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*

Varicocele (VC) is a common cause of male infertility, yet there is a lack of molecular information for VC-associated male infertility. This study investigated alterations in the seminal plasma metabolomic and lipidomic profiles of infertile male VC patients. Twenty infertile males with VC and twenty-three age-matched healthy controls (HCs) were recruited from Peking Union Medical College Hospital (Beijing, China) between October 2019 and April 2021. Untargeted metabolite and lipid profiles from seminal plasma were analyzed using mass spectrometry. Four hundred and seventy-six metabolites and seventeen lipids were significantly different in infertile male VC patients compared to HCs. The top enriched pathways among these significantly different metabolites are protein digestion and absorption, aminoacyl-transfer RNA (tRNA) biosynthesis, and biosynthesis of amino acids. Different key lipid species, including triglyceride (TG), diacylglycerol (DG), ceramides (Cer), and phosphatidylserine (PS), varied between VC and HC groups. The distinct metabolites and lipids were moderately correlated. DL-3-phenyllactic acid is a potential diagnostic biomarker for VC-related male infertility (area under the curve [AUC] = 0.893), positively correlating with sperm count, concentration, and motility. Furthermore, DL-3-phenyllactic acid is the only metabolite shared by all four comparisons (VC vs HC, VC-induced oligoasthenospermia [OAS] vs VC-induced asthenospermia [AS], OAS vs HC, and AS vs HC). DL-3-phenyllactic acid significantly decreased in OAS than AS. Metabolite-targeting gene analysis revealed carbonic anhydrase 9 (CA9) might be the strongest candidate associated with the onset and severity of VC. The seminal plasma metabolite and lipid profiles of infertile males with VC differ significantly from those of HCs. DL-3-phenyllactic acid could be a promising biomarker.
Humans ; Male ; Varicocele/complications* ; Infertility, Male/etiology* ; Semen/metabolism* ; Lipidomics ; Adult ; Metabolomics ; Case-Control Studies ; Biomarkers/metabolism*

Male factor infertility has been rising, which accounts for up to 30% of infertility cases and contributes to 50% of overall cases. The aim of this review is to explore the recent advances that have emerged in the field through a narrative review. A comprehensive literature search was conducted using multiple databases, including the Cochrane Library, PubMed, Scopus, and Web of Science. Gray literature was also reviewed through ClinicalTrials.gov and the World Health Organization International Clinical Trials Registry Platform. The findings were presented narratively to encompass the extensive range of published data on male infertility. Significant strides have been made in the field of male infertility, particularly with biomarkers, shear wave elastography, 3-dimensional (3D) bioprinting, artificial intelligence (AI), and robotic and microsurgical treatment, offering promising avenues for diagnosis and treatment. Continued research and technological innovation are essential to further improve outcomes for patients facing male factor infertility.
Humans ; Male ; Infertility, Male/diagnosis* ; Artificial Intelligence ; Elasticity Imaging Techniques ; Bioprinting ; Microsurgery ; Biomarkers ; Robotic Surgical Procedures

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

OBJECTIVES: To identify potential plasma lipidomic biomarkers that distinguish non-metastatic medulloblastoma (nmMB) from metastatic medulloblastoma (mMB) in children. METHODS: In this prospective study, 17 children with mMB and 20 matched children with nmMB were enrolled. Plasma samples were analyzed using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. Lipid metabolites were evaluated for their associations and diagnostic performance. RESULTS: Orthogonal partial least squares discriminant analysis based on lipid profiles clearly separated nmMB from mMB, and 14 differential lipids were identified, including DG(18:2/20:4/0:0) and SM(d18:1/20:0). Receiver operating characteristic analysis showed nine metabolites with area under the curve greater than 0.7. Differential lipids were enriched in sphingolipid, glycerophospholipid, and arachidonic acid metabolism, suggesting an association with the metastatic phenotype. CONCLUSIONS Plasma lipidomics provides a new approach to identify mMB, and the identified lipid metabolites may support early diagnosis and treatment, prognostic assessment, and selection of therapeutic targets for metastatic medulloblastoma.
Humans ; Medulloblastoma/diagnosis* ; Lipidomics ; Child ; Male ; Female ; Child, Preschool ; Cerebellar Neoplasms/blood* ; Biomarkers, Tumor/blood* ; Neoplasm Metastasis ; Prospective Studies ; Adolescent ; Lipids/blood*

OBJECTIVES: To study the predictive value of serum macrophage M1/M2 markers for the risk of cardiac lesions in obese children. METHODS: A total of 60 obese children (mild-to-moderate obesity, n=32; severe obesity, n=28) and 50 healthy controls who visited the Second Affiliated Hospital of Nanchang University from June 2024 to December 2024 were included. The baseline characteristics and the levels of laboratory indicators, echocardiographic parameters, and macrophage markers (MCP-1, Arg-1, CD206, and CD86) were compared among the three groups. The correlation between macrophage marker levels and echocardiographic parameters and the influencing factors of cardiac lesions in obese children were analyzed. The receiver operating characteristic curve analysis was used to evaluate the predictive performance of each influencing factor for cardiac lesions in obese children. RESULTS: Multiple echocardiographic parameters differed significantly among the mild-to-moderate obesity, severe obesity, and control groups (P<0.01). Significant differences were also observed in MCP-1 and Arg-1 levels, CD206 positivity rate, and the CD86/CD206 ratio among the three groups (P<0.05). In obese children, MCP-1 and Arg-1 levels, as well as CD86 and CD206 positivity rates, were correlated with echocardiographic parameters (P<0.05). Univariate logistic regression identified MCP-1, Arg-1, the CD86/CD206 ratio, and the CD206 positivity rate as factors associated with cardiac lesions (P<0.05). The combined prediction model based on these markers yielded an area under the receiver operating characteristic curve of 0.887 (P<0.01). CONCLUSIONS Combined measurement of macrophage markers can predict the risk of early cardiac lesions in obese children.
Humans ; Male ; Female ; Child ; Biomarkers/blood* ; Macrophages ; Obesity/blood* ; Chemokine CCL2/blood* ; ROC Curve ; Adolescent ; Child, Preschool ; Heart Diseases/diagnosis* ; Echocardiography

OBJECTIVES: To analyze the association between biological aging markers (phenotypic age and phenotypic age acceleration) and valvular heart diseases. METHODS: Research subjects who met the inclusion and exclusion criteria were selected from the UK Biobank from 2006 to 2010. The phenotypic age and phenotypic age acceleration were calculated. Cox multivariate analysis was used to examine the relationship between the aging markers and valvular heart diseases. Sensitivity analysis was conducted by removing missing values and subgroup analysis. The predictive accuracy of phenotypic age and phenotypic age acceleration for valvular heart diseases was analyzed using receiver operating characteristic (ROC) curves, and a clinical decision curve was generated based on logistic regression. RESULTS: A total of 411 687 subjects were included in the study, among whom there were 14 258 patients with valvular heart diseases. The overall median follow-up time was 12.80 years, the median follow-up time for patients with non-rheumatic aortic valve diseases (n=5238), non-rheumatic mitral valve diseases (n=4558), and non-rheumatic tricuspid valve diseases (n=411) were 12.82 years, 12.83 years and 12.84 years, respectively. After adjusting for demographic factors (gender, race, education, Townsend deprivation index), anthropometric factors (body mass index), lifestyle factors (smoking, alcohol consumption, Dietary Approaches to Stop Hypertension score), hypertension and hyperlipidemia, Cox multivariate analysis showed phenotypic age and phenotypic age acceleration were independent risk factors for valvular heart diseases, including non-rheumatic aortic valve diseases, non-rheumatic mitral valve diseases, and non-rheumatic tricuspid valve diseases (phenotypic age: corrected HR=1.04, P<0.01; phenotypic age acceleration: corrected HR=1.03, P<0.01), which was also confirmed by sensitivity analysis. ROC curves and clinical decision curves demonstrated that compared with the phenotypic age acceleration, phenotypic age had higher accuracy (the areas and the curves were 0.721 and 0.599) and higher net benefit in predicting valvular heart diseases. Moreover, compared with a single indicator, the combination of the two indicators had higher accuracy (the area under the curve was 0.725) and higher net benefit. CONCLUSIONS Phenotypic age and phenotypic age acceleration,as markers of biological aging, are independent risk factors for valvular heart diseases. Compared with phenotypic age acceleration, phenotypic age has a greater advantage in predicting valvular heart diseases. Overall, the combination of the two indicators offers a more effective approach for predicting valvular heart diseases.
Humans ; Male ; Female ; Heart Valve Diseases/epidemiology* ; Middle Aged ; Aged ; Aging ; Adult ; Biomarkers ; Phenotype ; Risk Factors ; Aged, 80 and over