1
|
Sezer G, Sahin F, Onses MS, Cumaoglu A. Activation of epidermal growth factor receptors in triple-negative breast cancer cells by morphine; analysis through Raman spectroscopy and machine learning. Talanta 2024; 272:125827. [PMID: 38432124 DOI: 10.1016/j.talanta.2024.125827] [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: 12/16/2023] [Revised: 02/20/2024] [Accepted: 02/21/2024] [Indexed: 03/05/2024]
Abstract
Triple negative breast cancer (TNBC) is a very aggressive form of breast cancer, and the analgesic drug morphine has been shown to promote the proliferation of TNBC cells. This article investigates whether morphine causes activation of epidermal growth factor receptors (EGFR), the roles of μ-opioid and EGFR receptors on TNBC cell proliferation and migration. While examining the changes with molecular techniques, we also aimed to investigate the analysis ability of Raman spectroscopy and machine learning-based approach. Effects of morphine on the proliferation and migration of MDA.MB.231 cells were evaluated by MTT and scratch wound-healing tests, respectively. Morphine-induced phosphorylation of the EGFR was analyzed by western blotting in the presence and absence of μ-receptor antagonist naltrexone and the EGFR-tyrosine kinase inhibitor gefitinib. Morphine-induced EGFR phosphorylation and cell migration were significantly inhibited by pretreatments with both naltrexone and gefitinib; however, morphine-increased cell proliferation was inhibited only by naltrexone. While morphine-induced changes were observed in the Raman scatterings of the cells, the inhibitory effect of naltrexone was analyzed with similarity to the control group. Principal component analysis (PCA) of the Raman confirmed the epidermal growth factor (EGF)-like effect of morphine and was inhibited by naltrexone and partly by gefitinib pretreatments. Our in vitro results suggest that combining morphine with an EGFR inhibitor or a peripherally acting opioidergic receptor antagonist may be a good strategy for pain relief without triggering cancer proliferation and migration in TNBC patients. In addition, our results demonstrated the feasibility of the Raman spectroscopy and machine learning-based approach as an effective method to investigate the effects of agents in cancer cells without the need for complex and time-consuming sample preparation. The support vector machine (SVM) with linear kernel automatically classified the effects of drugs on cancer cells with ∼95% accuracy.
Collapse
Affiliation(s)
- Gulay Sezer
- Department of Pharmacology, Faculty of Medicine, Erciyes University, 38039, Kayseri, Turkey; Genkok Genome and Stem Cell Center, Erciyes University, 38039, Kayseri, Turkey.
| | - Furkan Sahin
- Department of Biomedical Engineering, Faculty of Engineering and Architecture, Beykent University, 34398, Istanbul, Turkey; ERNAM - Erciyes University Nanotechnology Application and Research Center, 38039, Kayseri, Turkey
| | - M Serdar Onses
- ERNAM - Erciyes University Nanotechnology Application and Research Center, 38039, Kayseri, Turkey; Department of Materials Science and Engineering, Erciyes University, 38039, Kayseri, Turkey; UNAM-National Nanotechnology Research Center, Institute of Materials Science and Nanotechnology, Bilkent University, 06800, Ankara, Turkey
| | - Ahmet Cumaoglu
- Department of Biochemistry, School of Pharmacy, Erciyes University, Kayseri, Turkey
| |
Collapse
|
2
|
Hano H, Lawrie CH, Suarez B, Paredes Lario A, Elejoste Echeverría I, Gómez Mediavilla J, Crespo Cruz MI, Lopez E, Seifert A. Power of Light: Raman Spectroscopy and Machine Learning for the Detection of Lung Cancer. ACS OMEGA 2024; 9:14084-14091. [PMID: 38559992 PMCID: PMC10975667 DOI: 10.1021/acsomega.3c09537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 02/22/2024] [Accepted: 02/27/2024] [Indexed: 04/04/2024]
Abstract
Lung cancer is the leading cause of cancer-related deaths worldwide, emphasizing the urgent need for reliable and efficient diagnostic methods. Conventional approaches often involve invasive procedures and can be time-consuming and costly, thereby delaying the effective treatment. The current study explores the potential of Raman spectroscopy, as a promising noninvasive technique, by analyzing human blood plasma samples from lung cancer patients and healthy controls. In a benchmark study, 16 machine learning models were evaluated by employing four strategies: the combination of dimensionality reduction with classifiers; application of feature selection prior to classification; stand-alone classifiers; and a unified predictive model. The models showed different performances due to the inherent complexity of the data, achieving accuracies from 0.77 to 0.85 and areas under the curve for receiver operating characteristics from 0.85 to 0.94. Hybrid methods incorporating dimensionality reduction and feature selection algorithms present the highest figures of merit. Nevertheless, all machine learning models deliver creditable scores and demonstrate that Raman spectroscopy represents a powerful method for future in vitro diagnostics of lung cancer.
Collapse
Affiliation(s)
- Harun Hano
- CIC
nanoGUNE BRTA, 20018 San Sebastián, Spain
- Department
of Physics, University of the Basque Country
(UPV/EHU), 20018 San Sebastián, Spain
| | - Charles H. Lawrie
- IKERBASQUE—Basque
Foundation for Science, 48009 Bilbao, Spain
- Biogipuzkoa
Health Research Institute, 20014 San Sebastián, Spain
- Sino-Swiss
Institute of Advanced Technology (SSIAT), University of Shanghai, 201800 Shanghai, China
- Radcliffe
Department of Medicine, University of Oxford, OX3 9DU Oxford, U.K.
| | - Beatriz Suarez
- Faculty
of Nursing and Medicine, University of the
Basque Country (UPV/EHU), 20014 San Sebastián, Spain
- Biogipuzkoa
Health Research Institute, 20014 San Sebastián, Spain
| | - Alfredo Paredes Lario
- Servicio
de Oncología Médica, Hospital
Universitario Donostia, 20014 San Sebastián, Spain
| | | | | | | | - Eneko Lopez
- CIC
nanoGUNE BRTA, 20018 San Sebastián, Spain
- Department
of Physics, University of the Basque Country
(UPV/EHU), 20018 San Sebastián, Spain
| | - Andreas Seifert
- CIC
nanoGUNE BRTA, 20018 San Sebastián, Spain
- IKERBASQUE—Basque
Foundation for Science, 48009 Bilbao, Spain
| |
Collapse
|
3
|
Wang Y, Fang L, Wang Y, Xiong Z. Current Trends of Raman Spectroscopy in Clinic Settings: Opportunities and Challenges. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2300668. [PMID: 38072672 PMCID: PMC10870035 DOI: 10.1002/advs.202300668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 09/08/2023] [Indexed: 02/17/2024]
Abstract
Early clinical diagnosis, effective intraoperative guidance, and an accurate prognosis can lead to timely and effective medical treatment. The current conventional clinical methods have several limitations. Therefore, there is a need to develop faster and more reliable clinical detection, treatment, and monitoring methods to enhance their clinical applications. Raman spectroscopy is noninvasive and provides highly specific information about the molecular structure and biochemical composition of analytes in a rapid and accurate manner. It has a wide range of applications in biomedicine, materials, and clinical settings. This review primarily focuses on the application of Raman spectroscopy in clinical medicine. The advantages and limitations of Raman spectroscopy over traditional clinical methods are discussed. In addition, the advantages of combining Raman spectroscopy with machine learning, nanoparticles, and probes are demonstrated, thereby extending its applicability to different clinical phases. Examples of the clinical applications of Raman spectroscopy over the last 3 years are also integrated. Finally, various prospective approaches based on Raman spectroscopy in clinical studies are surveyed, and current challenges are discussed.
Collapse
Affiliation(s)
- Yumei Wang
- Department of NephrologyUnion HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430022China
| | - Liuru Fang
- Hubei Province Key Laboratory of Systems Science in Metallurgical ProcessWuhan University of Science and TechnologyWuhan430081China
| | - Yuhua Wang
- Hubei Province Key Laboratory of Systems Science in Metallurgical ProcessWuhan University of Science and TechnologyWuhan430081China
| | - Zuzhao Xiong
- Hubei Province Key Laboratory of Systems Science in Metallurgical ProcessWuhan University of Science and TechnologyWuhan430081China
| |
Collapse
|
4
|
Wang X, Chen C, Chen C, Zuo E, Han S, Yang J, Yan Z, Lv X, Hou J, Jia Z. Novel SERS biosensor for rapid detection of breast cancer based on Ag 2O-Ag-PSi nanochips. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 303:123226. [PMID: 37567026 DOI: 10.1016/j.saa.2023.123226] [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: 05/19/2023] [Revised: 07/11/2023] [Accepted: 08/01/2023] [Indexed: 08/13/2023]
Abstract
Ag2O-Ag-PSi (porous silicon) surface-enhanced Raman scattering (SERS) chip was successfully synthesized by electrochemical corrosion, in situ reduction and heat treatment technology. The influence of different heat treatment temperature on SERS performance of the chip is studied. The results show that the chip treated at 300 °C has the best SERS performance. The chip was composed of Ag2O-Ag nano core shell with a diameter of 40-60 nm and porous silicon substrate. Then, the optimized chip was used to perform SERS test on serum samples from 30 healthy volunteers and 30 early breast cancer patients, and the baseline was corrected by LabSpec6 software. Finally, the data were analyzed by principal component analysis combined with t-distributed Stochastic Neighbor Embedding (PCA-t-SNE). The results showed that the accuracy of the improved substrate combined with multivariate statistical method was 98%. The shelf life of the chips exceeded six months due to the presence of the Ag2O shell. This study provides a basis for developing a low-cost rapid and sensitive early screening technology for breast cancer.
Collapse
Affiliation(s)
- Xuehua Wang
- College of Physical Science and Technology, 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
| | - Enguang Zuo
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Shibin Han
- College of Physical Science and Technology, Xinjiang University, Urumqi 830046, China
| | - Jie Yang
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Ziwei Yan
- College of Software, Xinjiang University, Urumqi 830046, China
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi 830046, China.
| | - Junwei Hou
- State Key Laboratory of Heavy Oil Processing, China University of Petroleum-Beijing at Karamay, Karamay 834000, China.
| | - Zhenhong Jia
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
| |
Collapse
|
5
|
Lin R, Peng B, Li L, He X, Yan H, Tian C, Luo H, Yin G. Application of serum Raman spectroscopy combined with classification model for rapid breast cancer screening. Front Oncol 2023; 13:1258436. [PMID: 37965448 PMCID: PMC10640987 DOI: 10.3389/fonc.2023.1258436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 10/13/2023] [Indexed: 11/16/2023] Open
Abstract
Introduction This study aimed to evaluate the feasibility of using general Raman spectroscopy as a method to screen for breast cancer. The objective was to develop a machine learning model that utilizes Raman spectroscopy to detect serum samples from breast cancer patients, benign cases, and healthy subjects, with puncture biopsy as the gold standard for comparison. The goal was to explore the value of Raman spectroscopy in the differential diagnosis of breast cancer, benign lesions, and healthy individuals. Methods In this study, blood serum samples were collected from a total of 333 participants. Among them, there were 129 cases of tumors (pathologically diagnosed as breast cancer and labeled as cancer), 91 cases of benign lesions (pathologically diagnosed as benign and labeled as benign), and 113 cases of healthy controls (labeled as normal). Raman spectra of the serum samples from each group were collected. To classify the normal, benign, and cancer sample groups, principal component analysis (PCA) combined with support vector machine (SVM) was used. The SVM model was evaluated using a cross-validation method. Results The results of the study revealed significant differences in the mean Raman spectra of the serum samples between the normal and tumor/benign groups. Although the mean Raman spectra showed slight variations between the cancer and benign groups, the SVM model achieved a remarkable prediction accuracy of up to 98% for classifying cancer, benign, and normal groups. Discussion In conclusion, this exploratory study has demonstrated the tremendous potential of general Raman spectroscopy as a clinical adjunctive diagnostic and rapid screening tool for breast cancer.
Collapse
Affiliation(s)
- Runrui Lin
- School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Bowen Peng
- School of Electronic Science and Engineering, Nanjing University, Nanjing, China
| | - Lintao Li
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaoliang He
- School of Clinical Medicine, Southwest Medical University, Luzhou, China
| | - Huan Yan
- School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Chao Tian
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Huaichao Luo
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Gang Yin
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| |
Collapse
|
6
|
Idrees BS, Teng G, Israr A, Zaib H, Jamil Y, Bilal M, Bashir S, Khan MN, Wang Q. Comparison of whole blood and serum samples of breast cancer based on laser-induced breakdown spectroscopy with machine learning. BIOMEDICAL OPTICS EXPRESS 2023; 14:2492-2509. [PMID: 37342687 PMCID: PMC10278612 DOI: 10.1364/boe.489513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/09/2023] [Accepted: 04/17/2023] [Indexed: 06/23/2023]
Abstract
To identify cancer from non-cancer is one of the most challenging issues nowadays in the early diagnosis of cancer. The primary issue of early detection is to choose a suitable type of sample collection to diagnose cancer. A comparison of whole blood and serum samples of breast cancer was studied using laser-induced breakdown spectroscopy (LIBS) with machine learning methods. For LIBS spectra measurement, blood samples were dropped on a substrate of boric acid. For the discrimination of breast cancer and non-cancer samples, eight machine learning models were applied to LIBS spectral data, including decision tree, discrimination analysis, logistic regression, naïve byes, support vector machine, k-nearest neighbor, ensemble and neural networks classifiers. Discrimination between whole blood samples showed that narrow neural networks and trilayer neural networks both provided 91.7% highest prediction accuracy and serum samples showed that all the decision tree models provided 89.7% highest prediction accuracy. However, using whole blood as sample achieved the strong emission lines of spectra, better discrimination results of PCA and maximum prediction accuracy of machine learning models as compared to using serum samples. These merits concluded that whole blood samples could be a good option for the rapid detection of breast cancer. This preliminary research may provide the complementary method for early detection of breast cancer.
Collapse
Affiliation(s)
- Bushra Sana Idrees
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, 100081 Beijing, China
| | - Geer Teng
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, OX3 7LD, United Kingdom
| | - Ayesha Israr
- Laser Spectroscopy Lab, Department of Physics, University of Agriculture Faisalabad, 38090, Pakistan
| | - Huma Zaib
- Laser Spectroscopy Lab, Department of Physics, University of Agriculture Faisalabad, 38090, Pakistan
| | - Yasir Jamil
- Laser Spectroscopy Lab, Department of Physics, University of Agriculture Faisalabad, 38090, Pakistan
| | - Muhammad Bilal
- Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Sajid Bashir
- Punjab Institute of Nuclear Medicine Hospital, Faisalabad 2019, Pakistan
| | - M Nouman Khan
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, 100081 Beijing, China
| | - Qianqian Wang
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, 100081 Beijing, China
- Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing 314033, China
| |
Collapse
|
7
|
Lu B, Tian F, Chen C, Wu W, Tian X, Chen C, Lv X. Identification of Chinese red wine origins based on Raman spectroscopy and deep learning. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 291:122355. [PMID: 36641919 DOI: 10.1016/j.saa.2023.122355] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 12/07/2022] [Accepted: 01/08/2023] [Indexed: 06/17/2023]
Abstract
In this study, we combined Raman spectroscopy with deep learning for the first time to establish an accurate, simple, and fast method to identify the origin of red wines. We collected Raman spectra from 200 red wine samples of the Cabernet Sauvignon variety from four different origins with a portable Raman spectrometer. The red wine samples, made in 2021, were from the same producer in China. Differences were found by analyzing the Raman spectra of red wine samples. These differences are mainly caused by ethanol, carboxylic acids, and polyphenols. After further analysis, for different origins, the different performances of these substances on the Raman spectrum are related to the climate and geographical conditions of the origin. The Raman spectra were analyzed by principal component analysis (PCA). The data with PCA dimensionality reduction were imported into an artificial neural network (ANN), multifeature fusion convolutional neural network (MCNN), GoogLeNet, and residual neural network (ResNet) to establish red wine origin identification models. The classification results of the model prove that climate, geography, and other conditions can provide support for the classification of red wine origin. The experiments showed that all four models performed well, among which MCNN performed the best with 93.2% classification accuracy, and the area under the curve (AUC) was 0.987. This study provides a new means to classify the origin of red wine and opens up new ideas for identifying origins in the food field.
Collapse
Affiliation(s)
- Bingxu Lu
- College of Software, Xinjiang University, Urumqi 830046, China
| | - Feng Tian
- National Institute of Metrology, China, Beijing 100000, China
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi 830046, China.
| | - Wei Wu
- College of Software, Xinjiang University, Urumqi 830046, China
| | - Xuecong Tian
- College of Software, Xinjiang University, Urumqi 830046, China
| | - Chen Chen
- College of Software, Xinjiang University, Urumqi 830046, China
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi 830046, China.
| |
Collapse
|
8
|
Zhang J, Sun J, Li C, Qiao H, Hussain Z. Functionalization of curcumin nanomedicines: a recent promising adaptation to maximize pharmacokinetic profile, specific cell internalization and anticancer efficacy against breast cancer. J Nanobiotechnology 2023; 21:106. [PMID: 36964547 PMCID: PMC10039588 DOI: 10.1186/s12951-023-01854-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 03/09/2023] [Indexed: 03/26/2023] Open
Abstract
Owing to its diverse heterogeneity, aggressive nature, enormous metastatic potential, and high remission rate, the breast cancer (BC) is among the most prevalent types of cancer associated with high mortality. Curcumin (Cur) is a potent phytoconstituent that has gained remarkable recognition due to exceptional biomedical viability against a wide range of ailments including the BC. Despite exhibiting a strong anticancer potential, the clinical translation of Cur is restricted due to intrinsic physicochemical properties such as low aqueous solubility, chemical instability, low bioavailability, and short plasma half-life. To overcome these shortcomings, nanotechnology-aided developments have been extensively deployed. The implication of nanotechnology has pointedly improved the physicochemical properties, pharmacokinetic profile, cell internalization, and anticancer efficacy of Cur; however, majority of Cur-nanomedicines are still facing grandeur challenges. The advent of various functionalization strategies such as PEGylation, surface decoration with different moieties, stimuli-responsiveness (i.e., pH, light, temperature, heat, etc.), tethering of specific targeting ligand(s) based on the biochemical targets (e.g., folic acid receptors, transferrin receptors, CD44, etc.), and multifunctionalization (multiple functionalities) has revolutionized the fate of Cur-nanomedicines. This study ponders the biomedical significance of various Cur-nanomedicines and adaptable functionalizations for amplifying the physicochemical properties, cytotoxicity via induction of apoptosis, cell internalization, bioavailability, passive and active targeting to the tumor microenvironment (TME), and anticancer efficacy of the Cur while reversing the multidrug resistance (MDR) and reoccurrence in BC. Nevertheless, the therapeutic outcomes of Cur-nanomedicines against the BC have been remarkably improved after adaptation of various functionalizations; however, this evolving strategy still demands extensive research for scalable clinical translation.
Collapse
Affiliation(s)
- Jinku Zhang
- Department of Pathology, Baoding First Central Hospital, Baoding, 071000, Hebei, China.
| | - Jirui Sun
- Department of Pathology, Baoding First Central Hospital, Baoding, 071000, Hebei, China
| | - Chong Li
- Core Facility for Protein Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Haizhi Qiao
- Department of Pathology, Baoding First Central Hospital, Baoding, 071000, Hebei, China
| | - Zahid Hussain
- Department of Pharmaceutics and Pharmaceutical Technology, College of Pharmacy, University of Sharjah, 27272, Sharjah, United Arab Emirates
- Research Institute for Medical and Health Sciences, University of Sharjah, 27272, Sharjah, United Arab Emirates
| |
Collapse
|
9
|
Pereira de Souza NM, Machado BH, Padoin LV, Prá D, Fay AP, Corbellini VA, Rieger A. Rapid and low-cost liquid biopsy with ATR-FTIR spectroscopy to discriminate the molecular subtypes of breast cancer. Talanta 2023; 254:123858. [PMID: 36470017 DOI: 10.1016/j.talanta.2022.123858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 08/14/2022] [Accepted: 08/17/2022] [Indexed: 01/07/2023]
Abstract
Breast cancer (BC) is the most prevalent cancer worldwide. The prognosis and survival of these patients are directly related to the diagnostic stage. Even so, the gold standard screening method (mammography) has a long waiting period, high rates of false positives, anxiety for patients, and consequently delays the diagnosis by core needle biopsy (invasive method). Alternatively, the Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectroscopy is a noninvasive, low-cost, rapid, and reagent-free technique that generates the spectral metabolomic profile of biomolecules. This makes it possible to assess systemic repercussions, such as the BC carcinogenesis process. Blood plasma samples (n = 56 BC and n = 18 controls) were analyzed in the spectrophotometer in the ATR-FTIR mode. For the exploratory analysis of the data, interval Principal Component Analysis (iPCA) was used, and for predictive chemometric modeling, the Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) algorithm with validation by leave-one-out cross-validation. iPCA in the region of 1118-1052 cm-1 (predominantly DNA/RNA bands) showed significant clustering of molecular subtypes and control. The OPLS-DA model achieved 100% accuracy with only 1 latent variable and Root Mean Square Error of Cross-Validation (RMSECV) < 0.005 for all molecular subtypes and control. The wavenumbers (cm-1) with the highest iPCA peaks (loadings: 1117, 1089, 1081, 1075, 1057, and 1052) were used as input to MANOVA (Wilks' Lambda, p < 0.001 between molecular subtypes and control). The rapid and low-cost detection of BC molecular subtypes by ATR-FTIR spectroscopy would plausibly allow initial screening and clinical management, improving prognosis, reducing mortality and costs for the health system.
Collapse
Affiliation(s)
| | - Brenda Hunter Machado
- International affairs, International University Center, Santa Cruz do Sul, RS, Brazil.
| | - Licerio Vicente Padoin
- Mastology Service at the Hospital of the Federal University of Santa Maria, Santa Maria, RS, Brazil.
| | - Daniel Prá
- Department of Life Sciences, University of Santa Cruz do Sul, Santa Cruz do Sul, RS Brazil; Postgraduate Program in Health Promotion, University of Santa Cruz do Sul, Santa Cruz do Sul, RS, Brazil.
| | - André Poisl Fay
- Postgraduate Program in Medicine and Health Sciences, School of Medicine, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, RS, Brazil.
| | - Valeriano Antonio Corbellini
- Postgraduate Program in Health Promotion, University of Santa Cruz do Sul, Santa Cruz do Sul, RS, Brazil; Department of Sciences, Humanities and Education, University of Santa Cruz do Sul, Santa Cruz do Sul, RS, Brazil; Postgraduate Program in Environmental Technology, University of Santa Cruz do Sul, RS, Brazil.
| | - Alexandre Rieger
- Department of Life Sciences, University of Santa Cruz do Sul, Santa Cruz do Sul, RS Brazil; Postgraduate Program in Health Promotion, University of Santa Cruz do Sul, Santa Cruz do Sul, RS, Brazil; Postgraduate Program in Environmental Technology, University of Santa Cruz do Sul, RS, Brazil.
| |
Collapse
|
10
|
Zhang S, Chen S, Zhu R. Electroporation-Assisted Surface-Enhanced Raman Detection for Long-Term, Label-Free, and Noninvasive Molecular Profiling of Live Single Cells. ACS Sens 2023; 8:555-564. [PMID: 36399395 DOI: 10.1021/acssensors.2c01582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Molecule characterization of live single cells is greatly important in disease diagnoses and personalized treatments. Conventional molecule detection methods, such as mass spectrography, gene sequencing, or immunofluorescence, are usually destructive or labeled and unable to monitor the dynamic change of live cellular molecules. Herein, we propose an electroporation-assisted surface-enhanced Raman scattering (EP-SERS) method using a microchip to implement label-free, noninvasive, and continuous detections of the molecules of live single cells. The microchip containing microelectrodes with nanostructured EP-SERS probes has a multifunction of cell positioning, electroporation, and SERS detection. The EP-SERS method capably detects both the intracellular and extracellular molecules of live single cells without losing cell viability so as to enable long-term monitoring of the molecular pathological process in situ. We detect the molecules of single cells for two breast cancer cell lines with different malignancies (MCF-7 and MDA-MB-231), one liver cancer cell line (Huh-7), and one normal cell line (293T) using the EP-SERS method and classify these cell types to achieve high accuracies of 91.4-98.3% using their SERS spectra. Furthermore, 24 h continuous monitoring of the heterogeneous molecular responses of different cancer cell lines under doxorubicin treatment is successfully implemented using the EP-SERS method. This work provides a long-term, label-free, and biocompatible approach to simultaneously detect intracellular and extracellular molecules of live single cells on a chip, which would facilitate research and applications of cancer diagnoses and personalized treatments.
Collapse
Affiliation(s)
- Shengsen Zhang
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing100084, China
| | - Shengjie Chen
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing100084, China
| | - Rong Zhu
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing100084, China
| |
Collapse
|
11
|
Zhang B, Zhang Z, Gao B, Zhang F, Tian L, Zeng H, Wang S. Raman microspectroscopy based TNM staging and grading of breast cancer. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 285:121937. [PMID: 36201869 DOI: 10.1016/j.saa.2022.121937] [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: 08/13/2022] [Revised: 09/23/2022] [Accepted: 09/27/2022] [Indexed: 06/16/2023]
Abstract
The tumor-node-metastasis (TNM) system is the most common way that doctors determine the anatomical extent of cancer on the basis of clinical and pathological criteria. In this study, a spectral histopathological study has been carried out to bridge Raman micro spectroscopy with the breast cancer TNM system. A total of seventy breast tissue samples, including healthy tissue, early, middle, and advanced cancer, were investigated to provide detailed insights into compositional and structural variations that accompany breast malignant evolution. After evaluating the main spectral variations in all tissue types, the generalized discriminant analysis (GDA) pathological diagnostic model was established to discriminate the TNM staging and grading information. Moreover, micro-Raman images were reconstructed by K-means clustering analysis (KCA) for visualizing the lobular acinar in healthy tissue and ductal structures in all early, middle and advanced breast cancer tissue groups. While, univariate imaging techniques were adapted to describe the distribution differences of biochemical components such as tryptophan, β-carotene, proteins, and lipids in the scanned regions. The achieved spectral histopathological results not only established a spectra-structure correlations via tissue biochemical profiles but also provided important data and discriminative model references for in vivo Raman-based breast cancer diagnosis.
Collapse
Affiliation(s)
- Baoping Zhang
- Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi 710127, China
| | - Zhanqin Zhang
- Department of Anesthesiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Bingran Gao
- Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi 710127, China
| | - Furong Zhang
- Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi 710127, China
| | - Lu Tian
- Department of Physics, Northwest University, Xi'an, Shaanxi 710127, China
| | - Haishan Zeng
- Imaging Unit - Integrative Oncology Department, BC Cancer Research Center, Vancouver, BC V5Z 1L3, Canada
| | - Shuang Wang
- Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi 710127, China.
| |
Collapse
|
12
|
Li H, Wang S, Zeng Q, Chen C, Lv X, Ma M, Su H, Ma B, Chen C, Fang J. Serum Raman spectroscopy combined with multiple classification models for rapid diagnosis of breast cancer. Photodiagnosis Photodyn Ther 2022; 40:103115. [PMID: 36096439 DOI: 10.1016/j.pdpdt.2022.103115] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 09/03/2022] [Accepted: 09/07/2022] [Indexed: 12/14/2022]
Abstract
Breast cancer is a malignant tumor with the highest incidence rate in women. Current diagnostic methods are time-consuming, costly, and dependent on physician experience. In this study, we used serum Raman spectroscopy combined with multiple classification algorithms to implement an auxiliary diagnosis method for breast cancer, which will help in the early diagnosis of breast cancer patients. We analyzed the serum Raman spectra of 171 invasive ductal carcinoma (IDC) and 100 healthy volunteers; The analysis showed differences in nucleic acids, carotenoids, amino acids, and lipid concentrations in their blood. These differences provide a theoretical basis for this experiment. First, we used adaptive iteratively reweighted penalized least squares (airPLS) and Savitzky-Golay (SG) for baseline correction and smoothing denoising to remove the effect of noise on the experiment. Then, the Principal component analysis (PCA) algorithm was used to extract features. Finally, we built four classification models: support vector machine (SVM), decision tree (DT), linear discriminant analysis (LDA), and Neural Network Language Model (NNLM). The LDA, SVM, and NNLM achieve 100% accuracy. As supplementary, we added the classification experiment of the raw data. By comparing the experimental results of the two groups, We concluded that the NNLM was the best model. The results show the reliability of the combination of serum Raman spectroscopy and classification models under large sample conditions.
Collapse
Affiliation(s)
- Hongtao Li
- Department of Breast, Head and Neck Surgery, Xinjiang Medical University Affiliated Tumor Hospital, Urumqi, China
| | | | - Qinggang Zeng
- College of Software, Xinjiang University, Urumqi 830046, China; College of Information Science and Engineering 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
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi 830046, China; College of Information Science and Engineering Xinjiang University, Urumqi 830046, China
| | - Mingrui Ma
- Department of Breast, Head and Neck Surgery, Xinjiang Medical University Affiliated Tumor Hospital, Urumqi, China
| | - Haihua Su
- Hospital of Xinjiang Production and Construction Corps, Urumqi 830092, China
| | - Binlin Ma
- Department of Breast, Head and Neck Surgery, Xinjiang Medical University Affiliated Tumor Hospital, Urumqi, China.
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi 830046, China.
| | - Jingjing Fang
- Department of Breast, Head and Neck Surgery, Xinjiang Medical University Affiliated Tumor Hospital, Urumqi, China
| |
Collapse
|
13
|
Wang MH, Liu X, Wang Q, Zhang HW. Diagnosis accuracy of Raman spectroscopy in the diagnosis of breast cancer: a meta-analysis. Anal Bioanal Chem 2022; 414:7911-7922. [PMID: 36138121 DOI: 10.1007/s00216-022-04326-7] [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: 07/08/2022] [Revised: 08/31/2022] [Accepted: 09/05/2022] [Indexed: 11/25/2022]
Abstract
To investigate the diagnostic efficiency of Raman spectroscopy for the diagnosis of breast cancer, we searched PubMed, Web of Science, Cochrane Library, and Embase for articles published from the database establishment to May 20, 2022. Pooled sensitivity, specificity, diagnostic odds ratio, and area under the receiver pooled operating characteristic curve were derived for the included studies as outcome measures. The methodological quality was assessed according to the questionnaires and criteria suggested by the Diagnostic Accuracy Research Quality Assessment-2 tool. Sixteen studies were included in this meta-analysis. The pooled sensitivity and specificity of Raman spectroscopy for breast cancer diagnosis were 0.97 (95% CI, [0.92-0.99]) and 0.96 (95% CI, [0.91-0.98]). The diagnostic odds ratio was 720.89 (95% CI, [135.73-3828.88]) and the area under the curve of summary receiver operating characteristic curves was 0.99 (95% CI, [0.98-1]). Subgroup analysis revealed that all subgroup types in our analysis, including different races, sample types, diagnostic algorithms, number of spectra, instrument types, and laser wavelengths, turned out to have a sensitivity and specificity greater than 0.9. Significant heterogeneity was found between studies. Deeks' funnel plot demonstrated that publication bias was acceptable. This meta-analysis suggests that Raman spectroscopy may be an effective and accurate tool to differentiate breast cancer from normal breast tissue, which will help us diagnose and treat breast cancer.
Collapse
Affiliation(s)
- Mei-Huan Wang
- Department of Ultrasound, Shandong Provincial Hospital affiliated to Shandong First Medical University, No. 324 Jing 5 Rd, Shandong Provincial Hospital, Jinan, Shandong, 250021, People's Republic of China
| | - Xiao Liu
- Department of Ultrasound, Shandong Provincial Hospital affiliated to Shandong First Medical University, No. 324 Jing 5 Rd, Shandong Provincial Hospital, Jinan, Shandong, 250021, People's Republic of China
| | - Qian Wang
- Department of Ultrasound, Shandong Provincial Hospital affiliated to Shandong First Medical University, No. 324 Jing 5 Rd, Shandong Provincial Hospital, Jinan, Shandong, 250021, People's Republic of China.
- Department of Ultrasound, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250021, Shandong, China.
| | - Hua-Wei Zhang
- Department of Ultrasound, Shandong Provincial Hospital affiliated to Shandong First Medical University, No. 324 Jing 5 Rd, Shandong Provincial Hospital, Jinan, Shandong, 250021, People's Republic of China.
- Department of Ultrasound, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250021, Shandong, China.
| |
Collapse
|
14
|
Huang W, Shang Q, Xiao X, Zhang H, Gu Y, Yang L, Shi G, Yang Y, Hu Y, Yuan Y, Ji A, Chen L. Raman spectroscopy and machine learning for the classification of esophageal squamous carcinoma. SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 281:121654. [PMID: 35878494 DOI: 10.1016/j.saa.2022.121654] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 07/15/2022] [Accepted: 07/17/2022] [Indexed: 02/05/2023]
Abstract
Early diagnosis of esophageal squamous cell carcinoma (ESCC), a common malignant tumor with a low overall survival rate due to metastasis and recurrence, is critical for effective treatment and improved prognosis. Raman spectroscopy, an advanced detection technology for esophageal cancer, was developed to improve diagnosis sensitivity, specificity, and accuracy. This study proposed a novel, effective, and noninvasive Raman spectroscopy technique to differentiate and classify ESCC cell lines. Seven ESCC cell lines and tissues of an ESCC patient with staging of T3N1M0 and T3N2M0 at low and high differentiation levels were investigated through Raman spectroscopy. Raman spectral data analysis was performed with four machine learning algorithms, namely principal components analysis (PCA)- linear discriminant analysis (LDA), PCA-eXtreme gradient boosting (XGB), PCA- support vector machine (SVM), and PCA- (LDA, XGB, SVM)-stacked Gradient Boosting Machine (GBM). Four machine learning algorithms were able to classifiy ESCC cell subtypes from normal esophageal cells. The PCA-XGB model achieved an overall predictive accuracy of 85% for classifying ESCC and adjacent tissues. Moreover, an overall predictive accuracy of 90.3% was achieved in distinguishing low differentiation and high differentiation ESCC tissues with the same stage when PCA-LDA, XGM, and SVM models were combined. This study illustrated the Raman spectral traits of ESCC cell lines and esophageal tissues related to clinical pathological diagnosis. Future studies should investigate the role of Raman spectral features in ESCC pathogenesis.
Collapse
Affiliation(s)
- Wenhua Huang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Qixin Shang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Xin Xiao
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Hanlu Zhang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yimin Gu
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Lin Yang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Guidong Shi
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yushang Yang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yang Hu
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yong Yuan
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Aifang Ji
- Heping Hospital Affiliated to Changzhi Medical University, No. 161 Jiefang East Street, Changzhi 046000, China.
| | - Longqi Chen
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China.
| |
Collapse
|
15
|
Shi G, Shen X, Ren H, Rao Y, Weng S, Tang X. Kernel principal component analysis and differential non-linear feature extraction of pesticide residues on fruit surface based on surface-enhanced Raman spectroscopy. FRONTIERS IN PLANT SCIENCE 2022; 13:956778. [PMID: 35928706 PMCID: PMC9344007 DOI: 10.3389/fpls.2022.956778] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 06/29/2022] [Indexed: 06/15/2023]
Abstract
Surface-enhanced Raman spectroscopy (SERS) has attracted much attention because of its high sensitivity, high speed, and simple sample processing, and has great potential for application in the field of pesticide residue detection. However, SERS is susceptible to the influence of a complex detection environment in the detection of pesticide residues on the surface of fruits, facing problems such as interference from the spectral peaks of detected impurities, unclear dimension of effective correlation data, and poor linearity of sensing signals. In this work, the enhanced raw data of the pesticide thiram residues on the fruit surface using gold nanoparticle (Au-NPs) solution are formed into the raw data set of Raman signal in the IoT environment of Raman spectroscopy principal component detection. Considering the non-linear characteristics of sensing data, this work adopts kernel principal component analysis (KPCA) including radial basis function (RBF) to extract the main features for the spectra in the ranges of 653∼683 cm-1, 705∼728 cm-1, and 847∼872 cm-1, and discusses the effects of different kernel function widths (σ) to construct a qualitative analysis of pesticide residues based on SERS spectral data model, so that the SERS spectral data produce more useful dimensionality reduction with minimal loss, higher mean squared error for cross-validation in non-linear scenarios, and effectively weaken the interference features of detecting impurity spectral peaks, unclear dimensionality of effective correlation data, and poor linearity of sensing signals, reflecting better extraction effects than conventional principal component analysis (PCA) models.
Collapse
Affiliation(s)
- Guolong Shi
- School of Information and Computer, Anhui Agricultural University, Hefei, China
- School of Electrical Engineering and Automation, Wuhan University, Wuhan, China
| | - Xinyi Shen
- School of Information and Computer, Anhui Agricultural University, Hefei, China
| | - Huan Ren
- School of Information and Computer, Anhui Agricultural University, Hefei, China
| | - Yuan Rao
- School of Information and Computer, Anhui Agricultural University, Hefei, China
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Hefei, China
| | - Shizhuang Weng
- National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei, China
| | - Xianghu Tang
- School of Information and Computer, Anhui Agricultural University, Hefei, China
- Institute of Solid State Physics, Hefei Institutes of Physical Science (HFIPS), Chinese Academy of Sciences, Hefei, China
| |
Collapse
|
16
|
Raman spectroscopy: current applications in breast cancer diagnosis, challenges and future prospects. Br J Cancer 2022; 126:1125-1139. [PMID: 34893761 PMCID: PMC8661339 DOI: 10.1038/s41416-021-01659-5] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 11/11/2021] [Accepted: 11/25/2021] [Indexed: 12/26/2022] Open
Abstract
Despite significant improvements in the way breast cancer is managed and treated, it continues to persist as a leading cause of death worldwide. If detected and diagnosed early, when tumours are small and localised, there is a considerably higher chance of survival. However, current methods for detection and diagnosis lack the required sensitivity and specificity for identifying breast cancer at the asymptomatic or very early stages. Thus, there is a need to develop more rapid and reliable methods, capable of detecting disease earlier, for improved disease management and patient outcome. Raman spectroscopy is a non-destructive analytical technique that can rapidly provide highly specific information on the biochemical composition and molecular structure of samples. In cancer, it has the capacity to probe very early biochemical changes that accompany malignant transformation, even prior to the onset of morphological changes, to produce a fingerprint of disease. This review explores the application of Raman spectroscopy in breast cancer, including discussion on its capabilities in analysing both ex-vivo tissue and liquid biopsy samples, and its potential in vivo applications. The review also addresses current challenges and potential future uses of this technology in cancer research and translational clinical application.
Collapse
|
17
|
Schiemer R, Furniss D, Phang S, Seddon AB, Atiomo W, Gajjar KB. Vibrational Biospectroscopy: An Alternative Approach to Endometrial Cancer Diagnosis and Screening. Int J Mol Sci 2022; 23:ijms23094859. [PMID: 35563249 PMCID: PMC9102412 DOI: 10.3390/ijms23094859] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 04/25/2022] [Accepted: 04/26/2022] [Indexed: 01/27/2023] Open
Abstract
Endometrial cancer (EC) is the sixth most common cancer and the fourth leading cause of death among women worldwide. Early detection and treatment are associated with a favourable prognosis and reduction in mortality. Unlike other common cancers, however, screening strategies lack the required sensitivity, specificity and accuracy to be successfully implemented in clinical practice and current diagnostic approaches are invasive, costly and time consuming. Such limitations highlight the unmet need to develop diagnostic and screening alternatives for EC, which should be accurate, rapid, minimally invasive and cost-effective. Vibrational spectroscopic techniques, Mid-Infrared Absorption Spectroscopy and Raman, exploit the atomic vibrational absorption induced by interaction of light and a biological sample, to generate a unique spectral response: a “biochemical fingerprint”. These are non-destructive techniques and, combined with multivariate statistical analysis, have been shown over the last decade to provide discrimination between cancerous and healthy samples, demonstrating a promising role in both cancer screening and diagnosis. The aim of this review is to collate available evidence, in order to provide insight into the present status of the application of vibrational biospectroscopy in endometrial cancer diagnosis and screening, and to assess future prospects.
Collapse
Affiliation(s)
- Roberta Schiemer
- Division of Child Health, Obstetrics and Gynaecology, University of Nottingham, Nottingham NG5 1PB, UK;
- Correspondence:
| | - David Furniss
- Mid-Infrared Photonics Group, George Green Institute for Electromagnetics Research, Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, UK; (D.F.); (S.P.); (A.B.S.)
| | - Sendy Phang
- Mid-Infrared Photonics Group, George Green Institute for Electromagnetics Research, Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, UK; (D.F.); (S.P.); (A.B.S.)
| | - Angela B. Seddon
- Mid-Infrared Photonics Group, George Green Institute for Electromagnetics Research, Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, UK; (D.F.); (S.P.); (A.B.S.)
| | - William Atiomo
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU), Dubai P.O. Box 505055, United Arab Emirates;
| | - Ketankumar B. Gajjar
- Division of Child Health, Obstetrics and Gynaecology, University of Nottingham, Nottingham NG5 1PB, UK;
| |
Collapse
|
18
|
Aitekenov S, Sultangaziyev A, Abdirova P, Yussupova L, Gaipov A, Utegulov Z, Bukasov R. Raman, Infrared and Brillouin Spectroscopies of Biofluids for Medical Diagnostics and for Detection of Biomarkers. Crit Rev Anal Chem 2022; 53:1561-1590. [PMID: 35157535 DOI: 10.1080/10408347.2022.2036941] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/23/2023]
Abstract
This review surveys Infrared, Raman/SERS and Brillouin spectroscopies for medical diagnostics and detection of biomarkers in biofluids, that include urine, blood, saliva and other biofluids. These optical sensing techniques are non-contact, noninvasive and relatively rapid, accurate, label-free and affordable. However, those techniques still have to overcome some challenges to be widely adopted in routine clinical diagnostics. This review summarizes and provides insights on recent advancements in research within the field of vibrational spectroscopy for medical diagnostics and its use in detection of many health conditions such as kidney injury, cancers, cardiovascular and infectious diseases. The six comprehensive tables in the review and four tables in supplementary information summarize a few dozen experimental papers in terms of such analytical parameters as limit of detection, range, diagnostic sensitivity and specificity, and other figures of merits. Critical comparison between SERS and FTIR methods of analysis reveals that on average the reported sensitivity for biomarkers in biofluids for SERS vs FTIR is about 103 to 105 times higher, since LOD SERS are lower than LOD FTIR by about this factor. High sensitivity gives SERS an edge in detection of many biomarkers present in biofluids at low concentration (nM and sub nM), which can be particularly advantageous for example in early diagnostics of cancer or viral infections.HighlightsRaman, Infrared spectroscopies use low volume of biofluidic samples, little sample preparation, fast time of analysis and relatively inexpensive instrumentation.Applications of SERS may be a bit more complicated than applications of FTIR (e.g., limited shelf life for nanoparticles and substrates, etc.), but this can be generously compensated by much higher (by several order of magnitude) sensitivity in comparison to FTIR.High sensitivity makes SERS a noninvasive analytical method of choice for detection, quantification and diagnostics of many health conditions, metabolites, and drugs, particularly in diagnostics of cancer, including diagnostics of its early stages.FTIR, particularly ATR-FTIR can be a method of choice for efficient sensing of many biomarkers, present in urine, blood and other biofluids at sufficiently high concentrations (mM and even a few µM)Brillouin scattering spectroscopy detecting visco-elastic properties of probed liquid medium, may also find application in clinical analysis of some biofluids, such as cerebrospinal fluid and urine.
Collapse
Affiliation(s)
- Sultan Aitekenov
- Department of Chemistry, School of Sciences and Humanities (SSH), Nazarbayev University, Nur-Sultan, Kazakhstan
| | - Alisher Sultangaziyev
- Department of Chemistry, School of Sciences and Humanities (SSH), Nazarbayev University, Nur-Sultan, Kazakhstan
| | - Perizat Abdirova
- Department of Chemistry, School of Sciences and Humanities (SSH), Nazarbayev University, Nur-Sultan, Kazakhstan
| | - Lyailya Yussupova
- Department of Chemistry, School of Sciences and Humanities (SSH), Nazarbayev University, Nur-Sultan, Kazakhstan
| | | | - Zhandos Utegulov
- Department of Physics, School of Sciences and Humanities (SSH), Nazarbayev University, Nur-Sultan, Kazakhstan
| | - Rostislav Bukasov
- Department of Chemistry, School of Sciences and Humanities (SSH), Nazarbayev University, Nur-Sultan, Kazakhstan
| |
Collapse
|
19
|
Chen F, Sun C, Yue Z, Zhang Y, Xu W, Shabbir S, Zou L, Lu W, Wang W, Xie Z, Zhou L, Lu Y, Yu J. Screening ovarian cancers with Raman spectroscopy of blood plasma coupled with machine learning data processing. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 265:120355. [PMID: 34530200 DOI: 10.1016/j.saa.2021.120355] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 08/30/2021] [Accepted: 08/31/2021] [Indexed: 06/13/2023]
Abstract
The mortality of ovarian cancer is closely related to its poor rate of early detection. In the search of an efficient diagnosis method, Raman spectroscopy of blood features as a promising technique allowing simple, rapid, minimally-invasive and cost-effective detection of cancers, in particular ovarian cancer. Although Raman spectroscopy has been demonstrated to be effective to detect ovarian cancers with respect to normal controls, a binary classification remains idealized with respect to the real clinical practice. This work considered a population of 95 woman patients initially suspected of an ovarian cancer and finally fixed with a cancer or a cyst. Additionally, 79 normal controls completed the ensemble of samples. Such sample collection proposed us a study case where a ternary classification should be realized with Raman spectroscopy of the collected blood samples coupled with suitable spectroscopic data treatment algorithms. In the medical as well as data points of view, the appearance of the cyst case considerably reduces the distances among the different populations and makes their distinction much more difficult, since the intermediate cyst case can share the specific features of the both cancer and normal cases. After a proper spectrum pretreatment, we first demonstrated the evidence of different behaviors among the Raman spectra of the 3 types of samples. Such difference was further visualized in a high dimensional space, where the data points of the cancer and the normal cases are separately clustered, whereas the data of the cyst case were scattered into the areas respectively occupied by the cancer and normal cases. We finally developed and tested an ensemble of models for a ternary classification with 2 consequent steps of binary classifications, based on machine learning algorithms, allowing identification with sensitivity and specificity of 81.0% and 97.3% for cancer samples, 63.6% and 91.5% for cyst samples, 100% and 90.6% for normal samples.
Collapse
Affiliation(s)
- Fengye Chen
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Chen Sun
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Zengqi Yue
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yuqing Zhang
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Weijie Xu
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Sahar Shabbir
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Long Zou
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Weiguo Lu
- Center for Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Women's Reproductive Health Key Laboratory of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310011, China; Department of Gynecologic Oncology, Women's Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310011, China
| | - Wei Wang
- Department of Clinical Laboratory, Tongde Hospital of Zhejiang Province, Hangzhou 310012, China
| | - Zhenwei Xie
- Center for Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Women's Reproductive Health Key Laboratory of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310011, China; Department of Gynecologic Oncology, Women's Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310011, China
| | - Lanyun Zhou
- Center for Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Women's Reproductive Health Key Laboratory of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310011, China; Department of Gynecologic Oncology, Women's Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310011, China
| | - Yan Lu
- Center for Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Women's Reproductive Health Key Laboratory of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310011, China; Department of Gynecologic Oncology, Women's Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310011, China.
| | - Jin Yu
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China.
| |
Collapse
|
20
|
Zhang L, Li C, Peng D, Yi X, He S, Liu F, Zheng X, Huang WE, Zhao L, Huang X. Raman spectroscopy and machine learning for the classification of breast cancers. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 264:120300. [PMID: 34455388 DOI: 10.1016/j.saa.2021.120300] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 07/26/2021] [Accepted: 08/16/2021] [Indexed: 06/13/2023]
Abstract
Breast cancer is a major health threat for women. The drug responses associated with different breast cancer subtypes have obvious effects on therapeutic outcomes; therefore, the accurate classification of breast cancer subtypes is critical. Breast cancer subtype classification has recently been examined using various methods, and Raman spectroscopy has emerged as an effective technique that can be used for noninvasive breast cancer analysis. However, the accurate and rapid classification of breast cancer subtypes currently requires a great deal of effort and experience with the processing and analysis of Raman spectra data. Here, we adopted Raman spectroscopy and machine learning techniques to simplify and accelerate the process used to distinguish normal from breast cancer cells and classify breast cancer subtypes. Raman spectra were obtained from cultured breast cancer cell lines, and the data were analyzed by two machine learning algorithms: principal component analysis (PCA)-discriminant function analysis (DFA) and PCA-support vector machine (SVM). The accuracies with which these two algorithms were able to distinguish normal breast cells from breast cancer cells were both greater than 97%, and the accuracies of breast cancer subtype classification for both algorithms were both greater than 92%. Moreover, our results showed evidence to support the use of characteristic Raman spectral features as cancer cell biomarkers, such as the intensity of intrinsic Raman bands, which increased in cancer cells. Raman spectroscopy combined with machine learning techniques provides a rapid method for breast cancer analysis able to reveal differences in intracellular compositions and molecular structures among subtypes.
Collapse
Affiliation(s)
- Lihao Zhang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Keling Road, Suzhou, Jiangsu Province, 215163, China
| | - Chengjian Li
- Department of Pharmacy, Shanghai Baoshan Luodian Hospital, Baoshan District, Shanghai, 201908, China; Luodian Clinical Drug Research Center, Institute for Translational Medicine Research, Shanghai University, Shanghai, 200444, China
| | - Di Peng
- Shanghai D-band Medical Instrument Co., Ltd, Huyi Highway, Jiading District, Shanghai, 201800, China
| | - Xiaofei Yi
- Shanghai D-band Medical Instrument Co., Ltd, Huyi Highway, Jiading District, Shanghai, 201800, China
| | - Shuai He
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Keling Road, Suzhou, Jiangsu Province, 215163, China
| | - Fengxiang Liu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Keling Road, Suzhou, Jiangsu Province, 215163, China
| | - Xiangtai Zheng
- Luodian Clinical Drug Research Center, Institute for Translational Medicine Research, Shanghai University, Shanghai, 200444, China
| | - Wei E Huang
- Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, UK
| | - Liang Zhao
- Department of Pharmacy, Shanghai Baoshan Luodian Hospital, Baoshan District, Shanghai, 201908, China; Luodian Clinical Drug Research Center, Institute for Translational Medicine Research, Shanghai University, Shanghai, 200444, China.
| | - Xia Huang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Keling Road, Suzhou, Jiangsu Province, 215163, China; Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China.
| |
Collapse
|
21
|
Coupling Micro-Physiological Systems and Biosensors for Improving Cancer Biomarkers Detection. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1379:307-318. [DOI: 10.1007/978-3-031-04039-9_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
|
22
|
Akbar S, Majeed MI, Nawaz H, Rashid N, Tariq A, Hameed W, Shakeel S, Dastgir G, Bari RZA, Iqbal M, Nawaz A, Akram M. Surface-Enhanced Raman Spectroscopic (SERS) Characterization of Low Molecular Weight Fraction of the Serum of Breast Cancer Patients with Principal Component Analysis (PCA) and Partial Least Square-Discriminant Analysis (PLS-DA). ANAL LETT 2021. [DOI: 10.1080/00032719.2021.2017948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Saba Akbar
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | | | - Haq Nawaz
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Nosheen Rashid
- Department of Chemistry, University of Education, Faisalabad, Pakistan
| | - Ayesha Tariq
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Wajeeha Hameed
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Samra Shakeel
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Ghulam Dastgir
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Rana Zaki Abdul Bari
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Maham Iqbal
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Amna Nawaz
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Maria Akram
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| |
Collapse
|
23
|
Becker L, Janssen N, Layland SL, Mürdter TE, Nies AT, Schenke-Layland K, Marzi J. Raman Imaging and Fluorescence Lifetime Imaging Microscopy for Diagnosis of Cancer State and Metabolic Monitoring. Cancers (Basel) 2021; 13:cancers13225682. [PMID: 34830837 PMCID: PMC8616063 DOI: 10.3390/cancers13225682] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 11/05/2021] [Accepted: 11/10/2021] [Indexed: 02/08/2023] Open
Abstract
Hurdles for effective tumor therapy are delayed detection and limited effectiveness of systemic drug therapies by patient-specific multidrug resistance. Non-invasive bioimaging tools such as fluorescence lifetime imaging microscopy (FLIM) and Raman-microspectroscopy have evolved over the last decade, providing the potential to be translated into clinics for early-stage disease detection, in vitro drug screening, and drug efficacy studies in personalized medicine. Accessing tissue- and cell-specific spectral signatures, Raman microspectroscopy has emerged as a diagnostic tool to identify precancerous lesions, cancer stages, or cell malignancy. In vivo Raman measurements have been enabled by recent technological advances in Raman endoscopy and signal-enhancing setups such as coherent anti-stokes Raman spectroscopy or surface-enhanced Raman spectroscopy. FLIM enables in situ investigations of metabolic processes such as glycolysis, oxidative stress, or mitochondrial activity by using the autofluorescence of co-enzymes NADH and FAD, which are associated with intrinsic proteins as a direct measure of tumor metabolism, cell death stages and drug efficacy. The combination of non-invasive and molecular-sensitive in situ techniques and advanced 3D tumor models such as patient-derived organoids or microtumors allows the recapitulation of tumor physiology and metabolism in vitro and facilitates the screening for patient-individualized drug treatment options.
Collapse
Affiliation(s)
- Lucas Becker
- Department for Medical Technologies and Regenerative Medicine, Institute of Biomedical Engineering, University of Tübingen, 72076 Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, 72076 Tübingen, Germany
| | - Nicole Janssen
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, University of Tübingen, 72076 Tübingen, Germany
| | - Shannon L Layland
- Department for Medical Technologies and Regenerative Medicine, Institute of Biomedical Engineering, University of Tübingen, 72076 Tübingen, Germany
| | - Thomas E Mürdter
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, University of Tübingen, 72076 Tübingen, Germany
| | - Anne T Nies
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, 72076 Tübingen, Germany
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, University of Tübingen, 72076 Tübingen, Germany
| | - Katja Schenke-Layland
- Department for Medical Technologies and Regenerative Medicine, Institute of Biomedical Engineering, University of Tübingen, 72076 Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, 72076 Tübingen, Germany
- NMI Natural and Medical Sciences Institute at the University of Tübingen, 72770 Reutlingen, Germany
- Cardiovascular Research Laboratories, Department of Medicine/Cardiology, David Geffen School of Medicine, UCLA, Los Angeles, CA 90073, USA
| | - Julia Marzi
- Department for Medical Technologies and Regenerative Medicine, Institute of Biomedical Engineering, University of Tübingen, 72076 Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, 72076 Tübingen, Germany
- NMI Natural and Medical Sciences Institute at the University of Tübingen, 72770 Reutlingen, Germany
| |
Collapse
|
24
|
Giamougiannis P, Silva RVO, Freitas DLD, Lima KMG, Anagnostopoulos A, Angelopoulos G, Naik R, Wood NJ, Martin-Hirsch PL, Martin FL. Raman spectroscopy of blood and urine liquid biopsies for ovarian cancer diagnosis: identification of chemotherapy effects. JOURNAL OF BIOPHOTONICS 2021; 14:e202100195. [PMID: 34296515 DOI: 10.1002/jbio.202100195] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 07/19/2021] [Accepted: 07/20/2021] [Indexed: 06/13/2023]
Abstract
Blood plasma and serum Raman spectroscopy for ovarian cancer diagnosis has been applied in pilot studies, with promising results. Herein, a comparative analysis of these biofluids, with a novel assessment of urine, was conducted by Raman spectroscopy application in a large patient cohort. Spectra were obtained through samples measurements from 116 ovarian cancer patients and 307 controls. Principal component analysis identified significant spectral differences between cancers without previous treatment (n = 71) and following neo-adjuvant chemotherapy (NACT), (n = 45). Application of five classification algorithms achieved up to 73% sensitivity for plasma, high specificities and accuracies for both blood biofluids, and lower performance for urine. A drop in sensitivities for the NACT group in plasma and serum, with an opposite trend in urine, suggest that Raman spectroscopy could identify chemotherapy-related changes. This study confirms that biofluids' Raman spectroscopy can contribute in ovarian cancer's diagnostic work-up and demonstrates its potential in monitoring treatment response.
Collapse
Affiliation(s)
- Panagiotis Giamougiannis
- Department of Obstetrics and Gynaecology, Lancashire Teaching Hospitals NHS Foundation Trust, Preston, UK
- School of Pharmacy and Biomedical Sciences, University of Central Lancashire, Preston, UK
| | - Raissa V O Silva
- Institute of Chemistry, Biological Chemistry and Chemometrics, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Daniel L D Freitas
- Institute of Chemistry, Biological Chemistry and Chemometrics, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Kássio M G Lima
- Institute of Chemistry, Biological Chemistry and Chemometrics, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Antonios Anagnostopoulos
- Department of Obstetrics and Gynaecology, Lancashire Teaching Hospitals NHS Foundation Trust, Preston, UK
| | - Georgios Angelopoulos
- Department of Obstetrics and Gynaecology, Lancashire Teaching Hospitals NHS Foundation Trust, Preston, UK
| | - Raj Naik
- Department of Obstetrics and Gynaecology, Lancashire Teaching Hospitals NHS Foundation Trust, Preston, UK
| | - Nicholas J Wood
- Department of Obstetrics and Gynaecology, Lancashire Teaching Hospitals NHS Foundation Trust, Preston, UK
| | - Pierre L Martin-Hirsch
- Department of Obstetrics and Gynaecology, Lancashire Teaching Hospitals NHS Foundation Trust, Preston, UK
| | | |
Collapse
|
25
|
Githaiga JI, Angeyo HK, Kaduki KA, Bulimo WD, Ojuka DK. Quantitative Raman spectroscopy of breast cancer malignancy utilizing higher-order principal components: A preliminary study. SCIENTIFIC AFRICAN 2021. [DOI: 10.1016/j.sciaf.2021.e01035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
|
26
|
A New Look into Cancer-A Review on the Contribution of Vibrational Spectroscopy on Early Diagnosis and Surgery Guidance. Cancers (Basel) 2021; 13:cancers13215336. [PMID: 34771500 PMCID: PMC8582426 DOI: 10.3390/cancers13215336] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 10/14/2021] [Accepted: 10/18/2021] [Indexed: 02/05/2023] Open
Abstract
Simple Summary Cancer is a leading cause of death worldwide, with the detection of the disease in its early stages, as well as a correct assessment of the tumour margins, being paramount for a successful recovery. While breast cancer is one of most common types of cancer, head and neck cancer is one of the types of cancer with a lower prognosis and poor aesthetic results. Vibrational spectroscopy detects molecular vibrations, being sensitive to different sample compositions, even when the difference was slight. The use of spectroscopy in biomedicine has been extensively explored, since it allows a broader assessment of the biochemical fingerprint of several diseases. This literature review covers the most recent advances in breast and head and neck cancer early diagnosis and intraoperative margin assessment, through Raman and Fourier transform infrared spectroscopies. The rising field of spectral histopathology was also approached. The authors aimed at expounding in a more concise and simple way the challenges faced by clinicians and how vibrational spectroscopy has evolved to respond to those needs for the two types of cancer with the highest potential for improvement regarding an early diagnosis, surgical margin assessment and histopathology. Abstract In 2020, approximately 10 million people died of cancer, rendering this disease the second leading cause of death worldwide. Detecting cancer in its early stages is paramount for patients’ prognosis and survival. Hence, the scientific and medical communities are engaged in improving both therapeutic strategies and diagnostic methodologies, beyond prevention. Optical vibrational spectroscopy has been shown to be an ideal diagnostic method for early cancer diagnosis and surgical margins assessment, as a complement to histopathological analysis. Being highly sensitive, non-invasive and capable of real-time molecular imaging, Raman and Fourier transform infrared (FTIR) spectroscopies give information on the biochemical profile of the tissue under analysis, detecting the metabolic differences between healthy and cancerous portions of the same sample. This constitutes tremendous progress in the field, since the cancer-prompted morphological alterations often occur after the biochemical imbalances in the oncogenic process. Therefore, the early cancer-associated metabolic changes are unnoticed by the histopathologist. Additionally, Raman and FTIR spectroscopies significantly reduce the subjectivity linked to cancer diagnosis. This review focuses on breast and head and neck cancers, their clinical needs and the progress made to date using vibrational spectroscopy as a diagnostic technique prior to surgical intervention and intraoperative margin assessment.
Collapse
|
27
|
Grieve S, Puvvada N, Phinyomark A, Russell K, Murugesan A, Zed E, Hassan A, Legare JF, Kienesberger PC, Pulinilkunnil T, Reiman T, Scheme E, Brunt KR. Nanoparticle surface-enhanced Raman spectroscopy as a noninvasive, label-free tool to monitor hematological malignancy. Nanomedicine (Lond) 2021; 16:2175-2188. [PMID: 34547916 DOI: 10.2217/nnm-2021-0076] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
Aim: Monitoring minimal residual disease remains a challenge to the effective medical management of hematological malignancies; yet surface-enhanced Raman spectroscopy (SERS) has emerged as a potential clinical tool to do so. Materials & methods: We developed a cell-free, label-free SERS approach using gold nanoparticles (nanoSERS) to classify hematological malignancies referenced against two control cohorts: healthy and noncancer cardiovascular disease. A predictive model was built using machine-learning algorithms to incorporate disease burden scores for patients under standard treatment upon. Results: Linear- and quadratic-discriminant analysis distinguished three cohorts with 69.8 and 71.4% accuracies, respectively. A predictive nanoSERS model correlated (MSE = 1.6) with established clinical parameters. Conclusion: This study offers a proof-of-concept for the noninvasive monitoring of disease progression, highlighting the potential to incorporate nanoSERS into translational medicine.
Collapse
Affiliation(s)
- Stacy Grieve
- Department of Biology, University of New Brunswick, Saint John, New Brunswick, Canada.,IMPART investigator team, Canada
| | - Nagaprasad Puvvada
- Department of Pharmacology, Dalhousie University, Saint John, New Brunswick, Canada.,Department of Chemistry, Indrashil University, Gujarat, India
| | - Angkoon Phinyomark
- IMPART investigator team, Canada.,Institute of Biomedical Engineering, University of New Brunswick, Fredericton, New Brunswick, Canada
| | - Kevin Russell
- Faculty of Medicine, Dalhousie University, Saint John, New Brunswick, Canada
| | - Alli Murugesan
- Department of Biology, University of New Brunswick, Saint John, New Brunswick, Canada.,Faculty of Medicine, Dalhousie University, Saint John, New Brunswick, Canada
| | - Elizabeth Zed
- Department of Oncology, Saint John Regional Hospital, Saint John, New Brunswick, Canada
| | - Ansar Hassan
- IMPART investigator team, Canada.,Department of Cardiac Surgery, Saint John Regional Hospital, Saint John, New Brunswick, Canada
| | - Jean-Francois Legare
- IMPART investigator team, Canada.,Department of Cardiac Surgery, Saint John Regional Hospital, Saint John, New Brunswick, Canada
| | - Petra C Kienesberger
- IMPART investigator team, Canada.,Faculty of Medicine, Dalhousie University, Saint John, New Brunswick, Canada.,Department of Biochemistry & Molecular Biology, Dalhousie University, Saint John, New Brunswick, Canada
| | - Thomas Pulinilkunnil
- IMPART investigator team, Canada.,Faculty of Medicine, Dalhousie University, Saint John, New Brunswick, Canada.,Department of Biochemistry & Molecular Biology, Dalhousie University, Saint John, New Brunswick, Canada
| | - Tony Reiman
- Department of Biology, University of New Brunswick, Saint John, New Brunswick, Canada.,IMPART investigator team, Canada.,Faculty of Medicine, Dalhousie University, Saint John, New Brunswick, Canada.,Department of Oncology, Saint John Regional Hospital, Saint John, New Brunswick, Canada
| | - Erik Scheme
- IMPART investigator team, Canada.,Institute of Biomedical Engineering, University of New Brunswick, Fredericton, New Brunswick, Canada.,Faculty of Medicine, Dalhousie University, Saint John, New Brunswick, Canada
| | - Keith R Brunt
- IMPART investigator team, Canada.,Department of Pharmacology, Dalhousie University, Saint John, New Brunswick, Canada.,Faculty of Medicine, Dalhousie University, Saint John, New Brunswick, Canada
| |
Collapse
|
28
|
Hoyos W, Aguilar J, Toro M. Dengue models based on machine learning techniques: A systematic literature review. Artif Intell Med 2021; 119:102157. [PMID: 34531010 DOI: 10.1016/j.artmed.2021.102157] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 05/08/2021] [Accepted: 08/17/2021] [Indexed: 12/16/2022]
Abstract
BACKGROUND Dengue modeling is a research topic that has increased in recent years. Early prediction and decision-making are key factors to control dengue. This Systematic Literature Review (SLR) analyzes three modeling approaches of dengue: diagnostic, epidemic, intervention. These approaches require models of prediction, prescription and optimization. This SLR establishes the state-of-the-art in dengue modeling, using machine learning, in the last years. METHODS Several databases were selected to search the articles. The selection was made based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. Sixty-four articles were obtained and analyzed to describe their strengths and limitations. Finally, challenges and opportunities for research on machine-learning for dengue modeling were identified. RESULTS Logistic regression was the most used modeling approach for the diagnosis of dengue (59.1%). The analysis of the epidemic approach showed that linear regression (17.4%) is the most used technique within the spatial analysis. Finally, the most used intervention modeling is General Linear Model with 70%. CONCLUSIONS We conclude that cause-effect models may improve diagnosis and understanding of dengue. Models that manage uncertainty can also be helpful, because of low data-quality in healthcare. Finally, decentralization of data, using federated learning, may decrease computational costs and allow model building without compromising data security.
Collapse
Affiliation(s)
- William Hoyos
- Grupo de Investigaciones Microbiológicas y Biomédicas de Córdoba, Universidad de Córdoba, Montería, Colombia; Grupo de Investigación en I+D+i en TIC, Universidad EAFIT, Medellín, Colombia.
| | - Jose Aguilar
- Grupo de Investigación en I+D+i en TIC, Universidad EAFIT, Medellín, Colombia; Centro de Estudios en Microelectrónica y Sistemas Distribuidos, Universidad de Los Andes, Mérida, Venezuela; Universidad de Alcalá, Depto. de Automática, Alcalá de Henares, Spain
| | - Mauricio Toro
- Grupo de Investigación en I+D+i en TIC, Universidad EAFIT, Medellín, Colombia
| |
Collapse
|
29
|
N M, Lukose J, Mohan G, Shastry S, Chidangil S. Single cell spectroscopy of red blood cells in intravenous crystalloid fluids. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 257:119726. [PMID: 33848954 DOI: 10.1016/j.saa.2021.119726] [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: 12/30/2020] [Revised: 03/06/2021] [Accepted: 03/15/2021] [Indexed: 06/12/2023]
Abstract
Crystalloid fluids, a subset of intravenous (IV) fluid solutions are commonly used in clinical settings. The influence of these fluids on the functions of blood components are least explored. Raman spectroscopy combined with optical trapping has been widely used to evaluate the impact of external stress agents on red blood cells. The present study investigates the impact of commonly used crystalloid fluids on red blood cells in comparison with that of blood plasma using Raman Tweezers spectroscopy. The red blood cells suspended in crystalloid fluids undergo deoxygenation readily than that in blood plasma. In addition, cells in blood plasma were able to withstand laser induced deoxygenation comparatively better than that in crystalloid fluids at higher laser powers. Principle component analysis of the Raman spectral data has clearly demonstrated the discrimination of cells in plasma with that of crystalloid fluids demonstrating the effect of external induced stress on RBCs.
Collapse
Affiliation(s)
- Mithun N
- Centre of Excellence for Biophotonics, Department of Atomic and Molecular Physics, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India
| | - Jijo Lukose
- Centre of Excellence for Biophotonics, Department of Atomic and Molecular Physics, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India
| | - Ganesh Mohan
- Department of Immunohematology and Blood Transfusion, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Karnataka 576104, India
| | - Shamee Shastry
- Department of Immunohematology and Blood Transfusion, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Karnataka 576104, India
| | - Santhosh Chidangil
- Centre of Excellence for Biophotonics, Department of Atomic and Molecular Physics, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India.
| |
Collapse
|
30
|
Wang S, Li H, Ren Y, Yu F, Song D, Zhu L, Yu S, Jiang S, Zeng H. Studying the pathological and biochemical features in breast cancer progression by confocal Raman microspectral imaging of excised tissue samples. JOURNAL OF PHOTOCHEMISTRY AND PHOTOBIOLOGY B-BIOLOGY 2021; 222:112280. [PMID: 34375907 DOI: 10.1016/j.jphotobiol.2021.112280] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 09/29/2020] [Accepted: 08/02/2021] [Indexed: 11/19/2022]
Abstract
Confocal Raman microspectral imaging (CRMI) has been used to detect the spectra-pathological features of ductal carcinoma in situ (DCIS) and lobular hyperplasia (LH) compared with the heathy (H) breast tissue. A total of 15-20 spectra were measured from healthy tissue, LH tissue, and DCIS tissue. One-way ANOVA and Tukey's honest significant difference (HSD) post hoc multiple tests were used to evaluate the peak intensity variations in all three tissue types. Besides that, linear discrimination analysis (LDA) algorithm was adopted in combination with principal component analysis (PCA) to classify the spectral features from tissues at different stages along the continuum to breast cancer. Moreover, by using the point-by-point scanning methodology, spectral datasets were obtained and reconstructed for further pathologic visualization by multivariate imaging methods, including K-mean clustering analysis (KCA) and PCA. Univariate imaging of individual Raman bands was also used to describe the differences in the distribution of specific molecular components in the scanning area. After a detailed spectral feature analysis from 800 to 1800 cm-1 and 2800 to 3000 cm-1 for all the three tissue types, the histopathological features were visualized based on the content and structural variations of lipids, proteins, phenylalanine, carotenoids and collagen, as well as the calcification phenomena. The results obtained not only allowed a detailed Raman spectroscopy-based understanding of the malignant transformation process of breast cancer, but also provided a solid spectral data support for developing Raman based breast cancer clinical diagnostic techniques.
Collapse
Affiliation(s)
- Shuang Wang
- Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi 710069, China.
| | - Heping Li
- Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi 710069, China
| | - Yu Ren
- Department of breast surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Fan Yu
- Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi 710069, China
| | - Dongliang Song
- Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi 710069, China
| | - Lizhe Zhu
- Department of breast surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Shibo Yu
- Department of breast surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Siyuan Jiang
- Department of breast surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Haishan Zeng
- Imaging Unit - Integrative Oncology Department, BC Cancer Research Center, Vancouver, BC V5Z 1L3, Canada.
| |
Collapse
|
31
|
Li Y, Chen C, Chen F, Chen C, Gao R, Yang B, Si R, Lv X. Serum Raman spectroscopy combined with Deep Neural Network for analysis and rapid screening of hyperthyroidism and hypothyroidism. Photodiagnosis Photodyn Ther 2021; 35:102382. [PMID: 34091096 DOI: 10.1016/j.pdpdt.2021.102382] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 05/18/2021] [Accepted: 06/01/2021] [Indexed: 10/21/2022]
Abstract
Hyperthyroidism and hypothyroidism may cause a series of clinical complications have a high incidence, and early diagnosis is beneficial to treatment. Based on Raman spectroscopy and deep learning algorithms, we propose a rapid screening method to distinguish serum samples of hyperthyroidism patients, hypothyroidism patients and control subjects. We collected 99 serum samples, including 38 cases from hyperthyroidism patients, 32 cases from hypothyroidism patients and 29 cases from control subjects. By comparing and analyzing the Raman spectra of the three, we found differences in the peak intensity of the spectra, indicating that Raman spectra can be used for the subsequent identification of diseases. After collecting the spectral data, Vancouver Raman algorithm (VRA) was used to remove the fluorescence background of the data, and kernel principal component analysis (KPCA) was used to extract the spectral data features with a cumulative explained variance ratio of 0.9999. Then, five neural network models, the adjusted AlexNet, LSTM-CNN, IndRNNCNN, the adjusted GoogLeNet and the adjusted ResNet, were constructed for classifications. The total accuracy was 91%, 84%, 82%, 75% and 71% respectively. The results of our study show that it is feasible to use Raman spectroscopy combined with deep learning to distinguish hyperthyroidism, hypothyroidism and control subjects. After comparing the models, we found that as the neural network deepens and the complexity of the model increases, the classification effect of Raman spectroscopy gradually deteriorates, and we put forward three conjectures for this.
Collapse
Affiliation(s)
- Yizhe Li
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Cheng Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China; Key Laboratory of signal detection and processing, Xinjiang University, Urumqi 830046, Xinjiang, China.
| | - Fangfang Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Rui Gao
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Bo Yang
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Rumeng Si
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Xiaoyi Lv
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China; College of Software, Xinjiang University, Urumqi 830046, Xinjiang, China; Key Laboratory of signal detection and processing, Xinjiang University, Urumqi 830046, Xinjiang, China
| |
Collapse
|
32
|
Kim BC, Kim J, Kim K, Byun BH, Lim I, Kong CB, Song WS, Koh JS, Woo SK. Preliminary Radiogenomic Evidence for the Prediction of Metastasis and Chemotherapy Response in Pediatric Patients with Osteosarcoma Using 18F-FDF PET/CT, EZRIN and KI67. Cancers (Basel) 2021; 13:cancers13112671. [PMID: 34071614 PMCID: PMC8198322 DOI: 10.3390/cancers13112671] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 05/25/2021] [Accepted: 05/26/2021] [Indexed: 01/08/2023] Open
Abstract
Simple Summary Pediatric osteosarcoma is one of the most aggressive cancers, and predictions of metastasis and chemotherapy response have a significant impact on pediatric patient survival. Radiogenomics, as methods of analyzing gene expression or image texture features, have previously been used for the diagnosis of chemotherapy responses and metastasis and can reveal the current state of cancer. In this study, we aimed to generate a predictive model using gene expression and 18F-FDG PET/CT image texture features in pediatric osteosarcoma in relation to metastasis and chemotherapy response. A predictive model using radiogenomics technology that incorporates both imaging features and gene expression can accurately predict metastasis and chemotherapy responses to improve patient outcomes. Abstract Chemotherapy response and metastasis prediction play important roles in the treatment of pediatric osteosarcoma, which is prone to metastasis and has a high mortality rate. This study aimed to estimate the prediction model using gene expression and image texture features. 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images of 52 pediatric osteosarcoma patients were used to estimate the machine learning algorithm. An appropriate algorithm was selected by estimating the machine learning accuracy. 18F-FDG PET/CT images of 21 patients were selected for prediction model development based on simultaneous KI67 and EZRIN expression. The prediction model for chemotherapy response and metastasis was estimated using area under the curve (AUC) maximum image texture features (AUC_max) and gene expression. The machine learning algorithm with the highest test accuracy in chemotherapy response and metastasis was selected using the random forest algorithm. The chemotherapy response and metastasis test accuracy with image texture features was 0.83 and 0.76, respectively. The highest test accuracy and AUC of chemotherapy response with AUC_max, KI67, and EZRIN were estimated to be 0.85 and 0.89, respectively. The highest test accuracy and AUC of metastasis with AUC_max, KI67, and EZRIN were estimated to be 0.85 and 0.8, respectively. The metastasis prediction accuracy increased by 10% using radiogenomics data.
Collapse
Affiliation(s)
- Byung-Chul Kim
- Department of Nuclear Medicine, Korea Institute of Radiological and Medical Sciences, Seoul 01812, Korea; (B.-C.K.); (B.H.B.); (I.L.)
| | - Jingyu Kim
- Radiological & Medico-Oncological Sciences, University of Science & Technology, Seoul 34113, Korea;
| | - Kangsan Kim
- Division of Applied RI, Korea Institute of Radiological and Medical Science, Seoul 01812, Korea;
| | - Byung Hyun Byun
- Department of Nuclear Medicine, Korea Institute of Radiological and Medical Sciences, Seoul 01812, Korea; (B.-C.K.); (B.H.B.); (I.L.)
| | - Ilhan Lim
- Department of Nuclear Medicine, Korea Institute of Radiological and Medical Sciences, Seoul 01812, Korea; (B.-C.K.); (B.H.B.); (I.L.)
| | - Chang-Bae Kong
- Department of Orthopaedic Surgery, Seoul National University Hospital, Seoul 03080, Korea; (C.-B.K.); (W.S.S.)
| | - Won Seok Song
- Department of Orthopaedic Surgery, Seoul National University Hospital, Seoul 03080, Korea; (C.-B.K.); (W.S.S.)
| | - Jae-Soo Koh
- Department of Pathology, Korea Institute of Radiological and Medical Sciences, Seoul 01812, Korea;
| | - Sang-Keun Woo
- Radiological & Medico-Oncological Sciences, University of Science & Technology, Seoul 34113, Korea;
- Division of Applied RI, Korea Institute of Radiological and Medical Science, Seoul 01812, Korea;
- Correspondence: ; Tel.: +82-2-970-1659
| |
Collapse
|
33
|
Tipatet KS, Davison-Gates L, Tewes TJ, Fiagbedzi EK, Elfick A, Neu B, Downes A. Detection of acquired radioresistance in breast cancer cell lines using Raman spectroscopy and machine learning. Analyst 2021; 146:3709-3716. [PMID: 33969839 DOI: 10.1039/d1an00387a] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Radioresistance-a living cell's response to, and development of resistance to ionising radiation-can lead to radiotherapy failure and/or tumour recurrence. We used Raman spectroscopy and machine learning to characterise biochemical changes that occur in acquired radioresistance for breast cancer cells. We were able to distinguish between wild-type and acquired radioresistant cells by changes in chemical composition using Raman spectroscopy and machine learning with 100% accuracy. In studying both hormone receptor positive and negative cells, we found similar changes in chemical composition that occur with the development of acquired radioresistance; these radioresistant cells contained less lipids and proteins compared to their parental counterparts. As well as characterising acquired radioresistance in vitro, this approach has the potential to be translated into a clinical setting, to look for Raman signals of radioresistance in tumours or biopsies; that would lead to tailored clinical treatments.
Collapse
Affiliation(s)
- Kevin Saruni Tipatet
- Institute for BioEngineering, University of Edinburgh, UK. and Faculty of Life Sciences, Rhine Waal University of Applied Sciences, Kleve, Germany
| | | | - Thomas Johann Tewes
- Faculty of Life Sciences, Rhine Waal University of Applied Sciences, Kleve, Germany
| | | | | | - Björn Neu
- Faculty of Life Sciences, Rhine Waal University of Applied Sciences, Kleve, Germany
| | - Andrew Downes
- Institute for BioEngineering, University of Edinburgh, UK.
| |
Collapse
|
34
|
Schutz S, Aidar FJ, Souza RLM, dos Santos JL, Voltarelli FA, Vieira Junior RC, Soares NMM, Marçal AC. Different Methods of Physical Training Applied to Women Breast Cancer Survivors: A Systematic Review. Front Physiol 2021; 12:639406. [PMID: 33935799 PMCID: PMC8079809 DOI: 10.3389/fphys.2021.639406] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 03/22/2021] [Indexed: 01/15/2023] Open
Abstract
Objective: The objective of this systematic review was to identify the effects of different training methods in women who have survived breast cancer (WSBC). Data Sources: Studies were identified by searching SportDiscus, Web of Science, PubMed, Scopus, Scielo, and Bireme. Study Selection: The inclusion criteria were articles that addressed only breast cancer in women, were randomized clinical trials, and interventions involving physical training with Consort ≥80. Data Extraction: The PICO and CONSORT strategies were used for the selection of articles and quality assessment of randomized clinical trials, respectively. Two independent reviewers searched for articles among the databases. Disagreements were discussed, and in the case of an impasse, a third reviewer was consulted. Data Synthesis: Evidence that demonstrated the beneficial effects of physical exercise programs carried out by WSBC. Moderate or high-intensity exercise sessions have been shown to benefit women survivors of breast cancer. Among the modalities, the resistance exercise showed effects from 55% of one-repetition maximum (1 RM), exclusively or associated with other training regimes, such as aerobic (from 48% of heart rate), high-intensity interval training (HIIT), or impact. The main benefits include increased muscle strength, promoted by the practice of resistance exercise in combination with other types of exercises or alone; decreased fatigue; improved quality of life; improved psychosocial effects, and increased leisure time. Conclusions: Physical training performed at a moderate or high intensity (aerobic or anaerobic) can reduce fatigue, improve quality of life, improve sleep quality, and increase bone mineral density in women survivors of breast cancer.
Collapse
Affiliation(s)
- Silvia Schutz
- Department of Physical Education, Universidade Federal de Sergipe, São Cristóvão, Brazil
| | - Felipe J. Aidar
- Department of Physical Education, Universidade Federal de Sergipe, São Cristóvão, Brazil
- Group of Studies and Research of Performance, Sport, Health, and Paralympic Sports, Universidade Federal de Sergipe, São Cristóvão, Brazil
| | | | - Jymmys Lopes dos Santos
- Department of Physical Education, Universidade Federal de Sergipe, São Cristóvão, Brazil
- Department of Physiology, Universidade Federal de Sergipe, São Cristóvão, Brazil
| | - Fabrício Azevedo Voltarelli
- Graduate Program of Health Sciences, Faculty of Medicine, Universidade Federal de Mato Grosso, Cuiabá, Brazil
| | | | | | | |
Collapse
|
35
|
Lan W, Renard CMGC, Jaillais B, Buergy A, Leca A, Chen S, Bureau S. Mid-infrared technique to forecast cooked puree properties from raw apples: A potential strategy towards sustainability and precision processing. Food Chem 2021; 355:129636. [PMID: 33799241 DOI: 10.1016/j.foodchem.2021.129636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 02/05/2021] [Accepted: 03/13/2021] [Indexed: 10/21/2022]
Abstract
The potential of MIRS was investigated to: i) differentiate cooked purees issued from different apples and process conditions, and ii) predict the puree quality characteristics from the spectra of homogenized raw apples. Partial least squares (PLS) regression was tested both, on the real spectra of cooked purees and their reconstructed spectra calculated from the spectra of homogenized raw apples by direct standardization. The cooked purees were well-classified according to apple thinning practices and cold storage durations, and to different heating and grinding conditions. PLS models using the spectra of homogenized raw apples can anticipate the titratable acidity (the residual predictive deviation (RPD) = 2.9), soluble solid content (RPD = 2.8), particle averaged size (RPD = 2.6) and viscosity (RPD ≥ 2.5) of cooked purees. MIR technique can provide sustainable evaluations of puree quality, and even forecast texture and taste of purees based on the prior information of raw materials.
Collapse
Affiliation(s)
- Weijie Lan
- INRAE, Avignon University, UMR408 Sécurité et Qualité des Produits d'Origine Végétale, F-84000 Avignon, France.
| | - Catherine M G C Renard
- INRAE, Avignon University, UMR408 Sécurité et Qualité des Produits d'Origine Végétale, F-84000 Avignon, France; INRAE, TRANSFORM Division, F-44000 Nantes, France.
| | - Benoit Jaillais
- INRAE, ONIRIS, Unité Statistiques, Sensométrie, Chimiométrie (StatSC), F-44322 Nantes, France.
| | - Alexandra Buergy
- INRAE, Avignon University, UMR408 Sécurité et Qualité des Produits d'Origine Végétale, F-84000 Avignon, France.
| | - Alexandre Leca
- INRAE, Avignon University, UMR408 Sécurité et Qualité des Produits d'Origine Végétale, F-84000 Avignon, France.
| | | | - Sylvie Bureau
- INRAE, Avignon University, UMR408 Sécurité et Qualité des Produits d'Origine Végétale, F-84000 Avignon, France.
| |
Collapse
|
36
|
Jin L, Wang S, Cheng Y. A Raman spectroscopy analysis method for rapidly determining saccharides and its application to monitoring the extraction process of Wenxin granule manufacturing procedure. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 241:118603. [PMID: 32622050 DOI: 10.1016/j.saa.2020.118603] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Revised: 06/08/2020] [Accepted: 06/08/2020] [Indexed: 06/11/2023]
Abstract
Saccharides are the major constituents of many herbs, and they are often utilized as quality indicators of many botanical drugs, such as Chinese medicines. A method for the rapid determination of saccharides in the in-process extract solutions is beneficial for process monitoring and ensuring consistency in the quality of the end-products during the manufacturing of Chinese medicines. In this work, a method based on Raman spectroscopy and a competitive adaptive reweighted sampling-partial least squares (CARS-PLS) model was established for the rapid quantification of saccharides. The accuracy and precision of this method were confirmed by employing one monosaccharide (glucose), one oligosaccharide (maltotriose), and two polysaccharides (Codonopsis radix polysaccharides and Polygonati rhizome polysaccharides) as reference substances. The determined results correlated well with the reference values of the four substances with the coefficient of determination of prediction (Rp2) ≥ 0.9939 and the root-mean-square error of prediction (RMSEP) ≤ 1.1052 mg/mL. Then, the method was applied to monitoring the simulated extraction process for Wenxin granule manufacture using total saccharides as a quality indicator. The CARS-PLS model exhibited satisfactory fitting and predictive capability, with Rp2 and RMSEP values of 0.9743 and 1.4931 mg/mL, respectively. Our work demonstrated that Raman spectroscopy coupled with chemometrics can offer a reliable and nondestructive alternative for the determination of different types of saccharides, in addition to being useful for real-time monitoring of the extraction process during the manufacturing of Wenxin granules. The presented approach is expected to be applicable to other Chinese medicines.
Collapse
Affiliation(s)
- Lei Jin
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, PR China
| | - Shufang Wang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, PR China.
| | - Yiyu Cheng
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, PR China.
| |
Collapse
|
37
|
Lan W, Renard CM, Jaillais B, Leca A, Bureau S. Fresh, freeze-dried or cell wall samples: Which is the most appropriate to determine chemical, structural and rheological variations during apple processing using ATR-FTIR spectroscopy? Food Chem 2020; 330:127357. [DOI: 10.1016/j.foodchem.2020.127357] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 06/12/2020] [Accepted: 06/14/2020] [Indexed: 12/21/2022]
|
38
|
Lan W, Jaillais B, Leca A, Renard CM, Bureau S. A new application of NIR spectroscopy to describe and predict purees quality from the non-destructive apple measurements. Food Chem 2020; 310:125944. [DOI: 10.1016/j.foodchem.2019.125944] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Revised: 11/19/2019] [Accepted: 11/20/2019] [Indexed: 02/06/2023]
|
39
|
Burgara-Estrella AJ, Acosta-Elías MA, Álvarez-Bajo O, Silva-Campa E, Angulo-Molina A, Rodríguez-Hernández IDC, Sarabia-Sainz HM, Escalante-Lugo VM, Pedroza-Montero MR. Atomic force microscopy and Raman spectra profile of blood components associated with exposure to cigarette smoking. RSC Adv 2020; 10:11971-11981. [PMID: 35496627 PMCID: PMC9050830 DOI: 10.1039/d0ra01384f] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Accepted: 03/06/2020] [Indexed: 11/21/2022] Open
Abstract
Tobacco smoke contains several compounds with oxidant and pro-oxidant properties with the capability of producing structural changes in biomolecules, as well as cell damage. This work aimed to describe and analyse the effect of tobacco smoke on human blood components, red blood cell (RBC) membrane, haemoglobin (Hb) and blood plasma by Atomic Force Microscopy (AFM) and Raman spectroscopy. Our results indicate that tobacco induced RBC membrane nano-alterations characterized by diminished RBC diameter and increased nano-vesicles formation, and RBC fragility. The Raman spectra profile suggests modifications in chemical composition specifically found in peaks 1135 cm−1, 1156 cm−1, 1452 cm−1 and intensity relation of peaks 1195 cm−1 and 1210 cm−1 of blood plasma and by change of peaks 1338 cm−1, 1357 cm−1, 1549 cm−1 and 1605 cm−1 associated with the pyrrole ring of Hb. The relevance of these results lies in the identification of a profile of structural and chemical alterations that serves as a biomarker of physiological and pathological conditions in the human blood components induced by tobacco exposure using AFM and the Raman spectroscopy as tools for monitoring them. Tobacco smoke contains several compounds with oxidant and pro-oxidant properties with the capability of producing structural changes in biomolecules, as well as cell damage.![]()
Collapse
Affiliation(s)
| | | | - Osiris Álvarez-Bajo
- CONACYT-Departamento de Investigación en Física
- Universidad de Sonora
- Hermosillo
- Mexico
| | - Erika Silva-Campa
- Departamento de Investigación en Física
- Universidad de Sonora
- Hermosillo
- Mexico
| | - Aracely Angulo-Molina
- Departamento de Investigación en Física
- Universidad de Sonora
- Hermosillo
- Mexico
- Departamento de Ciencias Químico Biológicas
| | | | - Héctor M. Sarabia-Sainz
- Departamento de Ciencias del Deporte y de la Actividad Física
- Universidad de Sonora
- Hermosillo
- Mexico
| | | | | |
Collapse
|
40
|
Ralbovsky NM, Lednev IK. Towards development of a novel universal medical diagnostic method: Raman spectroscopy and machine learning. Chem Soc Rev 2020; 49:7428-7453. [DOI: 10.1039/d0cs01019g] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
This review summarizes recent progress made using Raman spectroscopy and machine learning for potential universal medical diagnostic applications.
Collapse
Affiliation(s)
| | - Igor K. Lednev
- Department of Chemistry
- University at Albany
- SUNY
- Albany
- USA
| |
Collapse
|