{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T17:25:58Z","timestamp":1787937958490,"version":"build-2784847793"},"reference-count":50,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Technology, the Republic of China","award":["MOST 111-2221-E-194-007"],"award-info":[{"award-number":["MOST 111-2221-E-194-007"]}]},{"name":"Ministry of Science and Technology, the Republic of China","award":["MND-MAB-110-141"],"award-info":[{"award-number":["MND-MAB-110-141"]}]},{"name":"Ministry of Science and Technology, the Republic of China","award":["DTCRD112-C-11"],"award-info":[{"award-number":["DTCRD112-C-11"]}]},{"name":"Kaohsiung Armed Forces General Hospital research project","award":["MOST 111-2221-E-194-007"],"award-info":[{"award-number":["MOST 111-2221-E-194-007"]}]},{"name":"Kaohsiung Armed Forces General Hospital research project","award":["MND-MAB-110-141"],"award-info":[{"award-number":["MND-MAB-110-141"]}]},{"name":"Kaohsiung Armed Forces General Hospital research project","award":["DTCRD112-C-11"],"award-info":[{"award-number":["DTCRD112-C-11"]}]},{"name":"Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation-National Chung Cheng University Joint Research Program","award":["MOST 111-2221-E-194-007"],"award-info":[{"award-number":["MOST 111-2221-E-194-007"]}]},{"name":"Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation-National Chung Cheng University Joint Research Program","award":["MND-MAB-110-141"],"award-info":[{"award-number":["MND-MAB-110-141"]}]},{"name":"Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation-National Chung Cheng University Joint Research Program","award":["DTCRD112-C-11"],"award-info":[{"award-number":["DTCRD112-C-11"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JPM"],"abstract":"<jats:p>The severity of diabetic retinopathy (DR) is directly correlated to changes in both the oxygen utilization rate of retinal tissue as well as the blood oxygen saturation of both arteries and veins. Therefore, the current stage of DR in a patient can be identified by analyzing the oxygen content in blood vessels through fundus images. This enables medical professionals to make accurate and prompt judgments regarding the patient\u2019s condition. However, in order to use this method to implement supplementary medical treatment, blood vessels under fundus images need to be determined first, and arteries and veins then need to be differentiated from one another. Therefore, the entire study was split into three sections. After first removing the background from the fundus images using image processing, the blood vessels in the images were then separated from the background. Second, the method of hyperspectral imaging (HSI) was utilized in order to construct the spectral data. The HSI algorithm was utilized in order to perform analysis and simulations on the overall reflection spectrum of the retinal image. Thirdly, principal component analysis (PCA) was performed in order to both simplify the data and acquire the major principal components score plot for retinopathy in arteries and veins at all stages. In the final step, arteries and veins in the original fundus images were separated using the principal components score plots for each stage. As retinopathy progresses, the difference in reflectance between the arteries and veins gradually decreases. This results in a more difficult differentiation of PCA results in later stages, along with decreased precision and sensitivity. As a consequence of this, the precision and sensitivity of the HSI method in DR patients who are in the normal stage and those who are in the proliferative DR (PDR) stage are the highest and lowest, respectively. On the other hand, the indicator values are comparable between the background DR (BDR) and pre-proliferative DR (PPDR) stages due to the fact that both stages exhibit comparable clinical-pathological severity characteristics. The results indicate that the sensitivity values of arteries are 82.4%, 77.5%, 78.1%, and 72.9% in the normal, BDR, PPDR, and PDR, while for veins, these values are 88.5%, 85.4%, 81.4%, and 75.1% in the normal, BDR, PPDR, and PDR, respectively.<\/jats:p>","DOI":"10.3390\/jpm13060939","type":"journal-article","created":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T03:06:00Z","timestamp":1685588760000},"page":"939","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Optical Identification of Diabetic Retinopathy Using Hyperspectral Imaging"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8213-7873","authenticated-orcid":false,"given":"Ching-Yu","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Ophthalmology, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi 62247, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7741-3722","authenticated-orcid":false,"given":"Arvind","family":"Mukundan","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Sin","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Ming","family":"Tsao","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fen-Chi","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Kaohsiung Armed Forces General Hospital, Kaohsiung 80284, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen-Shuang","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi 62247, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4107-2062","authenticated-orcid":false,"given":"Hsiang-Chen","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan"},{"name":"Director of Technology Development, Hitspectra Intelligent Technology Co., Ltd., Kaohsiung 80661, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,1]]},"reference":[{"key":"ref_1","unstructured":"Kanski, J.J. (2007). Clinical Ophthalmology: A Systematic Approach, Elsevier Brasil."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1109\/RBME.2010.2084567","article-title":"Retinal imaging and image analysis","volume":"3","author":"Garvin","year":"2010","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Galdran, A., Meyer, M., Costa, P., and Campilho, A. (2019, January 8\u201311). Uncertainty-aware artery\/vein classification on retinal images. Proceedings of the 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy.","DOI":"10.1109\/ISBI.2019.8759380"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.artmed.2019.02.004","article-title":"Joint segmentation and classification of retinal arteries\/veins from fundus images","volume":"94","author":"Girard","year":"2019","journal-title":"Artif. Intell. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"267","DOI":"10.2147\/IJGM.S83749","article-title":"Changes in retinal microvascular diameter in patients with diabetes","volume":"8","author":"Gouvea","year":"2015","journal-title":"Int. J. Gen. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"102116","DOI":"10.1016\/j.artmed.2021.102116","article-title":"Simultaneous segmentation and classification of the retinal arteries and veins from color fundus images","volume":"118","author":"Morano","year":"2021","journal-title":"Artif. Intell. Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1109\/TMI.2004.825627","article-title":"Ridge-based vessel segmentation in color images of the retina","volume":"23","author":"Staal","year":"2004","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1109\/TPAMI.2003.1159954","article-title":"Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images","volume":"25","author":"Jiang","year":"2003","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Nain, D., Yezzi, A., and Turk, G. (2004, January 26\u201329). Vessel segmentation using a shape driven flow. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Saint-Malo, France.","DOI":"10.1007\/978-3-540-30135-6_7"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1109\/42.700738","article-title":"A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering","volume":"17","author":"Tolias","year":"1998","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"902","DOI":"10.1136\/bjo.83.8.902","article-title":"Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images","volume":"83","author":"Sinthanayothin","year":"1999","journal-title":"Br. J. Ophthalmol."},{"key":"ref_12","first-page":"146","article-title":"A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features","volume":"30","author":"Aquino","year":"2010","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.1109\/TMI.2016.2546227","article-title":"Segmenting retinal blood vessels with deep neural networks","volume":"35","author":"Liskowski","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Fu, H., Xu, Y., Lin, S., Kee Wong, D.W., and Liu, J. (2016, January 17\u201321). Deepvessel: Retinal vessel segmentation via deep learning and conditional random field. Proceedings of the International conference on medical image computing and computer-assisted intervention, Athens, Greece.","DOI":"10.1007\/978-3-319-46723-8_16"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Fu, H., Xu, Y., Wong, D.W.K., and Liu, J. (2016, January 13\u201316). Retinal vessel segmentation via deep learning network and fully-connected conditional random fields. Proceedings of the 2016 IEEE 13th international symposium on biomedical imaging (ISBI), Prague, Czech Republic.","DOI":"10.1109\/ISBI.2016.7493362"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Dasgupta, A., and Singh, S. (2017, January 18\u201321). A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation. Proceedings of the 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, VIC, Australia.","DOI":"10.1109\/ISBI.2017.7950512"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compmedimag.2018.04.005","article-title":"Retinal blood vessel segmentation using fully convolutional network with transfer learning","volume":"68","author":"Jiang","year":"2018","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lu, B., Dao, P.D., Liu, J., He, Y., and Shang, J. (2020). Recent advances of hyperspectral imaging technology and applications in agriculture. Remote Sens., 12.","DOI":"10.3390\/rs12162659"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mukundan, A., Patel, A., Saraswat, K.D., Tomar, A., and Kuhn, T. (2022, January 3\u20137). Kalam Rover. Proceedings of the AIAA SCITECH 2022 Forum, San Diego, CA, USA.","DOI":"10.2514\/6.2022-1047"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Gross, W., Queck, F., V\u00f6gtli, M., Schreiner, S., Kuester, J., B\u00f6hler, J., Mispelhorn, J., Kneub\u00fchler, M., and Middelmann, W. (2021, January 13\u201318). A multi-temporal hyperspectral target detection experiment: Evaluation of military setups. Proceedings of the Target and Background Signatures VII, Online, Spain.","DOI":"10.1117\/12.2597991"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mukundan, A., Feng, S.-W., Weng, Y.-H., Tsao, Y.-M., Artemkina, S.B., Fedorov, V.E., Lin, Y.-S., Huang, Y.-C., and Wang, H.-C. (2022). Optical and Material Characteristics of MoS2\/Cu2O Sensor for Detection of Lung Cancer Cell Types in Hydroplegia. Int. J. Mol. Sci., 23.","DOI":"10.3390\/ijms23094745"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hsiao, Y.-P., Mukundan, A., Chen, W.-C., Wu, M.-T., Hsieh, S.-C., and Wang, H.-C. (2022). Design of a Lab-On-Chip for Cancer Cell Detection through Impedance and Photoelectrochemical Response Analysis. Biosensors, 12.","DOI":"10.3390\/bios12060405"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Mukundan, A., Tsao, Y.-M., Artemkina, S.B., Fedorov, V.E., and Wang, H.-C. (2021). Growth Mechanism of Periodic-Structured MoS2 by Transmission Electron Microscopy. Nanomaterials, 12.","DOI":"10.3390\/nano12010135"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, C.-W., Tseng, Y.-S., Mukundan, A., and Wang, H.-C. (2021). Air Pollution: Sensitive Detection of PM2. 5 and PM10 Concentration Using Hyperspectral Imaging. Appl. Sci., 11.","DOI":"10.3390\/app11104543"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Mukundan, A., Huang, C.-C., Men, T.-C., Lin, F.-C., and Wang, H.-C. (2022). Air Pollution Detection Using a Novel Snap-Shot Hyperspectral Imaging Technique. Sensors, 22.","DOI":"10.3390\/s22166231"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Gerhards, M., Schlerf, M., Mallick, K., and Udelhoven, T. (2019). Challenges and future perspectives of multi-\/Hyperspectral thermal infrared remote sensing for crop water-stress detection: A review. Remote Sens., 11.","DOI":"10.3390\/rs11101240"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lee, C.-H., Mukundan, A., Chang, S.-C., Wang, Y.-L., Lu, S.-H., Huang, Y.-C., and Wang, H.-C. (2021). Comparative Analysis of Stress and Deformation between One-Fenced and Three-Fenced Dental Implants Using Finite Element Analysis. J. Clin. Med., 10.","DOI":"10.3390\/jcm10173986"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Stuart, M.B., McGonigle, A.J., and Willmott, J.R. (2019). Hyperspectral imaging in environmental monitoring: A review of recent developments and technological advances in compact field deployable systems. Sensors, 19.","DOI":"10.3390\/s19143071"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Mukundan, A., and Wang, H.-C. (2021). Simplified Approach to Detect Satellite Maneuvers Using TLE Data and Simplified Perturbation Model Utilizing Orbital Element Variation. Appl. Sci., 11.","DOI":"10.3390\/app112110181"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Tsai, C.-L., Mukundan, A., Chung, C.-S., Chen, Y.-H., Wang, Y.-K., Chen, T.-H., Tseng, Y.-S., Huang, C.-W., Wu, I.-C., and Wang, H.-C. (2021). Hyperspectral Imaging Combined with Artificial Intelligence in the Early Detection of Esophageal Cancer. Cancers, 13.","DOI":"10.3390\/cancers13184593"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Tsai, T.-J., Mukundan, A., Chi, Y.-S., Tsao, Y.-M., Wang, Y.-K., Chen, T.-H., Wu, I.-C., Huang, C.-W., and Wang, H.-C. (2022). Intelligent Identification of Early Esophageal Cancer by Band-Selective Hyperspectral Imaging. Cancers, 14.","DOI":"10.3390\/cancers14174292"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Fang, Y.-J., Mukundan, A., Tsao, Y.-M., Huang, C.-W., and Wang, H.-C. (2022). Identification of Early Esophageal Cancer by Semantic Segmentation. J. Pers. Med., 12.","DOI":"10.3390\/jpm12081204"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Vangi, E., D\u2019Amico, G., Francini, S., Giannetti, F., Lasserre, B., Marchetti, M., and Chirici, G. (2021). The new hyperspectral satellite PRISMA: Imagery for forest types discrimination. Sensors, 21.","DOI":"10.3390\/s21041182"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Huang, S.-Y., Mukundan, A., Tsao, Y.-M., Kim, Y., Lin, F.-C., and Wang, H.-C. (2022). Recent Advances in Counterfeit Art, Document, Photo, Hologram, and Currency Detection Using Hyperspectral Imaging. Sensors, 22.","DOI":"10.3390\/s22197308"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, X., Han, L., Dong, Y., Shi, Y., Huang, W., Han, L., Gonz\u00e1lez-Moreno, P., Ma, H., Ye, H., and Sobeih, T. (2019). A deep learning-based approach for automated yellow rust disease detection from high-resolution hyperspectral UAV images. Remote Sens., 11.","DOI":"10.3390\/rs11131554"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hennessy, A., Clarke, K., and Lewis, M. (2020). Hyperspectral classification of plants: A review of waveband selection generalisability. Remote Sens., 12.","DOI":"10.3390\/rs12010113"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Terentev, A., Dolzhenko, V., Fedotov, A., and Eremenko, D. (2022). Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review. Sensors, 22.","DOI":"10.3390\/s22030757"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"De La Rosa, R., Tolosana-Delgado, R., Kirsch, M., and Gloaguen, R. (2022). Automated Multi-Scale and Multivariate Geological Logging from Drill-Core Hyperspectral Data. Remote Sens., 14.","DOI":"10.3390\/rs14112676"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yao, H.-Y., Tseng, K.-W., Nguyen, H.-T., Kuo, C.-T., and Wang, H.-C. (2020). Hyperspectral Ophthalmoscope Images for the Diagnosis of Diabetic Retinopathy Stage. J. Clin. Med., 9.","DOI":"10.3390\/jcm9061613"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.diabres.2011.10.029","article-title":"IDF diabetes atlas: Global estimates of the prevalence of diabetes for 2011 and 2030","volume":"94","author":"Whiting","year":"2011","journal-title":"Diabetes Res. Clin. Pract."},{"key":"ref_41","first-page":"2540","article-title":"Klein RK Diabetic retinopathy","volume":"27","author":"Fong","year":"2004","journal-title":"Ophthalmol. L"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1608","DOI":"10.1016\/j.ophtha.2018.04.007","article-title":"Guidelines on diabetic eye care: The international council of ophthalmology recommendations for screening, follow-up, referral, and treatment based on resource settings","volume":"125","author":"Wong","year":"2018","journal-title":"Ophthalmology"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"412","DOI":"10.2337\/dc16-2641","article-title":"Diabetic retinopathy: A position statement by the American Diabetes Association","volume":"40","author":"Solomon","year":"2017","journal-title":"Diabetes Care"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1556","DOI":"10.2337\/dc09-0565","article-title":"Advances in the medical treatment of diabetic retinopathy","volume":"32","year":"2009","journal-title":"Diabetes Care"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1450","DOI":"10.1096\/fj.03-1476fje","article-title":"A central role for inflammation in the pathogenesis of diabetic retinopathy","volume":"18","author":"Joussen","year":"2004","journal-title":"FASEB J."},{"key":"ref_46","first-page":"2423333","article-title":"Correlation of Platelet Function with Postpartum Hemorrhage and Venous Thromboembolism in Patients with Gestational Hypertension Complicated with Diabetes","volume":"2022","author":"Li","year":"2022","journal-title":"Comput. Math. Methods Med."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/S0749-0690(18)30072-7","article-title":"Gradual painless visual loss: Retinal causes","volume":"15","author":"Einer","year":"1999","journal-title":"Clin. Geriatr. Med."},{"key":"ref_48","unstructured":"Littmann, H. (1995). Die Zeiss Funduskamera Ber. 59. Zusammenkunft Deutsch. Ophthalmolog. Gesellsch., Heidelberg 1955, Verlag Bergmann."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1364\/OL.25.000111","article-title":"Spectroscopic optical coherencetomography","volume":"25","author":"Morgner","year":"2000","journal-title":"Opt. Lett."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"101","DOI":"10.4236\/jbise.2010.31015","article-title":"Automatic detection of multiple oriented blood vessels in retinal images","volume":"3","author":"Siddalingaswamy","year":"2010","journal-title":"J. Biomed. Sci. Eng."}],"container-title":["Journal of Personalized Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4426\/13\/6\/939\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:46:51Z","timestamp":1760125611000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4426\/13\/6\/939"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,1]]},"references-count":50,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["jpm13060939"],"URL":"https:\/\/doi.org\/10.3390\/jpm13060939","relation":{},"ISSN":["2075-4426"],"issn-type":[{"value":"2075-4426","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,1]]}}}