This paper proposes a two-stage method integrating visual foundation models （VFM） and diffusion models. The segment anything model （SAM） as VFM is combined with the SegRefiner diffusion model to construct the SAM-SegRefiner framework for automated segmentation of edema， inflammation， and thrombus regions in histopathological images of hemorrhoidal tissue， providing a reproducible technical tool for the objective quantification of pathological morphology and its application in traditional Chinese medicine （TCM） syndrome research. Trained and validated on multi-center retrospective data， the SAM-SegRefiner model achieved an average pixel accuracy of 95.32% and a mean intersection over union （mIoU） of 66.81% on an independent test set， significantly outperfor-ming comparative models such as U-Net， MixU-Net， and SAM-Med2D， and also demonstrating robust cross-center generalization capability. Furthermore， by correlating the quantitatively segmented results from the model with the patients' TCM syndrome types， the potential associations between pathomorphological features and TCM syndrome differentiation have been explored. The analysis revealed no statistically significant differences in the degree of inflammatory infiltration and thrombus formation among different syndrome types， suggesting a complex relationship between local pathological changes and systemic syndrome manifestations.

This consensus will introduce the characteristics of fillers used in the surgical cavities of domestic nasal surgery patients based on relevant literature and expert opinions. It will also provide recommendations for the selection of cavity fillers for different nasal diseases, with chronic sinusitis as a representative example.
Humans ; Nasal Cavity/surgery* ; Nasal Surgical Procedures ; China ; Consensus ; Sinusitis/surgery* ; Dermal Fillers

OBJECTIVES: To construct an intelligent analysis model for classifying fetal orientation during intrapartum ultrasound videos based on multi-feature fusion. METHODS: The proposed model consists of the Input, Backbone Network and Classification Head modules. The Input module carries out data augmentation to improve the sample quality and generalization ability of the model. The Backbone Network was responsible for feature extraction based on Yolov8 combined with CBAM, ECA, PSA attention mechanism and AIFI feature interaction module. The Classification Head consists of a convolutional layer and a softmax function to output the final probability value of each class. The images of the key structures (the eyes, face, head, thalamus, and spine) were annotated with frames by physicians for model training to improve the classification accuracy of the anterior occipital, posterior occipital, and transverse occipital orientations. RESULTS: The experimental results showed that the proposed model had excellent performance in the tire orientation classification task with the classification accuracy reaching 0.984, an area under the PR curve (average accuracy) of 0.993, and area under the ROC curve of 0.984, and a kappa consistency test score of 0.974. The prediction results by the deep learning model were highly consistent with the actual classification results. CONCLUSIONS The multi-feature fusion model proposed in this study can efficiently and accurately classify fetal orientation in intrapartum ultrasound videos.
Humans ; Female ; Ultrasonography, Prenatal/methods* ; Pregnancy ; Fetus/diagnostic imaging* ; Neural Networks, Computer ; Video Recording

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Objective To evaluate the therapeutic efficacy for surgical treatments among patients with hepatic cystic echinococcosis in Gansu Province from 2006 to 2023, so as to provide insights into optimization of the diagnosis and treatment strategies against hepatic cystic echinococcosis. Methods The demographic and clinical data of all echinococcosis cases included in central government fiscal transfer payment program for echinococcosis control and undergoing surgical treatments in Gansu Province from 2006 to 2023 were captured. Hepatic cystic echinococcosis patients with complete medical records and follow-up data were included in the study, and patients’ characteristics, including hospital where patients received diagnosis and treatment, methods of case identification, year of surgery, classification of lesions, number of lesions, size of lesions, course of disease, surgical methods, and post-surgical follow-up data. The cure and recurrence of hepatic cystic echinococcosis were evaluated according to the Guidelines for Management of Echinococcosis Patients in the Central Government Fiscal Transfer Payment Program, and the cure and recurrent rates were calculated. Results Data were collected from 1 686 surgical patients with hepatic cystic echinococcosis. According to the inclusion and exclusion criteria, 1 222 hepatic cystic echinococcosis patients undergoing surgical treatments were included during the period from 2006 to 2022, including 1 166 cured patients (95.42%) and 88 patients with postsurgical recurrence (7.20%), and the cure rate of surgical treatments appeared a tendency towards a rise among patients with hepatic cystic echinococcosis from 2008 to 2022 (χ²_trend = 19.39, P < 0.05). The cure rates of hepatic cystic echinococcosis were 100% (177/177), 94.81% (128/135) and 94.62% (861/910) among patients detected through regular physical examinations, screened by the central government fiscal transfer payment program for echinococcosis control, and those who passively sought healthcare services, respectively (χ² = 9.95, P < 0.05). The cure rates of hepatic cystic echinococcosis were 95.96% (1 046/1 090) among patients with a disease course of 2 years and less and 90.90% (120/132) among patients with a disease course of over 2 years (χ² = 6.87, P < 0.05), and there were significant differences in the cure rates among patients with hepatic cystic echinococcosis in terms of number of lesions (χ² = 24.44, P < 0.05) and surgical methods (P < 0.05). The cure rate of hepatic cystic echinococcosis patients was significantly higher following initiation of the central government fiscal transfer payment program for echinococcosis control (96.06%, 1 096/1 141) than before the program (86.42%, 70/81) (χ² = 16.06, P < 0.05), and the cure rate of hepatic cystic echinococcosis patients was significantly higher in designated hospitals (96.48%, 741/768) than in non-designated hospitals (93.37%, 366/392) (χ² = 5.78, P < 0.05). The median follow-up period was 4 (interquartile range, 7) years among 1 222 hepatic cystic echinococcosis patients undergoing surgical treatments. The recurrent rate of hepatic cystic echinococcosis appeared a tendency towards a decline from 2008 to 2022 (χ²_trend = 36.86, P < 0.05), with a reduction from 23.08% (9/39) in 2008 to 1.85% (1/54) in 2021, and the post-surgical recurrence rate of hepatic cystic echinococcosis was lower following initiation of the central government fiscal transfer payment program for echinococcosis control (5.87%, 67 / 1 141) than before the program (25.93%, 21/81) (χ² = 45.51, P < 0.05). In addition, the post-surgical recurrence rate of hepatic cystic echinococcosis was higher in non-designated hospitals (10.46%, 41/392) than in designated hospitals (5.60%, 43/768) (χ² = 9.12, P < 0.05), and there was a significant difference in the post-surgical recurrence rate among patients with hepatic cystic echinococcosis in terms of surgical methods (P < 0.05), with the highest recurrence rate (11.54%) seen among patients undergoing percutaneous fine-needle aspiration of cyst fluids-based surgical procedures (P < 0.05). Conclusion Since the initiation of the central government fiscal transfer payment program for echinococcosis control in Gansu Province in 2006, an increase in the surgical cure rate and a reduction in the recurrence of hepatic cystic echinococcosis had been found among patients with hepatic cystic echinococcosis, indicating a high overall therapeutic efficacy.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.