Highly sensitive molecular assay based on Identical Multi-Repeat Sequence (IMRS) algorithm for the detection of Trichomonas vaginalis infection
| cg.authorship.types | CGIAR and developing country institute | |
| cg.authorship.types | CGIAR and advanced research institute | |
| cg.contributor.affiliation | Mount Kenya University | |
| cg.contributor.affiliation | Jigsaw Bio Solutions Private Limited | |
| cg.contributor.affiliation | International Livestock Research Institute | |
| cg.contributor.affiliation | Stockholm University | |
| cg.contributor.affiliation | Boston University | |
| cg.contributor.donor | Royal Society, United Kingdom | |
| cg.creator.identifier | Hussein Abkallo: 0000-0002-5594-4418 | |
| cg.howPublished | Formally Published | |
| cg.identifier.dataurl | https://doi.org/10.5281/zenodo.14589291 | en |
| cg.identifier.dataurl | https://doi.org/10.5281/zenodo.11176058 | en |
| cg.identifier.doi | https://doi.org/10.1371/journal.pone.0317958 | |
| cg.isijournal | ISI Journal | |
| cg.issn | 1932-6203 | |
| cg.issue | 2 | |
| cg.journal | PLOS One | |
| cg.number | e0317958 | |
| cg.reviewStatus | Peer Review | |
| cg.species | Trichomonas vaginalis | |
| cg.subject.ilri | DIAGNOSTICS | |
| cg.subject.ilri | HUMAN HEALTH | |
| cg.subject.impactArea | Nutrition, health and food security | |
| cg.subject.sdg | SDG 3 - Good health and well-being | |
| cg.volume | 20 | |
| dc.contributor.author | Shiluli, C. | |
| dc.contributor.author | Kamath, S. | |
| dc.contributor.author | Kanoi, B.N. | |
| dc.contributor.author | Kimani, R. | |
| dc.contributor.author | Oduor, Bernard | |
| dc.contributor.author | Abkallo, Hussein M. | |
| dc.contributor.author | Maina, M. | |
| dc.contributor.author | Waweru, H. | |
| dc.contributor.author | Kamita, M. | |
| dc.contributor.author | Pamme, N. | |
| dc.contributor.author | Dupaty, J. | |
| dc.contributor.author | Klapperich, C.M. | |
| dc.contributor.author | Lolabattu, S.R. | |
| dc.contributor.author | Gitaka, J. | |
| dc.date.accessioned | 2025-02-12T05:59:35Z | en |
| dc.date.available | 2025-02-12T05:59:35Z | en |
| dc.identifier.uri | https://hdl.handle.net/10568/172975 | |
| dc.title | Highly sensitive molecular assay based on Identical Multi-Repeat Sequence (IMRS) algorithm for the detection of Trichomonas vaginalis infection | en |
| dcterms.abstract | Introduction: Annually, approximately 174 million people globally are affected by <i>Trichomonas vaginalis</i> (<i>T. vaginalis</i>) infection. Half of these infections occur in resource-limited regions. Untreated <i>T. vaginalis</i> infections are associated with complications such as pelvic inflammatory disease and adverse pregnancy outcomes mostly seen in women. In resource-limited regions, the World Health Organization (WHO) advocates for syndromic case management. However, this can lead to unnecessary treatment. Accurate diagnosis of <i>T. vaginalis</i> is required for effective and prompt treatment. Molecular tests such as Polymerase Chain Reaction (PCR) have the advantage of having a short turn-around time and allow the use of non-invasive specimens such as urine and vaginal swabs. However, these diagnostic techniques have numerous disadvantages such as high infrastructure costs, false negative and positive results, and interstrain variation among others. This study aimed to evaluate the use of identic | en |
| dcterms.accessRights | Open Access | |
| dcterms.audience | Academics | |
| dcterms.audience | Scientists | |
| dcterms.available | 2025-02-07 | |
| dcterms.bibliographicCitation | Shiluli, C., Kamath, S., Kanoi, B.N., Kimani, R., Oduor, B., Abkallo, H.M., Maina, M., Waweru, H., Kamita, M., Pamme, N., Dupaty, J., Klapperich, C.M., Lolabattu, S.R. and Gitaka, J. 2025. Highly sensitive molecular assay based on Identical Multi-Repeat Sequence (IMRS) algorithm for the detection of <i>Trichomonas vaginalis</i> infection. PLOS ONE 20(2): e0317958. | en |
| dcterms.issued | 2025-02-07 | |
| dcterms.language | en | |
| dcterms.license | CC0-1.0 | |
| dcterms.publisher | Public Library of Science | |
| dcterms.subject | diagnosis | en |
| dcterms.subject | health | en |
| dcterms.type | Journal Article |
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