Mathematical modelling using predictive biomarkers for the outcome of canine Leishmaniasis upon chemotherapy
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/1822/65507 |
Resumo: | Prediction parameters of possible outcomes of canine leishmaniasis (CanL) therapy might help with therapeutic decisions and animal health care. Here, we aimed to develop a diagnostic method with predictive value by analyzing two groups of dogs with CanL, those that exhibited a decrease in parasite load upon antiparasitic treatment (group: responders) and those that maintained high parasite load despite the treatment (group: non-responders). The parameters analyzed were parasitic load determined by q-PCR, hemogram, serum biochemistry and immune system-related gene expression signature. A mathematical model was applied to the analysis of these parameters to predict how efficient their response to therapy would be. Responder dogs restored hematological and biochemical parameters to the reference values and exhibited a Th1 cell activation profile with a linear tendency to reach mild clinical alteration stages. Differently, non-responders developed a mixed Th1/Th2 response and exhibited markers of liver and kidney injury. Erythrocyte counts and serum phosphorus were identified as predictive markers of therapeutic response at an early period of assessment of CanL. The results presented in this study are highly encouraging and may represent a new paradigm for future assistance to clinicians to interfere precociously in the therapeutic approach, with a more precise definition in the patient’s prognosis. |
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Mathematical modelling using predictive biomarkers for the outcome of canine Leishmaniasis upon chemotherapymathematical modeltreatmenthematological parametersbiochemical parametersLeishmaniaCiências Médicas::Ciências da SaúdeScience & TechnologyPrediction parameters of possible outcomes of canine leishmaniasis (CanL) therapy might help with therapeutic decisions and animal health care. Here, we aimed to develop a diagnostic method with predictive value by analyzing two groups of dogs with CanL, those that exhibited a decrease in parasite load upon antiparasitic treatment (group: responders) and those that maintained high parasite load despite the treatment (group: non-responders). The parameters analyzed were parasitic load determined by q-PCR, hemogram, serum biochemistry and immune system-related gene expression signature. A mathematical model was applied to the analysis of these parameters to predict how efficient their response to therapy would be. Responder dogs restored hematological and biochemical parameters to the reference values and exhibited a Th1 cell activation profile with a linear tendency to reach mild clinical alteration stages. Differently, non-responders developed a mixed Th1/Th2 response and exhibited markers of liver and kidney injury. Erythrocyte counts and serum phosphorus were identified as predictive markers of therapeutic response at an early period of assessment of CanL. The results presented in this study are highly encouraging and may represent a new paradigm for future assistance to clinicians to interfere precociously in the therapeutic approach, with a more precise definition in the patient’s prognosis.This work was funded by the Brazilian agencies Bahia Research Foundation—FAPESB (Grant nº PRONEM 498/2011-PNE 0002/2011 to S.M.B-M), National Council for Scientific and Technological Development —CNPq (PQ scholarship nº 307813/2018-5 to SMBM, and nº 303621/2015-0 to HG) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior —CAPES (PDSE scholarship nº 88881.189587/2018-01 to R.S.G; Finance Code 001 and PV scholarship nº 23066.033859/2018-73 to R.S.). This work was supported by grants from CESPU (TramTap-CESPU-2016, Chronic-TramTap_CESPU_2017 and TraTapMDMA-CESPU-2018), from the Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER) (NORTE-01-0145-FEDER-000013), funded by FEDER funds through COMPETE2020—Programa Operacional Competitividade e Internacionalização (POCI) and the Fundação para a Ciência e Tecnologia (FCT) (contract IF/00021/2014 to R.S.), Infect-Era (project INLEISH to R.S.) and Proyecto SNIP N◦ 292900 “Creación del Servicio de Laboratorio de Enfermedades Infecciosas y Parasitarias de Animales Domésticos de la Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas.Instituto de Investigación en Ganadería y Biotecnología-IGBI. Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas.Multidisciplinary Digital Publishing InstituteUniversidade do MinhoGonçalves, Rafaela de SousaAlves de Pinho, FlavianeDinis-Oliveira, Ricardo JorgeAzevedo, RuiGaifem, JoanaFarias Larangeira, DanielaRamos-Sanchez, Eduardo MiltonGoto, HiroSilvestre, RicardoBarrouin-Melo, Stella Maria2020-05-152020-05-15T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/65507engde Sousa Gonçalves, R.; Alves de Pinho, F.; Dinis-Oliveira, R.J.; Azevedo, R.; Gaifem, J.; Farias Larangeira, D.; Ramos-Sanchez, E.M.; Goto, H.; Silvestre, R.; Barrouin-Melo, S.M. Mathematical Modelling Using Predictive Biomarkers for the Outcome of Canine Leishmaniasis upon Chemotherapy. Microorganisms 2020, 8, 745.2076-260710.3390/microorganisms8050745https://www.mdpi.com/2076-2607/8/5/745info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:48:41Zoai:repositorium.sdum.uminho.pt:1822/65507Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:46:59.633796Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Mathematical modelling using predictive biomarkers for the outcome of canine Leishmaniasis upon chemotherapy |
title |
Mathematical modelling using predictive biomarkers for the outcome of canine Leishmaniasis upon chemotherapy |
spellingShingle |
Mathematical modelling using predictive biomarkers for the outcome of canine Leishmaniasis upon chemotherapy Gonçalves, Rafaela de Sousa mathematical model treatment hematological parameters biochemical parameters Leishmania Ciências Médicas::Ciências da Saúde Science & Technology |
title_short |
Mathematical modelling using predictive biomarkers for the outcome of canine Leishmaniasis upon chemotherapy |
title_full |
Mathematical modelling using predictive biomarkers for the outcome of canine Leishmaniasis upon chemotherapy |
title_fullStr |
Mathematical modelling using predictive biomarkers for the outcome of canine Leishmaniasis upon chemotherapy |
title_full_unstemmed |
Mathematical modelling using predictive biomarkers for the outcome of canine Leishmaniasis upon chemotherapy |
title_sort |
Mathematical modelling using predictive biomarkers for the outcome of canine Leishmaniasis upon chemotherapy |
author |
Gonçalves, Rafaela de Sousa |
author_facet |
Gonçalves, Rafaela de Sousa Alves de Pinho, Flaviane Dinis-Oliveira, Ricardo Jorge Azevedo, Rui Gaifem, Joana Farias Larangeira, Daniela Ramos-Sanchez, Eduardo Milton Goto, Hiro Silvestre, Ricardo Barrouin-Melo, Stella Maria |
author_role |
author |
author2 |
Alves de Pinho, Flaviane Dinis-Oliveira, Ricardo Jorge Azevedo, Rui Gaifem, Joana Farias Larangeira, Daniela Ramos-Sanchez, Eduardo Milton Goto, Hiro Silvestre, Ricardo Barrouin-Melo, Stella Maria |
author2_role |
author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Gonçalves, Rafaela de Sousa Alves de Pinho, Flaviane Dinis-Oliveira, Ricardo Jorge Azevedo, Rui Gaifem, Joana Farias Larangeira, Daniela Ramos-Sanchez, Eduardo Milton Goto, Hiro Silvestre, Ricardo Barrouin-Melo, Stella Maria |
dc.subject.por.fl_str_mv |
mathematical model treatment hematological parameters biochemical parameters Leishmania Ciências Médicas::Ciências da Saúde Science & Technology |
topic |
mathematical model treatment hematological parameters biochemical parameters Leishmania Ciências Médicas::Ciências da Saúde Science & Technology |
description |
Prediction parameters of possible outcomes of canine leishmaniasis (CanL) therapy might help with therapeutic decisions and animal health care. Here, we aimed to develop a diagnostic method with predictive value by analyzing two groups of dogs with CanL, those that exhibited a decrease in parasite load upon antiparasitic treatment (group: responders) and those that maintained high parasite load despite the treatment (group: non-responders). The parameters analyzed were parasitic load determined by q-PCR, hemogram, serum biochemistry and immune system-related gene expression signature. A mathematical model was applied to the analysis of these parameters to predict how efficient their response to therapy would be. Responder dogs restored hematological and biochemical parameters to the reference values and exhibited a Th1 cell activation profile with a linear tendency to reach mild clinical alteration stages. Differently, non-responders developed a mixed Th1/Th2 response and exhibited markers of liver and kidney injury. Erythrocyte counts and serum phosphorus were identified as predictive markers of therapeutic response at an early period of assessment of CanL. The results presented in this study are highly encouraging and may represent a new paradigm for future assistance to clinicians to interfere precociously in the therapeutic approach, with a more precise definition in the patient’s prognosis. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-05-15 2020-05-15T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1822/65507 |
url |
http://hdl.handle.net/1822/65507 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
de Sousa Gonçalves, R.; Alves de Pinho, F.; Dinis-Oliveira, R.J.; Azevedo, R.; Gaifem, J.; Farias Larangeira, D.; Ramos-Sanchez, E.M.; Goto, H.; Silvestre, R.; Barrouin-Melo, S.M. Mathematical Modelling Using Predictive Biomarkers for the Outcome of Canine Leishmaniasis upon Chemotherapy. Microorganisms 2020, 8, 745. 2076-2607 10.3390/microorganisms8050745 https://www.mdpi.com/2076-2607/8/5/745 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Multidisciplinary Digital Publishing Institute |
publisher.none.fl_str_mv |
Multidisciplinary Digital Publishing Institute |
dc.source.none.fl_str_mv |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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