ObjectiveTo explore the relationship between blood glucose variability index and persistent organ failure (POF) in acute pancreatitis (AP). MethodsWe prospectively included those patients who were diagnosed with AP with hyperglycemia and were hospitalized in the West China Center of Excellence for Pancreatitis of West China Hospital of Sichuan University from July 2019 to November 2021. The patients were given blood glucose monitoring at least 4 times a day for at least 3 consecutive days. The predictive value of blood glucose variability index for POF in patients with AP was analyzed. ResultsA total of 559 patients with AP were included, including 95 cases of POF. Comparing with those without POF, patients with AP complicated by POF had higher levels of admission glucose (11.0 mmol/L vs. 9.6 mmol/L), minimum blood glucose (6.8 mmol/L vs. 5.8 mmol/L), mean blood glucose (9.6 mmol/L vs. 8.7 mmol/L), and lower level of coefficient of variation of blood glucose (16.6 % vs. 19.0 %), P<0.05. Logistic regression analyses after adjustment for confounding factors showed that the risk of POF increased with the increase of admission glucose [OR=1.11, 95%CI (1.04, 1.19), P=0.002], minimum blood glucose [OR=1.28, 95%CI (1.10, 1.48), P=0.001] and mean blood glucose [OR=1.18, 95%CI (1.04, 1.33), P=0.010]; with the higher level of coefficient of variation of blood glucose [OR=0.95, 95%CI (0.92, 0.99), P=0.021], the risk of POF decreased. The results of area under the curve (AUC) of the receiver operator curves showed that AG [AUC=0.787, 95%CI (0.735, 0.840)] had the highest accuracy in predicting POF, with sensitivities of 60.0% and specificities of 84.7%. ConclusionHigh admission glucose, minimum blood glucose, mean blood glucose, and low coefficient of variation of blood glucose were risk factors for the development of POF in patients with hyperglycemic AP on admission.
Objective To evaluate the accuracy of newer-generation home blood glucose meter (Accu-Check? Integra) in patients with impaired glucose regulation (IGR) and newly-diagnosed type 2 diabetes mellitus. Methods A cross-sectional study was performed on 109 cases with newly-diagnosed type 2 diabetes or IGR who were asked to take oral glucose tolerance test (OGTT), while paired samples, that were Accu-Check? Integra in capillary blood glucose (CBG) and laboratory glucose in venous plasma glucose (VPG ), were taken simultaneously. Taking VPG as the reference value, the accuracy of the home glucose meter was assessed according to the international standardization organization (ISO), including, the accuracy was studied by means of Median absolute difference (Median AD) and Median absolute relative difference (Median RAD), the consistency of CBG and VPG was studied by Clarke Error Grid analysis, the correlation of CBG and VPG was analyzed according to liner regression analysis, and the sensitivity and specificity for hyperglycemia were also calculated. Results There were 292 VPG values paired with CBG values, among which 93.49% of CBG values met ISO home glucose meter criteria, the median AD was 7.2 mg/dL, and the median RAD was 4.76%. Paired glucose measurements from the Accu-Check Integra meter and laboratory glucose measurement demonstrated that 100% of paired points in the overall subject population fell in zones A and B of the Clarke Error Grid. The CBG value was well correlated to VPG value in the overall level, and the sensitivity and specificity were 94.6% and 95.7% respectively for hyperglycemia. Conclusion The newer-generation home blood glucose meter (Accu-Check? Integra) demonstrates a high degree of accuracy, and it can precisely report the real value of blood glucose.
Most of the existing near-infrared noninvasive blood glucose detection models focus on the relationship between near-infrared absorbance and blood glucose concentration, but do not consider the impact of human physiological state on blood glucose concentration. In order to improve the performance of prediction model, particle swarm optimization (PSO) algorithm was used to train the structure paramters of back propagation (BP) neural network. Moreover, systolic blood pressure, pulse rate, body temperature and 1 550 nm absorbance were introduced as input variables of blood glucose concentration prediction model, and BP neural network was used as prediction model. In order to solve the problem that traditional BP neural network is easy to fall into local optimization, a hybrid model based on PSO-BP was introduced in this paper. The results showed that the prediction effect of PSO-BP model was better than that of traditional BP neural network. The prediction root mean square error and correlation coefficient of ten-fold cross-validation were 0.95 mmol/L and 0.74, respectively. The Clarke error grid analysis results showed that the proportion of model prediction results falling into region A was 84.39%, and the proportion falling into region B was 15.61%, which met the clinical requirements. The model can quickly measure the blood glucose concentration of the subject, and has relatively high accuracy.
ObjectiveTo understand the relation between blood glucose regulating hormones and gastric cancer, so as to provide some new ideas for diagnosis and treatment of gastric cancer. MethodBy reviewing and screening relevant domestic and foreign literatures, the latest researches on the relation between blood glucose regulating hormones and gastric cancer were summarized. ResultsThe insulin, glucagon, adrenaline, growth hormone, and the other blood glucose regulating hormones all played the roles in promoting the occurrence and development of gastric cancer. However, glucocorticoids and somatostatin were protective hormones that maintained gastric homeostasis and inhibited the proliferation of gastric cancer cells. ConclusionBlood glucose regulating hormones play some roles in diagnosis and treatment of gastric cancer, but specific mechanisms such as interaction between blood glucose regulating hormones, role of glucose metabolism in biological behavior of gastric cancer, and effect of blood glucose regulating hormones on oncogene initiation are unclear, so prospective clinical control studies still need to be studied.
Blood glucose monitoring has become the weakest point in the overall management of diabetes in China. Long-term monitoring of blood glucose levels in diabetic patients has become an important means of controlling the development of diabetes and its complications, so that technological innovations in blood glucose testing methods have far-reaching implications for accurate blood glucose testing. This article discusses the basic principles of minimally invasive and non-invasive blood glucose testing assays, including urine glucose assays, tear assays, methods of extravasation of tissue fluid, and optical detection methods, etc., focuses on the advantages of minimally invasive and non-invasive blood glucose testing methods and the latest relevant results, and summarizes the current problems of various testing methods and prospects for future development trends.
ObjectiveTo investigate the change of blood glucose and its clinical significance in patients with acute pancreatitis (AP). MethodsThe regularity of blood glucose change and the relation between the regularity and the prognosis were analyzed in 115 patients with AP and hyperglycemia.ResultsBlood glucose was increased with a median (M) of 8.7 mmol/L，18.45 mmol/L and 27.22 mmol/L, which gradually decreased to normal value within 3-17 days, 7-26 days and 24-46 days after treatment,respectively in patients with mild AP, type Ⅰ of severe acute pancreatitis (SAP) and type Ⅱ of SAP. There was marked statistical difference among the three groups. A smaller dose of regular insulin was used for 36 patients with mild AP; however, a larger dose of regular insulin was used for all 30 patients with SAP.ConclusionThe level of blood glucose, the dose of regular insulin and the duration of hyperglycemia increase with the severity of AP.
The use of non-invasive blood glucose detection techniques can help diabetic patients to alleviate the pain of intrusive detection, reduce the cost of detection, and achieve real-time monitoring and effective control of blood glucose. Given the existing limitations of the minimally invasive or invasive blood glucose detection methods, such as low detection accuracy, high cost and complex operation, and the laser source's wavelength and cost, this paper, based on the non-invasive blood glucose detector developed by the research group, designs a non-invasive blood glucose detection method. It is founded on dual-wavelength near-infrared light diffuse reflection by using the 1 550 nm near-infrared light as measuring light to collect blood glucose information and the 1 310 nm near-infrared light as reference light to remove the effects of water molecules in the blood. Fourteen volunteers were recruited for in vivo experiments using the instrument to verify the effectiveness of the method. The results indicated that 90.27% of the measured values of non-invasive blood glucose were distributed in the region A of Clarke error grid and 9.73% in the region B of Clarke error grid, all meeting clinical requirements. It is also confirmed that the proposed non-invasive blood glucose detection method realizes relatively ideal measurement accuracy and stability.
Objective To investigate the application effect of ndividualized dietary care based on a multidisciplinary collaboration model on glycemic control, neurological recovery, dietary self-management, and satisfaction in stroke patients with abnormal blood glucose. Methods Patients with stroke and abnormal blood glucose admitted to the Department of Neurology, Shangjin Hospital, West China Hospital, Sichuan University between March and October 2024 were enrolled. Using SPSS 26.0 software, a random allocation sequence was generated to divide participants into an observation group and a control group. The control group received comprehensive nursing interventions, while the observation group received additional multidisciplinary collaboration model based individualized dietary care. Both groups were intervened until discharge. Glycemic indicators [glycated albumin (GA), fasting blood glucose (FBG), 2-hour postprandial blood glucose (2hPG)], neurological recovery, dietary adherence, and patient satisfaction were compared pre-intervention and post-intervention (at discharge). Results A total of 112 patients were included, with 56 patients in each group. At the post-intervention stage, GA, FBG and 2hPG in the observation group were lower than those in the control group (P<0.05), and the scores of the Dietary Compliance Scale for Type 2 Diabetes were higher than those in the control group (P<0.05). Except for admission (3.27±0.86 vs. 3.25±0.90, P＞0.05), the modified Rankin Scale scores of the observation group were lower than those of the control group at discharge (3.14±0.86 vs. 3.17±0.86), 30-days follow-up (2.93±0.76 vs. 3.02±0.84), and 90-days follow-up (1.05±0.80 vs.1.43±1.01) (P<0.05). The comparison results within the group showed that, there were significant differences in GA, FBG, 2hPG, modified Rankin Scale scores and Dietary Compliance Scale for Type 2 Diabetes between admission and discharge (P<0.05). The satisfaction rate of the observation group was higher than that of the control group (97.78% vs. 86.76%; χ2=3.877, P=0.049). Conclusion Multidisciplinary collaboration model based individualized dietary care improves short-term glycemic control, promotes long-term neurological recovery, enhances dietary adherence, and increases patient satisfaction in stroke patients with abnormal blood glucose, demonstrating clinical value for widespread application.
Existing near-infrared non-invasive blood glucose detection modelings mostly detect multi-spectral signals with different wavelength, which is not conducive to the popularization of non-invasive glucose meter at home and does not consider the physiological glucose dynamics of individuals. In order to solve these problems, this study presented a non-invasive blood glucose detection model combining particle swarm optimization (PSO) and artificial neural network (ANN) by using the 1 550 nm near-infrared absorbance as the independent variable and the concentration of blood glucose as the dependent variable, named as PSO-2ANN. The PSO-2ANN model was based on two sub-modules of neural networks with certain structures and arguments, and was built up after optimizing the weight coefficients of the two networks by particle swarm optimization. The results of 10 volunteers were predicted by PSO-2ANN. It was indicated that the relative error of 9 volunteers was less than 20%; 98.28% of the predictions of blood glucose by PSO-2ANN were distributed in the regions A and B of Clarke error grid, which confirmed that PSO-2ANN could offer higher prediction accuracy and better robustness by comparison with ANN. Additionally, even the physiological glucose dynamics of individuals may be different due to the influence of environment, temper, mental state and so on, PSO-2ANN can correct this difference only by adjusting one argument. The PSO-2ANN model provided us a new prospect to overcome individual differences in blood glucose prediction.
For the near-infrared (NIR) spectral analysis of the concentration of blood glucose, the calibration accuracy can be affected because of the existing of outlier samples. In this research, a Monte-Carlo cross validation (MCCV) method is constructed for eliminating outlier samples. The human blood plasma experiment in vitro and the human body experiment in vivo were introduced to evaluate the MCCV method for its application effect in NIR spectral analysis of blood glucose. And the uninformative sample elimination method based on modified uninformative variable elimination (MUVE-USE) was employed in this study for the comparison with MCCV. The results indicated that, like the MUVE-USE method, the outlier samples elimination method based on MCCV could be used to eliminate the outlier samples which came from gross errors (such as bad sample) or system errors (such as baseline drift). In addition, the outlier samples from the random errors of uncertain causes which affect model accuracy can be eliminated simultaneously by MCCV. The elimination of multiple outlier samples is beneficial to the improvement of prediction accuracy of calibration model.