Advanced Science
·
2024
·
IF 14.3
Ruiyuan Weng,Yudian Xu,Xinjie Gao,Linlin Cao,Jiabin Su,Heng Yang,He Li,Chenhuan Ding,Jun Pu,Meng Zhang,Jiheng Hao,Wei Xu,Wei Ni,Kun Qian,Yuxiang Gu
Moyamoya disease (MMD) is a progressive cerebrovascular disorder that increases the risk of intracranial ischemia and hemorrhage. Timely diagnosis and intervention can significantly reduce the risk of new-onset stroke in patients with MMD. However, the current diagnostic methods are invasive and expensive, and non-invasive diagnosis using biomarkers of MMD is rarely reported. To address this issue, nanoparticle-enhanced laser desorption/ionization mass spectrometry (LDI MS) was employed to record serum metabolic fingerprints (SMFs) with the aim of establishing a non-invasive diagnosis method for MMD. Subsequently, a diagnostic model was developed based on deep learning algorithms, which exhibited high accuracy in differentiating the MMD group from the HC group (AUC = 0.958, 95% CI of 0.911 to 1.000). Additionally, hierarchical clustering analysis revealed a significant association between SMFs across different groups and vascular cognitive impairment in MMD. This approach holds promise as a novel and intuitive diagnostic method for MMD. Furthermore, the study may have broader implications for the diagnosis of other neurological disorders.
研究领域:
医学
·
神经科学
·
生物信息学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
AMINO ACIDS
·
2023
·Vol. 55
·期 9
·1189-1200
·
IF 3.5
Hussain Ahad,Xie Li,Deng Guozhe,Kang Xuejun
Amino acids not only play a vital role in the synthesis of biological molecules such as proteins in cancer malignant cells, they are also essential metabolites for immune cell activation and antitumor effects in the tumor microenvironment. The abnormal changes in amino acid metabolism are closely related to the occurrence and development of tumors and immunity. Intestinal microorganisms play an essential role in amino acid metabolism, and tryptophan and its intestinal microbial metabolites are typical representatives. However, it is known that the cyclic amino acid profile is affected by specific cancer types, so relevant studies mainly focus on one type of cancer and rarely study different cancer forms at the same time. The objective of this study was to examine the PFAA profile of five cancer patients and the characteristics of tryptophan intestinal microbial metabolites to determine whether there are general amino acid changes across tumors. Plasma samples were collected from esophageal ( n = 53), lung ( n = 73), colorectal ( n = 94), gastric ( n = 55), breast cancer ( n = 25), and healthy control (HC) ( n = 139) subjects. PFAA profile and tryptophan metabolites were measured, and their perioperative changes were examined using high-performance liquid chromatography. Univariate analysis revealed significant differences between cancer patients and HC. Furthermore, multivariate analysis discriminated cancer patients from HC. Regression diagnosis models were established for each cancer group using differential amino acids from univariate analysis. Receiver-operating characteristic analysis was applied to evaluate these diagnosis models. Finally, GABA, arginine, tryptophan, taurine, glutamic acid, and melatonin showed common alterations across all types of cancer patients. Metabolic pathway analysis shows that the most significant enrichment pathways were tryptophan, arginine, and proline metabolism. This study provides evidence that common alterations of the metabolites mentioned above suggest their role in the pathogenesis of each cancer patient. It was suggested that multivariate models based on PFAA profiles and tryptophan metabolites might be applicable in the screening of cancer patients.
研究领域:
肿瘤学
·
代谢组学
·
微生物学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
Hepatology International
·
2022
·Vol. 17
·期 2
·339-349
·
IF 9.029
Gao Feng,Lu De-Chan,Zheng Tian-Lei,Geng Shi,Sha Jun-Cheng,Huang Ou-Yang,Tang Liang-Jie,Zhu Pei-Wu,Li Yang-Yang,Chen Li-Li,Targher Giovanni,Byrne Christopher D.,Huang Zu-Fang,Zheng Ming-Hua
Background/purpose of the study There is a need to find a standardized and low-risk diagnostic tool that can non-invasively detect non-alcoholic steatohepatitis (NASH). Surface enhanced Raman spectroscopy (SERS), which is a technique combining Raman spectroscopy (RS) with nanotechnology, has recently received considerable attention due to its potential for improving medical diagnostics. We aimed to investigate combining SERS and neural network approaches, using a liver biopsy dataset to develop and validate a new diagnostic model for non-invasively identifying NASH. Methods Silver nanoparticles as the SERS-active nanostructures were mixed with blood serum to enhance the Raman scattering signals. The spectral data set was used to train the NASH classification model by a neural network primarily consisting of a fully connected residual module. Results Data on 261 Chinese individuals with biopsy-proven NAFLD were included and a prediction model for NASH was built based on SERS spectra and neural network approaches. The model yielded an AUROC of 0.83 (95% confidence interval [CI] 0.70–0.92) in the validation set, which was better than AUROCs of both serum CK-18-M30 levels (AUROC 0.63, 95% CI 0.48–0.76, p = 0.044) and the HAIR score (AUROC 0.65, 95% CI 0.51–0.77, p = 0.040). Subgroup analyses showed that the model performed well in different patient subgroups. Conclusions Fully connected neural network-based serum SERS analysis is a rapid and practical tool for the non-invasive identification of NASH. The online calculator website for the estimated risk of NASH is freely available to healthcare providers and researchers ( http://www.pan-chess.cn/calculator/RAMAN_score ).
研究领域:
医学
·
生物技术
✓ 已验证
产品关联
DOI
PubMed
高影响因子
MedComm
·
2025
·Vol. 6
·期 8
·e70303
·
IF 10.7
Jun Liu,Yi Yu,Qiqi Lu,Shan-Qiang Zhang,Ji-Cheng Li
Metabolic disorders play a crucial role in the occurrence of acute myocardial infarction (AMI). The objective of this research was to elucidate the characteristic metabolic profile of AMI and provide potential biomarkers for AMI. This study employed a targeted metabolomics approach utilizing the Ultra Performance Liquid Chromatography Tandem Mass Spectrometry (UPLC-MS/MS) system to identify both hydrophilic and hydrophobic metabolites present in plasma samples. Among 1498 detected metabolites, 78 were the most significantly expressed in the AMI group. Functional synergy analysis showed prominent enrichment in the pathways of steroid hormone biosynthesis, biosynthesis of unsaturated fatty acids, bile secretion, and ABC transporters. The metabolites 2-Hydroxy-6-Aminopurine, 17α-Hydroxyprogesterone, and S -(methyl) glutathione have been identified as potential metabolic biomarkers linked to the pathogenesis of AMI. The diagnostic model that integrates these three metabolites exhibited exceptional performance in both the discovery and validation cohorts, attaining an area under the curve (AUC) value greater than 0.9. In addition, based on the follow-up data, we also found that the three metabolites were potential predictive biomarkers for poor prognosis of AMI. This study delineated the characteristic metabolic profile of AMI and assessed the value of metabolic molecules in the diagnosis and prognosis of AMI. This may provide insights for understanding the AMI occurrence and progression.
研究领域:
心血管病学
·
代谢组学
·
分子医学
·
临床生物化学
·
转化医学
·
疾病 biomarker 研究
✓ 已验证
产品关联
DOI
PubMed
高影响因子
Advanced Healthcare Materials
·
2025
·
IF 9.6
Kexin Meng,Yao Shen,Dongni Hou,Hanbing Hu,Shouzhi Yang,Ziyue Zhang,Ayizekeranmu Yiming,Dingyitai Liang,Weibin Tian,Ludan He,Shuoyan Wei,Ying Wang,Jie Shen,Yuanlin Song,Sai Gu,Haiyang Su,Jian Zhou,Kun Qian
Rapid and accurate recognition of severe community-acquired pneumonia (CAP) would facilitate the optimal intervention. Currently, the diagnosis of severe CAP is commonly based on the criteria established by Infectious Disease Society of America (IDSA)/American Thoracic Society (ATS), which include 2 primary and 9 secondary criteria, making the process cumbersome and time-consuming. Here, a porous PtCu alloy-assisted laser desorption/ionization mass spectrometry (LDI MS) is designed for the extraction of plasma metabolic fingerprints (PMFs), coupling with machine learning for the diagnosis of severe CAP. The PtCu alloys with optimal particle size exhibit excellent sensitivity, reproducibility, and universality for metabolite detection, due to the porous structure, promising photoelectric effect, and improved melting surface structure. Further, the nanoplatform successfully records the PMFs within seconds, using only 0.5 µL native plasma. Machine learning of PMFs on 69 individuals produces a diagnostic model with an area under curve (AUC) of 0.832. Particularly, a three metabolic biomarker panel demonstrates enhanced diagnostic efficiency (AUC of 0.846), outperforming reported biomarkers (AUC of 0.560–0.770). Notably, the diagnosis can be completed in ≈35 min. The work affords a rapid and precise method for CAP management through metabolite analysis.
研究领域:
医学诊断
·
代谢组学
·
材料科学
·
人工智能
✓ 已验证
产品关联
DOI
PubMed
高影响因子
Frontiers in Immunology
·
2024
·
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
·
生物信息学
·
药理学
·
机器学习
✓ 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）