Influence of short-term dietary measures on dioxin concentrations in human milk.

Breast-feeding may expose infants to high levels of toxic chlorinated dioxins. To diminish intake of these lipophilic compounds by the baby, two diets were tested for their ability to reduce concentrations of dioxins in human milk. The diets were a low-fat/high- carbohydrate/low-dioxin diet. (about 20% of energy intake derived from fat) and a high fat /low-carbohydrate/low-dioxin diet. These diets were tested in 16 and 18 breast-feeding women, respectively. The test diets were followed for 5 consecutive days in the fourth week after delivery. Milk was sampled before and at the end of the dietary regimen, and dioxin concentrations and fatty acid concentrations were determined. Despite significant influences of these diets on the fatty acid profiles, no significant influence on the dioxin concentrations in breast milk could be found. We conclude that short-term dietary measures will not reduce dioxin concentration in human milk

Left ventricular mass assessment by CMR; how to define the optimal index

Vascular Biology and Interventio

Cardiovascular dynamics in ischemic cardiomyopathy during exercise

Cardiac Dysfunction and Arrhythmia

Deep learning-based recognition of key anatomical structures during robot-assisted minimally invasive esophagectomy

Objective: To develop a deep learning algorithm for anatomy recognition in thoracoscopic video frames from robot-assisted minimally invasive esophagectomy (RAMIE) procedures using deep learning. Background: RAMIE is a complex operation with substantial perioperative morbidity and a considerable learning curve. Automatic anatomy recognition may improve surgical orientation and recognition of anatomical structures and might contribute to reducing morbidity or learning curves. Studies regarding anatomy recognition in complex surgical procedures are currently lacking. Methods: Eighty-three videos of consecutive RAMIE procedures between 2018 and 2022 were retrospectively collected at University Medical Center Utrecht. A surgical PhD candidate and an expert surgeon annotated the azygos vein and vena cava, aorta, and right lung on 1050 thoracoscopic frames. 850 frames were used for training of a convolutional neural network (CNN) to segment the anatomical structures. The remaining 200 frames of the dataset were used for testing the CNN. The Dice and 95% Hausdorff distance (95HD) were calculated to assess algorithm accuracy. Results: The median Dice of the algorithm was 0.79 (IQR = 0.20) for segmentation of the azygos vein and/or vena cava. A median Dice coefficient of 0.74 (IQR = 0.86) and 0.89 (IQR = 0.30) were obtained for segmentation of the aorta and lung, respectively. Inference time was 0.026 s (39 Hz). The prediction of the deep learning algorithm was compared with the expert surgeon annotations, showing an accuracy measured in median Dice of 0.70 (IQR = 0.19), 0.88 (IQR = 0.07), and 0.90 (0.10) for the vena cava and/or azygos vein, aorta, and lung, respectively. Conclusion: This study shows that deep learning-based semantic segmentation has potential for anatomy recognition in RAMIE video frames. The inference time of the algorithm facilitated real-time anatomy recognition. Clinical applicability should be assessed in prospective clinical studies.</p

Cardiac magnetic resonance imaging analysis in STEMI: quantitative or still visual?

Cardiovascular Aspects of Radiolog

Automated measurement of brain and white matter lesion volume in type 2 diabetes mellitus

Aims/hypothesis: Type 2 diabetes mellitus has been associated with brain atrophy and cognitive decline, but the association with ischaemic white matter lesions is unclear. Previous neuroimaging studies have mainly used semiquantitative rating scales to measure atrophy and white matter lesions (WMLs). In this study we used an automated segmentation technique to investigate the association of type 2 diabetes, several diabetes-related risk factors and cognition with cerebral tissue and WML volumes. Subjects and methods: Magnetic resonance images of 99 patients with type 2 diabetes and 46 control participants from a population-based sample were segmented using a k-nearest neighbour classifier trained on ten manually segmented data sets. White matter, grey matter, lateral ventricles, cerebrospinal fluid not including lateral ventricles, and WML volumes were assessed. Analyses were adjusted for age, sex, level of education and intracranial volume. Results: Type 2 diabetes was associated with a smaller volume of grey matter (-21.8 ml; 95% CI -34.2, -9.4) and with larger lateral ventricle volume (7.1 ml; 95% CI 2.3, 12.0) and with larger white matter lesion volume (56.5%; 95% CI 4.0, 135.8), whereas white matter volume was not affected. In separate analyses for men and women, the effects of diabetes were only significant in women. Conclusions/interpretation: The combination of atrophy with larger WML volume indicates that type 2 diabetes is associated with mixed pathology in the brain. The observed sex differences were unexpected and need to be addressed in further studies. © 2007 Springer-Verlag

Positron emission tomography; viable tool in patients pre-CABG?

Vascular Biology and Interventio

Corneal Pachymetry by AS-OCT after Descemet's Membrane Endothelial Keratoplasty

Corneal thickness (pachymetry) maps can be used to monitor restoration of corneal endothelial function, for example after Descemet's membrane endothelial keratoplasty (DMEK). Automated delineation of the corneal interfaces in anterior segment optical coherence tomography (AS-OCT) can be challenging for corneas that are irregularly shaped due to pathology, or as a consequence of surgery, leading to incorrect thickness measurements. In this research, deep learning is used to automatically delineate the corneal interfaces and measure corneal thickness with high accuracy in post-DMEK AS-OCT B-scans. Three different deep learning strategies were developed based on 960 B-scans from 50 patients. On an independent test set of 320 B-scans, corneal thickness could be measured with an error of 13.98 to 15.50 micrometer for the central 9 mm range, which is less than 3% of the average corneal thickness. The accurate thickness measurements were used to construct detailed pachymetry maps. Moreover, follow-up scans could be registered based on anatomical landmarks to obtain differential pachymetry maps. These maps may enable a more comprehensive understanding of the restoration of the endothelial function after DMEK, where thickness often varies throughout different regions of the cornea, and subsequently contribute to a standardized postoperative regime.Comment: Fixed typo in abstract: The development set consists of 960 B-scans from 50 patients (instead of 68). The B-scans from the other 18 patients were used for testing onl

Stress imaging in patients with diabetes; routine practice?

Ventricular Dysfunction & Heart Failur