Publications by authors named "Paula Mc Kenna"

Introduction: Large-scale vaccination campaigns can benefit from using digital health tools, particularly in low- and middle-income countries (LMICs). Selecting the best tool to fit into a pre-existing digital landscape can be challenging.

Areas Covered: We conducted a narrative review in PubMed and the grey literature for data available within 5 years to provide an overview of digital health tools used in large-scale vaccination campaigns for outbreak response in LMICs.

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Background: A partnership between the University of Antwerp and the University of Kinshasa implemented the EBOVAC3 clinical trial with an Ebola vaccine regimen administered to health care provider participants in Tshuapa Province, Democratic Republic of the Congo. This randomized controlled trial was part of an Ebola outbreak preparedness initiative financed through Innovative Medicines Initiative-European Union. The EBOVAC3 clinical trial used iris scan technology to identify all health care provider participants enrolled in the vaccine trial, to ensure that the right participant received the right vaccine at the right visit.

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Continued outbreaks of Ebola virus disease, including recent outbreaks in the Democratic Republic of the Congo (DRC), highlight the need for effective vaccine programs to combat future outbreaks. Given the population flow between DRC and Rwanda, the Rwanda Ministry of Health initiated a preventive vaccination campaign supported by a vaccination monitoring platform (VMP). The campaign aimed to vaccinate approximately 200,000 people from Rwanda's Rubavu and Rusizi districts with the two-dose vaccine regimen Ad26.

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Background: The Ministry of Health in Sierra Leone has developed and operationalized the national Digital Health Strategy to guide integrated roll out of e-health/mobile health solutions. The goal is that "by 2023 an effective and efficient ICT enabled system supports delivery of quality, accessible, affordable, equitable, and timely healthcare services and moves Sierra Leone closer to achieving universal health coverage". Investing in digital platforms for the education of community health workers (CHWs) in Sierra Leone is a critical strategic approach to strengthening the country's readiness for future Ebola outbreaks.

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This British hantavirus IgG prevalence study, aimed at 119 asymptomatic farmers in England, and using indirect immunofluorescence assay (IFA) as screening technique, concluded that rat-transmitted Seoul virus (SEOV) might be the main suspect as hantaviral pathogen in the UK. Exactly the same conclusion, using the same IFA screening technique, resulted from a 1994 serosurvey in the same country, and in 627 clinical cases plus 100 healthy controls. SEOV-positive study subjects were also mainly farmers with heavy rat-exposure, but residing in Northern-Ireland, a region where all other known rodent reservoirs for pathogenic hantaviruses are known to be absent, except the wild rat.

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Objectives: A wide array of monitoring tests is commercially available to gauge HIV-1 disease progression and the overall health status of an HIV-1-infected patient. Viral load tests provide a picture of viral activity, while CD4 cell counts shed light on the immune status and can help physicians to prevent the development of opportunistic infections in patients. On the other hand, genotypic and phenotypic resistance testing and therapeutic drug monitoring help to optimize HIV-1 antiretroviral therapy.

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We describe the selection of a previously unreported 21-base pair insertion following codon 69 of the HIV-1 reverse transcriptase (RT) from a patient undergoing multiple nucleoside analogue therapy. This insertion was a direct duplication of the preceding 21 bases of HIV-RT, and was selected in a background of NRTI-resistance mutations including substitutions at RT codons 41, 67, 184, 210, and 215. Longitudinal genotypic and phenotypic resistance tests performed before and after selection of the insertion suggested that the insertion conferred an additional decrease in susceptibility to some nucleoside analogues, most notably didanosine, stavudine, abacavir, and tenofovir.

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Article Synopsis
  • The study aims to analyze and compare how different HIV-1 subtypes (C, F, G, and CRF02_AG) respond to protease inhibitors to better understand variations in susceptibility and related genetic factors.
  • Using samples from drug-naive patients, researchers employed both phenotyping and genotyping techniques to identify specific polymorphisms linked to drug susceptibility, revealing unique susceptibility patterns across the subtypes.
  • Results indicated that certain polymorphisms, like 70R and 37D/S/T, significantly influenced drug response, suggesting that understanding these genetic variations can enhance the accuracy of genotyping algorithms across different HIV subtypes.
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Background: There is still considerable uncertainty as to the best algorithm for interpreting human immunodeficiency virus (HIV) genotyping results.

Methods: A total of 318 subjects with HIV RNA levels of >1000 copies/mL were enrolled in 41 centers throughout Italy from 2001 through 2003, stratified on the basis of their drug history, randomized (1:1) to 2 arms to have their treatments modified on the basis of the results of HIV genotyping (as interpreted by virtual phenotype analysis or with use of a rule-based interpretation system), and followed up for 48 weeks. At least 1 nucleoside reverse-transcriptase inhibitor and 1 protease inhibitor had to be included in any new regimen; nonnucleoside reverse-transcriptase inhibitor-naive patients were also prescribed a nonnucleoside reverse-transcriptase inhibitor.

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Objective: To investigate whether and how mutations at position 89 of HIV-1 protease were associated with protease inhibitor (PI) failure, and what is the impact of the HIV-1 subtype.

Methods: In a database containing pol nucleotide sequences and treatment history, the correlation between PI experience and mutations at codon 89 was determined separately for subtype B and several non-B subtypes. A Bayesian network model was used to map the resistance pathways in which M89I/V is involved for subtype G.

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Background: The K103N mutation in HIV-1 reverse transcriptase (RT) confers high-level resistance to current non-nucleoside reverse transcriptase inhibitors (NNRTI). The prevalence and resistance profile of HIV-1 with other substitutions at RT codon 103 is less well documented.

Methods: K103 substitutions among over 70,000 clinical samples submitted for routine antiretroviral resistance testing at two independent centres were examined.

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The long-term efficacy of making resistance testing routinely available to clinicians has not been established. We conducted a clinical trial at 6 US military hospitals in which volunteers infected with human immunodeficiency virus type-1 were randomized to have routine access to phenotype resistance testing (PT arm), access to genotype resistance testing (GT arm), or no access to either test (VB arm). The primary outcome measure was time to persistent treatment failure despite change(s) in antiretroviral therapy (ART) regimen.

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For the 127 Spanish patients enrolled in the Combine Study, a resistance substudy was performed with 100 (79%) plasma samples obtained at baseline and with 18 samples obtained from 19 patients at the time they experienced treatment failure. At baseline, primary mutations to nonnucleoside reverse-transcriptase inhibitors and protease inhibitors were not detected, whereas mutations to nucleoside reverse-transcriptase inhibitors were observed in 10% of patients. At failure, mutations were detected in 7 of 16 patients.

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The Y318F substitution in the 3' region of human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT) has been linked to nonnucleoside RT inhibitor (NNRTI) resistance in vitro. A systematic search of a large phenotypic-genotypic database (Virco) linked the Y318F substitution with a >10-fold decrease in NNRTI susceptibility in >85% of clinically derived isolates. There was a significant association between Y318F and use of delavirdine (P = 10(-11)) and nevirapine (P = 10(-6)) but not efavirenz (P = 0.

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