1
|
Guo Z, Ma M, Lu S, Ma Y, Yu Y, Guo Q. Applications of Raman spectroscopy in ocular biofluid detection. Front Chem 2024; 12:1407754. [PMID: 38915903 PMCID: PMC11194368 DOI: 10.3389/fchem.2024.1407754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 05/20/2024] [Indexed: 06/26/2024] Open
Abstract
Ophthalmic and many systemic diseases may damage the eyes, resulting in changes in the composition and content of biomolecules in ocular biofluids such as aqueous humor and tear. Therefore, the biomolecules in biofluids are potential biomarkers to reveal pathological processes and diagnose diseases. Raman spectroscopy is a non-invasive, label-free, and cost-effective technique to provide chemical bond information of biomolecules and shows great potential in the detection of ocular biofluids. This review demonstrates the applications of Raman spectroscopy technology in detecting biochemical components in aqueous humor and tear, then summarizes the current problems encountered for clinical applications of Raman spectroscopy and looks forward to possible approaches to overcome technical bottlenecks. This work may provide a reference for wider applications of Raman spectroscopy in biofluid detection and inspire new ideas for the diagnosis of diseases using ocular biofluids.
Collapse
Affiliation(s)
- Zhijun Guo
- Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Academy of Safety Engineering and Technology, Beijing, China
| | - Miaoli Ma
- Beijing Institute of Petrochemical Technology, Beijing, China
| | - Sichao Lu
- Beijing Institute of Petrochemical Technology, Beijing, China
| | - Ying Ma
- Beijing Institute of Petrochemical Technology, Beijing, China
| | - Yansuo Yu
- Beijing Institute of Petrochemical Technology, Beijing, China
| | - Qianjin Guo
- Beijing Institute of Petrochemical Technology, Beijing, China
| |
Collapse
|
2
|
Thomas KM, Ajithaprasad S, N M, Pavithran M S, Chidangil S, Lukose J. Raman spectroscopy assisted tear analysis: A label free, optical approach for noninvasive disease diagnostics. Exp Eye Res 2024; 243:109913. [PMID: 38679225 DOI: 10.1016/j.exer.2024.109913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 03/25/2024] [Accepted: 04/23/2024] [Indexed: 05/01/2024]
Abstract
In recent times, tear fluid analysis has garnered considerable attention in the field of biomarker-based diagnostics due to its noninvasive sample collection method. Tears encompass a reservoir of biomarkers that assist in diagnosing not only ocular disorders but also a diverse list of systemic diseases. This highlights the necessity for sensitive and dependable screening methods to employ tear fluid as a potential noninvasive diagnostic specimen in clinical environments. Considerable research has been conducted to investigate the potential of Raman spectroscopy-based investigations for tear analysis in various diagnostic applications. Raman Spectroscopy (RS) is a highly sensitive and label free spectroscopic technique which aids in investigating the molecular structure of samples by evaluating the vibrational frequencies of molecular bonds. Due to the distinct chemical compositions of different samples, it is possible to obtain a sample-specific spectral fingerprint. The distinctive spectral fingerprints obtained from Raman spectroscopy enable researchers to identify specific compounds or functional groups present in a sample, aiding in diverse biomedical applications. Its sensitivity to changes in molecular structure or environment provides invaluable insights into subtle alterations associated with various diseases. Thus, Raman Spectroscopy has the potential to assist in diagnosis and treatment as well as prognostic evaluation. Raman spectroscopy possesses several advantages, such as the non-destructive examination of samples, remarkable sensitivity to structural variations, minimal prerequisites for sample preparation, negligible interference from water, and the aptness for real-time investigation of tear samples. The purpose of this review is to highlight the potential of Raman spectroscopic technique in facilitating the clinical diagnosis of various ophthalmic and systemic disorders through non-invasive tear analysis. Additionally, the review delves into the advancements made in Raman spectroscopy with regards to paper-based sensing substrates and tear analysis methods integrated into contact lenses. Furthermore, the review also addresses the obstacles and future possibilities associated with implementing Raman spectroscopy as a routine diagnostic tool based on tear analysis in clinical settings.
Collapse
Affiliation(s)
- Keziah Mary Thomas
- Dr. Agarwal's Eye Hospital and Eye Research Centre, Chennai, Tamil Nadu, India
| | - Sreeprasad Ajithaprasad
- Department of Basic Science, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan
| | - Mithun N
- Centre of Excellence for Biophotonics, Department of Atomic and Molecular Physics, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Sanoop Pavithran M
- Centre of Excellence for Biophotonics, Department of Atomic and Molecular Physics, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Santhosh Chidangil
- Centre of Excellence for Biophotonics, Department of Atomic and Molecular Physics, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Jijo Lukose
- Centre of Excellence for Biophotonics, Department of Atomic and Molecular Physics, Manipal Academy of Higher Education, Manipal, Karnataka, India.
| |
Collapse
|
3
|
Chu HO, Buchan E, Smith D, Goldberg Oppenheimer P. Development and application of an optimised Bayesian shrinkage prior for spectroscopic biomedical diagnostics. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 245:108014. [PMID: 38246097 DOI: 10.1016/j.cmpb.2024.108014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 01/06/2024] [Accepted: 01/08/2024] [Indexed: 01/23/2024]
Abstract
BACKGROUND AND OBJECTIVE Classification of vibrational spectra is often challenging for biological substances containing similar molecular bonds, interfering with spectral outputs. To address this, various approaches are widely studied. However, whilst providing powerful estimations, these techniques are computationally extensive and frequently overfit the data. Shrinkage priors, which favour models with relatively few predictor variables, are often applied in Bayesian penalisation techniques to avoid overfitting. METHODS Using the logit-normal continuous analogue of the spike-and-slab (LN-CASS) as the shrinkage prior and modelling, we have established classification for accurate analysis, with the established system found to be faster than conventional least absolute shrinkage and selection operator, horseshoe or spike-and-slab. These were examined versus coefficient data based on a linear regression model and vibrational spectra produced via density functional theory calculations. Then applied to Raman spectra from saliva to classify the sample sex. RESULTS Subsequently applied to the acquired spectra from saliva, the evaluated models exhibited high accuracy (AUC>90 %) even when number of parameters was higher than the number of observations. Analyses of spectra for all Bayesian models yielded high-classification accuracy upon cross-validation. Further, for saliva sensing, LN-CASS was found to be the only classifier with 100 %-accuracy in predicting the output based on a leave-one-out cross validation. CONCLUSIONS With potential applications in aiding diagnosis from small spectroscopic datasets and are compatible with a range of spectroscopic data formats. As seen with the classification of IR and Raman spectra. These results are highly promising for emerging developments of spectroscopic platforms for biomedical diagnostic sensing systems.
Collapse
Affiliation(s)
- Hin On Chu
- School of Chemical Engineering, University of Birmingham, Birmingham B15 2TT, UK
| | - Emma Buchan
- School of Chemical Engineering, University of Birmingham, Birmingham B15 2TT, UK
| | - David Smith
- School of Mathematics, Watson Building, University of Birmingham, Birmingham B15 2TT, UK
| | - Pola Goldberg Oppenheimer
- School of Chemical Engineering, University of Birmingham, Birmingham B15 2TT, UK; Healthcare Technologies Institute, Institute of Translational Medicine, Mindelsohn Way, Birmingham B15 2TH, UK.
| |
Collapse
|
4
|
Li Z, Wang L, Wu X, Jiang J, Qiang W, Xie H, Zhou H, Wu S, Shao Y, Chen W. Artificial intelligence in ophthalmology: The path to the real-world clinic. Cell Rep Med 2023:101095. [PMID: 37385253 PMCID: PMC10394169 DOI: 10.1016/j.xcrm.2023.101095] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 04/17/2023] [Accepted: 06/07/2023] [Indexed: 07/01/2023]
Abstract
Artificial intelligence (AI) has great potential to transform healthcare by enhancing the workflow and productivity of clinicians, enabling existing staff to serve more patients, improving patient outcomes, and reducing health disparities. In the field of ophthalmology, AI systems have shown performance comparable with or even better than experienced ophthalmologists in tasks such as diabetic retinopathy detection and grading. However, despite these quite good results, very few AI systems have been deployed in real-world clinical settings, challenging the true value of these systems. This review provides an overview of the current main AI applications in ophthalmology, describes the challenges that need to be overcome prior to clinical implementation of the AI systems, and discusses the strategies that may pave the way to the clinical translation of these systems.
Collapse
Affiliation(s)
- Zhongwen Li
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China; School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China.
| | - Lei Wang
- School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Xuefang Wu
- Guizhou Provincial People's Hospital, Guizhou University, Guiyang 550002, China
| | - Jiewei Jiang
- School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
| | - Wei Qiang
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China
| | - He Xie
- School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Hongjian Zhou
- Department of Computer Science, University of Oxford, Oxford, Oxfordshire OX1 2JD, UK
| | - Shanjun Wu
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China
| | - Yi Shao
- Department of Ophthalmology, the First Affiliated Hospital of Nanchang University, Nanchang 330006, China.
| | - Wei Chen
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China; School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China.
| |
Collapse
|
5
|
Zeng Q, Chen C, Chen C, Song H, Li M, Yan J, Lv X. Serum Raman spectroscopy combined with convolutional neural network for rapid diagnosis of HER2-positive and triple-negative breast cancer. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 286:122000. [PMID: 36279798 DOI: 10.1016/j.saa.2022.122000] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 09/26/2022] [Accepted: 10/13/2022] [Indexed: 06/16/2023]
Abstract
Breast cancer is common in women, and its number of patients ranks first among female malignant tumors. Breast cancer is highly heterogeneous, and different types of breast cancer have different biological behaviors and prognoses. Therefore, identifying the different types of breast cancer is of great help in formulating individualized treatment plans. Based on serum Raman spectroscopy and deep learning algorithms, we propose a fast and low-cost diagnosis method for screening triple-negative breast cancer, human epidermal growth factor receptor 2 (HER2)-positive breast cancer, and healthy controls. We collected 75 serum samples in this study, including 23 triple-negative breast cancers, 22 HER2-positive breast cancers, and 30 healthy controls. Using the preprocessed Raman spectra as the input of deep learning, three deep learning models, neural network language model (NNLM), bidirectional long-short-term memory network (BiLSTM), and convolutional neural network (CNN), were established, and the accuracy rates of the three models were 87.78%, 90.37%, and 91.11%, respectively. The experimental results demonstrate the feasibility of serum Raman spectroscopy combined with deep learning algorithms to diagnose breast cancer, which can be used as an effective auxiliary diagnosis method for breast cancer.
Collapse
Affiliation(s)
- Qinggang Zeng
- College of Information Science and Engineering Xinjiang University, Urumqi 830046, China
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi 830046, China.
| | - Chen Chen
- College of Information Science and Engineering Xinjiang University, Urumqi 830046, China; Xinjiang Cloud Computing Application Laboratory, Karamay 834099, China
| | - Haitao Song
- Department of Breast, Head and Neck Surgery, Xinjiang Medical University Affiliated Tumor Hospital, Urumqi, China
| | - Min Li
- College of Information Science and Engineering Xinjiang University, Urumqi 830046, China
| | - Junyi Yan
- College of Software, Xinjiang University, Urumqi 830046, China
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi 830046, China; Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, Xinjiang, China
| |
Collapse
|