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:Exosomes derived from mesenchymal stem cells play pivotal roles in cell communication and epigenetic regulation due to their low immunogenicity and targeted delivery effects,and have been clinically applied in the treatment of various diseases.OBJECTIVE:To review the isolation,purification,identification methods,and application progress of mesenchymal stem cell-derived exosomes,and to facilitate the development of large-scale preparation techniques and clinical translation of mesenchymal stem cell-derived exosomes.METHODS:The Chinese search terms"exosome,mesenchymal stem cells,isolation,purification,characterization,clinical application"and the English search terms"exosome,extracellular vesicles,mesenchymal stem cells,isolation,characterization,application"were used to search the literature published before September 2024 in CNKI,PubMed,and Web of Science databases.Articles with poor relevance to the topic,outdated,or duplicated content were excluded,and finally,109 articles were included for review.RESULTS AND CONCLUSION:(1)This paper reviews recent methods for isolating and purifying exosomes,comparing the characteristics of ultracentrifugation,ultrafiltration,size-exclusion chromatography,polymer precipitation,immunoaffinity,microfluidic methods,and other novel approaches based on their underlying principles.(2)Methods for identifying exosomes can be categorized into physical and biochemical analyses,characterizing exosomes based on their shape,size,and characteristic proteins.(3)Mesenchymal stem cell-derived exosomes have broad applications in multiple fields such as medical aesthetics,wound repair,and cancer treatment,due to their immune-regulatory properties and ability to cross biological barriers.(4)The clinical translation of exosomes faces challenges due to their complex structure,lack of universal isolation techniques,and poor stability,making it difficult to achieve in a short period of time.

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.

This article systematically reviews and verifies the name， origin， production area， quality evaluation， harvesting， processing and other aspects of Cynanchi Atrati Radix et Rhizoma（CARR） by consulting relevant ancient and modern literature， in order to provide a basis for the development and utilization of famous classical formulas containing this herb. Through textual research， Baiwei has been the official name for CARR， though it also bears alternative names such as Chuncao， Popo Zhenxianbao， Longdan Baiwei. The mainstream base is the roots and rhizomes of Cynanchum atratum. Historical records indicate primary producing areas include Shandong， Anhui， Jiangsu， Shaanxi and Shanxi. Since the late Ming dynasty， varieties from Juxian， Yishui and Rizhao in Shandong have been highly regarded as authentic， commonly known as eastern Baiwei. Since modern times， its quality has been summarized as fine， slender， and straight fibrous roots， pale yellow exterior， whiter interior， and dryness with easy breakability are considered superior. The harvesting time before the Song dynasty was on the third day of the third lunar month， but after the Song dynasty， harvesting was possible in both spring and autumn. The initial processing methods of CARR in ancient times included drying in the shade， removing Lu（the little rhizomes which are on tap of roots）， and removing mustaches， modern methods involve washing and sun-drying. During the Northern and Southern dynasties， processing methods included steaming. In the Song dynasty， drying and light stir-frying were predominant， while wine washing emerged in the Ming dynasty. Modern practices primarily involve using raw， stir-frying or honey processing. Regarding the medicinal properties of CARR， both ancient and modern texts agree it has a bitter and salty taste and is non-toxic. Records prior to the Qing dynasty predominantly describe its nature as extremely cold， while mainstream herbal texts after the Qing dynasty generally characterize it as cold. Before the Ming dynasty， there were no records of its meridian tropism. It was not until the Qing dynasty that it was recorded in the lung meridian. Modern records mainly refer to the stomach， liver， and kidney meridians. Throughout history， its main functions have been to clear heat， diuresis， nourish Yin， and replenish essence， primarily treating Yin deficiency and fever syndrome. Based on the research results， it is suggested that when developing famous classical formulas containing CARR， the dried roots and rhizomes of C. atratum can be selected as its medicinal source. If there are no specific processing requirements， raw products can be selected as medicine. If the processing requirements are specified， corresponding processed products can be selected as medicine according to the original formula requirements.

Neurocritical care involves complex pathophysiological mechanisms, and its incidence is higher, injuries are more severe, and treatment is more challenging in high-altitude environments. This consensus, based on the latest domestic and international evidence-based medical data, establishes a standardized, goal-oriented framework for neurocritical care management applicable in high-altitude regions and nationwide. The consensus was developed following international standards for evidence quality assessment and underwent two rounds of Delphi expert consultation, resulting in 32 recommendation statements covering three parts: management systems, monitoring and assessment, and core strategies. Key updates include: advocating for the establishment of independent neurocritical care units and implementing precise tiered diagnosis and treatment based on the "Five Differences in Critical Care" concept; constructing a "trinity" multimodal brain monitoring system centered on cerebral blood flow, cerebral oxygenation, and brain function, emphasizing routine bedside transcranial Doppler ultrasound, cerebral oximetry, and continuous electroencephalography monitoring; shifting management strategies from mild hypothermia therapy to targeted temperature management, and defining the "446" target management pathway for the supercritical stage; emphasizing the assessment of static and dynamic cerebrovascular autoregulation functions through multimodal methods to achieve individualized optimal mean arterial pressure management; elevating cerebrospinal fluid management goals to the level of "glymphatic system" function maintenance; implementing a multidisciplinary collaborative, whole-process management model focusing on patients' long-term neurological functional outcomes; de-escalation criteria include multidimensional indicators such as recovery of brain structure, restoration of cerebrovascular autoregulation, improvement in cerebrospinal fluid dynamics, and reduction in biomarker levels; and integrating cutting-edge technologies like artificial intelligence into post-critical care management and rehabilitation planning. This consensus systematically integrates the entire process of neurocritical care management, reflecting the modern connotation of goal-oriented, dynamic, and multimodal integration in neurocritical care medicine. It aims to adapt to new trends such as deepening understanding of pathophysiological mechanisms, the integration of medicine and engineering, and the empowerment of artificial intelligence, thereby further advancing the discipline of critical care medicine.

Objective: To identify the structure of the complementarity determining region (CDRs), the V(D)J rearrangement and somatic hypermutational characteristics of the heavy and light chains of a red blood cell blood group-specific monoclonal antibody. Methods: The hybridoma cell line secreting human IgM κ monoclonal anti-P antibody was used as the research object. Total RNA was extracted from cultured monoclonal cell line, and cDNA was obtained by reverse transcription PCR (RT-PCR) using random hexamers primers. It was then amplified and sequenced using primers specific for variable regions of the immunoglobulin heavy and light chains encoding the anti-P antibody. The sequences were aligned against the NCBI database using online Immunoglobulin BLAST (Ig-BLAST) tool. Results: The study determined the structure of the CDRs and framework regions (FRs) of the variable regions of human monoclonal anti-P immunoglobulin, as well as the characteristics of V(D)J rearrangement. Moreover, the closest VH, VD, and VJ germline alleles for the heavy chain and VL and VJ germline alleles for the light chain were also identified. The IgH gene rearrangment pattern of the monoclonal anti-P was IGHV6-1 * 01—IGHD5-18 02—IGHJ4 02 and IgL gene was IGκV1-12 01—IGκJ3 01. Nine base mutations occurred within the germline gene IGHV6-1 01 in variable region of heavy chain, whereas 5 base mutations were found in the germline gene IGκV1-12 01 in variable region of light chain, respectively. Conclusion: This study characterized the CDR structure in monoclonal antibody cell line targeting the high-frequency red blood cell P antigen, and provided a foundation for the construction of recombinant antibody expressing plasmids and transfomation of the immunoglobulin type.