Xuewei Zhuang

Background: Gallstone disease constitutes a substantial global health burden, characterized by notable clinical heterogeneity and a complex risk of recurrence. However, large-scale studies systematically characterizing this heterogeneity and translating these findings into clinically applicable prediction tools remain limited. This study sought to systematically elucidate the clinical heterogeneity within a large cohort of gallstone patients, identify independent risk factors for postoperative recurrence, and develop a machine learning-based model for postoperative recurrence prediction. Methods: We conducted a retrospective analysis of 9,939 patients undergoing gallstone surgery between January 2017 and December 2023 at the Third Hospital of Shandong Province. Clinical data, including demographics, gallstone type, size, comorbidities, and recurrence status, were extracted. Statistical analyses compared subgroups based on sex, age, stone type, complication status, and recurrence. Age- and sex-matched non-recurrence controls were selected in a 1:1 case-control design. Independent recurrence-associated factors were identified using univariate and multivariable logistic regression analyses. A Random Forest model was subsequently constructed using routinely available clinical variables and evaluated in independent training and testing datasets. Results: Marked clinical heterogeneity was observed across sex, age groups, stone locations, and recurrence status. Distinct associations were identified between specific stone distributions and comorbidity patterns. Multivariable logistic regression identified stone size, diabetes mellitus, venous thrombosis, liver cirrhosis, and malignant tumors as independent factors associated with recurrence. The Random Forest prediction model, incorporating these factors along with sex and age, demonstrated excellent performance with an area under the curve (AUC) of 0.873 on the test set. Variable importance analysis highlighted stone size and cirrhosis as the most influential predictors. Conclusions: Gallstone disease is characterized by substantial clinical heterogeneity. The identified recurrence-associated factors and machine learning model provide a potential framework for individualized recurrence risk assessment and may facilitate postoperative management following further external and prospective validation.

Background: The diagnostic delay associated with standard urine culture necessitates rapid, accurate alternatives for urinary tract infection (UTI) management. Volatile organic compounds (VOCs) emitted by microbes represent a promising source of metabolic biomarkers for infection diagnosis. Objective: To develop and validate a diagnostic model for UTI by integrating urine VOCs profiles obtained via gas chromatography-ion mobility spectrometry (GC-IMS) with clinical features using machine learning. Methods: We conducted a prospective cohort study of 258 adults with suspected UTI. Clean-catch midstream urine samples were collected for clinical urinalysis, culture (reference standard), and GC-IMS-based VOCs analysis. VOCs and clinical data were used to train and test machine learning models (Logistic Regression, Random Forest, Support Vector Machine). Model performance was assessed by area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and decision curve analysis. Results: Among 258 enrolled patients, 152 (58.9%) were culture-positive. We identified 11 differentially expressed VOCs between infected and non-infected groups, with acetic acid, benzaldehyde, and furan being the most significant (Bonferroni-adjusted p < 0.05). A Random Forest model integrating both VOCs and clinical features demonstrated superior performance (AUC of 0.914, with an accuracy of 82.1% (95% CI: 71.8-89.8%), sensitivity of 87.0%, specificity of 75.0%, and an F1-score of 0.851) compared to models using clinical-only (AUC 0.831) or VOC-only (AUC 0.850). Multivariate analysis confirmed acetic acid (OR 3.27) and benzaldehyde (OR 4.95) as strong independent predictors of UTI. Furthermore, VOCs profiles allowed moderate discrimination between Gram-positive and Gram-negative bacterial infections (AUC 0.800) and exhibited pathogen-specific patterns. Conclusion: The integration of urine VOCs profiles obtained by GC-IMS with routine clinical parameters using machine learning achieves high diagnostic accuracy for UTI and shows potential for rapid pathogen differentiation. This strategy could improve UTI diagnostics, enabling faster, more precise antibiotic therapy.

To evaluate the noninvasive, early identification capability of urine volatile organic compounds (VOCs) obtained via gas chromatography-ion mobility spectrometry (GC-IMS), combined with machine learning models, for gallstones complicated by cholecystitis. A single-center study enrolled 100 patients with gallstone-cholecystitis and 100 healthy controls (n = 200 total). Midstream urine samples were uniformly collected and stored at − 80 °C. GC-IMS acquired two-dimensional fingerprints, which underwent RIP normalization and manual peak quality control. Unreliably identified peaks were excluded before modeling. The data were randomly divided into training (70%) and testing (30%) sets. Feature selection was performed on the training set to construct Random Forest (RF), Support Vector Machine (SVM), Neural Network (NN), and Decision Tree (DT) models. These models were optimized using 10-fold cross-validation and evaluated on the testing set, with Area Under the ROC Curve (AUC) as the primary metric. Simultaneously, model performance and single biomarker performance are evaluated based on leading compounds (e.g., Linalool, Propyl-propenyl disulphide, Methylthiobutyrate-M, Butylamine). On the test set, RF, SVM, and NN achieved AUC values of 0.905, 0.887, and 0.870 respectively, demonstrating overall superior discrimination compared to DT (AUC = 0.658). For the small model constructed using the aforementioned four VOCs, NN, RF, and SVM yielded AUC values of 0.81, 0.77, and 0.76 respectively; Regarding individual markers, Linalool (AUC = 0.777), Propyl-propenyl disulphide (AUC = 0.768), Methylthiobutyrate-M (AUC = 0.768), and Butylamine (AUC = 0.731) all demonstrated certain discriminatory capabilities. The combination of urine VOCs-GC-IMS with machine learning demonstrates favorable discriminatory performance in the early, non-invasive identification of gallstones and cholecystitis.

Introduction: Metabolic dysfunction-associated fatty liver disease (MAFLD) is a complex metabolic disorder and one of the leading causes of chronic liver disease worldwide. Current diagnostic tools, such as ultrasound, lack sufficient sensitivity for detecting early-stage disease, emphasizing the urgent need for novel and non-invasive diagnostic strategies. Metabolomics, particularly the profiling of volatile organic compounds (VOCs) in biofluids, has emerged as a promising approach for biomarker discovery in metabolic diseases. Methods: In this preliminary single-center study, serum samples were collected from 199 participants, including 110 MAFLD patients and 89 healthy controls. Volatile organic compounds were analyzed using gas chromatography–ion mobility spectrometry (GC-IMS). Machine learning algorithms, including random forest, were applied to construct diagnostic models and identify key discriminatory metabolites. Clinical and biochemical parameters such as age, body mass index, liver function, and lipid profiles were also compared between groups. Results: A total of 79 serum VOCs were detected, among which 54 showed significant differences between MAFLD patients and controls (29 identified and 25 unidentified). The random forest model exhibited the best diagnostic performance, achieving a test AUC of 0.941, with 86.7% sensitivity and 88.5% specificity. Seven key VOCs were identified as important contributors to the model, including two upregulated compounds (2-Butoxyethanol and Cyclopentanone-D) and five downregulated compounds ((E)-3-hexenoic acid, 2-Ethylbutanal, 2-Propyl acetate, Benzaldehyde-M, and Furaneol). Notably, 2-pentylfuran displayed significant variation across different pathological grades of MAFLD, suggesting potential as a stage-specific biomarker. Discussion: This study demonstrates that serum VOC profiling using GC-IMS combined with machine learning can effectively distinguish MAFLD patients from healthy individuals. The identified VOC signatures, particularly 2-pentylfuran, may serve as non-invasive biomarkers for MAFLD diagnosis and staging. However, due to the limited sample size and single-center design, these findings require validation in larger, multi-center, and longitudinal studies to confirm their clinical applicability, especially for early disease detection.

Rapid, nondestructive, high-throughput volatile constituent screening and detection is becoming more and more crucial in the medical field's illness analysis. GC-IMS, or gas chromatography-ion mobility spectrometry, is a potent method for the sensitive identification and separation of volatile organic molecules. GC-IMS enables rapid detection with high sensitivity, allowing for the identification of volatile organic compounds (VOCs) in a short time, making it particularly suitable for rapid screening and on-site analysis. This technique is user-friendly, requiring no complex sample preparation and enabling direct analysis, thereby streamlining the workflow and reducing time costs. Moreover, the equipment is portable and has low maintenance costs, offering significant potential for widespread application. The operating idea of GC-IMS is briefly explained in this study, along with recent research that has used GC-IMS on various human metabolites, such as urine, feces, bile, serum, and exhaled breath, for purposes like disease and pathogenic microbe differential detection. Lastly, a summary and recommendation are made on the future development direction of GC-IMS.

Cervical cancer(CCa) remains a significant global public health concern, with early diagnosis and treatment being crucial. Moreover, the molecular mechanisms underlying its pathogenesis remain incompletely elucidated. F2RL1 is closely associated with various tumors. However, its relationship with CCa is poorly understood. We accessed data from 309 patients diagnosed with CCa from TCGA database. The Limma package facilitated differential expression analysis to identify differentially expressed mRNAs (DEmRNAs). Survival analysis and ROC analysis were conducted via the XIANTAO database. Immune-related genes were identified with F2RL1-related genes through ImmPort database analysis. Functional enrichment analysis was carried out using GO, KEGG, and GSEA. We gathered cervical cells and serum from participants to test for HPV and TCT, and then used qPCR to check the levels of F2RL1 mRNA expression. We also verified the expression of F2RL1 protein through WB and ELISA techniques. Our investigation has unveiled a fascinating discovery—the levels of F2RL1 expression in CCa tissues are notably elevated when compared to normal tissues, showcasing intriguing variations among various pathological types. Moreover, the presence of high F2RL1 expression is linked to reduce Overall Survival (OS), Progression Free Interval (PFI), Progression Free Survival (PFS). F2RL1 rocked the ROC analysis with an AUC of 0.996. Furthermore, F2RL1 expression levels significantly impact CCa in different N stages, pathological tissue types, treatment statuses, and racial groups, allowing us to develop a predictive model. Additionally, we identified 43 immune-related genes. Enrichment analysis highlighting their association with pathways related to cell movement and T cell activation. Through analysis, we discovered an inverse proportion between F2RL1 expression and the infiltration of most immune cells, particularly TFH and cytotoxic cells, suggesting a potential link to immune evasion in CCa. Molecular biology experiments also confirmed a significant increase in F2RL1 expression in cervical exfoliated cells and serum. Our research uncovers the predictive and early detection significance of F2RL1 in CCa and its correlation with immune infiltration for the first time. F2RL1 is strongly linked to the progression of CCa and could serve as a biomarker for the early diagnosis and prognosis of CCa patients.

Histone lactylation plays a crucial role in cancer progression, but its impact on breast cancer (BC) tumorigenesis is still unclear. We utilized chromatin immunoprecipitation sequencing with H3K18la antibodies, transcriptomics of clinical BC samples, and proteomics and ATAC-seq analyses of in vivo tumors to identify the genes regulated by H3K18la and the transcription factor PPARD. qPCR and Western blot assays were used to detect expressions of molecules. We discovered that H3K18la levels were higher in BC tissues compared to adjacent non-cancerous tissues. H3K18la promoted the expression of PPARD, which in turn influenced the transcription of AKT, but not ILK. ATAC-seq analysis revealed that glycolysis in BC cells enhanced chromatin accessibility. Additionally, we confirmed that HDAC2 and HDAC3 act as “erasers” for H3 lysine lactylation. During the proteomics analysis, AKT-phosphorylation in the aerobic respiration inhibitor group exhibited an apparent disparity and activity. Our study demonstrated that changes in H3K18la in BC and its downstream transcription factor PPARD support cell survival under anaerobic glycolysis conditions. PPARD accelerated cancer proliferation by promoting the transcription and phosphorylation of AKT. This highlights the therapeutic potential of targeting the H3K18la/PPARD/AKT axis in breast cancer, providing new insights into epigenetic regulation and cancer metabolism (Trial registration: The study was approved by the Research Ethics Committee Shandong Provincial Third Hospital (KYLL-2023057; https://www.medicalresearch.org.cn/)).

Background: Early diagnosis of cholangiocarcinoma (CCA) is still unreliable, resulting in poor patient outcomes. Volatile organic compounds (VOCs), emitted by the body, could help detect disease-related metabolic changes, aiding diagnosis. Objective： This study aims to assess if Gas chromatography ion mobility spectroscopy (GC-IMS) detection of serum VOCs can differentiate CCA from benign biliary diseases (BBD) and evaluate its diagnostic effectiveness. Method：Serum samples from 46 CCA and 46 BBD patients were collected. GC-IMS detected VOCs in the serum groups, and PCA was performed to analyze group differences. Utilize the Receiver Operating Characteristic (ROC) curve to assess the diagnostic efficacy of VOCs in CCA and analyze the correlation between VOCs and pathological characteristics. Resuit: Using GC-IMS, we detected 27 VOCs in two serum samples, including 23 known and 4 unknown. A PCA 3D scatter plot highlights distinct VOC patterns and clustering for CCA patients versus BBD. Statistical analysis reveals significant differences in 14 substances between the BBD and CCA groups, with 8 VOCs exhibiting upregulation and 6 demonstrating downregulation in the CCA group. ROC analysis reveals that (E) -2-nonenal exhibits the highest diagnostic efficiency for CCA, with an AUC value of 0.761 (95%CI: 0.662 - 0.860). Conclusion: Serum VOC analysis could aid the diagnosis and surveillance of CCA.

Introduction: The exact triggers of gallstone formation remain incompletely understood, but research indicates that microbial infection is a significant factor and can interfere with treatment. There is no consensus on the bile microbial culture profiles in previous studies, and determining the microbial profile could aid in targeted prevention and treatment. The primary aim of this study is to investigate the differences in microbial communities cultured from bile specimens of patients with gallstones.Methods: We collected the clinical characteristics and bile microbial status of 9,939 gallstone patients. Statistical analysis was employed to assess the relationship between microbes and clinical features, and a random forest model was utilized to predict recurrence.Results: Results showed a higher proportion of females among patients, with the age group of 60-74 years being the most prevalent. The most common type of gallstone was solitary gallbladder stones. A total of 76 microbes were cultured from 5,153 patients, with Escherichia coli, Klebsiella pneumoniae, and Enterococcus faecalis being the most frequently identified. Significant differences in microbial diversity and positive detection rates were observed across different age groups, types of gallstones, and recurrence status. Positive frequencies of E. coli, Enterococcus faecium, and K. pneumoniae varied significantly by age group and gallstone type. The microbial diversity in the recurrence group was significantly lower compared to the non-recurrence group. The recurrence rate was significantly higher in the group with single microbial species compared to those with no microbes or multiple microbes. For the recurrence group, there were significant differences in the frequencies of seven microbes (Aeromonas hydrophila, Enterococcus casseliflavus, Enterococcus faecium, E. coli, K. pneumoniae, Proteus mirabilis, Pseudomonas aeruginosa) before and after recurrence, with these microbes appearing in a higher number of patients after recurrence. Regression analysis identified patient age, stone size, diabetes, venous thrombosis, liver cirrhosis, malignancy, coronary heart disease, and the number of microbial species as important predictors of recurrence. A random forest model constructed using these variables demonstrated good performance and high predictive ability (ROC-AUC = 0.862).Discussion: These findings highlight the significant role of microbial communities in gallstone formation and recurrence. Furthermore, the identified predictors of recurrence, including clinical factors and microbial diversity, may help develop personalized prevention and recurrence strategies for gallstone patients.