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.

Alzheimer’s disease （AD） is an age-related neurodegenerative disorder. Marrow-sea insufficiency serves as the fundamental basis for the onset of AD. Early syndrome differentiation-based intervention helps to delay disease progression， and improve patients’ cognitive function. Qifu yin is a representative specialized prescription for AD with marrow-sea insufficiency syndrome. Studies demonstrate that Qifu yin exerts neuroprotective effects through multiple pathways， including inhibiting the abnormal deposition of amyloid β -protein and hyperphosphorylation of tau protein， alleviating neuroinflammation， regulating oxidative stress and mitochondrial dysfunction， modulating the cholinergic system， and improving synaptic plasticity. Qifu yin combined with Western medicine such as donepezil， memantine， and butylphthalide， or combined with external therapies such as acupuncture， can effectively improve cognitive function and activities of daily living in AD patients with favorable safety. Future research should focus on the core pathogenesis and key targets of AD with marrow-sea insufficiency syndrome， provide in-depth elucidation of the scientific connotation of Qifu yin’s “tonifying the kidney to produce marrow”， and further conduct high-quality clinical studies to provide scientific evidence for the prevention and treatment of AD with marrow-sea insufficiency syndrome.

The c-Jun N-terminal kinase （JNK） signaling pathway， a key member of the mitogen-activated protein kinase （MAPK） family， plays a central role in the pathogenesis and progression of central nervous system （CNS） diseases by regulating core biological processes such as apoptosis， inflammatory responses， synaptic plasticity， and autophagy. This article sorts out and analyzes relevant literature published domestically and internationally in recent years， summarizing the mechanisms of action of the JNK signaling pathway in common CNS diseases and the research progress in traditional Chinese medicine （TCM） interventions in CNS diseases through the regulation of the JNK signaling pathway. Studies have shown that active components of TCM， such as berberine， paeoniflorin， and astragaloside Ⅳ， as well as compound formulations like Heixiaoyao san， Ditan tang， and Buyang huanwu tang， can exert neuroprotective effects in various CNS disorders， including Alzheimer’s disease， Parkinson’s disease， cerebral ischemia-reperfusion injury， and epilepsy， by inhibiting the aberrant activation of the JNK signaling pathway， thereby alleviating neuroinflammation， oxidative stress， and neuronal apoptosis， while improving synaptic function and cognitive behavioral deficits， regulating autophagy， and maintaining blood-brain barrier integrity.

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.

Objective:To propose an alarm method for ventilator fault and abnormality of machine signs based on convolutional neural network(CNN)model,which aimed at the problem of low detection rate of ventilator faults and abnormalities of machine sign,and to analyze its application value in patients with chronic obstructive pulmonary disease(COPD)in using non-invasive ventilator.Methods:This study established a identification model(CNN-SG)for ventilator fault based on stochastic gradient descent by mini-batch method that was introduced by CNN network,and a multi-task CNN identification model for abnormalities of machine signs of ventilator was established on the basis of CNN model.The respiratory waveform data of 60 patients with chronic obstructive pulmonary disease(COPD)who admitted to Wusong Hospital,Zhongshan Hospital Affiliated to Fudan University from January 2019 to January 2024 were collected.These data were divided into train set(42 cases)and validation set(18 cases)as ratio of 7 to 3.The training set was used in the learning process of model,while the validation set was used to assess the model's performance for unknown data.One-hot encoding was used to represent fault states,and to predict whether occurred abnormal data failures within specific time intervals.Two independent convolutional networks were employed respectively to detect and identify invalid inspiratory effort and double-triggering abnormalities in abnormal machine signs.Model performance was assessed by using accuracy,precision,recall rate,negative predictive value(NPV),and specificity.Chi-square test was used to compare the differences of various assessment indicators between different models.The F1-score was calculated to comprehensively assess the performance of model.Results:In the validation set,the accuracy(99.98%),precision(99.90%),recall rate(99.97%),NPV(98.68%),and specificity(98.24%)of CNN-SG model were higher than those of CNN model in identify the fault of ventilator.In detecting invalid inspiratory effort,the accuracy,sensitivity,specificity and F1-scores of CNN-MTL model were respectively 98.83%,98.81%,97.68%and 98.85.The accuracy,sensitivity,specificity and F1-scores of CNN-MTL model were respectively 98.70%,98.83%,97.62%,and 98.75 in identifying double-triggering positive features,as well as 98.80%,98.81%,98.88%and 98.99 for double-triggering negative features.All of the above indicators of CNN-SG were significantly better than those of the conventional CNN model.Conclusion:The established deep learning algorithm model based on CNN can effectively identify faults and abnormalities of machine signs of non-invasive ventilator.

Objective To investigate the effects of a continuous-dose administration versus different dosage regimens of polyethylene glycol electrolyte solution(PEG)taken in two doses with a 12-hour interval on bowel cleansing efficacy,with the goal of optimizing bowel preparation protocols and improving patient tolerability.Methods 232 patients who underwent painless colonoscopy and used PEG as a bowel cleanser from June 2024 to September 2024 were selected as study subjects.Participants were divided into three groups:the control group(3.00 L PEG continuous dose),experimental group A(0.75 L+2.25 L PEG),and experimental group B(1.50 L+1.50 L PEG).All patients underwent painless colonoscopy within 4～6 h after completing PEG intake.The interval between the two doses of PEG in group A and group B was 12 h.The bowel cleansing efficacy was assessed by using the Boston bowel preparation scale(BBPS),and the rates of colon polyp detection,adverse reactions,sleep duration,and tolerability were recorded.Results There were no significant statistical differences in BBPS scores and colon polyp detection rates among the three groups(P>0.05).Experimental group B experienced the least adverse reactions,followed by experimental group A,while the control group reported the most significant adverse reactions(P<0.05).The timing of PEG administration did not have a significant impact on sleep duration among the three groups(P>0.05).Patients in experimental group B showed good tolerability to PEG and were willing to accept this bowel preparation regimen,followed by group A,while the control group exhibited the poorest tolerability,with significant statistical differences among the three groups(P<0.05).Conclusion The continuous administration and divided administration of PEG have no significant impact on the effectiveness of intestinal cleansing and the detection rate of colonic polyps.However,the divided PEG regimen with a 12 h interval results in fewer adverse reactions and better tolerance,especially the optimal regimen of taking 1.50 L PEG in two doses with a 12 h interval.

Objective To establish a rat model of venous thrombosis in a plateau hypobaric hypoxic environment and to investigate the effect of this environment on venous thrombosis.Methods A total of 144 healthy male SD rats were assigned randomly to four groups(n=36 rats per group):a plains sham operation(A)group,plains operation(B)group,plateau altitude 6000 m+sham operation(C)group,and plateau altitude 6000 m+surgery(D)group.Rats in A and B groups were maintained in a plains normoxic environment,while rats in C and D groups C and D were subjected to a plateau environment.Rats in the surgical groups underwent quantitative constriction to incompletely obstruct the inferior vena cava blood flow.Each group was further divided into subgroups based on time:1,3,5,7,14,and 21 d(n=6 rats per group).Regular vascular ultrasound monitoring was conducted,and blood samples were taken for whole blood viscosity testing and the assessment of inflammatory indicators,including endothelin-1(ET-1),interleukin-6(IL-6)and tissue factor(TF).Coagulation function was evaluated through the activated partial thromboplastin time(APTT),prothrombin time(PT),thrombin time(TT),fibrinogen(FIB)and D-dimer.After the observation period,the experimental animals were sacrificed and the limbs were removed.Thrombus samples were stained with hematoxylin/eosin(HE),and the thrombus wet mass was measured.Results The thrombosis incidence was significantly higher in the plateau D group than in B group,accompanied by a marked increase in blood viscosity and hematocrit(P＜0.01).Additionally,levels of ET-1,IL-6,and TF were significantly elevated(P＜0.05),indicating a coagulation disorder.Conclusions A plateau hypoxic environment model can be successfully simulated by quantitative coarctation of the inferior vena cava,combined with a specialized environmental chamber.The findings of this study suggest that a plateau hypoxic environment promotes venous thrombosis.

Objective:To propose an alarm method for ventilator fault and abnormality of machine signs based on convolutional neural network(CNN)model,which aimed at the problem of low detection rate of ventilator faults and abnormalities of machine sign,and to analyze its application value in patients with chronic obstructive pulmonary disease(COPD)in using non-invasive ventilator.Methods:This study established a identification model(CNN-SG)for ventilator fault based on stochastic gradient descent by mini-batch method that was introduced by CNN network,and a multi-task CNN identification model for abnormalities of machine signs of ventilator was established on the basis of CNN model.The respiratory waveform data of 60 patients with chronic obstructive pulmonary disease(COPD)who admitted to Wusong Hospital,Zhongshan Hospital Affiliated to Fudan University from January 2019 to January 2024 were collected.These data were divided into train set(42 cases)and validation set(18 cases)as ratio of 7 to 3.The training set was used in the learning process of model,while the validation set was used to assess the model's performance for unknown data.One-hot encoding was used to represent fault states,and to predict whether occurred abnormal data failures within specific time intervals.Two independent convolutional networks were employed respectively to detect and identify invalid inspiratory effort and double-triggering abnormalities in abnormal machine signs.Model performance was assessed by using accuracy,precision,recall rate,negative predictive value(NPV),and specificity.Chi-square test was used to compare the differences of various assessment indicators between different models.The F1-score was calculated to comprehensively assess the performance of model.Results:In the validation set,the accuracy(99.98%),precision(99.90%),recall rate(99.97%),NPV(98.68%),and specificity(98.24%)of CNN-SG model were higher than those of CNN model in identify the fault of ventilator.In detecting invalid inspiratory effort,the accuracy,sensitivity,specificity and F1-scores of CNN-MTL model were respectively 98.83%,98.81%,97.68%and 98.85.The accuracy,sensitivity,specificity and F1-scores of CNN-MTL model were respectively 98.70%,98.83%,97.62%,and 98.75 in identifying double-triggering positive features,as well as 98.80%,98.81%,98.88%and 98.99 for double-triggering negative features.All of the above indicators of CNN-SG were significantly better than those of the conventional CNN model.Conclusion:The established deep learning algorithm model based on CNN can effectively identify faults and abnormalities of machine signs of non-invasive ventilator.

Objective To investigate the effects of a continuous-dose administration versus different dosage regimens of polyethylene glycol electrolyte solution(PEG)taken in two doses with a 12-hour interval on bowel cleansing efficacy,with the goal of optimizing bowel preparation protocols and improving patient tolerability.Methods 232 patients who underwent painless colonoscopy and used PEG as a bowel cleanser from June 2024 to September 2024 were selected as study subjects.Participants were divided into three groups:the control group(3.00 L PEG continuous dose),experimental group A(0.75 L+2.25 L PEG),and experimental group B(1.50 L+1.50 L PEG).All patients underwent painless colonoscopy within 4～6 h after completing PEG intake.The interval between the two doses of PEG in group A and group B was 12 h.The bowel cleansing efficacy was assessed by using the Boston bowel preparation scale(BBPS),and the rates of colon polyp detection,adverse reactions,sleep duration,and tolerability were recorded.Results There were no significant statistical differences in BBPS scores and colon polyp detection rates among the three groups(P>0.05).Experimental group B experienced the least adverse reactions,followed by experimental group A,while the control group reported the most significant adverse reactions(P<0.05).The timing of PEG administration did not have a significant impact on sleep duration among the three groups(P>0.05).Patients in experimental group B showed good tolerability to PEG and were willing to accept this bowel preparation regimen,followed by group A,while the control group exhibited the poorest tolerability,with significant statistical differences among the three groups(P<0.05).Conclusion The continuous administration and divided administration of PEG have no significant impact on the effectiveness of intestinal cleansing and the detection rate of colonic polyps.However,the divided PEG regimen with a 12 h interval results in fewer adverse reactions and better tolerance,especially the optimal regimen of taking 1.50 L PEG in two doses with a 12 h interval.

Objective:To explore the effects of multidisciplinary continuous nursing based on the transtheoretical model (TTM) in patients after radiofrequency ablation for atrial fibrillation.Methods:Using a convenience sampling method, 280 patients who underwent radiofrequency ablation for atrial fibrillation at the China-Japan Union Hospital of Jilin University from January to December 2024 were enrolled. Patients treated from January to June 2024 were assigned to the control group ( n=140), and those treated from July to December 2024 were assigned to the intervention group ( n=140). The control group received routine continuous nursing, while the intervention group received multidisciplinary continuous nursing guided by the transtheoretical model. The 8-item Morisky Medication Adherence Scale (MMAS-8), Self-Rated Abilities for Health Practices Scale (SRAHP), Depression Anxiety and Stress Scale (DASS-21), and the Chinese Quality of Life Questionnaire for Cardiovascular Patients (CQQC) were used to evaluate the intervention effects. Results:A total of 135 patients in the control group and 137 patients in the intervention group completed the study. After the intervention, the MMAS-8, CQQC, and SRAHP scores of the intervention group were higher than those of the control group, with statistically significant differences ( P<0.05). The anxiety and stress subscale scores of the DASS-21 were lower in the intervention group compared with the control group, with statistically significant differences ( P<0.05) . Conclusions:Multidisciplinary continuous nursing based on the transtheoretical model can improve medication adherence, reduce negative psychological states, promote healthy behavior formation, and enhance the quality of life of patients after radiofrequency ablation for atrial fibrillation.