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Research

Detection of bile acids in bronchoalveolar lavage fluid defines the inflammatory and microbial landscape of the lower airways in infants with cystic fibrosis

Cystic Fibrosis (CF) is a genetic condition characterized by neutrophilic inflammation and recurrent infection of the airways. How these processes are initiated and perpetuated in CF remains largely unknown. We have demonstrated a link between the intestinal microbiota-related metabolites bile acids and inflammation in the bronchoalveolar lavage fluid from children with stable CF lung disease.

Research

Ventilatory response and stability of oxygen saturation during a hypoxic challenge in very preterm infants

Preterm infants have immature control of breathing and impaired pulmonary gas exchange. We hypothesized that infants with bronchopulmonary dysplasia (BPD) have a blunted ventilatory response and peripheral oxygen saturation (SpO2 ) instability during a hypoxic challenge.

Research

Health service utilisation for acute respiratory infections in infants graduating from the neonatal intensive care unit: a population-based cohort study

Despite advances in neonatal intensive care, babies admitted to Neonatal Intensive Care Units (NICU) suffer from adverse outcomes. We aim to describe the longer-term respiratory infectious morbidity of infants discharged from NICU using state-wide population-based linked data in Western Australia.

Research

Fathers’ preconception smoking and offspring DNA methylation

Experimental studies suggest that exposures may impact respiratory health across generations via epigenetic changes transmitted specifically through male germ cells. Studies in humans are, however, limited. We aim to identify epigenetic marks in offspring associated with father's preconception smoking.

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.