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.

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:Bone relies on the close connection between blood vessels and bone cells to maintain its integrity.Bones are in a physiologically hypoxic environment.Therefore,the study of angiogenesis and osteogenesis in hypoxic environment is closer to the microenvironment in vivo. OBJECTIVE:To explore the influence of Wenshen Tongluo Zhitong(WSTLZT)formula on H-type bone microvascular endothelial cell/bone marrow mesenchymal stem cell co-culture system in hypoxia environment and its related mechanism. METHODS:Enzyme digestion method and flow sorting technique were used to isolate and identify H-type bone microvascular endothelial cells.Mouse bone marrow mesenchymal stem cells were isolated and obtained by bone marrow adhesion method.H-type bone microvascular endothelial cell/bone marrow mesenchymal stem cell hypoxic co-culture system was established using Transwell chamber and anoxic culture workstation.WSTLZT formula powder was used to intervene in each group at a mass concentration of 50 and 100 μg/mL.The angiogenic function of H-type bone microvascular endothelial cells in the co-culture system was evaluated by scratch migration test and tube formation test.The osteogenic differentiation ability of bone marrow mesenchymal stem cells in the co-cultured system was evaluated by alkaline phosphatase staining and alizarin red staining.The protein and mRNA expression changes of PDGF/PI3K/AKT signal axis related molecules in H-type bone microvascular endothelial cells in the co-cultured system were detected by Western Blotting and q-PCR,respectively. RESULTS AND CONCLUSION:(1)Compared with the normal oxygen group,the scratch mobility and new blood vessel length of H-type bone microvascular endothelial cells were significantly higher(P<0.05);the osteogenic differentiation capacity of bone marrow mesenchymal stem cells was higher(P<0.05);the expression of PDGF/PI3K/AKT axis-related molecular protein and mRNA increased(P<0.05)in the hypoxia group.(2)Compared with the hypoxia group,scratch mobility and new blood vessel length were significantly increased in the H-type bone microvascular endothelial cells(P<0.05);bone marrow mesenchymal stem cells had stronger osteogenic function(P<0.05);the expression of PDGF/PI3K/AKT axis-related molecular proteins and mRNA further increased(P<0.05)after treatment with different dose concentrations of WSTLZT formula.These findings conclude that H-type angiogenesis and osteogenesis under hypoxia may be related to the PDGF/PI3K/AKT signaling axis,and WSTLZT formula may promote H-type vasculo-dependent bone formation by activating the PDGF/PI3K/AKT signaling axis,thereby preventing and treating osteoporosis.

Real-time,noninvasive programmed death-ligand 1(PD-L1)testing using molecular imaging has enhanced our understanding of the immune environments of neoplasms and has served as a guide for immunotherapy.However,the utilization of radiotracers in the imaging of human brain tumors using positron emission tomography/computed tomography(PET/CT)remains limited.This investigation involved the synthesis of[18F]AlF-NOTA-PCP2,which is a novel peptide-based radiolabeled tracer that targets PD-L1,and evaluated its imaging capabilities in orthotopic glioblastoma(GBM)models.Using this tracer,we could noninvasively monitor radiation-induced PD-L1 changes in GBM.[18F]AlF-NOTA-PCP2 exhibited high radiochemical purity(＞95％)and stability up to 4 h after synthesis.It demonstrated specific,high-affinity binding to PD-L1 in vitro and in vivo,with a dissociation constant of 0.24 nM.PET/CT imaging,integrated with contrast-enhanced magnetic resonance imaging,revealed significant accumulation of[18F]AlF-NOTA-PCP2 in orthotopic tumors,correlating with blood-brain barrier disruption.After radiotherapy(15 Gy),[18F]AlF-NOTA-PCP2 uptake in tumors increased from 9.51％±0.73％to 12.04％±1.43％,indicating enhanced PD-L1 expression consistent with immunohisto-chemistry findings.Fractionated radiation(5 Gy × 3)further amplified PD-L1 upregulation(13.9％±1.54％ID/cc)compared with a single dose(11.48％±1.05％ID/cc).Taken together,[18F]AlF-NOTA-PCP2 may be a valuable tool for noninvasively monitoring PD-L1 expression in brain tumors after radiotherapy.

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.

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