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Research

Primary Nasal Epithelial Cells as a Surrogate Cell Culture Model for Type-II Alveolar Cells to Study ABCA-3 Deficiency

ATP Binding Cassette Subfamily A Member 3 (ABCA-3) is a lipid transporter protein highly expressed in type-II alveolar (AT-II) cells. Mutations in ABCA3 can result in severe respiratory disease in infants and children. To study ABCA-3 deficiency in vitro, primary AT-II cells would be the cell culture of choice although sample accessibility is limited. Our aim was to investigate the suitability of primary nasal epithelial cells, as a surrogate culture model for AT-II cells, to study ABCA-3 deficiency.

Research

Role of Tris-CaEDTA as an adjuvant with nebulised tobramycin in cystic fibrosis patients with Pseudomonas aeruginosa lung infections: A randomised controlled trial

We tested if disrupting iron utilisation by P. aeruginosa by adding the Tris-buffered chelating agent CaEDTA to nebulised tobramycin would enhance bacterial clearance and improve lung function in CF patients.

Research

Associations between respiratory and vascular function in early childhood

The link between respiratory and vascular health is well documented in adult populations. Impaired lung function is consistently associated with thicker arteries and higher incidence of cardiovascular disease. However, there are limited data on this relationship in young children and the studies that exist have focussed on populations at high risk of cardiorespiratory morbidity.

Research

Phage therapy for multi-drug resistant respiratory tract infections

The emergence of multi-drug resistant (MDR) bacteria is recognised today as one of the greatest challenges to public health. As traditional antimicrobials are becoming ineffective and research into new antibiotics is diminishing, a number of alternative treatments for MDR bacteria have been receiving greater attention. Bacteriophage therapies are being revisited and present a promising opportunity to reduce the burden of bacterial infection in this post-antibiotic era.

Research

Differential cell counts using center-point networks achieves human-level accuracy and efficiency over segmentation

Differential cell counts is a challenging task when applying computer vision algorithms to pathology. Existing approaches to train cell recognition require high availability of multi-class segmentation and/or bounding box annotations and suffer in performance when objects are tightly clustered.