Objective:To build a big data analysis model of comprehensive performance of medical equipment,so as to improve the management efficiency and fine management level for medical equipment of hospital.Methods:The big data analysis model of comprehensive performances of medical equipment was developed through building a data integration platform,and integrating multi-business system data of hospital,and including quantified social benefit data and relative data of the process of procurement and maintenance,which established index system of performance analysis with multi-dimensions and multi-layers.It realized the presentation of evaluation results of performance of medical equipment in the form of visual data reports.Results:The big data analysis model for the comprehensive performance of medical equipment has set a system with more than 10 indicators,which included social-benefit indicators,evaluation indicators of comprehensive performance,income indicators,and the indicator of recovery period of investment,and visual report with 35 pages.Each layer of the performance of equipment was analyzed from 35 angles,which realized a comprehensive,multi-dimensional and refined evaluation for the performance of medical equipment of hospital.Its'agile,easy-to-use,efficient response and automation features can significantly improve work efficiency of business.The model realized automatic data collection through interfaces and other means,and reduced manual input errors,which data accuracy rate was＞99%.The results of testing the performance data of equipment for three consecutive years by using this model indicated that the consuming time of single calculation of business system of hospital was shortened from＞20 min to 5-8 s.Conclusion:The big data analysis model of comprehensive performance of medical equipment can help hospital to identify,prevent and control risks in advance,and improve the efficiency of internal control,and enhance management efficiency and fine management level for hospital,and provide support for management decision for medical equipment of hospital.

Objective:To investigate the efficacy and safety of bendamustine combined with anti-CD20 monoclonal antibody in the first-line treatment of older patients with indolent B-cell non-Hodgkin lymphoma (B-iNHL) .Methods:The clinical data of 159 patients with B-iNHL enrolled in 16 hospitals from Jiangsu Cooperative Lymphoma Group from December 1, 2019, to April 20, 2024, were analyzed for regimen efficacy and safety. Bendamustine plus rituximab (BR) and bendamustine plus obinutuzumab (BG) were administered to 139 (87.4% ) and 20 (12.6% ) patients, respectively.Results:Among the 159 patients, 101 (63.5% ) were male and 58 (36.5% ) were female, with a median age of 69 years (range: 60–84). Efficacy could be assessed in 138 (86.8% ) patients. The efficacy assessment demonstrated that the overall response rate was 92.0% with complete and partial remissions in 75 (54.3% ) and 52 (37.7% ) cases, respectively. With a median follow-up of 24 months (range: 4–64), the progression-free survival rate was (87.5 ± 3.0) % and the overall survival rate was (83.2 ± 3.3) %. Of the 27 patients who died, 6 (22.2% ) died due to disease progression. The mean applied dose of bendamustine per cycle was 73.0 (50.8–89.7) mg/m 2 per day, administered on days 1 and 2. Adverse events of grade 3 or higher were reported in 53 (33.3% ) patients, with infection (30 cases,18.9% ) and neutropenia (24 cases, 15.1% ) demonstrating the highest incidence. Conclusion:Bendamustine combined with anti-CD20 monoclonal antibody demonstrated good efficacy and is well-tolerated in the first-line treatment of elderly patients with B-iNHL.

AIM To establish the quantitative models for gallic acid,mononucleoside,loganin,resveratrol,and rhein in Yishen Xiezhuo Mixture.METHODS HPLC was adopted in the content determination of various effective components,after which the near-infrared spectroscopy(NIRS)data were collected in 128 batches of samples and pretreatment was conducted,competitive adaptive reweighting sampling(CARS)algorithm was used for screening wavelength,partial least square method(PLS)regression analysis was performed.RESULTS There were no significant differences between the predicted values obtained by PLS models and measured values obtained by HPLC for various effective components(P＞0.05).CONCLUSION The quantitative models established by NIRS combined with chemometrics display good predictive performance,which can be used for the rapid determination of effective components in Yishen Xiezhuo Mixture,and provide a reference for the rapid monitoring of other traditional Chinese medicine preparations in production processes.

AIM To establish the quantitative models for gallic acid,mononucleoside,loganin,resveratrol,and rhein in Yishen Xiezhuo Mixture.METHODS HPLC was adopted in the content determination of various effective components,after which the near-infrared spectroscopy(NIRS)data were collected in 128 batches of samples and pretreatment was conducted,competitive adaptive reweighting sampling(CARS)algorithm was used for screening wavelength,partial least square method(PLS)regression analysis was performed.RESULTS There were no significant differences between the predicted values obtained by PLS models and measured values obtained by HPLC for various effective components(P＞0.05).CONCLUSION The quantitative models established by NIRS combined with chemometrics display good predictive performance,which can be used for the rapid determination of effective components in Yishen Xiezhuo Mixture,and provide a reference for the rapid monitoring of other traditional Chinese medicine preparations in production processes.

Objective:To build a big data analysis model of comprehensive performance of medical equipment,so as to improve the management efficiency and fine management level for medical equipment of hospital.Methods:The big data analysis model of comprehensive performances of medical equipment was developed through building a data integration platform,and integrating multi-business system data of hospital,and including quantified social benefit data and relative data of the process of procurement and maintenance,which established index system of performance analysis with multi-dimensions and multi-layers.It realized the presentation of evaluation results of performance of medical equipment in the form of visual data reports.Results:The big data analysis model for the comprehensive performance of medical equipment has set a system with more than 10 indicators,which included social-benefit indicators,evaluation indicators of comprehensive performance,income indicators,and the indicator of recovery period of investment,and visual report with 35 pages.Each layer of the performance of equipment was analyzed from 35 angles,which realized a comprehensive,multi-dimensional and refined evaluation for the performance of medical equipment of hospital.Its'agile,easy-to-use,efficient response and automation features can significantly improve work efficiency of business.The model realized automatic data collection through interfaces and other means,and reduced manual input errors,which data accuracy rate was＞99%.The results of testing the performance data of equipment for three consecutive years by using this model indicated that the consuming time of single calculation of business system of hospital was shortened from＞20 min to 5-8 s.Conclusion:The big data analysis model of comprehensive performance of medical equipment can help hospital to identify,prevent and control risks in advance,and improve the efficiency of internal control,and enhance management efficiency and fine management level for hospital,and provide support for management decision for medical equipment of hospital.

Objective:To investigate the efficacy and safety of bendamustine combined with anti-CD20 monoclonal antibody in the first-line treatment of older patients with indolent B-cell non-Hodgkin lymphoma (B-iNHL) .Methods:The clinical data of 159 patients with B-iNHL enrolled in 16 hospitals from Jiangsu Cooperative Lymphoma Group from December 1, 2019, to April 20, 2024, were analyzed for regimen efficacy and safety. Bendamustine plus rituximab (BR) and bendamustine plus obinutuzumab (BG) were administered to 139 (87.4% ) and 20 (12.6% ) patients, respectively.Results:Among the 159 patients, 101 (63.5% ) were male and 58 (36.5% ) were female, with a median age of 69 years (range: 60–84). Efficacy could be assessed in 138 (86.8% ) patients. The efficacy assessment demonstrated that the overall response rate was 92.0% with complete and partial remissions in 75 (54.3% ) and 52 (37.7% ) cases, respectively. With a median follow-up of 24 months (range: 4–64), the progression-free survival rate was (87.5 ± 3.0) % and the overall survival rate was (83.2 ± 3.3) %. Of the 27 patients who died, 6 (22.2% ) died due to disease progression. The mean applied dose of bendamustine per cycle was 73.0 (50.8–89.7) mg/m 2 per day, administered on days 1 and 2. Adverse events of grade 3 or higher were reported in 53 (33.3% ) patients, with infection (30 cases,18.9% ) and neutropenia (24 cases, 15.1% ) demonstrating the highest incidence. Conclusion:Bendamustine combined with anti-CD20 monoclonal antibody demonstrated good efficacy and is well-tolerated in the first-line treatment of elderly patients with B-iNHL.

ObjectiveTo observe the pharmacodynamic effects of Cinnamomi Cortex on the incretin effect in the rat model of diabetes mellites （DM） induced by streptozotocin （STZ） and explore the underlying mechanism from glucagon-like peptide-1 （GLP-1） and dipeptidyl peptidase-4 （DPP-4）. MethodForty SD rats were randomly assigned into blank， model， sitagliptin （0.1 g·kg^-1）， and low- and high-dose Cinnamomi Cortex （0.45 and 0.9 g·kg^-1， respectively） groups. The DM rat model was established by a high-fat diet combined with intraperitoneal injection of 40 mg·kg^-1 STZ in other groups except the blank group. The intervention lasted for 8 weeks. The status， body weight， water intake， food intake， and fasting blood glucose （FBG） of the rats were observed and determined. Hematoxylin-eosin staining was employed to reveal the pathological changes of the pancreas， and immunohistochemistry to detect the expression of glucagon in the pancreas. Biochemical assay was employed to measure the serum levels of lipid metabolism indexes such as total cholesterol （TC）， triglyceride （TG）， low-density lipoprotein （LDL）， and high-density lipoprotein （HDL）. Enzyme-linked immunosorbent assay was employed to determine the levels of glycosylated hemoglobin， insulin， glucagon， GLP-1， and glucose-dependent insulinotropic polypeptide （GIP） in rat serum， and Western blot to determine the protein levels of GLP-1 and DPP-4 in the pancreas. ResultAfter 8 weeks of intervention， the model group showed higher body weight， FBG， TC， TG， LDL， glycosylated hemoglobin， glucagon， insulin， and insulin resistance index and lower HDL， GLP-1， and GIP than the blank group （P<0.05， P<0.01）. The Cinnamomi Cortex groups showed lower body weight， FBG， TC， TG， LDL， glycosylated hemoglobin， glucagon， insulin， and insulin resistance index and higher HDL， GLP-1， and GIP than the model group （P<0.05， P<0.01）. The Cinnamomi Cortex groups showed recovered morphology of islet cells and no nucleus aggregation. Compared with the model group， the Cinnamomi Cortex groups showed declined levels of glucagon in the center of islet cells. Compared with the blank group， the model group showed up-regulated protein level of DPP-4 and down-regulated protein level of GLP-1 （P<0.01）. Compared with the model group， the high-dose Cinnamomi Cortex groups showed down-regulated protein level of DPP-4 and up-regulated protein level of GLP-1 （P<0.05）. ConclusionCinnamomi Cortex may reduce blood glucose and improve incretin effect to lower the blood glucose level by regulating DPP-4 and GLP-1 in DM rats.

The latest epidemiological data suggests that the situation of adult diabetes in China is severe, and metabolic diseases have become significant chronic illnesses that have a serious impact on public health and social development. After more than six years of practice, the National Metabolic Management Center(MMC) has developed distinctive approaches to manage metabolic patients and has achieved a series of positive outcomes, continuously advancing the standardized diagnosis and treatment model. In order to further improve the efficiency, based on the first edition, the second edition guideline was composed by incorporating experience of the past six years in conjunction with the latest international and domestic guidelines.

Objective: Compare and analyze the results of the domestic Lanyi AH600 glycated hemoglobin analyzer and other different detection systems to understand the comparability of the detection results of different detectors, and establish the best cut point of Lanyi AH600 determination of haemoglobin A1c (HbA1c) in the diagnosis of diabetes. Methods: Multi center cohort study was adopted. The clinical laboratory departments of 18 medical institutions independently collected test samples from their respective hospitals from March to April 2022, and independently completed comparative analysis of the evaluated instrument (Lanyi AH600) and the reference instrument HbA1c. The reference instruments include four different brands of glycosylated hemoglobin meters, including Arkray, Bio-Rad, DOSOH, and Huizhong. Scatter plot was used to calculate the correlation between the results of different detection systems, and the regression equation was calculated. The consistency analysis between the results of different detection systems was evaluated by Bland Altman method. Consistency judgment principles: (1) When the 95% limits of agreement (95% LoA) of the measurement difference was within 0.4% HbA1c and the measurement score was≥80 points, the comparison consistency was good; (2) When the measurement difference of 95% LoA exceeded 0.4% HbA1c, and the measurement score was≥80 points, the comparison consistency was relatively good; (3) The measurement score was less than 80 points, the comparison consistency was poor. The difference between the results of different detection systems was tested by paired sample T test or Wilcoxon paired sign rank sum test; The best cut-off point of diabetes was analyzed by receiver operating characteristic curve (ROC). Results: The correlation coefficient R² of results between Lanyi AH600 and the reference instrument in 16 hospitals is≥0.99; The Bland Altman consistency analysis showed that the difference of 95% LoA in Nanjing Maternity and Child Health Care Hospital in Jiangsu Province (reference instrument: Arkray HA8180) was -0.486%-0.325%, and the measurement score was 94.6 points (473/500); The difference of 95% LoA in the Tibetan Traditional Medical Hospital of TAR (reference instrument: Bio-Rad Variant II) was -0.727%-0.612%, and the measurement score was 89.8 points; The difference of 95% LoA in the People's Hospital of Chongqing Liang Jiang New Area (reference instrument: Huizhong MQ-2000PT) was -0.231%-0.461%, and the measurement score was 96.6 points; The difference of 95% LoA in the Taihe Hospital of traditional Chinese Medicine in Anhui Province (reference instrument: Huizhong MQ-2000PT) was -0.469%-0.479%, and the measurement score was 91.9 points. The other 14 hospitals, Lanyi AH600, were compared with 4 reference instrument brands, the difference of 95% LoA was less than 0.4% HbA1c, and the scores were all greater than 95 points. The results of paired sample T test or Wilcoxon paired sign rank sum test showed that there was no statistically significant difference between Lanyi AH600 and the reference instrument Arkray HA8180 (Z=1.665,P=0.096), with no statistical difference. The mean difference between the measured values of the two instruments was 0.004%. The comparison data of Lanyi AH600 and the reference instrument of all other institutions had significant differences (all P<0.001), however, it was necessary to consider whether it was within the clinical acceptable range in combination with the results of the Bland-Altman consistency analysis. The ROC curve of HbA1c detected by Lanyi AH600 in 985 patients with diabetes and 3 423 patients with non-diabetes was analyzed, the area under curve (AUC) was 0.877, the standard error was 0.007, and the 95% confidence interval 95%CI was (0.864, 0.891), which was statistically significant (P<0.001). The maximum value of Youden index was 0.634, and the corresponding HbA1c cut point was 6.235%. The sensitivity and specificity of diabetes diagnosis were 76.2% and 87.2%, respectively. Conclusion: Among the hospitals and instruments currently included in this study, among these four hospitals included Nanjing Maternity and Child Health Care Hospital in Jiangsu Province (reference instrument: Arkray HA8180), Tibetan Traditional Medical Hospital of TAR (reference instrument: Bio-Rad Variant Ⅱ), the People's Hospital of Chongqing Liang Jiang New Area (reference instrument: Huizhong MQ-2000PT), and the Taihe Hospital of traditional Chinese Medicine in Anhui Province (reference instrument: Huizhong MQ-2000PT), the comparison between Lanyi AH600 and the reference instruments showed relatively good consistency, while the other 14 hospitals involved four different brands of reference instruments: Arkray, Bio-Rad, DOSOH, and Huizhong, Lanyi AH600 had good consistency with its comparison. The best cut point of the domestic Lanyi AH600 for detecting HbA1c in the diagnosis of diabetes is 6.235%.
Pregnancy ; Child ; Humans ; Female ; Glycated Hemoglobin ; Cohort Studies ; Diabetes Mellitus/diagnosis* ; Sensitivity and Specificity ; ROC Curve

Objective: To investigate the association between long-term fasting blood glucose (FPG) variability and all-cause mortality in patients with type 2 diabetes. Methods: A total of 7 174 type 2 diabetic patients included in National Basic Public Health Service Program in Changshu of Jiangsu Province were recruited as participants. Long-term glucose variability was assessed using standard deviation (SD), coefficient of variation (CV), average real variability (ARV), and variability independent of the mean (VIM) across FPG measurements at the more than three visits. Death information were mainly obtained from the death registry system in Jiangsu. Then Cox proportional hazards regression models were used to estimate the associations of four variability indicators and all-cause mortality's hazard ratios (HRs) and their 95%CIs. Results: Among 55 058.50 person-years of the follow-up, the mean follow-up time was 7.67 years, and 898 deaths occurred during the follow-up period. After adjustment, compared with T₁ group, the Cox regression model showed that HRs of T₃ group in SD, CV, ARV and VIM were 1.24 (95%CI: 1.03-1.49), 1.20 (95%CI: 1.01-1.43), 1.28 (95%CI: 1.07-1.55) and 1.20 (95%CI:1.01-1.41), respectively. HRs of per 1 SD higher SD, CV, ARV and VIM were 1.13 (95%CI: 1.06-1.21), 1.08 (95%CI: 1.01-1.15), 1.05 (95%CI: 1.00-1.12) and 1.09 (95%CI: 1.02-1.16) for all-cause mortality, respectively. In the stratified analysis, age, gender, hypoglycemic agent and insulin uses had no effect on the above associations (all P for interaction >0.05). Conclusion: Long-term FPG glycemic variability was positively associated with the risk of all-cause mortality in type 2 diabetes patients.