Follicular lymphoma(FL)is a type of non-Hodgkin's lymphoma,and its treatment options face many challenges.This paper discusses the pathogenesis and treatment of FL based on the clear-turbid theory in traditional Chinese medicine(TCM)."The clear and the turbid being related,and the rise and fall of qi being disorderly"is the basic pathogenesis of FL.As the disease progres-ses,"evil qi being blocked inside,and turbid evil harming the clear"aggravates,and finally"evil qi is strong and the disease progres-ses,and evil poison is generated inside".Based on this theory,the method of raising the clear and lowering the turbid and the method of dispersing the clear and removing the turbid are proposed to treat FL.The emphasis of raising the clear and lowering the turbid is to take raising and lowering as the key,movement and stillness as the pivot,and to treat the middle jiao;the emphasis of dispersing the clear and removing the turbid is to clear the triple jiao,warm the yang and invigorate the qi,and harmonize the spleen and kidney.Ca-ses are attached to illustrate,providing new ideas for the TCM treatment of FL.

Objective:To investigate the effectiveness of combined teaching method integrating case-based teaching method(CBL),problem-based teaching method(PBL),and team-based teaching method(TBL)in clinical nursing teaching in pediatric intensive care unit(PICU).Methods:A total of 60 nursing students who trained in our PICU from Jan 2021 to Jun 2022 were selected as the control group,and 60 nursing students who trained in our PICU from Jul 2022 to Jun 2024 were selected as the research group.The control group received conventional nursing teaching mode,while the research group received combined teaching mode of CBL,PBL,and TBL.The scores of academic performance,logical thinking ability,self-learning ability,and clinical nursing comprehensive ability between the two groups were compared.Result:The scores of theoretical knowledge,operational skills,post-teaching Critical Thinking Disposition Inventory-Chinese Version(CTDI-CV)scale,self-directed learning readiness scale for nursing,and Mini-clinical evaluation exercise scale(Mini-CEX)in the research group were higher than those in the control group(P＜0.05).Conclusion:The combined teaching method of CBL,PBL,and TBL can effectively improve clinical nursing teaching results and comprehensive abilities of nursing students.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

Histone methylation modification,as one of the post-translational modifications,has been increasingly shown by studies to play a crucial role in the development of cardiovascular diseases(CVD).Due to its reversibility,tar-geting related modifying enzymes is expected to provide new strategies for the clinical diagnosis and treatment of CVD.This article reviews the relevant methylation modifications and their important regulatory mechanisms in CVD,and discusses the research progress of histone methylation inhibitors in the cardiovascular field.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

Histone methylation modification,as one of the post-translational modifications,has been increasingly shown by studies to play a crucial role in the development of cardiovascular diseases(CVD).Due to its reversibility,tar-geting related modifying enzymes is expected to provide new strategies for the clinical diagnosis and treatment of CVD.This article reviews the relevant methylation modifications and their important regulatory mechanisms in CVD,and discusses the research progress of histone methylation inhibitors in the cardiovascular field.

McCune-Albright syndrome(MAS) is a postzygotic somatic mutation disorder caused by activating mutations in the GNAS gene, which encodes the α subunit of the stimulatory G protein. Its clinical features typically include polyostotic fibrous dysplasia, cafe-au-lait skin pigmentation, and endocrine hyperactivity, such as Cushing′s syndrome, hyperthyroidism, and growth hormone excess. Here, we report two rare cases of MAS complicated with adrenocorticotropic hormone(ACTH)-independent Cushing syndrome, and provide a review and analysis of previously reported MAS cases associated with Cushing′s syndrome.

OBJECTIVES: To evaluate the feasibility, efficacy and safety of Sentinel cerebral embolic protection device (CEPD) during transcatheter aortic valve replacement (TAVR). This study is a subgroup analysis of the China Moderate to Severe Valvular Heart Disease Registry, which has been registered at the Chinese Clinical Trial Registry (ChiCTR2300075006). METHODS: Patients undergoing TAVR with the Sentinel CEPD from October 2023 to September 2024 were retrospectively enrolled. A total of 80 patients were included, with a median age of 72 (68, 76) years, including 52 males (65.0%) and 28 females (35.0%); 62 patients (77.5%) with tricuspid valves, and 18 patients (22.5%) with bicuspid valves; 34 patients (42.5%) with type Ⅰ aortic arch, 24 patients (30.0%) with type Ⅱ aortic arch, 12 patients (15.0%) with type Ⅲ aortic arch, and 10 patients (12.5%) with bovine-type aortic arch. Clinical data of the patients were summarized and analyzed. The primary endpoints were success rate of Sentinel CEPD implantation, as well as all-cause death, symptomatic stroke, transient ischemic attack, and Sentinel CEPD access vessel complications during hospitalization and within 30 days postoperatively. RESULTS: In the 80 patients, self-expanding valves were used in 68 cases (85.0%) and balloon-expandable valves in 12 cases (15.0%). Seventy-nine patients (98.8%) successfully underwent TAVR with Sentinel CEPD deployment. Macroscopically visible debris was captured in 92.5% (74/80) by filters of Sentinel CEPD. Although the procedure time for Sentinel CEPD placement was slightly longer in patients with bovine-type aortic arch, there was no statistically significant difference in deployment time among different aortic arch types (P>0.05). During hospitalization and within 30 days postoperatively, only one case of transient ischemic attack occurred, and there was no all-cause mortality, symptomatic stroke, or access-site vascular complications related to the Sentinel CEPD observed. CONCLUSIONS The Sentinel CEPD demonstrates high feasibility across aortic arch types, potential efficacy in embolic capture, and excellent safety in TAVR.
Humans ; Transcatheter Aortic Valve Replacement/adverse effects* ; Male ; Female ; Aged ; Embolic Protection Devices ; Retrospective Studies ; Intracranial Embolism/prevention & control* ; Aged, 80 and over ; Aortic Valve Stenosis/surgery* ; Aortic Valve/surgery*

In the mammalian central nervous system (CNS), astrocytes are the ubiquitous glial cells that have complex morphological and molecular characteristics. These fascinating cells play essential neurosupportive and homeostatic roles in the healthy CNS and undergo morphological, molecular, and functional changes to adopt so-called 'reactive' states in response to CNS injury or disease. In recent years, interest in astrocyte research has increased dramatically and some new biological features and roles of astrocytes in physiological and pathological conditions have been discovered thanks to technological advances. Here, we will review and discuss the well-established and emerging astroglial biology and functions, with emphasis on their potential as therapeutic targets for CNS injury, including traumatic and ischemic injury. This review article will highlight the importance of astrocytes in the neuropathological process and repair of CNS injury.
Astrocytes/drug effects* ; Humans ; Animals ; Central Nervous System/pathology* ; Central Nervous System Diseases/physiopathology*