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Iwata T, Okura Y, Saeki M, Yoshikawa T. Optimization of Temperature Modulation for Gas Classification Based on Bayesian Optimization. SENSORS (BASEL, SWITZERLAND) 2024; 24:2941. [PMID: 38733048 PMCID: PMC11086154 DOI: 10.3390/s24092941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Revised: 05/02/2024] [Accepted: 05/03/2024] [Indexed: 05/13/2024]
Abstract
This study proposes an optimization method for temperature modulation in chemiresistor-type gas sensors based on Bayesian optimization (BO), and its applicability was investigated. As voltage for a sensor heater, our previously proposed waveform was employed, and the parameters determining the voltage range were optimized. Employing the Bouldin-Davies index (DBI) as an objective function (OBJ), BO was utilized to minimize the DBI calculated from a feature matrix built from the collected data followed by pre-processing. The sensor responses were measured using five test gases with five concentrations, amounting to 2500 data points per parameter set. After seven trials with four initial parameter sets (ten parameter sets were tested in total), the DBI was successfully reduced from 2.1 to 1.5. The classification accuracy for the test gases based on the support vector machine tends to increase with decreasing the DBI, indicating that the DBI acts as a good OBJ. Additionally, the accuracy itself increased from 85.4% to 93.2% through optimization. The deviation from the tendency that the accuracy increases with decreasing the DBI for some parameter sets was also discussed. Consequently, it was demonstrated that the proposed optimization method based on BO is promising for temperature modulation.
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Affiliation(s)
- Tatsuya Iwata
- Department of Electrical and Electronic Engineering, Toyama Prefectural University, Imizu 939-0398, Japan (T.Y.)
| | - Yuki Okura
- Department of Information Systems Engineering, Toyama Prefectural University, Imizu 939-0398, Japan;
| | - Maaki Saeki
- Department of Electrical and Electronic Engineering, Toyama Prefectural University, Imizu 939-0398, Japan (T.Y.)
| | - Takefumi Yoshikawa
- Department of Electrical and Electronic Engineering, Toyama Prefectural University, Imizu 939-0398, Japan (T.Y.)
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Zhou M, Wang Q, Lu X, Zhang P, Yang R, Chen Y, Xia J, Chen D. Exhaled breath and urinary volatile organic compounds (VOCs) for cancer diagnoses, and microbial-related VOC metabolic pathway analysis: a systematic review and meta-analysis. Int J Surg 2024; 110:1755-1769. [PMID: 38484261 PMCID: PMC10942174 DOI: 10.1097/js9.0000000000000999] [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: 09/13/2023] [Accepted: 12/04/2023] [Indexed: 03/17/2024]
Abstract
BACKGROUND The gradual evolution of the detection and quantification of volatile organic compounds (VOCs) has been instrumental in cancer diagnosis. The primary objective of this study was to assess the diagnostic potential of exhaled breath and urinary VOCs in cancer detection. As VOCs are indicative of tumor and human metabolism, our work also sought to investigate the metabolic pathways linked to the development of cancerous tumors. MATERIALS AND METHODS An electronic search was performed in the PubMed database. Original studies on VOCs within exhaled breath and urine for cancer detection with a control group were included. A meta-analysis was conducted using a bivariate model to assess the sensitivity and specificity of the VOCs for cancer detection. Fagan's nomogram was designed to leverage the findings from our diagnostic analysis for the purpose of estimating the likelihood of cancer in patients. Ultimately, MetOrigin was employed to conduct an analysis of the metabolic pathways associated with VOCs in relation to both human and/or microbiota. RESULTS The pooled sensitivity, specificity and the area under the curve for cancer screening utilizing exhaled breath and urinary VOCs were determined to be 0.89, 0.88, and 0.95, respectively. A pretest probability of 51% can be considered as the threshold for diagnosing cancers with VOCs. As the estimated pretest probability of cancer exceeds 51%, it becomes more appropriate to emphasize the 'ruling in' approach. Conversely, when the estimated pretest probability of cancer falls below 51%, it is more suitable to emphasize the 'ruling out' approach. A total of 14, 14, 6, and 7 microbiota-related VOCs were identified in relation to lung, colorectal, breast, and liver cancers, respectively. The enrichment analysis of volatile metabolites revealed a significant enrichment of butanoate metabolism in the aforementioned tumor types. CONCLUSIONS The analysis of exhaled breath and urinary VOCs showed promise for cancer screening. In addition, the enrichment analysis of volatile metabolites revealed a significant enrichment of butanoate metabolism in four tumor types, namely lung, colorectum, breast and liver. These findings hold significant implications for the prospective clinical application of multiomics correlation in disease management and the exploration of potential therapeutic targets.
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Affiliation(s)
- Min Zhou
- Department of Breast Surgery, The Affiliated Wuxi Maternity and Child Health Care Hospital of Nanjing Medical University, Wuxi Maternity and Child Health Care Hospital
| | - Qinghua Wang
- Research Institute for Reproductive Health and Genetic Diseases, Women’s Hospital of Jiangnan University
| | - Xinyi Lu
- Department of Breast Surgery, The Affiliated Wuxi Maternity and Child Health Care Hospital of Nanjing Medical University, Wuxi Maternity and Child Health Care Hospital
| | - Ping Zhang
- Department of Breast Surgery, The Affiliated Wuxi Maternity and Child Health Care Hospital of Nanjing Medical University, Wuxi Maternity and Child Health Care Hospital
| | - Rui Yang
- Research Institute for Reproductive Health and Genetic Diseases, Women’s Hospital of Jiangnan University
| | - Yu Chen
- Research Institute for Reproductive Health and Genetic Diseases, Women’s Hospital of Jiangnan University
| | - Jiazeng Xia
- Department of General Surgery and Translational Medicine Center, The Affiliated Wuxi No. 2 People’s Hospital of Nanjing Medical University, Jiangnan University Medical Center, Wuxi, People’s Republic of China
| | - Daozhen Chen
- Department of Breast Surgery, The Affiliated Wuxi Maternity and Child Health Care Hospital of Nanjing Medical University, Wuxi Maternity and Child Health Care Hospital
- Research Institute for Reproductive Health and Genetic Diseases, Women’s Hospital of Jiangnan University
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Matza LS, Howell TA, Fung ET, Janes SM, Seiden M, Hackshaw A, Nadauld L, Karn H, Chung KC. Health State Utilities Associated with False-Positive Cancer Screening Results. PHARMACOECONOMICS - OPEN 2024; 8:263-276. [PMID: 38189869 PMCID: PMC10884390 DOI: 10.1007/s41669-023-00443-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/14/2023] [Indexed: 01/09/2024]
Abstract
INTRODUCTION Early cancer detection can significantly improve patient outcomes and reduce mortality rates. Novel cancer screening approaches, including multi-cancer early detection tests, have been developed. Cost-utility analyses will be needed to examine their value, and these models require health state utilities. The purpose of this study was to estimate the disutility (i.e., decrease in health state utility) associated with false-positive cancer screening results. METHODS In composite time trade-off interviews using a 1-year time horizon, UK general population participants valued 10 health state vignettes describing cancer screening with true-negative or false-positive results. Each false-positive vignette described a common diagnostic pathway following a false-positive result suggesting lung, colorectal, breast, or pancreatic cancer. Every pathway ended with a negative result (no cancer detected). The disutility of each false positive was calculated as the difference between the true-negative and each false-positive health state, and because of the 1-year time horizon, each disutility can be interpreted as a quality-adjusted life-year decrement associated with each type of false-positive experience. RESULTS A total of 203 participants completed interviews (49.8% male; mean age = 42.0 years). The mean (SD) utility for the health state describing a true-negative result was 0.958 (0.065). Utilities for false-positive health states ranged from 0.847 (0.145) to 0.932 (0.059). Disutilities for false positives ranged from - 0.031 to - 0.111 (- 0.041 to - 0.111 for lung cancer; - 0.079 for colorectal cancer; - 0.031 to - 0.067 for breast cancer; - 0.048 to - 0.088 for pancreatic cancer). CONCLUSION All false-positive results were associated with a disutility. Greater disutility was associated with more invasive follow-up diagnostic procedures, longer duration of uncertainty regarding the eventual diagnosis, and perceived severity of the suspected cancer type. Utility values estimated in this study would be useful for economic modeling examining the value of cancer screening procedures.
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Affiliation(s)
| | | | - Eric T Fung
- GRAIL, LLC., a subsidiary of Illumina Inc., Menlo Park, CA, USA
| | - Sam M Janes
- UCL Respiratory, University College London, London, UK
| | - Michael Seiden
- Physician in Residence, GRAIL, LLC., Menlo Park, CA, USA
| | | | | | | | - Karen C Chung
- GRAIL, LLC., a subsidiary of Illumina Inc., Menlo Park, CA, USA
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Mehrotra S, Sharma S, Pandey RK. A journey from omics to clinicomics in solid cancers: Success stories and challenges. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2024; 139:89-139. [PMID: 38448145 DOI: 10.1016/bs.apcsb.2023.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
The word 'cancer' encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading cause of death worldwide, responsible for one in six deaths and hence imposes a significant burden on global healthcare systems. High-throughput omics technologies combined with advanced imaging tools, have revolutionized our ability to interrogate the molecular landscape of tumors and has provided unprecedented understanding of the disease. Yet, there is a gap between basic research discoveries and their translation into clinically meaningful therapies for improving patient care. To bridge this gap, there is a need to analyse the vast amounts of high dimensional datasets from multi-omics platforms. The integration of multi-omics data with clinical information like patient history, histological examination and imaging has led to the novel concept of clinicomics and may expedite the bench-to-bedside transition in cancer. The journey from omics to clinicomics has gained momentum with development of radiomics which involves extracting quantitative features from medical imaging data with the help of deep learning and artificial intelligence (AI) tools. These features capture detailed information about the tumor's shape, texture, intensity, and spatial distribution. Together, the related fields of multiomics, translational bioinformatics, radiomics and clinicomics may provide evidence-based recommendations tailored to the individual cancer patient's molecular profile and clinical characteristics. In this chapter, we summarize multiomics studies in solid cancers with a specific focus on breast cancer. We also review machine learning and AI based algorithms and their use in cancer diagnosis, subtyping, prognosis and predicting treatment resistance and relapse.
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Vassilenko V, Moura PC, Raposo M. Diagnosis of Carcinogenic Pathologies through Breath Biomarkers: Present and Future Trends. Biomedicines 2023; 11:3029. [PMID: 38002028 PMCID: PMC10669878 DOI: 10.3390/biomedicines11113029] [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: 10/04/2023] [Revised: 11/08/2023] [Accepted: 11/09/2023] [Indexed: 11/26/2023] Open
Abstract
The assessment of volatile breath biomarkers has been targeted with a lot of interest by the scientific and medical communities during the past decades due to their suitability for an accurate, painless, non-invasive, and rapid diagnosis of health states and pathological conditions. This paper reviews the most relevant bibliographic sources aiming to gather the most pertinent volatile organic compounds (VOCs) already identified as putative cancer biomarkers. Here, a total of 265 VOCs and the respective bibliographic sources are addressed regarding their scientifically proven suitability to diagnose a total of six carcinogenic diseases, namely lung, breast, gastric, colorectal, prostate, and squamous cell (oesophageal and laryngeal) cancers. In addition, future trends in the identification of five other forms of cancer, such as bladder, liver, ovarian, pancreatic, and thyroid cancer, through perspective volatile breath biomarkers are equally presented and discussed. All the results already achieved in the detection, identification, and quantification of endogenous metabolites produced by all kinds of normal and abnormal processes in the human body denote a promising and auspicious future for this alternative diagnostic tool, whose future passes by the development and employment of newer and more accurate collection and analysis techniques, and the certification for utilisation in real clinical scenarios.
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Affiliation(s)
- Valentina Vassilenko
- Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics (LIBPhys-UNL), Department of Physics, NOVA School of Science and Technology, NOVA University of Lisbon, Campus FCT-UNL, 2829-516 Caparica, Portugal;
| | - Pedro Catalão Moura
- Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics (LIBPhys-UNL), Department of Physics, NOVA School of Science and Technology, NOVA University of Lisbon, Campus FCT-UNL, 2829-516 Caparica, Portugal;
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Kokabi M, Tahir MN, Singh D, Javanmard M. Advancing Healthcare: Synergizing Biosensors and Machine Learning for Early Cancer Diagnosis. BIOSENSORS 2023; 13:884. [PMID: 37754118 PMCID: PMC10526782 DOI: 10.3390/bios13090884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 09/08/2023] [Accepted: 09/09/2023] [Indexed: 09/28/2023]
Abstract
Cancer is a fatal disease and a significant cause of millions of deaths. Traditional methods for cancer detection often have limitations in identifying the disease in its early stages, and they can be expensive and time-consuming. Since cancer typically lacks symptoms and is often only detected at advanced stages, it is crucial to use affordable technologies that can provide quick results at the point of care for early diagnosis. Biosensors that target specific biomarkers associated with different types of cancer offer an alternative diagnostic approach at the point of care. Recent advancements in manufacturing and design technologies have enabled the miniaturization and cost reduction of point-of-care devices, making them practical for diagnosing various cancer diseases. Furthermore, machine learning (ML) algorithms have been employed to analyze sensor data and extract valuable information through the use of statistical techniques. In this review paper, we provide details on how various machine learning algorithms contribute to the ongoing development of advanced data processing techniques for biosensors, which are continually emerging. We also provide information on the various technologies used in point-of-care cancer diagnostic biosensors, along with a comparison of the performance of different ML algorithms and sensing modalities in terms of classification accuracy.
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Affiliation(s)
| | | | | | - Mehdi Javanmard
- Department of Electrical and Computer Engineering, Rutgers the State University of New Jersey, Piscataway, NJ 08854, USA; (M.K.); (M.N.T.); (D.S.)
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Bittla P, Kaur S, Sojitra V, Zahra A, Hutchinson J, Folawemi O, Khan S. Exploring Circulating Tumor DNA (CtDNA) and Its Role in Early Detection of Cancer: A Systematic Review. Cureus 2023; 15:e45784. [PMID: 37745752 PMCID: PMC10516512 DOI: 10.7759/cureus.45784] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 09/22/2023] [Indexed: 09/26/2023] Open
Abstract
There is a significant increase in the need for an efficient screening method that might identify cancer at an early stage and could improve patients' long-term survival due to the continued rise in cancer incidence and associated mortality. One such effort involved using circulating tumor DNA (ctDNA) as a rescue agent for a non-invasive blood test that may identify many tumors. A tumor marker called ctDNA is created by cells with the same DNA alterations. Due to its shorter half-life, it may be useful for both early cancer detection and real-time monitoring of tumor development, therapeutic response, and tumor outcomes. We obtained 156 papers from PUBMED using the MeSH approach in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) criteria and ten articles from additional online resources. After removing articles with irrelevant titles and screening the abstract and full text of the articles that contained information unrelated to or not specific to the title query using inclusion and exclusion criteria, 18 out of 166 articles were chosen for the quality check. Fourteen medium to high-quality papers were chosen out of the 18 publications to be included in the study design. The reviewed literature showed no significant utility of ctDNA in detecting early-stage tumors of size less than 1 cm diameter. Still, the ideal screening test would require the assay to detect a size <5 mm tumor, which is nearly impossible with the current data. The sensitivity and specificity of the assay ranged from 69% to 98% and 99%, respectively. Furthermore, CancerSEEK achieves tumor origin localization in 83% of cases, while targeted error correction sequencing (TEC-Seq) assays demonstrate a cancer detection rate ranging from 59% to 71%, depending on the type of cancer. However, it could be of great value as a prognostic indicator, and the levels are associated with progression-free survival (PFS) and overall survival (OS) rates, wherein the positive detection of ctDNA is associated with worse OS compared to the tumors detected through standard procedures, with an odds ratio (OS) of 4.83. We conclude that ctDNA could be better applied in cancer patients for prognosis, disease progression monitoring, and treatment outcomes compared to its use in early cancer detection. Due to its specific feature of recognizing the tumor-related mutations, it could be implemented as a supplemental tool to assess the nature of the tumor, grade, and size of the tumor and for predicting the outcomes by pre-operative and post-operative evaluation of the tumor marker, ctDNA, and thereby estimating PFS and OS depending on the level of marker present. A vast amount of research is required in early detection to determine the sensitivity, specificity, false positive rates, and false negative rates in evaluating its true potential as a screening tool. Even if the test could detect the mutations, an extensive workup for the search of tumor is required as the assay could only detect but cannot localize the disease. Establishing the clinical validity and utility of ctDNA is imperative for its implementation in future clinical practice.
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Affiliation(s)
- Parikshit Bittla
- Internal Medicine, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA
| | - Simran Kaur
- Internal Medicine, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA
| | - Vani Sojitra
- Internal Medicine, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA
| | - Anam Zahra
- Surgery, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA
| | - Jhenelle Hutchinson
- Internal Medicine, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA
| | - Oluwa Folawemi
- Internal Medicine, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA
| | - Safeera Khan
- Internal Medicine, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA
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Liu J, Chen H, Li Y, Fang Y, Guo Y, Li S, Xu J, Jia Z, Zou J, Liu G, Xu H, Wang T, Wang D, Jiang Y, Wang Y, Tang X, Qiao G, Zhou Y, Bai L, Zhou R, Lu C, Wen H, Li J, Huang Y, Zhang S, Feng Y, Chen H, Xu S, Zhang B, Liu Z, Wang X. A novel non-invasive exhaled breath biopsy for the diagnosis and screening of breast cancer. J Hematol Oncol 2023; 16:63. [PMID: 37328852 PMCID: PMC10276488 DOI: 10.1186/s13045-023-01459-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 05/25/2023] [Indexed: 06/18/2023] Open
Abstract
BACKGROUND Early detection is critical for improving the survival of breast cancer (BC) patients. Exhaled breath testing as a non-invasive technique might help to improve BC detection. However, the breath test accuracy for BC diagnosis is unclear. METHODS This multi-center cohort study consecutively recruited 5047 women from four areas of China who underwent BC screening. Breath samples were collected through standardized breath collection procedures. Volatile organic compound (VOC) markers were identified from a high-throughput breathomics analysis by the high-pressure photon ionization-time-of-flight mass spectrometry (HPPI-TOFMS). Diagnostic models were constructed using the random forest algorithm in the discovery cohort and tested in three external validation cohorts. RESULTS A total of 465 (9.21%) participants were identified with BC. Ten optimal VOC markers were identified to distinguish the breath samples of BC patients from those of non-cancer women. A diagnostic model (BreathBC) consisting of 10 optimal VOC markers showed an area under the curve (AUC) of 0.87 in external validation cohorts. BreathBC-Plus, which combined 10 VOC markers with risk factors, achieved better performance (AUC = 0.94 in the external validation cohorts), superior to that of mammography and ultrasound. Overall, the BreathBC-Plus detection rates were 96.97% for ductal carcinoma in situ, 85.06%, 90.00%, 88.24%, and 100% for stages I, II, III, and IV BC, respectively, with a specificity of 87.70% in the external validation cohorts. CONCLUSIONS This is the largest study on breath tests to date. Considering the easy-to-perform procedure and high accuracy, these findings exemplify the potential applicability of breath tests in BC screening.
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Affiliation(s)
- Jiaqi Liu
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuannanli, Chaoyang, Beijing, 100021, People's Republic of China
- State Key Laboratory of Molecular Oncology, National Cancer Center /National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuannanli, Chaoyang, Beijing, 100021, People's Republic of China
| | - Haibin Chen
- Breax Laboratory, PCAB Research Center of Breath and Metabolism, Beijing, 100071, People's Republic of China
| | - Yalun Li
- Department of Breast Surgery, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264000, People's Republic of China
| | - Yanman Fang
- Department of Breast Surgery, Guiyang Maternal and Child Healthcare Hospital, Guiyang, 550001, People's Republic of China
| | - Yang Guo
- Department of Breast Surgery, Yanqing Maternal and Child Healthcare Hospital of Beijing, Beijing, 101399, People's Republic of China
| | - Shuangquan Li
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People's Republic of China
| | - Juan Xu
- Department of Breast Surgery, Daxing Maternal and Child Healthcare Hospital of Beijing, Beijing, 100162, People's Republic of China
| | - Ziqi Jia
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuannanli, Chaoyang, Beijing, 100021, People's Republic of China
| | - Jiali Zou
- Department of Breast Surgery, Guiyang Maternal and Child Healthcare Hospital, Guiyang, 550001, People's Republic of China
| | - Gang Liu
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuannanli, Chaoyang, Beijing, 100021, People's Republic of China
| | - Hengyi Xu
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuannanli, Chaoyang, Beijing, 100021, People's Republic of China
- Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100005, People's Republic of China
| | - Tao Wang
- Department of Neurosurgery, Xuanwu Hospital, China International Neuroscience Institute, National Center for Neurological Disorders, Capital Medical University, Beijing, 100053, People's Republic of China
| | - Dingyuan Wang
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuannanli, Chaoyang, Beijing, 100021, People's Republic of China
| | - Yiwen Jiang
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuannanli, Chaoyang, Beijing, 100021, People's Republic of China
- Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100005, People's Republic of China
| | - Yang Wang
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuannanli, Chaoyang, Beijing, 100021, People's Republic of China
| | - Xuejie Tang
- Department of Breast Surgery, Guiyang Maternal and Child Healthcare Hospital, Guiyang, 550001, People's Republic of China
| | - Guangdong Qiao
- Department of Breast Surgery, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264000, People's Republic of China
| | - Yeqing Zhou
- Department of Breast Surgery, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264000, People's Republic of China
| | - Lan Bai
- Department of Breast Surgery, Daxing Maternal and Child Healthcare Hospital of Beijing, Beijing, 100162, People's Republic of China
| | - Ran Zhou
- Department of Breast Surgery, Yanqing Maternal and Child Healthcare Hospital of Beijing, Beijing, 101399, People's Republic of China
| | - Can Lu
- Department of Breast Surgery, Daxing Maternal and Child Healthcare Hospital of Beijing, Beijing, 100162, People's Republic of China
| | - Hongwei Wen
- Department of Breast Surgery, Yanqing Maternal and Child Healthcare Hospital of Beijing, Beijing, 101399, People's Republic of China
| | - Jiayi Li
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuannanli, Chaoyang, Beijing, 100021, People's Republic of China
- Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100005, People's Republic of China
| | - Yansong Huang
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuannanli, Chaoyang, Beijing, 100021, People's Republic of China
- Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100005, People's Republic of China
| | - Shuo Zhang
- Department of Breast Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050019, Hebei, People's Republic of China
| | - Yong Feng
- Breax Laboratory, PCAB Research Center of Breath and Metabolism, Beijing, 100071, People's Republic of China
| | - Hongyan Chen
- State Key Laboratory of Molecular Oncology, National Cancer Center /National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuannanli, Chaoyang, Beijing, 100021, People's Republic of China
| | - Shouping Xu
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, 150081, People's Republic of China
| | - Bailin Zhang
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuannanli, Chaoyang, Beijing, 100021, People's Republic of China.
| | - Zhihua Liu
- State Key Laboratory of Molecular Oncology, National Cancer Center /National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuannanli, Chaoyang, Beijing, 100021, People's Republic of China.
| | - Xiang Wang
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuannanli, Chaoyang, Beijing, 100021, People's Republic of China.
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Non-invasive screening of breast cancer from fingertip smears-a proof of concept study. Sci Rep 2023; 13:1868. [PMID: 36725900 PMCID: PMC9892587 DOI: 10.1038/s41598-023-29036-7] [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: 06/30/2022] [Accepted: 01/30/2023] [Indexed: 02/03/2023] Open
Abstract
Breast cancer is a global health issue affecting 2.3 million women per year, causing death in over 600,000. Mammography (and biopsy) is the gold standard for screening and diagnosis. Whilst effective, this test exposes individuals to radiation, has limitations to its sensitivity and specificity and may cause moderate to severe discomfort. Some women may also find this test culturally unacceptable. This proof-of-concept study, combining bottom-up proteomics with Matrix Assisted Laser Desorption Ionisation Mass Spectrometry (MALDI MS) detection, explores the potential for a non-invasive technique for the early detection of breast cancer from fingertip smears. A cohort of 15 women with either benign breast disease (n = 5), early breast cancer (n = 5) or metastatic breast cancer (n = 5) were recruited from a single UK breast unit. Fingertips smears were taken from each patient and from each of the ten digits, either at the time of diagnosis or, for metastatic patients, during active treatment. A number of statistical analyses and machine learning approaches were investigated and applied to the resulting mass spectral dataset. The highest performing predictive method, a 3-class Multilayer Perceptron neural network, yielded an accuracy score of 97.8% when categorising unseen MALDI MS spectra as either the benign, early or metastatic cancer classes. These findings support the need for further research into the use of sweat deposits (in the form of fingertip smears or fingerprints) for non-invasive screening of breast cancer.
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Dong M, Cao L, Cui R, Xie Y. The connection between innervation and metabolic rearrangements in pancreatic cancer through serine. Front Oncol 2022; 12:992927. [PMID: 36582785 PMCID: PMC9793709 DOI: 10.3389/fonc.2022.992927] [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: 07/13/2022] [Accepted: 10/31/2022] [Indexed: 12/15/2022] Open
Abstract
Pancreatic cancer is a kind of aggressive tumor famous for its lethality and intractability, and pancreatic ductal adenocarcinoma is the most common type. Patients with pancreatic cancer often suffer a rapid loss of weight and abdominal neuropathic pain in their early stages and then go through cachexia in the advanced stage. These features of patients are considered to be related to metabolic reprogramming of pancreatic cancer and abundant nerve innervation responsible for the pain. With increasing literature certifying the relationship between nerves and pancreatic ductal adenocarcinoma (PDAC), more evidence point out that innervation's role is not limited to neuropathic pain but explore its anti/pro-tumor functions in PDAC, especially the neural-metabolic crosstalks. This review aims to unite pancreatic cancer's innervation and metabolic rearrangements with terminated published articles. Hopefully, this article could explore the pathogenesis of PDAC and further promote promising detecting or therapeutic measurements for PDAC according to the lavish innervation in PDAC.
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Affiliation(s)
- Mengmeng Dong
- Jilin Provincial Key Laboratory on Molecular and Chemical Genetics, The Second Hospital of Jilin University, Changchun, China
| | - Lidong Cao
- Department of Hepatobiliary and Pancreatic Surgery, Second Hospital of Jilin University, Changchun, China,Jilin Engineering Laboratory for Translational Medicine of Hepatobiliary and Pancreatic Diseases, Second Hospital of Jilin University, Changchun, China,Department of Hepatobiliary and Pancreatic Surgery, Zhejiang Provincial Peoples Hospital, Hangzhou, China
| | - Ranji Cui
- Jilin Provincial Key Laboratory on Molecular and Chemical Genetics, The Second Hospital of Jilin University, Changchun, China,*Correspondence: Ranji Cui, ; Yingjun Xie,
| | - Yingjun Xie
- Department of Hepatobiliary and Pancreatic Surgery, Second Hospital of Jilin University, Changchun, China,Jilin Engineering Laboratory for Translational Medicine of Hepatobiliary and Pancreatic Diseases, Second Hospital of Jilin University, Changchun, China,*Correspondence: Ranji Cui, ; Yingjun Xie,
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11
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Alafeef M, Pan D. Diagnostic Approaches For COVID-19: Lessons Learned and the Path Forward. ACS NANO 2022; 16:11545-11576. [PMID: 35921264 PMCID: PMC9364978 DOI: 10.1021/acsnano.2c01697] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 07/12/2022] [Indexed: 05/17/2023]
Abstract
Coronavirus disease 2019 (COVID-19) is a transmitted respiratory disease caused by the infection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although humankind has experienced several outbreaks of infectious diseases, the COVID-19 pandemic has the highest rate of infection and has had high levels of social and economic repercussions. The current COVID-19 pandemic has highlighted the limitations of existing virological tests, which have failed to be adopted at a rate to properly slow the rapid spread of SARS-CoV-2. Pandemic preparedness has developed as a focus of many governments around the world in the event of a future outbreak. Despite the largely widespread availability of vaccines, the importance of testing has not diminished to monitor the evolution of the virus and the resulting stages of the pandemic. Therefore, developing diagnostic technology that serves as a line of defense has become imperative. In particular, that test should satisfy three criteria to be widely adopted: simplicity, economic feasibility, and accessibility. At the heart of it all, it must enable early diagnosis in the course of infection to reduce spread. However, diagnostic manufacturers need guidance on the optimal characteristics of a virological test to ensure pandemic preparedness and to aid in the effective treatment of viral infections. Nanomaterials are a decisive element in developing COVID-19 diagnostic kits as well as a key contributor to enhance the performance of existing tests. Our objective is to develop a profile of the criteria that should be available in a platform as the target product. In this work, virus detection tests were evaluated from the perspective of the COVID-19 pandemic, and then we generalized the requirements to develop a target product profile for a platform for virus detection.
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Affiliation(s)
- Maha Alafeef
- Department of Chemical, Biochemical and Environmental
Engineering, University of Maryland Baltimore County, Interdisciplinary
Health Sciences Facility, 1000 Hilltop Circle, Baltimore, Maryland 21250,
United States
- Departments of Diagnostic Radiology and Nuclear
Medicine and Pediatrics, Center for Blood Oxygen Transport and Hemostasis,
University of Maryland Baltimore School of Medicine, Health Sciences
Research Facility III, 670 W Baltimore Street, Baltimore, Maryland 21201,
United States
- Department of Bioengineering, the
University of Illinois at Urbana−Champaign, Urbana, Illinois 61801,
United States
- Biomedical Engineering Department, Jordan
University of Science and Technology, Irbid 22110,
Jordan
| | - Dipanjan Pan
- Department of Chemical, Biochemical and Environmental
Engineering, University of Maryland Baltimore County, Interdisciplinary
Health Sciences Facility, 1000 Hilltop Circle, Baltimore, Maryland 21250,
United States
- Departments of Diagnostic Radiology and Nuclear
Medicine and Pediatrics, Center for Blood Oxygen Transport and Hemostasis,
University of Maryland Baltimore School of Medicine, Health Sciences
Research Facility III, 670 W Baltimore Street, Baltimore, Maryland 21201,
United States
- Department of Bioengineering, the
University of Illinois at Urbana−Champaign, Urbana, Illinois 61801,
United States
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12
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Orrantia-Borunda E, Acuña-Aguilar LE, Ramírez-Valdespino CA. Nanomaterials for Breast Cancer. Breast Cancer 2022. [DOI: 10.36255/exon-publications-breast-cancer-nanomaterials] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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13
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Scheepers MHMC, Al-Difaie Z, Brandts L, Peeters A, van Grinsven B, Bouvy ND. Diagnostic Performance of Electronic Noses in Cancer Diagnoses Using Exhaled Breath: A Systematic Review and Meta-analysis. JAMA Netw Open 2022; 5:e2219372. [PMID: 35767259 PMCID: PMC9244610 DOI: 10.1001/jamanetworkopen.2022.19372] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
IMPORTANCE There has been a growing interest in the use of electronic noses (e-noses) in detecting volatile organic compounds in exhaled breath for the diagnosis of cancer. However, no systematic evaluation has been performed of the overall diagnostic accuracy and methodologic challenges of using e-noses for cancer detection in exhaled breath. OBJECTIVE To provide an overview of the diagnostic accuracy and methodologic challenges of using e-noses for the detection of cancer. DATA SOURCES An electronic search was performed in the PubMed and Embase databases (January 1, 2000, to July 1, 2021). STUDY SELECTION Inclusion criteria were the following: (1) use of e-nose technology, (2) detection of cancer, and (3) analysis of exhaled breath. Exclusion criteria were (1) studies published before 2000; (2) studies not performed in humans; (3) studies not performed in adults; (4) studies that only analyzed biofluids; and (5) studies that exclusively used gas chromatography-mass spectrometry to analyze exhaled breath samples. DATA EXTRACTION AND SYNTHESIS PRISMA guidelines were used for the identification, screening, eligibility, and selection process. Quality assessment was performed using Quality Assessment of Diagnostic Accuracy Studies 2. Generalized mixed-effects bivariate meta-analysis was performed. MAIN OUTCOMES AND MEASURES Main outcomes were sensitivity, specificity, and mean area under the receiver operating characteristic curve. RESULTS This review identified 52 articles with a total of 3677 patients with cancer. All studies were feasibility studies. The sensitivity of e-noses ranged from 48.3% to 95.8% and the specificity from 10.0% to 100.0%. Pooled analysis resulted in a mean (SE) area under the receiver operating characteristic curve of 94% (95% CI, 92%-96%), a sensitivity of 90% (95% CI, 88%-92%), and a specificity of 87% (95% CI, 81%-92%). Considerable heterogeneity existed among the studies because of differences in the selection of patients, endogenous and exogenous factors, and collection of exhaled breath. CONCLUSIONS AND RELEVANCE Results of this review indicate that e-noses have a high diagnostic accuracy for the detection of cancer in exhaled breath. However, most studies were feasibility studies with small sample sizes, a lack of standardization, and a high risk of bias. The lack of standardization and reproducibility of e-nose research should be addressed in future research.
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Affiliation(s)
- Max H. M. C. Scheepers
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - Zaid Al-Difaie
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - Lloyd Brandts
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre, the Netherlands
| | - Andrea Peeters
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre, the Netherlands
| | - Bart van Grinsven
- Sensor Engineering, Faculty of Science and Engineering, Maastricht University, Maastricht, the Netherlands
| | - Nicole D. Bouvy
- Department of Surgery, Maastricht University Medical Center, Maastricht, the Netherlands
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14
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Abstract
Healthcare is undergoing large transformations, and it is imperative to leverage new technologies to support the advent of personalized medicine and disease prevention. It is now well accepted that the levels of certain biological molecules found in blood and other bodily fluids, as well as in exhaled breath, are an indication of the onset of many human diseases and reflect the health status of the person. Blood, urine, sweat, or saliva biomarkers can therefore serve in early diagnosis of diseases such as cancer, but also in monitoring disease progression, detecting metabolic disfunctions, and predicting response to a given therapy. For most point-of-care sensors, the requirement that patients themselves can use and apply them is crucial not only regarding the diagnostic part, but also at the sample collection level. This has stimulated the development of such diagnostic approaches for the non-invasive analysis of disease-relevant analytes. Considering these timely efforts, this review article focuses on novel, sensitive, and selective sensing systems for the detection of different endogenous target biomarkers in bodily fluids as well as in exhaled breath, which are associated with human diseases.
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15
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Walking motion real-time detection method based on walking stick, IoT, COPOD and improved LightGBM. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03264-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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16
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Xuan W, Zheng L, Bunes BR, Crane N, Zhou F, Zang L. Engineering solutions to breath tests based on an e-nose system for silicosis screening and early detection in miners. J Breath Res 2022; 16. [PMID: 35303733 DOI: 10.1088/1752-7163/ac5f13] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 03/18/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVES This study aims to develop an engineering solution to breath tests using an electronic nose (e-nose), and evaluate its diagnosis accuracy for silicosis. Influencing factors of this technique were explored. METHODS 398 non-silicosis miners and 221 silicosis miners were enrolled in this cross-sectional study. Exhaled breath was analyzed by an array of 16 organic nanofiber sensors along with a customized sample processing system. Principal Component Analysis was used to visualize the breath data, and classifiers were trained by two improved cost-sensitive ensemble algorithms (RF and XGBoost) and two classical algorithms (KNN and SVM). All subjects were included to train the screening model, and an early detection model was run with silicosis cases in stage I. Both 5-fold cross-validation and external validation were adopted. Difference in classifiers caused by algorithms and subjects was quantified using a two-factor analysis of variance. The association between personal smoking habits and classification was investigated by the chi-square test. RESULTS Classifiers of ensemble learning performed well in both screening and early detection model, with an accuracy range of 0.817 to 0.987. Classical classifiers showed relatively worse performance. Besides, the ensemble algorithm type and silicosis cases inclusion had no significant effect on classification (p>0.05). There was no connection between personal smoking habits and classification accuracy. CONCLUSION Breath tests based on an e-nose consisted of 16x sensor array performed well in silicosis screening and early detection. Raw data input showed a more significant effect on classification compared with the algorithm. Personal smoking habits had little impact on models, supporting the applicability of models in large-scale silicosis screening. The e-nose technique and the breath analysis methods reported are expected to provide a quick and accurate screening for silicosis, and extensible for other diseases.
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Affiliation(s)
- Wufan Xuan
- China University of Mining and Technology, School of Safety Engineering, Xuzhou, 221116, CHINA
| | - Lina Zheng
- China University of Mining and Technology, School of Safety Engineering, Xuzhou, 221116, CHINA
| | - Benjamin R Bunes
- Vaporsens, Inc, 419 Wakara Way, Salt Lake City, Utah, 84108, UNITED STATES
| | - Nichole Crane
- Vaporsens, Inc, 419 Wakara Way, Salt Lake City, Utah, UT 84108, UNITED STATES
| | - Fubao Zhou
- China University of Mining and Technology, School of Safety Engineering, Xuzhou, 221116, CHINA
| | - Ling Zang
- Nano Institute of Utah, 36 South Wasatch Drive, Salt Lake City, Utah, 84112-8924, UNITED STATES
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17
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Kim A, Chung KC, Keir C, Patrick DL. Patient-reported outcomes associated with cancer screening: a systematic review. BMC Cancer 2022; 22:223. [PMID: 35232405 PMCID: PMC8886782 DOI: 10.1186/s12885-022-09261-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 02/03/2022] [Indexed: 12/16/2022] Open
Abstract
Background Multi-cancer early detection tests have been developed to enable earlier detection of multiple cancer types through screening. As reflected by patient-reported outcomes (PROs), the psychosocial impact of cancer screening is not yet clear. Our aim is to evaluate the impact of cancer screening through PRO assessment. Methods A systematic review was conducted using MEDLINE, EMBASE, and reference lists of articles from January 2000 to August 2020 for relevant publications assessing the psychosocial impact of cancer screening before and within 1 year after screening in the general asymptomatic population, including following receipt of results. Studies focused on diagnostic evaluation or involving patients previously diagnosed with cancer were excluded. Results In total, 31 studies (12 randomized controlled trials; 19 observational studies) were included, reflecting PRO assessments associated with lung, breast, colorectal, anal, ovarian, cervical, and prostate cancer screening procedures. The most commonly assessed construct was symptoms of anxiety, using the State-Trait Anxiety Inventory. Cancer-specific distress and worry were also assessed using a broad range of measures. Overall, individuals tolerated screening procedures well with no major psychosocial effects. Of note, increases in symptoms of anxiety and levels of distress and worry were generally found prior to communication of screening results and following communication of indeterminate or positive results that required further testing. These negative psychosocial effects were, however, not long-lasting and returned to baseline relatively soon after screening. Furthermore, individuals with higher cancer risk, such as current smokers and those with a family history of cancer, tended to have higher levels of anxiety and distress throughout the screening process, including following negative or indeterminate results. Conclusions The psychosocial impact of cancer screening is relatively low overall and short-lived, even following false-positive test results. Individuals with a higher risk of cancer tend to experience more symptoms of anxiety and distress during the screening process; thus, more attention to this group is recommended. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-09261-5.
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Affiliation(s)
- Ashley Kim
- GRAIL, LLC, a subsidiary of Illumina, Inc., CA, Menlo Park, USA.
| | - Karen C Chung
- GRAIL, LLC, a subsidiary of Illumina, Inc., CA, Menlo Park, USA
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18
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A pilot study for the prediction of liver function related scores using breath biomarkers and machine learning. Sci Rep 2022; 12:2032. [PMID: 35132067 PMCID: PMC8821604 DOI: 10.1038/s41598-022-05808-5] [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: 08/09/2021] [Accepted: 01/13/2022] [Indexed: 02/07/2023] Open
Abstract
Volatile organic compounds (VOCs) present in exhaled breath can help in analysing biochemical processes in the human body. Liver diseases can be traced using VOCs as biomarkers for physiological and pathophysiological conditions. In this work, we propose non-invasive and quick breath monitoring approach for early detection and progress monitoring of liver diseases using Isoprene, Limonene, and Dimethyl sulphide (DMS) as potential biomarkers. A pilot study is performed to design a dataset that includes the biomarkers concentration analysed from the breath sample before and after study subjects performed an exercise. A machine learning approach is applied for the prediction of scores for liver function diagnosis. Four regression methods are performed to predict the clinical scores using breath biomarkers data as features set by the machine learning techniques. A significant difference was observed for isoprene concentration (p < 0.01) and for DMS concentration (p < 0.0001) between liver patients and healthy subject's breath sample. The R-square value between actual clinical score and predicted clinical score is found to be 0.78, 0.82, and 0.85 for CTP score, APRI score, and MELD score, respectively. Our results have shown a promising result with significant different breath profiles between liver patients and healthy volunteers. The use of machine learning for the prediction of scores is found very promising for use of breath biomarkers for liver function diagnosis.
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19
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Faura G, Boix-Lemonche G, Holmeide AK, Verkauskiene R, Volke V, Sokolovska J, Petrovski G. Colorimetric and Electrochemical Screening for Early Detection of Diabetes Mellitus and Diabetic Retinopathy-Application of Sensor Arrays and Machine Learning. SENSORS 2022; 22:s22030718. [PMID: 35161465 PMCID: PMC8839630 DOI: 10.3390/s22030718] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 12/20/2021] [Accepted: 12/26/2021] [Indexed: 12/13/2022]
Abstract
In this review, a selection of works on the sensing of biomarkers related to diabetes mellitus (DM) and diabetic retinopathy (DR) are presented, with the scope of helping and encouraging researchers to design sensor-array machine-learning (ML)-supported devices for robust, fast, and cost-effective early detection of these devastating diseases. First, we highlight the social relevance of developing systematic screening programs for such diseases and how sensor-arrays and ML approaches could ease their early diagnosis. Then, we present diverse works related to the colorimetric and electrochemical sensing of biomarkers related to DM and DR with non-invasive sampling (e.g., urine, saliva, breath, tears, and sweat samples), with a special mention to some already-existing sensor arrays and ML approaches. We finally highlight the great potential of the latter approaches for the fast and reliable early diagnosis of DM and DR.
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Affiliation(s)
- Georgina Faura
- Center for Eye Research, Department of Ophthalmology, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Kirkeveien 166, 0450 Oslo, Norway; (G.F.); (G.B.-L.)
- Department of Medical Biochemistry, Institute of Clinical Medicine, University of Oslo, 0424 Oslo, Norway
| | - Gerard Boix-Lemonche
- Center for Eye Research, Department of Ophthalmology, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Kirkeveien 166, 0450 Oslo, Norway; (G.F.); (G.B.-L.)
| | | | - Rasa Verkauskiene
- Institute of Endocrinology, Medical Academy, Lithuanian University of Health Sciences, LT-50009 Kaunas, Lithuania;
| | - Vallo Volke
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411 Tartu, Estonia;
- Institute of Biomedical and Transplant Medicine, Department of Medical Sciences, Tartu University Hospital, L. Puusepa Street, 51014 Tartu, Estonia
| | | | - Goran Petrovski
- Center for Eye Research, Department of Ophthalmology, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Kirkeveien 166, 0450 Oslo, Norway; (G.F.); (G.B.-L.)
- Department of Ophthalmology, Oslo University Hospital, 0450 Oslo, Norway
- Correspondence: ; Tel.: +47-9222-6158
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20
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Paleczek A, Rydosz AM. Review of the algorithms used in exhaled breath analysis for the detection of diabetes. J Breath Res 2022; 16. [PMID: 34996056 DOI: 10.1088/1752-7163/ac4916] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 01/07/2022] [Indexed: 11/11/2022]
Abstract
Currently, intensive work is underway on the development of truly noninvasive medical diagnostic systems, including respiratory analysers based on the detection of biomarkers of several diseases including diabetes. In terms of diabetes, acetone is considered as a one of the potential biomarker, although is not the single one. Therefore, the selective detection is crucial. Most often, the analysers of exhaled breath are based on the utilization of several commercially available gas sensors or on specially designed and manufactured gas sensors to obtain the highest selectivity and sensitivity to diabetes biomarkers present in the exhaled air. An important part of each system are the algorithms that are trained to detect diabetes based on data obtained from sensor matrices. The prepared review of the literature showed that there are many limitations in the development of the versatile breath analyser, such as high metabolic variability between patients, but the results obtained by researchers using the algorithms described in this paper are very promising and most of them achieve over 90% accuracy in the detection of diabetes in exhaled air. This paper summarizes the results using various measurement systems, feature extraction and feature selection methods as well as algorithms such as Support Vector Machines, k-Nearest Neighbours and various variations of Neural Networks for the detection of diabetes in patient samples and simulated artificial breath samples.
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Affiliation(s)
- Anna Paleczek
- Institute of Electronics, AGH University of Science and Technology Faculty of Computer Science Electronics and Telecommunications, al. A. Mickiewicza 30, Krakow, 30-059, POLAND
| | - Artur Maciej Rydosz
- Institute of Electronics, AGH University of Science and Technology Faculty of Computer Science Electronics and Telecommunications, Al. Mickiewicza 30, Krakow, 30-059, POLAND
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21
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Yang HY, Chen WC, Tsai RC. Accuracy of the Electronic Nose Breath Tests in Clinical Application: A Systematic Review and Meta-Analysis. BIOSENSORS 2021; 11:bios11110469. [PMID: 34821685 PMCID: PMC8615633 DOI: 10.3390/bios11110469] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Revised: 11/20/2021] [Accepted: 11/20/2021] [Indexed: 05/25/2023]
Abstract
(1) Background: An electronic nose applies a sensor array to detect volatile biomarkers in exhaled breath to diagnose diseases. The overall diagnostic accuracy remains unknown. The objective of this review was to provide an estimate of the diagnostic accuracy of sensor-based breath tests for the diagnosis of diseases. (2) Methods: We searched the PubMed and Web of Science databases for studies published between 1 January 2010 and 14 October 2021. The search was limited to human studies published in the English language. Clinical trials were not included in this review. (3) Results: Of the 2418 records identified, 44 publications were eligible, and 5728 patients were included in the final analyses. The pooled sensitivity was 90.0% (95% CI, 86.3-92.8%, I2 = 47.7%), the specificity was 88.4% (95% CI, 87.1-89.5%, I2 = 81.4%), and the pooled area under the curve was 0.93 (95% CI 0.91-0.95). (4) Conclusion: The findings of our review suggest that a standardized report of diagnostic accuracy and a report of the accuracy in a test set are needed. Sensor array systems of electronic noses have the potential for noninvasiveness at the point-of-care in hospitals. Nevertheless, the procedure for reporting the accuracy of a diagnostic test must be standardized.
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Affiliation(s)
- Hsiao-Yu Yang
- Institute of Environmental and Occupational Health Sciences, National Taiwan University, Taipei 10055, Taiwan; (W.-C.C.); (R.-C.T.)
- Department of Public Health, National Taiwan University College of Public Health, Taipei 10055, Taiwan
- Department of Environmental and Occupational Medicine, National Taiwan University Hospital, Taipei 10002, Taiwan
| | - Wan-Chin Chen
- Institute of Environmental and Occupational Health Sciences, National Taiwan University, Taipei 10055, Taiwan; (W.-C.C.); (R.-C.T.)
- Department of Family Medicine, Changhua Christian Hospital, Changhua 50006, Taiwan
| | - Rodger-Chen Tsai
- Institute of Environmental and Occupational Health Sciences, National Taiwan University, Taipei 10055, Taiwan; (W.-C.C.); (R.-C.T.)
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22
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Exhaled-Breath Testing Using an Electronic Nose during Spinal Cord Stimulation in Patients with Failed Back Surgery Syndrome: An Experimental Pilot Study. J Clin Med 2021; 10:jcm10132921. [PMID: 34209972 PMCID: PMC8269089 DOI: 10.3390/jcm10132921] [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: 05/28/2021] [Revised: 06/17/2021] [Accepted: 06/29/2021] [Indexed: 11/17/2022] Open
Abstract
The increased awareness of discrepancies between self-reporting outcome measurements and objective outcome measurements within the field of neuromodulation has accelerated the search towards more objective measurements. The aim of this study was to evaluate whether an electronic nose can differentiate between chronic pain patients in whom Spinal Cord Stimulation (SCS) was activated versus deactivated. Twenty-seven patients with Failed Back Surgery Syndrome (FBSS) participated in this prospective pilot study. Volatile organic compounds in exhaled breath were measured with electronic nose technology (Aeonose™) during SCS on and off states. Random forest was used with a leave-10%-out cross-validation method to determine accuracy of discriminating between SCS on and off states. Our random forest showed an accuracy of 0.56, with an area under the curve of 0.62, a sensitivity of 62% (95% CI: 41–79%) and a specificity of 50% (95% CI: 30–70%). Pain intensity scores were significantly different between both SCS states. Our findings indicate that we cannot discriminate between SCS off and on states based on exhaled breath with the Aeonose™ in patients with FBSS. In clinical practice, these findings imply that with a noninvasive electronic nose, exhaled breath cannot be used as an additional marker of the effect of neuromodulation.
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23
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Tomić M, Šetka M, Vojkůvka L, Vallejos S. VOCs Sensing by Metal Oxides, Conductive Polymers, and Carbon-Based Materials. NANOMATERIALS (BASEL, SWITZERLAND) 2021; 11:552. [PMID: 33671783 PMCID: PMC7926866 DOI: 10.3390/nano11020552] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 01/31/2021] [Accepted: 02/07/2021] [Indexed: 12/24/2022]
Abstract
This review summarizes the recent research efforts and developments in nanomaterials for sensing volatile organic compounds (VOCs). The discussion focuses on key materials such as metal oxides (e.g., ZnO, SnO2, TiO2 WO3), conductive polymers (e.g., polypyrrole, polythiophene, poly(3,4-ethylenedioxythiophene)), and carbon-based materials (e.g., graphene, graphene oxide, carbon nanotubes), and their mutual combination due to their representativeness in VOCs sensing. Moreover, it delves into the main characteristics and tuning of these materials to achieve enhanced functionality (sensitivity, selectivity, speed of response, and stability). The usual synthesis methods and their advantages towards their integration with microsystems for practical applications are also remarked on. The literature survey shows the most successful systems include structured morphologies, particularly hierarchical structures at the nanometric scale, with intentionally introduced tunable "decorative impurities" or well-defined interfaces forming bilayer structures. These groups of modified or functionalized structures, in which metal oxides are still the main protagonists either as host or guest elements, have proved improvements in VOCs sensing. The work also identifies the need to explore new hybrid material combinations, as well as the convenience of incorporating other transducing principles further than resistive that allow the exploitation of mixed output concepts (e.g., electric, optic, mechanic).
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Affiliation(s)
- Milena Tomić
- Institute of Microelectronics of Barcelona (IMB-CNM, CSIC), Campus UAB, 08193 Cerdanyola del Vallès, Barcelona, Spain;
- Department of Electronic Engineering, Autonomous University of Barcelona (UAB), Campus UAB, 08193 Cerdanyola del Vallès, Barcelona, Spain
| | - Milena Šetka
- CEITEC—Central European Institute of Technology, Brno University of Technology, 61200 Brno, Czech Republic;
| | - Lukaš Vojkůvka
- Silicon Austria Labs, Microsystem Technologies, High Tech Campus Villach, Europastraβe 12, A-9524 Villach, Austria;
| | - Stella Vallejos
- Institute of Microelectronics of Barcelona (IMB-CNM, CSIC), Campus UAB, 08193 Cerdanyola del Vallès, Barcelona, Spain;
- CEITEC—Central European Institute of Technology, Brno University of Technology, 61200 Brno, Czech Republic;
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