BACKGROUND:It has been shown that Gushukang affects bone metabolism by regulating nucleotide and amino acid metabolism and immune mechanisms.Current research on the mechanism of Gushukang in the treatment of osteoporosis primarily focuses on osteoblast regulation and requires further improvement from the perspective of osteoclasts. OBJECTIVE:To investigate the mechanism by which Gushukang interferes with osteoclasts in the treatment of osteoporosis using RAW264.7 cells as the research model. METHODS:Twenty-four 8-week-old female Sprague-Dawley rats were randomly divided into four groups(n=6 per group):the three experimental groups were given 1,2 and 4 g/kg osteoporosis solution by gavage(2 times per day),and the control group was given an equal amount of distilled water by gavage(2 times per day).After 7 days of intragastric administration,aortic blood samples were extracted to collect serum samples using centrifugation,and serum samples from the same groups were combined to obtain the low-,medium-,and high-concentration Gushukang-containing and normal sera for the subsequent experiments.(1)RAW264.7 cells were cultured in six groups:normal serum was added to the control group;low,medium,and high concentration groups were added with low,medium,and high concentrations of Gushukang-containing serum,respectively;ML385,a nuclear factor erythroid 2-related factor 2(Nrf2)inhibitor was given in the Nrf2 inhibitor group;and t-BHQ,a Nrf2 activator,was added in the Nrf2 activator group.Cell viability was detected using the cell counting kit-8 assay.(2)The 3rd generation RAW 264.7 cells were cultured and divided into five groups:the blank control group was added with normal serum,the osteoclast group was added with receptor activator of nuclear factor κB ligand(RANKL),and the low-,medium-,and high-concentration groups were added with low-,medium-,and high-concentration Gushukang-containing serum based on the addition of RANKL.Tartrate-resistant acid phosphate staining was performed after 5 days of culture.(3)RAW264.7 cells were cultured and divided into five groups:blank control group was cultured with normal serum,osteoclast group cultured with normal serum and RANKL,high concentration+osteoclast group cultured with RANKL+high concentration Gushukang-containing serum,osteoclast+Nrf2 agonist group cultured with RANKL+t-BHQ,and high concentration+osteoclast+Nrf2 inhibitor group cultured with RANKL+high concentration Gushukang-containing serum+ML385.Western blot assay and determination of reactive oxygen content were performed after 5 days of culture. RESULTS AND CONCLUSION:The cell counting kit-8 results indicated that Gushukang-containing serum,NRF2 inhibitor or agonist had no significant effect on RAW264.7 cell viability.Tartrate-resistant acid phosphate staining results demonstrated that Gushukang-containing serum exhibited a concentration-dependent inhibitory effect on osteoclast differentiation.Western blot analysis and determination of reactive oxygen species revealed that compared with the blank control group,Nrf2 protein expression was decreased in the osteoclast group(P<0.05),while c-Fos and NFATc1 protein expression and reactive oxygen species content were elevated(P<0.05);compared with the osteoclast group,Nrf2 protein expression was elevated and reactive oxygen species content was decreased in the high-concentration+osteoclast group,osteoclast+Nrf2 agonist group,and high-concentration+osteoclast+Nrf2 inhibitor group(P<0.05),while c-Fos and NFATc1 protein expression was decreased in the high concentration+osteoclast group and osteoclast+Nrf2 agonist group(P<0.05);compared with the high concentration+osteoclast group,Nrf2 protein expression was decreased(P<0.05)and reactive oxygen species content was elevated(P<0.05)in the high concentration+osteoclast+Nrf2 inhibitor group.To conclude,Gushukang reduces reactive oxygen species production by activating Nrf2,thereby inhibiting downstream of the c-Fos/NFATc1 pathway and suppressing osteoclast differentiation.

BACKGROUND:Cervical instability is a common orthopedic disease in young and middle-aged people,and is the early manifestation of cervical spondylosis,which has a great impact on the quality of life of patients.Therefore,early diagnosis of cervical instability to implement early intervention has positive clinical and social significance.OBJECTIVE:The clinical prediction model of cervical instability in young and middle-aged people was constructed based on machine learning to realize early screening of cervical instability in young and middle-aged people before X-ray examination.METHODS:From September 2022 to October 2023,155 young and middle-aged adults with cervical instability and 88 with non-cervical instability recruited through recruitment advertisements and spinal department outpatient of Wangjing Hospital,China Academy of Chinese Medical Sciences were selected as research subjects.The research subjects'general information,living and working habits,discomfort symptoms,visual analog scale score,Neck Disability Index,and 36-ltem Short Form Health Survey were collected on site based on questionnaires.The above information was used as predictive factors.After screening,six machine learning algorithms of Support Vector Machine,LightGBM,RandomForest,Logistic,AdaBoost,and XGBClassifier were used to train the model by ten-fold cross-validation method,and the clinical prediction model of cervical instability was constructed.Area under the curve was used as the main evaluation index.Univariate analysis was performed on the predictors,and SHAP method was used to rank the importance of the predictors.Correlation heat maps were used to show the degree of linear correlation between the predictors and the cervical instability.RESULTS AND CONCLUSION:(1)Among the six machine learning models,RandomForest model was chosen as the final prediction model,including nine predictors,such as age,body mass index,neck circumference/neck length,visual analog scale score,Neck Disability Index,bodily pain,general health,vitality,and mental health,area under the curve=0.725 4,and the calibration degree was good.It could be used as a reference tool for early screening of cervical instability in young and middle-aged people.(2)There were significant differences in age,visual analog scale score,Neck Disability Index,bodily pain,general health,and vitality between the two groups(P＜0.05).(3)The order of importance of predictors was age,Neck Disability Index,visual analog scale score,general health,body mass index,vitality,bodily pain,neck circumference/neck length,mental health,among which age,visual analog scale score,Neck Disability Index were positively correlated with cervical instability,while general health,body mass index,vitality,bodily pain,neck circumference/neck length,and mental health were negatively correlated with cervical instability.

BACKGROUND:Cervical instability is a common orthopedic disease in young and middle-aged people,and is the early manifestation of cervical spondylosis,which has a great impact on the quality of life of patients.Therefore,early diagnosis of cervical instability to implement early intervention has positive clinical and social significance.OBJECTIVE:The clinical prediction model of cervical instability in young and middle-aged people was constructed based on machine learning to realize early screening of cervical instability in young and middle-aged people before X-ray examination.METHODS:From September 2022 to October 2023,155 young and middle-aged adults with cervical instability and 88 with non-cervical instability recruited through recruitment advertisements and spinal department outpatient of Wangjing Hospital,China Academy of Chinese Medical Sciences were selected as research subjects.The research subjects'general information,living and working habits,discomfort symptoms,visual analog scale score,Neck Disability Index,and 36-ltem Short Form Health Survey were collected on site based on questionnaires.The above information was used as predictive factors.After screening,six machine learning algorithms of Support Vector Machine,LightGBM,RandomForest,Logistic,AdaBoost,and XGBClassifier were used to train the model by ten-fold cross-validation method,and the clinical prediction model of cervical instability was constructed.Area under the curve was used as the main evaluation index.Univariate analysis was performed on the predictors,and SHAP method was used to rank the importance of the predictors.Correlation heat maps were used to show the degree of linear correlation between the predictors and the cervical instability.RESULTS AND CONCLUSION:(1)Among the six machine learning models,RandomForest model was chosen as the final prediction model,including nine predictors,such as age,body mass index,neck circumference/neck length,visual analog scale score,Neck Disability Index,bodily pain,general health,vitality,and mental health,area under the curve=0.725 4,and the calibration degree was good.It could be used as a reference tool for early screening of cervical instability in young and middle-aged people.(2)There were significant differences in age,visual analog scale score,Neck Disability Index,bodily pain,general health,and vitality between the two groups(P＜0.05).(3)The order of importance of predictors was age,Neck Disability Index,visual analog scale score,general health,body mass index,vitality,bodily pain,neck circumference/neck length,mental health,among which age,visual analog scale score,Neck Disability Index were positively correlated with cervical instability,while general health,body mass index,vitality,bodily pain,neck circumference/neck length,and mental health were negatively correlated with cervical instability.

BACKGROUND:Previous studies on cervical instability failed to explain the dynamic and static interaction relationship and pathological characteristics changes in the development of cervical lesions under the traditional imaging examination.In recent years,the emerging nuclear magnetic resonance imaging(MRI)radiomics can provide a new way for in-depth research on cervical instability. OBJECTIVE:To investigate the application value of MRI radiomics in the study of cervical instability. METHODS:Through recruitment advertisements and the Second Department of Spine of Wangjing Hospital,China Academy of Chinese Medical Sciences,young cervical vertebra unstable subjects and non-unstable subjects aged 18-45 years were included in the cervical vertebra nuclear magnetic image collection.Five specific regions of interest,including the intervertebral disc region,the facet region,the prevertebral muscle region,the deep region of the posterior cervical muscle group,and the superficial region of the posterior cervical muscle group,were manually segmented to extract and screen the image features.Finally,the cervical instability diagnosis classification model was constructed,and the effectiveness of the model was evaluated using the area under the curve. RESULTS AND CONCLUSION:(1)A total of 56 subjects with cervical instability and 55 subjects with non-instability were included,and 1 688 imaging features were extracted for each region of interest.After screening,300 sets of specific image feature combinations were obtained,with 60 sets of regions of interest for each group.(2)Five regions of interest diagnostic classification models for cervical instability were initially established.Among them,the support vector machine model for the articular process region and the support vector machine model for the deep cervical muscle group had certain accuracy for the classification of instability and non-instability,and the average area under the curve of ten-fold cross-validation was 0.719 7 and 0.703 3,respectively.(3)The Logistic model in the intervertebral disc region,the LightGBM model in the prevertebral muscle region,and the Logistic model in the superficial posterior cervical muscle region were generally accurate in the classification of instability and non-instability,and the average area under the curve of ten-fold cross-validation was 0.650 4,0.620 7,and 0.644 2,respectively.(4)This study proved the feasibility of MRI radiomics in the study of cervical instability,further deepened the understanding of the pathogenesis of cervical instability,and also provided an objective basis for the accurate diagnosis of cervical instability.