ObjectiveWe evaluated the influence of the renin–angiotensin system (RAS) on intestinal inflammation and fibrosis.DesignCultured human colonic myofibroblast proliferation and collagen secretion were assessed following treatment with angiotensin (Ang) II and Ang (1–7), their receptor antagonists candesartan and A779, and the ACE inhibitor captopril. Circulating and intestinal RAS components were evaluated in patients with and without IBD. Disease outcomes in patients with IBD treated with ACE inhibitors and angiotensin receptor blockers (ARBs) were assessed in retrospective studies.ResultsHuman colonic myofibroblast proliferation was reduced by Ang (1–7) in a dose-dependent manner (p<0.05). Ang II marginally but not significantly increased proliferation, an effect reversed by candesartan (p<0.001). Colonic myofibroblast collagen secretion was reduced by Ang (1–7) (p<0.05) and captopril (p<0.001), and was increased by Ang II (p<0.001). Patients with IBD had higher circulating renin (mean 25.4 vs 18.6 mIU/L, p=0.026) and ACE2:ACE ratio (mean 0.92 vs 0.69, p=0.015) than controls without IBD. RAS gene transcripts and peptides were identified in healthy and diseased bowels. Colonic mucosal Masson’s trichrome staining correlated with Ang II (r=0.346, p=0.010) and inversely with ACE2 activity (r=−0.373, p=0.006). Patients with IBD who required surgery (1/37 vs 12/75, p=0.034) and hospitalisation (0/34 vs 8/68, p=0.049) over 2 years were less often treated with ACE inhibitors and ARBs than patients not requiring surgery or hospitalisation.ConclusionsThe RAS mediates fibrosis in human cell cultures, is expressed in the intestine and perturbed in intestinal inflammation, and agents targeting this system are associated with improved disease outcomes.

There has been an increasing prevalence of neurodegenerative diseases with the rapid increase in aging societies worldwide. Biomarkers that can be used to detect pathological changes before the development of severe neuronal loss and consequently facilitate early intervention with disease-modifying therapeutic modalities are therefore urgently needed. Diffusion magnetic resonance imaging (MRI) is a promising tool that can be used to infer microstructural characteristics of the brain, such as microstructural integrity and complexity, as well as axonal density, order, and myelination, through the utilization of water molecules that are diffused within the tissue, with displacement at the micron scale. Diffusion tensor imaging is the most commonly used diffusion MRI technique to assess the pathophysiology of neurodegenerative diseases. However, diffusion tensor imaging has several limitations, and new technologies, including neurite orientation dispersion and density imaging, diffusion kurtosis imaging, and free-water imaging, have been recently developed as approaches to overcome these constraints. This review provides an overview of these technologies and their potential as biomarkers for the early diagnosis and disease progression of major neurodegenerative diseases.

Introduction: Incorporating artificial intelligence (AI) in diagnostic medical imaging reports has the potential to improve efficiency. Although perception of radiologists, radiographers, medical students and patients on AI use in image reporting has been explored, there is limited literature on nonradiologist clinicians' opinion on this topic. Method: Single-centre online survey targeting non-radiologist medical staff conducted from May to August 2021 at a tertiary referral hospital in Melbourne, Australia. Survey questions revolved around clinicians' level of comfort acting on AI-generated reports with varying levels of radiologist involvement and scan complexity, opinion on medicolegal responsibility for erroneous AI-issued reports and perception of data privacy and security. Results: Eighty-eight responses were collected, including 47.9% of consultants. Non-radiologist clinicians across all seniorities and specialties felt significantly less comfortable acting on AI-issued reports compared with radiologist-issued reports (mean comfort radiologist 6.44/7, mean comfort AI 3.35/7, P < 0.001) but felt equally comfortable with an AI-hybrid model of care (mean comfort hybrid 6.38/7, P = 0.676). Non-radiologist clinicians believed that medicolegal responsibility with errors in AI-issued reports mostly lay with hospitals or health service providers (65.9%) and radiologists (54.5%). Regarding data privacy and security, non-radiologist clinicians felt significantly less comfortable with AI issuing image reports instead of radiologists (P < 0.001). Conclusion: A hybrid AI-generated radiologist-confirmed method of image reporting may be the ideal way of integrating AI into clinical practice based on the perception of our referring non-radiologist medical colleagues. Formal guidelines on medicolegal responsibility and data privacy should be established prior to utilising AI in the clinical setting.

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