Article
Chemistry, Analytical
Emma Buchan, Liam Kelleher, Michael Clancy, Jonathan James Stanley Rickard, Pola Goldberg Oppenheimer
Summary: This study systematically investigated the molecular spectral fingerprint of saliva and developed a non-destructive molecular profiling approach using hybrid artificial neural network algorithms and Raman spectroscopy. The classification algorithm successfully identified gender and age information from saliva, laying the platform for applications in forensics and biosensing. The discernible spectral molecular 'barcodes' primarily stemmed from amino acid, protein, and lipid changes in saliva.
ANALYTICA CHIMICA ACTA
(2021)
Article
Chemistry, Analytical
Haoyue Liang, Xuelian Cheng, Shuxu Dong, Haoyu Wang, Ertao Liu, Yongxin Ru, Yinghui Li, Xiaodong Kong, Yingdai Gao
Summary: A simple and non-invasive detection method for acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) was established using Raman spectroscopy and orthogonal partial least squares discriminant analysis (OPLS-DA). The method can accurately distinguish the control group from AML and ALL groups, as well as differentiate different subtypes of AML. Additionally, Raman spectroscopy can identify important variables related to leukemia prognosis.
JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS
(2022)
Article
Engineering, Environmental
Yinglei Zhao, Jinnuo Zhang, Mostafa Gouda, Chenghao Zhang, Lei Lin, Pengcheng Nie, Hongbao Ye, Wei Huang, Yunxiang Ye, Chengquan Zhou, Yong He
Summary: This study used confocal Raman spectroscopy combined with density functional theory and deep learning to characterize phytochelatin2 (PC2) and heavy metal-PC2 mixtures. The results provide a general protocol for structure analysis and noninvasive detection of heavy metal-PCs complexes in plants, and offer a novel idea for simplifying identification of high phytoremediation cultivars and assessment of heavy metal related food safeties.
JOURNAL OF HAZARDOUS MATERIALS
(2022)
Article
Biochemical Research Methods
Xianchang Li, Hongjun Chen, Shiding Zhang, Haijun Yang, Shanshan Gao, Haisheng Xu, Lidong Wang, Ruiping Xu, Fuyou Zhou, Jiming Hu, Jianhua Zhao, Haishan Zeng
Summary: A novel method utilizing blood plasma resonance Raman spectroscopy combined with multivariate analysis was proposed to detect esophageal cancer, showing reduced levels of carotenoids in plasma of cancer patients. The technique, combined with wavenumber selection and PC-LDA algorithms, could potentially serve as a biomarker for screening esophageal cancer.
JOURNAL OF BIOPHOTONICS
(2021)
Article
Spectroscopy
Anang Kumar Singh, Himadri Karjee, Sambuddha Ghosh, Jyotirmoy Chatterjee, Anushree Roy
Summary: Resonance Raman spectroscopy and multivariate analysis of human serum spectral data are effective tools for detecting retinal degeneration and distinguishing between different stages of the disease. Machine learning algorithm shows a high accuracy of 94% for classification, indicating a potential therapeutic role of dietary carotenoids in diabetic retinopathy.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2022)
Review
Medicine, General & Internal
Siddra Maryam, Marcelo Saito Nogueira, Rekha Gautam, Shree Krishnamoorthy, Sanathana Konugolu Venkata Sekar, Kiang Wei Kho, Huihui Lu, Richeal Ni Riordain, Linda Feeley, Patrick Sheahan, Ray Burke, Stefan Andersson-Engels
Summary: Oral cancer is the 16th most common cancer worldwide, and early diagnosis is challenging. Optical spectroscopy could be a non-invasive and cost-effective diagnostic technique with advantages such as cost, speed, objectivity, sensitivity, painlessness, and ease-of-use. This review provides a comprehensive overview and recent developments in optical spectroscopy for oral cancer diagnosis, emphasizing the importance of saliva-based potential biomarkers for early-stage diagnosis.
Review
Cardiac & Cardiovascular Systems
Tuttolomondo Domenico, Antonelli Rita, Setti Giacomo, Ardissino Diego, Pertinhez Thelma, Gallo Mariana, Niccoli Giampaolo, Nicolini Francesco, Georgaki Maria, Formica Francesco, Borrello Bruno, Meleti Marco, Cassi Diana
Summary: Salivary biomarkers, such as cardiac troponin, C-reactive protein, and adiponectin, show potential for the early identification of acute myocardial infarction (AMI). However, further studies are needed to confirm their effectiveness as substitutes for serological markers.
INTERNATIONAL JOURNAL OF CARDIOLOGY
(2023)
Article
Biology
Mohammad Ali Sheikh Beig Goharrizi, Saeed Ghodsi, Majid Mokhtari, Sayyed Sajjad Moravveji
Summary: This study identified the STEMI-related non-invasive markers and found that ARGL, CLEC4E, and EIF3D genes showed significant differential expression in Iranian patients. The ROC curve for CLEC4E gene had an AUC of 0.786 in predicting STEMI. Additionally, a prognostic model was developed to stratify the risk of heart failure progression, and SI00AI2 was a common biomarker between STEMI and NSTEMI patients.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Review
Cardiac & Cardiovascular Systems
Thomas A. Kite, Andrew Ladwiniec, J. Ranjit Arnold, Gerry P. McCann, Alastair J. Moss
Summary: NSTE-ACS presents a wide range of diseases, making accurate diagnosis challenging and potentially leading to unnecessary ICA. Better risk stratification and more research are needed to improve patient management in both invasive and non-invasive strategies.
Article
Chemistry, Analytical
Chenlei Cai, Yujie Liu, Jiayu Li, Lei Wang, Kun Zhang
Summary: In this study, a spectroscopic method based on SLIPSERS was developed for rapid identification of lung cancer. By using SLIPSERS, vibrational fingerprints of serum molecules can be obtained from a small amount of serum sample in minutes. The method is capable of discriminating lung cancer from healthy controls and holds great potential for non-invasive diagnosis.
Article
Chemistry, Multidisciplinary
Jay Karhade, Samit Kumar Ghosh, Pranjali Gajbhiye, Rajesh Kumar Tripathy, U. Rajendra Acharya
Summary: The paper proposes a deep learning-based approach for detecting and localizing myocardial infarction using VCG signals, improving detection accuracy and performance through multi-channel multi-scale methods.
APPLIED SCIENCES-BASEL
(2021)
Article
Chemistry, Analytical
Shengrong Du, Qun Zhang, Haohao Guan, Guannan Chen, Sisi Wang, Yan Sun, Yuling Li, Rong Chen, Youwu He, Zufang Huang
Summary: This study presents a Raman spectroscopy method combined with EMSC for the assessment of sperm DNA integrity directly on glass slides. The Raman results were validated with clinical sperm staining, and although there were similarities in overall spectral patterns, specific differences were observed. By employing multivariate statistical analysis, the PLS-DA model demonstrated better diagnostic sensitivity, specificity, and classification rate compared to PCA-LDA. The results suggest the potential of Raman-based label-free DNA assessment of sperm cells as a simple method for clinical applications.
Article
Agriculture, Dairy & Animal Science
Haichao Yuan, Muhua Liu, Shuanggen Huang, Jinhui Zhao, Jinjiang Tao
Summary: This study developed a rapid and simple method based on surface-enhanced Raman spectroscopy (SERS) combined with multivariate analysis for the qualitative and quantitative analysis of testosterone propionate (TP) and nandrolone (NT) residues in duck meat. The developed models achieved high classification accuracies for identifying different groups of duck meat samples and accurate prediction of TP and NT values in the samples. Surface-enhanced Raman spectroscopy technology shows great potential as a tool for the analysis of TP and NT residues in duck meat extract.
Article
Spectroscopy
Paula A. Pimiento, Natalie E. Dunn, Bhavya Sharma
Summary: Dopamine is a crucial neurotransmitter in the brain responsible for pleasure, memory, motivation, and movement control. Electrochemistry is currently used to monitor dopamine concentrations, but lacks specificity for differentiating similar molecules. Surface-enhanced Raman spectroscopy (SERS) with multivariate analysis is shown to be optimal for detecting and distinguishing dopamine and its metabolites at physiologically relevant concentrations, offering molecule-specific information with high sensitivity and non-destructive sampling.
JOURNAL OF RAMAN SPECTROSCOPY
(2023)
Article
Spectroscopy
Caroleny Villalba-Hernandez, Maria de los Angeles Moyaho-Bernal, Freddy Narea-Jimenez, Hector Nahum Chavarria-Lizarraga, Maria Cecilia Galeazzi-Minutti, Rosendo Carrasco-Gutierrez, Jorge Castro-Ramos
Summary: This study predicts periodontitis by analyzing Raman spectra and biomarkers in saliva, such as albumin and alanine aminotransferase (ALT). The study uses MATLAB for data processing and analysis, ORCA software to predict fundamental frequencies and intensities, and support vector machines for spectral distinction.
JOURNAL OF RAMAN SPECTROSCOPY
(2022)
Article
Biochemical Research Methods
Weilin Wu, Jinyong Lin, Shu Wang, Yan Li, Mingyu Liu, Gaoqiang Liu, Jianyong Cai, Guannan Chen, Rong Chen
JOURNAL OF BIOPHOTONICS
(2017)
Article
Biochemical Research Methods
Yudong Lu, Yisheng Lin, Zuci Zheng, Xiaoqiong Tang, Jinyong Lin, Xiujie Liu, Mengmeng Liu, Guannan Chen, Sufang Qiu, Ting Zhou, Yao Lin, Shangyuan Feng
BIOMEDICAL OPTICS EXPRESS
(2018)
Article
Physics, Applied
Hongxin Lin, Chao Wei, Ning Cao, Hu Chen, Guangxing Wang, Jianxin Chen, Guannan Chen, Shuangmu Zhuo
JOURNAL OF PHYSICS D-APPLIED PHYSICS
(2019)
Article
Biotechnology & Applied Microbiology
Jianqing Gao, Guannan Chen, Wenru Lin
BIOMED RESEARCH INTERNATIONAL
(2020)
Article
Engineering, Electrical & Electronic
Chao Wei, Hongxin Lin, Lijuan Tang, Weiping Liu, Mengzhen Jiang, Junfeng Wang, Guannan Chen
Summary: This paper introduces a fusion method for enhancing low brightness images, which combines various techniques to obtain natural and detailed image derivatives. By utilizing a novel model and weight matrix design, the proposed algorithm shows superior performance in enhancing overall visual information.
CIRCUITS SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Sijing Cai, Yunxian Tian, Harvey Lui, Haishan Zeng, Yi Wu, Guannan Chen
QUANTITATIVE IMAGING IN MEDICINE AND SURGERY
(2020)
Article
Engineering, Electrical & Electronic
Qing Wang, Yi Zhang, Guannan Chen, Zhihao Chen, Hwan Ing Hee
Summary: A novel optical fiber sensor utilizing a deep learning model was developed to measure perioperative heart rate and respiratory rate signals. The model outperformed traditional algorithms and showed potential for future clinical applications in monitoring non-stationary vibration signals like HR and RR.
IEEE SENSORS JOURNAL
(2021)
Article
Biochemical Research Methods
Qing Wang, Weiping Liu, Xinghong Chen, Xiumei Wang, Guannan Chen, Xiaoqin Zhu
Summary: A novel method combining SHG imaging and deep learning algorithm is proposed for analyzing and diagnosing human scar texture. The model can accurately construct a regression model of scar texture and predict the age of the scar.
BIOMEDICAL OPTICS EXPRESS
(2021)
Article
Physics, Applied
Gangqin Xi, Qing Wang, Huiling Zhan, Deyong Kang, Yulan Liu, Tianyi Luo, Mingyu Xu, Qinglin Kong, Liqin Zheng, Guannan Chen, Jianxin Chen, Shuangmu Zhuo
Summary: In this study, label-free multiphoton microscopy (MPM) was used to acquire subcellular-resolution images of unstained breast cancer tissues. A deep-learning algorithm based on the generative adversarial network (GAN) was introduced to learn a representation using only MPM images without the histological grade information. The fusion of MPM and the GAN-based deep learning algorithm showed high classification accuracies for different tumor grades, suggesting its potential as a clinical tool for computer-aided intelligent pathological diagnosis of breast cancer.
JOURNAL OF PHYSICS D-APPLIED PHYSICS
(2023)
Article
Computer Science, Artificial Intelligence
Weiping Liu, Xiaozhen Lin, Xinghong Chen, Qing Wang, Xiumei Wang, Bin Yang, Naiqing Cai, Rong Chen, Guannan Chen, Yu Lin
Summary: This study proposed a novel model, GTSN, to estimate the MDS-UPDRS score of PD tremors based on video analysis. The results demonstrated the effectiveness of computer-assisted assessment for PD tremors.
MEDICAL IMAGE ANALYSIS
(2023)
Article
Automation & Control Systems
Chenglin Wu, Huanqiang Hu, Kean Lin, Qing Wang, Tianjian Liu, Guannan Chen
Summary: Considerable results have been achieved in research on appearance gaze estimation based on deep learning. However, the precision of gaze estimation is reduced due to a lack of information in the extracted local blocks, as CNNs do not prioritize the information within the key picture blocks for face image estimation. In this study, fine-grained visual information is extracted from local blocks using a Transformer in Transformer (TNT) model that emphasizes interactions between local blocks.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Biomedical
Bin Yang, Weiping Liu, Xinghong Chen, Guannan Chen, Xiaoqin Zhu
Summary: In this study, a novel multi-frame wavelet generation adversarial network (MWGAN) is proposed to improve the scattering reconstruction capability of structured illumination microscopy (SIM) images. The proposed method achieves superior performance compared to state-of-the-art methods in both subjective and objective evaluation, as demonstrated through testing multiple low-quality SIM image datasets.
PHYSICS IN MEDICINE AND BIOLOGY
(2023)
Article
Engineering, Civil
Qing Wang, Weiping Liu, Xiumei Wang, Xinghong Chen, Guannan Chen, Qingxiang Wu
Summary: Traffic flow prediction is crucial for digitized urban transportation management and control. The complexity and non-linearity of traffic flow data can be addressed by establishing models with spatial correlations and time dynamics. Current methods mainly rely on historical time series information, but this approach lacks information and leads to poor accuracy. To solve these issues, this study proposes a graph multi-head attention neural network (GMHANN) that compresses data into a hidden space representation and reconstructs the output using a decoder. Additionally, a novel gated recurrent unit (AGRU) based on multi-head attention is introduced for effective spatial and temporal feature extraction. Experimental results show that the proposed method outperforms state-of-the-art techniques.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Qing Wang, Weiping Liu, Xiumei Wang, Xinghong Chen, Guannan Chen, Qingxiang Wu
Summary: With the development of artificial intelligence, speech recognition and prediction have become important research fields. This study focuses on the prediction of pronunciation characteristics in Chinese poetry reading using a novel spatial-temporal graph model based on multihead attention. The proposed model effectively extracts the spatial and temporal features of MFCC data and demonstrates clear advantages over state-of-the-art methods in six datasets.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Chemistry, Multidisciplinary
Duo Lin, Tianxun Gong, Sufang Qiu, Qiong Wu, Ching-Yu Tseng, Kien Voon Kong, Guannan Chen, Rong Chen
CHEMICAL COMMUNICATIONS
(2019)
