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 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

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 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

PURPOSE: To screen laboratory markers with predictive value in early spinal surgical site infections (SSI) that are diagnosed within 30 days postoperatively. METHODS: Patients who underwent surgical treatment for internal spinal fixation between March 2022 and March 2023 in our hospital were retrospectively studied. The inclusion criteria were aged >18 years, undergoing internal fixation surgery, complete medical records with >30 days of postoperative follow-up, diagnosis was made within 30 days postoperatively, and an informed consent form was obtained. The exclusion criteria were abnormal white blood cell count or neutrophil percentage in the preoperative blood routine and combined diseases that may affect the C-reactive protein (CRP) or procalcitonin (PCT) values, including lower respiratory tract infection, renal insufficiency, and liver disease. We collected patients' personal information, surgical information, and blood laboratory data, including CRP, PCT, lymphocyte-neutrophil ratio, platelet-neutrophil ratio, and routine blood tests on preoperative and postoperative days 3, 5, and 7, from these patients. These data were statistically analyzed to determine which laboratory markers were statistically significant. The diagnostic value and optimal diagnostic threshold of these laboratory markers were further determined by receiver operating characteristic curve analysis. RESULTS: A total of 106 patients were enrolled in this study, of whom 8 patients were diagnosed with early SSI. A total of 4 laboratory markers were screened, namely, CRP on postoperative day 7 (optimal diagnostic threshold of ≥64.1 mg/L, sensitivity of 100%, specificity of 76.5%, area under the curve (AUC) of 0.908), PCT on postoperative day 7 (optimal diagnostic threshold of ≥0.2 ng/mL, sensitivity of 87.5%, specificity of 94.1%, AUC of 0.967), lymphocyte count on postoperative day 5 (optimal diagnostic threshold of ≤0.67 × 10⁹/L, sensitivity of 50%, specificity of 95.9%, AUC of 0.760), and lymphocyte count on postoperative day 7 (optimal diagnostic threshold of ≤1.32 × 10⁹/L, sensitivity of 87.5%, specificity of 55.1%, AUC of 0.721). CONCLUSION We concluded that CRP and PCT levels on postoperative day 7 and lymphocyte counts on postoperative days 5 and 7 are useful markers in screening for early spinal SSI.
Humans ; Retrospective Studies ; Male ; Female ; Biomarkers/blood* ; Middle Aged ; C-Reactive Protein/analysis* ; Surgical Wound Infection/blood* ; Procalcitonin/blood* ; Adult ; Aged ; Postoperative Period ; ROC Curve ; Predictive Value of Tests ; Spine/surgery*

Parameters of peripheral blood cell have been shown as the potential predictors of erectile dysfunction (ED). To investigate the clinical significance of hematological parameters for predicting the risk of rapid ejaculation, we established a rat copulatory model on the basis of ejaculation distribution theory. Blood samples from different ejaculatory groups were collected for peripheral blood cell counts and serum serotonin (5-HT) tests. Meanwhile, the relationship between hematological parameters and ejaculatory behaviors was assessed. Final analysis included 11 rapid ejaculators, 10 normal ejaculators, and 10 sluggish ejaculators whose complete data were available. The platelet (PLT) count in rapid ejaculators was significantly lower than that in normal and sluggish ejaculators, whereas the platelet distribution width (PDW) and mean platelet volume (MPV) were significantly greater in rapid ejaculators. Multivariate logistic regression analysis and receiver operating characteristic (ROC) curve analysis showed that the PLT was an independent protective factor for rapid ejaculation. Meanwhile, rapid ejaculators were found to have the lowest serum 5-HT compared to normal and sluggish ejaculators ( P < 0.001). Furthermore, there was a positive correlation between the PLT and serum 5-HT ( r = 0.662, P < 0.001), indicating that the PLT could indirectly reflect the serum 5-HT concentration. In addition, we assessed the association between the PLT and ejaculatory parameters. There was a negative correlation between ejaculation frequency (EF) and the PLT ( r = -0.595, P < 0.001), whereas there was a positive correlation between ejaculation latency (EL) and the PLT ( r = 0.740, P < 0.001). This study indicated that the PLT might be a useful and convenient diagnostic marker for predicting the risk of rapid ejaculation.
Male ; Animals ; Ejaculation/physiology* ; Rats ; Platelet Count ; Pilot Projects ; Serotonin/blood* ; Biomarkers/blood* ; Mean Platelet Volume ; Rats, Sprague-Dawley ; ROC Curve ; Erectile Dysfunction/physiopathology*

Heart failure is a complex clinical syndrome and pediatric heart failure (PHF) has a high mortality rate. Early diagnosis is crucial for treatment and management of PHF. In clinical practice, various tests and examinations play a key role in the diagnosis of PHF, including continuously updated biomarkers, echocardiography, and cardiac magnetic resonance imaging. This article focuses on summarizing relevant research on biomarkers, examinations, combined testing, clinical models, and the grading and staging of PHF diagnosis, aiming to provide insights and directions for the diagnosis of PHF.
Humans ; Heart Failure/diagnosis* ; Child ; Biomarkers/blood* ; Echocardiography ; Magnetic Resonance Imaging

OBJECTIVES: To explore the lipidomic characteristics of children with bronchial asthma (hereafter referred to as asthma) and identify potential biomarkers for asthma. METHODS: A total of 26 asthmatic children were prospectively enrolled as the asthma group, and 20 healthy children served as the healthy control group. The asthma group was further divided into atopic (n=13) and non-atopic (n=13) subgroups based on IgE levels. Serum lipid metabolites were analyzed using liquid chromatography-mass spectrometry, followed by statistical analysis and data visualization. RESULTS: A total of 1 435 lipids were detected in the 46 children, primarily glycerophospholipids (625/1 435, 43.55%). Significant differences were observed in serum lipid profiles between the asthma and control groups. Twelve significantly differential lipids were identified, with receiver operating characteristic curve analysis showing that phosphatidylserine (PS)(18:0/20:4) and ceramide (Cer)(c16:0) exhibited the highest diagnostic value for asthma. The relative abundances of PS(18:0/20:4) and PS(18:0/22:6) were higher in the atopic subgroup than in the non-atopic subgroup (P<0.05) and positively correlated with total IgE levels in asthmatic children (r=0.675 and 0.740, respectively; P<0.05). CONCLUSIONS Asthmatic children exhibit significant lipid metabolic disturbances, primarily characterized by abnormal glycerophospholipid metabolism. Among these, PS(18:0/20:4) and Cer(c16:0) demonstrate specific alterations and may serve as potential diagnostic biomarkers for asthma. Furthermore, the positive correlation between PS(18:0/20:4) and PS(18:0/22:6) levels and serum total IgE suggests their possible involvement in immune regulation in asthma.
Humans ; Asthma/metabolism* ; Male ; Child ; Female ; Prospective Studies ; Mass Spectrometry/methods* ; Lipids/blood* ; Chromatography, Liquid/methods* ; Child, Preschool ; Immunoglobulin E/blood* ; Biomarkers/blood* ; Adolescent ; Liquid Chromatography-Mass Spectrometry

OBJECTIVES: To investigate the clinical and immunological features of children with primary immune thrombocytopenia (pITP) or connective tissue disease (CTD) with thrombocytopenia as the initial manifestation at initial diagnosis, and to provide a basis for early differentiation. METHODS: A retrospective study was performed on 236 children with pITP (pITP group) or CTD with thrombocytopenia as the initial manifestation (CTD-TP group) who were admitted from January 2019 to August 2024. Clinical and immunological indicators were compared between the two groups to identify potential influencing factors for early differentiation and their discriminative validity. RESULTS: Compared with the pITP group, the CTD-TP group had a significantly older age of onset and significantly lower leukocyte count, eosinophil count, lymphocyte count, and complement C4 level (P<0.05), as well as significantly higher levels of C-reactive protein, IgE, and IgM (P<0.05). The logistic regression analysis showed that age, IgE, IgM, total B cells, and complement C4 were predictive factors for early differentiation between pITP and CTD-TP (P<0.05). The receiver operating characteristic curve analysis showed that a combination of these five factors had a good discriminative validity, with an area under the curve of 0.944. The correlation analysis showed a negative correlation between IgG and platelet count in the pITP group (r_s=-0.363, P<0.05) and a positive correlation between NK cells and platelet count in the CTD-TP group (r_s=0.713, P<0.05). CONCLUSIONS There is heterogeneity in the clinical and immunological indicators between children with pITP and CTD-TP at initial diagnosis, and these research findings can help with the early differentiation between the two diseases.
Purpura, Thrombocytopenic, Idiopathic/immunology* ; Diagnosis, Differential ; Connective Tissue Diseases/immunology* ; Retrospective Studies ; Early Diagnosis ; Age of Onset ; Leukocyte Count ; Complement C4/immunology* ; C-Reactive Protein/immunology* ; Immunoglobulin E/immunology* ; Immunoglobulin M/immunology* ; Humans ; Male ; Female ; Infant ; Child, Preschool ; Child ; Adolescent ; Biomarkers/blood*

OBJECTIVES: To investigate the correlation between bone mineral density (BMD) and bone metabolic markers in preschool children and the influencing factors for BMD, and to provide a clinical basis for promoting bone health in children. METHODS: A retrospective analysis was performed for the data of 127 preschool children who underwent physical examination in the Department of Child Health Care of the First Affiliated Hospital of Xinjiang Medical University, from June to December 2024. BMD and bone metabolic markers were measured, and physical examination was performed. A multiple linear regression analysis was used to investigate the effect of general information on BMD Z-score in preschool children. Spearman's rank correlation test was used to investigate the correlation of BMD Z-score with 25-hydroxyvitamin D (25-OHD), serum bone Gla protein (BGP), and parathyroid hormone (PTH). RESULTS: BMD Z-score significantly differed by ethnicity, weight category, and height category (all P<0.05). The multiple linear regression analysis indicated that weight and height significantly influenced BMD Z-score (P<0.05), whereas sex, age, ethnicity, and parental education level did not (P>0.05). In children, BMD Z-score was positively correlated with 25-OHD level (r_s=0.260, P<0.001) and BGP level (r_s=0.075, P=0.025) and was negatively correlated with PTH level (r_s=-0.043, P=0.032). CONCLUSIONS Weight, height, 25-OHD, BGP, and PTH are influencing factors for BMD in preschool children. In clinical practice, combined measurement of bone metabolic markers may provide a scientific basis for early identification of children with abnormal BMD and prevention of osteoporosis and osteomalacia.
Humans ; Bone Density ; Child, Preschool ; Female ; Male ; Retrospective Studies ; Vitamin D/blood* ; Parathyroid Hormone/blood* ; Biomarkers/blood* ; Osteocalcin/blood* ; Bone and Bones/metabolism* ; Calcium-Binding Proteins/blood* ; Linear Models ; Matrix Gla Protein ; Extracellular Matrix Proteins/blood* ; Body Weight ; Infant