Three-dimensional modeling of human placental terminal villi.

INTRODUCTION: Placental transport is the main factor affecting the health and development of the fetus. Due to the placenta's geometrical and mathematical complexity, the structure-function relations of placental terminal villi have not been successfully modeled. Hence, a novel modeling approach is proposed. METHODS: Computational models of four different specimens were generated from the three-dimensional reconstruction of confocal laser scanning microscopic image stacks. To evaluate the capabilities of the proposed methodology, stationary oxygen diffusion transport was calculated in the terminal villus volumes. RESULTS: The reconstructions automatically provided the spatial arrangement of the fetal capillaries inside the terminal villi. The surface and volume ratios between the fetal capillaries and the villus were also calculated, and the effects of model parameters on the placental diffusive capacity were assessed by parametric analysis. DISCUSSION: The potential of three-dimensional reconstructions combined with finite element analysis as a research tool for the human placenta was tested. The methodology herein could serve in the future as a simulation platform for complicated in vivo and in vitro scenarios.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.placenta.2016.05.00

Motivation of owners to purchase pedigree cats, with specific focus on the acquisition of brachycephalic cats

Background: Cats are globally popular pets and pedigree cats are increasingly prevalent, with brachycephalic breeds being the most registered breeds. How owners decide upon and acquire their cats is poorly understood. Moreover, there are growing concerns about the health and welfare of brachycephalic (BC) dogs and recent studies are raising the awareness of health and welfare problems in BC cats. Methods: An online survey investigated owners’ motivations, perceptions and behaviours prior to, during and following acquisition of non-pedigree (NP), extreme brachycephalic pedigree (BC; i.e., Persian and Exotic Shorthair) and mild to non-BC pedigree (P) cats. Results: The survey received 1367 valid responses (NP n = 882, P n = 400, BC n = 85 (6.2%)). There were marked differences between NP, P and BC owners’ perception of their cats’ health and welfare, reason(s) for acquisition and its process. Owners of NP were less influenced by appearance, behaviour and other features than P or BC owners. In contrast, P and BC owners were highly influenced by appearance, with P owners also placing greater importance on good breed health than BC owners. BC owners were less likely to recommend their breeds to prospective cat owners, apparently concerned by high maintenance requirements. Conclusion: Further research is needed to determine how decision-making is constructed and how it may be improved, especially in respect of welfare outcomes for extreme BC cats given the increased weighting given to appearance over health

Can we accurately classify schizophrenia patients from healthy controls using magnetic resonance imaging and machine learning?:A multi-method and multi-dataset study

Machine learning is a powerful tool that has previously been used to classify schizophrenia (SZ) patients from healthy controls (HC) using magnetic resonance images. Each study, however, uses different datasets, classification algorithms, and validation techniques. Here, we perform a critical appraisal of the accuracy of machine learning methodologies used in SZ/HC classifications studies by comparing three machine learning algorithms (logistic regression [LR], support vector machines [SVMs], and linear discriminant analysis [LDA]) on three independent datasets (435 subjects total) using two tissue density estimates and cortical thickness (CT). Performance is assessed using 10-fold cross-validation, as well as a held-out validation set. Classification using CT outperformed tissue densities, but there was no clear effect of dataset. LR, SVMs, and LDA each yielded the highest accuracies for a different feature set and validation paradigm, but most accuracies were between 55 and 70%, well below previously reported values. The highest accuracy achieved was 73.5% using CT data and an SVM. Taken together, these results illustrate some of the obstacles to constructing effective disease classifiers, and suggest that tissue densities and CT may not be sufficiently sensitive for SZ/HC classification given current available methodologies and sample sizes

Glutathione and glutamate in schizophrenia: a 7T MRS study

In schizophrenia, abnormal neural metabolite concentrations may arise from cortical damage following neuroinflammatory processes implicated in acute episodes. Inflammation is associated with increased glutamate, whereas the antioxidant glutathione may protect against inflammation-induced oxidative stress. We hypothesized that patients with stable schizophrenia would exhibit a reduction in glutathione, glutamate and/or glutamine in the cerebral cortex, consistent with a postinflammatory response, and that this reduction would be most marked in patients with residual schizophrenia an early stage with positive psychotic symptoms has progressed to a late stage characterised by long-term negative symptoms and impairments. We recruited 28 patients with stable schizophrenia and 45 healthy participants matched for age, gender and parental socio-economic status. We measured glutathione, glutamate and glutamine concentrations in the anterior cingulate cortex (ACC), left insula, and visual cortex using 7T proton Magnetic Resonance Spectroscopy (MRS). Glutathione and glutamate were significantly correlated in all three voxels. Glutamine concentrations across the three voxels were significantly correlated with each other. Principal Components Analysis (PCA) produced three clear components: an ACC glutathione-glutamate component; an insula-visual glutathione-glutamate component; and a glutamine component. Patients with stable schizophrenia had significantly lower scores on the ACC glutathione-glutamate component, an effect almost entirely leveraged by the sub-group of patients with residual schizophrenia. All three metabolite concentration values in the ACC were significantly reduced in this group. These findings are consistent with the hypothesis that excito-toxicity during the acute phase of illness leads to reduced glutathione and glutamate in the residual phase of the illness

Placenta Imaging Workshop 2018 report:Multiscale and multimodal approaches

The Centre for Medical Image Computing (CMIC) at University College London (UCL) hosted a two-day workshop on placenta imaging on April 12th and 13th 2018. The workshop consisted of 10 invited talks, 3 contributed talks, a poster session, a public interaction session and a panel discussion about the future direction of placental imaging. With approximately 50 placental researchers in attendance, the workshop was a platform for engineers, clinicians and medical experts in the field to network and exchange ideas. Attendees had the chance to explore over 20 posters with subjects ranging from the movement of blood within the placenta to the efficient segmentation of fetal MRI using deep learning tools. UCL public engagement specialists also presented a poster, encouraging attendees to learn more about how to engage patients and the public with their research, creating spaces for mutual learning and dialogue