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Yuan Q, Yao LF, Tang JW, Ma ZW, Mou JY, Wen XR, Usman M, Wu X, Wang L. Rapid discrimination and ratio quantification of mixed antibiotics in aqueous solution through integrative analysis of SERS spectra via CNN combined with NN-EN model. J Adv Res 2024:S2090-1232(24)00116-4. [PMID: 38531495 DOI: 10.1016/j.jare.2024.03.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 02/14/2024] [Accepted: 03/23/2024] [Indexed: 03/28/2024] Open
Abstract
INTRODUCTION Abusing antibiotic residues in the natural environment has become a severe public health and ecological environmental problem. The side effects of its biochemical and physiological consequences are severe. To avoid antibiotic contamination in water, implementing universal and rapid antibiotic residue detection technology is critical to maintaining antibiotic safety in aquatic environments. Surface-enhanced Raman spectroscopy (SERS) provides a powerful tool for identifying small molecular components with high sensitivity and selectivity. However, it remains a challenge to identify pure antibiotics from SERS spectra due to coexisting components in the mixture. OBJECTIVES In this study, an intelligent analysis model for the SERS spectrum based on a deep learning algorithm was proposed for rapid identification of the antibiotic components in the mixture and quantitative determination of the ratios of these components. METHODS We established a water environment system containing three antibiotic residues of ciprofloxacin, doxycycline, and levofloxacin. To facilitate qualitative and quantitative analysis of the SERS spectra antibiotic mixture datasets, we developed a computational framework integrating a convolutional neural network (CNN) and a non-negative elastic network (NN-EN) method. RESULTS The experimental results demonstrate that the CNN model has a recognition accuracy of 98.68%, and the interpretation analysis of Shapley Additive exPlanations (SHAP) shows that our model can specifically focus on the characteristic peak distribution. In contrast, the NN-EN model can accurately quantify each component's ratio in the mixture. CONCLUSION Integrating the SERS technique assisted by the CNN combined with the NN-EN model exhibits great potential for rapid identification and high-precision quantification of antibiotic residues in aquatic environments.
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Affiliation(s)
- Quan Yuan
- Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province 510080, China; School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu Province 210000, China
| | - Lin-Fei Yao
- Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province 510080, China
| | - Jia-Wei Tang
- School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu Province 210000, China
| | - Zhang-Wen Ma
- Department of Pharmaceutical Analysis, School of Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu Province 210000, China
| | - Jing-Yi Mou
- Department of Clinical Medicine, School of 1st Clinical Medicine, Xuzhou Medical University, Xuzhou, Jiangsu Province 210000, China
| | - Xin-Ru Wen
- Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province 510080, China
| | - Muhammad Usman
- School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu Province 210000, China.
| | - Xiang Wu
- School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu Province 210000, China.
| | - Liang Wang
- Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province 510080, China.
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Mou JY, Ma ZW, Zhang MY, Yuan Q, Wang ZY, Liu QH, Li F, Liu Z, Wang L. Structural abnormality of hepatic glycogen in rat liver with diethylnitrosamine-induced carcinogenic injury. Int J Biol Macromol 2024; 260:129432. [PMID: 38228208 DOI: 10.1016/j.ijbiomac.2024.129432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 01/01/2024] [Accepted: 01/09/2024] [Indexed: 01/18/2024]
Abstract
Growing evidence confirms associations between glycogen metabolic re-wiring and the development of liver cancer. Previous studies showed that glycogen structure changes abnormally in liver diseases such as cystic fibrosis, diabetes, etc. However, few studies focus on glycogen molecular structural characteristics during liver cancer development, which is worthy of further exploration. In this study, a rat model with carcinogenic liver injury induced by diethylnitrosamine (DEN) was successfully constructed, and hepatic glycogen structure was characterized. Compared with glycogen structure in the healthy rat liver, glycogen chain length distribution (CLD) shifts towards a short region. In contrast, glycogen particles were mainly present in small-sized β particles in DEN-damaged carcinogenic rat liver. Comparative transcriptomic analysis revealed significant expression changes of genes and pathways involved in carcinogenic liver injury. A combination of transcriptomic analysis, RT-qPCR, and western blot showed that the two genes, Gsy1 encoding glycogen synthase and Gbe1 encoding glycogen branching enzyme, were significantly altered and might be responsible for the structural abnormality of hepatic glycogen in carcinogenic liver injury. Taken together, this study confirmed that carcinogenic liver injury led to structural abnormality of hepatic glycogen, which provided clues to the future development of novel drug targets for potential therapeutics of carcinogenic liver injury.
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Affiliation(s)
- Jing-Yi Mou
- Department of Clinical Medicine, School of 1(st) Clinical Medicine, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Zhang-Wen Ma
- Department of Pharmaceutical Analysis, School of Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Meng-Ying Zhang
- School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Quan Yuan
- School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Zi-Yi Wang
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Queensland, Australia
| | - Qing-Hua Liu
- State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Taipa, Macau SAR, China
| | - Fen Li
- Laboratory Medicine, The Fifth People's Hospital of Huai'an, Huai'an, Jiangsu Province, China
| | - Zhao Liu
- Department of Clinical Medicine, School of 1(st) Clinical Medicine, Xuzhou Medical University, Xuzhou, Jiangsu Province, China; Department of Thyroid and Breast Surgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
| | - Liang Wang
- School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu Province, China; Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China; School of Agriculture and Food Sciences, The University of Queensland, Brisbane, Queensland, Australia.
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Liu W, Tang JW, Mou JY, Lyu JW, Di YW, Liao YL, Luo YF, Li ZK, Wu X, Wang L. Rapid discrimination of Shigella spp. and Escherichia coli via label-free surface enhanced Raman spectroscopy coupled with machine learning algorithms. Front Microbiol 2023; 14:1101357. [PMID: 36970678 PMCID: PMC10030586 DOI: 10.3389/fmicb.2023.1101357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 02/20/2023] [Indexed: 03/11/2023] Open
Abstract
Shigella and enterotoxigenic Escherichia coli (ETEC) are major bacterial pathogens of diarrheal disease that is the second leading cause of childhood mortality globally. Currently, it is well known that Shigella spp., and E. coli are very closely related with many common characteristics. Evolutionarily speaking, Shigella spp., are positioned within the phylogenetic tree of E. coli. Therefore, discrimination of Shigella spp., from E. coli is very difficult. Many methods have been developed with the aim of differentiating the two species, which include but not limited to biochemical tests, nucleic acids amplification, and mass spectrometry, etc. However, these methods suffer from high false positive rates and complicated operation procedures, which requires the development of novel methods for accurate and rapid identification of Shigella spp., and E. coli. As a low-cost and non-invasive method, surface enhanced Raman spectroscopy (SERS) is currently under intensive study for its diagnostic potential in bacterial pathogens, which is worthy of further investigation for its application in bacterial discrimination. In this study, we focused on clinically isolated E. coli strains and Shigella species (spp.), that is, S. dysenteriae, S. boydii, S. flexneri, and S. sonnei, based on which SERS spectra were generated and characteristic peaks for Shigella spp., and E. coli were identified, revealing unique molecular components in the two bacterial groups. Further comparative analysis of machine learning algorithms showed that, the Convolutional Neural Network (CNN) achieved the best performance and robustness in bacterial discrimination capacity when compared with Random Forest (RF) and Support Vector Machine (SVM) algorithms. Taken together, this study confirmed that SERS paired with machine learning could achieve high accuracy in discriminating Shigella spp., from E. coli, which facilitated its application potential for diarrheal prevention and control in clinical settings.
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Affiliation(s)
- Wei Liu
- School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Jia-Wei Tang
- School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Laboratory Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China
| | - Jing-Yi Mou
- The First School of Clinical Medicine, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Jing-Wen Lyu
- Laboratory Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China
| | - Yu-Wei Di
- Laboratory Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China
| | - Ya-Long Liao
- Laboratory Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China
| | - Yan-Fei Luo
- Laboratory Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China
| | - Zheng-Kang Li
- Laboratory Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China
- *Correspondence: Zheng-Kang Li,
| | - Xiang Wu
- School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Xiang Wu,
| | - Liang Wang
- Laboratory Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China
- Liang Wang,
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Ma ZW, Tang JW, Liu QH, Mou JY, Qiao R, Du Y, Wu CY, Tang DQ, Wang L. Identification of geographic origins of Morus alba Linn. through surfaced enhanced Raman spectrometry and machine learning algorithms. J Biomol Struct Dyn 2023; 41:14285-14298. [PMID: 36803175 DOI: 10.1080/07391102.2023.2180433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 02/08/2023] [Indexed: 02/22/2023]
Abstract
The leaves of Morus alba Linn., which is also known as white mulberry, have been commonly used in many of traditional systems of medicine for centuries. In traditional Chinese medicine (TCM), mulberry leaf is mainly used for anti-diabetic purpose due to its enrichment in bioactive compounds such as alkaloids, flavonoids and polysaccharides. However, these components are variable due to the different habitats of the mulberry plant. Therefore, geographic origin is an important feature because it is closely associated with bioactive ingredient composition that further influences medicinal qualities and effects. As a low-cost and non-invasive method, surface enhanced Raman spectrometry (SERS) is able to generate the overall fingerprints of chemical compounds in medicinal plants, which holds the potential for the rapid identification of their geographic origins. In this study, we collected mulberry leaves from five representative provinces in China, namely, Anhui, Guangdong, Hebei, Henan and Jiangsu. SERS spectrometry was applied to characterize the fingerprints of both ethanol and water extracts of mulberry leaves, respectively. Through the combination of SERS spectra and machine learning algorithms, mulberry leaves were well discriminated with high accuracies in terms of their geographic origins, among which the deep learning algorithm convolutional neural network (CNN) showed the best performance. Taken together, our study established a novel method for predicting the geographic origins of mulberry leaves through the combination of SERS spectra with machine learning algorithms, which strengthened the application potential of the method in the quality evaluation, control and assurance of mulberry leaves.
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Affiliation(s)
- Zhang-Wen Ma
- Department of Pharmaceutical Analysis, School of Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, School of Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Jia-Wei Tang
- Department of Intelligent Medical Engineering, School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, Jiangsu Province, China
| | - Qing-Hua Liu
- State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Taipa, Macau, China
| | - Jing-Yi Mou
- The First School of Clinical Medicine, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Rui Qiao
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, School of Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
- Department of Clinical Pharmacy, School of Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Yan Du
- Department of Pharmaceutical Analysis, School of Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, School of Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Chang-Yu Wu
- Department of Biomedical Engineering, School of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Dao-Quan Tang
- Department of Pharmaceutical Analysis, School of Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, School of Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Liang Wang
- Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
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Li WW, Liu B, Dong SQ, He SQ, Liu YY, Wei SY, Mou JY, Zhang JX, Liu Z. Bioinformatics and Experimental Analysis of the Prognostic and Predictive Value of the CHPF Gene on Breast Cancer. Front Oncol 2022; 12:856712. [PMID: 35372047 PMCID: PMC8965246 DOI: 10.3389/fonc.2022.856712] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 02/16/2022] [Indexed: 01/08/2023] Open
Abstract
Background Recent studies in the United States have shown that breast cancer accounts for 30% of all new cancer diagnoses in women and has become the leading cause of cancer deaths in women worldwide. Chondroitin Polymerizing Factor (CHPF), is an enzyme involved in chondroitin sulfate (CS) elongation and a novel key molecule in the poor prognosis of many cancers. However, its role in the development and progression of breast cancer remains unclear. Methods The transcript expression of CHPF in the Cancer Genome Atlas-Breast Cancer (TCGA-BRCA), Gene Expression Omnibus (GEO) database was analyzed separately using the limma package of R software, and the relationship between CHPF transcriptional expression and CHPF DNA methylation was investigated in TCGA-BRCA. Kaplan-Meier curves were plotted using the Survival package to further assess the prognostic impact of CHPF DNA methylation/expression. The association between CHPF transcript expression/DNA methylation and cancer immune infiltration and immune markers was investigated using the TIMER and TISIDB databases. We also performed gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis with the clusterProfiler package. Western blotting and RT-PCR were used to verify the protein level and mRNA level of CHPF in breast tissue and cell lines, respectively. Small interfering plasmids and lentiviral plasmids were constructed for transient and stable transfection of breast cancer cell lines MCF-7 and SUM1315, respectively, followed by proliferation-related functional assays, such as CCK8, EDU, clone formation assays; migration and invasion-related functional assays, such as wound healing assay and transwell assays. We also conducted a preliminary study of the mechanism. Results We observed that CHPF was significantly upregulated in breast cancer tissues and correlated with poor prognosis. CHPF gene transcriptional expression and methylation are associated with immune infiltration immune markers. CHPF promotes proliferation, migration, invasion of the breast cancer cell lines MCF-7 and SUM1315, and is significantly enriched in pathways associated with the ECM-receptor interaction and PI3K-AKT pathway. Conclusion CHPF transcriptional expression and DNA methylation correlate with immune infiltration and immune markers. Upregulation of CHPF in breast cancer promotes malignant behavior of cancer cells and is associated with poorer survival in breast cancer, possibly through ECM-receptor interactions and the PI3K-AKT pathway.
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Affiliation(s)
- Wan-Wan Li
- Department of General Surgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.,Institute of Digestive Diseases, Xuzhou Medical University, Xuzhou, China
| | - Bin Liu
- Department of General Surgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Shu-Qing Dong
- Department of General Surgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.,Institute of Digestive Diseases, Xuzhou Medical University, Xuzhou, China
| | - Shi-Qing He
- Department of General Surgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.,Institute of Digestive Diseases, Xuzhou Medical University, Xuzhou, China
| | - Yu-Ying Liu
- Department of General Surgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.,Institute of Digestive Diseases, Xuzhou Medical University, Xuzhou, China
| | - Si-Yu Wei
- Department of General Surgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.,Institute of Digestive Diseases, Xuzhou Medical University, Xuzhou, China
| | - Jing-Yi Mou
- Department of General Surgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.,Institute of Digestive Diseases, Xuzhou Medical University, Xuzhou, China
| | - Jia-Xin Zhang
- Department of General Surgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Zhao Liu
- Department of General Surgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
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