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Cai M, Li X, Liang J, Liao M, Han Y. An effective deep learning fusion method for predicting the TVB-N and TVC contents of chicken breasts using dual hyperspectral imaging systems. Food Chem 2024; 456:139847. [PMID: 38925007 DOI: 10.1016/j.foodchem.2024.139847] [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: 01/22/2024] [Revised: 05/06/2024] [Accepted: 05/24/2024] [Indexed: 06/28/2024]
Abstract
Total volatile basic nitrogen (TVB-N) and total viable count (TVC) are important freshness indicators of meat. Hyperspectral imaging combined with chemometrics has been proven to be effective in meat detection. However, a challenge with chemometrics is the lack of a universally applicable processing combination, requiring trial-and-error experiments with different datasets. This study proposes an end-to-end deep learning model, pyramid attention features fusion model (PAFFM), integrating CNN, attention mechanism and pyramid structure. PAFFM fuses the raw visible and near-infrared range (VNIR) and shortwave near-infrared range (SWIR) spectral data for predicting TVB-N and TVC in chicken breasts. Compared with the CNN and chemometric models, PAFFM obtains excellent results without a complicated processing combinatorial optimization process. Important wavelengths that contributed significantly to PAFFM performance are visualized and interpreted. This study offers valuable references and technical support for the market application of spectral detection, benefiting related research and practical fields.
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Affiliation(s)
- Mingrui Cai
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
| | - Xiaoxin Li
- College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, 486 Wushan Road, Guangzhou 510642, China; National Center for International Collaboration Research on Precision Agricultural Aviation Pesticides Spraying Technology, South China Agricultural University, Guangzhou 510642, China.
| | - Juntao Liang
- College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, 486 Wushan Road, Guangzhou 510642, China; National Center for International Collaboration Research on Precision Agricultural Aviation Pesticides Spraying Technology, South China Agricultural University, Guangzhou 510642, China.
| | - Ming Liao
- State Key Laboratory of Swine and Poultry Breeding Industry, Guangzhou 510640, China; Key Laboratory for Prevention and Control of Avian Influenza and Other Major Poultry Diseases, Ministry of Agriculture and Rural Affairs, Guangzhou 510640, China; Key Laboratory of Livestock Disease Prevention of Guangdong Province, Guangzhou 510640, China.
| | - Yuxing Han
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
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2
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Tian Y, Fan X, Chen K, Chen X, Peng W, Wang L, Wang F. Optical biomarker analysis for renal cell carcinoma obtained from preoperative and postoperative patients using ATR-FTIR spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 318:124426. [PMID: 38763020 DOI: 10.1016/j.saa.2024.124426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 04/19/2024] [Accepted: 05/07/2024] [Indexed: 05/21/2024]
Abstract
Renal cell carcinoma (RCC) is the most common malignant tumor in the urinary system, accounting for 80 % to 90 % for all renal malignancies. Traditional diagnostic methods like magnetic resonance imaging (MRI) and computed tomography (CT) lack the sensitivity and specificity as they lack specific biomarkers. These limitations impede effective monitoring of tumor recurrence. This study aims to employ Attenuated Total Reflection (ATR)-Fourier transform infrared (FTIR) spectroscopy, an optical technology sensitive to molecular groups, to analyze the potential optical biomarkers in urine and plasma samples from RCC patients pre- and post-surgery. The results reveal distinctive spectral information from both plasma and urine samples. Post-surgery urine spectra exhibit complexity compared to plasma, showing reduced content at 1072 cm-1, 1347 cm-1 and 1654 cm-1 bands, while increased content at 1112 cm-1, 1143 cm-1, 1447 cm-1, 3334 cm-1 and 3420 cm-1 bands. Utilizing machine learning models such as eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), partial least squares (PLS), and artificial neural network (ANN), the study evaluated plasma and urine samples pre- and post-surgery. Remarkably, the XGBoost method excelled in distinguishing between tumor conditions and recovery, achieving an impressive AUC value of 0.99. These results underscore the potential of ATR-FTIR technology in identifying RCC optical biomarkers, with XGBoost showing promise as a valuable screening tool for RCC recurrence diagnosis.
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Affiliation(s)
- Yuchen Tian
- Department of Biochemistry and Molecular Biology, Shaanxi Provincial Key Laboratory of Clinical Genetics, School of Basic Medicine, Air Force Medical University, Xi'an 710032, China
| | - Xiaozheng Fan
- Department of Urology, Xijing Hospital, Fourth Military Medical University, Xi'an 710032, China
| | - Kun Chen
- Department of Anatomy, Histology and Embryology and K.K. Leung Brain Research Centre, School of Basic Medicine, Air Force Medical University, Xi'an 710032, China
| | - Xinyu Chen
- Department of Biochemistry and Molecular Biology, Shaanxi Provincial Key Laboratory of Clinical Genetics, School of Basic Medicine, Air Force Medical University, Xi'an 710032, China
| | - Wenyu Peng
- Department of Biochemistry and Molecular Biology, Shaanxi Provincial Key Laboratory of Clinical Genetics, School of Basic Medicine, Air Force Medical University, Xi'an 710032, China
| | - Li Wang
- School of Aerospace Medicine, Air Force Medical University, Xi'an, China
| | - Fuli Wang
- Department of Urology, Xijing Hospital, Fourth Military Medical University, Xi'an 710032, China.
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3
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Chang C, Liu H, Chen C, Wu L, Lv X, Xie X, Chen C. Rapid diagnosis of systemic lupus erythematosus by Raman spectroscopy combined with spiking neural network. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 310:123904. [PMID: 38262298 DOI: 10.1016/j.saa.2024.123904] [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: 09/04/2023] [Revised: 11/30/2023] [Accepted: 01/15/2024] [Indexed: 01/25/2024]
Abstract
Multiple organs are affected by the autoimmune inflammatory connective tissue disease known as systemic lupus erythematosus (SLE). If not diagnosed and treated in a timely manner, it can lead to nephritis and damage to the blood system in severe cases, resulting in the patient's death. Therefore, correct and timely diagnosis and treatment are essential for patients. In this study, a framework based on neural network algorithm and Raman spectroscopy technique was established to diagnose SLE patients. Firstly, we pre-processed the obtained Raman data by three methods: baseline correction, smoothing processing and normalization methods, before using it as input for the model, and then ANN, ResNet and SNN classification models were established. The respective classification accuracies for SLE patients were 89.61%, 85.71%, and 95.65% for the three models, with corresponding AUC values of 0.8772, 0.8100, and 0.9555. The results of the experimental indicate that SNN possesses a good classification effect, and the number of model parameters is only 525,826, which is 414,221 less than that of ResNet model. Since the network only uses 0 and 1 to transmit information, and only has basic operations such as summation, compared with the second-generation artificial neural network, which simplifies the product operation of floating point numbers into multiple addition operations, the network has low energy consumption and is suitable for embedding portable Raman spectrometer for clinical diagnosis. This research highlights the significant potential for quick and precise SLE patient discrimination offered by Raman spectroscopy in conjunction with spiking neural networks.
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Affiliation(s)
- Chenjie Chang
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Hao Liu
- College of Software, Xinjiang University, Urumqi 830046, China
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China; Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, China; Xinjiang Cloud Computing Application Laboratory, Karamay 834099, China; Xinjiang Aiqiside Testing Technology Co., Ltd, Urumqi 830000, China
| | - Lijun Wu
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi 830001, China; Xinjiang Clinical Research Center for Rheumatoid Arthritis, Urumqi 830001, China
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi 830046, China; Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, China
| | - Xiaodong Xie
- Xinjiang Uygur Autonomous Region People's Hospital, Urumqi 830001, China.
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi 830046, China.
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4
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Xu S, Dawuti W, Maimaitiaili M, Dou J, Aizezi M, Aimulajiang K, Lü X, Lü G. Rapid and non-invasive detection of cystic echinococcosis in sheep based on serum fluorescence spectrum combined with machine learning algorithms. JOURNAL OF BIOPHOTONICS 2024; 17:e202300357. [PMID: 38263544 DOI: 10.1002/jbio.202300357] [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: 09/03/2023] [Revised: 11/15/2023] [Accepted: 12/14/2023] [Indexed: 01/25/2024]
Abstract
Cystic echinococcosis (CE) is a grievous zoonotic parasitic disease. Currently, the traditional technology of screening CE is laborious and expensive, developing an innovative technology is urgent. In this study, we combined serum fluorescence spectroscopy with machine learning algorithms to develop an innovative screening technique to diagnose CE in sheep. Serum fluorescence spectra of Echinococcus granulosus sensu stricto-infected group (n = 63) and uninfected E. granulosus s.s. group (n = 60) under excitation at 405 nm were recorded. The linear support vector machine (Linear SVM), Quadratic SVM, medium radial basis function (RBF) SVM, K-nearest neighbor (KNN), and principal component analysis-linear discriminant analysis (PCA-LDA) were used to analyze the spectra data. The results showed that Quadratic SVM had the great classification capacity, its sensitivity, specificity, and accuracy were 85.0%, 93.8%, and 88.9%, respectively. In short, serum fluorescence spectroscopy combined with Quadratic SVM algorithm has great potential in the innovative diagnosis of CE in sheep.
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Affiliation(s)
- Shengke Xu
- College of Life Sciences and Technology, Xinjiang University, Urumqi, Xinjiang, China
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
- Xinjiang Key Laboratory of Echinococcosis, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Wubulitalifu Dawuti
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Maierhaba Maimaitiaili
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
- Xinjiang Key Laboratory of Echinococcosis, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Jingrui Dou
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Malike Aizezi
- Animal Health Supervision Institute of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, PR China
| | - Kalibixiati Aimulajiang
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
- Xinjiang Key Laboratory of Echinococcosis, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Xiaoyi Lü
- College of Software, Xinjiang University, Urumqi, Xinjiang, China
| | - Guodong Lü
- College of Life Sciences and Technology, Xinjiang University, Urumqi, Xinjiang, China
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
- Xinjiang Key Laboratory of Echinococcosis, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
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Chen X, Chen C, Tian X, He L, Zuo E, Liu P, Xue Y, Yang J, Chen C, Lv X. DBAN: An improved dual branch attention network combined with serum Raman spectroscopy for diagnosis of diabetic kidney disease. Talanta 2024; 266:125052. [PMID: 37574605 DOI: 10.1016/j.talanta.2023.125052] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 08/02/2023] [Accepted: 08/05/2023] [Indexed: 08/15/2023]
Abstract
Diabetic kidney disease (DKD) is one of the most common kidney diseases worldwide. It is estimated that approximately 537 million adults worldwide have diabetes, and up to 30%-40% of diabetic patients are at risk of developing nephropathy. The pathogenesis of DKD is complex, and its onset is insidious. Currently, the clinical diagnosis of DKD primarily relies on the increase of urinary albumin and the decrease in glomerular filtration rate in diabetic patients. However, the excretion of urinary albumin is influenced by various factors, such as physical activity, infections, fever, and high blood glucose, making it challenging to achieve an objective and accurate diagnosis. Therefore, there is an urgent need to develop an efficient, fast, and low-cost auxiliary diagnostic technology for DKD. In this study, an improved Dual Branch Attention Network (DBAN) was developed to quickly identify DKD. Serum Raman spectroscopy samples were collected from 32 DKD patients and 32 healthy volunteers. The collected data were preprocessed using the adaptive iteratively reweighted penalized least squares (airPLS) algorithm, and the DBAN was used to classify the serum Raman spectroscopy data of DKD. The model consists of a dual branch structure that extracts features using Convolutional Neural Network (CNN) and bottleneck layer modules. The attention module allows the model to learn features specifically, and lateral connections are added between the dual branches to achieve multi-level and multi-scale fusion of shallow and deep features, as well as local and global features, improving the classification accuracy of the experiment. The results of the study showed that compared to traditional deep learning algorithms such as Artificial Neural Network (ANN), CNN, GoogleNet, ResNet, and AlexNet, our proposed DBAN classification model achieved 95.4% accuracy, 98.0% precision, 96.5% sensitivity, and 97.2% specificity, demonstrating the best classification performance. This is the best method for identifying DKD, and has important reference value for the diagnosis of DKD patients, as well as improving the accuracy of medical auxiliary diagnosis.
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Affiliation(s)
- Xinya 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
| | - Xuecong Tian
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China
| | - Liang He
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China; Xinjiang Key Laboratory of Signal Detection and Processing, Urumqi, 830017,China; Department of Electronic Engineering, and Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, 100084, China
| | - Enguang Zuo
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China
| | - Pei Liu
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China
| | - You Xue
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China
| | - Jie Yang
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi, 830046, China.
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi, 830046, China; The Key Laboratory of Signal Detection and Processing, Xinjiang Uygur Autonomous Region, Xinjiang University, Urumqi, 840046, China.
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He X, Su Y, Liu P, Chen C, Chen C, Guan H, Lv X, Guo W. Machine learning-based immune prognostic model and ceRNA network construction for lung adenocarcinoma. J Cancer Res Clin Oncol 2023; 149:7379-7392. [PMID: 36939925 DOI: 10.1007/s00432-023-04609-1] [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: 10/20/2022] [Accepted: 01/27/2023] [Indexed: 03/21/2023]
Abstract
PURPOSE Lung adenocarcinoma (LUAD) is a malignant tumor with a high lethality rate. Immunotherapy has become a breakthrough in cancer treatment and improves patient survival and prognosis. Therefore, it is necessary to find new immune-related markers. However, the current research on immune-related markers in LUAD is not sufficient. Therefore, there is a need to find new immune-related biomarkers to help treat LUAD patients. METHODS In this study, a bioinformatics approach combined with a machine learning approach screened reliable immune-related markers to construct a prognostic model to predict the overall survival (OS) of LUAD patients, thus promoting the clinical application of immunotherapy in LUAD. The experimental data were obtained from The Cancer Genome Atlas (TCGA) database, including 535 LUAD and 59 healthy control samples. Firstly, the Hub gene was screened using a bioinformatics approach combined with the Support Vector Machine Recursive Feature Elimination algorithm; then, a multifactorial Cox regression analysis by constructing an immune prognostic model for LUAD and a nomogram to predict the OS rate of LUAD patients. Finally, the regulatory mechanism of Hub genes in LUAD was analyzed by ceRNA. RESULTS Five genes, ADM2, CDH17, DKK1, PTX3, and AC145343.1, were screened as potential immune-related genes in LUAD. Among them, ADM2 and AC145343.1 had a good prognosis in LUAD patients (HR < 1) and were novel markers. The remaining three genes screened were associated with poor prognosis in LUAD patients (HR > 1). In addition, the experimental results showed that patients in the low-risk group had better OS rates than those in the high-risk group (P < 0.001). CONCLUSION In this paper, we propose an immune prognostic model to predict OS rate in LUAD patients and show the correlation between five immune genes and the level of immune-related cell infiltration. It provides new markers and additional ideas for immunotherapy in patients with LUAD.
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Affiliation(s)
- Xiaoqian He
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China
| | - Ying Su
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China
| | - Pei Liu
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi, 830046, China.
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China
| | - Haoqin Guan
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi, 830046, China.
| | - Wenjia Guo
- Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, 830011, China.
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Lin XW, Liu RH, Wang S, Yang JW, Tao NP, Wang XC, Zhou Q, Xu CH. Direct Identification and Quantitation of Protein Peptide Powders Based on Multi-Molecular Infrared Spectroscopy and Multivariate Data Fusion. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023. [PMID: 37406208 DOI: 10.1021/acs.jafc.3c01841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/07/2023]
Abstract
Given that protein peptide powders (PPPs) from different biological sources were inherited with diverse healthcare functions, which aroused adulteration of PPPs. A high-throughput and rapid methodology, united multi-molecular infrared (MM-IR) spectroscopy with data fusion, could determine the types and component content of PPPs from seven sources as examples. The chemical fingerprints of PPPs were thoroughly interpreted by tri-step infrared (IR) spectroscopy, and the defined spectral fingerprint region of protein peptide, total sugar, and fat was 3600-950 cm-1, which constituted MIR finger-print region. Moreover, the mid-level data fusion model was of great applicability in qualitative analysis, in which the F1-score reached 1 and the total accuracy was 100%, and a robust quantitative model was established with excellent predictive capacity (Rp: 0.9935, RMSEP: 1.288, and RPD: 7.97). MM-IR coordinated data fusion strategies to achieve high-throughput, multi-dimensional analysis of PPPs with better accuracy and robustness which meant a significant potential for the comprehensive analysis of other powders in food as well.
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Affiliation(s)
- Xiao-Wen Lin
- College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, P. R. China
- Shanghai Qinpu Biotechnology Pte Ltd, Shanghai 201306, China
| | - Run-Hui Liu
- College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, P. R. China
| | - Song Wang
- College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, P. R. China
- Shanghai Qinpu Biotechnology Pte Ltd, Shanghai 201306, China
| | - Jie-Wen Yang
- College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, P. R. China
| | - Ning-Ping Tao
- College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, P. R. China
- Shanghai Engineering Research Center of Aquatic-Product Processing & Preservation, Shanghai 201306, China
| | - Xi-Chang Wang
- College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, P. R. China
- Shanghai Engineering Research Center of Aquatic-Product Processing & Preservation, Shanghai 201306, China
| | - Qun Zhou
- Department of Chemistry, Tsinghua University, Beijing 100084, China
| | - Chang-Hua Xu
- College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, P. R. China
- Shanghai Engineering Research Center of Aquatic-Product Processing & Preservation, Shanghai 201306, China
- Ministry of Agriculture, Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), Shanghai 201306, China
- National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Shanghai 201306, China
- Shanghai Qinpu Biotechnology Pte Ltd, Shanghai 201306, China
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8
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Dawuti W, Dou J, Zheng X, Lü X, Zhao H, Yang L, Lin R, Lü G. Rapid and accurate screening of cystic echinococcosis in sheep based on serum Fourier-transform infrared spectroscopy combined with machine learning algorithms. JOURNAL OF BIOPHOTONICS 2023; 16:e202200320. [PMID: 36707914 DOI: 10.1002/jbio.202200320] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 01/20/2023] [Accepted: 01/25/2023] [Indexed: 05/17/2023]
Abstract
Cystic echinococcosis (CE) in sheep is a serious zoonotic parasitic disease caused by Echinococcus granulosus sensu stricto (s.s.). Presently, the screening technology for CE in sheep is time-consuming and inaccurate, and novel screening technology is urgently needed. In this work, we combined machine-learning algorithms with Fourier transform infrared (FT-IR) spectroscopy of serum to establish a quick and accurate screening approach for CE in sheep. Serum samples from 77 E. granulosus s.s.-infected sheep to 121 healthy control sheep were measured by FT-IR spectrometer. To optimize the classification accuracy of the serum FI-TR method for the E. granulosus s.s.-infected sheep and healthy control sheep, principal component analysis (PCA), linear discriminant analysis, and support vector machine (SVM) algorithms were used to analyze the data. Among all the bands, 1500-1700 cm-1 band has the best classification effect; its diagnostic sensitivity, specificity, and accuracy of PCA-SVM were 100%, 95.74%, and 96.66%, respectively. The study showed that serum FT-IR spectroscopy combined with machine learning algorithms has great potential for rapid and accurate screening methods for the CE in sheep.
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Affiliation(s)
- Wubulitalifu Dawuti
- School of Public Health, Xinjiang Medical University, Urumqi, China
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Jingrui Dou
- School of Public Health, Xinjiang Medical University, Urumqi, China
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Xiangxiang Zheng
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Xiaoyi Lü
- College of Software, Xinjiang University, Urumqi, China
| | - Hui Zhao
- Department of Clinical Laboratory, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Lingfei Yang
- Department of Abdominal Ultrasound Diagnosis, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Renyong Lin
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Guodong Lü
- School of Public Health, Xinjiang Medical University, Urumqi, China
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
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Xie HF, Mao ZZ, Xiao D, Li ZN. Rapid detection of molybdenum ore grade based on visible-infrared spectroscopy and MTSVD-TGJO-ELM. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 298:122789. [PMID: 37156173 DOI: 10.1016/j.saa.2023.122789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 03/27/2023] [Accepted: 04/24/2023] [Indexed: 05/10/2023]
Abstract
The rapid determination of ore grade can improve the efficiency of beneficiation. The existing molybdenum ore grade determination methods lag behind the beneficiation work. Therefore, this paper proposes a method based on a combination of Visible-infrared spectroscopy and machine learning to rapidly determine molybdenum ore grade. Firstly, 128 molybdenum ores were collected as spectral test samples to obtain spectral data. Then 13 latent variables were extracted from the 973 spectral features using partial least square. The Durbin-Watson test and the runs test were used to detect the partial residual plots and augmented partial residual plots of LV1 and LV2 to determine the non-linear relationship between spectral signal and molybdenum content. Extreme Learning Machine (ELM) was used instead of linear modeling methods to model the grade of molybdenum ores because of the non-linear behavior of the spectral data. In this paper, the Golden Jackal Optimization of adaptive T-distribution was used to optimize the parameters of the ELM to solve the problem of unreasonable parameters. Aiming at solving ill-posed problems by ELM, this paper decomposes the ELM output matrix by using the improved truncated singular value decomposition. Finally, this paper proposes an extreme learning machine method based on a modified truncated singular value decomposition and a Golden Jackal Optimization of adaptive T-distribution (MTSVD-TGJO-ELM). Compared with other classical machine learning algorithms, MTSVD-TGJO-ELM has the highest accuracy. This provides a new method for rapid detection of ore grade in the mining process and facilitates accurate beneficiation of molybdenum ores to improve ore recovery rate.
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Affiliation(s)
- Hong-Fei Xie
- Information Science and Engineering School, Northeastern University, 110819 Shenyang, China
| | - Zhi-Zhong Mao
- Information Science and Engineering School, Northeastern University, 110819 Shenyang, China.
| | - Dong Xiao
- Information Science and Engineering School, Northeastern University, 110819 Shenyang, China
| | - Zhen-Ni Li
- Information Science and Engineering School, Northeastern University, 110819 Shenyang, China
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Dou J, Dawuti W, Zheng X, Zhu Y, Lin R, Lü G, Zhang Y. Rapid discrimination of Brucellosis in sheep using serum Fourier transform infrared spectroscopy combined with PCA-LDA algorithm. Photodiagnosis Photodyn Ther 2023; 42:103567. [PMID: 37084931 DOI: 10.1016/j.pdpdt.2023.103567] [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: 02/25/2023] [Revised: 04/09/2023] [Accepted: 04/11/2023] [Indexed: 04/23/2023]
Abstract
Brucellosis in sheep is an infectious disease caused by Brucella melitensis in sheep. The current conventional serological methods for screening Brucella-infected sheep have the disadvantage of time consuming and low accuracy, so a simple, rapid and highly accurate screening method is needed. The aim of this study was to evaluate the feasibility of diagnosing Brucella-infected sheep by serum samples based on the Fourier transform infrared (FTIR) spectroscopy. In this study, FTIR spectroscopy of serum from Brucella-infected sheep (n=102) and healthy sheep (n=125) revealed abnormal protein and lipid metabolism in serum from Brucella-infected sheep compared to healthy sheep. Principal component analysis-Linear discriminant analysis (PCA-LDA) method was used to differentiate the FTIR spectra of serum from Brucella-infected sheep and healthy sheep in the protein band (3700-3090 cm-1) and lipid band (3000-2800 cm-1), and its overall diagnostic accuracy was 100% (sensitivity 100%, specificity 100%). In conclusion, our results suggest that serum FTIR spectroscopy combined with PCA-LDA algorithm has great potential for brucellosis in sheep screening.
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Affiliation(s)
- Jingrui Dou
- School of Public Health, Xinjiang Medical University, Urumqi 830054, China; State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
| | - Wubulitalifu Dawuti
- School of Public Health, Xinjiang Medical University, Urumqi 830054, China; State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
| | - Xiangxiang Zheng
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Yousen Zhu
- Clinical Laboratory, The First Affiliated Hospital of Xinjiang Medical University, Urumqi Xinjiang 830054, China
| | - Renyong Lin
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
| | - Guodong Lü
- School of Public Health, Xinjiang Medical University, Urumqi 830054, China; State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China.
| | - Yujiang Zhang
- School of Public Health, Xinjiang Medical University, Urumqi 830054, China; The Center for Disease Control and Prevention of Xinjiang Uygur Autonomous Region, Urumqi 830002, China.
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11
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Dawuti W, Dou J, Li J, Zhang R, Zhou J, Maimaitiaili M, Zhou R, Lin R, Lü G. Label-free surface-enhanced Raman spectroscopy of serum with machine-learning algorithms for gallbladder cancer diagnosis. Photodiagnosis Photodyn Ther 2023; 42:103544. [PMID: 37004836 DOI: 10.1016/j.pdpdt.2023.103544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 03/24/2023] [Accepted: 03/28/2023] [Indexed: 04/03/2023]
Abstract
Gallbladder cancer (GBC) is a rare but frequently fatal biliary tract malignancy that is typically only discovered when it is already advanced. In the search of an efficient diagnosis method. Therefore, in this study, we investigated a novel technique for the quick and non-invasive diagnosis of GBC based on serum surface-enhanced Raman spectroscopy (SERS). SERS spectra of serum from 41 patients with GBC and 72 normal subjects were recorded. Principal component analysis-linear discriminant analysis (PCA-LDA), and PCA-support vector machine (PCA-SVM), Linear SVM and Gaussian radial basis function-SVM (RBF-SVM) algorithms were used to establish the classification models, respectively. When the Linear SVM was used, the overall diagnostic accuracy for classifying the two groups could achieve 97.1%, and when RBF-SVM was used, the diagnostic sensitivity of GBC was 100%. The results demonstrated that SERS in combination with a machine learning algorithm is a promising candidate to be one of the diagnostic tools for GBC in the future.
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Affiliation(s)
- Wubulitalifu Dawuti
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China; School of Public Health, Xinjiang Medical University, Urumqi 830054, China
| | - Jingrui Dou
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China; School of Public Health, Xinjiang Medical University, Urumqi 830054, China
| | - Jintian Li
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China; School of Public Health, Xinjiang Medical University, Urumqi 830054, China
| | - Rui Zhang
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China; School of Public Health, Xinjiang Medical University, Urumqi 830054, China
| | - Jing Zhou
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China; College of Pharmacy, Xinjiang Medical University, Urumqi 830054, China
| | - Maierhaba Maimaitiaili
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China; College of Pharmacy, Xinjiang Medical University, Urumqi 830054, China
| | - Run Zhou
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China; College of Pharmacy, Xinjiang Medical University, Urumqi 830054, China
| | - Renyong Lin
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China.
| | - Guodong Lü
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China.
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12
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Jin Y, Liu B, Li C, Shi S. Origin identification of Cornus officinalis based on PCA-SVM combined model. PLoS One 2023; 18:e0282429. [PMID: 36854014 PMCID: PMC9974136 DOI: 10.1371/journal.pone.0282429] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 02/14/2023] [Indexed: 03/02/2023] Open
Abstract
Infrared spectroscopy can quickly and non-destructively extract analytical information from samples. It can be applied to the authenticity identification of various Chinese herbal medicines, the prediction of the mixing amount of defective products, and the analysis of the origin. In this paper, the spectral information of Cornus officinalis from 11 origins was used as the research object, and the origin identification model of Cornus officinalis based on mid-infrared spectroscopy was established. First, principal component analysis was used to extract the absorbance data of Cornus officinalis in the wavenumber range of 551~3998 cm-1. The extracted principal components contain more than 99.8% of the information of the original data. Second, the extracted principal component information was used as input, and the origin category was used as output, and the origin identification model was trained with the help of support vector machine. In this paper, this combined model is called PCA-SVM combined model. Finally, the generalization ability of the PCA-SVM model is evaluated through an external test set. The three indicators of Accuracy, F1-Score, and Kappa coefficient are used to compare this model with other commonly used classification models such as naive Bayes model, decision trees, linear discriminant analysis, radial basis function neural network and partial least square discriminant analysis. The results show that PCA-SVM model is superior to other commonly used models in accuracy, F1 score and Kappa coefficient. In addition, compared with the SVM model with full spectrum data, the PCA-SVM model not only reduces the redundant variables in the model, but also has higher accuracy. Using this model to identify the origin of Cornus officinalis, the accuracy rate is 84.8%.
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Affiliation(s)
- Yueqiang Jin
- Public Foundational Courses Department, Nanjing Vocational University of Industry Technology, Nanjing, China
- * E-mail:
| | - Bing Liu
- Public Foundational Courses Department, Nanjing Vocational University of Industry Technology, Nanjing, China
| | - Chaoning Li
- Research and Development Department, Nanjing Changxingyang Intelligent Home Company Limited, Nanjing, China
| | - Shasha Shi
- School of Science, Jiangsu Ocean University, Lianyungang, China
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13
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Zeng Q, Chen C, Chen C, Song H, Li M, Yan J, Lv X. Serum Raman spectroscopy combined with convolutional neural network for rapid diagnosis of HER2-positive and triple-negative breast cancer. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 286:122000. [PMID: 36279798 DOI: 10.1016/j.saa.2022.122000] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 09/26/2022] [Accepted: 10/13/2022] [Indexed: 06/16/2023]
Abstract
Breast cancer is common in women, and its number of patients ranks first among female malignant tumors. Breast cancer is highly heterogeneous, and different types of breast cancer have different biological behaviors and prognoses. Therefore, identifying the different types of breast cancer is of great help in formulating individualized treatment plans. Based on serum Raman spectroscopy and deep learning algorithms, we propose a fast and low-cost diagnosis method for screening triple-negative breast cancer, human epidermal growth factor receptor 2 (HER2)-positive breast cancer, and healthy controls. We collected 75 serum samples in this study, including 23 triple-negative breast cancers, 22 HER2-positive breast cancers, and 30 healthy controls. Using the preprocessed Raman spectra as the input of deep learning, three deep learning models, neural network language model (NNLM), bidirectional long-short-term memory network (BiLSTM), and convolutional neural network (CNN), were established, and the accuracy rates of the three models were 87.78%, 90.37%, and 91.11%, respectively. The experimental results demonstrate the feasibility of serum Raman spectroscopy combined with deep learning algorithms to diagnose breast cancer, which can be used as an effective auxiliary diagnosis method for breast cancer.
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Affiliation(s)
- Qinggang Zeng
- College of Information Science and Engineering Xinjiang University, Urumqi 830046, China
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi 830046, China.
| | - Chen Chen
- College of Information Science and Engineering Xinjiang University, Urumqi 830046, China; Xinjiang Cloud Computing Application Laboratory, Karamay 834099, China
| | - Haitao Song
- Department of Breast, Head and Neck Surgery, Xinjiang Medical University Affiliated Tumor Hospital, Urumqi, China
| | - Min Li
- College of Information Science and Engineering Xinjiang University, Urumqi 830046, China
| | - Junyi Yan
- College of Software, Xinjiang University, Urumqi 830046, China
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi 830046, China; Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, Xinjiang, China
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14
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Feng Y, Chen C, Liu S, Dong B, Yu Y, Chen C, Lv X. A novel technology of structural distance feature of Raman spectra and convolutional neural network for alcohol dependence diagnosis. Microchem J 2023. [DOI: 10.1016/j.microc.2023.108485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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15
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Sun J, Xu X, Feng S, Zhang H, Xu L, Jiang H, Sun B, Meng Y, Chen W. Rapid identification of salmonella serovars by using Raman spectroscopy and machine learning algorithm. Talanta 2023; 253:123807. [PMID: 36115103 DOI: 10.1016/j.talanta.2022.123807] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/26/2022] [Accepted: 07/29/2022] [Indexed: 12/13/2022]
Abstract
A widespread and escalating public health problem worldwide is foodborne illness, and foodborne Salmonella infection is one of the most common causes of human illness.For the three most pathogenic Salmonella serotypes, Raman spectroscopy was employed to acquire spectral data.As machine learning offers high efficiency and accuracy, we have chosen the convolutional neural network(CNN), which is suitable for solving multi-classification problems, to do in-depth mining and analysis of Raman spectral data.To optimize the instrument parameters, we compared three laser wavelengths: 532, 638, and 785 nm.Ultimately, the 532 nm wavelength was chosen as the most effective for detecting Salmonella.A pre-processing step is necessary to remove interference from the background noise of the Raman spectrum.Our study compared the effects of five spectral preprocessing methods, Savitzky-Golay smoothing (SG), Multivariate Scatter Correction (MSC), Standard Normal Variate (SNV), and Hilbert Transform (HT), on the predictive power of CNN models.Accuracy(ACC), Precision, Recall, and F1-score 4 machine learning evaluation indicators are used to evaluate the model performance under different preprocessing methods.In the results, SG combined with SNV was found to be the most accurate spectral pre-processing method for predicting Salmonella serotypes using Raman spectroscopy, achieving an accuracy of 98.7% for the training set and over 98.5% for the test set in CNN model.Pre-processing spectral data using this method yields higher accuracy than other methods.As a conclusion, the results of this study demonstrate that Raman spectroscopy when used in conjunction with a convolutional neural network model enables the rapid identification of three Salmonella serotypes at the single-cell level, and that the model has a great deal of potential for distinguishing between different serotypes of pathogenic bacteria and closely related bacterial species.This is vital to preventing outbreaks of foodborne illness and the spread of foodborne pathogens.
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Affiliation(s)
- Jiazheng Sun
- College of Criminal Investigation, People's Public Security University of China, Beijing, 100038, PR China
| | - Xuefang Xu
- State Key Laboratory of Communicable Disease Prevention and Control, Institute for Communicable Disease Prevention and Control, Chinese Center for Disease Control and Prevention, Beijing, 102206, PR China
| | - Songsong Feng
- College of Information and Cyber Security,People's Public Security University of China, Beijing, 100038, PR China
| | - Hanyu Zhang
- School of Criminology,People's Public Security University of China, Beijing, 100038, PR China
| | - Lingfeng Xu
- College of Criminal Investigation, People's Public Security University of China, Beijing, 100038, PR China
| | - Hong Jiang
- College of Criminal Investigation, People's Public Security University of China, Beijing, 100038, PR China.
| | - Baibing Sun
- College of Information and Cyber Security,People's Public Security University of China, Beijing, 100038, PR China
| | - Yuyan Meng
- College of Information and Cyber Security,People's Public Security University of China, Beijing, 100038, PR China
| | - Weizhou Chen
- School of Law,People's Public Security University of China, Beijing, 100038, PR China
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16
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Leng H, Chen C, Chen C, Chen F, Du Z, Chen J, Yang B, Zuo E, Xiao M, Lv X, Liu P. Raman spectroscopy and FTIR spectroscopy fusion technology combined with deep learning: A novel cancer prediction method. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 285:121839. [PMID: 36191438 DOI: 10.1016/j.saa.2022.121839] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 09/01/2022] [Accepted: 09/02/2022] [Indexed: 06/16/2023]
Abstract
According to the limited molecular information reflected by single spectroscopy, and the complementarity of FTIR spectroscopy and Raman spectroscopy, we propose a novel diagnostic technology combining multispectral fusion and deep learning. We used serum samples from 45 healthy controls, 44 non-small cell lung cancer (NSCLC), 38 glioma and 37 esophageal cancer patients, and the Raman spectra and FTIR spectra were collected respectively. Then we performed low-level fusion and feature fusion on the spectral, and used SVM, Convolutional Neural Network-Long-Short Term Memory (CNN-LSTM) and the multi-scale convolutional fusion neural network (MFCNN). The accuracy of low-level fusion and feature fusion models are improved by about 10% compared with single spectral models.
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Affiliation(s)
- Hongyong Leng
- School of Computer Science & Technology, Beijing Institute of Technology, Beijing 100081, China; College of Software, Xinjiang University, Urumqi 830046, Xinjiang, China
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi 830046, Xinjiang, China; Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, China; Xinjiang Cloud Computing Application Laboratory, Xinjiang Cloud Computing Engineering Technology Research Center, Karamay 834000, China.
| | - Chen Chen
- College of Software, Xinjiang University, Urumqi 830046, Xinjiang, China; Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, China; Xinjiang Cloud Computing Application Laboratory, Xinjiang Cloud Computing Engineering Technology Research Center, Karamay 834000, China
| | - Fangfang Chen
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou 511483, Guangdong, China
| | - Zijun Du
- University of Macau, Macao Special Administrative Region, 999078, China
| | - Jiajia Chen
- Changji Vocational and Technical College, Changji 831100, China
| | - Bo Yang
- The Fourth Affiliated Hospital of Wulumqi, Urumqi 830046, China
| | - Enguang Zuo
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Meng Xiao
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi 830046, Xinjiang, China; College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Pei Liu
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
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17
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A novel fast method for identifying the origin of Maojian using NIR spectroscopy with deep learning algorithms. Sci Rep 2022; 12:21418. [PMID: 36496531 PMCID: PMC9741623 DOI: 10.1038/s41598-022-25671-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 12/02/2022] [Indexed: 12/13/2022] Open
Abstract
Maojian is one of China's traditional famous teas. There are many Maojian-producing areas in China. Because of different producing areas and production processes, different Maojian have different market prices. Many merchants will mix Maojian in different regions for profit, seriously disrupting the healthy tea market. Due to the similar appearance of Maojian produced in different regions, it is impossible to make a quick and objective distinction. It often requires experienced experts to identify them through multiple steps. Therefore, it is of great significance to develop a rapid and accurate method to identify different regions of Maojian to promote the standardization of the Maojian market and the development of detection technology. In this study, we propose a new method based on Near infra-red (NIR) with deep learning algorithms to distinguish different origins of Maojian. In this experiment, the NIR spectral data of Maojian from different origins are combined with the back propagation neural network (BPNN), improved AlexNet, and improved RepSet models for classification. Among them, improved RepSet has the highest accuracy of 99.30%, which is 8.67% and 0.70% higher than BPNN and improved AlexNet, respectively. The overall results show that it is feasible to use NIR and deep learning methods to quickly and accurately identify Maojian from different origins and prove an effective alternative method to discriminate different origins of Maojian.
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18
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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.
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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
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19
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Duma ZS, Sihvonen T, Havukainen J, Reinikainen V, Reinikainen SP. Optimizing energy dispersive X-Ray Spectroscopy (EDS) image fusion to Scanning Electron Microscopy (SEM) images. Micron 2022; 163:103361. [DOI: 10.1016/j.micron.2022.103361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 09/20/2022] [Accepted: 09/26/2022] [Indexed: 11/06/2022]
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20
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Agustika DK, Mercuriani I, Purnomo CW, Hartono S, Triyana K, Iliescu DD, Leeson MS. Fourier transform infrared spectrum pre-processing technique selection for detecting PYLCV-infected chilli plants. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 278:121339. [PMID: 35537256 DOI: 10.1016/j.saa.2022.121339] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 04/12/2022] [Accepted: 04/29/2022] [Indexed: 06/14/2023]
Abstract
Pre-processing is a crucial step in analyzing spectra from Fourier transform infrared (FTIR) spectroscopy because it can reduce unwanted noise and enhance system performance. Here, we present the results of pre-processing technique optimization to facilitate the detection of pepper yellow leaf curl virus (PYLCV)-infected chilli plants using FTIR spectroscopy. Optimization of a range of pre-processing techniques was undertaken, namely baseline correction, normalization (standard normal variate, vector, and min-max), and de-noising (Savitzky-Golay (SG) smoothing, 1st and 2 derivatives). The pre-processing was applied to the mid-infrared spectral range (4000 - 400 cm-1) and the biofingerprint region (1800 - 900 cm-1) then the discrete wavelet transform (DWT) was used for dimension reduction. The pre-processed data were then used as an input for classification using a multilayer perceptron neural network, a support vector machine, and linear discriminant analysis. The pre-processing method with the highest classification model accuracy was selected for the further use in the processing. It was seen that only the SG 1st derivative method applied to both wavenumber ranges could produce 100% accuracy. This result was supported by principal component analysis clustering. Thus, we have demonstrated that by using the right pre-processing technique, classification success can be increased, and the process simplified by optimization and minimization of the technique used.
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Affiliation(s)
- Dyah K Agustika
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK; Department of Physics Education, Universitas Negeri Yogyakarta, Yogyakarta, 55281 Indonesia.
| | - Ixora Mercuriani
- Department of Biology Education, Universitas Negeri Yogyakarta, Yogyakarta, 55281 Indonesia
| | - Chandra W Purnomo
- Department of Chemical Engineering, Universitas Gadjah Mada, Sekip Utara Yogyakarta, 55281 Indonesia
| | - Sedyo Hartono
- Department of Plant Protection, Faculty of Agriculture, Universitas Gadjah Mada. Jl, Flora 1, Bulaksumur, Sleman 55281, Yogyakarta
| | - Kuwat Triyana
- Department of Physics, Universitas Gadjah Mada, Sekip Utara Yogyakarta, 55281 Indonesia
| | - Doina D Iliescu
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK
| | - Mark S Leeson
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK.
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21
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Liu P, Guo W, Su Y, Chen C, Ma Y, Ma P, Chen C, Lv X. Multi-Omics Analysis of GNL3L Expression, Prognosis, and Immune Value in Pan-Cancer. Cancers (Basel) 2022; 14:cancers14194595. [PMID: 36230520 PMCID: PMC9558978 DOI: 10.3390/cancers14194595] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/09/2022] [Accepted: 09/14/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Guanine nucleotide-binding protein-like 3-like (GNL3L) is a novel GTP-binding nucleolar protein. In this study, we analyzed the expression, prognosis, and immune roles of GNL3L in pan-cancer from multiple omics analyses. The final results showed that GNL3L is differentially expressed in a variety of cancers, plays a prognostic role, and has good immune value. Moreover, GNL3L may affect the occurrence of cancer through processes such as ribonucleoprotein, ribosomal RNA processing, and cell proliferation. At the same time, we established an esophageal cancer (ESCA) prediction model with strong predictive ability and proved that GNL3L can significantly affect the proliferation ability of esophageal cancer cells through clone formation assays. In conclusion, GNL3L is an important biomarker. Abstract Guanine nucleotide-binding protein-like 3-like protein (GNL3L) is a novel, evolutionarily conserved, GTP-binding nucleolar protein. This study aimed to investigate the expression, prognosis, and immune value of GNL3L in pan-cancer from multiple omics analyses. Firstly, the expression and prognostic value of GNL3L in pan-cancer were discussed using the TIMER2 database, the GEPIA database, the cBioportal database, COX regression analysis, and enrichment analysis. The association of GNL3L with tumor mutational burden (TMB), tumor microsatellite instability (MSI), mismatch repair (MMR) genes, and immune cells was then analyzed. Finally, an esophageal cancer (ESCA) prediction model was established, and GNL3L clone formation assays were performed. The final results showed that GNL3L is differentially expressed in the vast majority of cancers, is associated with the prognosis of various cancers, and may affect cancer occurrence through processes such as ribonucleoprotein, ribosomal RNA processing, and cell proliferation. At the same time, it was found that the correlation between GNL3L and TMB, MSI, MMR, and various immune cells is significant. The established ESCA prediction model had a strong predictive ability, and GNL3L could significantly affect the proliferation of esophageal cancer cells. In conclusion, GNL3L may serve as an important prognostic biomarker and play an immunomodulatory role in tumors.
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Affiliation(s)
- Pei Liu
- College of Information Science, Engineering Xinjiang University, Urumqi 830046, China
- College of Software, Xinjiang University, Urumqi 830046, China
| | - Wenjia Guo
- Xinjiang Medical University Affiliated Tumor Hospital, Urumqi 830011, China
| | - Ying Su
- College of Software, Xinjiang University, Urumqi 830046, China
| | - Chen Chen
- College of Information Science, Engineering Xinjiang University, Urumqi 830046, China
- Xinjiang Cloud Computing Application Laboratory, Karamay 834099, China
| | - Yuhua Ma
- Karamay Central Hospital, Karamay 834099, China
| | - Ping Ma
- College of Software, Xinjiang University, Urumqi 830046, China
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi 830046, China
- Correspondence: (C.C.); (X.L.)
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi 830046, China
- Correspondence: (C.C.); (X.L.)
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22
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MDFNet: application of multimodal fusion method based on skin image and clinical data to skin cancer classification. J Cancer Res Clin Oncol 2022:10.1007/s00432-022-04180-1. [PMID: 35918465 DOI: 10.1007/s00432-022-04180-1] [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: 05/15/2022] [Accepted: 06/27/2022] [Indexed: 10/16/2022]
Abstract
PURPOSE Skin cancer is one of the ten most common cancer types in the world. Early diagnosis and treatment can effectively reduce the mortality of patients. Therefore, it is of great significance to develop an intelligent diagnosis system for skin cancer. According to the survey, at present, most intelligent diagnosis systems of skin cancer only use skin image data, but the multi-modal cross-fusion analysis using image data and patient clinical data is limited. Therefore, to further explore the complementary relationship between image data and patient clinical data, we propose multimode data fusion diagnosis network (MDFNet), a framework for skin cancer based on data fusion strategy. METHODS MDFNet establishes an effective mapping among heterogeneous data features, effectively fuses clinical skin images and patient clinical data, and effectively solves the problems of feature paucity and insufficient feature richness that only use single-mode data. RESULTS The experimental results present that our proposed smart skin cancer diagnosis model has an accuracy of 80.42%, which is an improvement of about 9% compared with the model accuracy using only medical images, thus effectively confirming the unique fusion advantages exhibited by MDFNet. CONCLUSIONS This illustrates that MDFNet can not only be applied as an effective auxiliary diagnostic tool for skin cancer diagnosis, help physicians improve clinical decision-making ability and effectively improve the efficiency of clinical medicine diagnosis, but also its proposed data fusion method fully exerts the advantage of information convergence and has a certain reference value for the intelligent diagnosis of numerous clinical diseases.
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23
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Ma Y, Liang F, Zhu M, Chen C, Chen C, Lv X. FT-IR combined with PSO-CNN algorithm for rapid screening of cervical tumors. Photodiagnosis Photodyn Ther 2022; 39:103023. [PMID: 35868522 DOI: 10.1016/j.pdpdt.2022.103023] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 07/11/2022] [Accepted: 07/18/2022] [Indexed: 01/11/2023]
Abstract
Cervical cancer is the most common gynecological malignancy with a slow onset. Early and accurate identification of the stage of cervical cancer patients can greatly improve the cure rate. In this study, serum sample data were collected from patients with cervical cancer, CIN (cervical intraepithelial neoplasia) I, CIN II, CIN III and hysteromyoma using FT-IR technology. PSO-CNN model for early screening of cervical cancer was designed using a particle swarm algorithm to automatically build a CNN structure with variable number of layers and variable layer class parameters. The experimental results showed that PSO-CNN was the best compared with the classical Lenet, AlexNet, VGG16 and GoogLeNet deep learning models, and the accuracy of PSO-CNN in discriminating five types of samples can reach 87.2%. This study showed that FT-IR technology combined with PSO-CNN model had great potential for non-invasive, rapid and accurate identification of patients with cervical cancer, and can provide a reference for intelligent diagnosis of other diseases.
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Affiliation(s)
- Yuhua Ma
- Department of Oncology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, China; Department of Pathology, Karamay central Hosptial of XinJiang Karamay, Karamay, Xinjiang Uygur Autonomous Region 834000, China
| | - Fei Liang
- Department of Pathology, Karamay central Hosptial of XinJiang Karamay, Karamay, Xinjiang Uygur Autonomous Region 834000, China
| | - Min Zhu
- College of Software, Xinjiang University, No. 448, Northwest Road, Shayibake District, Urumqi, Xinjiang 830046, China.
| | - Cheng Chen
- College of Software, Xinjiang University, No. 448, Northwest Road, Shayibake District, Urumqi, Xinjiang 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, No. 448, Northwest Road, Shayibake District, Urumqi, Xinjiang 830046, China
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24
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Wu J, Cui X, Kang Z, Wang S, Zhu G, Yang S, Wang S, Li H, Lu C, Lv X. Rapid diagnosis of diabetes based on ResNet and Raman spectroscopy. Photodiagnosis Photodyn Ther 2022; 39:103007. [PMID: 35817371 DOI: 10.1016/j.pdpdt.2022.103007] [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: 06/27/2022] [Revised: 07/06/2022] [Accepted: 07/07/2022] [Indexed: 10/17/2022]
Abstract
Diabetes mellitus is a global public health problem, and the epidemic situation in China is particularly serious. The prevalence of the disease has been increasing in recent years, and the number of patients is the highest in the world. Diabetes has become another chronic non-communicable disease that seriously endangers the health of our people after cardiovascular and cerebrovascular diseases and tumors. In this study, urine sample data were collected from 37 diabetic patients and 37 healthy volunteers using Raman spectroscopy. The collected data were preprocessed using an adaptive iterative reweighted penalized least squares (airPLS) algorithm and a polynomial Savitzky-Golay smoothing algorithm. After extracting features using principal component analysis (PCA) dimensionality reduction algorithm, ResNet, support vector machine (SVM) and linear discriminant analysis (LDA) classification models were selected to classify and identify diabetic patients and healthy controls. The results show that ResNet has the best discrimination effect, and the average accuracy, recall and F1-score can reach 84.28%, 86.20% and 84.02% respectively after five cross-validations, and the area under the subject working characteristic (ROC) curve is 0.93. The experimental results show that the model established in this paper is simple to operate, highly accurate and has good reference value for rapid screening of diabetes.
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Affiliation(s)
- Jianying Wu
- Xinjiang Key Laboratory for Luminescence Minerals and Optical Functional Materials, School of Physics and Electronic Engineering, Xinjiang Normal University, Urumqi, Xinjiang 830054, China
| | - Xinyue Cui
- Shihezi University, Shihezi, Xinjiang 832003, China
| | - Zhenping Kang
- College of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang 830046, China
| | - Shanshan Wang
- Xinjiang Medical University, Urumqi, Xinjiang 830054, China
| | - Guoqiang Zhu
- Xinjiang Medical University, Urumqi, Xinjiang 830054, China
| | - Shufen Yang
- Department of Nephrology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang 830001, China
| | - Shun Wang
- Department of Nephrology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang 830001, China
| | - Hongtao Li
- Xinjiang Medical University Affiliated Tumor Hospital, Urumqi, Xinjiang 830011, China
| | - Chen Lu
- Department of Nephrology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang 830011, China.
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi, Xinjiang 830046, China.
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25
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Jakubczyk P, Paja W, Pancerz K, Cebulski J, Depciuch J, Uzun Ö, Tarhan N, Guleken Z. Determination of idiopathic female infertility from infrared spectra of follicle fluid combined with gonadotrophin levels, multivariate analysis and machine learning methods. Photodiagnosis Photodyn Ther 2022; 38:102883. [PMID: 35487430 DOI: 10.1016/j.pdpdt.2022.102883] [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: 03/08/2022] [Revised: 04/20/2022] [Accepted: 04/25/2022] [Indexed: 01/24/2023]
Abstract
By in vitro fertilization, oocytes can be removed and the embryo can be cultured, and then trans cervically replaced when they reach cleavage or when the blastocyst stage. The characterization of the follicular fluid is important for the treatment process. Women who applied to the Academic Hospital in vitro fertilization (IVF) Center diagnosed with idiopathic female infertility (IFI) were sought in the patient group. Demographics and clinical gonadotropin measurements of the study population were recorded. Of the 116 follicular fluid samples (n=58 male-induced infertility; n=58 control) were analyzed using the FTIR system. To identify FTIR spectral characteristics of follicular fluids associated with an ovarian reserve and reproductive hormone levels from control and IFI, six machine learning methods and multivariate analysis were used. To assess the quantitative information about the total biochemical composition of a follicular fluid across various diagnoses. FTIR spectra showed a higher level of vibrations corresponding to lipids and a lower level of amide vibrations in the IFI group. Furthermore, the T square plot from Partial Last Square (PLS) analysis showed, that these vibrations can be used to distinguish IFI from the control group which was obtained by principal component analysis (PCA). Proteins and lipids play an important role in the development of IFI. The absorption dynamics of FTIR spectra showed wavenumbers with around 100% discrimination probability, which means, that the presented wavenumbers can be used as a spectroscopic marker of IFI. Also, six machine learning methods showed, that classification accuracy for the original set was from 93.75% to 100% depending on the learning algorithm used. These results can inform about IFI women's follicular fluid has biomacromolecular differentiation in their follicular fluid. By using a safe and effective tool for the characterization of changes in follicular fluid during in vitro fertilization, this study builds upon a comprehensive examination of the idiopathic female infertility remodeling process in human studies. We anticipate that this technology will be a valuable adjunct for clinical studies.
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Affiliation(s)
| | - Wiesław Paja
- Institute of Computer Science, University of Rzeszów, Poland
| | - Krzysztof Pancerz
- Institute of Technology and Computer Science, Academy of Zamosc, Poland
| | | | - Joanna Depciuch
- Institute of Nuclear Physics Polish Academy of Science, 31-342 Krakow, Poland, Turkey.
| | - Özgur Uzun
- Istanbul University-Cerrahpaşa, Cerrahpasa Faculty of Medicine, Department of Histology and Embryology, Istanbul, Turkey
| | | | - Zozan Guleken
- Uskudar University, Faculty of Medicine, Department of Physiology, Istanbul Turkey.
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