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Record W2738480806 · doi:10.5451/unibas-006715239

Secondary bacterial infection in Buruli ulcer

2016· dissertation· en· W2738480806 on OpenAlexfundno aff
Grace Semabia Kpeli

Bibliographic record

Venueedoc (University of Basel) · 2016
Typedissertation
Languageen
FieldMedicine
TopicMycobacterium research and diagnosis
Canadian institutionsnot available
FundersInstitute of Infection and ImmunityNoguchi Memorial Institute for Medical Research, University of GhanaUBS Optimus FoundationVolkswagen Foundation
KeywordsBuruli ulcerMycobacterium ulceransMedicineAntibioticsDiseaseAntibiotic therapyPathogenTransmission (telecommunications)Intensive care medicineTropical diseaseInternal medicineImmunologyBiologyMicrobiology

Abstract

fetched live from OpenAlex

Abstract
\nBuruli ulcer (BU) is a chronic debilitating disease of the skin and soft tissues caused by Mycobacterium ulcerans. It is one of the 17 neglected tropical diseases according to the World Health Organization and has been reported in over 30 countries with tropical and sub-tropical conditions globally. M. ulcerans is traditionally considered as an environmental pathogen and even though BU was discovered over half a century ago, the environmental reservoir and exact mode of transmission of this pathogen remain obscure. This makes it challenging to formulate strategies for its prevention. As such, control strategies geared towards the early detection and treatment of cases are vital to minimize morbidity, disability and the socio-economic burden associated with the disease. The introduction of antibiotic therapy for treatment in 2004 to replace surgery as first-line therapy has brought about an improvement in the management of the disease. However, despite reported successful outcomes with the antibiotic treatment, the healing process is still characterized by long hospitalizations as a result of delayed wound closure.
\nIn this thesis, we explored the factors which could contribute to the observed delayed wound healing in two BU treatment centers in Ghana; the Ga-West Municipal Hospital and the Obom Health Center. Through a combination of clinical, microbiological and histopathological analysis, we identified secondary infection of BU lesions by other bacteria as a major cause of delayed healing. Through quantitative microbiological studies, we analysed the evolution of the bacterial burden and identified increased loads of bacteria post treatment which could negatively impact on the healing potential of the wounds. Furthermore, we explored co-infection with Human immunodeficiency virus (HIV) in the Ga-West Municipal Hospital as a challenge to the management of BU and described challenges associated with the management of this co-infection. Studying the isolated bacterial species through phenotypic, molecular and whole genome approaches helped to identify health-care associated transmission through health workers and equipment as well as self transmission as potential sources of wound infection within the health centers. With these results, we made recommendations for the improvement of wound management in the health centers and made a case for the need for wound management guidelines which were absent in the health centers. We followed this up with the development of local guidelines for wound care and the implementation of several interventions in the health centers. We also identified antibiotic resistance as an increasing problem and described in detail through whole genome sequencing, a recently emerged and rapidly spreading clone of community acquired methicillin resistant Staphylococcus aureus with sequence type 88 in Ghana which has the potential to become a serious public health threat with implications for healthcare. This alarming result therefore calls for the urgent establishment of a surveillance system to monitor the use and distribution of antibiotics in Ghana and the emergence of antibiotic resistant pathogens.
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Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.559
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0140.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.245
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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