Background Volatile organic compounds (VOCs) in exhaled breath are closely associated with respiratory diseases and are linked to various metabolic reactions in the human body. A quantitative analytical method can provide technical support for studying VOCs related to various diseases. Objective To establish a thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS) method for the determination of 27 VOCs in exhaled breath. Methods VOCs in exhaled breath were collected using a Bio-VOC sampler and enriched with Tenax TA thermal desorption tubes before TD-GC-MS analysis. Standards were collected using thermal desorption tubes and optimized for thermal desorption conditions as well as chromatographic and mass spectrometric conditions: The separation of the 27 VOCs was achieved by an optimized temperature program, the improvement of sensitivity by optimizing quantitative ions, and the increase of VOCs desorption efficiency by optimizing thermal desorption time and temperature. Limit of detection, limit of quantification, accuracy, precision, and stability of the proposed method were investigated by spiking with a blank gas bag, and exhaled breath samples from 20 healthy individuals were collected for an application study of the proposed method. Results The thermal desorption temperature was 280 ℃, and desorption time was 6 min. A VF-624ms chromatographic column was selected for the separation of target substances. The initial temperature of heating program was 35 ℃, maintained for 1 min, and then increased to 100 ℃ at a heating rate of 3 ℃·min⁻¹ for 1 min, followed by increasing to 210 ℃ at a heating rate of 28 ℃·min⁻¹ for 5 min. A quantitative analysis was conducted with a single ion monitoring (SIM) mode. Under these conditions, the 27 VOCs showed good linear relationships in their respective concentration ranges and the correlation coefficients were higher than 0.9990. The limits of detection of the method were in the range of 0.01-0.13 nmol·mol⁻¹, the limits of quantification were in the range of 0.02-0.44 nmol·mol⁻¹, and the spiked recoveries were in the range of 80.1%-120.5%, with intra-batch and inter-batch precision ≤ 18.8% and 17.9% respectively. All substances can be stored at room temperature (23-28 °C) for 7 d and at 4 °C for 14 d. The proposed method was applied to exhaled breath samples from 20 subjects with detection rates≥ 80% (except for trans-2-pentene and decane) and a concentration range of 0.00-465.50 nmol·mol⁻¹. Conclusion The established TD-GC-MS method for quantification of VOCs in exhaled breath is characterized by high sensitivity and good accuracy, and is suitable for quantitative determination of VOCs in exhaled breath, which can provide technical support for the study of exhaled breath VOCs.

Objective:To construct a diabetes foot prediction model for adult patients with type 2 diabetes based on retrospective cohort study using data from a regional health data platform.Methods:Using Yinzhou Health Information Platform of Ningbo, adult patients with newly diagnosed type 2 diabetes from January 1, 2015 to December 31, 2022 were included in this study and divided randomly the train and test sets according to the ratio of 7∶3. LASSO regression model and bidirectional stepwise regression model were used to identify risk factors, and model comparisons were conducted with net reclassification index, integrated discrimination improvement and concordance index. Univariate and multivariate Cox proportional hazard regression models were constructed, and a nomogram plot was drawn. Area under the curve (AUC) was calculated as a discriminant evaluation indicator for model validation test its calibration ability, and calibration curves were drawn to test its calibration ability.Results:No significant difference existed between LASSO regression model and bidirectional stepwise regression model, but the better bidirectional stepwise regression model was selected as the final model. The risk factors included age of onset, gender, hemoglobin A1c, estimated glomerular filtration rate, taking angiotensin receptor blocker and smoking history. AUC values (95% CI) of risk outcome prediction at year 5 and 7 were 0.700 (0.650-0.749) and 0.715(0.668-0.762) for the train set and 0.738 (0.667-0.801) and 0.723 (0.663-0.783) for the test set, respectively. The calibration curves were close to the ideal curve, and the model discrimination and calibration powers were both good. Conclusions:This study established a convenient prediction model for diabetic foot and classified the risk levels. The model has strong interpretability, good discrimination power, and satisfactory calibration and can be used to predict the incidence of diabetes foot in adult patients with type 2 diabetes to provide a basis for self-assessment and clinical prediction of diabetic foot disease risk.

Objective:To develop a prediction model for the risk of diabetic retinopathy (DR) in patients with newly diagnosed type 2 diabetes mellitus (T2DM).Methods:Patients with new diagnosis of T2DM recorded in Yinzhou Regional Health Information Platform between January 1, 2015 and December 31, 2022 were included in the study. The predictor variables were selected by using Lasso-Cox proportional hazards regression model. Cox proportional hazards regression models were used to establish the prediction model for the risk of DR. Bootstrap method (500 resamples) was used for internal validation, and the performance of the model was assessed by C-index, the receiver operating characteristic curve and area under the curve (AUC), and calibration curve.Results:The predictor variables included in the final model were age of T2DM onset, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, estimated glomerular filtration rate, and history of lipid-lowering agent and angiotensin converting enzyme inhibitor uses. The C-index of the final model was 0.622, and the mean corrected C-index was 0.623 (95% CI: 0.607-0.634). The AUC values for predicting the risk of DR after 3, 5, and 7 years were 0.631, 0.620, and 0.624, respectively, with a high degree of overlap of the calibration curves with the ideal curves. Conclusion:In this study, a simple and practical risk prediction model for DR risk prediction was developed, which could be used as a reference for individualized DR screening and intervention in newly diagnosed T2DM patients.

Objective:To construct a risk prediction model for diabetes kidney disease (DKD).Methods:Patients newly diagnosed with type 2 diabetes mellitus (T2DM) between January 1, 2015, and December 31, 2022, were selected as study subjects from the Yinzhou Regional Health Information Platform in Ningbo City. The Lasso method was used to screen the risk factors, and the DKD risk prediction model was established using Cox proportional hazard regression models. Bootstrap 500 resampling was applied for internal validation.Results:The study included 49 706 subjects, with an median ( Q1, Q3) age of 60.00 (50.00, 68.00) years old, and 55% were male. A total of 4 405 subjects eventually developed DKD. Age at first diagnosis of T2DM, BMI, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, past medical history (hyperuricemia, rheumatic diseases), triglycerides, and estimated glomerular filtration rate were included in the final model. The final model's C-index was 0.653, with an average of 0.654 after Bootstrap correction. The final model's area under the receiver operating characteristic curve for predicting 4-year, 5-year, and 6-year was 0.657, 0.659, and 0.664, respectively. The calibration curve was closely aligned with the ideal curve. Conclusions:This study constructed a DKD risk prediction model for newly diagnosed T2DM patients based on real-world data that is simple, easy to use, and highly practical. It provides a reliable basis for screening high-risk groups for DKD.

Objective:Construct the hospital"three in one"quality control system suitable for medical insurance DRG payment,and realize the safe,stable and efficient operation of medical insurance DRG actual payment. Methods:By using data aggregation and fusion technology,through standardized governance of data across the entire basin,optimization of the entire process quality control system and comprehensive intelligent quality control system management,the visualization and interactive analysis of data could be achieved,thereby improving the precision support of hospital operation efficiency. Results:After actual operation,the disease enrollment rate reached to 99％,the Case Mix Index (CMI) boosted from 0.96 to 1.32,the average length of hospital stay decreased by 1.07 days,the time consumption index and the cost consumption index both fell below 1. The supervision method extended from manual sampling to intelligent quality control,the supervision process extended from a fixed process to a dynamic process and the focus of supervision extends from the review of the settlement list to the supervision of medical behavior. Conclusion:Construct the hospital"three in one"quality control system is a necessary way to promote DRG paying medical institutions to enter the fast lane of standardization,collaboration and refined management,which made a beneficial attempt for the practical application of DRG payment in hospitals.

Objective:Construct the hospital"three in one"quality control system suitable for medical insurance DRG payment,and realize the safe,stable and efficient operation of medical insurance DRG actual payment. Methods:By using data aggregation and fusion technology,through standardized governance of data across the entire basin,optimization of the entire process quality control system and comprehensive intelligent quality control system management,the visualization and interactive analysis of data could be achieved,thereby improving the precision support of hospital operation efficiency. Results:After actual operation,the disease enrollment rate reached to 99％,the Case Mix Index (CMI) boosted from 0.96 to 1.32,the average length of hospital stay decreased by 1.07 days,the time consumption index and the cost consumption index both fell below 1. The supervision method extended from manual sampling to intelligent quality control,the supervision process extended from a fixed process to a dynamic process and the focus of supervision extends from the review of the settlement list to the supervision of medical behavior. Conclusion:Construct the hospital"three in one"quality control system is a necessary way to promote DRG paying medical institutions to enter the fast lane of standardization,collaboration and refined management,which made a beneficial attempt for the practical application of DRG payment in hospitals.

Bioactive glass (BG) has been widely used in the preparation of artificial bone scaffolds due to its excellent biological properties and non-cytotoxicity, which can promote bone and soft tissue regeneration. However, due to the brittleness, poor mechanical strength, easy agglomeration and uncontrollable structure of glass material, its application in various fields is limited. In this regard, most current researches mainly focus on mixing BG with organic or inorganic materials by freeze-drying method, sol-gel method, etc., to improve its mechanical properties and brittleness, so as to increase its clinical application and expand its application field. This review introduces the combination of BG with natural organic materials, metallic materials and non-metallic materials, and demonstrates the latest technology and future prospects of BG composite materials through the development of scaffolds, injectable fillers, membranes, hydrogels and coatings. The previous studies show that the addition of BG improves the mechanical properties, biological activity and regeneration potential of the composites, and broadens the application of BG in the field of bone tissue engineering. By reviewing the recent BG researches on bone regeneration, the research potential of new materials is demonstrated, in order to provide a reference for future related research.
Bone Regeneration ; Bone and Bones ; Freeze Drying ; Glass ; Hydrogels

In addition to the typical respiratory symptoms, COVID-19 patients present with clinical manifestations of cardiovascular system damage, and they are at higher risk of intensive care or mortality. The mechanism of COVID-19's impact on the cardiovascular system is still unclear, therefore we need to pay close attention to it. Based on the existing research, this paper focused on the concurrent characteristics of the cardiovascular system diseases, summarized the possible mechanisms of cardiovascular system damage, including the coronavirus invaders directly into cardiomyocytes and its conduction system, with complications or accompanying conditions such as cytokine storm, imbalance of oxygen supply and demand, drug influence, stress and other damage to cardiovascular system. Meanwhile, current clinical manifestations and treatment methods of COVID-19 were summarized.

Objective: To investigate the feasibility of cine three-dimensional (3D) balanced steady-state free precession (b-SSFP) imaging combined with a non-local means (NLM) algorithm for image denoising in evaluating cardiac function in children with repaired tetralogy of Fallot (rTOF). Materials and Methods: Thirty-five patients with rTOF (mean age, 12 years; range, 7–18 years) were enrolled to undergo cardiac cine image acquisition, including two-dimensional (2D) b-SSFP, 3D b-SSFP, and 3D b-SSFP combined with NLM. End-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), and ejection fraction (EF) of the two ventricles were measured and indexed by body surface index. Acquisition time and image quality were recorded and compared among the three imaging sequences. Results: 3D b-SSFP with denoising vs. 2D b-SSFP had high correlation coefficients for EDV, ESV, SV, and EF of the left (0.959– 0.991; p < 0.001) as well as right (0.755–0.965; p < 0.001) ventricular metrics. The image acquisition time ± standard deviation (SD) was 25.1 ± 2.4 seconds for 3D b-SSFP compared with 277.6 ± 0.7 seconds for 2D b-SSFP, indicating a significantly shorter time with the 3D than the 2D sequence (p < 0.001). Image quality score was better with 3D b-SSFP combined with denoising than with 3D b-SSFP (mean ± SD, 3.8 ± 0.6 vs. 3.5 ± 0.6; p = 0.005). Signal-to-noise ratios for blood and myocardium as well as contrast between blood and myocardium were higher for 3D b-SSFP combined with denoising than for 3D b-SSFP (p < 0.05 for all but septal myocardium). Conclusion The 3D b-SSFP sequence can significantly reduce acquisition time compared to the 2D b-SSFP sequence for cine imaging in the evaluation of ventricular function in children with rTOF, and its quality can be further improved by combining it with an NLM denoising method.

Objective: To investigate the feasibility of cine three-dimensional (3D) balanced steady-state free precession (b-SSFP) imaging combined with a non-local means (NLM) algorithm for image denoising in evaluating cardiac function in children with repaired tetralogy of Fallot (rTOF). Materials and Methods: Thirty-five patients with rTOF (mean age, 12 years; range, 7–18 years) were enrolled to undergo cardiac cine image acquisition, including two-dimensional (2D) b-SSFP, 3D b-SSFP, and 3D b-SSFP combined with NLM. End-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), and ejection fraction (EF) of the two ventricles were measured and indexed by body surface index. Acquisition time and image quality were recorded and compared among the three imaging sequences. Results: 3D b-SSFP with denoising vs. 2D b-SSFP had high correlation coefficients for EDV, ESV, SV, and EF of the left (0.959– 0.991; p < 0.001) as well as right (0.755–0.965; p < 0.001) ventricular metrics. The image acquisition time ± standard deviation (SD) was 25.1 ± 2.4 seconds for 3D b-SSFP compared with 277.6 ± 0.7 seconds for 2D b-SSFP, indicating a significantly shorter time with the 3D than the 2D sequence (p < 0.001). Image quality score was better with 3D b-SSFP combined with denoising than with 3D b-SSFP (mean ± SD, 3.8 ± 0.6 vs. 3.5 ± 0.6; p = 0.005). Signal-to-noise ratios for blood and myocardium as well as contrast between blood and myocardium were higher for 3D b-SSFP combined with denoising than for 3D b-SSFP (p < 0.05 for all but septal myocardium). Conclusion The 3D b-SSFP sequence can significantly reduce acquisition time compared to the 2D b-SSFP sequence for cine imaging in the evaluation of ventricular function in children with rTOF, and its quality can be further improved by combining it with an NLM denoising method.