Physiological Impact of Abnormal Lipoxin A 4

Lipoxin A4 has been described as a major signal for the resolution of inflammation and is abnormally produced in the lungs of patients with cystic fibrosis (CF). In CF, the loss of chloride transport caused by the mutation in the cystic fibrosis transmembrane conductance regulator (CFTR) Cl− channel gene results in dehydration, mucus plugging, and reduction of the airway surface liquid layer (ASL) height which favour chronic lung infection and neutrophil based inflammation leading to progressive lung destruction and early death of people with CF. This review highlights the unique ability of LXA4 to restore airway surface hydration, to stimulate airway epithelial repair, and to antagonise the proinflammatory program of the CF airway, circumventing some of the most difficult aspects of CF pathophysiology. The report points out novel aspects of the cellular mechanism involved in the physiological response to LXA4, including release of ATP from airway epithelial cell via pannexin channel and subsequent activation of and P2Y11 purinoreceptor. Therefore, inadequate endogenous LXA4 biosynthesis reported in CF exacerbates the ion transport abnormality and defective mucociliary clearance, in addition to impairing the resolution of inflammation, thus amplifying the vicious circle of airway dehydration, chronic infection, and inflammation

MACHINE LEARNING APPROACHES ALONG THE RADIOLOGY VALUE CHAIN – RETHINKING VALUE PROPOSITIONS

Radiology is experiencing an increased interest in machine learning with its ability to use a large amount of available data. However, it remains unclear how and to what extent machine learning will affect radiology businesses. Conducting a systematic literature review and expert interviews, we compile the opportunities and challenges of machine learning along the radiology value chain to discuss their implications for the radiology business. Machine learning can improve diagnostic quality by reducing human errors, accurately analysing large amounts of data, quantifying reports, and integrating data. Hence, it strengthens radiology businesses seeking product or service leadership. Machine learning fosters efficiency by automating accompanying activities such as generating study protocols or reports, avoiding duplicate work due to low image quality, and supporting radiologists. These efficiency improvements advance the operational excellence strategy. By providing personnel and proactive medical solutions beyond the radiology silo, machine learning supports a customer intimacy strategy. However, the opportunities face challenges that are technical (i.e., lack of data, weak labelling, and generalisation), legal (i.e., regulatory approval and privacy laws), and persuasive (i.e., radiologists’ resistance and patients’ distrust). Our findings shed light on the strategic positioning of radiology businesses, contributing to academic discourse and practical decision-making