{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T03:14:19Z","timestamp":1788491659949,"version":"build-2803163510"},"update-to":[{"DOI":"10.1371\/journal.pntd.0013991","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T00:00:00Z","timestamp":1773100800000}}],"reference-count":57,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T00:00:00Z","timestamp":1771459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62322301"],"award-info":[{"award-number":["62322301"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172002"],"award-info":[{"award-number":["62172002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The University Outstanding Youth Research Project of the Education Commission of Anhui Province","award":["2022AH020010"],"award-info":[{"award-number":["2022AH020010"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82273977"],"award-info":[{"award-number":["82273977"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Joint International Research Laboratory of Prevention and Control of Major Diseases in Central Asia Fund","award":["JIRL-MDCA-2024-GH1"],"award-info":[{"award-number":["JIRL-MDCA-2024-GH1"]}]}],"content-domain":{"domain":["www.plosntds.org"],"crossmark-restriction":false},"short-container-title":["PLoS Negl Trop Dis"],"abstract":"<jats:p>\n                    Parasitic diseases impose a substantial global health burden due to the widespread transmission and diversity of protozoa and helminths, which cause numerous infections and regional outbreaks. Despite the availability of various antiparasitic drugs, their clinical utility is often constrained by high cost, toxicity, severe side effects, and the growing threat of drug resistance. Combination therapy, designed to enhance efficacy through synergistic effects while reducing toxicity, represents a promising strategy to improve treatment outcomes for parasitic diseases. In this work, we propose MetaSynMT, a novel multi-task learning framework designed to predict synergistic and safe drug combinations, with a specific focus on parasitic diseases. The model integrates a meta-path aggregation mechanism to capture both structural and high-order semantic features of drugs. Alongside the primary task of synergy prediction, we introduce a secondary task of side effect prediction, enabling the joint identification of combinations with high synergy and low toxicity. Experimental results demonstrate that MetaSynMT outperforms several state-of-the-art baselines on parasitic disease dataset and exhibits strong generalization capability across diverse real-world settings. Furthermore, based on MetaSynMT\u2019s predictions, we identified allicin and sodium stibogluconate as a promising combination therapy for echinococcosis. In vitro protoscolex culture experiments showed that the combination achieved a 100% inhibition rate at concentrations of 850\n                    <jats:italic>\u03bc<\/jats:italic>\n                    M allicin and 36.3\n                    <jats:italic>\u03bc<\/jats:italic>\n                    M sodium stibogluconate, significantly surpassing monotherapies. Overall, this work provides a novel computational tool and theoretical foundation for optimizing antiparasitic drug combinations and discovering potential therapeutic strategies.\n                  <\/jats:p>","DOI":"10.1371\/journal.pntd.0013991","type":"journal-article","created":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T18:45:48Z","timestamp":1771526748000},"page":"e0013991","update-policy":"https:\/\/doi.org\/10.1371\/journal.pntd.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Systematic modeling predicts synergistic and safe drug combinations for parasitic diseases"],"prefix":"10.1371","volume":"20","author":[{"given":"Yansen","family":"Su","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guodong","family":"Lv","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4208-2027","authenticated-orcid":true,"given":"Hanjing","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,2,19]]},"reference":[{"key":"pntd.0013991.ref001","doi-asserted-by":"crossref","first-page":"102193","DOI":"10.1016\/j.mib.2022.102193","article-title":"Plasmodium falciparum resistance to artemisinin-based combination therapies","volume":"69","author":"KE Ward","year":"2022","journal-title":"Curr Opin Microbiol."},{"issue":"1","key":"pntd.0013991.ref002","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1186\/s12936-024-04924-z","article-title":"Optimal balance of benefit versus risk for tafenoquine in the treatment of Plasmodium vivax malaria","volume":"23","author":"R Sharma","year":"2024","journal-title":"Malar J."},{"key":"pntd.0013991.ref003","doi-asserted-by":"crossref","DOI":"10.3389\/fddsv.2023.1201419","article-title":"Drug discovery and development: introduction to the general public and patient groups","volume":"3","author":"N Singh","year":"2023","journal-title":"Front Drug Discov."},{"key":"pntd.0013991.ref004","unstructured":"Ertabaklar H, Malatyal E, Ertu S. Drug resistance in parasitic diseases. 2020."},{"issue":"24","key":"pntd.0013991.ref005","doi-asserted-by":"crossref","first-page":"13310","DOI":"10.3390\/ijms222413310","article-title":"The framework for human host immune responses to four types of parasitic infections and relevant key JAK\/STAT signaling","volume":"22","author":"T-H Wen","year":"2021","journal-title":"Int J Mol Sci."},{"issue":"4","key":"pntd.0013991.ref006","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.pt.2023.01.010","article-title":"Drug discovery for parasitic diseases: powered by technology, enabled by pharmacology, informed by clinical science","volume":"39","author":"SPS Rao","year":"2023","journal-title":"Trends Parasitol."},{"issue":"2","key":"pntd.0013991.ref007","doi-asserted-by":"crossref","first-page":"231","DOI":"10.4269\/ajtmh.2012.12-0025","article-title":"Artemisinin-resistant malaria: research challenges, opportunities, and public health implications","volume":"87","author":"RM Fairhurst","year":"2012","journal-title":"Am J Trop Med Hyg."},{"issue":"2","key":"pntd.0013991.ref008","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1111\/bcp.14474","article-title":"Advances and roadblocks in the treatment of malaria","volume":"88","author":"B Hanboonkunupakarn","year":"2022","journal-title":"Br J Clin Pharmacol."},{"key":"pntd.0013991.ref009","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.ijpddr.2021.06.003","article-title":"Mefloquine as a prophylaxis for malaria needs to be revisited","volume":"17","author":"SS Ahmad","year":"2021","journal-title":"Int J Parasitol Drugs Drug Resist."},{"issue":"13","key":"pntd.0013991.ref010","doi-asserted-by":"crossref","first-page":"7977","DOI":"10.1073\/pnas.1337088100","article-title":"Systematic discovery of multicomponent therapeutics","volume":"100","author":"AA Borisy","year":"2003","journal-title":"Proc Natl Acad Sci U S A."},{"issue":"1","key":"pntd.0013991.ref011","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1186\/s40360-022-00580-8","article-title":"Synergistic anti-SARS-CoV-2 activity of repurposed anti-parasitic drug combinations","volume":"23","author":"K Jitobaom","year":"2022","journal-title":"BMC Pharmacol Toxicol."},{"issue":"4","key":"pntd.0013991.ref012","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.pt.2023.01.010","article-title":"Drug discovery for parasitic diseases: powered by technology, enabled by pharmacology, informed by clinical science","volume":"39","author":"SPS Rao","year":"2023","journal-title":"Trends Parasitol."},{"issue":"15","key":"pntd.0013991.ref013","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.4155\/fmc-2018-0439","article-title":"Host-directed therapies for parasitic diseases","volume":"11","author":"B Singh","year":"2019","journal-title":"Future Med Chem."},{"key":"pntd.0013991.ref014","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.3389\/fphar.2018.01096","article-title":"Using machine learning to predict synergistic antimalarial compound combinations with novel structures","volume":"9","author":"DJ Mason","year":"2018","journal-title":"Front Pharmacol."},{"key":"pntd.0013991.ref015","unstructured":"Ferreira Junior MA. Reposicionamento de f\u00e1rmacos para o tratamento da doen\u00e7a de Chagas: desenvolvimento de modelos computacionais baseados em ligantes ativos fenotipicamente contra o Trypanosoma cruzi e na inibi\u00e7\u00e3o da diidroorotato desidrog.. Universidade de S\u00e3o Paulo; 2021."},{"issue":"1","key":"pntd.0013991.ref016","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/s11686-023-00765-z","article-title":"Antimalarial drug combination predictions using the machine learning synergy predictor (MLSyPred) tool","volume":"69","author":"A Roche-Lima","year":"2024","journal-title":"Acta Parasitologica."},{"issue":"07","key":"pntd.0013991.ref017","first-page":"3897","article-title":"Machine learning and deep learning: a review of methods and applications","volume":"10","author":"K Sharifani","year":"2023","journal-title":"World Information Technology and Engineering Journal."},{"issue":"1","key":"pntd.0013991.ref018","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab390","article-title":"DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations","volume":"23","author":"J Wang","year":"2022","journal-title":"Brief Bioinform."},{"issue":"2","key":"pntd.0013991.ref019","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1007\/s12539-023-00558-y","article-title":"Predicting drug synergy and discovering new drug combinations based on a graph autoencoder and convolutional neural network","volume":"15","author":"H Li","year":"2023","journal-title":"Interdiscip Sci."},{"key":"pntd.0013991.ref020","doi-asserted-by":"crossref","first-page":"109","DOI":"10.3389\/fphar.2019.00109","article-title":"A comparative study of cluster detection algorithms in protein-protein interaction for drug target discovery and drug repurposing","volume":"10","author":"J Ma","year":"2019","journal-title":"Front Pharmacol."},{"issue":"11","key":"pntd.0013991.ref021","doi-asserted-by":"crossref","first-page":"2336","DOI":"10.1093\/jamia\/ocab162","article-title":"GraphSynergy: a network-inspired deep learning model for anticancer drug combination prediction","volume":"28","author":"J Yang","year":"2021","journal-title":"J Am Med Inform Assoc."},{"issue":"11","key":"pntd.0013991.ref022","doi-asserted-by":"crossref","first-page":"7945","DOI":"10.1109\/JBHI.2025.3567108","article-title":"Synergistic drug combination prediction via dual-level feature aggregation and knowledge graph-based deep neural network","volume":"29","author":"Y Zuo","year":"2025","journal-title":"IEEE J Biomed Health Inform."},{"issue":"9","key":"pntd.0013991.ref023","doi-asserted-by":"crossref","first-page":"17086","DOI":"10.1109\/TNNLS.2025.3553385","article-title":"MRHGNN: enhanced multimodal relational hypergraph neural network for synergistic drug combination forecasting","volume":"36","author":"M Chen","year":"2025","journal-title":"IEEE Trans Neural Netw Learn Syst."},{"key":"pntd.0013991.ref024","doi-asserted-by":"crossref","unstructured":"Xue Q, Ni J, Ji C. HGCLSynergy: heterogeneous graph contrastive learning for drug synergy prediction. in: Proceedings of the 2025 5th International Conference on Bioinformatics and Intelligent Computing. 2025. p. 94\u2013100. https:\/\/doi.org\/10.1145\/3724979.3724995","DOI":"10.1145\/3724979.3724995"},{"issue":"1","key":"pntd.0013991.ref025","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1109\/TKDE.2019.2922956","article-title":"Metagraph-based learning on heterogeneous graphs","volume":"33","author":"Y Fang","year":"2021","journal-title":"IEEE Trans Knowl Data Eng."},{"key":"pntd.0013991.ref026","doi-asserted-by":"crossref","unstructured":"Meng C, Cheng R, Maniu S, Senellart P, Zhang W. Discovering meta-paths in large heterogeneous information networks. In: Proceedings of the 24th International Conference on World Wide Web. 2015. p. 754\u201364. https:\/\/doi.org\/10.1145\/2736277.2741123","DOI":"10.1145\/2736277.2741123"},{"issue":"11","key":"pntd.0013991.ref027","article-title":"Combination therapy with risperidone and high-dose melatonin is effective despite reversible side effects including breast budding","volume":"16","author":"A Nakamura","year":"2024","journal-title":"Cureus."},{"issue":"3","key":"pntd.0013991.ref028","doi-asserted-by":"crossref","first-page":"232","DOI":"10.4274\/meandros.galenos.2023.59320","article-title":"Evaluation of the efficacy, side effects, and drug survival data with methotrexate-leflunomide combination therapy in patients with inflammatory arthritis","volume":"24","author":"P Talu Erten","year":"2023","journal-title":"meandros."},{"issue":"8","key":"pntd.0013991.ref029","doi-asserted-by":"crossref","first-page":"4516","DOI":"10.3390\/ijms25084516","article-title":"Advancing adverse drug reaction prediction with deep chemical language model for drug safety evaluation","volume":"25","author":"J Lin","year":"2024","journal-title":"Int J Mol Sci."},{"key":"pntd.0013991.ref030","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gkw1012","article-title":"MalaCards: an amalgamated human disease compendium with diverse clinical and genetic annotation and structured search","volume":"45","author":"N Rappaport","year":"2017","journal-title":"Nucleic Acids Res."},{"key":"pntd.0013991.ref031","doi-asserted-by":"crossref","DOI":"10.1093\/database\/baw100","article-title":"The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins","volume":"2016","author":"AD Rouillard","year":"2016","journal-title":"Database (Oxford)."},{"issue":"4","key":"pntd.0013991.ref032","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1080\/02763869.2020.1826228","article-title":"PubMed 2.0","volume":"39","author":"J White","year":"2020","journal-title":"Medical Reference Services Quarterly."},{"key":"pntd.0013991.ref033","article-title":"The GeneCards suite: from gene data mining to disease genome sequence analyses","volume":"54","author":"G Stelzer","year":"2016","journal-title":"Curr Protoc Bioinformatics."},{"key":"pntd.0013991.ref034","unstructured":"Niwattanakul S, Singthongchai J, Naenudorn E, Wanapu S. Using of Jaccard coefficient for keywords similarity. In: Proceedings of the international multiconference of engineers and computer scientists. vol. 1; 2013. p. 380\u20134."},{"key":"pntd.0013991.ref035","unstructured":"Kipf TN, Welling M. Semi-supervised classification with graph convolutional networks. arXiv preprint 2016. https:\/\/arxiv.org\/abs\/1609.02907"},{"key":"pntd.0013991.ref036","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y. Graph attention networks. arXiv preprint 2017. https:\/\/arxiv.org\/abs\/1710.10903"},{"issue":"5","key":"pntd.0013991.ref037","doi-asserted-by":"crossref","first-page":"104345","DOI":"10.1016\/j.drudis.2025.104345","article-title":"Computational modeling approaches and regulatory pathways for drug combinations","volume":"30","author":"L Fillinger","year":"2025","journal-title":"Drug Discov Today."},{"issue":"1","key":"pntd.0013991.ref038","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1186\/s12859-024-05765-y","article-title":"MFSynDCP: multi-source feature collaborative interactive learning for drug combination synergy prediction","volume":"25","author":"Y Dong","year":"2024","journal-title":"BMC Bioinformatics."},{"issue":"7391","key":"pntd.0013991.ref039","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1038\/nature11003","article-title":"The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity","volume":"483","author":"J Barretina","year":"2012","journal-title":"Nature."},{"issue":"1","key":"pntd.0013991.ref040","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1017\/S1351324916000334","article-title":"Word2Vec","volume":"23","author":"KW Church","year":"2017","journal-title":"Natural Language Engineering."},{"issue":"1","key":"pntd.0013991.ref041","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1186\/s13321-020-00456-1","article-title":"An open source chemical structure curation pipeline using RDKit","volume":"12","author":"AP Bento","year":"2020","journal-title":"J Cheminform."},{"key":"pntd.0013991.ref042","doi-asserted-by":"crossref","unstructured":"Vig J, Madani A, Varshney LR, Xiong C, Socher R, Rajani NF. Bertology meets biology: interpreting attention in protein language models. arXiv preprint 2020. https:\/\/arxiv.org\/abs\/2006.15222","DOI":"10.1101\/2020.06.26.174417"},{"key":"pntd.0013991.ref043","unstructured":"Ahmad W, Simon E, Chithrananda S, Grand G, Ramsundar B. Chemberta-2: towards chemical foundation models. arXiv preprint 2022. https:\/\/arxiv.org\/abs\/2209.01712"},{"issue":"6637","key":"pntd.0013991.ref044","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1126\/science.ade2574","article-title":"Evolutionary-scale prediction of atomic-level protein structure with a language model","volume":"379","author":"Z Lin","year":"2023","journal-title":"Science."},{"key":"pntd.0013991.ref045","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gkad976","article-title":"DrugBank 6.0: the DrugBank Knowledgebase for 2024","volume":"52","author":"C Knox","year":"2024","journal-title":"Nucleic Acids Res."},{"key":"pntd.0013991.ref046","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gky1049","article-title":"UniProt: a worldwide hub of protein knowledge","volume":"47","author":"Consortium UniProt","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"pntd.0013991.ref047","doi-asserted-by":"crossref","unstructured":"Perozzi B, Al-Rfou R, Skiena S. DeepWalk: online learning of social representations. ACM. 2014.","DOI":"10.1145\/2623330.2623732"},{"issue":"5","key":"pntd.0013991.ref048","article-title":"Metabolic mechanism and pharmacological study of albendazole in secondary hepatic alveolar echinococcosis (HAE) model rats","volume":"68","author":"C Li","year":"2024","journal-title":"Antimicrobial Agents and Chemotherapy."},{"issue":"1","key":"pntd.0013991.ref049","article-title":"Antiviral effects, tissue exposure of tetrandrine against SARS-CoV-2 infection and COVID-19","volume":"4","author":"J Liu","year":"2023","journal-title":"MedComm 2020"},{"issue":"1","key":"pntd.0013991.ref050","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.bbamem.2016.10.008","article-title":"The cytotoxic activity of miltefosine against Leishmania and macrophages is associated with dynamic changes in plasma membrane proteins","volume":"1859","author":"KS Fernandes","year":"2017","journal-title":"Biochim Biophys Acta Biomembr."},{"issue":"1","key":"pntd.0013991.ref051","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pntd.0006116","article-title":"Combinations of registered drugs reduce treatment times required to deplete Wolbachia in the Litomosoides sigmodontis mouse model","volume":"12","author":"S Specht","year":"2018","journal-title":"PLoS Negl Trop Dis."},{"issue":"4","key":"pntd.0013991.ref052","first-page":"420","article-title":"Taxonomic rank of human parasites","volume":"34","author":"TW Jia","year":"2022","journal-title":"Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi."},{"issue":"9","key":"pntd.0013991.ref053","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pntd.0011633","article-title":"Safety and tolerability of moxidectin and ivermectin combination treatments for lymphatic filariasis in C\u00f4te d\u2019Ivoire: a randomized controlled superiority study","volume":"17","author":"CM Bjerum","year":"2023","journal-title":"PLoS Negl Trop Dis."},{"key":"pntd.0013991.ref054","doi-asserted-by":"crossref","first-page":"1196063","DOI":"10.3389\/fmed.2023.1196063","article-title":"Community-based treatment of cutaneous leishmaniasis using cryotherapy and miltefosine in Southwest Ethiopia: the way forward?","volume":"10","author":"S van Henten","year":"2023","journal-title":"Front Med (Lausanne)."},{"key":"pntd.0013991.ref055","doi-asserted-by":"crossref","first-page":"8843808","DOI":"10.1155\/2020\/8843808","article-title":"Activity of combinations of antioxidants and anthelmintic drugs against the adult stage of schistosoma mansoni","volume":"2020","author":"MJ Gouveia","year":"2020","journal-title":"J Parasitol Res."},{"issue":"6","key":"pntd.0013991.ref056","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.1134\/S0026893324700572","article-title":"Synthesis and antimicrobial activity of thiosulfinates and allicin analogues","volume":"58","author":"VM Puchkov","year":"2024","journal-title":"Molecular Biology."},{"key":"pntd.0013991.ref057","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gkac382","article-title":"SynergyFinder 3.0: an interactive analysis and consensus interpretation of multi-drug synergies across multiple samples","volume":"50","author":"A Ianevski","year":"2022","journal-title":"Nucleic Acids Res."}],"updated-by":[{"DOI":"10.1371\/journal.pntd.0013991","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T00:00:00Z","timestamp":1773100800000}}],"container-title":["PLOS Neglected Tropical Diseases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pntd.0013991","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T17:44:58Z","timestamp":1773164698000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pntd.0013991"}},"subtitle":[],"editor":[{"given":"Murtala","family":"Isah","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,2,19]]},"references-count":57,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2,19]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pntd.0013991","relation":{},"ISSN":["1935-2735"],"issn-type":[{"value":"1935-2735","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,19]]}}}