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Liu J, Wang P, Zhang H, Wu N. Distinguishing brain tumors by Label-free confocal micro-Raman spectroscopy. Photodiagnosis Photodyn Ther 2024; 45:104010. [PMID: 38336147 DOI: 10.1016/j.pdpdt.2024.104010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 01/31/2024] [Accepted: 02/06/2024] [Indexed: 02/12/2024]
Abstract
BACKGROUND Brain tumors have serious adverse effects on public health and social economy. Accurate detection of brain tumor types is critical for effective and proactive treatment, and thus improve the survival of patients. METHODS Four types of brain tumor tissue sections were detected by Raman spectroscopy. Principal component analysis (PCA) has been used to reduce the dimensionality of the Raman spectra data. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) methods were utilized to discriminate different types of brain tumors. RESULTS Raman spectra were collected from 40 brain tumors. Variations in intensity and shift were observed in the Raman spectra positioned at 721, 854, 1004, 1032, 1128, 1248, 1449 cm-1 for different brain tumor tissues. The PCA results indicated that glioma, pituitary adenoma, and meningioma are difficult to differentiate from each other, whereas acoustic neuroma is clearly distinguished from the other three tumors. Multivariate analysis including QDA and LDA methods showed the classification accuracy rate of the QDA model was 99.47 %, better than the rate of LDA model was 95.07 %. CONCLUSIONS Raman spectroscopy could be used to extract valuable fingerprint-type molecular and chemical information of biological samples. The demonstrated technique has the potential to be developed to a rapid, label-free, and intelligent approach to distinguish brain tumor types with high accuracy.
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Affiliation(s)
- Jie Liu
- Chongqing Medical University, Chongqing, 400016, China; Department of Neurosurgery, Chongqing General Hospital, Chongqing University, Chongqing, 401147, China; Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China; Chongqing School, University of Academy of Sciences, Chongqing, 400714, China
| | - Pan Wang
- Department of Neurosurgery, Chongqing General Hospital, Chongqing University, Chongqing, 401147, China
| | - Hua Zhang
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China
| | - Nan Wu
- Chongqing Medical University, Chongqing, 400016, China; Department of Neurosurgery, Chongqing General Hospital, Chongqing University, Chongqing, 401147, China; Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China; Chongqing School, University of Academy of Sciences, Chongqing, 400714, China.
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Zhou D, Xie J, Wang J, Zong J, Fang Q, Luo F, Zhang T, Ma H, Cao L, Yin H, Yin S, Li S. Establishment of a differential diagnosis method and an online prediction platform for AOSD and sepsis based on gradient boosting decision trees algorithm. Arthritis Res Ther 2023; 25:220. [PMID: 37974244 PMCID: PMC10652592 DOI: 10.1186/s13075-023-03207-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 11/03/2023] [Indexed: 11/19/2023] Open
Abstract
OBJECTIVE The differential diagnosis between adult-onset Still's disease (AOSD) and sepsis has always been a challenge. In this study, a machine learning model for differential diagnosis of AOSD and sepsis was developed and an online platform was developed to facilitate the clinical application of the model. METHODS All data were collected from 42 AOSD patients and 50 sepsis patients admitted to Affiliated Hospital of Xuzhou Medical University from December 2018 to December 2021. In addition, 5 AOSD patients and 10 sepsis patients diagnosed in our hospital after March 2022 were collected for external validation. All models were built using the scikit-learn library (version 1.0.2) in Python (version 3.9.7), and feature selection was performed using the SHAP (Shapley Additive exPlanation) package developed in Python. RESULTS The results showed that the gradient boosting decision tree(GBDT) optimization model based on arthralgia, ferritin × lymphocyte count, white blood cell count, ferritin × platelet count, and α1-acid glycoprotein/creatine kinase could well identify AOSD and sepsis. The training set interaction test (AUC: 0.9916, ACC: 0.9457, Sens: 0.9556, Spec: 0.9578) and the external validation also achieved satisfactory results (AUC: 0.9800, ACC: 0.9333, Sens: 0.8000, Spec: 1.000). We named this discrimination method AIADSS (AI-assisted discrimination of Still's disease and Sepsis) and created an online service platform for practical operation, the website is http://cppdd.cn/STILL1/ . CONCLUSION We created a method for the identification of AOSD and sepsis based on machine learning. This method can provide a reference for clinicians to formulate the next diagnosis and treatment plan.
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Affiliation(s)
- Dongmei Zhou
- The First Clinical College of Xuzhou Medical University, Xuzhou, 221004, China.
- Department of Rheumatology and Immunology, Affiliated Hospital of Xuzhou Medical University, Jiangsu Province, China.
| | - Jingzhi Xie
- Department of Rheumatology and Immunology, Affiliated Hospital of Xuzhou Medical University, Jiangsu Province, China
| | - Jiarui Wang
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, 221004, China
| | - Juan Zong
- Department of Rheumatology and Immunology, Affiliated Hospital of Xuzhou Medical University, Jiangsu Province, China
| | - Quanquan Fang
- Department of Rheumatology and Immunology, Affiliated Hospital of Xuzhou Medical University, Jiangsu Province, China
| | - Fei Luo
- Department of Rheumatology and Immunology, Affiliated Hospital of Xuzhou Medical University, Jiangsu Province, China
| | - Ting Zhang
- Department of Rheumatology and Immunology, Affiliated Hospital of Xuzhou Medical University, Jiangsu Province, China
| | - Hua Ma
- Department of Rheumatology and Immunology, Affiliated Hospital of Xuzhou Medical University, Jiangsu Province, China
| | - Lina Cao
- Department of Rheumatology and Immunology, Affiliated Hospital of Xuzhou Medical University, Jiangsu Province, China
| | - Hanqiu Yin
- Department of Rheumatology and Immunology, Affiliated Hospital of Xuzhou Medical University, Jiangsu Province, China.
| | - Songlou Yin
- Department of Rheumatology and Immunology, Affiliated Hospital of Xuzhou Medical University, Jiangsu Province, China.
| | - Shuyan Li
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, 221004, China.
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Shi Y, Wang Y, Hu X, Li Z, Huang X, Liang J, Zhang X, Zheng K, Zou X, Shi J. Nondestructive discrimination of analogous density foreign matter inside soy protein meat semi-finished products based on transmission hyperspectral imaging. Food Chem 2023; 411:135431. [PMID: 36681022 DOI: 10.1016/j.foodchem.2023.135431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 01/02/2023] [Accepted: 01/05/2023] [Indexed: 01/09/2023]
Abstract
Analogous density foreign matter (ADFM) embedded in soy protein meat semi-finished (SFSPM) is hidden by SFSPM and has similar acoustic impedance features to SFSPM, which makes non-destructive testing techniques such as computer vision (CV), reflectance spectroscopy and ultrasound imaging inappropriate for ADFM, which not only seriously affects the quality of soy protein meat (SPM) products but also increases the safety risk to consumers. In this study, to locate and separate ADFM by using transmission hyperspectral imaging (T-HSI) technique which is sensitive to chemical composition and highlight internal contours. The optimal discrimination model SVM + PCA + MSC + SPA was constructed using transmission spectral information with an accuracy of 95.00 %. The visualization results based on the optimal model showed clearer localization results than CV and ultrasound imaging. The study demonstrated that the advantages of T-HSI technology in detecting and locating ADFM inside SFSPM, which provides a basis for improving the production quality and safety of SPM.
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Affiliation(s)
- Yu Shi
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Yueying Wang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Xuetao Hu
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Zhihua Li
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Xiaowei Huang
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Jing Liang
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Xinai Zhang
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Kaiyi Zheng
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Xiaobo Zou
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China; International Joint Research Laboratory of Intelligent Agriculture and Agri-products Processing, Jiangsu University, Zhenjiang 212013, China
| | - Jiyong Shi
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China; International Joint Research Laboratory of Intelligent Agriculture and Agri-products Processing, Jiangsu University, Zhenjiang 212013, China.
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Chung IM, Kim YJ, Moon HS, Han JG, Kong WS, Yarnes CT, Kim SH. Improved accuracy of geographical origin identification of shiitake grown in sawdust medium: A compound-specific isotope model-based pilot study. Food Chem 2022; 369:130955. [PMID: 34488129 DOI: 10.1016/j.foodchem.2021.130955] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 08/05/2021] [Accepted: 08/22/2021] [Indexed: 11/29/2022]
Abstract
In countries like South Korea and the USA, origin labeling of shiitake grown using imported Chinese-inoculated medium is an issue. Therefore, we evaluated the use of compound-specific isotope analysis (CSIA) for the accurate identification of the geographical origin of shiitake (Korean, Chinese-inoculated medium, and Chinese); Chinese-inoculated medium shiitake were cultivated in Korea using inoculated sawdust medium from China. The CSIA-discriminant model showed an overall accuracy of 100% in the geographical classification of the original set and 96.4% for the cross-validated set. Glutamate and aspartate δ15N values were the most important variables for differentiating shiitake based on their origins. Compared to that observed upon using the bulk stable isotope analysis, the CSIA model was associated with significantly improved predictability of origin identification. Our findings elucidate the importance of isotope signatures in developing a reliable origin labeling method for shiitake cultured on the sawdust medium for the global market.
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Affiliation(s)
- Ill-Min Chung
- Department of Crop Science, College of Sanghuh Life Science, Konkuk University, Seoul 05029, Republic of Korea
| | - Yun-Ju Kim
- Department of Crop Science, College of Sanghuh Life Science, Konkuk University, Seoul 05029, Republic of Korea
| | - Hee-Sung Moon
- Department of Crop Science, College of Sanghuh Life Science, Konkuk University, Seoul 05029, Republic of Korea
| | - Jae-Gu Han
- National Institutes of Horticultural and Herbal Science, Rural Development Administration, 27709 Eumseong, Republic of Korea
| | - Won-Sik Kong
- National Institutes of Horticultural and Herbal Science, Rural Development Administration, 27709 Eumseong, Republic of Korea
| | - Christopher T Yarnes
- UC Davis Stable Isotope Facility, University of California, Davis, 1 Shields Avenue, Davis, CA 95616, USA
| | - Seung-Hyun Kim
- Department of Crop Science, College of Sanghuh Life Science, Konkuk University, Seoul 05029, Republic of Korea.
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Liu F, Bao G, Yan M, Lin G. A decision support system for primary headache developed through machine learning. PeerJ 2022; 10:e12743. [PMID: 35047235 PMCID: PMC8759354 DOI: 10.7717/peerj.12743] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 12/14/2021] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Primary headache is a disorder with a high incidence and low diagnostic accuracy; the incidence of migraine and tension-type headache ranks first among primary headaches. Artificial intelligence (AI) decision support systems have shown great potential in the medical field. Therefore, we attempt to use machine learning to build a clinical decision-making system for primary headaches. METHODS The demographic data and headache characteristics of 173 patients were collected by questionnaires. Decision tree, random forest, gradient boosting algorithm and support vector machine (SVM) models were used to construct a discriminant model and a confusion matrix was used to calculate the evaluation indicators of the models. Furthermore, we have carried out feature selection through univariate statistical analysis and machine learning. RESULTS In the models, the accuracy, F1 score were calculated through the confusion matrix. The logistic regression model has the best discrimination effect, with the accuracy reaching 0.84 and the area under the ROC curve also being the largest at 0.90. Furthermore, we identified the most important factors for distinguishing the two disorders through statistical analysis and machine learning: nausea/vomiting and photophobia/phonophobia. These two factors represent potential independent factors for the identification of migraines and tension-type headaches, with the accuracy reaching 0.74 and the area under the ROC curve being at 0.74. CONCLUSIONS Applying machine learning to the decision-making system for primary headaches can achieve a high diagnostic accuracy. Among them, the discrimination effect obtained by the integrated algorithm is significantly better than that of a single learner. Second, nausea/vomiting, photophobia/phonophobia may be the most important factors for distinguishing migraine from tension-type headaches.
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Affiliation(s)
- Fangfang Liu
- Shanghai Jiao Tong University, School of Medicine, Shanghai Ninth People’s Hospital, Shanghai, Huangpuqu, China
| | - Guanshui Bao
- Shanghai Jiao Tong University, School of Medicine, Shanghai Ninth People’s Hospital, Shanghai, Huangpuqu, China
| | - Mengxia Yan
- Shanghai Jiao Tong University, School of Medicine, Shanghai Ninth People’s Hospital, Shanghai, Huangpuqu, China
| | - Guiming Lin
- Shanghai Jiao Tong University, School of Medicine, Shanghai Ninth People’s Hospital, Shanghai, Huangpuqu, China
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Feng P, Li Y, Liao Z, Yao Z, Lin W, Xie S, Hu B, Huang C, Liu W, Xu H, Liu M, Gan W. An online alpha-thalassemia carrier discrimination model based on random forest and red blood cell parameters for low HbA 2 cases. Clin Chim Acta 2021; 525:1-5. [PMID: 34883090 DOI: 10.1016/j.cca.2021.12.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 12/02/2021] [Accepted: 12/02/2021] [Indexed: 11/15/2022]
Abstract
BACKGROUND Since screening of α-thalassemia carriers by low HbA2 has a low positive predictive value (PPV), the PPV was as low as 40.97% in our laboratory, other more effective screening methods need to be devised. This study aimed at developing a machine learning model by using red blood cell parameters to identify α-thalassemia carriers from low HbA2 patients. METHODS Laboratory data of 1213 patients with low HbA2 used for modeling was randomly divided into the training set (849 of 1213, 70%) and the internal validation set (364 of 1213, 30%). In addition, an external data set (n = 399) was used for model validation. Fourteen machine learning methods were applied to construct a discriminant model. Performance was evaluated with accuracy, sensitivity, specificity, etc. and compared with 7 previously published discriminant function formulae. RESULTS The optimal model was based on random forest with 5 clinical features. The PPV of the model was more than twice the PPV of HbA2, and the model had a high negative predictive value (NPV) at the same time. Compared with seven formulae in screening of α-thalassemia carriers, the model had a better accuracy (0.915), specificity (0.967), NPV (0.901), PPV (0.942) and area under the receiver operating characteristic curve (AUC, 0.948) in the independent test set. CONCLUSION Use of a random forest-based model enables rapid discrimination of α-thalassemia carriers from low HbA2 cases.
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Affiliation(s)
- Pinning Feng
- Department of Clinical Laboratory, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yuzhe Li
- Department of Clinical Laboratory, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zhihao Liao
- Department of Clinical Laboratory, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zhenrong Yao
- Department of Clinical Laboratory, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Wenbin Lin
- Department of Clinical Laboratory, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Shuhua Xie
- Department of Clinical Laboratory, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Beini Hu
- R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China
| | - Chencui Huang
- R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China
| | - Wei Liu
- R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China
| | - Hongxu Xu
- Department of Clinical Laboratory, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Min Liu
- Department of Clinical Laboratory, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
| | - Wenjia Gan
- Department of Clinical Laboratory, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
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Chung IM, Kim YJ, Moon HS, Chi HY, Kim SH. Long-term isotopic model study for ecofriendly rice (Oryza sativa L.) authentication: Updating a case study in South Korea. Food Chem 2021; 362:130215. [PMID: 34091166 DOI: 10.1016/j.foodchem.2021.130215] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 05/24/2021] [Accepted: 05/25/2021] [Indexed: 11/16/2022]
Abstract
To overcome the lack of consumer trust in ecofriendly products due to low reliability of ecofriendly certification and decreasing areas certified for growing ecofriendly agricultural products, alternative approaches for reliable certification are required. Isotopic-chemometric analysis has potential for determining organic authenticity, but previous studies have struggled to differentiate the authenticities of different rice types. The present study examined 5-year variations in δ13C and δ15N in ecofriendly and conventional rice sold at retail markets in South Korea, while assessing the feasibility of discriminant models for authentication of organic rice. Supporting vector machine analysis showed 4.4-14.6% better overall predictability of rice types than discriminant analysis and was effective in discriminating organic or conventional rice from pesticide-free rice, potentially enabling high-throughput screening to authenticate organic rice at marketplaces. Our findings provide reliable information for authenticating ecofriendly rice, with a potential to improve consumer safety and thus the confidence in organic products.
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Affiliation(s)
- Ill-Min Chung
- Department of Crop Science, College of Sanghuh Life Science, Konkuk University, Seoul 05029, Republic of Korea.
| | - Yun-Ju Kim
- Department of Crop Science, College of Sanghuh Life Science, Konkuk University, Seoul 05029, Republic of Korea.
| | - Hee-Sung Moon
- Department of Crop Science, College of Sanghuh Life Science, Konkuk University, Seoul 05029, Republic of Korea.
| | - Hee-Youn Chi
- Department of Crop Science, College of Sanghuh Life Science, Konkuk University, Seoul 05029, Republic of Korea.
| | - Seung-Hyun Kim
- Department of Crop Science, College of Sanghuh Life Science, Konkuk University, Seoul 05029, Republic of Korea.
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Khoder A, Dornaika F. An enhanced approach to the robust discriminant analysis and class sparsity based embedding. Neural Netw 2021; 136:11-16. [PMID: 33422928 DOI: 10.1016/j.neunet.2020.12.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Revised: 12/10/2020] [Accepted: 12/23/2020] [Indexed: 10/22/2022]
Abstract
In recent times, feature extraction attracted much attention in machine learning and pattern recognition fields. This paper extends and improves a scheme for linear feature extraction that can be used in supervised multi-class classification problems. Inspired by recent frameworks for robust sparse LDA and Inter-class sparsity, we propose a unifying criterion able to retain the advantages of these two powerful linear discriminant methods. We introduce an iterative alternating minimization scheme in order to estimate the linear transformation and the orthogonal matrix. The linear transformation is efficiently updated via the steepest descent gradient technique. The proposed framework is generic in the sense that it allows the combination and tuning of other linear discriminant embedding methods. We used our proposed method to fine tune the linear solutions delivered by two recent linear methods: RSLDA and RDA_FSIS. Experiments have been conducted on public image datasets of different types including objects, faces, and digits. The proposed framework compared favorably with several competing methods.
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Affiliation(s)
- A Khoder
- University of the Basque Country UPV/EHU, San Sebastian, Spain
| | - F Dornaika
- Henan University, Kaifeng, China; University of the Basque Country UPV/EHU, San Sebastian, Spain; IKERBASQUE, Basque Foundation for Science, Bilbao, Spain.
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Zhai T, Fu ZL, Qiu YB, Chen Q, Luo D, Chen K. Application of combined cerebrospinal fluid physicochemical parameters to detect intracranial infection in neurosurgery patients. BMC Neurol 2020; 20:213. [PMID: 32460716 PMCID: PMC7251726 DOI: 10.1186/s12883-020-01781-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Accepted: 05/12/2020] [Indexed: 02/07/2023] Open
Abstract
Routine test of cerebrospinal fluid (CSF), such as glucose concentrations, chloride ion, protein and leukocyte, as well as color, turbidity and clot, were important indicators for intracranial infection. However, there were no models to predict the intracranial infection with these parameters. We collected data of 221 cases with CSF positive-culture and 50 cases with CSF negative culture from January 1, 2016 to December 31, 2018 in the First Affiliated Hospital of Nanchang University, China. SPSS17.0 software was used to establish the model by adopting seven described indicators, and P < 0.05 was considered as statistically significant. Meanwhile, 40 cases with positive-culture and 10 cases with negative-culture were selected to verify the sensitivity and specificity of the model. The results showed that each parameter was significant in the model establishment (P < 0.05). To extract the above seven parameters, the interpretation model C was established, and C = 0.952–0.183 × glucose value (mmol/L) - 0.024 × chloride ion value (mmol/L)- 0.000122 × protein value (mg/L) - 0.0000859 × number of leukocytes per microliter (× 106/L) + 1.354 × color number code + 0.236 × turbidity number code + 0.691 × clot number code. In addition, the diagnostic sensitivity and specificity of the model were 85.0 and 100%, respectively. The combining application of seven physicochemical parameters of CSF might be of great value in the diagnosis of intracranial infection for adult patients.
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Affiliation(s)
- Tiantian Zhai
- Clinical Laboratory, the First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, China.,School of Public health of Nanchang University, Nanchang, 330006, Jiangxi, China
| | - Zhong Lian Fu
- Department of Preschool education and special education, Yuzhang Normal College, Nanchang, 330103, Jiangxi, China
| | - Yan Bing Qiu
- School of Public health of Nanchang University, Nanchang, 330006, Jiangxi, China
| | - Qiang Chen
- Clinical Laboratory, the First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, China
| | - Dong Luo
- Clinical Laboratory, the First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, China
| | - Kaisen Chen
- Clinical Laboratory, the First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, China.
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