In this paper, to improve the compatibility of poly(vinylidene difluoride) (PVDF) with barium titanate (BaTiO 3 ) and the piezoelectric property of wearable piezoelectric nanogenerators (PENGs), fluorinated BaTiO 3 (F-BaTiO 3 ) nanoparticles were prepared by the simple thermal annealing of BaTiO 3 prepolymer with PVDF powder, a modified composite nanofiber was prepared by electrospinning with a dispersed liquid consisting of F-BaTiO 3 nanoparticles and PVDF solution, and a PENG was prepared with the modified composite nanofiber as a piezoelectric functional material. Scanning electron microscopy (SEM) showed that F-BaTiO 3 nanoparticles were more uniformly dispersed in the modified composite nanofiber than BaTiO 3 nanoparticles, the analysis of Fourier infrared (FT-IR) spectroscopy showed that the β-phase content of the modified composite nanofiber compounded with F-BaTiO 3 (F-BaTiO 3 /PVDF nanofiber) was higher than that of the modified composite nanofiber compounded with BaTiO 3 (BaTiO 3 /PVDF nanofiber), and when the dosage of F-BaTiO 3 nanoparticles was 5 wt %, the β-phase content of the modified composite nanofiber reached the maximum value (91%), which was about three times that of BaTiO 3 /PVDF nanofiber. The output voltage of the PENG could reach as high as 1.5 V under an external force of 2N and does not decrease obviously after 300 cycles in a vertical pressing test. Furthermore, PENG was demonstrated to be sensitive to the detection of human motions, for instance, elbow flexion, hand slapping a table, and walking. These results indicated that by BaTiO 3 being fluorinated with PVDF, the dispersity of F-BaTiO 3 in PVDF nanofiber, the β-phase content of F-BaTiO 3 /PVDF nanofiber, and the output voltage of F-BaTiO 3 /PVDF PENG were improved.

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The objective of this study was to adopt the high-resolution computed tomography (HRCT) technology based on the faster-region recurrent convolutional neural network (Faster-RCNN) algorithm to evaluate the lung infection in patients with type 2 diabetes, so as to analyze the application value of imaging features in the assessment of pulmonary disease in type 2 diabetes. In this study, 176 patients with type 2 diabetes were selected as the research objects, and they were divided into different groups based on gender, course of disease, age, glycosylated hemoglobin level (HbA1c), 2 h C peptide (2 h C-P) after meal, fasting C peptide (FC-P), and complications. The research objects were performed with HRCT scan, and the Faster-RCNN algorithm model was built to obtain the imaging features. The relationships between HRCT imaging features and 2 h C-P, FC-P, HbA1c, gender, course of disease, age, and complications were analyzed comprehensively. The results showed that there were no significant differences in HRCT scores between male and female patients, patients of various ages, and patients with different HbA1c contents ( P > 0.05 ). As the course of disease and complications increased, HRCT scores of patients increased obviously ( P < 0.05 ). The HRCT score decreased dramatically with the increase in the contents of 2 h C-P and FC-P after the meal ( P < 0.05 ). In addition, the results of the Spearman rank correlation analysis showed that the course of disease and complications were positively correlated with the HRCT scores, while the 2 h C-P and FC-P levels after meal were negatively correlated with the HRCT scores. The receiver operating curve (ROC) showed that the accuracy, specificity, and sensitivity of HRCT imaging based on Faster-RCNN algorithm were 90.12%, 90.43%, and 83.64%, respectively, in diagnosing lung infection of patients with type 2 diabetes. In summary, the HRCT imaging features based on the Faster-RCNN algorithm can provide effective reference information for the diagnosis and condition assessment of lung infection in patients with type 2 diabetes.

This study investigates the value of magnetic resonance imaging (MRI) based on a deep learning algorithm in the diagnosis of diabetic macular edema (DME) patients. A total of 96 patients with DME were randomly divided into the experimental group (N  = 48) and the control group (N  = 48). A deep learning 3D convolutional neural network (3D-CNN) algorithm for MRI images of patients with DME was designed. The application value of this algorithm was comprehensively evaluated by MRI image segmentation Dice value, sensitivity, specificity, and other indicators and diagnostic accuracy. The results showed that the quality of MRI images processed by the 3D-CNN algorithm based on deep learning was significantly improved, and the Dice value, sensitivity, and specificity index data were significantly better than those of the traditional CNN algorithm ( P < 0.05 ). In addition, the diagnostic accuracy of MRI images processed by this algorithm was 93.78 ± 5.32%, which was significantly better than the diagnostic accuracy of 64.25 ± 10.24% of traditional MRI images in the control group ( P < 0.05 ). In summary, the 3D-CNN algorithm based on deep learning can significantly improve the accuracy and sensitivity of MRI image recognition and segmentation in patients with DME, can significantly improve the diagnostic accuracy of MRI in patients with DME, and has a good clinical application value.

Insulin (INS) is easily degraded when administered orally and loading it into polylactic acid/glycolic acid (PLGA) polymer nanoparticles can enhance the efficacy of the drug. The W/O/W double emulsion solvent volatilization method was adopted to prepare INS-loaded PLGA nanoparticles. The preparation formula of nanoparticles was determined according to the type, concentration, and PLGA concentration of the emulsifier. Then, the morphology, particle size, and drug encapsulation efficiency of nanoparticles were characterized. Phosphate buffered solution (PBS) with pH = 7.4 was utilized as the release medium, and the prepared nanoparticles were analyzed for in vitro release performance. In addition, the rat diabetes model was constructed, and subcutaneous injection of nanoparticle in vitro release solution was performed to observe its hypoglycemic effect, which was used for the treatment of diabetic patients. Patients were rolled into experimental group and control group. The changes of the patients’ HbA1c, blood lipids (total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C)), C peptide, and aminopeptidase N (APN) were observed before and after treatment. Through the test, the appearance of the prepared nanoparticles was round, the average particle size was 416.7 nm, and the INS encapsulation efficiency was (36.82±2.26)%. After 36 h, the cumulative release of INS reached (60.58 ±1.45)%, and then the release rate gradually slowed down. The drug release tended to be balanced after 72 h, and the best hypoglycemic effect was achieved after subcutaneous administration 3 h (P < 0.01). The blood glucose level of the rat diabetes model was greatly decreased after 3 h injection of 36.8 IU/kg PLGA polymer nanoparticles (P < 0.05), and the blood glucose dropped to the lowest at 8 h (P < 0.01), which was only (38.8 ± 3.72)% of the initial blood glucose. HbA1C of diabetic patients increased remarkably after treatment (P < 0.05), TG, TC, and LDL-C in blood lipids decreased, and HDL-C increased, without statistically considerable differences (P > 0.05). The serum APN level increased greatly (P < 0.01). In short, the prepared PLGA polymer nanoparticles can effectively reduce blood glucose, help diabetic patients to relieve the toxicity of high glucose in the body, and improve the secretion function of INS.