Dataset Information

Bacterial isolation and antibiotic susceptibility from diabetic foot ulcers in Kenya using microbiological tests and comparison with RT-PCR in detection of S. aureus and MRSA.

ABSTRACT:

Objectives

Diabetic foot ulcers (DFUs) often lead to hospital admissions, amputations and deaths; however, there is no up-to-date information on microbial isolates from DFUs and no mention of utilization of molecular techniques in Sub-Saharan Africa. We conducted a cross-sectional study among 83 adult patients at a tertiary hospital in Kenya over 12 months. The study aimed to isolate, identify bacteria, their antibiotic susceptibility patterns in active DFUs, and to compare standard microbiological methods versus a real-time PCR commercial kit in the detection of Staphylococcus aureus DNA and methicillin-resistant S. aureus (MRSA) DNA.

Results

Eighty swabs (94%) were culture-positive; 29% were Gram-positive and 65% were Gram-negative. The main organisms isolated were S. aureus (16%), Escherichia coli (15%), Proteus mirabilis (11%), Klebsiella pneumoniae (7%) and Pseudomonas aeruginosa (7%). The bacterial isolates showed resistance to commonly used antibiotics such as ampicillin, amoxicillin, cefepime, ceftazidime, cefuroxime, clindamycin, erythromycin, piperacillin-tazobactam, tetracycline and trimethoprim-sulphamethoxazole (TMPSMX). Thirty-one percent of the S. aureus isolated and 40% of the Gram-negatives were multi-drug resistant organisms (MDROs). There was a high prevalence of nosocomial bacteria. MRSA were not identified using culture methods but were identified using PCR. PCR was more sensitive but less specific than culture-based methods to identify S. aureus.

SUBMITTER: Mutonga DM

PROVIDER: S-EPMC6489269 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Publications

Bacterial isolation and antibiotic susceptibility from diabetic foot ulcers in Kenya using microbiological tests and comparison with RT-PCR in detection of S. aureus and MRSA.

Mutonga Daniel M DM Mureithi Marianne W MW Ngugi Nancy N NN Otieno Fredrick C F FCF

BMC research notes 20190429 1

<h4>Objectives</h4>Diabetic foot ulcers (DFUs) often lead to hospital admissions, amputations and deaths; however, there is no up-to-date information on microbial isolates from DFUs and no mention of utilization of molecular techniques in Sub-Saharan Africa. We conducted a cross-sectional study among 83 adult patients at a tertiary hospital in Kenya over 12 months. The study aimed to isolate, identify bacteria, their antibiotic susceptibility patterns in active DFUs, and to compare standard micr ...[more]

PMID: 31036061

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Project description:BackgroundThe emergency of new COVID-19 variants over the past three years posed a serious challenge to the public health. Cities in China implemented mass daily RT-PCR tests by pooling strategies. However, a random delay exists between an infection and its first positive RT-PCR test. It is valuable for disease control to know the delay pattern and daily infection incidences reconstructed from RT-PCR test observations.MethodsWe formulated the convolution model between daily incidences and positive RT-PCR test counts as a linear inverse problem with positivity restrictions. Consequently, the Richard-Lucy deconvolution algorithm was used to reconstruct COVID-19 incidences from daily PCR tests. A real-time deconvolution was further developed based on the same mathematical principle. The method was applied to an Omicron epidemic data set of a bar outbreak in Beijing and another in Wuxi in June 2022. We estimated the delay function by maximizing likelihood via an E-M algorithm.ResultsThe delay function of the bar-outbreak in 2022 differs from that reported in 2020. Its mode was shortened to 4 days by one day. A 95% confidence interval of the mean delay is [4.43,5.55] as evaluated by bootstrap. In addition, the deconvolved infection incidences successfully detected two associated infection events after the bar was closed. The application of the real-time deconvolution to the Wuxi data identified all explosive incidence increases. The results revealed the progression of the two COVID-19 outbreaks and provided new insights for prevention and control strategies, especially for the role of mass daily RT-PCR testing.ConclusionsThe proposed deconvolution method is generally applicable to other infectious diseases if the delay model can be assumed to be approximately valid. To ensure a fair reconstruction of daily infection incidences, the delay function should be estimated in a similar context in terms of virus variant and test protocol. Both the delay estimate from the E-M algorithm and the incidences resulted from deconvolution are valuable for epidemic prevention and control. The real-time feedback is particularly useful during the epidemic's acute phase because it can help the local disease control authorities modify the control measures more promptly and precisely.

Dataset Information

Bacterial isolation and antibiotic susceptibility from diabetic foot ulcers in Kenya using microbiological tests and comparison with RT-PCR in detection of S. aureus and MRSA.

Objectives

Results

Publications

Bacterial isolation and antibiotic susceptibility from diabetic foot ulcers in Kenya using microbiological tests and comparison with RT-PCR in detection of S. aureus and MRSA.

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