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Flynn CD, Chang D. Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities. Diagnostics (Basel) 2024; 14:1100. [PMID: 38893627 PMCID: PMC11172335 DOI: 10.3390/diagnostics14111100] [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: 05/05/2024] [Revised: 05/22/2024] [Accepted: 05/24/2024] [Indexed: 06/21/2024] Open
Abstract
The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the potential to revolutionize diagnostic methodologies by offering rapid, accurate, and accessible health assessment directly at the patient level. This review paper explores the transformative impact of AI technologies on POC biosensing, emphasizing recent computational advancements, ongoing challenges, and future prospects in the field. We provide an overview of core biosensing technologies and their use at the POC, highlighting ongoing issues and challenges that may be solved with AI. We follow with an overview of AI methodologies that can be applied to biosensing, including machine learning algorithms, neural networks, and data processing frameworks that facilitate real-time analytical decision-making. We explore the applications of AI at each stage of the biosensor development process, highlighting the diverse opportunities beyond simple data analysis procedures. We include a thorough analysis of outstanding challenges in the field of AI-assisted biosensing, focusing on the technical and ethical challenges regarding the widespread adoption of these technologies, such as data security, algorithmic bias, and regulatory compliance. Through this review, we aim to emphasize the role of AI in advancing POC biosensing and inform researchers, clinicians, and policymakers about the potential of these technologies in reshaping global healthcare landscapes.
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Affiliation(s)
- Connor D. Flynn
- Department of Chemistry, Weinberg College of Arts & Sciences, Northwestern University, Evanston, IL 60208, USA
| | - Dingran Chang
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL 60208, USA
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Fukuzawa F, Yanagita Y, Yokokawa D, Uchida S, Yamashita S, Li Y, Shikino K, Tsukamoto T, Noda K, Uehara T, Ikusaka M. Importance of Patient History in Artificial Intelligence-Assisted Medical Diagnosis: Comparison Study. JMIR MEDICAL EDUCATION 2024; 10:e52674. [PMID: 38602313 PMCID: PMC11024399 DOI: 10.2196/52674] [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: 09/12/2023] [Revised: 01/31/2024] [Accepted: 02/15/2024] [Indexed: 04/12/2024]
Abstract
Background Medical history contributes approximately 80% to a diagnosis, although physical examinations and laboratory investigations increase a physician's confidence in the medical diagnosis. The concept of artificial intelligence (AI) was first proposed more than 70 years ago. Recently, its role in various fields of medicine has grown remarkably. However, no studies have evaluated the importance of patient history in AI-assisted medical diagnosis. Objective This study explored the contribution of patient history to AI-assisted medical diagnoses and assessed the accuracy of ChatGPT in reaching a clinical diagnosis based on the medical history provided. Methods Using clinical vignettes of 30 cases identified in The BMJ, we evaluated the accuracy of diagnoses generated by ChatGPT. We compared the diagnoses made by ChatGPT based solely on medical history with the correct diagnoses. We also compared the diagnoses made by ChatGPT after incorporating additional physical examination findings and laboratory data alongside history with the correct diagnoses. Results ChatGPT accurately diagnosed 76.6% (23/30) of the cases with only the medical history, consistent with previous research targeting physicians. We also found that this rate was 93.3% (28/30) when additional information was included. Conclusions Although adding additional information improves diagnostic accuracy, patient history remains a significant factor in AI-assisted medical diagnosis. Thus, when using AI in medical diagnosis, it is crucial to include pertinent and correct patient histories for an accurate diagnosis. Our findings emphasize the continued significance of patient history in clinical diagnoses in this age and highlight the need for its integration into AI-assisted medical diagnosis systems.
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Affiliation(s)
- Fumitoshi Fukuzawa
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
| | - Yasutaka Yanagita
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
| | - Daiki Yokokawa
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
| | - Shun Uchida
- Uchida Internal Medicine Clinic, Saitama-shi, Japan
| | - Shiho Yamashita
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
| | - Yu Li
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
| | - Kiyoshi Shikino
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
| | - Tomoko Tsukamoto
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
| | - Kazutaka Noda
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
| | - Takanori Uehara
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
| | - Masatomi Ikusaka
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
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Leme DEDC, de Oliveira C. Machine Learning Models to Predict Future Frailty in Community-Dwelling Middle-Aged and Older Adults: The ELSA Cohort Study. J Gerontol A Biol Sci Med Sci 2023; 78:2176-2184. [PMID: 37209408 PMCID: PMC10613015 DOI: 10.1093/gerona/glad127] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Indexed: 05/22/2023] Open
Abstract
BACKGROUND Machine learning (ML) models can be used to predict future frailty in the community setting. However, outcome variables for epidemiologic data sets such as frailty usually have an imbalance between categories, that is, there are far fewer individuals classified as frail than as nonfrail, adversely affecting the performance of ML models when predicting the syndrome. METHODS A retrospective cohort study with participants (50 years or older) from the English Longitudinal Study of Ageing who were nonfrail at baseline (2008-2009) and reassessed for the frailty phenotype at 4-year follow-up (2012-2013). Social, clinical, and psychosocial baseline predictors were selected to predict frailty at follow-up in ML models (Logistic Regression, Random Forest [RF], Support Vector Machine, Neural Network, K-nearest neighbor, and Naive Bayes classifier). RESULTS Of all the 4 378 nonfrail participants at baseline, 347 became frail at follow-up. The proposed combined oversampling and undersampling method to adjust imbalanced data improved the performance of the models, and RF had the best performance, with areas under the receiver-operating characteristic curve and the precision-recall curve of 0.92 and 0.97, respectively, specificity of 0.83, sensitivity of 0.88, and balanced accuracy of 85.5% for balanced data. Age, chair-rise test, household wealth, balance problems, and self-rated health were the most important frailty predictors in most of the models trained with balanced data. CONCLUSIONS ML proved useful in identifying individuals who became frail over time, and this result was made possible by balancing the data set. This study highlighted factors that may be useful in the early detection of frailty.
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Affiliation(s)
| | - Cesar de Oliveira
- Department of Epidemiology and Public Health, University College London, London, UK
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Ni X, Zhao H, Li R, Su H, Jiao J, Yang Z, Lv Y, Pang G, Sun M, Hu C, Yuan H. Development of a model for the prediction of biological age. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107686. [PMID: 37421874 DOI: 10.1016/j.cmpb.2023.107686] [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: 04/03/2023] [Revised: 06/04/2023] [Accepted: 06/20/2023] [Indexed: 07/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Rates of aging vary markedly among individuals, and biological age serves as a more reliable predictor of current health status than does chronological age. As such, the ability to predict biological age can support appropriate and timely active interventions aimed at improving coping with the aging process. However, the aging process is highly complex and multifactorial. Therefore, it is more scientific to construct a prediction model for biological age from multiple dimensions systematically. METHODS Physiological and biochemical parameters were evaluated to gage individual health status. Then, age-related indices were screened for inclusion in a model capable of predicting biological age. For subsequent modeling analyses, samples were divided into training and validation sets for subsequent deep learning model-based analyses (e.g. linear regression, lasso model, ridge regression, bayesian ridge regression, elasticity network, k-nearest neighbor, linear support vector machine, support vector machine, and decision tree models, and so on), with the model exhibiting the best ability to predict biological age thereby being identified. RESULTS First, we defined the individual biological age according to the individual health status. Then, after 22 candidate indices (DNA methylation, leukocyte telomere length, and specific physiological and biochemical indicators) were screened for inclusion in a model capable of predicting biological age, 14 age-related indices and gender were used to construct a model via the Bagged Trees method, which was found to be the most reliable qualitative prediction model for biological age (accuracy=75.6%, AUC=0.84) by comparing 30 different classification algorithm models. The most reliable quantitative predictive model for biological age was found to be the model developed using the Rational Quadratic method (R2=0.85, RMSE=8.731 years) by comparing 24 regression algorithm models. CONCLUSIONS Both qualitative model and quantitative model of biological age were successfully constructed from a multi-dimensional and systematic perspective. The predictive performance of our models was similar in both smaller and larger datasets, making it well-suited to predicting a given individual's biological age.
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Affiliation(s)
- Xiaolin Ni
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, PR China
| | - Hanqing Zhao
- College of Traditional Chinese Medicine, Hebei University, Baoding, 071000, PR China
| | - Rongqiao Li
- Jiangbin Hospital, Guangxi Zhuang Autonomous Region, Nanning, 530021, PR China
| | - Huabin Su
- Jiangbin Hospital, Guangxi Zhuang Autonomous Region, Nanning, 530021, PR China
| | - Juan Jiao
- Clinical Lab, The Seventh Medical Center of Chinese People's Liberation Army General Hospital, Beijing 100700, China
| | - Ze Yang
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, PR China
| | - Yuan Lv
- Jiangbin Hospital, Guangxi Zhuang Autonomous Region, Nanning, 530021, PR China
| | - Guofang Pang
- Jiangbin Hospital, Guangxi Zhuang Autonomous Region, Nanning, 530021, PR China
| | - Meiqi Sun
- College of Traditional Chinese Medicine, Hebei University, Baoding, 071000, PR China
| | - Caiyou Hu
- Jiangbin Hospital, Guangxi Zhuang Autonomous Region, Nanning, 530021, PR China.
| | - Huiping Yuan
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, PR China.
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Chakraborty N, Lawrence A, Campbell R, Yang R, Hammamieh R. Biomarker discovery process at binomial decision point (2BDP): Analytical pipeline to construct biomarker panel. Comput Struct Biotechnol J 2023; 21:4729-4742. [PMID: 37822559 PMCID: PMC10562676 DOI: 10.1016/j.csbj.2023.09.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 09/21/2023] [Accepted: 09/21/2023] [Indexed: 10/13/2023] Open
Abstract
A clinical incident is typically manifested by several molecular events; therefore, it seems logical that a successful diagnosis, prognosis, or stratification of a clinical landmark require multiple biomarkers. In this report, we presented a machine learning pipeline, namely "Biomarker discovery process at binomial decision point" (2BDP) that took an integrative approach in systematically curating independent variables (e.g., multiple molecular markers) to explain an output variable (e.g., clinical landmark) of binary in nature. In a logical sequence, 2BDP includes feature selection, unsupervised model development and cross validation. In the present work, the efficiency of 2BDP was demonstrated by finding three biomarker panels that independently explained three stages of Alzheimer's disease (AD) marked as Braak stages I, II and III, respectively. We designed three assortments from the entire cohort based on these Braak stages; subsequently, each assortment was split into two populations at Braak score I, II or III. 2BDP systematically integrated random forest and logistic regression fitting model to find biomarker panels with minimum features that explained these three assortments, e.g., significantly differentiated two populations segregated by Braak stage I, II or III, respectively. Thereafter, the efficacies of these panels were measured by the area under the curve (AUC) values of the receiver operating characteristic (ROC) plot. The AUC-ROC was calculated by two cross-validation methods. Final set of gene markers was a mix of novel and a priori established AD signatures. These markers were weighted by unique coefficients and linearly connected in a group of 2-10 to explain Braak stage I, II or III by AUC ≥ 0.8. Small sample size and a lack of distinctly recruited Training and Test sets were the limitations of the present undertaking; yet 2BDP demonstrated its capability to curate a panel of optimum numbers of biomarkers to describe the outcome variable with high efficacy.
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Affiliation(s)
- Nabarun Chakraborty
- Medical Readiness Systems Biology, Center for Military Psychiatry and Neuroscience (CMPN), Walter Reed Army Institute of Research, Silver Spring, MD, USA
| | - Alexander Lawrence
- Medical Readiness Systems Biology, Center for Military Psychiatry and Neuroscience (CMPN), Walter Reed Army Institute of Research, Silver Spring, MD, USA
- ORISE, Walter Reed Army Institute of Research, Silver Spring, MD, USA
| | - Ross Campbell
- Medical Readiness Systems Biology, Center for Military Psychiatry and Neuroscience (CMPN), Walter Reed Army Institute of Research, Silver Spring, MD, USA
- Geneva Foundation, Walter Reed Army Institute of Research, Silver Spring, MD, USA
| | - Ruoting Yang
- Medical Readiness Systems Biology, Center for Military Psychiatry and Neuroscience (CMPN), Walter Reed Army Institute of Research, Silver Spring, MD, USA
| | - Rasha Hammamieh
- Medical Readiness Systems Biology, Center for Military Psychiatry and Neuroscience (CMPN), Walter Reed Army Institute of Research, Silver Spring, MD, USA
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Hardt LM, Herrmann HJ, Reljic D, Jaensch P, Zerth J, Neurath MF, Zopf Y. Are Guideline Recommendations on Supportive Nutrition and Exercise Therapy for Cancer Patients Implemented in Clinical Routine? A National Survey with Real-Life Data. Nutrients 2023; 15:3172. [PMID: 37513591 PMCID: PMC10383016 DOI: 10.3390/nu15143172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 07/13/2023] [Accepted: 07/15/2023] [Indexed: 07/30/2023] Open
Abstract
Malnutrition and cancer cachexia are highly prevalent comorbidities of cancer, limiting patients' quality of life and being relevant to prognosis. International and national clinical guidelines recommend supportive nutrition and exercise therapy for cancer patients. However, there is little current epidemiological evidence on the implementation of these guideline recommendations in clinical routine. To close this data gap, a national survey in Germany using an online questionnaire was conducted. There were 261 of a total of 5074 contacted hospitals and medical offices who participated in the survey (5.1% response rate). The data indicated that nutrition and exercise therapy for cancer patients is so far inadequately implemented, with 59% of the respondents reporting nutrition therapy as an integral part of oncological treatment, 66.7% having a nutrition specialist/team, and 65.1% routinely conducting a screening for nutritional status. Only half of the participants stated that there are defined goals in nutrition therapy. The majority of respondents (85.8%) generally recommend exercise therapy, but only a few of them provide specific offers at their own institution (19.6%) or at cooperation partners (31.7%). In order to implement the recommended combined nutrition and exercise therapy as part of regular care, there is a need for nationwide availability of multidisciplinary nutrition teams and targeted offers of individualized exercise therapy. Health policy support would be important to create the structural, financial, and staff conditions for appropriate guideline implementation in order to achieve the optimal treatment of cancer patients.
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Affiliation(s)
- Luisa M Hardt
- Hector-Center for Nutrition, Exercise and Sports, Department of Medicine 1, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, 91054 Erlangen, Germany
- Department of Medicine 1, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, 91054 Erlangen, Germany
| | - Hans J Herrmann
- Hector-Center for Nutrition, Exercise and Sports, Department of Medicine 1, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, 91054 Erlangen, Germany
- Department of Medicine 1, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, 91054 Erlangen, Germany
| | - Dejan Reljic
- Hector-Center for Nutrition, Exercise and Sports, Department of Medicine 1, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, 91054 Erlangen, Germany
- Department of Medicine 1, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, 91054 Erlangen, Germany
| | - Peter Jaensch
- IDC Research Institute, SRH University Wilhelm Löhe, 90763 Fürth, Germany
| | - Jürgen Zerth
- IDC Research Institute, SRH University Wilhelm Löhe, 90763 Fürth, Germany
- Faculty of Social Work, Catholic University Eichstätt-Ingolstadt, 85071 Eichstätt, Germany
| | - Markus F Neurath
- Department of Medicine 1, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, 91054 Erlangen, Germany
| | - Yurdagül Zopf
- Hector-Center for Nutrition, Exercise and Sports, Department of Medicine 1, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, 91054 Erlangen, Germany
- Department of Medicine 1, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, 91054 Erlangen, Germany
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Borrelli A, Pecoraro M, Del Giudice F, Cristofani L, Messina E, Dehghanpour A, Landini N, Roberto M, Perotti S, Muscaritoli M, Santini D, Catalano C, Panebianco V. Standardization of Body Composition Status in Patients with Advanced Urothelial Tumors: The Role of a CT-Based AI-Powered Software for the Assessment of Sarcopenia and Patient Outcome Correlation. Cancers (Basel) 2023; 15:cancers15112968. [PMID: 37296930 DOI: 10.3390/cancers15112968] [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: 03/25/2023] [Revised: 05/26/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023] Open
Abstract
BACKGROUND Sarcopenia is a well know prognostic factor in oncology, influencing patients' quality of life and survival. We aimed to investigate the role of sarcopenia, assessed by a Computed Tomography (CT)-based artificial intelligence (AI)-powered-software, as a predictor of objective clinical benefit in advanced urothelial tumors and its correlations with oncological outcomes. METHODS We retrospectively searched patients with advanced urothelial tumors, treated with systemic platinum-based chemotherapy and an available total body CT, performed before and after therapy. An AI-powered software was applied to CT to obtain the Skeletal Muscle Index (SMI-L3), derived from the area of the psoas, long spine, and abdominal muscles, at the level of L3 on CT axial images. Logistic and Cox-regression modeling was implemented to explore the association of sarcopenic status and anthropometric features to the clinical benefit rate and survival endpoints. RESULTS 97 patients were included, 66 with bladder cancer and 31 with upper-tract urothelial carcinoma. Clinical benefit outcomes showed a linear positive association with all the observed body composition variables variations. The chances of not experiencing disease progression were positively associated with ∆_SMI-L3, ∆_psoas, and ∆_long spine muscle when they ranged from ~10-20% up to ~45-55%. Greater survival chances were matched by patients achieving a wider ∆_SMI-L3, ∆_abdominal and ∆_long spine muscle. CONCLUSIONS A CT-based AI-powered software body composition and sarcopenia analysis provide prognostic assessments for objective clinical benefits and oncological outcomes.
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Affiliation(s)
- Antonella Borrelli
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, 00161 Rome, Italy
| | - Martina Pecoraro
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, 00161 Rome, Italy
| | - Francesco Del Giudice
- Department of Maternal Infant and Urologic Sciences, Sapienza University of Rome, 00161 Rome, Italy
| | - Leonardo Cristofani
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, 00161 Rome, Italy
| | - Emanuele Messina
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, 00161 Rome, Italy
| | - Ailin Dehghanpour
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, 00161 Rome, Italy
| | - Nicholas Landini
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, 00161 Rome, Italy
| | - Michela Roberto
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, 00161 Rome, Italy
| | - Stefano Perotti
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, 00161 Rome, Italy
| | - Maurizio Muscaritoli
- Department of Translational and Precision Medicine, Sapienza University of Rome, 00161 Rome, Italy
| | - Daniele Santini
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, 00161 Rome, Italy
| | - Carlo Catalano
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, 00161 Rome, Italy
| | - Valeria Panebianco
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, 00161 Rome, Italy
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Zhang H, Yin M, Liu Q, Ding F, Hou L, Deng Y, Cui T, Han Y, Pang W, Ye W, Yue J, He Y. Machine and deep learning-based clinical characteristics and laboratory markers for the prediction of sarcopenia. Chin Med J (Engl) 2023; 136:967-973. [PMID: 37098831 PMCID: PMC10278711 DOI: 10.1097/cm9.0000000000002633] [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: 05/20/2022] [Indexed: 04/27/2023] Open
Abstract
BACKGROUND Sarcopenia is an age-related progressive skeletal muscle disorder involving the loss of muscle mass or strength and physiological function. Efficient and precise AI algorithms may play a significant role in the diagnosis of sarcopenia. In this study, we aimed to develop a machine learning model for sarcopenia diagnosis using clinical characteristics and laboratory indicators of aging cohorts. METHODS We developed models of sarcopenia using the baseline data from the West China Health and Aging Trend (WCHAT) study. For external validation, we used the Xiamen Aging Trend (XMAT) cohort. We compared the support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGB), and Wide and Deep (W&D) models. The area under the receiver operating curve (AUC) and accuracy (ACC) were used to evaluate the diagnostic efficiency of the models. RESULTS The WCHAT cohort, which included a total of 4057 participants for the training and testing datasets, and the XMAT cohort, which consisted of 553 participants for the external validation dataset, were enrolled in this study. Among the four models, W&D had the best performance (AUC = 0.916 ± 0.006, ACC = 0.882 ± 0.006), followed by SVM (AUC =0.907 ± 0.004, ACC = 0.877 ± 0.006), XGB (AUC = 0.877 ± 0.005, ACC = 0.868 ± 0.005), and RF (AUC = 0.843 ± 0.031, ACC = 0.836 ± 0.024) in the training dataset. Meanwhile, in the testing dataset, the diagnostic efficiency of the models from large to small was W&D (AUC = 0.881, ACC = 0.862), XGB (AUC = 0.858, ACC = 0.861), RF (AUC = 0.843, ACC = 0.836), and SVM (AUC = 0.829, ACC = 0.857). In the external validation dataset, the performance of W&D (AUC = 0.970, ACC = 0.911) was the best among the four models, followed by RF (AUC = 0.830, ACC = 0.769), SVM (AUC = 0.766, ACC = 0.738), and XGB (AUC = 0.722, ACC = 0.749). CONCLUSIONS The W&D model not only had excellent diagnostic performance for sarcopenia but also showed good economic efficiency and timeliness. It could be widely used in primary health care institutions or developing areas with an aging population. TRIAL REGISTRATION Chictr.org, ChiCTR 1800018895.
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Affiliation(s)
- He Zhang
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Mengting Yin
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Qianhui Liu
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Fei Ding
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Lisha Hou
- Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Yiping Deng
- Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Tao Cui
- Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, Sichuan 610041, China
| | - Yixian Han
- Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, Sichuan 610041, China
| | - Weiguang Pang
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning 110819, China
| | - Wenbin Ye
- Department of Geriatrics, Xiamen Hospital of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Xiamen, Fujian 361015, China
| | - Jirong Yue
- Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Yong He
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
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Jo Y, Yeo MK, Dao T, Kwon J, Yi H, Ryu D. Machine learning-featured Secretogranin V is a circulating diagnostic biomarker for pancreatic adenocarcinomas associated with adipopenia. Front Oncol 2022; 12:942774. [PMID: 36059698 PMCID: PMC9428794 DOI: 10.3389/fonc.2022.942774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 07/25/2022] [Indexed: 11/13/2022] Open
Abstract
Background Pancreatic cancer is one of the most fatal malignancies of the gastrointestinal cancer, with a challenging early diagnosis due to lack of distinctive symptoms and specific biomarkers. The exact etiology of pancreatic cancer is unknown, making the development of reliable biomarkers difficult. The accumulation of patient-derived omics data along with technological advances in artificial intelligence is giving way to a new era in the discovery of suitable biomarkers. Methods We performed machine learning (ML)-based modeling using four independent transcriptomic datasets, including GSE16515, GSE62165, GSE71729, and the pancreatic adenocarcinoma (PAC) dataset of the Cancer Genome Atlas. To find candidates for circulating biomarkers, we exported expression profiles of 1,703 genes encoding secretory proteins. Integrating three transcriptomic datasets into either a training or test set, ML-based modeling distinguishing PAC from normal was carried out. Another ML-model classifying long-lived and short-lived patients with PAC was also built to select prognosis-associated features. Finally, circulating level of SCG5 in the plasma was determined from the independent cohort (non-tumor = 25 and pancreatic cancer = 25). We also investigated the impact of SCG5 on adipocyte biology using recombinant protein. Results Three distinctive ML-classifiers selected 29-, 64- and 18-featured genes, recognizing the only common gene, SCG5. As per the prediction of ML-models, the SCG5 transcripts was significantly reduced in PAC and decreased further with the progression of the tumor, indicating its potential as a diagnostic as well as prognostic marker for PAC. External validation of SCG5 using plasma samples from patients with PAC confirmed that SCG5 was reduced significantly in patients with PAC when compared to controls. Interestingly, plasma SCG5 levels were correlated with the body mass index and age of donors, implying pancreas-originated SCG5 could regulate energy metabolism systemically. Additionally, analyses using publicly available Genotype-Tissue Expression datasets, including adipose tissue histology and pancreatic SCG5 expression, further validated the association between pancreatic SCG5 expression and the size of subcutaneous adipocytes in humans. However, we could not observe any definite effect of rSCG5 on the cultured adipocyte, in 2D in vitro culture. Conclusion Circulating SCG5, which may be associated with adipopenia, is a promising diagnostic biomarker for PAC.
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Affiliation(s)
- Yunju Jo
- Department of Molecular Cell Biology, Sungkyunkwan University (SKKU) School of Medicine, Suwon, South Korea
| | - Min-Kyung Yeo
- Department of Pathology, Chungnam National University School of Medicine, Daejeon, South Korea
| | - Tam Dao
- Department of Molecular Cell Biology, Sungkyunkwan University (SKKU) School of Medicine, Suwon, South Korea
| | - Jeongho Kwon
- Department of Molecular Cell Biology, Sungkyunkwan University (SKKU) School of Medicine, Suwon, South Korea
| | - Hyon‐Seung Yi
- Department of Medical Science, Chungnam National University School of Medicine, Daejeon, South Korea
- Laboratory of Endocrinology and Immune System, Chungnam National University School of Medicine, Daejeon, South Korea
- *Correspondence: Hyon‐Seung Yi, ; Dongryeol Ryu,
| | - Dongryeol Ryu
- Department of Molecular Cell Biology, Sungkyunkwan University (SKKU) School of Medicine, Suwon, South Korea
- *Correspondence: Hyon‐Seung Yi, ; Dongryeol Ryu,
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Kim EY, Lee JM. Transcriptional Control of Trpm6 by the Nuclear Receptor FXR. Int J Mol Sci 2022; 23:ijms23041980. [PMID: 35216094 PMCID: PMC8874704 DOI: 10.3390/ijms23041980] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 02/03/2022] [Accepted: 02/08/2022] [Indexed: 12/12/2022] Open
Abstract
Farnesoid x receptor (FXR) is a nuclear bile acid receptor that belongs to the nuclear receptor superfamily. It plays an essential role in bile acid biosynthesis, lipid and glucose metabolism, liver regeneration, and vertical sleeve gastrectomy. A loss of the FXR gene or dysregulations of FXR-mediated gene expression are associated with the development of progressive familial intrahepatic cholestasis, tumorigenesis, inflammation, and diabetes mellitus. Magnesium ion (Mg2+) is essential for mammalian physiology. Over 600 enzymes are dependent on Mg2+ for their activity. Here, we show that the Trpm6 gene encoding a Mg2+ channel is a direct FXR target gene in the intestinal epithelial cells of mice. FXR expressed in the intestinal epithelial cells is absolutely required for sustaining a basal expression of intestinal Trpm6 that can be robustly induced by the treatment of GW4064, a synthetic FXR agonist. Analysis of FXR ChIP-seq data revealed that intron regions of Trpm6 contain two prominent FXR binding peaks. Among them, the proximal peak from the transcription start site contains a functional inverted repeat 1 (IR1) response element that directly binds to the FXR-RXRα heterodimer. Based on these results, we proposed that an intestinal FXR-TRPM6 axis may link a bile acid signaling to Mg2+ homeostasis.
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Affiliation(s)
- Eun Young Kim
- Department of Biochemistry and Cell Biology, Cell and Matrix Research Institute, School of Medicine, Kyungpook National University, Daegu 41944, Korea;
| | - Jae Man Lee
- Department of Biochemistry and Cell Biology, Cell and Matrix Research Institute, School of Medicine, Kyungpook National University, Daegu 41944, Korea;
- BK21 FOUR KNU Biomedical Convergence Program, Department of Biomedical Science, Kyungpook National University, Daegu 41944, Korea
- Correspondence: ; Tel.: +82-53-420-4826
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