Objective:To develop a deep learning-based segmentation model for anterior segment ultrasound biomicroscopy (UBM) images to automatically segment the anterior segment tissues of patients with primary angle-closure glaucoma (PACG).Methods:A single-center retrospective case series was conducted.A small-scale dataset comprised 468 UBM images of the anterior chamber angle closure from 156 patients with PACG who underwent the UBM examination at Tianjin Medical University Eye Hospital between July 12, 2022, and February 20, 2023.The UBM images were randomly split into a training dataset of 228 images and a testing dataset of 152 images using a random seed method in a ratio of 6∶4.The models were trained using the PSPNet model with MobileNet V2 and ResNet50 as backbones, the DeepLab v3+ model with MobileNet V2 and Xception as backbones, and the SegFormer model with MiT-B0 and MiT-B2 as backbones.The testing dataset was used for result prediction and to achieve segmentation of four regions: the cornea and sclera, iris, ciliary body, and anterior lens surface.To evaluate the performance of the models in segmenting the anterior segment structures, multiple metrics were assessed, including the mean intersection over union (mIoU), Dice coefficient, precision, recall, false negative rate, and specificity.A comparative analysis of the test results across the different models was subsequently performed.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Tianjin Medical University Eye Hospital (No.2023KY-05).Results:The two models with the best segmentation performance were PSPNet and DeepLab v3+ .The PSPNet model with ResNet50 as the backbone achieved the mIoU of 85.11%, Dice coefficient of 91.38%, precision of 91.83%, recall of 90.94%, false negative rate of 9.06%, and specificity of 98.89%.The DeepLab v3+ model with MobileNet V2 as the backbone achieved an mIoU of 85.84%, Dice coefficient of 92.01%, precision of 92.67%, recall of 91.36%, false negative rate of 8.64%, and specificity of 98.90%.Among the five key metrics, mIoU, Dice coefficient, recall, false negative rate, and specificity, DeepLab v3+ exhibited the best segmentation performance.In addition, the DeepLab v3+ model with Xception as the backbone had the highest precision among all models, reaching 92.77%.Conclusions:The deep learning-based DeepLab v3+ model achieves precise segmentation of anterior segment tissue structures in PACG anterior segment UBM image segmentation, providing auxiliary support for clinical diagnosis.

Objective:To develop a deep learning-based segmentation model for anterior segment ultrasound biomicroscopy (UBM) images to automatically segment the anterior segment tissues of patients with primary angle-closure glaucoma (PACG).Methods:A single-center retrospective case series was conducted.A small-scale dataset comprised 468 UBM images of the anterior chamber angle closure from 156 patients with PACG who underwent the UBM examination at Tianjin Medical University Eye Hospital between July 12, 2022, and February 20, 2023.The UBM images were randomly split into a training dataset of 228 images and a testing dataset of 152 images using a random seed method in a ratio of 6∶4.The models were trained using the PSPNet model with MobileNet V2 and ResNet50 as backbones, the DeepLab v3+ model with MobileNet V2 and Xception as backbones, and the SegFormer model with MiT-B0 and MiT-B2 as backbones.The testing dataset was used for result prediction and to achieve segmentation of four regions: the cornea and sclera, iris, ciliary body, and anterior lens surface.To evaluate the performance of the models in segmenting the anterior segment structures, multiple metrics were assessed, including the mean intersection over union (mIoU), Dice coefficient, precision, recall, false negative rate, and specificity.A comparative analysis of the test results across the different models was subsequently performed.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Tianjin Medical University Eye Hospital (No.2023KY-05).Results:The two models with the best segmentation performance were PSPNet and DeepLab v3+ .The PSPNet model with ResNet50 as the backbone achieved the mIoU of 85.11%, Dice coefficient of 91.38%, precision of 91.83%, recall of 90.94%, false negative rate of 9.06%, and specificity of 98.89%.The DeepLab v3+ model with MobileNet V2 as the backbone achieved an mIoU of 85.84%, Dice coefficient of 92.01%, precision of 92.67%, recall of 91.36%, false negative rate of 8.64%, and specificity of 98.90%.Among the five key metrics, mIoU, Dice coefficient, recall, false negative rate, and specificity, DeepLab v3+ exhibited the best segmentation performance.In addition, the DeepLab v3+ model with Xception as the backbone had the highest precision among all models, reaching 92.77%.Conclusions:The deep learning-based DeepLab v3+ model achieves precise segmentation of anterior segment tissue structures in PACG anterior segment UBM image segmentation, providing auxiliary support for clinical diagnosis.

Objective:To analyze the effect of biofeedback electrical stimulation on the prevention of stress urinary incontinence and pelvic floor muscle strength during postpartum rehabilitation.Methods:A total of 200 parturients who gave birth in Ningde Municipal Hospital of Ningde Normal University from October 2021 to April 2022 were included as research objects and divided into the control group and the observation group according to different rehabilitation programs, with 100 cases in each group. The control group was given routine rehabilitation, and the observation group was given biofeedback electrical stimulation on the basis of the control group, the parturients in the two groups were treated for 3 months. The occurrence of stress urinary incontinence in the two groups was compared, and the pelvic floor muscle strength before and after treatment were compared between the two groups. The scores of International Consultation on Incontinence Questionnaire Short Form (ICI-Q-SF) and Pelvic Floor Impact Questionnaire-7 (PFIQ-7) were compared between the two groups.Results:The total incidence of stress urinary incontinence in the observation group was lower than that in the control group: 4.00%(4/100) vs. 15.00%(15/100), there was statistical difference( χ2 = 9.00, P<0.05). After treatment, the muscle strength grade, class Ⅰ muscle fiber, class Ⅱ muscle fiber and average voltage of pelvic floor muscle in the observation group were higher than those in the control group: (3.85 ± 0.27) grades vs. (3.74 ± 0.32) grades, (10.23 ± 1.17) μV vs. (8.84 ± 1.13) μV, (11.56 ± 0.19) μV vs. (10.98 ± 0.24) μV, (18.12 ± 3.24) μV vs. (14.69 ± 3.01) μV, there were statistical differences ( P<0.05). After treatment, the scores of ICI Q-SF and PFIQ-7 in the observation group were lower than those in the control group: (7.02 ± 1.26) scores vs. (8.26 ± 1.15) scores, (18.96 ± 4.31) scores vs. (24.17 ± 5.62) scores, there were statistical differences ( P<0.05). Conclusions:The application of biofeedback electrical stimulation in postpartum rehabilitation can reduce the incidence of stress urinary incontinence, improve postpartum pelvic floor muscle strength, and reduce the impact of stress urinary incontinence and pelvic floor muscle disorder on daily life.

Breast cancer is one of the most common cancers affecting women globally. With the advent of digital pathology slide scanners and the continuous evolution of deep learning algorithms, there has been a significant advancement in the application of artificial intelligence (AI) in the diagnosis and treatment of breast cancer. This article provided an overview of the current research and application status of AI in breast cancer pathological diagnosis, and summarized the challenges encountered as well as future directions in this field.

Objective:To prepare specific molecular probe 18F-AlF-1, 4, 7-triazacylononane-1, 4, 7-triacetic acid-(polyethylene glycol) 4-ZD2 ( 18F-AlF-NOTA-PEG 4-ZD2) for targeting extradomain-B fibronectin (EDB-FN), and evaluate its properties in vitro and in vivo. Methods:18F-AlF-NOTA-PEG 4-ZD2 was prepared by one-step chelation labeling with Al 18F. The radiochemical purity and in vitro stability were determined by high performance liquid chromatography (HPLC). The partition coefficient (logP) of 18F-AlF-NOTA-PEG 4-ZD2 was evaluated, and the cell uptake experiment was carried out (triple-negative breast cancer (MDA-MB-231) cells (1×10 6/tube) were divided into 3 groups ( n=3 per group); positive group, inhibition group, control group). MicroPET imaging was performed on MDA-MB-231 bearing nude mice ( n=3) after 18F-AlF-NOTA-PEG 4-ZD2 injection (30, 60, 90, 120 min) and compared with blocking group ( n=3, NOTA-PEG 4-ZQ 2 was preinjected at 0.5 h before 18F-AIF-NOTA-PEG a-ZD2 injection). Independent-sample t test was used to analyze the data. Results:18F-AlF-NOTA-PEG 4-ZD2 was successfully prepared. The optimized radiochemical yield was (33.8±2.1)% (undecay corrected, n=8) and the radiochemical purity was >96%. After incubating 120 min at 37 ℃, the radiochemical purity of 18F-AlF-NOTA-PEG 4-ZD2 in human serum and PBS was >93%, indicating its good stability in vitro. The specific activity was (11.1±3.2) GBq/μmol, and logP was -1.43±0.05. The uptake value of tumor cells was (1.77±0.28) percentage applied activity (%AR)/10 6 cells at 120 min post-injection in positive group, and the total uptake value of the inhibition group was (0.76±0.07) %AR/10 6 cells ( t=4.30, P=0.032). MicroPET imaging in tumor bearing nude mice showed that 18F-AlF-NOTA-PEG 4-ZD2 was mainly metabolized by the liver and kidneys. The tumor uptake value was (1.94±0.21) percentage activity of injection dose per gram of tissue (%ID/g) at 60 min post-injection and the tumor/muscle ratio was 3.80±0.25 at 90 min post-injection in the experimental group, while the tumor uptake value of tumor bearing nude mice in the blocking group was (0.43±0.09) %ID/g at 60 min post-injection ( t=3.18, P=0.006). Conclusions:18F-AlF-NOTA-PEG 4-ZD2 can be prepared simply with high labeling rate and good stability in vitro, with high tumor uptake and tumor/muscle ratio in microPET imaging, and good specificity and long tumor residence time. The probe has good application prospect in breast cancer with high expression of fibronectin subtype EDB-FN.

Objective:To analyze the 6-year operation faults of PHILIPS Brilliance big bore CT, identify the common problems, make corresponding maintenance plans, reduce the incidence of failures, and carry out simulation prediction of the occurrence rate of failures in the next few years.Methods:The failure data of Brilliance big bore CT from June 2012 to June 2018 were collected, and the curve estimation function in SPASS 19.0 software and the pareto diagram were used to analyze the relationship between the number of failures, time and failure types, and the prediction was made.Results:A total of 28 faults occurred during the 6-year opeation of Brilliance big bore CT. During the first half year, five times of faults occurred with the highest fault rate and then tended to stabilize. The linear function model was obtained using the curve estimation: y=-0.033 x+ 2.099( y for the number of fault, unit for times, x for the unit of time for half a year), the model of R2=0.003. In the next three years, approximately twice faults occurred within half year. The pareto chart showed that 16 faults occurred during data collection, including 3 faults in the treatment bed and 3 faults in the power supply system, respectively. The accumulative ratio of the above three faults was 71.4%, which were the main fault sources. Conclusion:The fault statistical analysis of Brilliance big bore CT is helpful for department maintenance personnel to better understand CT, develop effective maintenance programs, reduce the occurrence of faults, and predict the incidence of faults in the future.

Objective To explore the effect of multidimensional nursing based on empowerment theory on preoperative negative emotion in elderly patients undergoing endoscopic surgery. Methods A total of 243 cases of elderly patients undergoing laparoscopic surgery undergoing endoscopic surgery in the First Affiliated Hospital of Wenzhou Medical University, Zhejiang Province were selected by convenient sampling method. The intervention group (122 cases) and the control group (121 cases) were divided into two groups by random number table.According to the endoscopic surgery nursing routine,two groups of patients were given routine care,and the intervention group was given multidimensional nursing based on empowerment theory. Two groups were observed to evaluate the anxiety and depression of the patients during admission, preoperative night and 0.5 hour before surgery. Results The preoperative anxiety scores of the intervention group were 16.23 ± 3.45, 22.17 ± 4.13, 23.36 ± 6.55. The preoperative depression scores of the intervention group were 10.23±3.45,15.31±3.65,19.29±6.28.The preoperative anxiety scores of the control group were 15.56 ± 3.68, 30.17 ± 3.78, 36.36 ± 9.25. The preoperative depression scores of the control group were 9.56 ± 1.23, 20.56 ± 5.36, 34.24 ± 8.17. The anxiety and depression of the intervention group and the control group were gradually increased before the operation, but the anxiety and depression in the intervention group was better than that in the control group (P<0.01). Conclusions Multidimensional nursing based on empowerment theory can effectively improve the preoperative negative emotions of elderly laparoscopic surgery, and is conducive to the treatment and recovery of the disease.

Graded Zirconia-hydroxyapatite composite bioceramic and simplex Zirconia-hydroxyapatite composite bioceramic were produced, and the extractes of these two of materials were made to evaluate their immunocompatibility. Scanning electron microscope (SEM) was used to detect the character of the surface of the graded composite bioceramic. Holding and without holding phytohemagglutinin (PHA), proliferation and activation of peripheralblood monocytes(PBMCs) cultured in the two extracts were studied. Cultured in PHA after 72 hours, the proliforation rate of the graded composite group was significantly higher than the simplex composite group (P < 0.01). There was no difference of apoptosis of PBMCs of the two groups (P > 0.05). Cultured in PHA after 24 hours, the ratio of CD3/CD69 positive PBMCs of the simple composite group was significantly higher than that of the graded composite material group (P < 0.01). The numbers of PBMCs activated by the graded composite material group were less than that of the simply composite material group and the technique of graded composite will be helpful to improve its immunocompatibility.
Adult ; Cells, Cultured ; Dental Porcelain ; chemistry ; pharmacology ; Durapatite ; chemistry ; pharmacology ; Humans ; Materials Testing ; Monocytes ; drug effects ; immunology ; Zirconium ; chemistry ; pharmacology

AIM: To evaluate the immunogenicity of a novel orthopedics materials (graded zirconia-hydroxyapatite composite) in vitro by using peripheral blood mononuclear cells (PBMCs) from healthy young people, and simple zirconia-hydroxyapatite composited material was used as control materials. METHODS: Proliferation of PBMCs cultured in different liquid after 5 days was measured by MTT methods. ELISA was used to detect TNF-? and IL-6 concentration in the supernatant of PBMCs cultured in the extracts after 24 hours. Flow cytometery was used to measure CD69 and CD25 in activated PBMCs cultured in the extracts of the two kinds of materials after 24 hours. RESULTS: The proliferation rate of the simple composite group was significantly lower than that in negative group (P