{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T00:05:12Z","timestamp":1793232312355,"version":"4.5.0"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T00:00:00Z","timestamp":1764115200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T00:00:00Z","timestamp":1764115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100017526","name":"International Cooperation Project of Ningbo City","doi-asserted-by":"publisher","award":["2023H015"],"award-info":[{"award-number":["2023H015"]}],"id":[{"id":"10.13039\/501100017526","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Province \u201cThree Rural Nine-Party\u201d Science and Technology Cooperation Plan","award":["2023SNJF071"],"award-info":[{"award-number":["2023SNJF071"]}]},{"name":"Ningbo Welfare Project","award":["2024S142"],"award-info":[{"award-number":["2024S142"]}]},{"name":"The Norwegian Agency for Shared Services in Education and Research","award":["000"],"award-info":[{"award-number":["000"]}]},{"DOI":"10.13039\/100015995","name":"Nord University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100015995","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Parasites Vectors"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      Disease outbreaks, particularly cryptocaryoniasis caused by the ciliate\n                      <jats:italic>Cryptocaryon irritans<\/jats:italic>\n                      , pose significant barriers to sustainable marine fish aquaculture, undermining productivity, profitability, and biosecurity. Despite its impact, early warning tools for parasitic diseases leveraging advanced technologies remain underdeveloped.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We developed a machine learning (ML)-driven early warning system for cryptocaryoniasis, integrating seven years of outbreak surveillance data (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009429 events from 2016 to 2023) with 17 high-resolution oceanographic predictors influencing parasite life cycles along China\u2019s coast. Five supervised ML models: logistic regression (LR), support vector machine (SVM), random forest (RF), XGBoost (XGB), and artificial neural network (ANN), were trained using cross-validation and benchmarked in commercial open-sea cages and recirculating aquaculture systems (RAS).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The RF model achieved the highest sensitivity (98.6%), with RF and XGB excelling in F1 scores (0.93 and 0.938, respectively), identifying stocking density, water temperature, salinity, pH, and novel predictors such as silicate and nitrate as key risk factors. The predictive engine was deployed as an open-source web-based platform, delivering weekly, spatially resolved outbreak forecasts. Field validation across 12 open-sea cage events and weekly RAS monitoring confirmed high predictive accuracy (91.67% in sea cages; 87.5% in RAS), revealing seasonal and latitudinal disease trends.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study establishes a robust, scalable framework for real-time disease forecasting in marine aquaculture, adaptable to other aquatic pathogen-host species to support parasite surveillance and precision health management across diverse global aquaculture systems. While further validation with larger datasets and integration of pathogen and host data will enhance future models, this system provides a flexible foundation for advancing disease control in aquatic environments.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphical Abstract<\/jats:title>\n                  <\/jats:sec>","DOI":"10.1186\/s13071-025-07124-z","type":"journal-article","created":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T14:55:44Z","timestamp":1764168944000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A machine learning-driven early warning system for cryptocaryoniasis in marine aquaculture"],"prefix":"10.1186","volume":"18","author":[{"given":"Xiao","family":"Xie","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunyan","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kurt","family":"Buchmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suming","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuezhuo","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jorge","family":"Galindo-Villegas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,26]]},"reference":[{"key":"7124_CR1","volume-title":"The state of world fisheries and aquaculture","author":"FAO","year":"2024","unstructured":"FAO. The state of world fisheries and aquaculture. Rome: FAO; 2024."},{"key":"7124_CR2","doi-asserted-by":"publisher","first-page":"110838","DOI":"10.1016\/j.isci.2024.110838","volume":"27","author":"AF Rowley","year":"2024","unstructured":"Rowley AF, Baker-Austin C, Boerlage AS, Caillon C, Davies CE, Duperret L, et al. Diseases of marine fish and shellfish in an age of rapid climate change. iScience. 2024;27:110838. https:\/\/doi.org\/10.1016\/j.isci.2024.110838.","journal-title":"iScience"},{"key":"7124_CR3","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.ympev.2017.04.015","volume":"112","author":"H Chi","year":"2017","unstructured":"Chi H, Taik P, Foley EJ, Racicot AC, Gray HM, Guzzetta KE, et al. High genetic diversities between isolates of the fish parasite Cryptocaryon irritans (Ciliophora) suggest multiple cryptic species. Mol Phylogenet Evol. 2017;112:47\u201352. https:\/\/doi.org\/10.1016\/j.ympev.2017.04.015.","journal-title":"Mol Phylogenet Evol"},{"key":"7124_CR4","doi-asserted-by":"publisher","first-page":"108081","DOI":"10.1016\/j.exppara.2021.108081","volume":"223","author":"X Xie","year":"2021","unstructured":"Xie X, Zheng C, Zahid A, Kong J, Bushra, Qian D, et al. Updating specific PCR primer for detection of Cryptocaryon irritans from reared Larimichthys polyactis. Exp Parasitol. 2021;223:108081.","journal-title":"Exp Parasitol"},{"key":"7124_CR5","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1111\/raq.12594","volume":"14","author":"Y Li","year":"2022","unstructured":"Li Y, Jiang B, Mo Z, Li A, Dan X. Cryptocaryon irritans (Brown, 1951) is a serious threat to aquaculture of marine fish. Rev Aquacult. 2022;14:218\u201336.","journal-title":"Rev Aquacult"},{"key":"7124_CR6","doi-asserted-by":"publisher","first-page":"1424669","DOI":"10.3389\/fcimb.2024.1424669","volume":"14","author":"X Guo","year":"2024","unstructured":"Guo X, Huang W, Xu Y, Zhan Q, Sun P, Hu H. Metabolomic changes in Cryptocaryon irritans from Larimichthys crocea after exposure to copper plate. Front Cell Infect Microbiol. 2024;14:1424669.","journal-title":"Front Cell Infect Microbiol"},{"key":"7124_CR7","doi-asserted-by":"publisher","first-page":"850026","DOI":"10.3389\/fimmu.2022.850026","volume":"13","author":"J Galindo-Villegas","year":"2022","unstructured":"Galindo-Villegas J, Bossier P, Reyes-L\u00f3pez FE. Editorial: Oral immune-enhancing research in fish. Front Immunol. 2022;13:850026.","journal-title":"Front Immunol"},{"key":"7124_CR8","doi-asserted-by":"publisher","first-page":"739694","DOI":"10.1016\/j.aquaculture.2023.739694","volume":"574","author":"F Yin","year":"2023","unstructured":"Yin F, Yin J, Xie X, Jiang L. Water temperature affects Cryptocaryon irritans development, cryptocaryoniasis occurrence and an auxiliary treatment decision-making webpage. Aquaculture. 2023;574:739694.","journal-title":"Aquaculture"},{"key":"7124_CR9","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.aquaculture.2016.08.041","volume":"467","author":"D Standing","year":"2017","unstructured":"Standing D, Brunner T, Aruety T, Ronen Z, Gross A, Zilberg D. Mortality of Cryptocaryon irritans in sludge from a digester of a marine recirculating aquaculture system. Aquaculture. 2017;467:134\u20137.","journal-title":"Aquaculture"},{"key":"7124_CR10","doi-asserted-by":"publisher","first-page":"e0005822","DOI":"10.1128\/aem.00058-22","volume":"88","author":"L Zhou","year":"2022","unstructured":"Zhou L, Huang J, Jiang Y, Kong J, Xie X, Yin F. pH regulates the formation and hatching of Cryptocaryon irritans tomonts, which affects cryptocaryoniasis occurrence in Larimichthys crocea aquaculture. Appl Environ Microbiol. 2022;88:e0005822.","journal-title":"Appl Environ Microbiol"},{"key":"7124_CR11","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.pt.2021.10.007","volume":"38","author":"C Selbach","year":"2022","unstructured":"Selbach C, Mouritsen KN, Poulin R, Sures B, Smit NJ. Bridging the gap: aquatic parasites in the one health concept. Trends Parasitol. 2022;38:109\u201311.","journal-title":"Trends Parasitol"},{"key":"7124_CR12","doi-asserted-by":"publisher","DOI":"10.1080\/29932181.2024.2441506","author":"C Brugere","year":"2025","unstructured":"Brugere C, Kumar G, Bondad-Reantaso MG. Bridging aquatic organism health and economics in the analysis of disease impacts and biosecurity strategies in aquaculture: a conceptual framework. Crit. Insights Aquac. 2025;1,2441506. https:\/\/doi.org\/10.1080\/29932181.2024.2441506.","journal-title":"Crit Insights Aquacult"},{"key":"7124_CR13","doi-asserted-by":"publisher","first-page":"100250","DOI":"10.1016\/j.watbs.2024.100250","volume":"3","author":"RE Gozlan","year":"2024","unstructured":"Gozlan RE, Bommarito C, Caballero-Huertas M, Givens J, Mortillaro J-M, Pepey E, et al. A one-health approach to non-native species, aquaculture, and food security. Water Biol Security. 2024;3:100250.","journal-title":"Water Biol Security"},{"key":"7124_CR14","doi-asserted-by":"publisher","first-page":"736724","DOI":"10.1016\/j.aquaculture.2021.736724","volume":"540","author":"S Zhao","year":"2021","unstructured":"Zhao S, Zhang S, Liu J, Wang H, Zhu J, Li D, et al. Application of machine learning in intelligent fish aquaculture: a review. Aquaculture. 2021;540:736724.","journal-title":"Aquaculture"},{"key":"7124_CR15","doi-asserted-by":"publisher","first-page":"100964","DOI":"10.1016\/j.cois.2022.100964","volume":"54","author":"HE Tonnang","year":"2022","unstructured":"Tonnang HE, Salifu D, Mudereri BT, Tanui J, Espira A, Dubois T, et al. Advances in data-collection tools and analytics for crop pest and disease management. Curr Opin Insect Sci. 2022;54:100964.","journal-title":"Curr Opin Insect Sci"},{"key":"7124_CR16","doi-asserted-by":"publisher","first-page":"739155","DOI":"10.1016\/j.aquaculture.2022.739155","volume":"565","author":"SW Dahle","year":"2023","unstructured":"Dahle SW, Gaarden SI, Buhaug JF, Netzer R, Attramadal KJK, Busche T, et al. Long-term microbial community structures and dynamics in a commercial RAS during seven production batches of Atlantic salmon fry (Salmo salar). Aquaculture. 2023;565:739155.","journal-title":"Aquaculture"},{"key":"7124_CR17","first-page":"S1","volume":"11","author":"K von Schuckmann","year":"2018","unstructured":"von Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Brasseur P, Fennel K, et al. Copernicus marine service ocean state report. J Oper Oceanogr. 2018;11:S1\u2013142.","journal-title":"J Oper Oceanogr"},{"key":"7124_CR18","unstructured":"Hijmans RJ. terra: Spatial Data Analysis. The R Foundation. 2024."},{"key":"7124_CR19","unstructured":"R Core T. R: A Language and Environment for Statistical Computing. Computer software. Vienna, Austria.: Foundation for Statistical Computing; 2024."},{"key":"7124_CR20","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random forests. Mach Learn. 2001;45:5\u201332.","journal-title":"Mach Learn"},{"key":"7124_CR21","volume-title":"Statistical analysis with missing data","author":"R Little","year":"2019","unstructured":"Little R, Rubin D. Statistical analysis with missing data. 3rd ed. New York: Wiley; 2019.","edition":"3"},{"key":"7124_CR22","doi-asserted-by":"publisher","DOI":"10.32614\/RJ-2014-008","volume":"6","author":"N Lunardon","year":"2014","unstructured":"Lunardon N, Menardi G, Torelli N. ROSE: a package for binary imbalanced learning. R J. 2014;6:79.","journal-title":"R J"},{"key":"7124_CR23","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. JAIR. 2002;16:321\u201357.","journal-title":"JAIR."},{"key":"7124_CR24","unstructured":"Wickham H, Fran\u00e7ois R, Henry L, M\u00fcller K, Vaughan D. dplyr: A Grammar of Data Manipulation. The R Foundation. 2024."},{"key":"7124_CR25","unstructured":"Wickham H, Vaughan D, Girlich M. tidyr: Tidy Messy Data. The R Foundation. 2024."},{"key":"7124_CR26","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1007\/s11222-016-9646-1","volume":"27","author":"B Gregorutti","year":"2017","unstructured":"Gregorutti B, Michel B, Saint-Pierre P. Correlation and variable importance in random forests. Stat Comput. 2017;27:659\u201378.","journal-title":"Stat Comput"},{"key":"7124_CR27","unstructured":"Meyer D, Dimitriadou E, Hornik K, Weingessel A, Leisch F. e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. The R Foundation. 2024."},{"key":"7124_CR28","unstructured":"Breiman L, Cutler A, Liaw A, Wiener M. randomForest: Breiman and Cutlers Random Forests for Classification and Regression. The R Foundation. 2024."},{"key":"7124_CR29","volume-title":"xgboost: extreme gradient boosting","author":"T Chen","year":"2024","unstructured":"Chen T, He T, Benesty M, Khotilovich V, Tang Y, Cho H, et al. xgboost: extreme gradient boosting. Vienna: The R Foundation; 2024."},{"key":"7124_CR30","doi-asserted-by":"publisher","first-page":"30","DOI":"10.32614\/RJ-2010-006","volume":"2","author":"F G\u00fcnther","year":"2010","unstructured":"G\u00fcnther F, Fritsch S. Neuralnet: training of neural networks. R J. 2010;2:30.","journal-title":"R J"},{"key":"7124_CR31","doi-asserted-by":"publisher","first-page":"8377","DOI":"10.1038\/s41598-022-12316-z","volume":"12","author":"Q Bai","year":"2022","unstructured":"Bai Q, Su C, Tang W, Li Y. Machine learning to predict end stage kidney disease in chronic kidney disease. Sci Rep. 2022;12:8377.","journal-title":"Sci Rep"},{"key":"7124_CR32","doi-asserted-by":"publisher","first-page":"6262","DOI":"10.1038\/s41598-022-10346-1","volume":"12","author":"N Niimi","year":"2022","unstructured":"Niimi N, Shiraishi Y, Sawano M, Ikemura N, Inohara T, Ueda I, et al. Machine learning models for prediction of adverse events after percutaneous coronary intervention. Sci Rep. 2022;12:6262.","journal-title":"Sci Rep"},{"key":"7124_CR33","doi-asserted-by":"publisher","first-page":"8757","DOI":"10.1038\/s41598-022-12033-7","volume":"12","author":"X Xu","year":"2022","unstructured":"Xu X, Fairley CK, Chow EPF, Lee D, Aung ET, Zhang L, et al. Using machine learning approaches to predict timely clinic attendance and the uptake of HIV\/STI testing post clinic reminder messages. Sci Rep. 2022;12:8757.","journal-title":"Sci Rep"},{"key":"7124_CR34","doi-asserted-by":"publisher","first-page":"105886","DOI":"10.1016\/j.ypmed.2019.105886","volume":"130","author":"D-H Han","year":"2020","unstructured":"Han D-H, Lee S, Seo D-C. Using machine learning to predict opioid misuse among US adolescents. Prev Med. 2020;130:105886.","journal-title":"Prev Med"},{"key":"7124_CR35","doi-asserted-by":"publisher","DOI":"10.3390\/s20092649","author":"AU Haq","year":"2020","unstructured":"Haq AU, Li JP, Khan J, Memon MH, Nazir S, Ahmad S, et al. Intelligent machine learning approach for effective recognition of diabetes in E-healthcare using clinical data. Sensors 2020;20, 2649. https:\/\/doi.org\/10.3390\/s20092649.","journal-title":"Sensors"},{"key":"7124_CR36","doi-asserted-by":"publisher","first-page":"bbab415","DOI":"10.1093\/bib\/bbab415","volume":"23","author":"L Jia","year":"2022","unstructured":"Jia L, Yao W, Jiang Y, Li Y, Wang Z, Li H, et al. Development of interactive biological web applications with R\/Shiny. Brief Bioinform. 2022;23:bbab415. https:\/\/doi.org\/10.1093\/bib\/bbab415.","journal-title":"Brief Bioinform"},{"key":"7124_CR37","doi-asserted-by":"publisher","unstructured":"Zhou R, Sun K, Xie X, Yin F and Galindo-Villegas J, et al.  Integrated transcriptomic and immune enzymatic analyses uncover coordinated immunometabolic responses in large yellow croaker (Larimichthys crocea) to Metanophrys sp. infection. Front. Immunol. 2025;16:1636453. https:\/\/doi.org\/10.3389\/fimmu.2025.1636453","DOI":"10.3389\/fimmu.2025.1636453"},{"key":"7124_CR38","doi-asserted-by":"publisher","DOI":"10.32473\/edis-fa164-2009","author":"RP Yanong","year":"2010","unstructured":"Yanong RP. Cryptocaryon irritans infections (marine white spot disease) in fish. EDIS. 2010. https:\/\/doi.org\/10.32473\/edis-fa164-2009.","journal-title":"EDIS"},{"key":"7124_CR39","doi-asserted-by":"publisher","first-page":"740501","DOI":"10.1016\/j.aquaculture.2023.740501","volume":"582","author":"L Zhou","year":"2024","unstructured":"Zhou L, Zhou R, Xie X, Yin F. Characteristics and risk assessment of cryptocaryoniasis in large yellow croaker (Larimichthys crocea) at different densities in industrialized aquaculture. Aquaculture. 2024;582:740501.","journal-title":"Aquaculture"},{"key":"7124_CR40","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.aquaculture.2017.11.024","volume":"485","author":"J-L Wang","year":"2018","unstructured":"Wang J-L, Lao G-F, Li Y-W, Yang M, Mo Z-Q, Dan X-M. Effects of temperature and host species on the life cycle of Cryptocaryon irritans. Aquaculture. 2018;485:49\u201352.","journal-title":"Aquaculture"},{"key":"7124_CR41","doi-asserted-by":"publisher","first-page":"738166","DOI":"10.1016\/j.aquaculture.2022.738166","volume":"554","author":"J Kong","year":"2022","unstructured":"Kong J, Zhou L, Yuan Y, Wang L, Kang T, Wu J, et al. Salinity regulates the formation and hatching of Cryptocaryon irritans tomonts, affecting infectivity to Larimichthys crocea. Aquaculture. 2022;554:738166.","journal-title":"Aquaculture"},{"key":"7124_CR42","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1017\/S0025315400047652","volume":"74","author":"PJ Burgess","year":"1994","unstructured":"Burgess PJ, Matthews RA. Cryptocaryon irritans (Ciliophora): photoperiod and transmission in marine fish. J Mar Biol Ass. 1994;74:535\u201342.","journal-title":"J Mar Biol Ass"},{"key":"7124_CR43","doi-asserted-by":"publisher","first-page":"102812","DOI":"10.1016\/j.parint.2023.102812","volume":"98","author":"M Kotake","year":"2024","unstructured":"Kotake M, Watanabe Y, Itoh N, Yoshinaga T. Effect of light exposure on circadian rhythm in theront excystment in Cryptocaryon irritans. Parasitol Int. 2024;98:102812.","journal-title":"Parasitol Int"},{"key":"7124_CR44","doi-asserted-by":"publisher","first-page":"734557","DOI":"10.1016\/j.aquaculture.2019.734557","volume":"516","author":"DC Skilton","year":"2020","unstructured":"Skilton DC, Saunders RJ, Hutson KS. Parasite attractants: identifying trap baits for parasite management in aquaculture. Aquaculture. 2020;516:734557.","journal-title":"Aquaculture"},{"key":"7124_CR45","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/j.aquaculture.2017.11.026","volume":"485","author":"Y Watanabe","year":"2018","unstructured":"Watanabe Y, How KH, Zenke K, Itoh N, Yoshinaga T. Dormancy induced by a hypoxic environment in tomonts of Cryptocaryon irritans, a parasitic ciliate of marine teleosts. Aquaculture. 2018;485:131\u20139.","journal-title":"Aquaculture"},{"key":"7124_CR46","doi-asserted-by":"publisher","first-page":"38","DOI":"10.3147\/jsfp.55.38","volume":"55","author":"Y Watanabe","year":"2020","unstructured":"Watanabe Y, How KH, Zenke K, Itoh N, Yoshinaga T. Control of the daily rhythms by photoperiods in protomont detachment and theront excystment of the parasitic ciliate Cryptocaryon irritans. Fish Pathol. 2020;55:38\u201341.","journal-title":"Fish Pathol"},{"key":"7124_CR47","doi-asserted-by":"publisher","first-page":"720","DOI":"10.1017\/S0031182016002651","volume":"144","author":"R Ma","year":"2017","unstructured":"Ma R, Fan X, Yin F, Ni B, Gu F. Ultrastructural features of the tomont of Cryptocaryon irritans (Ciliophora: Prostomatea), a parasitic ciliate of marine fishes. Parasitology. 2017;144:720\u20139.","journal-title":"Parasitology"},{"key":"7124_CR48","doi-asserted-by":"publisher","DOI":"10.3390\/ma13184054","author":"X Wang","year":"2020","unstructured":"Wang X, Xie W, Li T, Ren J, Zhu J, Han N, et al. Molecular dynamics study on mechanical properties of interface between urea-formaldehyde resin and calcium-silicate-hydrates. Materials 2020;13, 4054. https:\/\/doi.org\/10.3390\/ma13184054.","journal-title":"Materials"},{"key":"7124_CR49","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/S0044-8486(00)00300-8","volume":"187","author":"P Leung","year":"2000","unstructured":"Leung P, Tran LT. Predicting shrimp disease occurrence: artificial neural networks vs. logistic regression. Aquaculture. 2000;187:35\u201349.","journal-title":"Aquaculture"},{"key":"7124_CR50","doi-asserted-by":"publisher","first-page":"89","DOI":"10.3390\/stats2010007","volume":"2","author":"H Hassani","year":"2019","unstructured":"Hassani H, Silva E, Combe M, Andreou D, Ghodsi M, Yeganegi M, et al. A support vector machine based approach for predicting the risk of freshwater disease emergence in England. Stats. 2019;2:89\u2013103.","journal-title":"Stats"},{"key":"7124_CR51","unstructured":"Cai XP. Preliminary establishment of large yellow croak disease diagnosis and pre-warning system. Doctoral dissertation. Xiamen University; 2013."},{"key":"7124_CR52","unstructured":"Zhou RJ. Studies on the forecasting of the main disease in cage-cultured Pseudosciaena crocea. Doctoral dissertation. Ningbo University; 2011."},{"key":"7124_CR53","doi-asserted-by":"publisher","DOI":"10.3389\/fmars.2019.00083","author":"F Gasparin","year":"2019","unstructured":"Gasparin F, Guinehut S, Mao C, Mirouze I, R\u00e9my E, King RR, et al. Requirements for an integrated in situ Atlantic Ocean observing system from coordinated observing system simulation experiments. Front. Mar. Sci. 2019;6, 83. https:\/\/doi.org\/10.3389\/fmars.2019.00083.","journal-title":"Front Mar Sci"},{"key":"7124_CR54","doi-asserted-by":"publisher","first-page":"e29022","DOI":"10.1016\/j.heliyon.2024.e29022","volume":"10","author":"NA Mohd Jais","year":"2024","unstructured":"Mohd Jais NA, Abdullah AF, Mohd Kassim MS, Abd Karim MM, Abdulsalam M, Muhadi NA. Improved accuracy in IoT-Based water quality monitoring for aquaculture tanks using low-cost sensors: Asian seabass fish farming. Heliyon. 2024;10:e29022.","journal-title":"Heliyon."},{"key":"7124_CR55","doi-asserted-by":"publisher","DOI":"10.3389\/fmars.2016.00213","author":"R Danovaro","year":"2016","unstructured":"Danovaro R, Carugati L, Berzano M, Cahill A, Carvalho S, Chenuil A, et al. Implementing and innovating marine monitoring approaches for assessing marine environmental status. Front. Mar. Sci. 2016;3, 213. https:\/\/doi.org\/10.3389\/fmars.2016.00213.","journal-title":"Front Mar Sci"},{"key":"7124_CR56","doi-asserted-by":"crossref","unstructured":"Ma J, Ma R, Pan Q, Liang X, Wang J, Ni X. A global review of progress in remote sensing and monitoring of marine pollution. Water. 2023;15:3491.","DOI":"10.3390\/w15193491"}],"container-title":["Parasites &amp; Vectors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13071-025-07124-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13071-025-07124-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13071-025-07124-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T14:55:49Z","timestamp":1764168949000},"score":1,"resource":{"primary":{"URL":"https:\/\/parasitesandvectors.biomedcentral.com\/articles\/10.1186\/s13071-025-07124-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,26]]},"references-count":56,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["7124"],"URL":"https:\/\/doi.org\/10.1186\/s13071-025-07124-z","relation":{},"ISSN":["1756-3305"],"issn-type":[{"value":"1756-3305","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,26]]},"assertion":[{"value":"14 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"490"}}