OBJECTIVE: To investigate the clinical utility of novel of new hematological markers in the preoperative diagnosis of periprosthetic joint infection (PJI). METHODS: A retrospective analysis was conducted on a total of 149 patients who underwent revision of total hip arthroplasty (THA) or total knee arthroplasty (TKA) at a single center between January 2016 and June 2022, including 63 males and 86 females, aged from 47 to 93 years old with an average of (69.5±11.8) years old. Of them, 46 were diagnosed as PJI(PJI group), including 22 males and 24 females. The mean age was (71.3±12.5) years old. The body mass index (BMI) was (26.4±3.1) kg·m^-2. And 103 patients were diagnosed as aseptic prosthesis loosening (aseptic group), including 41 males and 62 females. The mean age was (68.7±11.4) years old. The BMI was (25.8±3.5) kg·m^-2. Preoperatively analyzed clinical parameters included C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), albumin, globulin, albumin-to-globulin ratio (AGR), plasma D-dimer, and plasma fibrinogen. The receiver operating characteristic curve (ROC), sensitivity, and specificity analysis were employed to compare the diagnostic value of each blood marker in preoperative PJI diagnosis. RESULTS: In the PJI group, the levels of CRP were 16.6 (7.6, 4.5) mg·L^-1, ESR was 17.0 (12.8, 35.5) mm·h^-1, plasma D-dimer was 1.0 (0.5, 3.1) μg·L^-1, and plasma fibrinogen was 4.2 (3.2, 3.1) mg·L^-1;all of which were higher compared to the aseptic group with CRP at 4.2 (2.6, 7.8) mg·L^-1, ESR at 12.0(8.0, 20.0 )mm·h^-l, D-dimer at 0.4(0.2, 0.7)μg·L^-1, and fibrinogen at 2.8(2.4, 3.3 ) g·L^-1(P<0.05). However, the albumin level of 35.3 (32.3, 37.5) g·L^-1 and the WBC ratio of 1.0(0.9, 1.1) in the PJI group were significantly lower compared to the aseptic group with levels of 39.8 (36.1, 41.8) g·L^-1 and 1.4 (1.3, 1.5), respectively (P<0.05). Only the area under the curve (AUC) of AGR and plasma fibrinogen were greater than 0.8. The optimal predictive cut-1off, AUC, sensitivity and specificity were 3.4 g·L-1, 0.820, 69.57% and 84.47% for plasma fibrinogen; 1.18, 0.813, 82.61% and 78.64% for AGR, respectively. CONCLUSION AGR and plasma fibrinogen are promising blood markers for improving the diagnosis of PJI.
Humans ; Female ; Fibrinogen/metabolism* ; Male ; Middle Aged ; Aged ; Prosthesis-Related Infections/blood* ; Retrospective Studies ; Biomarkers/blood* ; Aged, 80 and over ; Arthroplasty, Replacement, Knee/adverse effects* ; Arthroplasty, Replacement, Hip/adverse effects* ; Leukocyte Count

OBJECTIVE: To investigate the variability of bone metabolism levels among different populations and its association with knee osteoarthritis (KOA) pain. METHODS: A total of 50 people (control group) who participated in physical examination from January 2023 to June 2023 were selected, including 26 males and 24 females, wtih a mean aged of (52.14±9.04) years old ranging 41 to 65 years old. The other 50 patients with knee osteoarthritis(case group) who attended the outpatient clinic of the Orthopedics and Traumatology Department in the same time period, including 19 males and 31 females, with a mean age of (53.60±7.76) years old ranging 40 to 65 years. The two groups of Western Ontario and McMaster Universities Osteoarthritis Index(WOMAC) and bone metabolism markers, such as 25-hydroxy-cholecalciferol[25(OH)D], β-isomerized typeⅠcollagen C-telopeptide breakdown products (β-CTX), total typeⅠprocollagen N-terminal propeptide (t-PINP), osteocalcin (OC), parathormone (PTH) levels were compared. Pearson correlation analysis was used to compare the correlation between two groups of bone metabolism related markers and WOMAC. RESULTS: The WOMAC score of the case group (39.90±2.34) was higher than that of the control group (3.60±0.57), with significant difference (P<0.05). There was no significant difference between the two groups of 25 (OH)D, β-CTX and PTH (P>0.05). The t-PINP and OC of the case group were (62.90±52.40) and (19.88±10.15) ng·ml-1, respectively, and those of the control group were (38.86±10.82) and (14.90±3.62) ng·ml^-1, respectively;the t-PINP and OC of the case group were higher than those of the control group, with significant difference (P<0.05). Pearson correlation analysis showed that t-PINP was positively correlated with WOMAC pain score in the case group (r²=0.045, P<0.01). CONCLUSION Bone metabolism levels in the serum of patients with knee osteoarthritis are different from those of healthy people, and the difference between OC and t-PINP is the most obvious, and the concentration of t-PINP levels is positively correlated with pain symptoms in patients with KOA. However, the specific mechanism of correlation between the bone metabolism levels of patients with KOA and their pain symptoms needs to be further elucidated by basic experimental research as well as by enlarging the samples.
Humans ; Female ; Male ; Middle Aged ; Osteoarthritis, Knee/metabolism* ; Aged ; Adult ; Bone and Bones/metabolism* ; Pain/etiology* ; Biomarkers/metabolism*

Objective To explore the clinical and immunological significance of CCDC97 in hepatocellular carcinoma (HCC). Methods Clinical data and RNA sequencing results from HCC patients were retrieved from TCGA and ICGC databases. Bioinformatics analysis and in vitro experiments were performed to investigate the role of CCDC97 in HCC. Results The expression level of CCDC97 was elevated in HCC patients and HCC cells, closely associated with pathological features and prognosis. CCDC97 was identified as a novel prognostic biomarker. It is linked to the spliceosome pathway, which is significantly active in tumors and potentially promotes carcinogenesis. CCDC97 is also highly expressed in various immune cells and is associated with microenvironment. Furthermore, knocking down CCDC97 in vitro suppressed cell migration, invasion, and proliferation. Conclusion CCDC97 plays a critical role in HCC progression and the immune microenvironment, making it a potential target for prognosis and therapeutic intervention.
Humans ; Carcinoma, Hepatocellular/metabolism* ; Liver Neoplasms/metabolism* ; Tumor Microenvironment/genetics* ; Cell Movement/genetics* ; Cell Proliferation ; Prognosis ; Cell Line, Tumor ; Gene Expression Regulation, Neoplastic ; Biomarkers, Tumor/genetics* ; Male

Objective To investigate the clinical relevance and diagnostic or prognostic value of ST3β-galactoside α-2, 3-sialyltransferase 1 (ST3GAL1) in glioma and to confirm its role in promoting malignant phenotypes. Methods Using data from The Cancer Genome Atlas (TCGA) database, we analyzed the correlation between ST3GAL1 expression levels in glioma and clinical parameters to evaluate its diagnostic and prognostic value. The impact of ST3GAL1 on malignant phenotypes of glioma cells-including proliferation, cell cycle progression, apoptosis, and invasion was further validated through ST3GAL1 knockdown experiments. Results The expression level of ST3GAL1 was significantly higher in glioma tissues compared to healthy brain tissues and showed a strong correlation with clinical characteristics of glioma patients. Survival analysis and receiver operating characteristic (ROC) curve demonstrated that ST3GAL1 could serve as a potential diagnostic and prognostic biomarker for glioma. Knockdown of ST3GAL1 suppressed proliferation, invasion, and migration capabilities of glioma cell lines, and induced G1-phase cell cycle arrest. Conclusion ST3GAL1 promotes malignant phenotypes in glioma and plays a critical role in its malignant progression, suggesting its potential as a biomarker for glioma diagnosis and prognosis.
Humans ; Sialyltransferases/metabolism* ; Glioma/diagnosis* ; Cell Proliferation/genetics* ; Cell Line, Tumor ; Brain Neoplasms/enzymology* ; beta-Galactoside alpha-2,3-Sialyltransferase ; Disease Progression ; Prognosis ; Cell Movement/genetics* ; Apoptosis/genetics* ; Male ; Female ; Gene Expression Regulation, Neoplastic ; Biomarkers, Tumor/metabolism* ; Middle Aged

Objective To mine and analyze the routine blood test data of children with allergic rhinitis (AR), identify routine blood parameters related to childhood allergic rhinitis, establish an effective diagnostic model, and evaluate the performance of the model. Methods This study was a retrospective study of clinical cases. The experimental group comprised a total of 1110 children diagnosed with AR at the First Affiliated Hospital of Air Force Medical University during the period from December 12, 2020 to December 12, 2021, while the control group included 1109 children without a history of allergic rhinitis or other allergic diseases who underwent routine physical examinations during the same period. Information such as age, sex and routine blood test results was collected for all subjects. The levels of routine blood test indicators were compared between AR children and healthy children using comprehensive intelligent baseline analysis, with indicators of P≥0.05 excluded; variables were screened by Lasso regression. Binary Logistic regression was used to further evaluate the influence of multiple routine blood indexes on the results. Five kinds of machine model algorithms were used, namely extreme value gradient lift (XGBoost), logistic regression (LR), gradient lift decision tree (LGBMC), Random forest (RF) and adaptive lift algorithm (AdaBoost), to establish the diagnostic models. The receiver operating characteristic (ROC) curve was used to screen the optimal model. The best LightGBM algorithm was used to build an online patient risk assessment tool for clinical application. Results Statistically significant differences were observed between the AR group and the control group in the following routine blood test indicators: mean cellular hemoglobin concentration (MCHC), hemoglobin (HGB), absolute value of basophils (BASO), absolute value of eosinophils (EOS), large platelet ratio (P-LCR), mean platelet volume (MPV), platelet distribution width (PDW), platelet count (PLT), absolute values of leukocyte neutrophil (W-LCC), leukocyte monocyte (W-MCC), leukocyte lymphocyte (W-SCC), and age. Lasso regression identified these variables as important predictors, and binary Logistic regression further analyzed the significant influence of these variables on the results. The optimal machine learning algorithm LightGBM was used to establish a multi-index joint detection model. The model showed robust prediction performance in the training set, with AUC values of 0.8512 and 0.8103 in the internal validation set. Conclusion The identified routine blood parameters can be used as potential biomarkers for early diagnosis and risk assessment of AR, which can improve the accuracy and efficiency of diagnosis. The established model provides scientific basis for more accurate diagnostic tools and personalized prevention strategies. Future studies should prospectively validate these findings and explore their applicability in other related diseases.
Humans ; Male ; Female ; Rhinitis, Allergic/blood* ; Child ; Biomarkers/blood* ; Retrospective Studies ; Early Diagnosis ; Child, Preschool ; ROC Curve ; Logistic Models ; Hematologic Tests ; Algorithms ; Adolescent ; Machine Learning

The early diagnosis and precise treatment of esophageal squamous cell carcinoma (ESCC) hold significant clinical value in improving patient survival rate. Current diagnostic methods for early-stage ESCC primarily rely on invasive procedures and endoscopy, which can cause discomfort and financial burden for patients. Therefore, non-invasive biomarkers with high sensitivity and specificity present a more suitable alternative for early tumor diagnosis. Tumor associated autoantibodies (TAAb), identified as potential biomarkers, have considerable clinical implications for the early diagnosis, treatment monitoring, and prognosis assessment of ESCC. Here in we aim to summarize recent research on ESCC-related autoantibodies, including their background, types and development, analyze the potential of those autoantibodies in clinical diagnosis, treatment monitoring, and prognosis assessment, and also discuss the limitations of existing research and future directions. The goal is to provide a theoretical foundation for the early diagnosis and personalized treatment of ESCC.
Humans ; Autoantibodies/immunology* ; Esophageal Neoplasms/therapy* ; Esophageal Squamous Cell Carcinoma/immunology* ; Biomarkers, Tumor/immunology* ; Prognosis ; Carcinoma, Squamous Cell/diagnosis* ; Animals

Objective To investigate the causal relationship between serum total bile acid (TBA) levels and gastric cancer (GC) using regression discontinuity design (RDD). Methods A total of 1244 GC patients and 1333 healthy controls were included in the study. Demographic characteristics, gallbladder disease history, tumor markers, and serum TBA levels were collected from both groups. Logistic regression was used to construct a risk prediction model to estimate the risk of GC. RDD was employed with serum TBA as the grouping variable and the individual risk of developing GC as the outcome variable. Results The predictive factors in the GC risk prediction model included age, sex, body mass index(BMI), serum TBA, carcinoembryoniv antigen(CEA), alpha fetoprotein(AFP), carbohydrate antigen 199(CA199), and CA125. Serum TBA was identified as an independent risk factor for GC (OR=1.054, 95% CI: 1.030 to 1.079). RDD analysis indicated that when serum TBA levels reached 8 μmol/L, the probability of developing GC increased sharply by 23.7%. The breakpoint remained statistically significant following validity and robustness assessments. Conclusion The study demonstrates a positive causal relationship between serum TBA levels and GC, when the serum TBA level reaches 8 μmol/L, the risk of an individual developing GC increases sharply.
Humans ; Stomach Neoplasms/etiology* ; Male ; Female ; Middle Aged ; Bile Acids and Salts/blood* ; Aged ; Adult ; Risk Factors ; Case-Control Studies ; Biomarkers, Tumor/blood* ; Logistic Models

Objective Through integrative bioinformatics analysis of multi-source transcriptomic data, potential biomarkers to asthma epithelial cells were identified. The expression of these candidate target was subsequently validated in lung tissues and epithelial cells from asthma models. Methods The gene expression profile data of epithelial cells from three asthma patient cohorts and corresponding healthy controls were integrated from the Gene Expression Omnibus (GEO) database. Differential expression analysis and gene co-expression network analysis were performed to identify key genes and biological pathways associated with asthma. The key genes were validated in lung tissues and epithelial cells in asthma animal models. Results Differential gene expression analysis revealed 1121 upregulated and 1484 downregulated genes in epithelial cells from asthma patients compared with healthy controls. The biological pathway enrichment analysis revealed that the upregulated genes were mainly involved in glycosylation processes, whereas the downregulated genes were mainly associated with immune cell differentiation process. The gene co-expression network analysis revealed that module 9, enriched in glycosylation-related pathways, was significantly positively correlated with asthma, whereas module 17, associated with insulin and other signaling pathways, showed a significant negative correlation with asthma. We identified the genes of polypeptide N-acetylgalactosaminyltransferase 5 (GALNT5), pyrroline-5-carboxylate reductase 1 (PYCR1), and carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5) as key genes within module 9, all of which were significantly upregulated in asthma. Finally, we validated that the expression levels of GALNT5, PYCR1, and CEACAM5 were significantly upregulated in epithelial cells from asthmatic lung tissue. Additionally, using a rat asthma model, we further confirmed that the protein levels of these three genes were significantly upregulated in lung tissues of the model group. Conclusion Through data integration and experimental validation, this study identified key genes and biological pathways closely associated with asthma pathogenesis. These findings provide a novel theoretical basis and potential targets for the diagnosis and treatment of asthma.
Asthma/metabolism* ; Humans ; Epithelial Cells/metabolism* ; Animals ; Biomarkers/metabolism* ; Gene Expression Profiling ; Transcriptome ; Gene Regulatory Networks ; Rats ; Computational Biology

Objective TXN is a thioredoxin (TXN) that participates in many redox reactions and plays a crucial role in various signaling pathways. However, the role of TXN in many cancers is still unclear. The objective of this study is to investigate and visualize the diagnostic, prognostic, and immunological implications of TXN expression across various cancer types. Methods The clinical data were downloaded from the cancer genome mapping project(TCGA) database to analyze the expression level of TXN in pan cancer, and the expression level was preliminarily verified by human protein mapping (HPA)(https://www.proteinatlas.org/)database. The ESTIMATE algorithm and CIBERSORT algorithm were applied to calculate the correlation between TXN expression and immune cell infiltration. The correlation between TXN and microsatellite instability (MSI) and tumor mutation burden (TMB) was analyzed using Spearman method. Gene Set Enrichment Analysis (GSEA) is used for gene biology functional analysis and sensitivity analysis of genes to pan cancer therapeutic drugs. Results TXN is highly expressed in most malignant tumors. The high expression of TXN is associated with overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression free interval (PFI) in various cancers. Moreover, TXN expression is associated with TMB, MSI, tumor microenvironment, chemotherapy sensitivity and so on. Conclusion TXN may become a potential prognostic biomarker in pan cancer, providing strong theoretical basis for future tumor diagnosis and prognosis judgment. The retinoic acid-inducible gene-I (RIG-I)-like receptor signaling pathway, Toll-like receptor (TLR) signaling pathway, and nucleotide binding oligomerization domain (NOD)-like receptor signaling pathway may be crucial pathways through which TXN influences tumor immunity.
Humans ; Prognosis ; Neoplasms/diagnosis* ; Thioredoxins/metabolism* ; Microsatellite Instability ; Gene Expression Regulation, Neoplastic ; Biomarkers, Tumor/genetics* ; Mutation ; Tumor Microenvironment

Objective To investigate the expression of blood basophil activation markers in patients with allergic rhinitis (AR) and the effects of allergens on their expression. Methods The blood samples were collected from the following four groups: healthy control (HC), AR patients with negative skin prick test (nAR), seasonal AR patients (sAR) and perennial AR patients (pAR). Flow cytometry was employed to analyze the expression of basophil activation markers Immunoglobulin E receptor I alpha(FcepsilonRIα), CD63 and CD203c in AR patients. Plasma levels of interleukin 4 (IL-4) and IL-8 were measured by liquid-phase chip technology, and their correlations with the percentages of activated basophils were further analyzed. An ovalbumin-induced AR mouse model was established, and the expression levels of FcepsilonRIα and CD63 on blood basophils were detected. Results The expression of FcepsilonRIα, CD203c and CD63 on basophils were increased in nAR, sAR and pAR patients. Allergens enhanced the mean florescence intensity expression of CD63 and CD203c on basophils of sAR and pAR patients. The plasma levels of IL-4 and IL-8 were elevated in nAR, sAR and pAR patients, showing moderate to high correlations with the expression levels of basophil activation markers. The FcepsilonRIαand CD63 expression on basophils of AR mice were increased. Conclusion Allergens may contribute to AR pathogenesis by upregulating the expression of FcepsilonRIα, CD63 and CD203c, as well as promoting the secretion of IL-4 and IL-8.
Basophils/metabolism* ; Humans ; Allergens/immunology* ; Animals ; Rhinitis, Allergic/blood* ; Female ; Male ; Adult ; Mice ; Biomarkers/blood* ; Tetraspanin 30/blood* ; Interleukin-4/blood* ; Interleukin-8/blood* ; Receptors, IgE/blood* ; Phosphoric Diester Hydrolases ; Young Adult ; Pyrophosphatases ; Middle Aged ; Mice, Inbred BALB C