Reversible disruption of the blood-brain barrier (BBB) occurs within hours after the onset of ischemic stroke (IS), offering a critical window for therapeutic intervention. However, the molecular characteristics and their potential as circulating biomarkers associated with this transient phase of BBB dysfunction remain poorly defined. To elucidate these mechanisms, we employed an oxygen-glucose deprivation (OGD) model in human cerebral microvascular endothelial cells (hCMEC/D3) to simulate early ischemic stress, and systematically profiled their secreted proteome and metabolome. By comparing with non-brain-derived human umbilical vein endothelial cells (HUVECs), we identified brain endothelium-specific hypoxic response signatures. These molecules were significantly enriched in pathways related to metabolic reprogramming, antioxidant defense, and epigenetic regulation pathways, indicating a coordinated adaptive response to preserve BBB homeostasis. Furthermore, integrative multi-omics analysis revealed 14 protein-metabolite pairs with potential functional synergy. Based on a multi-criteria screening strategy including brain specificity, functional relevance, and secretory potential, we prioritized 10 candidate circulating biomarkers: ALDH2, ITGA5, KYNU, TFRC, CD44, COL1A2, HEXB, HSPG2, THBS4, and DLD. Preliminary validation using serum from acute IS (AIS) patients and healthy controls showed significantly altered levels of ALDH2, ITGA5, KYNU, and TFRC, with TFRC exhibiting promising diagnostic performance both individually (AUC = 0.816) and in combination with the other three biomarkers (AUC = 0.876). Moreover, multivariate logistic regression analysis revealed that elevated TFRC was independently associated with poor 90-day outcomes (OR = 1.02, 95

This study aimed to develop a hepatocellular carcinoma (HCC) risk prediction model based on clinlabomics and develop an online prediction tool to provide a novel approach for early HCC diagnosis. We retrospectively collected clinical and laboratory data from 1,017 patients (576 HCC cases, 358 cirrhosis cases, and 83 chronic viral hepatitis cases), randomly dividing them into a training set (798 cases) and a validation set (219 cases). Key variables were selected using LASSO logistic regression and variable importance scoring, followed by the construction of seven machine learning models. Model performance was comprehensively evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The optimal model was interpreted using SHapley Additive exPlanations (SHAP). Subsequently, we developed an online prediction tool for real-time risk prediction and validated its predictive performance. A total of 16 variables significantly related to HCC were selected for model construction, including: age, liver nodules (≥ 1 cm), CRP, RBC, PDW, AFP, PIVKA-II, IBIL, TBA, GASR, AAAR, ALBI, CK, INR, FIB, and FDP. Among the seven machine learning models developed, the SVM model demonstrated optimal performance, achieving AUC values of 0.962 (training set) and 0.877 (validation set). In prospective validation, the SVM-based online prediction tool demonstrated 85

This study aimed to identify metabolic footprints associated with distinct phenotypes of acute ischemic stroke (AIS) using untargeted metabolomics. We included 20 samples each from AIS phenotype A (n = 251), B (n = 213), and C (n = 43) groups, along with 20 age- and gender-matched healthy controls (HCs). Plasma metabolic profiles were analyzed using liquid chromatography-mass spectrometry (LC-MS). Weighted gene correlation network analysis (WGCNA) evaluated associations between metabolite clusters and clinical traits, including the National Institutes of Health Stroke Scale (NIHSS) and the modified Rankin Scale (mRS). We identified three, five, and six key differential metabolites for diagnosing phenotypes A, B, and C, respectively, demonstrating high diagnostic performance. These metabolites were focused on fatty acids, sex hormones, amino acids, and their derivatives. WGCNA identified 12 core metabolites involved in phenotype progression. Notably, phenylalanylphenylalanine and phenylalanylleucine were inversely correlated with disease severity and disability. Metabolites related to energy supply and inflammation were common across phenotypes, with additional changes in ionic homeostasis in phenotype A and decreased neurotransmitter release in phenotype C. Biosynthesis of unsaturated fatty acids and the pentose phosphate pathway (PPP) were relevant across all phenotypes, while the folate biosynthesis pathway was linked to phenotype C and clinical scales. Key metabolites, including phenylalanylphenylalanine and phenylalanylleucine, and pathways such as folate biosynthesis, significantly contribute to AIS severity and differentiation of phenotypes. These findings offer new insights into the pathogenesis and mechanisms underlying AIS phenotypes.

BACKGROUND:This study investigates the relationships between folate intake, RBC folate, serum folate levels, and stroke risk, with an emphasis on the mediating roles of the dietary inflammatory index (DII) and systemic immune-inflammation index (SII). METHODS:A cross-sectional analysis was conducted using 24,106 participants from NHANES (2007-2018). Associations were assessed with weighted multivariate logistic regression, adjusting for key confounders. Propensity score matching (PSM) was applied, yielding 1,838 matched participants, respectively. Nonlinear relationships were analyzed with restricted cubic splines, and mediation analysis was performed for DII and SII. RESULTS:Post-PSM, folate intake in Q2 (252-350 μg/day), Q3 (350-484 μg/day), and Q4 (> 484 μg/day) was significantly inversely associated with stroke risk (trend P < 0.05), with adjusted ORs of 0.62 (95 % CI: 0.45-0.85), 0.65 (95 % CI: 0.46-0.90), and 0.60 (95 % CI: 0.42-0.86), respectively. Serum folate levels in Q3 (37.0 - 54.8 nmol/L) were also protective (OR: 0.47, 95 % CI: 0.32-0.68, trend P < 0.05). Serum folate levels exhibited a biphasic effect, with the lowest stroke risk at 41.9 nmol/L before PSM and 43.3 nmol/L after PSM. Mediation analysis showed DII mediated 45.2 % of the relationship between folate intake and stroke risk (P = 0.018), while SII's mediation effect was minimal (0.412 %, P = 0.016). No significant interactions were observed between folate intake, serum folate and stratified variables (P > 0.05) after PSM. CONCLUSION:Higher folate intake lowers stroke risk, with DII playing a significant mediating role, while serum folate presents a biphasic risk pattern. Personalized dietary strategies addressing folate intake and inflammation may be crucial for stroke prevention.

ABSTRACT Staphylococcus aureus is a pathogen responsible for diverse severe infections. The global spread of methicillin-resistant S. aureus (MRSA) necessitates innovative therapeutic approaches beyond traditional antibiotics. S. aureus virulence mechanisms remain a critical concern. Targeting histidinol dehydrogenase (HisD), a key enzyme in histidine biosynthesis, presents a novel anti-virulence strategy. We used the derivative strains of Newman strains (ΔhisD, ΔhisD::pRAB-hisD, and WT::pRAB-hisD) and clinical strains to study the role of HisD in the pathogenicity of S. aureus. HisD inhibition by pixantrone was further evaluated. The absence of hisD significantly reduced hemolytic activity and biofilm formation, accompanied by the downregulation of virulence genes (hla, coa, hlgA-C, lukE/S/F, and NWMN_1873) and the saeR/S two-component system (P < 0.05). The expression of biofilm-inhibiting proteases was elevated, notably Aur and ScpA. Murine challenge revealed that ΔhisD exhibited 5.6-fold higher LD50 (4.17 × 109 CFU/mL vs. WT 7.42 × 108 CFU/mL) and reduced organ colonization (P < 0.05). Through structure-based virtual screening and SPR affinity verification, we discovered pixantrone—a nitrogenated anthraquinone—as a potent HisD inhibitor binding via four hydrogen bonds and two salt bridges. Pixantrone dose-dependently (25–200 μM) suppressed virulence phenotypes in vitro, achieving hemolysis inhibition, virulence gene inhibition, and biofilm reduction. In vivo, pixantrone (30 mg/kg) decreased serum CRP, IL-6, and TNF-α levels while diminishing abscess sizes and splenic bacterial loads (P < 0.05 vs. untreated). These findings establish HisD as a pivotal virulence regulator in S. aureus through saeR/S-mediated pathways. Pixantrone demonstrates potent anti-virulence efficacy, positioning HisD inhibition as a promising therapeutic strategy against S. aureus infections. This study provides foundational insights for developing HisD-targeted agents to combat antibiotic-resistant staphylococcal pathogens.IMPORTANCEThe increase in drug-resistant Staphylococcus aureus (MRSA) demands therapies that block virulence without promoting resistance. We identify histidinol dehydrogenase (HisD), a histidine-synthesis enzyme, as a key controller of S. aureus pathogenicity. Disrupting HisD genetically or with pixantrone—a newly identified inhibitor—reduces bacterial toxicity, biofilm formation, and virulence gene activity while improving survival and reducing organ damage in infected mice. Pixantrone's dose-dependent suppression of infection severity and inflammation positions it as a therapeutic candidate. Unlike traditional antibiotics, this strategy disarms bacteria rather than killing them, reducing resistance risks. By uncovering HisD’s role in connecting metabolism to virulence through the saeR/S system, we reveal a druggable target for fighting multidrug-resistant infections. This work addresses the urgent need for innovative solutions to the global antibiotic resistance crisis, paving the way for therapies that outsmart evolving superbugs.

ObjectiveAcute ischemic stroke (AIS) is a heterogeneous condition. To stratify the heterogeneity, identify novel phenotypes, and develop Clinlabomics models of phenotypes that can conduct more personalized treatments for AIS.MethodsIn a retrospective analysis, consecutive AIS and non-AIS inpatients were enrolled. An unsupervised k-means clustering algorithm was used to classify AIS patients into distinct novel phenotypes. Besides, the intergroup comparisons across the phenotypes were performed in clinical and laboratory data. Next, the least absolute shrinkage and selection operator (LASSO) algorithm was used to select essential variables. In addition, Clinlabomics predictive models of phenotypes were established by a support vector machines (SVM) classifier. We used the area under curve (AUC), accuracy, sensitivity, and specificity to evaluate the performance of the models.ResultsOf the three derived phenotypes in 909 AIS patients [median age 64 (IQR: 17) years, 69% male], in phenotype 1 (N = 401), patients were relatively young and obese and had significantly elevated levels of lipids. Phenotype 2 (N = 463) was associated with abnormal ion levels. Phenotype 3 (N = 45) was characterized by the highest level of inflammation, accompanied by mild multiple-organ dysfunction. The external validation cohort prospectively collected 507 AIS patients [median age 60 (IQR: 18) years, 70% male]. Phenotype characteristics were similar in the validation cohort. After LASSO analysis, Clinlabomics models of phenotype 1 and 2 were constructed by the SVM algorithm, yielding high AUC (0.977, 95% CI: 0.961–0.993 and 0.984, 95% CI: 0.971–0.997), accuracy (0.936, 95% CI: 0.922–0.956 and 0.952, 95% CI: 0.938–0.972), sensitivity (0.984, 95% CI: 0.968–0.998 and 0.958, 95% CI: 0.939–0.984), and specificity (0.892, 95% CI: 0.874–0.926 and 0.945, 95% CI: 0.923–0.969).ConclusionIn this study, three novel phenotypes that reflected the abnormal variables of AIS patients were identified, and the Clinlabomics models of phenotypes were established, which are conducive to individualized treatments.

Objective In this study, serum markers of acute ischemic stroke (AICS) with carotid artery plaque were retrospectively evaluated to establish a basis for discovering serological indicators for early warning of acute ischemic stroke (AICS). Methods A total of 248 patients with AICS were enrolled in Lanzhou University Second Hospital from January 2019 to December 2020. The study population included 136 males and 112 females, 64 ± 11 years of age. Of these, there were 90 patients with a transient ischemic attack (TIA), including 60 males and 30 females, aged 64 ± 8 years old. Patients with AICS were stratified by carotid ultrasound into a plaque group (n = 154) and a non-plaque group (n = 94). A total of 160 healthy subjects were selected as the control group. Serum lipoprotein-associated phospholipase A2 (Lp-PLA2), amyloid A (SAA), immunoglobulin E (IgE), D-dimer (D-D), total cholesterol (TC), triglyceride (TG), and low-density lipoprotein cholesterol (LDL-C) were collected from all subjects. Multivariate logistic regression was used to analyze the risk factors of AICS with carotid plaque. ROC curve was used to analyze the diagnostic efficacy of AICS with carotid plaque. Results The IgE, Lp-PLA2, SAA, LDL-C, TC, TG, and D-D levels in the AICS group were higher than those in the TIA group and healthy control group (P < 0.05). The IgE level was significantly higher than that in the healthy control group and TIA group. The IgE level in the AICS plaque group was significantly higher than that in the AICS non-plaque group (P < 0.01), and the Lp-PLA2 level was also different (P < 0.05). The incidence of AICS was positively correlated with Lp-PLA2, TC, IgE, TG, D-D, SAA and LDL-C (r = 0.611, 0.499, 0.478, 0.431, 0.386, 0.332, 0.280, all P < 0.05). The incidence of AICS with plaque was only positively correlated with IgE and Lp-PLA2 (r = 0.588, 0.246, P < 0.05). Logistic regression analysis showed that IgE and Lp-PLA2 were independent risk factors for predicting the occurrence of AICS with carotid plaque (P < 0.05). ROC curve analysis showed that the AUC of IgE (0.849) was significantly higher than other indicators; its sensitivity and specificity were also the highest, indicating that IgE can improve the diagnostic efficiency of AICS with carotid plaque. Conclusion IgE is a serum laboratory indicator used to diagnose AICS disease with carotid plaque, which lays a foundation for further research on potential early warning indicators of AICS disease.

Objective: To investigate the diagnostic value of serum lipoprotein associated phospholipase A2 (Lp-PLA2), amyloid A (SAA) and immunoglobulin E (IgE) in patients with type 2 diabetes (T2DM) mellitus complicated with atherosclerotic disease. Methods: From June to December 2019, 224 patients with T2DM in the Second Hospital of Lanzhou University were selected, including 144 males and 80 females, aged (61±11) years. According to the results of imaging examination, the patients were divided into T2DM with AS group (T2DM-AS group, n=160) and T2DM group (n=64); Healthy subjects in the same period were selected as healthy control group (n=160). Lp-PLA2, IgE, SAA, hs-CRP, TC, TG, HDL-C, LDL-C and Hcy were detected in all patients and healthy controls. The correlation between the above indexes, gender, age and T2DM with AS was analyzed; Multivariate logistic regression was used to analyze the risk factors of T2DM with AS. Results: Compared with the healthy control group, the levels of IgE and Lp-PLA2 in T2DM-AS group and T2DM group were increased, and the levels of SAA in T2DM-AS group were increased (all P<0.05); Compared with T2DM group, the levels of Lp-PLA2, IgE and SAA were increased in T2DM-AS group (all P<0.05). T2DM with AS was positively correlated with age, IgE, Lp-PLA2 and SAA (r=0.468, 0.269, 0.486, 0.418, all P<0.05), and negatively correlated with HDL-C (r=-0.338, P<0.05). Multivariate logistic regression analysis showed that age (OR=0.865, 95%CI: 0.763-0.982, P<0.05), IgE (OR=0.910, 95%CI: 0.840-0.987, P<0.05) and Lp-PLA2 (OR=0.942, 95%CI: 0.910-0.986, P<0.05) were risk factors of T2DM with AS. ROC curve showed that the combined detection of Lp-PLA2, SAA and IgE could improve the diagnostic efficiency of T2DM with AS (AUC=0.895, P<0.05), the sensitivity was 80.0%, and the specificity was 93.7%. Conclusion: The levels of Lp-PLA2, IgE and SAA increase in T2DM patients with AS. The combined detection of Lp-PLA2, SAA and IgE can improve the diagnostic efficiency of T2DM patients with AS.

Objective:To systematically review the efficacy of common biomarkers of epithelial to mesenchymal transition (EMT) in HepG2 cells with hypoxia intervention.Methods:PubMed, Web of Science, Embase, CNKI, CBM, Wan Fang Data and VIP databases were electronically searched from the establishment of the database to March 2021. Then, meta-analysis was performed by using Stata 15.0 software.Results:A total of 14 vitro experiments were included. The results of meta-analysis showed that the expression level of E-cadherin of hypoxia intervention group was lower than that of negative control group [SMD=-3.8, 95% confidence interval ( CI): -5.85--1.76, P<0.05], and the expression level of Vimentin (SMD=4.48, 95% CI: 2.32-6.64, P<0.05), Snail (SMD=6.22, 95% CI: 0.55-11.9, P<0.05), Twist (SMD=4.06, 95% CI: 1.03-7.08, P<0.05) and β-Catenin (SMD=72, 95% CI: 2.46-141.54, P<0.05) of hypoxia intervention group was higher than that of negative control group. The differences between each two groups were statistically significant. However, the expression level of N-Cadherin (SMD=4.24, 95% CI: -0.18-8.670, P>0.05) and α-SMA (SMD=6.28, 95% CI: 0.01-12.54, P>0.05) between hypoxia intervention group and negative control group were not found statistically significant differences. Conclusion:Biomarkers E-cadherin, Vimentin, Snail, Twist and β-Catenin can be selected as experimental indicators in the vitro cell experiments of hypoxia-induced EMT in HepG2 cells.

目的:了解并验证罗氏公司Cobas8000全自动生化分析仪原装试剂的性能,以确保检测结果的准确性和可靠性.方法:根据美国临床实验室标准化委员会(CLSI)的标准指南要求,参照罗氏厂家提供的试剂性能验证方案,对该生化分析仪检测的36个配套试剂的检验项目的正确度、精密度、线性范围、最佳稀释倍数和参考区间进行性能评价.结果:36项检验项目检测结果的批内精密度、总精密度、正确度、线性范围、最佳稀释倍数和参考区间均在判定标准范围之内,均通过验证.结论:原装配套试剂在罗氏Cobas8000全自动生化分析仪上进行相关的血生化检测均达到质量标准的要求,满足临床标本检测分析的要求.

婴幼儿骨髓坏死情况复杂，故临床上疑似骨髓坏死的患者应多部位取材，避免误诊、漏诊，提高骨髓坏死判断，为原发疾病诊断争取时间。本研究通过患者血象与骨髓象涂片检查、骨髓组织病理检查、流式细胞术免疫分型检查，以查找该例幼儿骨髓坏死可能伴随的原发疾病。现报道如下。