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Episodic migraineurs comprise over half of the migraine population, yet the vast majority of current animal models of migraine appear to best represent chronic migraine states. While some of these models can be modified to reflect episodic migraine, there remains a need for non-invasive, validated models of episodic migraine to enhance the clinical translation of migraine research.

Episodic migraineurs comprise over half of the migraine population, yet the vast majority of current animal models of migraine appear to best represent chronic migraine states. While some of these models can be modified to reflect episodic migraine, there remains a need for non-invasive, validated models of episodic migraine to enhance the clinical translation of migraine research.

Society consensus guidelines are commonly used to guide management of pancreatic cystic neoplasms (PCNs). this website However, downsides of these guidelines include unnecessary surgery and missed malignancy. The aim of this study was to use computed tomography (CT)-guided deep learning techniques to predict malignancy of PCNs.

Patients with PCNs who underwent resection were retrospectively reviewed. Axial images of the mucinous cystic neoplasms were collected and based on final pathology were assigned a binary outcome of advanced neoplasia or benign. Advanced neoplasia was defined as adenocarcinoma or intraductal papillary mucinous neoplasm with high-grade dysplasia. A convolutional neural network (CNN) deep learning model was trained on 66% of images, and this trained model was used to test 33% of images. Predictions from the deep learning model were compared to Fukuoka guidelines.

Twenty-seven patients met the inclusion criteria, with 18 used for training and 9 for model testing. The trained deep learning model correctly predicted 3 of 3 malignant lesions and 5 of 6 benign lesions. Fukuoka guidelines correctly classified 2 of 3 malignant lesions as high risk and 4 of 6 benign lesions as worrisome. Following deep learning model predictions would have avoided 1 missed malignancy and 1 unnecessary operation.

In this pilot study, a deep learning model correctly classified 8 of 9 PCNs and performed better than consensus guidelines. Deep learning can be used to predict malignancy of PCNs; however, further model improvements are necessary before clinical use.

In this pilot study, a deep learning model correctly classified 8 of 9 PCNs and performed better than consensus guidelines. Deep learning can be used to predict malignancy of PCNs; however, further model improvements are necessary before clinical use.

Aortic root replacement (ARR) introduces several anatomic complexities relevant to valve-in-valve (VIV)-transcatheter aortic valve replacement (TAVR) that may (1) increase the risk of coronary obstruction, (2) necessitate transcatheter valve overexpansion to accommodate large annuli, and (3) require alternative vascular access to navigate aortic kinking. Therefore, we aimed to quantify the feasibility of VIV-TAVR in patients who underwent aortic root surgery.

Postoperative computed tomography scans were reviewed for consecutive patients who underwent ARR between 2005 and 2019 to obtain measurements relevant for VIV-TAVR planning. Virtual transcatheter valve to coronary ostia distance was measured to assess the risk of coronary obstruction. Root morphologies were classified into 1 of 4 groups based on aortic graft type, aortic diameter at the sinotubular junction, sinus height, estimated transcatheter heart valve height, and diameter. VIV-TAVR was projected to be complex in patients with an aortic kink, exheses may be at high risk for complex VIV-TAVR. Prospective evaluation is required to assess the impact of these conclusions on procedural feasibility.

ARR patients with atypical root morphologies or those who underwent valve replacement with stentless bioprostheses may be at high risk for complex VIV-TAVR. Prospective evaluation is required to assess the impact of these conclusions on procedural feasibility.

During fractional flow reserve (FFR) measurement, the simple presence of the guiding catheter (GC) within the coronary ostium might create artificial ostial stenosis, affecting the hyperemic flow. We aimed to investigate whether selective GC engagement of the coronary ostium might impede hyperemic flow, and therefore impact FFR measurements and related clinical decision-making.

In the DISENGAGE (Determination of Fractional Flow Reserve in Intermediate Coronary Stenosis With Guiding Catheter Disengagement) registry, FFR was prospectively measured twice (with GC engaged [FFR

] and disengaged [FFR

]) in 202 intermediate stenoses of 173 patients. We assessed (1) whether ΔFFR

-FFR

was significantly different from the intrinsic variability of repeated FFR measurements (test-retest repeatability); (2) whether the extent of ΔFFR

-FFR

could be clinically significant and therefore able to impact clinical decision-making; and (3) whether ΔFFR

-FFR

related to the stenosis location, that is, proximal and minical cutoff FFR value of 0.80 in 1 out of 5 measurements. This occurs mostly when the stenosis is located in proximal and middle coronary segments and the FFR value is close to the cutoff value.Retention of staff presents major challenges within children's palliative care; this has substantial implications for children, families and the nursing workforce. To address this, a programme was undertaken that provided pathways of professional development for senior nurses working in this field. This study reports the views of nurses completing this programme, the overall project manager (PM) and the day-to-day programme lead (PL) as well as factors that influence nurse retention within children's palliative care nursing. The study drew on an Appreciative Inquiry approach that comprised of interviews with the PM and PL as well as focus groups and questionnaires with senior nurses from the children's palliative care sector, who participated in the training programme. Thematic analysis of data from interviews and focus groups revealed factors influencing nurse retention speciality, positivity and making a difference, support, provision of adequate resources, tailored education/professional development and resilience.