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Yao Z, Kuang M, Li Z. Global trends of delayed graft function in kidney transplantation from 2013 to 2023: a bibliometric analysis. Ren Fail 2024; 46:2316277. [PMID: 38357764 PMCID: PMC10877662 DOI: 10.1080/0886022x.2024.2316277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 02/03/2024] [Indexed: 02/16/2024] Open
Abstract
Delayed graft function (DGF) is an early complication after kidney transplantation. The literature on DGF has experienced substantial growth. However, there is a lack of bibliometric analysis of DGF. This study aimed to analyze the scientific outputs of DGF and explore its hotspots from 2013 to 2023 by using CiteSpace and VOSviewer. The 2058 pieces of literature collected in the Web of Science Core Collection (WOSCC) from 1 January 2013 to 31 December 2023 were visually analyzed in terms of the annual number of publications, authors, countries, journals, literature co-citations, and keyword clustering by using CiteSpace and VOSviewer. We found that the number of papers published in the past ten years showed a trend of first increasing and then decreasing; 2021 was the year with the most posts. The largest number of papers was published by the University of California System, and the largest number of papers was published by the United States. The top five keyword frequency rankings are: 'delayed graft function', 'kidney transplantation', 'renal transplantation', 'survival', and 'recipients'. These emerging trends include 'brain death donors', 'blood absence re-injection injuries', 'tacrolimus', 'older donors and recipients', and 'artificial intelligence and DGF'. In summary, this study reveals the authors and institutions that could be cooperated with and discusses the research hotspots in the past ten years. It provides a reference and direction for future research and application of DGF.
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Affiliation(s)
- Zhiling Yao
- Department of Organ Transplantation, First Affiliated Hospital of Kunming Medical University, Kunming City, Yunnan Province, China
| | - Mingqian Kuang
- Department of Organ Transplantation, First Affiliated Hospital of Kunming Medical University, Kunming City, Yunnan Province, China
| | - Zhen Li
- Department of Organ Transplantation, First Affiliated Hospital of Kunming Medical University, Kunming City, Yunnan Province, China
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2
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Fang P, Feng R, Liu C, Wen R. Boundary sample-based class-weighted semi-supervised learning for malignant tumor classification of medical imaging. Med Biol Eng Comput 2024; 62:2987-2997. [PMID: 38727760 DOI: 10.1007/s11517-024-03114-y] [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: 11/24/2023] [Accepted: 04/28/2024] [Indexed: 09/07/2024]
Abstract
Medical image classification plays a pivotal role within the field of medicine. Existing models predominantly rely on supervised learning methods, which necessitate large volumes of labeled data for effective training. However, acquiring and annotating medical image data is both an expensive and time-consuming endeavor. In contrast, semi-supervised learning methods offer a promising approach by harnessing limited labeled data alongside abundant unlabeled data to enhance the performance of medical image classification. Nonetheless, current methods often encounter confirmation bias due to noise inherent in self-generated pseudo-labels and the presence of boundary samples from different classes. To overcome these challenges, this study introduces a novel framework known as boundary sample-based class-weighted semi-supervised learning (BSCSSL) for medical image classification. Our method aims to alleviate the impact of intra- and inter-class boundary samples derived from unlabeled data. Specifically, we address reliable confidential data and inter-class boundary samples separately through the utilization of an inter-class boundary sample mining module. Additionally, we implement an intra-class boundary sample weighting mechanism to extract class-aware features specific to intra-class boundary samples. Rather than discarding such intra-class boundary samples outright, our approach acknowledges their intrinsic value despite the difficulty associated with accurate classification, as they contribute significantly to model prediction. Experimental results on widely recognized medical image datasets demonstrate the superiority of our proposed BSCSSL method over existing semi-supervised learning approaches. By enhancing the accuracy and robustness of medical image classification, our BSCSSL approach yields considerable implications for advancing medical diagnosis and future research endeavors.
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Affiliation(s)
- Pei Fang
- China Comservice Enrising Information Technology Co., Ltd., Chengdu, Sichuan, 610041, China.
| | - Renwei Feng
- China Comservice Enrising Information Technology Co., Ltd., Chengdu, Sichuan, 610041, China
| | - Changdong Liu
- China Comservice Enrising Information Technology Co., Ltd., Chengdu, Sichuan, 610041, China
| | - Renjun Wen
- China Comservice Enrising Information Technology Co., Ltd., Chengdu, Sichuan, 610041, China
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3
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Liu J, Du H, Huang L, Xie W, Liu K, Zhang X, Chen S, Zhang Y, Li D, Pan H. AI-Powered Microfluidics: Shaping the Future of Phenotypic Drug Discovery. ACS APPLIED MATERIALS & INTERFACES 2024; 16:38832-38851. [PMID: 39016521 DOI: 10.1021/acsami.4c07665] [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: 07/18/2024]
Abstract
Phenotypic drug discovery (PDD), which involves harnessing biological systems directly to uncover effective drugs, has undergone a resurgence in recent years. The rapid advancement of artificial intelligence (AI) over the past few years presents numerous opportunities for augmenting phenotypic drug screening on microfluidic platforms, leveraging its predictive capabilities, data analysis, efficient data processing, etc. Microfluidics coupled with AI is poised to revolutionize the landscape of phenotypic drug discovery. By integrating advanced microfluidic platforms with AI algorithms, researchers can rapidly screen large libraries of compounds, identify novel drug candidates, and elucidate complex biological pathways with unprecedented speed and efficiency. This review provides an overview of recent advances and challenges in AI-based microfluidics and their applications in drug discovery. We discuss the synergistic combination of microfluidic systems for high-throughput screening and AI-driven analysis for phenotype characterization, drug-target interactions, and predictive modeling. In addition, we highlight the potential of AI-powered microfluidics to achieve an automated drug screening system. Overall, AI-powered microfluidics represents a promising approach to shaping the future of phenotypic drug discovery by enabling rapid, cost-effective, and accurate identification of therapeutically relevant compounds.
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Affiliation(s)
- Junchi Liu
- Department of Anesthesiology, The First Hospital of Jilin University, 71 Xinmin Street, Changchun 130012, China
| | - Hanze Du
- Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, Translation Medicine Centre, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
| | - Lei Huang
- Jilin Provincial Key Laboratory of Tooth Development and Bone Remodeling, School and Hospital of Stomatology, Jilin University, 1500 Qinghua Road, Changchun 130012, China
| | - Wangni Xie
- Jilin Provincial Key Laboratory of Tooth Development and Bone Remodeling, School and Hospital of Stomatology, Jilin University, 1500 Qinghua Road, Changchun 130012, China
| | - Kexuan Liu
- Jilin Provincial Key Laboratory of Tooth Development and Bone Remodeling, School and Hospital of Stomatology, Jilin University, 1500 Qinghua Road, Changchun 130012, China
| | - Xue Zhang
- Jilin Provincial Key Laboratory of Tooth Development and Bone Remodeling, School and Hospital of Stomatology, Jilin University, 1500 Qinghua Road, Changchun 130012, China
| | - Shi Chen
- Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, Translation Medicine Centre, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
| | - Yuan Zhang
- Department of Anesthesiology, The First Hospital of Jilin University, 71 Xinmin Street, Changchun 130012, China
| | - Daowei Li
- Jilin Provincial Key Laboratory of Tooth Development and Bone Remodeling, School and Hospital of Stomatology, Jilin University, 1500 Qinghua Road, Changchun 130012, China
| | - Hui Pan
- Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, Translation Medicine Centre, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
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4
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Ma T, Wang G, Guo R, Chen L, Ma J. Forest fire susceptibility assessment under small sample scenario: A semi-supervised learning approach using transductive support vector machine. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 359:120966. [PMID: 38677225 DOI: 10.1016/j.jenvman.2024.120966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 02/29/2024] [Accepted: 04/19/2024] [Indexed: 04/29/2024]
Abstract
Forest fires threaten global ecosystems, socio-economic structures, and public safety. Accurately assessing forest fire susceptibility is critical for effective environmental management. Supervised learning methods dominate this assessment, relying on a substantial dataset of forest fire occurrences for model training. However, obtaining precise forest fire location data remains challenging. To address this issue, semi-supervised learning emerges as a viable solution, leveraging both a limited set of collected samples and unlabeled data containing environmental factors for training. Our study employed the transductive support vector machine (TSVM), a key semi-supervised learning method, to assess forest fire susceptibility in scenarios with limited samples. We conducted a comparative analysis, evaluating its performance against widely used supervised learning methods. The assessment area for forest fire susceptibility lies in Dayu County, Jiangxi Province, China, renowned for its vast forest cover and frequent fire incidents. We analyzed and generated maps depicting forest fire susceptibility, evaluating prediction accuracies for both supervised and semi-supervised learning methods across various small sample scenarios (e.g., 4, 8, 12, 16, 20, 24, 28, and 32 samples). Our findings indicate that TSVM exhibits superior prediction accuracy compared to supervised learning with limited samples, yielding more plausible forest fire susceptibility maps. For instance, at sample sizes of 4, 16, and 28, TSVM achieves prediction accuracies of approximately 0.8037, 0.9257, and 0.9583, respectively. In contrast, random forests, the top performers in supervised learning, demonstrate accuracies of approximately 0.7424, 0.8916, and 0.9431, respectively, for the same small sample sizes. Additionally, we discussed three key aspects: TSVM parameter configuration, the impact of unlabeled sample size, and performance within typical sample sizes. Our findings support semi-supervised learning as a promising approach compared to supervised learning for forest fire susceptibility assessment and mapping, particularly in scenarios with small sample sizes.
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Affiliation(s)
- Tianwu Ma
- Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China; School of Geography, Nanjing Normal University, Nanjing, 210023, China
| | - Gang Wang
- School of Geography, Nanjing Normal University, Nanjing, 210023, China; School of Urban and Plan, Yancheng Teachers University, Yancheng, 224002, China.
| | - Rui Guo
- Administration of Zhejiang Qingliangfeng National Nature Reserve, Hangzhou, 311300, China
| | - Liang Chen
- Department of Environmental and Biological Sciences, University of Eastern Finland, Joensuu, 80101, Finland
| | - Junfei Ma
- Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China; School of Geography, Nanjing Normal University, Nanjing, 210023, China
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Hashemi Gheinani A, Kim J, You S, Adam RM. Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment. Nat Rev Urol 2024; 21:214-242. [PMID: 37604982 DOI: 10.1038/s41585-023-00805-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/13/2023] [Indexed: 08/23/2023]
Abstract
The application of bioinformatics has revolutionized the practice of medicine in the past 20 years. From early studies that uncovered subtypes of cancer to broad efforts spearheaded by the Cancer Genome Atlas initiative, the use of bioinformatics strategies to analyse high-dimensional data has provided unprecedented insights into the molecular basis of disease. In addition to the identification of disease subtypes - which enables risk stratification - informatics analysis has facilitated the identification of novel risk factors and drivers of disease, biomarkers of progression and treatment response, as well as possibilities for drug repurposing or repositioning; moreover, bioinformatics has guided research towards precision and personalized medicine. Implementation of specific computational approaches such as artificial intelligence, machine learning and molecular subtyping has yet to become widespread in urology clinical practice for reasons of cost, disruption of clinical workflow and need for prospective validation of informatics approaches in independent patient cohorts. Solving these challenges might accelerate routine integration of bioinformatics into clinical settings.
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Affiliation(s)
- Ali Hashemi Gheinani
- Department of Urology, Boston Children's Hospital, Boston, MA, USA
- Department of Surgery, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Urology, Inselspital, Bern, Switzerland
- Department for BioMedical Research, University of Bern, Bern, Switzerland
| | - Jina Kim
- Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sungyong You
- Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Rosalyn M Adam
- Department of Urology, Boston Children's Hospital, Boston, MA, USA.
- Department of Surgery, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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6
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Okumura N, Nishikawa T, Imafuku C, Matsuoka Y, Miyawaki Y, Kadowaki S, Nakahara M, Matsuoka Y, Koizumi N. U-Net Convolutional Neural Network for Real-Time Prediction of the Number of Cultured Corneal Endothelial Cells for Cellular Therapy. Bioengineering (Basel) 2024; 11:71. [PMID: 38247948 PMCID: PMC10813389 DOI: 10.3390/bioengineering11010071] [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: 12/29/2023] [Accepted: 01/08/2024] [Indexed: 01/23/2024] Open
Abstract
Corneal endothelial decompensation is treated by the corneal transplantation of donor corneas, but donor shortages and other problems associated with corneal transplantation have prompted investigations into tissue engineering therapies. For clinical use, cells used in tissue engineering must undergo strict quality control to ensure their safety and efficacy. In addition, efficient cell manufacturing processes are needed to make cell therapy a sustainable standard procedure with an acceptable economic burden. In this study, we obtained 3098 phase contrast images of cultured human corneal endothelial cells (HCECs). We labeled the images using semi-supervised learning and then trained a model that predicted the cell centers with a precision of 95.1%, a recall of 92.3%, and an F-value of 93.4%. The cell density calculated by the model showed a very strong correlation with the ground truth (Pearson's correlation coefficient = 0.97, p value = 8.10 × 10-52). The total cell numbers calculated by our model based on phase contrast images were close to the numbers calculated using a hemocytometer through passages 1 to 4. Our findings confirm the feasibility of using artificial intelligence-assisted quality control assessments in the field of regenerative medicine.
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Affiliation(s)
- Naoki Okumura
- Department of Biomedical Engineering, Faculty of Life and Medical Sciences, Doshisha University, 1-3 Miyakodani, Tatara, Kyotanabe-City 610-0394, Kyoto, Japan; (T.N.); (Y.M.); (Y.M.); (S.K.); (N.K.)
| | - Takeru Nishikawa
- Department of Biomedical Engineering, Faculty of Life and Medical Sciences, Doshisha University, 1-3 Miyakodani, Tatara, Kyotanabe-City 610-0394, Kyoto, Japan; (T.N.); (Y.M.); (Y.M.); (S.K.); (N.K.)
| | - Chiaki Imafuku
- Department of Biomedical Engineering, Faculty of Life and Medical Sciences, Doshisha University, 1-3 Miyakodani, Tatara, Kyotanabe-City 610-0394, Kyoto, Japan; (T.N.); (Y.M.); (Y.M.); (S.K.); (N.K.)
| | - Yuki Matsuoka
- Department of Biomedical Engineering, Faculty of Life and Medical Sciences, Doshisha University, 1-3 Miyakodani, Tatara, Kyotanabe-City 610-0394, Kyoto, Japan; (T.N.); (Y.M.); (Y.M.); (S.K.); (N.K.)
| | - Yuna Miyawaki
- Department of Biomedical Engineering, Faculty of Life and Medical Sciences, Doshisha University, 1-3 Miyakodani, Tatara, Kyotanabe-City 610-0394, Kyoto, Japan; (T.N.); (Y.M.); (Y.M.); (S.K.); (N.K.)
| | - Shinichi Kadowaki
- Department of Biomedical Engineering, Faculty of Life and Medical Sciences, Doshisha University, 1-3 Miyakodani, Tatara, Kyotanabe-City 610-0394, Kyoto, Japan; (T.N.); (Y.M.); (Y.M.); (S.K.); (N.K.)
| | - Makiko Nakahara
- ActualEyes Inc., D-egg, 1 Jizodani, Koudo, Kyotanabe-City 610-0332, Kyoto, Japan; (M.N.); (Y.M.)
| | - Yasushi Matsuoka
- ActualEyes Inc., D-egg, 1 Jizodani, Koudo, Kyotanabe-City 610-0332, Kyoto, Japan; (M.N.); (Y.M.)
| | - Noriko Koizumi
- Department of Biomedical Engineering, Faculty of Life and Medical Sciences, Doshisha University, 1-3 Miyakodani, Tatara, Kyotanabe-City 610-0394, Kyoto, Japan; (T.N.); (Y.M.); (Y.M.); (S.K.); (N.K.)
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7
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Mondello A, Dal Bo M, Toffoli G, Polano M. Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges. Front Pharmacol 2024; 14:1260276. [PMID: 38264526 PMCID: PMC10803549 DOI: 10.3389/fphar.2023.1260276] [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: 07/17/2023] [Accepted: 12/26/2023] [Indexed: 01/25/2024] Open
Abstract
Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.
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Affiliation(s)
| | | | | | - Maurizio Polano
- Experimental and Clinical Pharmacology Unit, Centro di Riferimento Oncologico di Aviano (CRO), Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Aviano, Italy
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Nagendra L, Pappachan JM, Fernandez CJ. Artificial intelligence in the diagnosis of thyroid cancer: Recent advances and future directions. Artif Intell Cancer 2023; 4:1-10. [DOI: 10.35713/aic.v4.i1.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 07/24/2023] [Accepted: 08/07/2023] [Indexed: 09/07/2023] Open
Abstract
The diagnosis and management of thyroid cancer is fraught with challenges despite the advent of innovative diagnostic, surgical, and chemotherapeutic modalities. Challenges like inaccuracy in prognostication, uncertainty in cytopathological diagnosis, trouble in differentiating follicular neoplasms, intra-observer and inter-observer variability on ultrasound imaging preclude personalised treatment in thyroid cancer. Artificial intelligence (AI) is bringing a paradigm shift to the healthcare, powered by quick advancement of the analytic techniques. Several recent studies have shown remarkable progress in thyroid cancer diagnostics based on AI-assisted algorithms. Application of AI techniques in thyroid ultrasonography and cytopathology have shown remarkable impro-vement in sensitivity and specificity over the traditional diagnostic modalities. AI has also been explored in the development of treatment algorithms for indeterminate nodules and for prognostication in the patients with thyroid cancer. The benefits of high repeatability and straightforward implementation of AI in the management of thyroid cancer suggest that it holds promise for clinical application. Limited clinical experience and lack of prospective validation studies remain the biggest drawbacks. Developing verified and trustworthy algorithms after extensive testing and validation using prospective, multi-centre trials is crucial for the future use of AI in the pipeline of precision medicine in the management of thyroid cancer.
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Affiliation(s)
- Lakshmi Nagendra
- Department of Endocrinology, JSS Medical College & JSS Academy of Higher Education and Research Center, Mysore 570015, India
| | - Joseph M Pappachan
- Department of Endocrinology & Metabolism, Lancashire Teaching Hospitals NHS Trust, Preston PR2 9HT, United Kingdom
- Faculty of Science, Manchester Metropolitan University, Manchester M15 6BH, M15 6BH, United Kingdom
- Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9PL, United Kingdom
| | - Cornelius James Fernandez
- Department of Endocrinology & Metabolism, Pilgrim Hospital, United Lincolnshire Hospitals NHS Trust, PE21 9QS PE21 9QS, United Kingdom
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Vitorino R, Barros AS, Guedes S, Caixeta DC, Sabino-Silva R. Diagnostic and monitoring applications using Near infrared (NIR) Spectroscopy in cancer and other diseases. Photodiagnosis Photodyn Ther 2023:103633. [PMID: 37245681 DOI: 10.1016/j.pdpdt.2023.103633] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 05/19/2023] [Accepted: 05/23/2023] [Indexed: 05/30/2023]
Abstract
Early cancer diagnosis plays a critical role in improving treatment outcomes and increasing survival rates for certain cancers. NIR spectroscopy offers a rapid and cost-effective approach to evaluate the optical properties of tissues at the microvessel level and provides valuable molecular insights. The integration of NIR spectroscopy with advanced data-driven algorithms in portable instruments has made it a cutting-edge technology for medical applications. NIR spectroscopy is a simple, non-invasive and affordable analytical tool that complements expensive imaging modalities such as functional magnetic resonance imaging, positron emission tomography and computed tomography. By examining tissue absorption, scattering, and concentrations of oxygen, water, and lipids, NIR spectroscopy can reveal inherent differences between tumor and normal tissue, often revealing specific patterns that help stratify disease. In addition, the ability of NIR spectroscopy to assess tumor blood flow, oxygenation, and oxygen metabolism provides a key paradigm for its application in cancer diagnosis. This review evaluates the effectiveness of NIR spectroscopy in the detection and characterization of disease, particularly in cancer, with or without the incorporation of chemometrics and machine learning algorithms. The report highlights the potential of NIR spectroscopy technology to significantly improve discrimination between benign and malignant tumors and accurately predict treatment outcomes. In addition, as more medical applications are studied in large patient cohorts, consistent advances in clinical implementation can be expected, making NIR spectroscopy a valuable adjunct technology for cancer therapy management. Ultimately, the integration of NIR spectroscopy into cancer diagnostics promises to improve prognosis by providing critical new insights into cancer patterns and physiology.
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Affiliation(s)
- Rui Vitorino
- Institute of Biomedicine-iBiMED, Department of Medical Sciences, University of Aveiro, 3810-193 Aveiro, Portugal; UnIC@RISE, Department of Surgery and Physiology, Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, 4200-319 Porto, Portugal; LAQV-REQUIMTE, Department of Chemistry, University of Aveiro, 3810-193 Aveiro, Portugal.
| | - António S Barros
- UnIC@RISE, Department of Surgery and Physiology, Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, 4200-319 Porto, Portugal
| | - Sofia Guedes
- LAQV-REQUIMTE, Department of Chemistry, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Douglas C Caixeta
- Innovation Center in Salivary Diagnostics and Nanobiotechnology, Department of Physiology, Institute of Biomedical Sciences, Federal University of Uberlandia, Minas Gerais, Brazil
| | - Robinson Sabino-Silva
- Innovation Center in Salivary Diagnostics and Nanobiotechnology, Department of Physiology, Institute of Biomedical Sciences, Federal University of Uberlandia, Minas Gerais, Brazil
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Juhas M. Artificial Intelligence in Microbiology. BRIEF LESSONS IN MICROBIOLOGY 2023:93-109. [DOI: 10.1007/978-3-031-29544-7_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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