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: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 develop and evaluate a nomogram prediction model for the 3-month mortality risk of patients with sepsis-associated acute kidney injury (S-AKI).Methods:Based on the American Medical Information Mart for Intensive Care-Ⅳ (MIMIC-Ⅳ), clinical data of S-AKI patients from 2008 to 2021 were collected.Initially, 58 relevant predictive factors were included, with all-cause mortality within 3 months as the outcome event. The data were divided into training and testing sets at a 7∶3 ratio. In the training set, univariate Logistic regression analysis was used for preliminary variable screening. Multicollinearity analysis, Lasso regression, and random forest algorithm were employed for variable selection, combined with the clinical application value of variables, to establish a multivariable Logistic regression model, visualized using a nomogram. In the testing set, the predictive value of the model was evaluated through internal validation. The receiver operator characteristic curve (ROC curve) was drawn, and the area under the curve (AUC) was calculated to evaluate the discrimination of nomogram model and Oxford acute severity of illness score (OASIS), sequential organ failure assessment (SOFA), and systemic inflammatory response syndrome score (SIRS). The calibration curve was used to evaluate the calibration, and decision curve analysis (DCA) was performed to assess the net benefit at different probability thresholds.Results:Based on the survival status at 3 months after diagnosis, patients were divided into 7?768 (68.54%) survivors and 3?566 (31.46%) death. In the training set, after multiple screenings, 7 variables were finally included in the nomogram model: Logistic organ dysfunction system (LODS), Charlson comorbidity index, urine output, international normalized ratio (INR), respiratory support mode, blood urea nitrogen, and age. Internal validation in the testing set showed that the AUC of nomogram model was 0.81 [95% confidence interval (95% CI) was 0.80-0.82], higher than the OASIS score's 0.70 (95% CI was 0.69-0.71) and significantly higher than the SOFA score's 0.57 (95% CI was 0.56-0.58) and SIRS score's 0.56 (95% CI was 0.55-0.57), indicating good discrimination. The calibration curve demonstrated that the nomogram model's calibration was better than the OASIS, SOFA, and SIRS scores. The DCA curve suggested that the nomogram model's clinical net benefit was better than the OASIS, SOFA, and SIRS scores at different probability thresholds. Conclusions:A nomogram prediction model for the 3-month mortality risk of S-AKI patients, based on clinical big data from MIMIC-Ⅳ and including seven variables, demonstrates good discriminative ability and calibration, providing an effective new tool for assessing the prognosis of S-AKI patients.

Recent studies have shown that TCM treatment for bone metabolic diseases, such as steroid-induced avascular necrosis of the femoral head, has definite efficacy and has received extensive attention. However, due to lack of molecular biological basis of pharmacodynamics mechanism, it has not yet reached the standard of scientific treatment. The discovery of OPG/RANK/RANKL system has become a new breakthrough point in the prevention and treatment of bone metabolic diseases. The pathogenesis of deficiency of spleen and kidney - blood stasis - caused by phlegm arthralgia caused by blood stasis is closely related to the system. The effect of the final control of the system from the deficiency theory can be expressed through the axial micro-information. This article discussed the TCM syndrome differentiation of steroid-induced osteonecrosis of the femoral head and the relation of regulation of bone metabolism combined with the OPG/RANK/RANKL system, and provided the basis for prevention and treatment of this disease.

In recent years, due to the irregular and abused use of glucocorticoid in clinical treatment, the incidence of steroid induced avascular necrosis of femoral head (SANFH) is gradually increasing. TCM for the prevention and treatment of SANFH has received much attention from many scholars. However, due to the lack of the scientific explanation of molecular biology level for its pharmacodynamics mechanism, it is difficult to achieve the purpose of standardized treatment. The discovery of OPG/RANK/RANKL signaling pathway has opened up new shortcuts for the prevention and treatment of bone metabolic diseases. OPG/RANK/RANKL signaling pathway is associated with the pathogenesis of spleen deficiency and phlegm - blood stasis caused by phlegm-blood stasis due to paralysis of SANFH. The treatment efficacy based on the phlegm theory can eventually axial control of the system through the micro-information to express. This article discussed the relevance between the phlegm in the treatment of SANFH and molecular biology mechanism of OPG/RANK/RANKL signal regulation mechanism, and combined the system of bone metabolism regulation mechanism to discuss TCM differentiation of SANFH, with a purpose to provide references for clinical and further study.

The theory of static and dynamic combination coincides with the effect of osteoblast stress, although there are few articles on molecular biology about the theory in recent years. The treatment of static and dynamic combination theory for fractures and other orthopedic disorders is to use the localized stress fracture, and the stress controls bone formation results through afffecting proliferation of osteoblast differentiation, which has proved by relevant experimental evidence. This article discussed the molecular biology basis about the correlation between theory of static and dynamic combination and osteoblast proliferation and differentiation by stress stimulation.

Objective To observe the effects of Najia method of midday-midnight point selection for acute ischemic stroke (AIS) model rats onthe contents of NSE and S100B protein in serum. Methods SPF SD male rats were chosen to establish the models by middle cerebral artery bolt method. Rats were divided into blank group, sham-operation group, model group, channel-point group, and Najia method group by random number table method. Blank group, sham-operation group, and model group were in the absence of treatment, while the channel-point group received acupuncture treatment according to differentiation syndrome. Najia method group used Najia method of midday-midnight point selection to conduct acupuncture treatment once a day. Improvement of neural function and cerebral infarction volume were observed. The contents of NSE and S100B protein in serum were detected. Results Compared with model group, neurological function score, infarct volume and infarct volume percentage, and the contents of NSE and S100B protein in serum decreased in Najia method group and channel-point group (P<0.05, P<0.01). The effects of Najia group were generally better than the channel-point group. Conclusion Najia method of midday-midnight point selection can decrease the content of NSE and S100B protein in serum of AIS model rats, so as to achieve the effects of neuroprotection and treatment.

The endemic situation of schistosomiasis in Tangyin Village of Duchang County from 2000 to 2007 showed the infection rates of residents between 2003 and 2007 were all around 10%.In 2000.2002 and 2007,the infection rates of snails were 0.32%,O.45%and 0.38%,respectively,and the densities of infected snails were 0.016,0.004 No./0.1 m~2.and 0.004 No./0.1 m~2,respectively.It is suggested that the snail control and health education should be strengthened and the self-protection awareness of residents should improve for schistosomiasis control.

Objective To observe the effect of psychological intervention on the anxiety of middle-aged and elderly patients with hypertension. Methods 378 middle-aged and elderly patients with hypertension accepted the intervention (health education and psychological intervention). They were assessed with the Zung Self-rating Anxiety of Scale (SAS) before and after the inertvention. Results The scores of the SAS in patients were higher than the Chinese normal. Female, younger, and higher eduction background were related to the anxiety symptom. The anxiety of the pateints were released after the intervention. Conclusion Health education and psychological intervention is effective on the anxiety of the middle-aged and elderly patients with hypertension at outpatient.

Objective To evaluate the effect of health education on the old male patients with primary hypertension.Methods 120 old male patients with primary hypertension were randomly selected from the whole group and educated with health knowledge related to blood hypertension, and the changes of life style and blood pressure after health education were recorded.Results Most of the patients changed their unhealthy life style. Among these patients, more than 80% were able to have a stable emotion, regular daily life, stop smoking and less drinking, there were 66% cases with a healthy diet. The systolic pressure of the patients declined significantly after health education (P<0.01).Conclusion The health education is an efficient way to control hypertension in the official-service outpatient department.