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.

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.

[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.

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.

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.

BACKGROUND:The specific molecular mechanism of the transformation from normal healthy people to acute cervical spondylotic radiculopathy has not been clear,which needs to be further studied. OBJECTIVE:To investigate the differential expression of serum proteomics between normal healthy people and patients with acute cervical spondylotic radiculopathy,and to find and identify potential specific serum markers between them. METHODS:The serum samples of eight patients with acute cervical spondylotic radiculopathy and eight normal healthy people were collected,and the proteomic screening and analysis were performed by tandem mass tag combined with liquid chromatography-tandem mass spectrometry technology,in order to explore and identify serum proteins differentially expressed in patients with acute cervical spondylotic radiculopathy. RESULTS AND CONCLUSION:A total of 183 significantly differential proteins were screened by tandem mass tag technology,and 11 significantly differential proteins were identified(P＜0.05).Compared with normal healthy people,three differential proteins were significantly up-regulated,including human leukocyte antigen-A,secretoglobin family 1a member 1,and protein 4-hydroxyphenylpyruvate dioxygenase,and seven differential proteins were significantly down-regulated,such as immunoglobulin heavy constant gamma 3,skin factor,and myosin light chain 3,in patients with acute cervical spondylotic radiculopathy.Gene ontology enrichment analysis showed that these differential proteins participated in antigen binding,immunoglobulin receptor binding and other molecular functions.Protein-protein interaction analysis showed that among the common differential proteins between normal healthy people and patients with acute cervical spondylotic radiculopathy,HLA-A,HPD,PSMA3,DMKN,SCGB1A1,and MYL3 were located at the nodes of the functional network,and were closely related to the systems of body immunity,cellular inflammatory response,energy metabolism,and mechanical pressure.The significantly differential proteins HLA-A,HPD and MYL3 were verified by western blot,and the results were consistent with those of proteomics.To conclude,tandem mass tag combined with liquid chromatography-tandem mass spectrometry technology can be used to find the differentially expressed proteins in serum between normal healthy people and patients with acute cervical spondylotic radiculopathy.It is preliminarily believed that HLA-A,HPD and MYL3 may be specific serum markers of acute cervical spondylotic radiculopathy,providing a new direction for further research on its pathogenesis.

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.

Objective:To explore potential predictors of refractory Mycoplasma pneumoniae pneumonia (RMPP) in early stage. Methods:The prospective multicenter study was conducted in Zhejiang, China from May 1 st, 2019 to January 31 st, 2020. A total of 1 428 patients with fever >48 hours to <120 hours were studied. Their clinical data and oral pharyngeal swab samples were collected; Mycoplasma pneumoniae DNA in pharyngeal swab specimens was detected. Patients with positive Mycoplasma pneumoniae DNA results underwent a series of tests, including chest X-ray, complete blood count, C-reactive protein, lactate dehydrogenase (LDH), and procalcitonin. According to the occurrence of RMPP, the patients were divided into two groups, RMPP group and general Mycoplasma pneumoniae pneumonia (GMPP) group. Measurement data between the 2 groups were compared using Mann-Whitney U test. Logistic regression analyses were used to examine the associations between clinical data and RMPP. Receiver operating characteristic (ROC) curves were used to analyse the power of the markers for predicting RMPP. Results:A total of 1 428 patients finished the study, with 801 boys and 627 girls, aged 4.3 (2.7, 6.3) years. Mycoplasma pneumoniae DNA was positive in 534 cases (37.4%), of whom 446 cases (83.5%) were diagnosed with Mycoplasma pneumoniae pneumonia, including 251 boys and 195 girls, aged 5.2 (3.3, 6.9) years. Macrolides-resistant variation was positive in 410 cases (91.9%). Fifty-five cases were with RMPP, 391 cases with GMPP. The peak body temperature before the first visit and LDH levels in RMPP patients were higher than that in GMPP patients (39.6 (39.1, 40.0) vs. 39.2 (38.9, 39.7) ℃, 333 (279, 392) vs. 311 (259, 359) U/L, both P<0.05). Logistic regression showed the prediction probability π=exp (-29.7+0.667×Peak body temperature (℃)+0.004×LDH (U/L))/(1+exp (-29.7+0.667×Peak body temperature (℃)+0.004 × LDH (U/L))), the cut-off value to predict RMPP was 0.12, with a consensus of probability forecast of 0.89, sensitivity of 0.89, and specificity of 0.67; and the area under ROC curve was 0.682 (95% CI 0.593-0.771, P<0.01). Conclusion:In MPP patients with fever over 48 to <120 hours, a prediction probability π of RMPP can be calculated based on the peak body temperature and LDH level before the first visit, which can facilitate early identification of RMPP.