Publicação
Harnessing complex metabolic signatures to predict drug efficacy in ovarian cancer
| Resumo: | "Ovarian carcinoma (OvC) is the most lethal gynecologic malignancy. Unfortunately, most cases are diagnosed at the later stages of the disease, impacting patient outcomes, resulting in a 5-year survival rate of less than 40%. The standard-of-care (SOC) treatment consists of cytoreduction surgery for tumor debulking and adjuvant chemotherapy, comprising a combination of platinum (cisplatin or carboplatin) with taxane (paclitaxel or docetaxel) agents.(...)" |
|---|---|
| Autores principais: | Mendes, Ana Rita Soares |
| Assunto: | precision medicine ovarian cancer |
| Ano: | 2022 |
| País: | Portugal |
| Tipo de documento: | tese de doutoramento |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| _version_ | 1868982525325475840 |
|---|---|
| author | Mendes, Ana Rita Soares |
| author_facet | Mendes, Ana Rita Soares |
| author_role | author |
| contributor_name_str_mv | Isidro, Inês Brito, Catarina RUN |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Mendes, Ana Rita Soares\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Isidro, Inês Brito, Catarina RUN |
| datacite.creators.creator.creatorName.fl_str_mv | Mendes, Ana Rita Soares |
| datacite.date.Accepted.fl_str_mv | 2022-07-04T00:00:00Z |
| datacite.date.available.fl_str_mv | 2023-07-31T00:33:51Z |
| datacite.date.embargoed.fl_str_mv | 2023-07-31T00:33:51Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | precision medicine ovarian cancer |
| datacite.titles.title.fl_str_mv | Harnessing complex metabolic signatures to predict drug efficacy in ovarian cancer |
| dc.contributor.none.fl_str_mv | Isidro, Inês Brito, Catarina RUN |
| dc.creator.none.fl_str_mv | Mendes, Ana Rita Soares |
| dc.date.Accepted.fl_str_mv | 2022-07-04T00:00:00Z |
| dc.date.available.fl_str_mv | 2023-07-31T00:33:51Z |
| dc.date.embargoed.fl_str_mv | 2023-07-31T00:33:51Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10362/143167 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | precision medicine ovarian cancer |
| dc.title.fl_str_mv | Harnessing complex metabolic signatures to predict drug efficacy in ovarian cancer |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_db06 |
| description | "Ovarian carcinoma (OvC) is the most lethal gynecologic malignancy. Unfortunately, most cases are diagnosed at the later stages of the disease, impacting patient outcomes, resulting in a 5-year survival rate of less than 40%. The standard-of-care (SOC) treatment consists of cytoreduction surgery for tumor debulking and adjuvant chemotherapy, comprising a combination of platinum (cisplatin or carboplatin) with taxane (paclitaxel or docetaxel) agents.(...)" |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | doctoralThesis |
| fulltext.url.fl_str_mv | https://run.unl.pt/bitstreams/622c4779-3dd3-4805-b0a9-ae17470fb440/download |
| funder_facet_str_mv | FCT{{{_:::_}}}Fundação para a Ciência e a Tecnologia |
| funding.funder.alternateName_str_mv | FCT |
| funding.funder.identifier_str_mv | http://doi.org/10.13039/501100001871 |
| funding.funder.name_str_mv | Fundação para a Ciência e a Tecnologia |
| id | run_739b84e5f46338cc8e5bde0c19f7b037 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10362/143167 |
| inst_facet_str | urn:organizationAcronym:unl{{{_:::_}}}Universidade Nova de Lisboa |
| instacron_str | unl |
| institution | Universidade Nova de Lisboa |
| instname_str | Universidade Nova de Lisboa |
| language | eng |
| network_acronym_str | run |
| network_name_str | Repositório Institucional da UNL |
| oai_identifier_str | oai:run.unl.pt:10362/143167 |
| organization_str_mv | urn:organizationAcronym:unl |
| person_str_mv | Mendes, Ana Rita Soares |
| publishDate | 2022 |
| repo_facet_str | urn:repositoryAcronym:run{{{_:::_}}}Repositório Institucional da UNL |
| reponame_str | Repositório Institucional da UNL |
| repository_id_str | urn:repositoryAcronym:run |
| service_str_mv | urn:repositoryAcronym:run |
| spelling | engpt_PT"Ovarian carcinoma (OvC) is the most lethal gynecologic malignancy. Unfortunately, most cases are diagnosed at the later stages of the disease, impacting patient outcomes, resulting in a 5-year survival rate of less than 40%. The standard-of-care (SOC) treatment consists of cytoreduction surgery for tumor debulking and adjuvant chemotherapy, comprising a combination of platinum (cisplatin or carboplatin) with taxane (paclitaxel or docetaxel) agents.(...)"application/pdfpt_PTHarnessing complex metabolic signatures to predict drug efficacy in ovarian cancerMendes, Ana Rita SoaresIsidro, InêsBrito, CatarinaHostingInstitutionOrganizationalRUNe-mailmailto:run@unl.ptrun@unl.ptURNurn:tid:1017245352023-07-31T00:33:51Z2022-07-042022-022022-07-04T00:00:00ZHandlehttp://hdl.handle.net/10362/143167http://purl.org/coar/access_right/c_abf2open accessprecision medicineovarian cancer8093444 bytesFundação para a Ciência e a Tecnologiaalterado para: A precision medicine platform using patient derived cancer cell models to predict drug efficacy.Crossref Funder IDhttp://doi.org/10.13039/501100001871literaturehttp://purl.org/coar/resource_type/c_db06doctoral thesishttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://run.unl.pt/bitstreams/622c4779-3dd3-4805-b0a9-ae17470fb440/download |
| spellingShingle | Harnessing complex metabolic signatures to predict drug efficacy in ovarian cancer Mendes, Ana Rita Soares precision medicine ovarian cancer |
| status | SINGLETON |
| subject.fl_str_mv | precision medicine ovarian cancer |
| title | Harnessing complex metabolic signatures to predict drug efficacy in ovarian cancer |
| title_full | Harnessing complex metabolic signatures to predict drug efficacy in ovarian cancer |
| title_fullStr | Harnessing complex metabolic signatures to predict drug efficacy in ovarian cancer |
| title_full_unstemmed | Harnessing complex metabolic signatures to predict drug efficacy in ovarian cancer |
| title_short | Harnessing complex metabolic signatures to predict drug efficacy in ovarian cancer |
| title_sort | Harnessing complex metabolic signatures to predict drug efficacy in ovarian cancer |
| topic | precision medicine ovarian cancer |
| topic_facet | precision medicine ovarian cancer |
| url | http://hdl.handle.net/10362/143167 |
| visible | 1 |