Biomarkers in Medicine
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2024
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IF 1.9
Zhenzhen Tang,Baoli Xu,Junjun Wang,Wenzhen Wang,Shanshan Sha,Yongjin Sun
Aim: To identify novel metabolic biomarkers for patients with acute ischemic stroke (AIS).Methods: The metabolites in the sera of 63 patients with AIS aged 45∼77 years and 60 healthy individuals were analyzed by liquid chromatography (LC)-mass spectrometry (MS)/MS. The efficiency of significantly altered metabolites as biomarkers of AIS was evaluated by ROC curve analysis.Results: Different metabolic profiles were revealed in AIS patients' sera compared with healthy persons. Twelve significantly altered metabolites had an area under the curve (AUC) value >0.80, demonstrating their potential as a biomarker of AIS. Among them, six metabolites are firstly reported to distinguish between AIS patients and healthy individuals.Conclusion: These 12 metabolites can be further researched as potential diagnostic biomarkers of AIS.
研究领域:
医学
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代谢组学
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产品关联
DOI
PubMed
高影响因子
JOURNAL OF CHROMATOGRAPHY B-ANALYTICAL TECHNOLOGIES IN THE BIOMEDICAL AND LIFE SCIENCES
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2021
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IF 3.205
Li Feng,Mengting Gao,Yuanyuan Zhai,Xin Li,Yifei Wang,Tong Xie,Weifeng Yao,Jinjun Shan,Li Zhang,Anwei Ding
The complexity of ingredients in traditional Chinese medicine (TCM) makes it challenging to clarify its efficacy in an acceptable and scientific approach. The present study was aimed to use quantification results from targeted cellular metabolomics to evaluate anti-aging efficacy of a famous Chinese medicine formula, Erzhi Wan (EZW), and screen possible effective extracts, depending on the developed strategy integrating multivariate receiver operating characteristic (ROC) curve and analytic hierarchy process (AHP). In this study, senescent NRK cells induced by D-galactose were treated with drug-containing serum of EZW and four kinds of extracts (petroleum ether, ethyl acetate, butanol and water). Intermediates of two major metabolic pathways for energy synthesis, tricarboxylic acid (TCA) cycle and glycolysis, were accurately quantified by GC–MS/MS to identify discriminate metabolites for clarifying therapeutic mechanism of EZW based on multivariate statistical analysis. Senescent and non-senescent cells were successfully distinguished using these metabolites by ROC curve analysis. Next, these metabolites were used as evaluation indexes to quantitatively reflect different effect of EZW and its extracts, according to the role of them in distinguishing groups and in conjunction with AHP. In vitro detection of senescence-associated β-galactosidase (SA-β-gal) activity was used to verify the reliability of evaluation results. The reversal after treatment of drug-containing serum of EZW and extracts was observed, and the petroleum ether extract might be the potential active extract responsible for the major anti-aging effect of EZW, which was in agreement with in vitro experiments. Altogether, metabolomics was a powerful approach for evaluation efficacy and elucidation action mechanisms of TCM. The integrated evaluation strategy in this paper with properties of high practicality, feasibility and effectivity was expected to provide a new insight into comprehensive and quantitative efficacy evaluation.
研究领域:
药理学
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代谢组学
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中医药学
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DOI
PubMed
高影响因子
RSC Advances
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2015
·Vol. 5
·期 102
·84164-84171
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IF 3.84
Guifang Du,Xiaomei Yang,Mu Hu,Chengcheng Hao,Yanan Gu,Xiuyi Zhi,Wen G. Jiang,Junqi He,Shan Cheng
NHERF1 might play a significant role in biological processes including oncogenic transformation and metastasis. Owing to the lack of highly sensitive and quantitative methods of NHERF1 in human plasma, there have been few reports on the plasma levels of NHERF1 and its correlation with cancer. Here, a novel amplified luminescent proximity homogeneous immunoassay (AlphaLISA) has been developed and validated for the quantification of NHERF1 in human plasma. This assay was based on an AlphaScreen detection technique with two different anti-NHERF1 antibodies coupled to donor and acceptor beads, respectively. The developed AlphaLISA assay was further optimized and validated in terms of linearity, limit of detection (LOD), limit of quantification (LOQ), precision, recovery, selectivity and interferences. The linear range of NHERF1 in human plasma was 5.00–100 ng mL−1, with an LOD of 2.00 ng mL−1. This AlphaLISA assay has been successfully applied to the quantification of NHERF1 in the plasma from 75 patients with non-small cell lung cancer (NSCLC). The levels of NHERF1 protein in plasma from patients with NSCLC were significantly higher than those in the healthy group (p = 0.0004). Based on the evaluation of the ROC curves, measuring the content of NHERF1 in human plasma could provide a potential diagnostic tool for NSCLC.
研究领域:
肿瘤学
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分子生物学
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临床化学
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癌症生物标志物
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DOI
高影响因子
ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY
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2022
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IF 7.129
Yiran Sun,Jiayu Pei,Xi Chen,Mengwen Lin,Yun Pan,Yingying Zhang,Wenlin Bai,Xingfan Zhou,Wenping Zhang
Chlorpyrifos (CPF) is associated with depression, cognitive dysfunction, and other neurological disorders. Increasing evidence has suggested that the gut microbiota plays a vital role in regulating the development of depression. However, it is unknown whether gut microbiota is associated with CPF-related depression. This study aimed to explore the effect of CPF on depressive-like behavior in mice and investigated the role of gut microbiota in this behavior. In our study, we selected fifty male C57BL/6 J mice for the model and subjected them to CPF poisoning by gavage for 14 days. The depressive-like behaviors of mice were assessed by the open field test (OFT), sucrose preference test (SPT), and forced swimming test (FST). Furthermore, we selected the high-dose group (CPF10) with obvious changes in depressive-like behaviors for the hippocampus and colon histopathological analysis, examined the changes in the gut microbiota by 16 S rRNA sequencing, screened the different microbiota among groups by linear discriminant analysis effect size (LEfSe), analyzed the correlation between intestinal bacteria and depression-like behavior indicators by Spearman analysis, and evaluated the predictive ability of different bacteria to CPF-induced depressive-like behavior using the receiver operator characteristic (ROC) curve. The results showed that CPF caused depressive-like behaviors with pathological changes in the hippocampus and colon. CPF induced changes in gut microbiota, including 49 differential bacteria. Among the top 10 abundant bacteria, Actinobacteria and Deferribacteres were increased, and Cyanobacteria , Patescibacteria and Verrucomicrobia were decreased at the phylum level. Muribaculum , Ruminococcaceae.UCG.014 and uncultured Bacteroides bacterium were decreased at the genus level. Correlation analysis demonstrated that 18 differentially abundant bacteria were correlated with CPF-induced depression. ROC curves revealed that Deferribacteres , Mucispirillum , Rikenella and GCA900066575 are potential biomarkers for depression caused by CPF. These findings will provide an experimental basis for the neurological health of the pesticide-exposed population.
研究领域:
神经科学
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微生物学
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毒理学
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行为科学
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DOI
PubMed
高影响因子
Frontiers in Endocrinology
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2022
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IF 6.055
Cao Lin-Lin,Han Yi,Wang Yuanxiao,Pei Lin,Yue Zhihong,Qin Li,Liu Boyu,Cui Jingwen,Jia Mei,Wang Hui
Metabolic syndrome (MetS) is an independent risk factor for hepatocellular cancer (HCC). Currently, there is no highly sensitive and specific biomarkers for HCC surveillance in MetS population. Metabolomics has been reported as a powerful technology for biomarker discovery. In the present study, we aimed to explore novel biomarkers with high sensitivity and specificity for MetS-positive [MetS(+)] HCC by metabolomic analysis. At first, many serum metabolites were found dysregulated in MetS(+) HCC individuals. Validation of the dysregulated metabolites by targeted metabolite analyses revealed that serum L-glutamic acid (L-glu), pipecolic acid (PA) and 7-methylguanine (7-mG) were increased in MetS(+) HCC compared to MetS group. Then a biomarker panel including L-glu, PA and alpha-fetoprotein (AFP) was identified as a novel biomarker for the diagnosis of MetS(+) HCC. Receiver operating characteristic (ROC) curve was drawn and the area under the ROC curve (AUC) was 0.87 for discriminating MetS(+) HCC from MetS group. The biomarker panel was capable of detecting small (AUC = 0.82) and early-stage (AUC = 0.78) tumors as well. Moreover, it exhibited great diagnostic performance (AUC = 0.93) for discriminating MetS(+) HCC from other MetS-associated cancers, including colorectal cancer and gastric cancer. Collectively, our study establishes a novel diagnostic tool for MetS(+) HCC.
研究领域:
肿瘤学
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代谢组学
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分子生物学
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癌症诊断
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产品关联
DOI
PubMed
高影响因子
ACS Nano
·
2023
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IF 17.1
Shicai Wang,Ruijun Deng,Zhiya Chen,Lili Huang,Yang Song,Dan Yuan,Yu Li,Haonan Liu,Fan Yang,Beiyuan Fan,Yun Xu,Zijian Zhao,Yanzhao Li,Yan Zhang
Alzheimer’s disease (AD) is the most common cause of dementia in older people. However, diagnosing AD through noncognitive methods, such as invasive cerebrospinal fluid sampling or radioactive positron emission tomography, has limited applications. Herein, the femtomolar levels of AD biomarkers amyloid β 40 (Aβ40), amyloid β 42 (Aβ42), phosphorylated tau 181 (P-tau181), phosphorylated tau 217 (P-tau217), and neurofilament light chain (NfL) were determined in human plasma in multicenter clinical cohorts using an ultrasensitive graphene field-effect transistor sensor. A machine-learning algorithm was also used to assemble these plasma biomarkers and optimize their performance in discriminating individual stages of Alzheimer’s dementia progression. The “composite-info” biomarker panel, which combines these biomarkers and clinical information, considerably improved the staging performance in AD progression. It achieved an area under the curve of >0.94 in the receiver operator characteristic (ROC) curve. In addition, the panel demonstrated an advantage in the individual-based stage assessment compared with that of the Mini-Mental State Examination/Montreal Cognitive Assessment and nuclear magnetic resonance imaging. This study provides a composite biomarker panel for the screening and early diagnosis of AD using a rapid detection system.
研究领域:
神经学
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生物标志物研究
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医学诊断
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机器学习
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石墨烯技术
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产品关联
DOI
PubMed
高影响因子
MICROCHEMICAL JOURNAL
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2024
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IF 4.9
Haiqin Li,Yueyue Li,Xiaoliang Zhang,Xiaojun Ren,Xiaochun Li
As a non-invasive monitoring method, urine analysis is one of the “three routines” of medical examination, which is of great significance for the early diagnosis, therapeutic effect judgment and long-term fine monitoring of chronic diseases such as metabolic system diseases and kidney diseases. It is imperative to develop a home urine routine detection system. In this paper, Smart Urine Analyzer is innovatively developed by combining smartphones with array urine dipsticks, and equipped with the unique design of adjustable bracket suitable for camera position of different smartphones. The optimal colorimetric value was selected for analysis based on chromogenic response characteristics combined with digital image colorimetry, and the breakthrough from qualitative semi-quantification to precise quantification of 9 kinds of urine biomarkers was completed. And a smartphone App for automated image recognition and processing was developed, which was applied for at-home routine healthcare. 100 clinical urine samples were used to evaluate the reliability of the system. Among them, the ROC curve showed a high consistency between Smart Urine Analyzer and the Dirui H-800 urine analyzer in hospital, which further verified the practicability. As a result, the user-friendly Smart Urine Analyzer can share health data and condition with doctors in real time, which provides a new scheme and means for the home detection of urine biomarkers, and has great practical application value as a potential home medical service assistant.
研究领域:
医学诊断
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泌尿学
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医疗技术
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移动健康(mHealth)
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临床化学
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产品关联
DOI
高影响因子
Frontiers in Immunology
·
2024
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IF 5.7
Yifei Geng,Yuchen Liu,Min Wang,Xi Dong,Xiao Sun,Yun Luo,Xiaobo Sun
Background Ischemic stroke (IS), caused by blood and oxygen deprivation due to cerebral thrombosis, has links to activated and aggregated platelets. Discovering platelet-related biomarkers, developing diagnostic models, and screening antiplatelet drugs are crucial for IS diagnosis and treatment. Methods and results Combining and normalizing {"type":"entrez-geo","attrs":{"text":"GSE16561","term_id":"16561"}} GSE16561 and {"type":"entrez-geo","attrs":{"text":"GSE22255","term_id":"22255"}} GSE22255 datasets identified 1,753 upregulated and 1,187 downregulated genes. Fifty-one genes in the platelet-related module were isolated using weighted gene co-expression network analysis (WGCNA) and other analyses, including 50 upregulated and one downregulated gene. Subsequent enrichment and network analyses resulted in 25 platelet-associated genes and six diagnostic markers for a risk assessment model. This model’s area under the ROC curve outperformed single genes, and in the peripheral blood of the high-risk group, immune infiltration indicated a higher proportion of CD4, resting CD4 memory, and activated CD4 memory T cells, along with a lower proportion of CD8 T cells in comparison to the low-risk group. Utilizing the gene expression matrix and the CMap database, we identified two potential drugs for IS. Finally, a rat MACO/R model was used to validate the diagnostic markers’ expression and the drugs’ predicted anticoagulant effects. Conclusion We identified six IS platelet-related biomarkers (APP, THBS1, F13A1, SRC, PPBP, and VCL) for a robust diagnostic model. The drugs alpha-linolenic acid and ciprofibrate have potential antiplatelet effects in IS. This study advances early IS diagnosis and treatment.
研究领域:
神经病学
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生物信息学
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药理学
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机器学习
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产品关联
DOI
PubMed
高影响因子
Frontiers in Immunology
·
2024
·
IF 5.7
Jijin Jiang,Yan Chen,Yue Su,Li Zhang,Hao Qian,Xinmiao Song,Jin-Fu Xu
Background Sepsis is an uncontrolled reaction to infection that causes severe organ dysfunction and is a primary cause of ARDS. Patients suffering both sepsis and ARDS have a poor prognosis and high mortality. However, the mechanisms behind their simultaneous occurrence are unclear. Methods We acquired sepsis and ARDS datasets from GEO and Arrayexpress databases and screened hub genes by WGCNA and machine learning algorithm. For diagnosis and prognosis, ROC curve and survival analysis were used. We performed GO, KEGG, GSEA, immune cell infiltration, drug prediction, molecular docking, transcription factor prediction, and constructed PPI and ceRNA networks to explore these genes and the common mechanisms of sepsis and ARDS. Single-cell data analysis compared immune cell profiles and hub gene localization. Finally, RT-qPCR and H&E staining confirmed the reliability of hub genes using PBMCs samples and mouse models. Results We identified 242 common differentially expressed genes in sepsis and ARDS. WGCNA analysis showed that the turquoise module in GSE95233 is strongly linked to sepsis occurrence and poor prognosis, while the black module in GSE10474 is associated with ARDS. Using WGCNA and three machine learning methods (LASSO, random forest and Boruta), we identified three key genes CX3CR1, PID1 and PTGDS. Models built with them showed high AUC values in ROC curve evaluations and were validated by external datasets, accurately predicting the occurrence and mortality. We further explored the immunological landscape of these genes using immune infiltration and single-cell analysis. Then, the ceRNA, predicted drugs and molecular docking were analyzed. Ultimately, we demonstrated that these genes are expressed differently in human and mouse samples with sepsis and ARDS. Conclusion This study identified three molecular signatures (CX3CR1, PID1 and PTGDS) linked to the diagnosis and poor prognosis of sepsis and ARDS, validated by RT-qPCR and H&E staining in both patient and mouse samples. This research may be valuable for identifying shared biological mechanisms and potential treatment targets for both diseases.
研究领域:
医学遗传学
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免疫学
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生物信息学
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分子生物学
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转化医学
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产品关联
DOI
PubMed
高影响因子
JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS
·
2024
·
IF 3.1
Yuzhu Xu,Menghe Huang,Yingting Chen,Lintao Yu,Meiran Wu,Shiyue Kang,Qiuyu Lin,Qiaoxuan Zhang,Liqiao Han,Haibiao Lin,Peifeng Ke,Wenjin Fu,Qizhi Tang,Jun Yan,Xianzhang Huang
Advanced glycation end products (AGEs), derived from the non-enzymatic glycation reaction, are defined as glycotoxins in various diseases including aging, diabetes and kidney injury. Exploring AGEs as potential biomarkers for these diseases holds paramount significance. Nevertheless, the high chemical structural similarity and great heterogeneity among AGEs present a formidable challenge when it comes to the comprehensive, simultaneous, and accurate detection of multiple AGEs in biological samples. In this study, an UPLC/MS/MS method for simultaneous quantification of 20 free AGEs in human serum was firstly established and applied to quantification of clinical samples from individuals with kidney injury. Simple sample preparation method through protein precipitation without derivatization was used. Method performances including imprecision, accuracy, sensitivity, linearity, and carryover were systematically validated. Intra- and inter- imprecision of 20 free AGEs were 1.93–5.94 % and 2.30–8.55 %, respectively. The method accuracy was confirmed with good recoveries ranging from 96.40 % to 103.25 %. The LOD and LOQ were 0.1–3.13 ng/mL and 0.5–6.25 ng/mL, respectively. Additionally, the 20 free AGEs displayed excellent linearity (R 2 >0.9974) across a wide linear range (1.56–400 ng/mL). Finally, through simultaneous quantitation of 20 Free AGEs in 100 participants including kidney injury patient and healthy controls, we identified six free AGEs, including N 6 -carboxyethyl-L-arginine (CEA), N 6 -carboxymethyl-L-lysine (CML), methylglyoxal-derived hydroimidazolones (MG-H), N 6 -formyl-lysine, N 6 -carboxymethyl-L-arginine (CMA), and glyoxal-derived hydroimidazolone (G-H), could well distinguish kidney injury patients and healthy individuals. Among them, the levels of four free AGEs including CML, CEA, MG-H, and G-H strongly correlate with traditionally clinical markers of kidney disease. The high area under the curve (AUC) values (AUC=0.965) in receiver operating characteristic (ROC) curve indicated that these four free AGEs can be served as combined diagnostic biomarkers for the diagnosis of kidney disease.
研究领域:
临床化学
·
分析化学
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肾病学
·
生物标志物研究
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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）