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Nguyen NTT, Nguyen TTT, Nguyen DTC, Tran TV. Functionalization strategies of metal-organic frameworks for biomedical applications and treatment of emerging pollutants: A review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 906:167295. [PMID: 37742958 DOI: 10.1016/j.scitotenv.2023.167295] [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: 05/12/2023] [Revised: 09/19/2023] [Accepted: 09/21/2023] [Indexed: 09/26/2023]
Abstract
One of the representative coordination polymers, metal-organic frameworks (MOFs) material, is of hotspot interest in the multi field thanks to their unique structural characteristics and properties. As a novel hierarchical structural class, MOFs show diverse topologies, intrinsic behaviors, flexibility, etc. However, bare MOFs have less desirable biofunction, high humid sensitivity and instability in water, restraining their efficiencies in biomedical and environmental applications. Thus, a structural modification is required to address such drawbacks. Herein, we pinpoint new strategies in the synthesis and functionalization of MOFs to meet demanding requirements in in vitro tests, i.e., antibacterial face masks against corona virus infection and in wound healing and nanocarriers for drug delivery in anticancer. Regarding the treatment of wastewater containing emerging pollutants such as POPs, PFAS, and PPCPs, functionalized MOFs showed excellent performance with high efficiency and selectivity. Challenges in toxicity, vast database of clinical trials for biomedical tests and production cost can be still presented. MOFs-based composites can be, however, a bright candidate for reasonable replacement of traditional nanomaterials in biomedical and wastewater treatment applications.
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Affiliation(s)
- Ngoan Thi Thao Nguyen
- Institute of Applied Technology and Sustainable Development, Nguyen Tat Thanh University, 298-300A Nguyen Tat Thanh, District 4, Ho Chi Minh City 755414, Vietnam; Faculty of Chemical Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam; Vietnam National University Ho Chi Minh City, Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam
| | - Thuy Thi Thanh Nguyen
- Faculty of Science, Nong Lam University, Thu Duc District, Ho Chi Minh City 700000, Vietnam
| | - Duyen Thi Cam Nguyen
- Institute of Applied Technology and Sustainable Development, Nguyen Tat Thanh University, 298-300A Nguyen Tat Thanh, District 4, Ho Chi Minh City 755414, Vietnam
| | - Thuan Van Tran
- Institute of Applied Technology and Sustainable Development, Nguyen Tat Thanh University, 298-300A Nguyen Tat Thanh, District 4, Ho Chi Minh City 755414, Vietnam.
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Yang Y, Sun K, Gao Y, Wang K, Yu G. Preparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance. Diagnostics (Basel) 2023; 13:3115. [PMID: 37835858 PMCID: PMC10572440 DOI: 10.3390/diagnostics13193115] [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: 08/31/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023] Open
Abstract
The pathology is decisive for disease diagnosis but relies heavily on experienced pathologists. In recent years, there has been growing interest in the use of artificial intelligence in pathology (AIP) to enhance diagnostic accuracy and efficiency. However, the impressive performance of deep learning-based AIP in laboratory settings often proves challenging to replicate in clinical practice. As the data preparation is important for AIP, the paper has reviewed AIP-related studies in the PubMed database published from January 2017 to February 2022, and 118 studies were included. An in-depth analysis of data preparation methods is conducted, encompassing the acquisition of pathological tissue slides, data cleaning, screening, and subsequent digitization. Expert review, image annotation, dataset division for model training and validation are also discussed. Furthermore, we delve into the reasons behind the challenges in reproducing the high performance of AIP in clinical settings and present effective strategies to enhance AIP's clinical performance. The robustness of AIP depends on a randomized collection of representative disease slides, incorporating rigorous quality control and screening, correction of digital discrepancies, reasonable annotation, and sufficient data volume. Digital pathology is fundamental in clinical-grade AIP, and the techniques of data standardization and weakly supervised learning methods based on whole slide image (WSI) are effective ways to overcome obstacles of performance reproduction. The key to performance reproducibility lies in having representative data, an adequate amount of labeling, and ensuring consistency across multiple centers. Digital pathology for clinical diagnosis, data standardization and the technique of WSI-based weakly supervised learning will hopefully build clinical-grade AIP.
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Affiliation(s)
- Yuanqing Yang
- Department of Biomedical Engineering, School of Basic Medical Sciences, Central South University, Changsha 410013, China; (Y.Y.); (K.S.)
- Department of Biomedical Engineering, School of Medical, Tsinghua University, Beijing 100084, China
| | - Kai Sun
- Department of Biomedical Engineering, School of Basic Medical Sciences, Central South University, Changsha 410013, China; (Y.Y.); (K.S.)
- Furong Laboratory, Changsha 410013, China
| | - Yanhua Gao
- Department of Ultrasound, Shaanxi Provincial People’s Hospital, Xi’an 710068, China;
| | - Kuansong Wang
- Department of Pathology, School of Basic Medical Sciences, Central South University, Changsha 410013, China;
- Department of Pathology, Xiangya Hospital, Central South University, Changsha 410013, China
| | - Gang Yu
- Department of Biomedical Engineering, School of Basic Medical Sciences, Central South University, Changsha 410013, China; (Y.Y.); (K.S.)
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Wuttisarnwattana P, Eck BL, Gargesha M, Wilson DL. Optimal slice thickness for improved accuracy of quantitative analysis of fluorescent cell and microsphere distribution in cryo-images. Sci Rep 2023; 13:10907. [PMID: 37407807 DOI: 10.1038/s41598-023-37927-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 06/29/2023] [Indexed: 07/07/2023] Open
Abstract
Cryo-imaging has been effectively used to study the biodistribution of fluorescent cells or microspheres in animal models. Sequential slice-by-slice fluorescent imaging enables detection of fluorescent cells or microspheres for corresponding quantification of their distribution in tissue. However, if slices are too thin, there will be data overload and excessive scan times. If slices are too thick, then cells can be missed. In this study, we developed a model for detection of fluorescent cells or microspheres to aid optimal slice thickness determination. Key factors include: section thickness (X), fluorescent cell intensity (Ifluo), effective tissue attenuation coefficient (μT), and a detection threshold (T). The model suggests an optimal slice thickness value that provides near-ideal sensitivity while minimizing scan time. The model also suggests a correction method to compensate for missed cells in the case that image data were acquired with overly large slice thickness. This approach allows cryo-imaging operators to use larger slice thickness to expedite the scan time without significant loss of cell count. We validated the model using real data from two independent studies: fluorescent microspheres in a pig heart and fluorescently labeled stem cells in a mouse model. Results show that slice thickness and detection sensitivity relationships from simulations and real data were well-matched with 99% correlation and 2% root-mean-square (RMS) error. We also discussed the detection characteristics in situations where key assumptions of the model were not met such as fluorescence intensity variation and spatial distribution. Finally, we show that with proper settings, cryo-imaging can provide accurate quantification of the fluorescent cell biodistribution with remarkably high recovery ratios (number of detections/delivery). As cryo-imaging technology has been used in many biological applications, our optimal slice thickness determination and data correction methods can play a crucial role in further advancing its usability and reliability.
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Affiliation(s)
- Patiwet Wuttisarnwattana
- Biomedical Engineering Institute, Department of Computer Engineering, Excellence Center in Infrastructure Technology and Transportation Engineering, Chiang Mai University, Chiang Mai, 50200, Thailand.
| | - Brendan L Eck
- Imaging Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA
| | | | - David L Wilson
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA.
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Wuttisarnwattana P, Auephanwiriyakul S. Spleen Tissue Segmentation Algorithm for Cryo-Imaging Data. J Digit Imaging 2023; 36:588-602. [PMID: 36441277 PMCID: PMC10039202 DOI: 10.1007/s10278-022-00736-2] [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: 05/18/2022] [Revised: 10/30/2022] [Accepted: 10/31/2022] [Indexed: 11/29/2022] Open
Abstract
Spleen tissue segmentation is an essential process for analyzing various immunological diseases as observed in the cryo-imaging data. Because manual labeling of the spleen tissue by human experts is not efficient, an automatic segmentation algorithm is needed. In this study, we developed a novel algorithm for automatically segmenting spleen substructures including white pulp and red pulp for the first time. The algorithm is designed for datasets created by a cryo-imaging system. This unique technology can effectively enable cellular tracking anywhere in the whole mouse with single-cell sensitivity. The proposed algorithm consists of four components: initial spleen mask creation, feature extraction, Supervised Patch-based Fuzzy c-Mean (spFCM) classification, and post-processing. The algorithm accurately and efficiently labeled spleen tissues in all experiment settings. The algorithm also improved the spleen segmentation throughput by 90 folds as compared to the manual segmentation. Moreover, we show that our novel spFCM algorithm outperformed traditional fast-learning classifiers as well as the U-Net deep-learning model in many aspects. Two major contributions of this paper are (1) an explainable algorithm for segmenting spleen tissues in cryo-images for the first time and (2) an spFCM algorithm as a new classifier. We also discussed that our work can be beneficial to researchers who work not only in the fields of graft-versus-host disease (GVHD) mouse models, but also in that of other immunological disease models where spleen analysis is essential. Future work building upon our research may lay the foundations for biomedical studies that utilize cryo-imaging technology.
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Affiliation(s)
- Patiwet Wuttisarnwattana
- Department of Computer Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, 50300, Thailand.
- Optimization Theory and Applications for Engineering Systems Research Group (OASYS), Chiang Mai University, Chiang Mai, 50300, Thailand.
- Excellence Center in Infrastructure Technology and Transportation Engineering (ExCITE), Chiang Mai University, Chiang Mai, 50300, Thailand.
- Biomedical Engineering Institute, Chiang Mai University, Chiang Mai, 50300, Thailand.
| | - Sansanee Auephanwiriyakul
- Department of Computer Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, 50300, Thailand.
- Excellence Center in Infrastructure Technology and Transportation Engineering (ExCITE), Chiang Mai University, Chiang Mai, 50300, Thailand.
- Biomedical Engineering Institute, Chiang Mai University, Chiang Mai, 50300, Thailand.
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Lu SY, Wang SH, Zhang YD. BCDNet: An Optimized Deep Network for Ultrasound Breast Cancer Detection. Ing Rech Biomed 2023. [DOI: 10.1016/j.irbm.2023.100774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
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Liu Y, Gargesha M, Scott B, Tchilibou Wane AO, Wilson DL. Deep learning multi-organ segmentation for whole mouse cryo-images including a comparison of 2D and 3D deep networks. Sci Rep 2022; 12:15161. [PMID: 36071089 PMCID: PMC9452525 DOI: 10.1038/s41598-022-19037-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Accepted: 08/23/2022] [Indexed: 11/25/2022] Open
Abstract
Cryo-imaging provided 3D whole-mouse microscopic color anatomy and fluorescence images that enables biotechnology applications (e.g., stem cells and metastatic cancer). In this report, we compared three methods of organ segmentation: 2D U-Net with 2D-slices and 3D U-Net with either 3D-whole-mouse or 3D-patches. We evaluated the brain, thymus, lung, heart, liver, stomach, spleen, left and right kidney, and bladder. Training with 63 mice, 2D-slices had the best performance, with median Dice scores of > 0.9 and median Hausdorff distances of < 1.2 mm in eightfold cross validation for all organs, except bladder, which is a problem organ due to variable filling and poor contrast. Results were comparable to those for a second analyst on the same data. Regression analyses were performed to fit learning curves, which showed that 2D-slices can succeed with fewer samples. Review and editing of 2D-slices segmentation results reduced human operator time from ~ 2-h to ~ 25-min, with reduced inter-observer variability. As demonstrations, we used organ segmentation to evaluate size changes in liver disease and to quantify the distribution of therapeutic mesenchymal stem cells in organs. With a 48-GB GPU, we determined that extra GPU RAM improved the performance of 3D deep learning because we could train at a higher resolution.
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Affiliation(s)
- Yiqiao Liu
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA
| | | | - Bryan Scott
- BioInVision Inc, Suite E 781 Beta Drive, Cleveland, OH, 44143, USA
| | - Arthure Olivia Tchilibou Wane
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA
| | - David L Wilson
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA. .,BioInVision Inc, Suite E 781 Beta Drive, Cleveland, OH, 44143, USA. .,Department of Radiology, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA.
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