Objective To explore the diagnosing value of serum 1,5-anhydroglucitol(1,5-AG)for gestational diabetes mellitus(GDM)by determining 1,5-AG in pregnant women with different glucose tolerance status.Methods The levels of serum 1,5-AG,fasting plasma glucose(FPG),1 hour plasma glucose(1hPG)and glycosylated hemoglobin(HbA1c)were determined in 30 cases GDM(GDM group),32 cases gestational impair glucose tolerance(GIGT group)and 43 cases gestational normal glucose tolerance (GNGT group).Results The level of serum 1,5-AG in GDM group[(11.69 ± 3.92)mg/L]was obviously lower than that in GIGT group[(20.93 ± 3.43)mg/L]and GNGT group[(27.83 ± 4.03)mg/L](P ＜ 0.01),GIGT group was obviously lower than that in GNGT group(P ＜0.05).The level of serum 1,5-AG had significantly negative correlation with FPG,1hPG and HbA1c respectively(P＜ 0.01).Conclusion The level of serum 1,5-AG has significantly correlation with glucose tolerance,1,5-AG can be used as useful indicator in the diagnosis of GDM.

Objective To investigate,the significance of serum high-sensitivity C-reactive protein (hs-CRP) concentration in subjects with various glucose tolerance and study the role of hs-CRP in patients of impaired glucose tolerance (IGT) and diabetes mellitus.Method All subjects in the study underwent oral glucose tolerance test (OGTT).Determination of serum hs-CRP levels in normal glucose tolerance(NGT),IGT and type 2 diabetes mellitus (T2DM) patients were made by immunologic projective turbidimetry method.Results The levels of serum hs-CRP were (1.57 + 0.46) mg/L in NGT patients,(2.84 ±0.48) mg/L in IGT patients,and(4.18± 0.76) mg/L in T2DM patients.The levels of serum hs-CRP were positively correlated with body mass index,systolic blood pressure,diastolic blood pressure,fasting plasma glucose,OGTr 2 hPG,triglyceride,and total cholesterol.ConclusionsInflammation has already existed not only in T2DM patients but also in subjects with IGT.Inflammation might participate in the oeeurrance and development of T2DM.hs-CRP as an inflammator factor can be used to predict and monitor T2DM.

Objective: To evaluate the relationship between bolus volume and hyoid displacement in dysphagia patients with nasopharyngeal carcinoma after radiation therapy. Methods: Twenty-three nasopharyngeal carcinoma patients with dysphagia were recruited and their swallowing of 3, 5, 10 and 20ml of liquid food was studied fluoroscopically. The vertical and horizontal displacement of the hyoid as well as its time in motion were measured, and the relationship between the bolus volume, hyoid displacement and time in motion time was evaluated. Results: The largest vertical displacement of the hyoid (1.01±0.65cm) was observed when swallowing a 10ml bolus. The hyoid showed the smallest average horizontal displacement (0.39±0.34cm), when swallowing a 3ml bolus. The average motion time of the hyoid was (2.11±0.65) seconds. It was shorter when swallowing a 10 or 20ml bolus than when dealing with a smaller one. Hyoid motion time was negatively correlated with the horizontal displacement of the hyoid bone, and the volume of a swallow was negatively correlated with the hyoid motion time but positively correlated with the penetration-aspiration scale score. Conclusion Bolus volume affects hyoid displacement and hyoid motion time in nasopharyngeal carcinoma patients with dysphagia after radiation therapy. For patients with a penetration-aspiration scale score of 5 or less, the optimum bolus volume is 5 to 10ml.

Objective: To study the clinical efficacy of biological ceramics (iRoot BP Plus ) and mineral trioxide aggregate (MTA) in direct pulp capping of mature permanent teeth, to provide referrence for clinical application. Methods : Seventy-four patients with pulp exposure due to deep caries or reversible pulpitis in 75 mature permanent teeth were selected and were randomly divided into two groups. iRoot BP Plus were used as pulp capping agents in the treatment group and MTA were used as pulp capping agents in the control group respectively. The clinical efficacy and imaging analysis were performed at 1, 3, 6 and 12 months after operation. Treatment success rate of the two groups were calculated, and the influence of various factors including gender, age, tooth position, cavity, number and size of pulp exposure on the efficacy of direct pulp capping were analyzed. Results : Sixty patients with 61 mature permanent teeth were selected. Twelve mouths after treatment, 61 teeth of 60 patents were completely investigated (iRoot group: 31 teeth 30 patients; MTA group: 30 teeth 30 patients). The success rates of the 2 groups were 90.3% (iRoot BP Plus) and 90.0% (MTA), respectively. There was no statistical difference between 2 groups (P>0.05). Statistical analysis also showed that gender, age, tooth position, cavity, number and size of pulp exposure had no significant difference between the two groups (P>0.05). Conclusion Both iRoot BP Plus and MTA are effective in direct pulp capping of mature permanent teeth with carious pulp exposure, while the operation of iRoot is simple and convenient.

BACKGROUND:MRI is important for the diagnosis of early knee osteoarthritis.MRI image recognition and intelligent segmentation of knee osteoarthritis using deep learning method is a hot topic in image diagnosis of artificial intelligence. OBJECTIVE:Through deep learning of MRI images of knee osteoarthritis,the segmentation of femur,tibia,patella,cartilage,meniscus,ligaments,muscles and effusion of knee can be automatically divided,and then volume of knee fluid and muscle content were measured. METHODS:100 normal knee joints and 100 knee osteoarthritis patients were selected and randomly divided into training dataset(n=160),validation dataset(n=20),and test dataset(n=20)according to the ratio of 8:1:1.The Coarse-to-Fine sequential training method was used to train the 3D-UNET network deep learning model.A Coarse MRI segmentation model of the knee sagittal plane was trained first,and the rough segmentation results were used as a mask,and then the fine segmentation model was trained.The T1WI and T2WI images of the sagittal surface of the knee joint and the marking files of each structure were input,and DeepLab v3 was used to segment bone,cartilage,ligament,meniscus,muscle,and effusion of knee,and 3D reconstruction was finally displayed and automatic measurement results(muscle content and volume of knee fluid)were displayed to complete the deep learning application program.The MRI data of 26 normal subjects and 38 patients with knee osteoarthritis were screened for validation. RESULTS AND CONCLUSION:(1)The 26 normal subjects were selected,including 13 females and 13 males,with a mean age of(34.88±11.75)years old.The mean muscle content of the knee joint was(1 051 322.94±2 007 249.00)mL,the mean median was 631 165.21 mL,and the mean volume of effusion was(291.85±559.59)mL.The mean median was 0 mL.(2)There were 38 patients with knee osteoarthritis,including 30 females and 8 males.The mean age was(68.53±9.87)years old.The mean muscle content was(782 409.18±331 392.56)mL,the mean median was 689 105.66 mL,and the mean volume of effusion was(1 625.23±5 014.03)mL.The mean median was 178.72 mL.(3)There was no significant difference in muscle content between normal people and knee osteoarthritis patients.The volume of effusion in patients with knee osteoarthritis was higher than that in normal subjects,and the difference was significant(P<0.05).(4)It is indicated that the intelligent segmentation of MRI images by deep learning can discard the defects of manual segmentation in the past.The more accuracy evaluation of knee osteoarthritis was necessary,and the image segmentation was processed more precisely in the future to improve the accuracy of the results.