Publications by authors named "J Malone-Lee"

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causing the COVID-19 pandemic, has had an enormous effect on conventional clinical practice. Telemedicine has emerged as critical to the provision of healthcare services when reducing the transmission of COVID-19 among patients, families, and clinicians. It has been an essential tool for continuing care for patients with lower urinary tract symptoms (LUTS) during the COVID-19 pandemic and has been the link between socially distant patient contact.

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Background: Contemporary studies have discredited the methods used to exclude urinary tract infection (UTI) when treating overactive bladder (OAB). Thus we must revisit the OAB phenotype to check that UTI has not been overlooked.

Aims: To examine the differences in urinary cytokines IL6 and lactoferrin in OAB patients compared to controls, with references to microscopy of urine and enhanced quantitative urine culture.

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A significant proportion of urinary tract infection (UTI) patients experience recurrent episodes, due to deep tissue infection and treatment-resistant bacterial reservoirs. Direct bladder instillation of antibiotics has proved disappointing in treating UTI, likely due to the failure of infused antibiotics to penetrate the bladder epithelium and accumulate to high enough levels to kill intracellular bacteria. This work investigates the use of nitrofurantoin loaded poly(lactic-co-glycolic acid) (PLGA) particles to improve delivery to intracellular targets for the treatment of chronic UTI.

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Introduction And Hypothesis: Midstream urine (MSU) is key in assessing lower urinary tract syndrome (LUTS), but contingent on some assumptions. The aim of this study was to compare the occurrence of contamination and the quality of substrates obtained from four different collections: MSU, catheter specimen urine (CSU), a commercial MSU collecting device (Peezy) and a natural void. Contamination was quantified by differential, uroplakin-positive, urothelial cell counts.

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We propose autoregressive Bayesian semi-parametric models for gap times between recurrent events. The aim is two-fold: inference on the effect of possibly time-varying covariates on the gap times and clustering of individuals based on the time trajectory of the recurrent event. Time-dependency between gap times is taken into account through the specification of an autoregressive component for the frailty parameters influencing the response at different times.

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