ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

ObjectiveObesity, a global chronic metabolic disease, is closely associated with disruptions in lipid metabolism and gut microbiota. Current intervention strategies still have limitations in terms of safety and microecological regulation, necessitating the exploration of novel natural regulatory approaches. Based on the early pathological characteristics of obesity, this study innovatively employs a rectal delivery method alongside a high-fat diet (HFD)-induced obesity model to systematically evaluate the inhibitory effects, safety, and gut microbiota regulation mechanisms of leek-derived and konjac-derived extracellular vesicles on obesity development. By simulating early clinical intervention scenarios, this study aims to explore the preventive potential of plant-derived extracellular vesicles during the initial stages of obesity onset. MethodsExtracellular vesicles from leek and konjac were isolated using ultracentrifugation combined with density gradient centrifugation. Their nanoscale properties were characterized by dynamic light scattering (DLS), transmission electron microscopy (TEM), and nanoparticle tracking analysis (NTA). Male C57BL/6J mice were randomly divided into four groups: normal control (NC), high-fat diet (HFD), leek-derived extracellular vesicles (LEVs), and konjac-derived extracellular vesicles (KEVs). Beginning simultaneously with HFD feeding, mice in the intervention groups received 20 g/L vesicles rectally every 3 d for 4 weeks. Body mass and body composition were monitored throughout. At endpoint, mouse serum, adipose tissue, and colonic contents were collected. Serum biochemical indices (lipid profile, liver and kidney function, cardiac markers) were assessed to evaluate safety and metabolic efficacy, while 16S rRNA sequencing was employed to analyze gut microbial structure and diversity. ResultsDLS, NTA, and TEM confirmed that both LEVs and KEVs exhibited typical cup-shaped nanostructures with average particle sizes of approximately 284 nm and 223 nm, respectively. LEVs and KEVs treatment significantly suppressed HFD-induced weight gain and elevation of body-fat percentage (P<0.05), and reduced accumulation of abdominal white and epididymal adipose tissue. Serological analyses showed that both vesicles lowered total cholesterol, triglycerides and LDL-cholesterol, and ameliorated liver enzyme profiles (ALT, AST), demonstrating lipid-metabolic regulation and hepatoprotective effects. No hepatic, renal or cardiac dysfunction was observed, indicating favorable safety. Gut microbiota analyses revealed that vesicle intervention partially restored HFD-depleted microbial diversity and reshaped community structure. Notably, LEVs markedly increased the relative abundance of the beneficial taxon Lachnospiraceae at the family level, which is known for producing short-chain fatty acids and enhancing intestinal barrier function. Furthermore, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) functional prediction suggested that LEVs and KEVs modulated gut microbial functions through distinct mechanisms: LEVs downregulated pathways related to ribosomes and DNA replication while enhancing xenobiotic degradation, whereas KEVs tended to upregulate energy metabolism and protein synthesis toward healthy levels. ConclusionRectally administered LEVs and KEVs exhibit excellent safety and pronounced metabolic benefits during the early phase of obesity, suppressing weight gain, correcting lipid dysregulation, and exerting effects via modulation of gut microbial composition and function. This study provides systematic experimental evidence supporting plant-derived exosome-like vesicles as an early intervention strategy against obesity.

ObjectiveObesity, a global chronic metabolic disease, is closely associated with disruptions in lipid metabolism and gut microbiota. Current intervention strategies still have limitations in terms of safety and microecological regulation, necessitating the exploration of novel natural regulatory approaches. Based on the early pathological characteristics of obesity, this study innovatively employs a rectal delivery method alongside a high-fat diet (HFD)-induced obesity model to systematically evaluate the inhibitory effects, safety, and gut microbiota regulation mechanisms of leek-derived and konjac-derived extracellular vesicles on obesity development. By simulating early clinical intervention scenarios, this study aims to explore the preventive potential of plant-derived extracellular vesicles during the initial stages of obesity onset. MethodsExtracellular vesicles from leek and konjac were isolated using ultracentrifugation combined with density gradient centrifugation. Their nanoscale properties were characterized by dynamic light scattering (DLS), transmission electron microscopy (TEM), and nanoparticle tracking analysis (NTA). Male C57BL/6J mice were randomly divided into four groups: normal control (NC), high-fat diet (HFD), leek-derived extracellular vesicles (LEVs), and konjac-derived extracellular vesicles (KEVs). Beginning simultaneously with HFD feeding, mice in the intervention groups received 20 g/L vesicles rectally every 3 d for 4 weeks. Body mass and body composition were monitored throughout. At endpoint, mouse serum, adipose tissue, and colonic contents were collected. Serum biochemical indices (lipid profile, liver and kidney function, cardiac markers) were assessed to evaluate safety and metabolic efficacy, while 16S rRNA sequencing was employed to analyze gut microbial structure and diversity. ResultsDLS, NTA, and TEM confirmed that both LEVs and KEVs exhibited typical cup-shaped nanostructures with average particle sizes of approximately 284 nm and 223 nm, respectively. LEVs and KEVs treatment significantly suppressed HFD-induced weight gain and elevation of body-fat percentage (P<0.05), and reduced accumulation of abdominal white and epididymal adipose tissue. Serological analyses showed that both vesicles lowered total cholesterol, triglycerides and LDL-cholesterol, and ameliorated liver enzyme profiles (ALT, AST), demonstrating lipid-metabolic regulation and hepatoprotective effects. No hepatic, renal or cardiac dysfunction was observed, indicating favorable safety. Gut microbiota analyses revealed that vesicle intervention partially restored HFD-depleted microbial diversity and reshaped community structure. Notably, LEVs markedly increased the relative abundance of the beneficial taxon Lachnospiraceae at the family level, which is known for producing short-chain fatty acids and enhancing intestinal barrier function. Furthermore, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) functional prediction suggested that LEVs and KEVs modulated gut microbial functions through distinct mechanisms: LEVs downregulated pathways related to ribosomes and DNA replication while enhancing xenobiotic degradation, whereas KEVs tended to upregulate energy metabolism and protein synthesis toward healthy levels. ConclusionRectally administered LEVs and KEVs exhibit excellent safety and pronounced metabolic benefits during the early phase of obesity, suppressing weight gain, correcting lipid dysregulation, and exerting effects via modulation of gut microbial composition and function. This study provides systematic experimental evidence supporting plant-derived exosome-like vesicles as an early intervention strategy against obesity.

ObjectiveHepatocellular carcinoma (HCC) represents 90% of all primary liver cancers. The main risk factors associated with HCC include viral hepatitis (B and/or C), alcohol abuse, and metabolic dysfunction-associated steatotic liver disease (MASLD), which progressively advance to liver fibrosis, cirrhosis, and ultimately evolve into HCC. Surgical resection represents the most effective treatment for HCC, while recent advances in immunotherapy, including immune checkpoint inhibitors and adoptive cell therapies, have provided improved treatment prospects for patients with unresectable HCC. However, the complex metabolic heterogeneity of HCC limits the therapeutic efficacy. Metabolic intermediates acyl-CoA not only provide energy and substrates for numerous biochemical reactions but also serve as donors for protein lysine acylation, a major class of post-translational modification (PTM). Therefore, a deeper understanding of the molecular mechanisms underlying protein lysine acylation and hepatocarcinogenesis is urgently needed. MethodsThe levels of protein lysine acylation and silence information regulator 5 (SIRT5) expression levels in clinical HCC samples were analyzed by Western blot. Quantitative malonylome and succinylome of HCC samples were analyzed by antibody-based affinity enrichment coupled with tandem mass spectrometry. The proliferation of HCC cells was analyzed with Cell Counting Kit-8 (CCK-8) assays, the apoptosis was quantified by Annexin V-FITC/propidium iodide (PI) staining coupled with flow cytometry, and the ability of cells to migrate was assayed by Transwell assays. The enzymatic activity of glutathione S-transferase Mu 1 (GSTM1) was quantified. Transgenic mice with hepatic overexpression of SIRT5 were constructed using CRISPR-Cas9, and primary hepatocarcinogenesis was induced by administration of diethylnitrosamine. ResultsWestern blot analysis indicated that the expression level of SIRT5 was elevated in clinical samples from HCC patients, and the levels of lysine malonylation, glutarylation, and succinylation were significantly reduced in HCC tissues. Knockout of SIRT5 in MHCC-97H and MHCC-97L hepatoma cells suppressed cell proliferation, and increased the percentage of apoptotic cells significantly. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the differentially malonylome and succinylome of HCC samples revealed significant enrichment in two major classes of biological processes: core energy metabolism (e.g., glycolysis/gluconeogenesis, tricarboxylic acid metabolic process, fatty acid beta oxidation) and detoxification and oxidative stress response (e.g., response to toxic substance, chemical carcinogenesis, reactive oxygen species (ROS)). SIRT5 removes malonylation from lysine residues in GSTM1 and restores its detoxification activity, which is crucial for the survival of hepatocytes under stressed conditions. More importantly, in vivo experiment indicated that hepatic-specific overexpression of SIRT5 in mice accelerated diethylnitrosamine-induced liver fibrosis and hepatocarcinogenesis, indicating the critical role of SIRT5 in HCC progression. ConclusionThis study highlights the previously unrecognized SIRT5-GSTM1 axis as a key regulator in hepatocarcinogenesis, and suggests a potential target for the treatment of patients with HCC.

Owing to its inherent advantages—such as being non-destructive, rapid, highly molecule-specific, and minimally interfered with by moisture—Raman spectroscopy has been widely adopted in the fields of skin barrier function assessment, monitoring the transdermal penetration of active cosmetic ingredients, and the identification and quality control of cosmetic products. Despite these strengths, the practical application of this technique faces a significant bottleneck: the strong fluorescence background generated by endogenous skin components and exogenous cosmetic additives. Endogenous skin substances, such as structural proteins (e.g., collagen and elastin), metabolic coenzymes (e.g., nicotinamide adenine dinucleotide), and pigments (e.g., melanin), together with exogenous cosmetic constituents like organic colorants, chemical sunscreens, and fragrances, often possess strong absorption and emission characteristics. When excited by lasers, these components produce a fluorescence background that can be 10⁶ to 10⁸ times stronger than the Raman scattering signals, effectively masking the inherently weak vibrational fingerprint information. In recent years, driven by the rapid development of optoelectronic hardware and artificial intelligence algorithms, fluorescence suppression strategies have evolved from isolated, single-method approaches into comprehensive, multi-level synergistic systems. These systems are categorized into three distinct tiers: sample preparation, signal acquisition, and data processing. At the sample preparation level, techniques such as photobleaching and surface-enhanced Raman spectroscopy (SERS) are employed to eliminate or bypass the generation of fluorescence at the source. At the signal acquisition level, instrumental improvements—including the use of long-wavelength near-infrared excitation (typically 785 nm or 1 064 nm), confocal spatial filtering, and shifted excitation Raman difference spectroscopy (SERDS)— are utilized to physically isolate Raman signals from the fluorescence background. Furthermore, at the data processing level, numerical baseline correction methods such as polynomial fitting, penalized least squares (e.g., airPLS, arPLS), wavelet transform, and derivative algorithms are increasingly integrated into the analytical pipeline to extract Raman spectral features from mixed signals without increasing hardware costs or acquisition time. This review provides a systematic categorization and critical evaluation of these fluorescence suppression methods, detailing their underlying principles, technical advantages, and inherent limitations in diverse experimental setups. By focusing on critical application scenarios—including skin barrier assessment, percutaneous absorption monitoring, the routine quality control of cosmetics, and the emerging field of portable on-site detection—this paper explores the current state of technique selection and optimization. Finally, the article discusses future development trends, emphasizing the necessity of constructing adaptive, tiered suppression strategies, developing intelligent and automated data processing algorithms, and promoting the integration of portable, multi-modal diagnostic devices. The objective of this review is to provide a comprehensive technical reference to facilitate the transition of Raman spectroscopy from a specialized laboratory tool into a routine, robust analytical platform for advancements in skin science and cosmetic research.

ObjectiveTo address the key challenges in breast electrical impedance tomography (EIT) reconstruction including the severe ill‑posedness of the inverse problem, limited measurement information from the electrodes, low contrast of small lesions, as well as the common drawbacks of traditional reconstruction methods such as boundary blurring, low spatial resolution, and weak small-target detection capability, and to overcome the limitations of existing deep learning approaches namely low training efficiency, insufficient physical consistency, and substantial information loss during the low‑to‑high dimensional mapping process, this study proposes a multi‑scale attention and dynamic sparse mask fusion network (MADS‑Net). The proposed method aims to enhance high‑resolution reconstruction accuracy, small-lesion detectability, noise robustness, and computational efficiency in breast‑screening-oriented EIT, thereby providing a feasible solution for the application of EIT in breast functional imaging. MethodsThe proposed network adopts a strategy of “locate first, reconstruct later”. It realizes adaptive allocation of computational resources through a dynamic mask module, alleviates information loss in the low-to-high dimensional mapping process via a multi-scale attention mechanism, and embeds physical constraints to improve the physical rationality of reconstruction results. The performance of the proposed method was validated through systematic numerical simulations, agar phantom experiments, and porcine tissue experiments. Comparisons were made with mainstream methods including the Gauss-Newton algorithm, convolutional neural networks, U-Net, and Deep D-Bar. The image correlation coefficient (ICC), root mean square error (RMSE), and peak signal-to-noise ratio (PSNR) were adopted as quantitative evaluation metrics. Furthermore, lesion‑level evaluation metrics were introduced to assess the capability of different methods in reconstructing abnormal target boundaries and small‑sized target regions. ResultsExperimental results demonstrate that MADS-Net outperforms all comparison methods across all test scenarios. In numerical simulations, it achieves higher accuracy in target morphology and spatial location recovery as well as better background suppression. In agar phantom experiments, its detection and imaging reliability for small targets is significantly superior to the comparison methods. In porcine tissue experiments, the model maintains good generalization performance and can adapt to complex biological tissue imaging scenarios. Quantitative results show that MADS-Net achieves an average ICC of 0.917 6±0.048 8, an RMSE as low as0.060 6±0.031 6, and a PSNR of (23.893 9±2.685 3) dB. It successfully detects tiny targets as small as 5 mm within a 100 mm diameter sensing field, reduces the total training duration by 23.5% compared with the baseline method, and maintains stable reconstruction performance even under strong noise interference. ConclusionThe results demonstrate that MADS-Net achieves favorable EIT reconstruction performance in numerical simulations, agar phantom experiments, and porcine tissue experiments, even under common practical imaging challenges including low signal-to-noise ratio and blurred tissue boundaries. This study provides a methodological reference for further research on EIT in breast-screening-related scenarios and other medical functional imaging tasks, and also offers a low-cost, radiation-free alternative for breast lesion screening that aligns with the clinical demand for non-invasive diagnostic tools. It should be noted that real clinical breast data have not yet been used for validation in this study, and the clinical applicability of the proposed method requires further investigation. Future work will prioritize collecting multi-center real clinical breast datasets, and further optimize the model’s generalization ability to adapt to individual differences in breast tissue composition to promote its clinical application.

Objective The aim of the study is to analyze the incidence patterns and epidemiological characteristics of parasitic and vector-borne diseases in the Minhang and Jinshan districts of Shanghai, and provide evidence-based information for optimizing prevention and control measures. Methods We collected information from epidemic reports and epidemiological investigation forms for cases of parasitic and vector-borne diseases from 2005 to 2024 in Shanghai's Minhang and Jinshan districts. Descriptive epidemiological method were adopted to conduct a comparative analysis of incidence trends, epidemic characteristics, and infection sources. Results A total of 229 cases in 7 categories were reported between 2005 and 2024, with an average annual incidence rate of 0.36/100 000. The average annual incidence rate in Minhang District was significantly higher than that in Jinshan District. The most prevalent diseases were malaria and dengue fever, followed by Japanese encephalitis. Monthly case reports were available, predominantly from June to October, with a peak in July-August. A statistically significant difference was observed between the two districts in case distribution during June-October (P＜0.001), whereas no significant difference was found for July-August (P=0.331). The majority of cases were young men (111/229,48.47%), aged 20-39 years. Migrant workers/farmers constituted the largest occupational group (55/229, 24.02%). Registered residents of other provinces and cities (147/229,64.19%), and the main source of infection was overseas importation (112/229,48.91%). There were no statistically significant differences in gender and household-registration composition between the two districts (P＞0.05). Significant differences existed in age, occupation, and source of infection (P＜0.05). The imported diseases were malaria and dengue fever, with Africa and Southeast Asia being the primary source locations. Malaria was the predominant disease imported from other provinces and cities, with cases having been imported from abroad since 2012. Local infections were mainly Japanese encephalitis, followed by malaria (with the last local case reported in 2010). Cases between 2005 and 2009 were predominantly imported from other provinces and cities. The proportion of cases imported from abroad increased sharply after 2010, particularly in Minhang District. Local infections sometimes arise in Jinshan District, with Japanese encephalitis serving as the primary type. Conclusions Incidence rates of parasitic and vector-borne diseases in Minhang and Jinshan districts have generally remained low. To further reduce this disease burden, it will be necessary to strengthen joint measures for prevention and control, eliminate the environmental risk factors of vector organisms, enhance monitoring and precise control of the environment and inbound personnel to prevent the occurrence and transmission of parasitic and vector-borne diseases.

The European Society of Cardiology (ESC) released the "2024 ESC guidelines for the management of elevated blood pressure and hypertension" on August 30, 2024. This guideline updates the 2018 "Guidelines for the management of arterial hypertension." One notable update is the introduction of the concept of "elevated blood pressure" (120-139/70-89 mm Hg). Additionally, a new systolic blood pressure target range of 120-129 mm Hg has been proposed for most patients receiving antihypertensive treatment. The guideline also includes numerous additions or revisions in areas such as non-pharmacological interventions and device-based treatments for hypertension. This article interprets the guideline's recommendations on definition and classification of elevated blood pressure and hypertension, and cardiovascular disease risk assessment, diagnosing hypertension and investigating underlying causes, preventing and treating elevated blood pressure and hypertension. We provide a comparison interpretation with the 2018 "Guidelines for the management of arterial hypertension" and the "2017 ACC/AHA guideline on the prevention, detection, evaluation, and management of high blood pressure in adults."

Objective To develop and evaluate a diagnostic model for autoimmune liver disease(AILD)using clinical indicators.Methods A total of 181 AILD patients(AILD group)diagnosed in the Department of Gastroenterology or Rheumatology and Immunology,the First Medical Center of Chinese PLA General Hospital,from June 2019 to February 2024,133 patients with hepatitis B virus cirrhosis(HBVC)(HBVC group),and 100 healthy individuals(HC group)were included in the study cohort for retrospective analysis.Clinical data,blood routine tests,and biochemical indicators were compared among the groups.Propensity score matching was used to balance the effects of factors such as gender and age.Multivariate logistic regression analysis was applied to identify characteristic indicators for AILD.Receiver operating characteristic(ROC)curves were used to evaluate the diagnostic efficacy of single and combined indicators,and the optimal diagnostic model for AILD was constructed.The Hosmer-Lemeshow test was performed to assess the model's goodness-of-fit,while ROC analysis was employed to evaluate the predictive validity.Additionally,an independent validation cohort,including 91 AILD patients(AILD validation group),82 HBVC patients(HBVC validation group),and 40 healthy individuals(HC validation group)who visited during the same period,was used to validate the diagnostic model.False-positive and false-negative rates were calculated to assess the diagnostic efficacy and clinical utility of the model.Results The age and the proportion of male patients in AILD and HBVC groups were higher than those in HC group,and the proportion of male patients in AILD group was higher than in HBVC group,the differences were statistically significant(P＜0.05).Prior to matching,compared with HC group,HBVC group and AILD group had significantly lower levels of hemoglobin(HB),red blood cell count(RBC),white blood cell count(WBC),platelet count(PLT),and albumin(ALB)(P＜0.05),but significantly higher levels of alanine aminotransferase(ALT),aspartate aminotransferase(AST),alkaline phosphatase(ALP),gamma-glutamyl transferase(GGT),total bilirubin(TBIL),and direct bilirubin(DBIL)(P＜0.05).Compared with HBVC group,AILD group had significantly higher levels of WBC,PLT,ALB,ALP,and GGT(P＜0.05 or P＜0.001)but significantly lower levels of TBIL and DBIL(P＜0.001).After maching,compared with HC group,AILD group still had significantly lower levels of HB,RBC,WBC,PLT,and ALB,but significantly higher levels of ALT,AST,ALP,GGT,TBIL,and DBIL(P＜0.05).Compared with HBVC group,AILD group had significantly higher levels of HB,RBC,WBC,PLT,ALB,ALT,ALP,and GGT,but significantly lower levels of DBIL(P＜0.05).ROC analysis results showed that AST,GGT,ALP,and DBIL had high predictive values for differentiating AILD from HC(AUC=0.843,0.804,0.802,0.773),while PLT,WBC,GGT,and ALP had high predictive values for differentiating AILD from HBVC(AUC=0.780,0.739,0.729,0.702).For distinguishing AILD from HC,the combined model of AST,ALP,and DBIL(Model e)had an AUC of 0.887,with a sensitivity of 82.2％,and a specificity of 94.7％.For distinguishing AILD from HBVC,the combined model of ALP,GGT,and PLT(Model D)had an AUC of 0.829,with a sensitivity of 78.5％and a specificity of 80.6％.Conclusions The combined diagnostic model based on AST,ALP,and DBIL can effectively differentiate healthy individuals without liver diseases from AILD patients,while the combined diagnostic model based on PLT,ALP,and GGT can effectively distinguish HBVC from AILD patients.