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1.
PLoS One ; 13(6): e0198246, 2018.
Artículo en Inglés | MEDLINE | ID: mdl-29953436

RESUMEN

BACKGROUND: ATCC HIV-1 drug resistance test kit was designed to detect HIV-1 drug resistance (HIVDR) mutations in the protease and reverse transcriptase genes for all HIV-1 group M subtypes and circulating recombinant forms. The test has been validated for both plasma and dried blood spot specimen types with viral load (VL) of ≥1000 copies/ml. We performed an in-country assessment on the kit to determine the genotyping sensitivity and its accuracy in detecting HIVDR mutations using plasma samples stored under suboptimal conditions. METHODS: Among 572 samples with VL ≥1000 copies/ml that had been genotyped by ViroSeq assay, 183 were randomly selected, including 85 successful genotyped and 98 unsuccessful genotyped samples. They were tested with ATCC kits following the manufacturer's instructions. Sequence identity and HIVDR patterns were analysed with Stanford University HIV Drug Resistance HIVdb program. RESULTS: Of the 183 samples, 127 (69.4%) were successfully genotyped by either method. While ViroSeq system genotyped 85/183 (46.5%) with median VL of 32,971 (IQR: 11,150-96,506) copies/ml, ATCC genotyped 115/183 (62.8%) samples with median VL of 23,068 (IQR: 7,397-86,086) copies/ml. Of the 98 unsuccessful genotyped samples with ViroSeq assay, 42 (42.9%) samples with lower median VL of 13,906 (IQR: 6,122-72,329) copies/ml were successfully genotyped using ATCC. Sequence identity analysis revealed that the sequences generated by both methods were >98% identical and yielded similar HIVDR profiles at individual patient level. CONCLUSION: This study confirms that ATCC kit showed greater sensitivity in genotyping plasma samples stored in suboptimal conditions experiencing frequent and prolonged power outage. Thus, it is more sensitive particularly for subtypes A and A/G HIV-1 in resource-limited settings.


Asunto(s)
Antirretrovirales , Farmacorresistencia Viral/genética , Genotipo , Técnicas de Genotipaje/métodos , VIH-1/genética , Juego de Reactivos para Diagnóstico , Farmacorresistencia Viral/efectos de los fármacos , Femenino , Técnicas de Genotipaje/normas , VIH-1/metabolismo , Humanos , Masculino , Distribución Aleatoria
2.
Idowu Bolade Olawoye; Paul Eniola Oluniyi; Edyth Parker; Judith Uche Oguzie; Jessica Nnenna Uwanibe; Tolulope Adeyemi Kayode; Fehintola Victoria Ajogbasile; Testimony Jesupamilerin Olumade; Philomena Eromon; Priscilla Abechi; Tope Sobajo; Chinedu Ugwu; George Uwem; Femi Ayoade; Kazeem Akano; Oluwasemilogo Oluwasekunolami Akinlo; Julie Oreoluwa Akin-John; Nicholas Oyejide; Olubukola Ayo-Ale; Benjamin Adegboyega; Grace Chizaramu Chukwu; Ayomide Adeleke; Grace Opemipo Ezekiel; Farida Brimmo; Olanrewaju Odunyemi Fayemi; Iyanuoluwa Fred-Akintunwa; Ibrahim F. Yusuf; Testimony Oluwatise Ipaye; Oluwagboadurami John; Ahmed Iluoreh Muhammad; Deborah Chisom Nwodo; Olusola Akinola Ogunsanya; Johnson Okolie; Abolade Esther Omoniyi; Iyobosa Beatrice Omwanghe; Oludayo Oluwaseyi Ope-ewe; Shobi Otitoola; Kemi Adedotun-Suleiman; Courage Philip; Mudasiru Femi Saibu; Ayotunde Elijah Sijuwola; Christabel Anamuma Terkuma; Augustine Abu; Johnson Adekunle Adeniji; Moses Olubusuyi Adewunmi; Olufemi Oluwapelumi Adeyemi; Rahaman Ahmed; Anthony Ahumibe; Anthony Nnennaya Ajayi; Olusola Akanbi; Olatunji Akande; Monilade Akinola; Afolabi Akinpelu; George Akpede; Ekanem Anieno; Antjony E. Atage; Oyeronke Ayansola; Marycelin Baba; Olajumoke Babatunde; Bamidele Soji Oderinde; Ebo Benevolence; Osiemi Blessing; Mienye Bob-Manuel; Andrew Bock-Oruma; Aire Chris; Chimaobi Chukwu; Funmi Daramola; Adomeh Donatus; Rosemay Duruihuoma; Yerumoh Edna; Matthew Ekeh; Erim Ndoma; Richard Ewah; Akinwumi Fajola; Enoch Olowatosin Fakayode; Adeola Fowotade; Galadima Gadzama; Daniel Igwe; Odia Ikponmwosa; Rafiu Olasunkanmi Isamotu; Agbukor Jacqueline; Aiyepada John; Julie Johnson Ekpo; Ibrahim Kida; Nwando Mba; Airende Micheal; Mirabeau Youtchou Tatfeng; Worbianueri Beatrice Moore-Igwe; Anietie Moses; Okonofua Naregose; Nsikak-Abasi Ntia; Ifeanyi Nwafor; Elizabeth Odeh; Ephraim Ogbaini; Kingsley Chiedozie Ojide; Sylvanus Okogbenin; Peter Okokhere; Sylvanus Okoro; Azuka Okwuraiwe; Olisa Olasunkanmi; Oluseyi Olayinka; Adesuyi Omoare; Ewean Chukwuma Omoruyi; Hannah E. Omunakwe; Emeka Onwe Ogah; Chika Onwuamah; Venatious Onyia; Akhilomen Patience; Ebhodaghe Paulson; Omiunu Racheal; Esumeh Rita; Giwa Rosemary; Joseph Shaibu; Joseph Shaibu; Ehikhametalor Solomon; Ngozi Ugwu; Collins Nwachi Ugwu; Kingsley Ukwuaja; Zara Wudiri; Nnaemeka Ndodo; Brittany Petros; Bronwyn Mcannis; Cyril Oshomah; Femi Oladiji; Katherine J. Siddle; Rosemary Audu; Babatunde Lawal Salako; Stephen Schaffner; Danny Park; Ifedayo Adetifa; Chikwe Ihekweazu; Oyewale Tomori; Anise Nkenjop Happi; Onikepe Folarin; Kristian G. Andersen; Pardis C. Sabeti; Christian Tientcha Happi.
Preprint en Inglés | PREPRINT-MEDRXIV | ID: ppmedrxiv-22280269

RESUMEN

Identifying the dissemination patterns and impacts of a virus of economic or health importance during a pandemic is crucial, as it informs the public on policies for containment in order to reduce the spread of the virus. In this study, we integrated genomic and travel data to investigate the emergence and spread of the B.1.1.318 and B.1.525 variants of interest in Nigeria and the wider Africa region. By integrating travel data and phylogeographic reconstructions, we find that these two variants that arose during the second wave emerged from within Africa, with the B.1.525 from Nigeria, and then spread to other parts of the world. Our results show how regional connectivity in downsampled regions like Africa can often influence virus transmissions between neighbouring countries. Our findings demonstrate the power of genomic analysis when combined with mobility and epidemiological data to identify the drivers of transmission in the region, generating actionable information for public health decision makers in the region.

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