BACKGROUND:Arthroscopic anchor repair has become the main treatment method for rotator cuff tears at present.Among them,the insertion status of the anchor is a key factor in the success or failure of the operation.However,currently,the impact of the insertion depth of the anchor on the stress of the bone tunnel and the anchor under different bone density conditions remains unclear.OBJECTIVE:To explore the stress distribution of the bone tunnel and the anchor when the insertion depth of the anchor varies under different bone density conditions by using three-dimensional finite element analysis technology.METHODS:The CT image data of the humerus of volunteers were collected,and the models of the humerus and the anchor were constructed by using Mimics,3-Matic,and Solidworks software.In 3-Matic,holes with distances of 0,2,4,6,and 8 mm from the surface of the humerus were respectively created at the same position of the humerus and assembled with the anchor.In Mimics,values were assigned based on the CT gray value to obtain a model with normal bone mass(T value ≥-1.0).The parameters were changed to construct models with reduced bone mass(-2.5＜T value＜-1.0)and osteoporosis(T value＜-2.5).In each model,a 70 N pulling force was applied to the anchor along the direction tangent to the inner edge of the bone tunnel.The stress distribution and magnitude of the bone tunnel and the anchor when inserted at different depths under different bone density conditions were observed.RESULTS AND CONCLUSION:(1)When the insertion depth was the same,as the bone density decreased,the maximum equivalent stress of the anchor increased,while the maximum equivalent stress of the bone tunnel decreased.(2)When the bone density was the same,as the insertion depth of the anchor increased,the maximum equivalent stress of the anchor decreased.When the insertion depth was 4 mm,the stress of the bone tunnel was the smallest and the distribution was relatively uniform.The stress of the anchor was mainly distributed around the lower anchor hole and the proximal thread,and the stress of the bone tunnel was mainly at the part in contact with the proximal thread.The increase in the insertion depth would change the uniformity and pattern of the stress distribution,while the bone density had a relatively small impact on the stress distribution pattern.(3)It is concluded that the bone density of the humerus is crucial for the anchor repair of rotator cuff tears.It is recommended that clinicians measure the bone density of the greater tuberosity of the humerus before the operation.Excessive insertion depth of the anchor does not significantly increase its stability.Clinicians can conduct personalized preoperative assessments by using the finite element analysis method in combination with the actual situation of patients to achieve the best surgical results.

BACKGROUND:Due to the complex movement of the scapula,which is a six-degree-of-freedom activity in three-dimensional space,it is difficult to measure it accurately using traditional methods.The image and model matching technology based on dual-plane X-ray is a three-dimensional measurement method that has gradually developed and matured in recent years.Two high-speed cameras are used to project and shoot from orthogonal directions.Compared with a single perspective,this method has advantages in observation range and reduction of out-of-plane errors,and is suitable for the study of scapula kinematics.OBJECTIVE:X-ray biplane and image-model registration technology were used to explore the differences in scapular kinematics between normal individuals and patients with rotator cuff tears,providing a basis for the treatment and rehabilitation of rotator cuff tear patients.METHODS:From April 2023 to January 2024,10 patients with normal shoulders and 10 patients with rotator cuff tears who met the inclusion criteria were enrolled from Guanghua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine.The subjects underwent a shoulder CT scan to create a 3D model and a local scapular coordinate system.X-ray biplane images were taken during shoulder abduction with two C-arm machines.The 2D and 3D images were registered to compare scapular kinematic differences between the two groups at 0°,15°,30°,45°,60°,75°,and 90° of abduction,including scapular rotation angle and displacement distance.RESULTS AND CONCLUSION:(1)During shoulder abduction,the scapula of both groups showed upward rotation,but the upward rotation of the rotator cuff tear group was greater than that of the control group,and the difference was significant when the abduction was 30°-90°(P＜0.01).At the same time,the scapula internal rotation of both groups gradually increased,but the rotator cuff tear group was significantly greater than the control group when the abduction was 45°-90°(P＜0.01).In addition,the anteroposterior tilt of the scapula of the two groups was significantly different when the abduction was 15°-90°(P＜0.01).The scapula posterior tilt of the control group gradually increased during abduction,while the scapula of the rotator cuff tear group tilted forward except for a slight posterior tilt at 15°-30° abduction.(2)In terms of displacement,the upward displacement of the rotator cuff tear group was less than that of the control group during abduction,and the difference was significant at 15°-90°(P＜0.05),but there was no significant difference in lateral and anterior-posterior displacement between the two groups(P＞0.05).(3)Rotator cuff tear can cause scapular dyskinesis,characterized by increased upward rotation,internal rotation,and abnormal forward tilt during shoulder abduction.Identifying and addressing scapular dyskinesis is crucial for treating rotator cuff tear.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

BACKGROUND:Arthroscopic anchor repair has become the main treatment method for rotator cuff tears at present.Among them,the insertion status of the anchor is a key factor in the success or failure of the operation.However,currently,the impact of the insertion depth of the anchor on the stress of the bone tunnel and the anchor under different bone density conditions remains unclear.OBJECTIVE:To explore the stress distribution of the bone tunnel and the anchor when the insertion depth of the anchor varies under different bone density conditions by using three-dimensional finite element analysis technology.METHODS:The CT image data of the humerus of volunteers were collected,and the models of the humerus and the anchor were constructed by using Mimics,3-Matic,and Solidworks software.In 3-Matic,holes with distances of 0,2,4,6,and 8 mm from the surface of the humerus were respectively created at the same position of the humerus and assembled with the anchor.In Mimics,values were assigned based on the CT gray value to obtain a model with normal bone mass(T value ≥-1.0).The parameters were changed to construct models with reduced bone mass(-2.5＜T value＜-1.0)and osteoporosis(T value＜-2.5).In each model,a 70 N pulling force was applied to the anchor along the direction tangent to the inner edge of the bone tunnel.The stress distribution and magnitude of the bone tunnel and the anchor when inserted at different depths under different bone density conditions were observed.RESULTS AND CONCLUSION:(1)When the insertion depth was the same,as the bone density decreased,the maximum equivalent stress of the anchor increased,while the maximum equivalent stress of the bone tunnel decreased.(2)When the bone density was the same,as the insertion depth of the anchor increased,the maximum equivalent stress of the anchor decreased.When the insertion depth was 4 mm,the stress of the bone tunnel was the smallest and the distribution was relatively uniform.The stress of the anchor was mainly distributed around the lower anchor hole and the proximal thread,and the stress of the bone tunnel was mainly at the part in contact with the proximal thread.The increase in the insertion depth would change the uniformity and pattern of the stress distribution,while the bone density had a relatively small impact on the stress distribution pattern.(3)It is concluded that the bone density of the humerus is crucial for the anchor repair of rotator cuff tears.It is recommended that clinicians measure the bone density of the greater tuberosity of the humerus before the operation.Excessive insertion depth of the anchor does not significantly increase its stability.Clinicians can conduct personalized preoperative assessments by using the finite element analysis method in combination with the actual situation of patients to achieve the best surgical results.

BACKGROUND:Due to the complex movement of the scapula,which is a six-degree-of-freedom activity in three-dimensional space,it is difficult to measure it accurately using traditional methods.The image and model matching technology based on dual-plane X-ray is a three-dimensional measurement method that has gradually developed and matured in recent years.Two high-speed cameras are used to project and shoot from orthogonal directions.Compared with a single perspective,this method has advantages in observation range and reduction of out-of-plane errors,and is suitable for the study of scapula kinematics.OBJECTIVE:X-ray biplane and image-model registration technology were used to explore the differences in scapular kinematics between normal individuals and patients with rotator cuff tears,providing a basis for the treatment and rehabilitation of rotator cuff tear patients.METHODS:From April 2023 to January 2024,10 patients with normal shoulders and 10 patients with rotator cuff tears who met the inclusion criteria were enrolled from Guanghua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine.The subjects underwent a shoulder CT scan to create a 3D model and a local scapular coordinate system.X-ray biplane images were taken during shoulder abduction with two C-arm machines.The 2D and 3D images were registered to compare scapular kinematic differences between the two groups at 0°,15°,30°,45°,60°,75°,and 90° of abduction,including scapular rotation angle and displacement distance.RESULTS AND CONCLUSION:(1)During shoulder abduction,the scapula of both groups showed upward rotation,but the upward rotation of the rotator cuff tear group was greater than that of the control group,and the difference was significant when the abduction was 30°-90°(P＜0.01).At the same time,the scapula internal rotation of both groups gradually increased,but the rotator cuff tear group was significantly greater than the control group when the abduction was 45°-90°(P＜0.01).In addition,the anteroposterior tilt of the scapula of the two groups was significantly different when the abduction was 15°-90°(P＜0.01).The scapula posterior tilt of the control group gradually increased during abduction,while the scapula of the rotator cuff tear group tilted forward except for a slight posterior tilt at 15°-30° abduction.(2)In terms of displacement,the upward displacement of the rotator cuff tear group was less than that of the control group during abduction,and the difference was significant at 15°-90°(P＜0.05),but there was no significant difference in lateral and anterior-posterior displacement between the two groups(P＞0.05).(3)Rotator cuff tear can cause scapular dyskinesis,characterized by increased upward rotation,internal rotation,and abnormal forward tilt during shoulder abduction.Identifying and addressing scapular dyskinesis is crucial for treating rotator cuff tear.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

ObjectiveTo investigate the effects of Schisandrae Chinensis Fructus lignans on schizophrenia induced by dizocilpine maleate （MK-801） in mice and to clarify its mechanism. MethodsMale mice of 4-6 weeks old were randomized into blank， model， positive drug， and low-， medium-， and high-dose （40， 80， 160 mg·kg^-1， respectively） Schisandrae Chinensis Fructus lignans groups. The blank group was administrated with distilled water， and the other groups were injected with 0.5 mg·kg^-1 MK-801 to induce schizophrenia symptoms. Meanwhile， risperidone was injected at 0.2 mg·kg^-1 in the positive drug group， and mice in the intervention groups were injected with corresponding drugs for 14 consecutive days. The behavioral changes of mice were observed by autonomous activity test， open field test， forced swimming test， and water maze test. The levels of dopamine （DA） and 5-hydroxytryptamine （5-HT） in the brain and tumor necrosis factor-α （TNF-α） and nuclear factor-κB （NF-κB） in peripheral blood were quantified by enzyme-linked immunosorbent assay （ELISA）. The changes in the prefrontal lobe of mice were observed by hematoxylin-eosin staining， and the changes of the hippocampal tissue were observed by Nissl staining. The protein levels of silencing information regulatory factor 1 （SIRT1） and forkhead box protein O3a （FoxO3a） in the hippocampus of mice were determined by Western blot. ResultsCompared with the model group， low， medium， and high doses of Schisandrae Chinensis Fructus lignans reduced the total number of autonomous activities， total distance in the open field test， immobile time in the forced swimming test， and levels of TNF-α and NF-κB in peripheral blood （P<0.05）， while increasing the number of platform crossings in the water maze test and DA and 5-HT levels in the brain tissue （P<0.05）. Compared with the model group， risperidone and low， medium， and high doses of Schisandrae Chinensis Fructus lignans improve the neural cell morphology in the CA1 region， with full cells in neatly dense arrangement and exhibiting clear membrane boundary. Schisandrae Chinensis Fructus lignans inhibited the expression of SIRT 1 and FoxO3a in the hippocampus （P<0.05）. ConclusionTo sum up， Schisandrae Chinensis Fructus lignans may improve the behavior of schizophrenic mice by activating the SIRT1/FoxO3a signaling pathway to exert neuroprotective effects.

Esophageal cancer （EC） is a highly prevalent malignant tumor in China. The phosphatidylinositol 3-kinase （PI3K）/protein kinase B （Akt） signaling pathway， as one of the key oncogenic pathways， can promote the cell cycle progression， proliferation， migration， and invasion， induce chemoresistance， and inhibit apoptosis and autophagy of EC cells. Traditional Chinese medicine （TCM）， with the advantages of targeting multiple points with multiple components to delay cancer progression， can target the PI3K/Akt signaling pathway for EC treatment. This article preliminarily discusses the molecular mechanism and role of the PI3K/Akt signaling pathway in EC and elaborates on the specific targets and efficacy of TCM in treating EC through intervention in the PI3K/Akt signaling pathway in the past five years. TCM materials and extracts inhibiting the PI3K/Akt signaling pathway in EC include Borneolum， spore powder of Ganoderma lucidum without spore coat， extract of Celastrus orbiculatus， root extract of Taraxacum， and Bruceae Fructus oil emulsion. TCM active ingredients exerting the effect include flavonoids， terpenoids， saponins， phenols， polysaccharides， alkaloids， and other compounds. TCM compound prescriptions with such effect include Qige San， Huqi San， Xuanfu Daizhetang， Tongyoutang and its decomposed prescriptions， Liujunzi Tang， and Xishenzhi Formula. In addition， TCM injections such as Compound Kushen Injection and Kang'ai injection also inhibit the PI3K/Akt signaling pathway in EC. This paper summarizes the role of the PI3K/Akt signaling pathway in EC and the TCM interventions， aiming to provide reference for the research and clinical application of new drugs for EC.

OBJECTIVE To explore a model for constructing a platform for medical institution formulation and provide insights for promoting their development. METHODS By systematically reviewing the development status and challenges of medical institution preparations in China， and based on the theory of industry-university-research collaborative innovation， the organizational structure， collaborative processes， and safeguard mechanisms of the platform were designed. RESULTS & CONCLUSIONS Medical institution formulations in China mainly faced challenges such as weak research and development （R&D） capacity， uneven quality standards， and blocked transformation pathways. This study established a full-chain， whole- industry collaborative innovation network covering the government， medical institutions， universities/research institutes， pharmaceutical enterprises， and the market， forming a new “government-industry-university-research-application” five-in-one platform model for medical institution formulations. By establishing mechanisms such as multi-entity collaborative cooperation， full- chain intellectual property management， contribution-based benefit distribution， staged risk-sharing， and third-party evaluation， the model clarified the responsibilities and collaborative pathways of all parties. The new model highlights the whole-process transformation of clinical experience-based prescriptions， enabling precise alignment between clinical needs and technological R&D， as well as between preparation achievements and industrial transformation. While breaking down the barriers of traditional platform construction， it effectively achieves optimal resource allocation and complementary advantages， addresses problems emerging in the development of medical institution preparations， and provides reference value for the formulation of relevant systems.

ObjectiveTo investigate the potential mechanism of Danggui Shaoyaosan （DSS） in ameliorating renal injury in db/db mice. MethodsThirty 8-week-old specific pathogen-free （SPF）-grade male db/db mice and six db/m mice were acclimated for one week. Urinary microalbumin and blood glucose levels were measured weekly in both db/db and db/m mice. Successful modeling was determined by significantly higher microalbuminuria in db/db mice compared to db/m mice and a fasting blood glucose ≥16.7 mmol·L^-1. The 30 db/db mice were randomly divided into five groups： the model group， the irbesartan （IBN） group， and three DSS dose groups （low-， medium-， and high-dose DSS groups， administered at 16.77， 33.54， 67.08 g·kg^-1·d^-1， respectively）. Additionally， the six db/m mice served as the normal control group. The IBN group received irbesartan at 0.025 g·kg^-1·d^-1 by gavage， while the three DSS groups received DSS at 16.77， 33.54， and 67.08 g·kg^-1·d^-1 by gavage， respectively. The normal and model groups were administered with an equivalent volume of normal saline by gavage. All interventions lasted for 8 consecutive weeks. After intervention， serum creatinine （SCr）， blood urea nitrogen （BUN）， urinary total protein （UTP）， triglyceride （TG）， and low-density lipoprotein cholesterol （LDL-C） were measured to evaluate the therapeutic efficacy of the treatments. Renal histopathological changes were observed with hematoxylin-eosin （HE） staining. Western blot was used to detect the protein expression of silencing information regulator 1 （SIRT1）， hypoxia-inducible factor-1α （HIF-1α）， very low-density lipoprotein receptor （VLDLr）， and cluster of differentiation 31 （CD31）. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） was used to detect the mRNA levels of HIF-1α and VLDLr. Immunohistochemistry was used to observe the expression and distribution of HIF-1α and Caspase-3. ResultsCompared to the normal group， the model group showed significantly increased SCr， BUN， UTP， TG， and LDL-C. HE staining revealed glomerulosclerosis， mesangial matrix hyperplasia， capillary loop distortion and thickening， with extensive inflammatory cell infiltration. Protein expression of SIRT1 and CD31 significantly decreased （P<0.05）， while HIF-1α and VLDLr protein and mRNA levels increased （P<0.05）. Immunohistochemistry showed increased expression of HIF-1α and Caspase-3 （P<0.05）， indicating hypoxia and apoptosis in renal cells. In all treatment groups， SCr， BUN， TG， and LDL-C were significantly reduced compared to the model group （P<0.05）， and UTP was significantly improved in the medium-dose DSS group （P<0.05）. Renal tissue structure and morphology were improved， inflammatory cells were reduced， and no vascular hyaline degeneration was observed. SIRT1 and CD31 protein expression was elevated to varying degrees compared to the model group （P<0.05）， while HIF-1α and VLDLr protein and mRNA levels decreased （P<0.05）. Immunohistochemistry showed reduced expression of HIF-1α and Caspase-3 in all treatment groups （P<0.05）， with the most significant improvement observed in the IBN group and medium-dose DSS group （P<0.05）. ConclusionDSS can effectively ameliorate glomerulosclerosis and lipid deposition in db/db mice， and its mechanism may involve the SIRT1/HIF-1α/VLDLr signaling pathway.