JOURNAL OF CHROMATOGRAPHIC SCIENCE
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2016
·Vol. 55
·期 1
·60-68
·
IF 1.32
Gu Liqiang,Shi Huiyan,Zhang Ruowen,Wei Zhe,Bi Kai-Shun,Chen Xiao-Hui
In clinical diagnosis, serum creatinine (CR) was commonly used as the routine markers for the evaluation of kidney injury. To investigate the specific and sensitive nephrotoxicity biomarkers in different drug-induced kidney injury (DKI) models, receiver operating characteristic (ROC) analysis was applied in this study. The quantification data were acquired by the LC–MS determination. Histopathology results showed that different kinds of kidney injuries were observed in different DKI models (gentamycin, cisplatin, methotrexate and amphotericin B models), indicating the injuries might be caused by different mechanisms. Then, five typical biomarkers were simultaneously determined by a newly developed and validated LC–MS method. Uric acid, CR, hippuric acid, 3-indoxyl sulfate and phenaceturic acid were separated by an Apollo C18 column and a methanol/water (5 mM ammonium acetate) gradient program. The prepared calibration curves showed good linearity with regression coefficients all above 0.9927. Of all the analytes, the precision were below 9.9% and the accuracy were from −10.3% to 9.2%. ROC results showed that different nephrotoxicity biomarkers were specific in different DKI models. The changes of different biomarkers might be induced by different nephrotoxic mechanisms in the DKI models. These specific and sensitive biomarkers in combination with serum CR are promising in the clinical diagnosis of DKI.
研究领域:
毒理学
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临床医学
·
分析化学
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产品关联
DOI
PubMed
高影响因子
CLINICA CHIMICA ACTA
·
2022
·
IF 6.314
Chenxi Yang,Sicheng Zhou,Jing Zhu,Huaying Sheng,Weimin Mao,Zhixuan Fu,Zhongjian Chen
Colorectal cancer is the second leading cause of cancer-related death across the world. So far, screening method for colorectal cancer are limited to blood test, imaging test, and digital rectal examination, that are either invasive or ineffective. So, this study aims to explore novel, more convenient and effective diagnostic method for colorectal cancer. First, the experiment cohort was randomly split to train set and test set, and LC-MS-based plasma lipidomics was applied to identify lipid features in colorectal cancer. Second, univariate and multivariate analyses were performed to screen for significantly differentially expressed lipids. Third, single-lipid-based ROC analysis and multiple-lipid-based machine learning modeling were conducted to assess differential lipids’ diagnostic performance. Lastly, survival analyses were used to evaluate lipids’ prognostic values. In total, 41 differential lipids were screened out, 10 were upregulated and 31 were downregulated in CRC. Only CerP(d15:0_22:0 + O) showed fine predictive accuracy in single-lipid-based ROC analysis. Among the four machine learning models, SVM showed best predictive performance with accuracy (in predicting test set) of 1.0000 (95 %CI: 0.8806, 1.0000), that can be reached by modeling with only 14 lipids. Four lipids had significant prognostic values, that were TG(11:0_18:0_18:0) (HR: 0.34), TG(18:0_18:0_18:1) (HR: 0.34), PC(22:1_12:3) (HR: 2.22), LPC(17:0) (HR: 3.16). In conclusion, this study discovered novel lipid features that have potential diagnostic and prognostic values, and showed combination of plasma lipidomics and machine learning modeling could have outstanding diagnostic performance and may serve as a convenient and more accessible way to aid in clinical diagnosis of colorectal cancer.
研究领域:
肿瘤学
·
生物信息学
·
代谢组学
·
医学诊断
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DOI
PubMed
高影响因子
Analytical Methods
·
2025
·
IF 2.6
Suxuan Li,Ningyang Mao,Cong Chen,Hui Zhao,Xiaoyu Chen,Liusheng Wang,Fuyun Cui,Wenning Feng,Zhiyong Wu
This study employed headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) technology combined with multivariate statistical analysis methods to analyze the flavor compounds in flue-cured tobacco from five different regions in China: Henan, Hunan, Yunnan, Chongqing, and Fujian. A total of 98 volatile aroma compounds were identified through HS-GC-IMS analysis, including esters, ketones, aldehydes, acids, alcohols, heterocyclic compounds, sulfur-containing compounds, other types of compounds, and 8 uncharacterized compounds. Principal Component Analysis (PCA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) were utilized to conduct dimensionality reduction and distinguish the samples, effectively recognizing differences in volatile compounds among tobacco leaves from various origins. A Random Forest (RF) classification model was constructed, and its reliability was validated through ROC (Receiver Operating Characteristic) analysis, achieving an AUC (Area Under the Curve) value of 0.980, which demonstrates exceptional predictive performance. PCA revealed distinct separations of tobacco leaf samples from different regions on the PCA score plot, and OPLS-DA analysis further validated these differences and confirmed the model's validity through permutation testing. Twenty key aroma compounds with VIP > 1.0 were screened by integrating OPLS-DA with the Random Forest classification model. These compounds showed significant differences in content among different samples, suggesting their potential as chemical markers for distinguishing the origin of flue-cured tobacco. This study not only provides a new method for identifying volatile compounds in tobacco but also offers novel insights into the geographical identification of flue-cured tobacco.
研究领域:
分析化学
·
食品科学
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植物学
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化学计量学
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农业科学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
危险品化学品经营许可证（带存储）