 CBCT image sets)used a conventional carbon fiber fixator combined with a thermoplastic mask.All the patients underwent daily cone-beam computed tomography(CBCT)image guidance.Bone registration combined with manual registration was used to obtain the setup error data in the left-right(X),cranio-caudal(Y)and anterior-posterior(Z)directions.The positioning errors of the two groups were compared by using the independent sample t-test,the Mann-Whitney U test and the chi-square test.The average positioning error,systematic positioning error(Σ)and random positioning error(δ)were calculated,and the CTV-PTV extension distance was calculated by using the(MPTV=2.5Σ+0.7δ).Results:The analysis of the setup errors in the three-dimensional direction showed significant differences between the two groups(all P＜0.01).Specifically,the median(quartile)absolute values of the errors in the X,Y,and Z di-rections of group A were[0.40(0.20,0.70)cm,0.50(0.30,0.80)cm,and 0.35(0.20,0.60)cm],respectively.In group B,the corresponding values were significantly reduced to[0.20(0.10,0.40)cm,0.40(0.20,0.70)cm and 0.20(0.10,0.40)cm].The results of Mann-Whitney U test showed that the differences in each direction were highly statistically significant(X:z=-6.86;Y:z=-2.76;Z:z=-5.71).The cumulative distribution ratio of the setup error displacement within 0.5 cm in the X,Y,and Z directions in group A and group B were 297(66.0％)and 408(88.5％)(P＜0.01),250(55.6％)and 285(61.8％)(P=0.055),308(68.4％)and 391(84.8％)(P＜0.01),re-spectively.The CTV-PTV margins in three directions were X 0.66 cm in group A and 0.35 cm in group B;Y0.67 cm and 0.45 cm;Z 0.54 cm and 0.42 cm.Conclusion:Conventional carbon fiber human body fixator combined with thermoplastic film can significantly reduce the setup error.However,the car-bon fiber whole-body fixator in the lithotomy position demonstrated larger setup errors in the X,Y,and Z directions compared with the conventional fixator combined with a thermoplastic mask,indicating the need for further workflow optimization.Given that the lithotomy position is essential for transperineal ultrasound-guided techniques,further research and improvements are required to achieve higher positioning accuracy.

Objective:To explore the distribution of Traditional Chinese Medicine constitution among patients with impaired awareness of hypoglycemia (IAH) and identify risk factors for IAH in patients with diabetes mellitus, to develop a risk prediction model. The aim is to validate the models′ predictive accuracy to facilitate early prevention and treatment of IAH.Methods:A case control study employing convenience sampling model was conducted on 1351 hospitalized patients with diabetes mellitus in the endocrinology departments of Changshu Hospital Affiliated to Nanjing University of Chinese Medicine and Affiliated Hospital of Jiangsu University, between August 2021 and December 2023. Traditional Chinese medicine constitution types were determined using the Traditional Chinese Medicine Constitution Classification and Judgment (ZYYXH/T157-2009). Data were divided into training and test sets at a ratio of 7∶3. Two prediction models were developed: Model 1, a conventional IAH prediction model for patients with diabetes mellitus, and Model 2, an IAH prediction model for patients with diabetes mellitus incorporating traditional Chinese medicine constitution. Nomograms were drawn for both models. The Hosmer-Lemeshow goodness-of-fit test, calibration curve, receiver operating characteristic (ROC) curve, and area under the curve (AUC) were calculated to evaluate the effectiveness of models 1 and 2. The improvement in prediction performance between Models 1 and 2 was assessed using Delong test, AUC, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA).Results:The study included 1 283 patients with diabetes mellitus, including 578 males and 705 females, aged (59.61 ± 14.09) years. The incidence of IAH among patients with diabetes mellitus was 20.50% (263/1283), with yang deficiency constitution being the most prevalent traditional Chinese medicine constitution type, at 47.53% (125/263). Multivariate analysis revealed that age, body mass index, course of diabetes, neurological hypoglycemia symptoms, hypoglycemia symptoms and severe hypoglycemia history were the influencing factors of Model 1 (all P<0.05); age, body mass index, neurological hypoglycemic symptoms, hypoglycemic symptoms, history of severe hypoglycemia, and traditional Chinese medicine constitution were the influencing factors of Model 2 (all P<0.05). The Hosmer-Lemeshow goodness-of-fit test showed a good fit of Model 2 [training set ( χ2=8.48, P>0.05), test set ( χ2=3.92, P>0.05)]. The Delong test results showed that the AUC for Model 2 was 0.96 for both the training and test sets, significantly higher than the AUCs of the 0.90 and 0.91 for Model 1 ( Z=-7.27, -3.70, both P<0.01). Furthermore, NRI was 0.66 ( 95%CI 0.53-0.79, P<0.01) and IDI was 0.02 (95% CI 0.01-0.03, P<0.05) for Model 2. Comparative analysis of clinical utility demonstrated that the net benefit of Model 2 for predicting IAH in patients with diabetes mellitus surpassed that of Model 1 across threshold probabilities ranging from 5% to 100%. Conclusions:The study constructed a nomogram prediction model included traditional Chinese medicine constitution with good predictive performance for IAH in patients with diabetes mellitus, and is of significant clinical value for identifying high-risk IAH populations.IAH patients mainly have a biased constitution, indicating that medical staff can reduce the incidence of IAH by improving the patients′ constitution.

Objective To investigate the incidence rate,clinical characteristics,and prognosis of neonatal stroke in Shenzhen,China.Methods Led by Shenzhen Children's Hospital,the Shenzhen Neonatal Data Collaboration Network organized 21 institutions to collect 36 cases of neonatal stroke from January 2020 to December 2022.The incidence,clinical characteristics,treatment,and prognosis of neonatal stroke in Shenzhen were analyzed.Results The incidence rate of neonatal stroke in 21 hospitals from 2020 to 2022 was 1/15 137,1/6 060,and 1/7 704,respectively.Ischemic stroke accounted for 75％(27/36);boys accounted for 64％(23/36).Among the 36 neonates,31(86％)had disease onset within 3 days after birth,and 19(53％)had convulsion as the initial presentation.Cerebral MRI showed that 22 neonates(61％)had left cerebral infarction and 13(36％)had basal ganglia infarction.Magnetic resonance angiography was performed for 12 neonates,among whom 9(75％)had involvement of the middle cerebral artery.Electroencephalography was performed for 29 neonates,with sharp waves in 21 neonates(72％)and seizures in 10 neonates(34％).Symptomatic/supportive treatment varied across different hospitals.Neonatal Behavioral Neurological Assessment was performed for 12 neonates(33％,12/36),with a mean score of(32±4)points.The prognosis of 27 neonates was followed up to around 12 months of age,with 44％(12/27)of the neonates having a good prognosis.Conclusions Ischemic stroke is the main type of neonatal stroke,often with convulsions as the initial presentation,involvement of the middle cerebral artery,sharp waves on electroencephalography,and a relatively low neurodevelopment score.Symptomatic/supportive treatment is the main treatment method,and some neonates tend to have a poor prognosis.

Objective:To investigate the predictive value of clinical factor model, deep learning model based on baseline plain CT images, and combination of both for predicting hematoma expansion in cerebral hemorrhage.Methods:The study was cross-sectional. Totally 471 cerebral hemorrhage patients who were firstly diagnosed in the Second Affiliated Hospital of Soochow University from January 2017 to December 2021 were collected retrospectively. These patients were randomly divided into a training dataset ( n=330) and a validation dataset ( n=141) at a ratio of 7∶3 by using the random function. All patients underwent two noncontrast CT examinations within 24 h and an increase in hematoma volume of >33% or an absolute increase in hematoma volume of >6 ml was considered hematoma enlargement. According to the presence or absence of hematoma enlargement, all patients were divided into hematoma enlargement group and hematoma non-enlargement group.Two-sample t test, Mann-Whitney U test or χ2 test were used for univariate analysis. The factors with statistically significant differences were included in multivariate logistic regression analysis, and independent influences related to hematoma enlargement were screened out to establish a clinical factor model. ITK-SNAP software was applied to manually label and segment the cerebral hemorrhage lesions on plain CT images to train and build a deep learning model based on ResNet50 architecture. A combination model for predicting hematoma expansion in cerebral hemorrhage was established by combining independent clinical influences with deep learning scores. The value of the clinical factor model, the deep learning model, and the combination model for predicting hematoma expansion in cerebral hemorrhage was evaluated using receiver operating characteristic (ROC) curves and decision curves in the training and validation datasets. Results:Among 471 cerebral hemorrhage patients, 136 cases were in the hematoma enlargement group and 335 cases were in the hematoma non-enlargement group. Regression analyses showed that male ( OR=1.790, 95% CI 1.136-2.819, P=0.012), time of occurrence ( OR=0.812, 95% CI 0.702-0.939, P=0.005), history of oral anticoagulants ( OR=2.157, 95% CI 1.100-4.229, P=0.025), admission Glasgow Coma Scale score ( OR=0.866, 95% CI 0.807-0.929, P<0.001) and red blood cell distribution width ( OR=1.045, 95% CI 1.010-1.081, P=0.011) were the independent factors for predicting hematoma expansion in cerebral hemorrhage. ROC curve analysis showed that in the training dataset, the area under the curve (AUC) of clinical factor model, deep learning model and combination model were 0.688 (95% CI 0.635-0.738), 0.695 (95% CI 0.642-0.744) and 0.747 (95% CI 0.697-0.793) respectively. The AUC of the combination model was better than that of the clinical model ( Z=0.54, P=0.011) and the deep learning model ( Z=2.44, P=0.015). In the validation dataset, the AUC of clinical factor model, deep learning model and combination model were 0.687 (95% CI 0.604-0.763), 0.683 (95% CI 0.599-0.759) and 0.736 (95% CI 0.655-0.806) respectively, with no statistical significance. Decision curves showed that the combination model had the highest net benefit rate and strong clinical practicability. Conclusions:Both the deep learning model and the clinical factor model established in this study have some predictive value for hematoma expansion in cerebral hemorrhage; the combination model established by the two together has the highest predictive value and can be applied to predict hematoma expansion.

Abstract Biomarkers could improve the understanding of the causes of obesity and its association with chronic diseases for people. The purpose of the review is to summarize recent advances in transcriptomic, proteomic, and metabolomic phenotypic biomarkers of obesity in order to deepen the understanding of the etiology of obesity and its metabolic consequences. In the precise prevention and control of childhood obesity, different groups of biomarkers can improve the accuracy of the word "obesity" and help early detection of specific biomarkers with risk characteristics, so as to realize the transformation of childhood obesity from a one size fits all prevention and control strategy to a personalized prevention and control plan during the development of obesity.

To explore the effectiveness of "near peer learning" (NPL) in the electromyography(EMG)teaching module for neurology residents.