Exosomes are ubiquitous in all types of body fluids, exhibiting a high degree of abundance and diversity. Given their distinctive structure and function, exosomes are involved in a range of life activities, including intercellular communication, material transport, and immune regulation. An increasing number of studies have identified exosomes as a source of diagnostic markers for diabetic retinopathy. Furthermore, exosomes represent a novel avenue for therapeutic intervention, with promising clinical applications. This paper examines the diagnostic and therapeutic mechanisms of exosomes in diabetic retinopathy, reviews the advancements in exosomes-based diagnostics and therapeutics for diabetic retinopathy, and aims to enhance the precision and efficiency of clinical diagnosis and treatment of diabetic retinopathy.

Objective: To summarize strategies for improving childhood hypertension, so as to provide evidencebased recommendations for poliymaking and practice childhood hypertension management in China. Methods: From March to April 2024, child health stakeholders from five districts in Hangzhou were selected using a combination of stratified and convenience sampling methods. Data were analyzed using a groundedtheory approach. During the indepth interview phase, six policymakers were interviewed. Focus group discussions were conducted with school administrators, healthcare providers, and parents, comprising a total of 62 participants. Results: Through threelevel coding, 116 initial categories were identified(e.g., "trend of younger age" "difficulty in behavior change"), 35 main categories (e.g., "higher incidence compared to the past" "caused by comprehensive influencing factors"), and 12 core categories (e.g., "epidemic status" "influencing factors"). Finally, the cognitive status, problem analysis, and management strategies of children hypertension were constructed. Conclusion Effective prevention and control of childhood hypertension requires coordinated efforts among governments, schools, families, and society to establish a comprehensive management system, with dynamic monitoring and evaluation to optimize policy implementation.

Cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders are the 3 major chronic diseases threatening human health, which are closely related and often coexist, significantly increasing the difficulty of disease management. In response, the American Heart Association (AHA) proposed a novel disease concept of “cardiovascular-kidney-metabolic (CKM) syndrome” in October 2023, which has triggered widespread concern about the co-treatment of heart and kidney diseases and the prevention and treatment of metabolic disorders around the world. This review posits that effectively managing CKM syndrome requires a new and multidimensional paradigm for diagnosis and risk prediction that integrates biological insights, advanced technology and social determinants of health (SDoH). We argue that the core pathological driver is a “metabolic toxic environment”, fueled by adipose tissue dysfunction and characterized by a vicious cycle of systemic inflammation and oxidative stress, which forms a common pathway to multi-organ injury. The at-risk population is defined not only by biological characteristics but also significantly impacted by adverse SDoH, which can elevate the risk of advanced CKM by a factor of 1.18 to 3.50, underscoring the critical need for equity in screening and care strategies. This review systematically charts the progression of diagnostic technologies. In diagnostics, we highlight a crucial shift from single-marker assessments to comprehensive multi-marker panels. The synergistic application of traditional biomarkers like NT-proBNP (reflecting cardiac stress) and UACR (indicating kidney damage) with emerging indicators such as systemic immune-inflammation index (SII) and Klotho protein facilitates a holistic evaluation of multi-organ health. Furthermore, this paper explores the pivotal role of non-invasive monitoring technologies in detecting subclinical disease. Techniques like multi-wavelength photoplethysmography (PPG) and impedance cardiography (ICG) provide a real-time window into microcirculatory and hemodynamic status, enabling the identification of early, often asymptomatic, functional abnormalities that precede overt organ failure. In imaging, progress is marked by a move towards precise, quantitative evaluation, exemplified by artificial intelligence-powered quantitative computed tomography (AI-QCT). By integrating AI-QCT with clinical risk factors, the predictive accuracy for cardiovascular events within 6 months significantly improves, with the area under the curve (AUC) increasing from 0.637 to 0.688, demonstrating its potential for reclassifying risk in CKM stage 3. In the domain of risk prediction, we trace the evolution from traditional statistical tools to next-generation models. The new PREVENT equation represents a major advancement by incorporating key kidney function markers (eGFR, UACR), which can enhance the detection rate of CKD in primary care by 20%-30%. However, we contend that the future lies in dynamic, machine learning-based models. Algorithms such as XGBoost have achieved an AUC of 0.82 for predicting 365-day cardiovascular events, while deep learning models like KFDeep have demonstrated exceptional performance in predicting kidney failure risk with an AUC of 0.946. Unlike static calculators, these AI-driven tools can process complex, multimodal data and continuously update risk profiles, paving the way for truly personalized and proactive medicine. In conclusion, this review advocates for a paradigm shift toward a holistic and technologically advanced framework for CKM management. Future efforts must focus on the deep integration of multimodal data, the development of novel AI-driven biomarkers, the implementation of refined SDoH-informed interventions, and the promotion of interdisciplinary collaboration to construct an efficient, equitable, and effective system for CKM screening and intervention.

This study aimed to explore the pharmacological mechanism of Yourenji Capsules(YRJ) in improving osteoporosis by combining network pharmacology and proteomics technologies. The SD rats were randomly divided into a blank control group and a 700 mg·kg~(-1) YRJ group. The rats were subjected to gavage administration with the corresponding drugs, and the blank serum, drug-containing serum, and YRJ samples were compared using ultra performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry(UPLC-Q-TOF-MS/MS) to analyze the main components absorbed into blood. Network pharmacology analysis was conducted based on the YRJ components absorbed into blood to obtain related targets of the components and target genes involved in osteoporosis, and Venn diagrams were used to identify the intersection of drug action targets and disease targets. The STRING database was used for protein-protein interaction(PPI) network analysis of potential target proteins to construct a PPI network. Gene Ontology(GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment were performed using Enrichr to investigate the potential mechanism of action of YRJ. Ovariectomy(OVX) was performed to establish a rat model of osteoporosis, and the rats were divided into a sham group, a model group, and a 700 mg·kg~(-1) YRJ group. The rats were given the corresponding drugs by gavage. The femurs of the rats were subjected to label-free proteomics analysis to detect differentially expressed proteins, and GO functional enrichment and KEGG pathway enrichment analyses were performed on the differentially expressed proteins. With the help of network pharmacology and proteomics results, the mechanism by which YRJ improves osteoporosis was predicted. The analysis of the YRJ components absorbed into blood revealed 23 bioactive components of YRJ, and network pharmacology results indicated that key targets involved include tumor necrosis factor(TNF), tumor protein p53(TP53), protein kinase(AKT1), and matrix metalloproteinase 9(MMP9). These targets are mainly involved in osteoclast differentiation, estrogen signaling pathways, and nuclear factor-kappa B(NF-κB) signaling pathways. Additionally, the proteomics analysis highlighted important pathways such as peroxisome proliferator-activated receptor(PPAR) signaling pathways, mitogen-activated protein kinase(MAPK) signaling pathways, and β-alanine metabolism. The combined approaches of network pharmacology and proteomics have revealed that the mechanism by which YRJ improves osteoporosis may be closely related to the regulation of inflammation, osteoblast, and osteoclast metabolic pathways. The main pathways involved include the NF-κB signaling pathways, MAPK signaling pathways, and PPAR signaling pathways, among others.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Osteoporosis/metabolism* ; Proteomics ; Rats ; Rats, Sprague-Dawley ; Network Pharmacology ; Female ; Protein Interaction Maps/drug effects* ; Capsules ; Humans ; Signal Transduction/drug effects*

The accurate segmentation of breast ultrasound images is an important precondition for the lesion determination. The existing segmentation approaches embrace massive parameters, sluggish inference speed, and huge memory consumption. To tackle this problem, we propose T ²KD Attention U-Net (dual-Teacher Knowledge Distillation Attention U-Net), a lightweight semantic segmentation method combined double-path joint distillation in breast ultrasound images. Primarily, we designed two teacher models to learn the fine-grained features from each class of images according to different feature representation and semantic information of benign and malignant breast lesions. Then we leveraged the joint distillation to train a lightweight student model. Finally, we constructed a novel weight balance loss to focus on the semantic feature of small objection, solving the unbalance problem of tumor and background. Specifically, the extensive experiments conducted on Dataset BUSI and Dataset B demonstrated that the T ²KD Attention U-Net outperformed various knowledge distillation counterparts. Concretely, the accuracy, recall, precision, Dice, and mIoU of proposed method were 95.26%, 86.23%, 85.09%, 83.59%and 77.78% on Dataset BUSI, respectively. And these performance indexes were 97.95%, 92.80%, 88.33%, 88.40% and 82.42% on Dataset B, respectively. Compared with other models, the performance of this model was significantly improved. Meanwhile, compared with the teacher model, the number, size, and complexity of student model were significantly reduced (2.2×10 ⁶ vs. 106.1×10 ⁶, 8.4 MB vs. 414 MB, 16.59 GFLOPs vs. 205.98 GFLOPs, respectively). Indeedy, the proposed model guarantees the performances while greatly decreasing the amount of computation, which provides a new method for the deployment of clinical medical scenarios.
Humans ; Breast Neoplasms/diagnostic imaging* ; Female ; Ultrasonography, Mammary/methods* ; Image Processing, Computer-Assisted/methods* ; Algorithms ; Neural Networks, Computer ; Breast/diagnostic imaging*

To facilitate the early intelligent screening of pediatric genu valgum, this study develops a deep learning-based gait recognition model tailored for clinical application. The model is constructed upon a three-dimensional residual network architecture and incorporates a triplet attention module alongside a spatial hierarchical pooling module, jointly enhancing feature interaction across temporal, spatial, and channel dimensions. This design ensures an optimal balance between representational capacity and computational efficiency. Evaluated on a self-constructed dataset, the model achieves precision of 98.0%, 97.1%, and 96.5%, recall rates of 97.5%, 97.0%, and 95.0%, and F ₁-scores of 0.98, 0.97, and 0.96 on the training, validation, and test sets, respectively, demonstrating excellent recognition performance and strong generalization ability. Ablation experiments confirm the importance of the proposed model's core components in improving performance, and comparative experiments further highlight its significant advantages in recognition accuracy and robustness. Visualization experiments reveal that the model effectively focuses on key regions of gait images, with attention regions aligning closely with clinical anatomical landmarks, thereby enhancing the interpretability of the model's decision-making in clinical applications. In summary, the proposed model not only offers an efficient and reliable technical solution for early intelligent screening of genu valgum in children, but also provides a practical pathway for applying gait recognition technology in medical diagnosis.
Humans ; Gait ; Deep Learning ; Genu Valgum/physiopathology* ; Child ; Neural Networks, Computer ; Algorithms

OBJECTIVE: To investigate the safety and prognostic factors influencing the treatment of upper urinary tract urothelial carcinoma (UTUC) combined with bladder cancer (BCa) by laparoscopic simultaneous radical cystectomy and nephroureterectomy (RCNU). METHODS: The clinical data of patients admitted to Peking University Third Hospital for laparoscopic RCNU surgery from January 2009 to September 2023 were analyzed retrospectively. Based on the same gender, age (±5 years), history of uroepithelial tumors, underlying diseases, T-stage, N-stage, M-stage, American Society of Anesthesiologists (ASA) score, Charlson comorbidity index, and body mass index (BMI) (±5), 34 patients with RCNU were matched 1 ∶1 with patients with bladder cancer who underwent laparoscopic radical cystectomy (RC) alone. Kaplan-Meier survival analysis was used to calculate patient survival, and Cox proportional regression risk model was used to analyze clinical factors affecting prognosis. RESULTS: Of the 68 patients enrolled, the follow-up rate was 100% with a median follow-up time of 27.0 (11.7, 60.2) months. Comparison of intraoperative conditions (including operation time, estimated intraoperative bleeding, intra-operative blood transfusion, etc.) between the two groups of patients showed no significant difference (P>0.05). Comparison of preoperative creatinine and postoperative creatinine between the two groups of patients showed significant differences (P < 0.05). The perioperative Clavien grade Ⅲ-Ⅳ complication rates were 2.9% (1/34) in the RC group and 5.9% (2/34) in the RCNU group. There was no significant difference in terms of perioperative complications between the two groups. Overall survival was significantly lower in the patients receiving RCNU compared with the matched group receiving RC alone (P < 0.05). Cox regression analysis suggested that two factors, high N stage and high postoperative creatinine, were independent risk factors affecting the prognosis of patients in the 2 groups (P < 0.05). CONCLUSION The overall survival prognosis of patients undergoing RCNU surgery was worse compared with laparoscopic RC surgery alone during the same period. There was no clinically significant difference between the two groups in terms of operation time, intraoperative bleeding, and perioperative complications, and there were clinically significant differences in preoperative renal function and post-operative renal function.
Humans ; Laparoscopy/methods* ; Nephroureterectomy/methods* ; Cystectomy/methods* ; Prognosis ; Male ; Retrospective Studies ; Female ; Urinary Bladder Neoplasms/mortality* ; Middle Aged ; Aged

Neoplasms characterized by the expression of markers of neuroendocrine differentiation in neoplastic cells are defined as neuroendocrine neoplasms. A case of neuroendocrine carcinomas (NECs) with a small amount of papillary adenocarcinoma and significantly vacuolar nucleus at the esophagogastric junction was reported in this article. A 77-year-old male had dysphagia for one week. Endoscopy revealed early-stage esophagogastric junction carcinoma, and biopsy was diagnosed as poorly differentiated carcinoma. Endoscopic submucosal dissection was performed. Histologically, the papillary adenocarcinoma progresses from typically branching papillary structures (well-differentiated) to hyperplasia of the lining epithelium of the papilla to form a cribriform structure (moderately differentiated), to solid area lacking papillary structures (poorly differentiated). There was a continuous process, and during this process, the vacuoles in the nuclei of tumor cells showed progressive changes from mild to obvious and finally to significant vacuoles. The tumor was mainly composed of solid areas (about 95%), with single cell, large cell, round or oval to irregular nuclei, and significantly vacuolar nuclei, nuclei with larger vacuoles appeared in a loop, a few thin weakly basophilic or weakly eosinophilic fine particles could be seen in the vacuoles, and the vacuoles had rough edges. The nucleus chromatin at the outer edge of the vacuoles was fine particles, and mitosis was common (20-30/mm²), atypical mitosis could be seen, and nucleoli could be seen easily, the cytoplasm was weakly eosinophilic, and the boundaries of cells were unclear. The cells were arranged in a nested, trabecular, or diffuse sheet shape, with some arranged in a glandular tube shape. Tumor thrombus was found in the vein of submucosa; the interstitial tissue rich in capillaries within the tumor was accompanied by a large number of neutrophil infiltration. Immunohistochemical staining showed that the solid area of the tumor was positive for synaptophysin (Syn) and chromogranin A (CgA), while papillary adenocarcinoma was negative. Mucin 5AC (MUC5AC) was diffusely positive in papillary adenocarcinoma, while the proportion of positive cells in the solid area of the tumor was about 10% to 15%. In a word, this case showed the extreme situation of the vacuolar nuclear characteristics of NECs, extremely rare, in a sense, this case expanded the boundary of the morphological spectrum of NECs. Understanding the extreme vacuolar features of this nucleus is helpful to make a correct pathological diagnosis.
Humans ; Male ; Esophagogastric Junction/pathology* ; Aged ; Carcinoma, Neuroendocrine/pathology* ; Vacuoles/pathology* ; Esophageal Neoplasms/pathology* ; Cell Nucleus/pathology* ; Adenocarcinoma, Papillary/pathology* ; Stomach Neoplasms/pathology*

OBJECTIVE: To evaluate the dynamic changes of glucocorticoid (GC) dose and the feasibility of GC discontinuation in rheumatoid arthritis (RA) patients under the background of Chinese medicine (CM). METHODS: This multicenter retrospective cohort study included 1,196 RA patients enrolled in the China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN) from September 1, 2019 to December 4, 2023, who initiated GC therapy. Participants were divided into the Western medicine (WM) and integrative medicine (IM, combination of CM and WM) groups based on medication regimen. Follow-up was performed at least every 3 months to assess dynamic changes in GC dose. Changes in GC dose were analyzed by generalized estimator equation, the probability of GC discontinuation was assessed using Kaplan-Meier curve, and predictors of GC discontinuation were analyzed by Cox regression. Patients with <12 months of follow-up were excluded for the sensitivity analysis. RESULTS: Among 1,196 patients (85.4% female; median age 56.4 years), 880 (73.6%) received IM. Over a median 12-month follow-up, 34.3% (410 cases) discontinued GC, with significantly higher rates in the IM group (40.8% vs. 16.1% in WM; P<0.05). GC dose declined progressively, with IM patients demonstrating faster reductions (median 3.75 mg vs. 5.00 mg in WM at 12 months; P<0.05). Multivariate Cox analysis identified age <60 years [P<0.001, hazard ratios (HR)=2.142, 95% confidence interval (CI): 1.523-3.012], IM therapy (P=0.001, HR=2.175, 95% CI: 1.369-3.456), baseline GC dose ⩽7.5 mg (P=0.003, HR=1.637, 95% CI: 1.177-2.275), and absence of non-steroidal anti-inflammatory drugs use (P=0.001, HR=2.546, 95% CI: 1.432-4.527) as significant predictors of GC discontinuation. Sensitivity analysis (545 cases) confirmed these findings. CONCLUSIONS RA patients receiving CM face difficulties in following guideline-recommended GC discontinuation protocols. IM can promote GC discontinuation and is a promising strategy to reduce GC dependency in RA management. (Trial registration: ClinicalTrials.gov, No. NCT05219214).
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Arthritis, Rheumatoid/drug therapy* ; Glucocorticoids/therapeutic use* ; Medicine, Chinese Traditional ; Retrospective Studies

Mycophenolic acid (MPA), the active moiety of both mycophenolate mofetil (MMF) and enteric-coated mycophenolate sodium (EC-MPS), serves as a primary immunosuppressant for maintaining solid organ transplants. Therapeutic drug monitoring (TDM) enhances treatment outcomes through tailored approaches. This study aimed to develop an evidence-based guideline for MPA TDM, facilitating its rational application in clinical settings. The guideline plan was drawn from the Institute of Medicine and World Health Organization (WHO) guidelines. Using the Delphi method, clinical questions and outcome indicators were generated. Systematic reviews, Grading of Recommendations Assessment, Development, and Evaluation (GRADE) evidence quality evaluations, expert opinions, and patient values guided evidence-based suggestions for the guideline. External reviews further refined the recommendations. The guideline for the TDM of MPA (IPGRP-2020CN099) consists of four sections and 16 recommendations encompassing target populations, monitoring strategies, dosage regimens, and influencing factors. High-risk populations, timing of TDM, area under the curve (AUC) versus trough concentration (C₀), target concentration ranges, monitoring frequency, and analytical methods are addressed. Formulation-specific recommendations, initial dosage regimens, populations with unique considerations, pharmacokinetic-informed dosing, body weight factors, pharmacogenetics, and drug‍-‍drug interactions are covered. The evidence-based guideline offers a comprehensive recommendation for solid organ transplant recipients undergoing MPA therapy, promoting standardization of MPA TDM, and enhancing treatment efficacy and safety.
Mycophenolic Acid/administration & dosage* ; Drug Monitoring/methods* ; Humans ; Organ Transplantation ; Immunosuppressive Agents/administration & dosage* ; Delphi Technique