Twelve tips for designing and implementing peer-led assessment writing schemes in health professions education.

Peer-led assessment (PLA) has gained increasing prominence within health professions education as an effective means of engaging learners in the process of assessment writing and practice. Involving students in various stages of the assessment lifecycle, including item writing, quality assurance, and feedback, not only facilitates the creation of high-quality item banks with minimal faculty input but also promotes the development of students' assessment literacy and fosters their growth as teachers. The advantages of involving students in the generation of assessments are evident from a pedagogical standpoint, benefiting both students and faculty. However, faculty members may face uncertainty when it comes to implementing such approaches effectively. To address this concern, this paper presents twelve tips that offer guidance on important considerations for the successful implementation of peer-led assessment schemes in the context of health professions education.

Genome-wide association meta-analysis identifies risk loci for abdominal aortic aneurysm and highlights PCSK9 as a therapeutic target.

Abdominal aortic aneurysm (AAA) is a common disease with substantial heritability. In this study, we performed a genome-wide association meta-analysis from 14 discovery cohorts and uncovered 141 independent associations, including 97 previously unreported loci. A polygenic risk score derived from meta-analysis explained AAA risk beyond clinical risk factors. Genes at AAA risk loci indicate involvement of lipid metabolism, vascular development and remodeling, extracellular matrix dysregulation and inflammation as key mechanisms in AAA pathogenesis. These genes also indicate overlap between the development of AAA and other monogenic aortopathies, particularly via transforming growth factor ß signaling. Motivated by the strong evidence for the role of lipid metabolism in AAA, we used Mendelian randomization to establish the central role of nonhigh-density lipoprotein cholesterol in AAA and identified the opportunity for repurposing of proprotein convertase, subtilisin/kexin-type 9 (PCSK9) inhibitors. This was supported by a study demonstrating that PCSK9 loss of function prevented the development of AAA in a preclinical mouse model.

Protective Role for Smooth Muscle Cell Hepcidin in Abdominal Aortic Aneurysm.

BACKGROUND: Hepcidin is a liver-derived hormone that controls systemic iron homeostasis, by inhibiting the iron exporter ferroportin in the gut and spleen, respective sites of iron absorption and recycling. Hepcidin is also expressed ectopically in the context of cardiovascular disease. However, the precise role of ectopic hepcidin in underlying pathophysiology is unknown. In patients with abdominal aortic aneurysm (AAA), hepcidin is markedly induced in smooth muscle cells (SMCs) of the aneurysm wall and inversely correlated with the expression of LCN2 (lipocalin-2), a protein implicated in AAA pathology. In addition, plasma hepcidin levels were inversely correlated with aneurysm growth, suggesting hepcidin has a potential disease-modifying role. METHODS: To probe the role of SMC-derived hepcidin in the setting of AAA, we applied AngII (Angiotensin-II)-induced AAA model to mice harbouring an inducible, SMC-specific deletion of hepcidin. To determine whether SMC-derived hepcidin acted cell-autonomously, we also used mice harboring an inducible SMC-specific knock-in of hepcidin-resistant ferroportinC326Y. The involvement of LCN2 was established using a LCN2-neutralizing antibody. RESULTS: Mice with SMC-specific deletion of hepcidin or knock-in of hepcidin-resistant ferroportinC326Y had a heightened AAA phenotype compared with controls. In both models, SMCs exhibited raised ferroportin expression and reduced iron retention, accompanied by failure to suppress LCN2, impaired autophagy in SMCs, and greater aortic neutrophil infiltration. Pretreatment with LCN2-neutralizing antibody restored autophagy, reduced neutrophil infiltration, and prevented the heightened AAA phenotype. Finally, plasma hepcidin levels were consistently lower in mice with SMC-specific deletion of hepcidin than in controls, indicating that SMC-derived hepcidin contributes to the circulating pool in AAA. CONCLUSIONS: Hepcidin elevation in SMCs plays a protective role in the setting of AAA. These findings are the first demonstration of a protective rather than deleterious role for hepcidin in cardiovascular disease. They highlight the need to further explore the prognostic and therapeutic value of hepcidin outside disorders of iron homeostasis.

A Deep Learning Approach to Visualize Aortic Aneurysm Morphology Without the Use of Intravenous Contrast Agents.

BACKGROUND: Intravenous contrast agents are routinely used in CT imaging to enable the visualization of intravascular pathology, such as with abdominal aortic aneurysms. However, the injection is contraindicated in patients with iodine allergy and is associated with renal complications. OBJECTIVES: In this study, we investigate if the raw data acquired from a noncontrast CT image contains sufficient information to differentiate blood and other soft tissue components. A deep learning pipeline underpinned by generative adversarial networks was developed to simulate contrast enhanced CTA images using noncontrast CTs. METHODS AND RESULTS: Two generative models (cycle- and conditional) are trained with paired noncontrast and contrast enhanced CTs from seventy-five patients (total of 11,243 pairs of images) with abdominal aortic aneurysms in a 3-fold cross-validation approach with a training/testing split of 50:25 patients. Subsequently, models were evaluated on an independent validation cohort of 200 patients (total of 29,468 pairs of images). Both deep learning generative models are able to perform this image transformation task with the Cycle-generative adversarial network (GAN) model outperforming the Conditional-GAN model as measured by aneurysm lumen segmentation accuracy (Cycle-GAN: 86.1% ± 12.2% vs Con-GAN: 85.7% ± 10.4%) and thrombus spatial morphology classification accuracy (Cycle-GAN: 93.5% vs Con-GAN: 85.7%). CONCLUSION: This pipeline implements deep learning methods to generate CTAs from noncontrast images, without the need of contrast injection, that bear strong concordance to the ground truth and enable the assessment ofimportant clinical metrics. Our pipeline is poised to disrupt clinical pathways requiring intravenous contrast.

Prediction of Abdominal Aortic Aneurysm Growth Using Geometric Assessment of Computerized Tomography Images Acquired During the Aneurysm Surveillance Period.

OBJECTIVE: We investigated the utility of geometric features for future AAA growth prediction. BACKGROUND: Novel methods for growth prediction of AAA are recognized as a research priority. Geometric feature have been used to predict cerebral aneurysm rupture, but not examined as predictor of AAA growth. METHODS: Computerized tomography (CT) scans from patients with infra-renal AAAs were analyzed. Aortic volumes were segmented using an automated pipeline to extract AAA diameter (APD), undulation index (UI), and radius of curvature (RC). Using a prospectively recruited cohort, we first examined the relation between these geometric measurements to patients' demographic features (n = 102). A separate 192 AAA patients with serial CT scans during AAA surveillance were identified from an ongoing clinical database. Multinomial logistic and multiple linear regression models were trained and optimized to predict future AAA growth in these patients. RESULTS: There was no correlation between the geometric measurements and patients' demographic features. APD (Spearman r = 0.25, P < 0.05), UI (Spearman r = 0.38, P < 0.001) and RC (Spearman r =-0.53, P < 0.001) significantly correlated with annual AAA growth. Using APD, UI, and RC as 3 input variables, the area under receiver operating characteristics curve for predicting slow growth (<2.5 mm/yr) or fast growth (>5 mm/yr) at 12 months are 0.80 and 0.79, respectively. The prediction or growth rate is within 2 mm error in 87% of cases. CONCLUSIONS: Geometric features of an AAA can predict its future growth. This method can be applied to routine clinical CT scans acquired from patients during their AAA surveillance pathway.

Erratum: Author Correction: Lipid-associated macrophages transition to an inflammatory state in human atherosclerosis, increasing the risk of cerebrovascular complications.

[This corrects the article DOI: 10.1038/s44161-023-00295-x.].

Lipid-associated macrophages transition to an inflammatory state in human atherosclerosis increasing the risk of cerebrovascular complications.

The immune system is integral to cardiovascular health and disease. Targeting inflammation ameliorates adverse cardiovascular outcomes. Atherosclerosis, a major underlying cause of cardiovascular disease (CVD), is conceptualised as a lipid-driven inflammation where macrophages play a non-redundant role. However, evidence emerging so far from single cell atlases suggests a dichotomy between lipid associated and inflammatory macrophage states. Here, we present an inclusive reference atlas of human intraplaque immune cell communities. Combining scRNASeq of human surgical carotid endarterectomies in a discovery cohort with bulk RNASeq and immunohistochemistry in a validation cohort (the Carotid Plaque Imaging Project-CPIP), we reveal the existence of PLIN2hi/TREM1hi macrophages as a toll-like receptor-dependent inflammatory lipid-associated macrophage state linked to cerebrovascular events. Our study shifts the current paradigm of lipid-driven inflammation by providing biological evidence for a pathogenic macrophage transition to an inflammatory lipid-associated phenotype and for its targeting as a new treatment strategy for CVD.

Genome-wide association analysis and replication in 810,625 individuals with varicose veins.

Varicose veins affect one-third of Western society, with a significant subset of patients developing venous ulceration, costing $14.9 billion annually in the USA. Current management consists of either compression stockings, or surgical ablation for more advanced disease. Most varicose veins patients report a positive family history, and heritability is ~17%. We describe the largest two-stage genome-wide association study of varicose veins in 401,656 individuals from UK Biobank, and replication in 408,969 individuals from 23andMe (total 135,514 cases and 675,111 controls). Forty-nine signals at 46 susceptibility loci were discovered. We map 237 genes to these loci, several of which are biologically plausible and tractable to therapeutic targeting. Pathway analysis identified enrichment in extracellular matrix biology, inflammation, (lymph)angiogenesis, vascular smooth muscle cell migration, and apoptosis. Using a polygenic risk score (PRS) derived in an independent cohort, we demonstrate its predictive utility and correlation with varicose veins surgery.

A Hand-Held Magnetic Acoustic Device With Integrated Real-Time Monitoring for Targeted Drug Delivery.

Advances in magnetic materials have enabled the development of new therapeutic agents that can be localized by external magnetic fields. These agents offer a potential means of improving treatment targeting and reducing the toxicity-related side effects associated with systemic delivery. Achieving sufficiently high magnetic fields at clinically relevant depths in vivo, however, remains a challenge. Similarly, there is a need for techniques for real-time monitoring that do not rely on magnetic resonance imaging (MRI). Here, we present a hand-held device to meet these requirements, combining an array of permanent magnets and a thin 64-element capacitive micromachined ultrasonic transducer (CMUT) interfaced to a real-time imaging system. Drug carrier localization was assessed by measuring the terminal velocity of magnetic microbubbles in a column of fluid above the magnetic array. It was found that the magnetic pull force was sufficient to overcome buoyancy at equivalent tissue depths of at least 35 mm and that the median terminal velocity ranged from 0.7 to 20 [Formula: see text]/s over the distances measured. A Monte Carlo study was performed to estimate capture effectiveness in tumor microvessels over a range of different tissue depths and flow rates. Finally, B-mode and contrast-enhanced ultrasound (CEUS) imaging were demonstrated using a gel flow phantom containing a 1.6-mm diameter vessel. Real-time monitoring provided visual confirmation of retention of magnetic microbubbles along the vessel wall at a flow rate of 0.5 mL/min. These results indicate that the system can successfully retain and image magnetic microbubbles at tissue depths and flow rates relevant for clinical applications such as molecular ultrasound imaging of atherosclerosis, sonodynamic and antimetabolite cancer therapy, and clot dissolution via sonothrombolysis.

A deep learning pipeline to simulate fluorodeoxyglucose (FDG) uptake in head and neck cancers using non-contrast CT images without the administration of radioactive tracer.

OBJECTIVES: Positron emission tomography (PET) imaging is a costly tracer-based imaging modality used to visualise abnormal metabolic activity for the management of malignancies. The objective of this study is to demonstrate that non-contrast CTs alone can be used to differentiate regions with different Fluorodeoxyglucose (FDG) uptake and simulate PET images to guide clinical management. METHODS: Paired FDG-PET and CT images (n = 298 patients) with diagnosed head and neck squamous cell carcinoma (HNSCC) were obtained from The cancer imaging archive. Random forest (RF) classification of CT-derived radiomic features was used to differentiate metabolically active (tumour) and inactive tissues (ex. thyroid tissue). Subsequently, a deep learning generative adversarial network (GAN) was trained for this CT to PET transformation task without tracer injection. The simulated PET images were evaluated for technical accuracy (PERCIST v.1 criteria) and their ability to predict clinical outcome [(1) locoregional recurrence, (2) distant metastasis and (3) patient survival]. RESULTS: From 298 patients, 683 hot spots of elevated FDG uptake (elevated SUV, 6.03 ± 1.71) were identified. RF models of intensity-based CT-derived radiomic features were able to differentiate regions of negligible, low and elevated FDG uptake within and surrounding the tumour. Using the GAN-simulated PET image alone, we were able to predict clinical outcome to the same accuracy as that achieved using FDG-PET images. CONCLUSION: This pipeline demonstrates a deep learning methodology to simulate PET images from CT images in HNSCC without the use of radioactive tracer. The same pipeline can be applied to other pathologies that require PET imaging.

Integrated Plasma and Tissue Proteomics Reveals Attractin Release by Intraluminal Thrombus of Abdominal Aortic Aneurysms and Improves Aneurysm Growth Prediction in Humans.

OBJECTIVE: Discovery of novel biomarkers for AAA growth prediction. BACKGROUND: Novel biomarker of AAA growth is a recognized priority in research. Our prior work implicated intraluminal thrombus (ILT) in AAAs to be a potential source of systemic mediators during AAA progression. Here we applied a mass spectrometry proteomics pipeline to discover novel biomarkers for AAA growth prediction. METHODS: Patients were prospectively recruited. Plasma samples were collected at baseline (n = 62). AAA growth was recorded at 12 months. In Experiment 1, plasma samples from the fastest and slowest growth patients (n = 10 each) were compared. In Experiment 2, plasma samples were collected before and at 10-12 weeks after surgery (n = 29). In Experiment 3, paired ILT and omental biopsies were collected intra-operatively during open surgical repair (n = 3). In Experiment 4, tissue secretome was obtained from ex-vivo culture of these paired tissue samples. Samples were subjected to a liquid chromatography tandem mass spectrometry workflow to discover novel biomarkers. RESULTS: We discovered 3 proteins that are: (i) present in ILT; (ii) released by ILT; (iii) reduced in circulation after AAA surgery; (iv) differs between fast and slow growth AAAs. One of these is Attractin. Plasma Attractin correlates significantly with future AAA growth (Spearman r = 0.35, P < 0.005). Using Attractin and AAA diameter as input variables, the area under receiver operating characteristics for predicting no growth and fast growth or AAA at 12 months is 85% and 76%, respectively. CONCLUSION: We show that ILT of AAAs releases mediators during the natural history of AAA growth. These are novel biomarkers for AAA growth prediction in humans.

A Deep Learning Pipeline to Automate High-Resolution Arterial Segmentation With or Without Intravenous Contrast.

BACKGROUND: Existing methods to reconstruct vascular structures from a computerized tomography (CT) angiogram rely on contrast injection to enhance the radio-density within the vessel lumen. However, pathological changes in the vasculature may be present that prevent accurate reconstruction. In aortic aneurysmal disease, a thrombus adherent to the aortic wall within the expanding aneurysmal sac is present in >90% of cases. These deformations prevent the automatic extraction of vital clinical information by existing image reconstruction methods. AIM: In this study, a deep learning architecture consisting of a modified U-Net with attention-gating was implemented to establish a high-throughput and automated segmentation pipeline of pathological blood vessels in CT images acquired with or without the use of a contrast agent. METHODS AND RESULTS: Seventy-Five patients with paired noncontrast and contrast-enhanced CT images were randomly selected from an ongoing study (Ethics Ref 13/SC/0250), manually annotated and used for model training and evaluation. Data augmentation was implemented to diversify the training data set in a ratio of 10:1. The performance of our Attention-based U-Net in extracting both the inner (blood flow) lumen and the wall structure of the aortic aneurysm from CT angiograms was compared against a generic 3-D U-Net and displayed superior results. Implementation of this network within the aortic segmentation pipeline for both contrast and noncontrast CT images has allowed for accurate and efficient extraction of the morphological and pathological features of the entire aortic volume. CONCLUSIONS: This extraction method can be used to standardize aneurysmal disease management and sets the foundation for complex geometric and morphological analysis. Furthermore, this pipeline can be extended to other vascular pathologies.

The role of dipeptidyl peptidase-IV in abdominal aortic aneurysm pathogenesis: A systematic review.

Abdominal aortic aneurysm (AAA) is an important vascular disease carrying significant mortality implications due to the risk of aneurysm rupture. Current management relies exclusively on surgical repair as there is no effective medical therapy. A key element of AAA pathogenesis is the chronic inflammation mediated by inflammatory cells releasing proteases, including the enzyme dipeptidyl peptidase IV (DPP-IV). This review sought to recapitulate available evidence on the involvement of DPP-IV in AAA development. Further, we assessed the experimental use of currently available DPP-IV inhibitors for AAA management in murine models. Embase, Medline, PubMed, and Web of Science databases were utilised to access the relevant studies. The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). A narrative synthesis approach was used. Sixty-four studies were identified from the searched databases; a final 11 were included in the analysis. DPP-IV was reported to be significantly increased in both AAA tissue and plasma of patients and correlated with AAA growth. DPP-IV inhibitors (sitagliptin, vildagliptin, alogliptin, and teneligliptin) were all shown to attenuate AAA formation in murine models by reducing monocyte differentiation, the release of reactive oxygen species (ROS), and metalloproteinases (MMP-2 and MMP-9). DPP-IV seems to play a role in AAA pathogenesis by propagating the inflammatory microenvironment. This is supported by observations of decreased AAA formation and reduction in macrophage infiltration, ROS, matrix MMPs, and interleukins following the use of DPP-IV inhibitors in murine models. There is an existing translational gap from preclinical observations to clinical trials in this important and novel mechanism of AAA pathogenesis. This prior literature highlights the need for further research on molecular targets involved in AAA formation.

Use of deep learning for detection, characterisation and prediction of metastatic disease from computerised tomography: a systematic review.

CT is widely used for diagnosis, staging and management of cancer. The presence of metastasis has significant implications on treatment and prognosis. Deep learning (DL), a form of machine learning, where layers of programmed algorithms interpret and recognise patterns, may have a potential role in CT image analysis. This review aims to provide an overview on the use of DL in CT image analysis in the diagnostic evaluation of metastatic disease. A total of 29 studies were included which could be grouped together into three areas of research: the use of deep learning on the detection of metastatic disease from CT imaging, characterisation of lesions on CT into metastasis and prediction of the presence or development of metastasis based on the primary tumour. In conclusion, DL in CT image analysis could have a potential role in evaluating metastatic disease; however, prospective clinical trials investigating its clinical value are required.

CD200 Limits Monopoiesis and Monocyte Recruitment in Atherosclerosis.

[Figure: see text].