Objective To explore a method for rapid and differential diagnosis of rejection after kidney transplantation through urine hyperspectral imaging technology. Methods Hyperspectral data information from urine samples of 118 recipients after kidney transplantation was collected, and a deep learning model was constructed to diagnose and classify the types of rejection. Results A deep learning diagnostic model based on the 34-layer residual network (ResNet-34) was constructed, and 118 patients were included and divided into the training set and the test set. Based on the pathological results of the transplanted kidney puncture, the urine samples of the patients were classified into five groups: the non-rejection group, the T-cell-mediated rejection group, the antibody-mediated rejection group, the mixed rejection group and the nephropathy recurrence group. The results showed that the diagnostic sensitivities of the model for the above five groups were 0.960, 0.980, 0.930, 0.940 and 0.943 respectively, and the diagnostic specificities were 0.983, 0.993, 0.997, 0.989 and 0.989 respectively. The overall diagnostic accuracy rate reached 95.7%. Conclusions The study provides a non-invasive, rapid and accurate auxiliary diagnostic method for the differential diagnosis of rejection after kidney transplantation.

ObjectiveThis study aimed to establish a monocrotaline （MCT）-induced pulmonary arterial hypertension （PAH） rat model to systematically evaluate the protective effect of Xiao Qinglongtang （XQLT） on right cardiac function in model rats and further elucidate the underlying regulatory mechanism. MethodsSixty male SD rats were randomly assigned to the normal group， model group， XQLT low-， medium-， and high-dose groups （XQLT-L/M/H）， and the beraprost sodium tablet group （BST）. Except for the normal group， rats in all other groups were given a single subcutaneous injection of MCT （60 mg·kg^-1） to induce PAH. Three weeks after injection， rats in the XQLT-L/M/H groups were administered XQLT intragastrically at 3.07， 6.14， 12.28 g·kg^-1·d^-1， respectively. Rats in the BST group received beraprost sodium at 12.6 μg·kg^-1·d^-1， and rats in the model group received an equal volume of saline. All treatments lasted for 3 weeks. Right ventricular systolic pressure （RVSP） was measured by right ventricular catheterization. Cardiac function was assessed by echocardiography. The right ventricle was weighed to calculate the right ventricular hypertrophy index （RVHI）. Hematoxylin-eosin （HE） staining， Masson staining， and transmission electron microscopy were used to observe myocardial morphology. Serum metabolomic changes were analyzed using ultra-performance liquid chromatography-tandem mass spectrometry （UPLC-MS/MS）. Data-independent acquisition （DIA） proteomics was used to detect differentially expressed （DE） proteins in the right ventricle， and Western blot was used to measure the expression of uncoupling protein 3 （UCP3）， phosphatidylinositol 3-kinase catalytic subunit p110α （PIK3CA）， L1 cell adhesion molecule （L1CAM）， and quinone oxidoreductase （CRYZ）. UPLC-MS/MS was used to analyze the chemical components of XQLT. ResultsCompared with the normal group， the model group showed significantly increased RVSP and RVHI （P<0.05）， along with pathological changes in myocardial morphology. Compared with the model group， all XQLT-treated groups exhibited reductions in RVSP and RVHI as well as significant improvements in cardiac function and myocardial morphology. Among the XQLT groups， XQLT-M showed the most pronounced effects （P<0.05）， comparable to the BST group. Serum metabolomics revealed 105 differential metabolites in the XQLT groups versus the model group ［variable importance in projection （VIP） >1， P<0.05］， including 58 upregulated and 47 downregulated metabolites. KEGG enrichment analysis indicated that XQLT intervention downregulated phenylalanine metabolism （P<0.01） and upregulated unsaturated fatty acid biosynthesis （P<0.05）. Proteomics analysis showed that 982 DE proteins were identified in the MCT groups versus the normal group， including 455 upregulated and 527 downregulated proteins （|fold change （FC）| >1.3， P<0.05）. Compared with the model group， 237 DE proteins were identified in the XQLT groups， including 124 upregulated and 113 downregulated proteins （|FC| >1.3， P<0.05）， with 57 overlapping DE proteins. KEGG enrichment suggested that XQLT mainly modulated pathways related to mineral absorption， ribosomal biogenesis， peroxisomes， glycolysis/gluconeogenesis， spliceosomes， and thyroid hormone signaling. Western blot analysis showed that， compared with the model group， XQLT increased the expression of UCP3， PIK3CA， and L1CAM， while decreasing the expression of CRYZ （P<0.05）. ConclusionXQLT exerts a protective effect on right heart function in MCT-induced PAH rats， and its mechanism is associated with maintaining myocardial homeostasis and alleviating right ventricular remodeling.

To summarize the distribution characteristics of human papillomavirus(HPV) infection types in patients with cervical squamous intraepithelial lesion(SIL) and cervical cancer(CC), and to explore the impact of HPV vaccination, HPV infection types, and general clinical data on different grades of cervical lesions.

ObjectiveTo explore the mechanism by which the ethanol extract of Epimedium brevicornu （EEBM） intervenes in mesenchymal adipose-derived stem cells （ADSCs） to delay aging in castrated rats. MethodsForty-five 3-month-old SPF female SD rats were ovariectomized and randomly divided into model group， ADSCs treatment group， and ADSCs groups treated with low， medium， and high concentrations of EEBM （1， 50， 100 μg·L^-1）， referred to as the AE low， medium， and high concentration groups， with 9 rats in each group. After tail vein injection of 200 μL of the corresponding stem cell suspension， aging-related indicators including cyclin-dependent kinase inhibitor （p21）， tumor suppressor gene （p53）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， superoxide dismutase （SOD）， malondialdehyde （MDA）， B-cell lymphoma-2 （Bcl-2）， Bcl-2-associated X protein （Bax）， cysteine-aspartic acid protease-3 （Caspase-3）， and lipofuscin were measured using enzyme-linked immunosorbent assay （ELISA） and Western blot. ResultsCompared with the model group， the IL-6 content in the AE low， medium， and high concentration groups was significantly decreased （P<0.05）. Lipofuscin， MDA， and IL-8 levels in the ADSCs treatment group and AE low， medium， and high concentration groups were significantly reduced （P<0.01）， while SOD content was significantly increased （P<0.05， P<0.01）. Compared with the ADSCs treatment group， lipofuscin and IL-8 levels in the AE low， medium， and high concentration groups were significantly reduced （P<0.05， P<0.01）. The MDA content was significantly decreased in the AE medium concentration group （P<0.01）. Compared with the model group， protein levels of p21， p53， Bax， and Caspase-3 in the ADSCs treatment group and AE low， medium， and high concentration groups were significantly reduced （P<0.05， P<0.01）， while the Bcl-2 protein level was significantly increased （P<0.01）. Compared with the ADSCs treatment group， protein levels of p21， p53， Bax， and Caspase-3 in the AE low， medium， and high concentration groups were significantly reduced （P<0.05， P<0.01）， and the Bcl-2 protein level in the AE low concentration group was significantly increased （P<0.01）. ConclusionThe results of this experiment show that EEBM-treated ADSCs or ADSCs may delay aging in castrated rats by inhibiting cell apoptosis， reducing cell cycle inhibitors and pro-inflammatory factors， enhancing antioxidant capacity， and reducing oxidative reactions. Moreover， EEBM-treated ADSCs demonstrate stronger anti-aging effects than ADSCs alone. This study provides experimental evidence supporting the clinical use of EEBM to intervene in ADSCs and delay aging.

The rapid development of artificial intelligence （AI） technology provides new opportunities for the modernisation of traditional Chinese medicine （TCM） diagnosis. By analysing the foundation， research progress and difficulties of the combination of AI and TCM diagnosis， it is concluded that AI has made remarkable development in intelligence-driven modernization of TCM tongue diagnosis， pulse diagnosis， listening and smelling diagnosis and text processing， and there are useful explorations in the field of constructing data-driven TCM diagnostic model and multidisciplinary integration of TCM diagnostic models. However， the current integration of AI technology in TCM diagnosis still faces many challenges， such as the scarcity and uneven quality of clinical data， the limited ability of AI algorithms to express TCM thinking model of syndrome differentiation and empirical knowledge， and the possible existence of ethical and privacy issues. By systematically sorting out the current research status and development direction of AI-empowered TCM diagnostics， it is proposed to promote the application of AI technology in TCM diagnostics in four aspects， namely， strengthening the construction of TCM big data and talent cultivation， encouraging cross-disciplinary cooperation， improving the legal and ethical framework， and promoting the popularity of the technology in primary care， so as to enhance the modernisation of TCM diagnostics.

ObjectiveThis paper proposes a novel real-time bedside pulmonary ventilation monitoring method for the diagnosis of chronic obstructive pulmonary disease (COPD), based on electrical impedance tomography (EIT). Four indicators—center of ventilation (CoV), global inhomogeneity index (GI), regional ventilation delay inhomogeneity (RVDI), and the ratio of forced expiratory volume in one second to forced vital capacity (FEV₁/FVC)—are calculated to enable the spatiotemporal assessment of COPD. MethodsA simulation of the respiratory cycles of COPD patients was first conducted, revealing significant differences in certain indicators compared to healthy individuals. The effectiveness of these indicators was then validated through experiments. A total of 93 subjects underwent multiple pulmonary function tests (PFTs) alongside simultaneous EIT measurements. Ventilation heterogeneity under different breathing patterns—including forced exhalation, forced inhalation, and quiet tidal breathing—was compared. EIT images and related indicators were analyzed to distinguish healthy individuals across different age groups from COPD patients. ResultsSimulation results demonstrated significant differences in CoV, GI, FEV₁/FVC, and RVDI between COPD patients and healthy individuals. Experimental findings indicated that, in terms of spatial heterogeneity, the GI values of COPD patients were significantly higher than those of the other two groups, while no significant differences were observed among healthy individuals. Regarding temporal heterogeneity, COPD patients exhibited significantly higher RVDI values than the other groups during both quiet breathing and forced inhalation. Moreover, during forced exhalation, the distribution of FEV₁/FVC values further highlighted the temporal delay heterogeneity of regional lung function in COPD patients, distinguishing them from healthy individuals of various ages. ConclusionEIT technology effectively reveals the spatiotemporal heterogeneity of regional lung function, which holds great promise for the diagnosis and management of COPD.

ObjectiveThe aim of this study was to investigate the prophylactic effects of caloric restriction (CR) on lipopolysaccharide (LPS)-induced septic cardiomyopathy (SCM) and to elucidate the mechanisms underlying the cardioprotective actions of CR. This research aims to provide innovative strategies and theoretical support for the prevention of SCM. MethodsA total of forty-eight 8-week-old male C57BL/6 mice, weighing between 20-25 g, were randomly assigned to 4 distinct groups, each consisting of 12 mice. The groups were designated as follows: CON (control), LPS, CR, and CR+LPS. Prior to the initiation of the CR protocol, the CR and CR+LPS groups underwent a 2-week acclimatization period during which individual food consumption was measured. The initial week of CR intervention was set at 80% of the baseline intake, followed by a reduction to 60% for the subsequent 5 weeks. After 6-week CR intervention, all 4 groups received an intraperitoneal injection of either normal saline or LPS (10 mg/kg). Twelve hours post-injection, heart function was assessed, and subsequently, heart and blood samples were collected. Serum inflammatory markers were quantified using enzyme-linked immunosorbent assay (ELISA). The serum myocardial enzyme spectrum was analyzed using an automated biochemical instrument. Myocardial tissue sections underwent hematoxylin and eosin (HE) staining and immunofluorescence (IF) staining. Western blot analysis was used to detect the expression of protein in myocardial tissue, including inflammatory markers (TNF-α, IL-9, IL-18), oxidative stress markers (iNOS, SOD2), pro-apoptotic markers (Bax/Bcl-2 ratio, CASP3), and SIRT3/SIRT6. ResultsTwelve hours after LPS injection, there was a significant decrease in ejection fraction (EF) and fractional shortening (FS) ratios, along with a notable increase in left ventricular end-systolic diameter (LVESD). Morphological and serum indicators (AST, LDH, CK, and CK-MB) indicated that LPS injection could induce myocardial structural disorders and myocardial injury. Furthermore, 6-week CR effectively prevented the myocardial injury. LPS injection also significantly increased the circulating inflammatory levels (IL-1β, TNF-α) in mice. IF and Western blot analyses revealed that LPS injection significantly up-regulating the expression of inflammatory-related proteins (TNF-α, IL-9, IL-18), oxidative stress-related proteins (iNOS, SOD2) and apoptotic proteins (Bax/Bcl-2 ratio, CASP3) in myocardial tissue. 6-week CR intervention significantly reduced circulating inflammatory levels and downregulated the expression of inflammatory, oxidative stress-related proteins and pro-apoptotic level in myocardial tissue. Additionally, LPS injection significantly downregulated the expression of SIRT3 and SIRT6 proteins in myocardial tissue, and CR intervention could restore the expression of SIRT3 proteins. ConclusionA 6-week CR could prevent LPS-induced septic cardiomyopathy, including cardiac function decline, myocardial structural damage, inflammation, oxidative stress, and apoptosis. The mechanism may be associated with the regulation of SIRT3 expression in myocardial tissue.

The risk prediction of paroxysmal atrial fibrillation (PAF) is a challenge in the field of biomedical engineering. This study integrated the advantages of machine learning feature engineering and end-to-end modeling of deep learning to propose a PAF risk prediction method based on multimodal feature fusion. Additionally, the study utilized four different feature selection methods and Pearson correlation analysis to determine the optimal multimodal feature set, and employed random forest for PAF risk assessment. The proposed method achieved accuracy of (92.3 ± 2.1)% and F1 score of (91.6 ± 2.9)% in a public dataset. In a clinical dataset, it achieved accuracy of (91.4 ± 2.0)% and F1 score of (90.8 ± 2.4)%. The method demonstrates generalization across multi-center datasets and holds promising clinical application prospects.
Humans ; Atrial Fibrillation/diagnosis* ; Machine Learning ; Deep Learning ; Risk Assessment/methods*

Fatigue driving is one of the leading causes of traffic accidents, posing a significant threat to drivers and road safety. Most existing methods focus on studying whole-brain multi-channel electroencephalogram (EEG) signals, which involve a large number of channels, complex data processing, and cumbersome wearable devices. To address this issue, this paper proposes a fatigue detection method based on frontal EEG signals and constructs a fatigue driving detection model using an asymptotic hierarchical fusion network. The model employed a hierarchical fusion strategy, integrating an attention mechanism module into the multi-level convolutional module. By utilizing both cross-attention and self-attention mechanisms, it effectively fused the hierarchical semantic features of power spectral density (PSD) and differential entropy (DE), enhancing the learning of feature dependencies and interactions. Experimental validation was conducted on the public SEED-VIG dataset. The proposed model achieved an accuracy of 89.80% using only four frontal EEG channels. Comparative experiments with existing methods demonstrate that the proposed model achieves high accuracy and superior practicality, providing valuable technical support for fatigue driving monitoring and prevention.
Humans ; Electroencephalography/methods* ; Automobile Driving ; Fatigue/diagnosis* ; Accidents, Traffic/prevention & control* ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; Algorithms ; Prefrontal Cortex/physiology*

Patients with periodontal disease often require combined periodontal-orthodontic interventions to restore periodontal health, function, and aesthetics, ensuring both patient satisfaction and long-term stability. Managing these patients involving orthodontic tooth movement can be particularly challenging due to compromised periodontal soft and hard tissues, especially in severe cases. Therefore, close collaboration between orthodontists and periodontists for comprehensive diagnosis and sequential treatment, along with diligent patient compliance throughout the entire process, is crucial for achieving favorable treatment outcomes. Moreover, long-term orthodontic retention and periodontal follow-up are essential to sustain treatment success. This expert consensus, informed by the latest clinical research and practical experience, addresses clinical considerations for orthodontic treatment of periodontal patients, delineating indications, objectives, procedures, and principles with the aim of providing clear and practical guidance for clinical practitioners.
Humans ; Consensus ; Orthodontics, Corrective/standards* ; Periodontal Diseases/complications* ; Tooth Movement Techniques/methods* ; Practice Guidelines as Topic