Drawing on recent advances in modern fascial anatomy and biomechanics， this paper interprets the biomechanical implications of yin-yang balance at the physical level from the perspective of holistic force lines. It posits that holistic force lines represent the functional expression of mechanical load transmission and integration within the myofascial network； the channel sinews system and myofascial chains exhibit significant similarities in terms of their distribution， functional attributes， and principles of holistic regulation， and can thus be understood as structural-functional carriers of yin-yang balance. Accordingly， this paper provides a biomechanical interpretation of the yin-yang relationship from four aspects， including interdependence， mutual restriction， dynamic balance through waxing and waning， and mutual transformation， proposing that in a healthy state， the body relies on the continuous integration， distribution， and readjustment of the myofascial tension network to maintain overall coordination， whereas disease states are often associated with compensatory transmission of local tension abnormalities along holistic force lines， the accumulation of imbalances， and structural rigidity. The holistic force-line perspective provides a biomechanical explanatory framework for the theory of yin-yang balance and offers a new theoretical basis for clinical diagnosis and treatment to shift from empirical holistic balancing toward holistic force-line balancing based on posture， function， and tension transmission pathways.

[Objective] To analyze the influencing factors of repeat blood donor lapsing using a zero-inflated poisson regression model (ZIP). [Methods] The blood donation behavior of 12 498 whole blood donors from 2020 was tracked until December 31, 2023. The factors influencing the frequency of blood donations in a given year was analyzed using ZIP, and donors with 0 blood donation in that year were considered to have lapsed. The changes in relevant influencing factors associated with each blood donation were measured and modeled for analysis. [Results] The zero-inflated part of ZIP showed that the risk of lapsing of male blood donors was 2.24 times that of female blood donors (OR 95% CI:1.864-2.696, P<0.001); the risk of lapsing of the 35-44 age group and over 45 age group was respectively 40% (OR 95% CI:0.455-0.790, P<0.001) and 61%(OR 95% CI:0.268-0.578, P<0.001) lower than that of the under 25 age group; the risk of lapsing for those who have donated blood twice and ≥3 times was respectively 50% (OR 95% CI:0.405-0.609, P<0.001) and 81% (OR 95% CI:0.154-0.225, P<0.001) lower than that of first-time donors; the risk of lapsing of those with junior high or high school education was 1.2 times that of those with a college degree or higher (OR 95% CI:1.033-1.384, P<0.05); the risk of lapsing for the divorced group was 2.02 times that of the married group (OR 95% CI:1.445-2.820, P<0.001); the risk of lapsing for those with an income (Yuan) of 10 000 to 50 000, 50 000 to 100 000 and more than 100 000 was respectively 0.67 (OR 95% CI:0.552-0.818, P<0.001), 0.72 (OR 95% CI:0.591-0.884, P=0.002) and 0.67 (OR 95% CI:0.535-0.834, P<0.001) times that of those with an income (Yuan) of less than 10 000. The results of the Poisson part are consistent with the results of the zero-inflated part in terms of age and education level. [Conclusion] Blood donor lapsing is overall related to factors such as gender, age, donation frequency, education, marital status and family income. It's essential to care for those blood donors prone to lapse to retain more regular blood donors.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective To compare the distribution characteristics of retinal capillary non-perfusion areas(NPAs)across different layers and regions in patients with neovascular glaucoma(NVG)secondary to proliferative diabetic retinop-athy(PDR)versus those with PDR alone through wide-field swept-source optical coherence tomography angiography(SS-OCTA)and to analyze the impacts of NPAs on the development of NVG.Methods This prospective cross-sectional study enrolled 33 patients with PDR(33 eyes,the PDR group)and 30 patients with NVG(30 eyes,the PDR+NVG group)diag-nosed at Affiliated Hospital of Shandong Second Medical University(formerly Weifang Medical University)from January 2022 to June 2023.The fundus examination was performed using SS-OCTA,and the NPA boundaries of the superficial capil-lary plexus(SCP)and deep capillary plexus(DCP)of the retina were manually delimited with the aid of ImageJ.The reti-na was divided based on two methods.Specifically,according to different concentric circles,the retina could be divided in-to the foveal area,parafoveal area,perifoveal area,annulus6-9,annulus9-12,annulus12-retinal boundary;besides,the ret-ina could also be divided into four quadrants(supratemporal,infratemporal,supranasal,and infranasal quadrants)based on the horizontal and vertical lines centered on the macular fovea.Based on that,the NPA area and ischemia index(ISI)in each layer and subdivision of the two groups of patients were counted.Additionally,the NPA and ISI in different concentric circles and different quadrants of the SCP and DCP were compared between the two groups.Moreover,the distribution characteristics of NPAs as well as the effect of NPAs on NVG were analyzed.Results(1)The NPA area and ISI in the DCP were larger than those in the SCP in both groups(all P＜0.001);the NPA area and ISI in the SCP and DCP of patients in the PDR+NVG group were larger than those in the PDR group(all P＜0.001).(2)In the supratemporal,infratemporal,supranasal,and infranasal quadrants,the NPA area and ISI in the SCP and DCP of patients in the PDR+NVG group were larger than those in the PDR group(all P＜0.01).The NPA area in the inferotemporal quadrant was the largest in the SCP and DCP,respectively,within each group(all P＜0.01).(3)The differences in the NPA area and ISI between the two groups were statistically significant in the annulus6-9,annulus9-12,and annulus12-retinal boundary in the SCP and DCP(all P＜0.01).The differences in the NPA area and ISI were statistically significant between different annular subdivisions in the SCP and DCP within each group(all P＜0.001).The multiple comparison results showed that the NPA area and ISI of the annulus12-retinal boundary in the SCP and DCP were larger than those in other annuli in both groups(all P＜0.05).The NPA area and ISI of the annulus9-12 were larger than those of the parafoveal and perifoveal areas;the NPA area and ISI of the annulus6-9 were larger than those of the parafoveal area(all P＜0.05).There was no statistically significant differ-ence in the NPA area and ISI in the remaining annuli(all P＞0.05).(4)The multivariate logistic regression analysis showed that the NPA area and ISI in the subnasal quadrant of the SCP were negatively correlated with the risk of NVG sec-ondary to PDR(P=0.036 and 0.038).The increased NPA area and ISI in the subnasal quadrant of the DCP were risk fac-tors for NVG secondary to PDR,and they may increase the risk of NVG(P=0.029 and 0.028).Conclusion The in-creased NPA area and ISI in the subnasal quadrant of the DCP were risk factors for secondary NVG in patients in the PDR group.

Pulpitis is a prevalent inflammatory disease in dentistry, and root canal therapy remains the primary clinical treatment for it. However, pulp removal leads to reduced tooth fracture resistance and complications such as secondary infection and tooth fracture. As a potential alternative, vital pulp therapy (VPT) relies on precise assessment of pulp status; yet current clinical diagnostic methods lack specificity. The establishment of appropriate animal models for pulpitis is crucial for investigating its pathogenesis, developing specific diagnostic biomarkers, and optimizing VPT strategies. This review systematically summarizes experimental animals selection based on anatomical compatibility and pathological similarity, as well as model construction methods and multimodal evaluation systems for pulpitis animal models, aiming to provide insights for related researches.

Objective To compare the distribution characteristics of retinal capillary non-perfusion areas(NPAs)across different layers and regions in patients with neovascular glaucoma(NVG)secondary to proliferative diabetic retinop-athy(PDR)versus those with PDR alone through wide-field swept-source optical coherence tomography angiography(SS-OCTA)and to analyze the impacts of NPAs on the development of NVG.Methods This prospective cross-sectional study enrolled 33 patients with PDR(33 eyes,the PDR group)and 30 patients with NVG(30 eyes,the PDR+NVG group)diag-nosed at Affiliated Hospital of Shandong Second Medical University(formerly Weifang Medical University)from January 2022 to June 2023.The fundus examination was performed using SS-OCTA,and the NPA boundaries of the superficial capil-lary plexus(SCP)and deep capillary plexus(DCP)of the retina were manually delimited with the aid of ImageJ.The reti-na was divided based on two methods.Specifically,according to different concentric circles,the retina could be divided in-to the foveal area,parafoveal area,perifoveal area,annulus6-9,annulus9-12,annulus12-retinal boundary;besides,the ret-ina could also be divided into four quadrants(supratemporal,infratemporal,supranasal,and infranasal quadrants)based on the horizontal and vertical lines centered on the macular fovea.Based on that,the NPA area and ischemia index(ISI)in each layer and subdivision of the two groups of patients were counted.Additionally,the NPA and ISI in different concentric circles and different quadrants of the SCP and DCP were compared between the two groups.Moreover,the distribution characteristics of NPAs as well as the effect of NPAs on NVG were analyzed.Results(1)The NPA area and ISI in the DCP were larger than those in the SCP in both groups(all P＜0.001);the NPA area and ISI in the SCP and DCP of patients in the PDR+NVG group were larger than those in the PDR group(all P＜0.001).(2)In the supratemporal,infratemporal,supranasal,and infranasal quadrants,the NPA area and ISI in the SCP and DCP of patients in the PDR+NVG group were larger than those in the PDR group(all P＜0.01).The NPA area in the inferotemporal quadrant was the largest in the SCP and DCP,respectively,within each group(all P＜0.01).(3)The differences in the NPA area and ISI between the two groups were statistically significant in the annulus6-9,annulus9-12,and annulus12-retinal boundary in the SCP and DCP(all P＜0.01).The differences in the NPA area and ISI were statistically significant between different annular subdivisions in the SCP and DCP within each group(all P＜0.001).The multiple comparison results showed that the NPA area and ISI of the annulus12-retinal boundary in the SCP and DCP were larger than those in other annuli in both groups(all P＜0.05).The NPA area and ISI of the annulus9-12 were larger than those of the parafoveal and perifoveal areas;the NPA area and ISI of the annulus6-9 were larger than those of the parafoveal area(all P＜0.05).There was no statistically significant differ-ence in the NPA area and ISI in the remaining annuli(all P＞0.05).(4)The multivariate logistic regression analysis showed that the NPA area and ISI in the subnasal quadrant of the SCP were negatively correlated with the risk of NVG sec-ondary to PDR(P=0.036 and 0.038).The increased NPA area and ISI in the subnasal quadrant of the DCP were risk fac-tors for NVG secondary to PDR,and they may increase the risk of NVG(P=0.029 and 0.028).Conclusion The in-creased NPA area and ISI in the subnasal quadrant of the DCP were risk factors for secondary NVG in patients in the PDR group.

Pulpitis is a prevalent inflammatory disease in dentistry, and root canal therapy remains the primary clinical treatment for it. However, pulp removal leads to reduced tooth fracture resistance and complications such as secondary infection and tooth fracture. As a potential alternative, vital pulp therapy (VPT) relies on precise assessment of pulp status; yet current clinical diagnostic methods lack specificity. The establishment of appropriate animal models for pulpitis is crucial for investigating its pathogenesis, developing specific diagnostic biomarkers, and optimizing VPT strategies. This review systematically summarizes experimental animals selection based on anatomical compatibility and pathological similarity, as well as model construction methods and multimodal evaluation systems for pulpitis animal models, aiming to provide insights for related researches.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective:To explore the effect and safety of endoscopic tubular musculoskeletal tumor surgery (ETMS) technology in spinal tumors.Methods:Clinical data were retrospectively collected from 18 spinal tumor patients who were treated with ETMS technology at Tianjin Medical University Cancer Institute and Hospital ( n=16) or the Affiliated Hospital of Qingdao University ( n=2) from November 2022 to December 2023. The total cohort included 11 males and 7 females, with the age at 60.3±8.6 years (range of 41-76). Two cases were diagnosed with benign tumors, four patients were diagnosed with spinal hematologic malignancies while other 12 cases were patients with spinal metastases. After localization under the C-arm X-ray machine, the spinal endoscopic channel is established using dilators. Soft tissue is dissected under endoscopic guidance to create an artificial cavity. Subsequently, the saline medium relied upon by the spinal endoscopic technique is removed, and posterior decompression and tumor curettage are performed using tubular techniques. Frankel grade classification and paraplegia index were used to evaluate the improvement of postoperative function and the VAS score was performed in pain scoring. The surgical complications and tumor evaluation were observed by postoperative outpatient and telephone follow-up. Results:The ETMS technology was successfully completed in all 18 patients with the mean operation time of 240.3±80.2 min. The median of intraoperative bleeding was 200.0(172.5, 350.0) ml and the mean postoperative drainage was 131.4±69.5 ml. The median value of postoperative hospitalization days was 6.0(4.0, 10.25) d. The paraplegia index decreased from 1.5(0, 3.0) preoperatively to 0(0, 1.25) postoperatively ( Z=-2.599, P=0.009). All the patients presented an improvement in Frankel grading after surgery except for one patient (downgrading from grade E to grade D). There was significantly difference in Frankel grading between preoperative and postoperative groups ( Z=2.812, P=0.005). The median value of preoperative VAS score was up to 5.5(4.0, 7.0) while the median value at postoperative, one month after surgery and three months after surgery were 1.5(1.0, 2.25), 1.0(0, 1.0) and 0(0, 1.0), respectively (χ 2=44.641, P<0.001). The 3-month postoperative VAS improvement rate was 91.2% (range 75%-100%). During a mean follow-up period of 7.6±6.2 months, none of the 18 patients presented surgical complications or tumor recurrence at surgical region. Only one patient died at 3.2 months after surgery until the last follow-up due to respiratory failure after lung tumor progression. The mean survival of the total cohort was up to 13.3 [95% CI (11.5, 15.0)] months. The 16 cases with spinal metastases or spinal hematological malignancies had a mean survival of 13.2 [95% CI (11.3, 15.0)] months. Conclusion:The ETMS technology presented good efficacy and safety in treatment of spinal tumors with low blood supply and with diameter less than 5cm.

BACKGROUND:Previous studies have found that qi deficiency and blood stasis syndrome is the main syndrome among various TCM syndromes of cervical spondylotic myelopathy.However,there is no report on proteomic markers as early diagnosis indicators for the transformation of developmental cervical spinal stenosis with qi deficiency and blood stasis syndrome to cervical spondylotic myelopathy. OBJECTIVE:To explore serum proteomics difference between developmental cervical spinal stenosis and cervical spondylotic myelopathy and to find and identify the potential serum biomarkers between them. METHODS:Serum samples of nine patients with cervical spondylotic myelopathy of qi deficiency and blood stasis syndrome(experimental group)and nine patients with developmental cervical spinal stenosis of qi deficiency and blood stasis syndrome(control group)were collected.The proteomic analysis was carried out by Tandem Mass Tag combined with liquid chromatography tandem mass spectrometry,so as to find and identify differentially expressed proteins. RESULTS AND CONCLUSION:A total of 1027 significantly differential proteins were initially screened by TMT technology and 89 significantly differential proteins were finally identified(P<0.05).Compared with the control group,there were 45 up-regulated proteins in the experimental group,such as α-actinin-4,α-actinin-1,cell division control protein 42 homolog,integrin-linked protein kinase and B-actin.Conversely,there were 44 down-regulated proteins in the experimental group compared with the control group,such as fibronectin,fibrinogen γ chain,fibrinogen α chain,fibrinogen β chain.Gene ontology enrichment analysis indicated that these differential proteins were involved in signal receptor binding,kinase binding,protein kinase activity,integrin binding,actin filament binding and other molecular functions.Based on the Kyoto Encyclopedia of Genes and Genomes pathway analysis,20 common differential signal/metabolic pathways were identified,including Rap1 signaling pathway,adherens junction,tight junction,platelet activation,and regulation of actin cytoskeleton.Protein-protein interaction analysis showed that ILK,FGA,FGB,FGG,FN1,Cdc42,ACTN1,ACTN4 and ACTB were located at the nodes of protein-protein interaction network and were closely related to bone formation and destruction system,nervous system,coagulation system,cellular inflammation and other systems.To conclude,the serum differentially expressed proteins between developmental cervical spinal stenosis and cervical spondylotic myelopathy can be successfully screened by Tandem Mass Tag combined with liquid chromatography tandem mass spectrometry.ILK,FN1,CDC42 and ACTN 4 are identified as specific markers for the transformation of developmental cervical spinal stenosis with qi deficiency and blood stasis syndrome into cervical spondylotic myelopathy.These findings provide a basis for further clarifying the transformation mechanism.