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In the head of an embryo, a layer of mesenchyme surrounds the brain underneath the surface ectoderm. This cranial mesenchyme gives rise to the meninges, the calvaria (top part of the skull), and the dermis of the scalp. Abnormal development of these structures, especially the meninges and the calvaria, is linked to significant congenital defects in humans. It has been known that different areas of the cranial mesenchyme have different fates. For example, the calvarial bone develops from the cranial mesenchyme on the baso-lateral side of the head just above the eye (supraorbital mesenchyme, SOM), but not from the mesenchyme apical to SOM (early migrating mesenchyme, EMM). However, the molecular basis of this difference is not fully understood. To answer this question, we compared the transcriptomes of EMM and SOM using high-throughput sequencing (RNA-seq). This experiment identified a large number of genes that were differentially expressed in EMM and SOM, and gene ontology analyses found very different terms enriched in each region. We verified the expression of about 40 genes in the head by RNA in situ hybridization, and the expression patterns were annotated to make a map of molecular markers for 6 subdivisions of the cranial mesenchyme. Our data also provided insights into potential novel regulators of cranial mesenchyme development, including several axon guidance pathways, lectin complement pathway, cyclic-adenosine monophosphate (cAMP) signaling pathway, and ZIC family transcription factors. Together, information in this paper will serve as a unique resource to guide future research on cranial mesenchyme development. • Cranial mesenchyme gives rise to the meninges, the calvaria, and the scalp. • Different regions of the cranial mesenchyme have different fates. • We performed RNAseq to compare gene expression in different regions. • We identified potential novel regulators of cranial mesenchyme patterning. [ABSTRACT FROM AUTHOR]

[Display omitted] • We describe a novel technique for detecting structural chromosomal abnormalities. • The Siamese architecture that we use mimics how human operators detect abnormalities. • This machine learning method takes as inputs both copies of each chromosome pair. • The method detects changes in the banding pattern of the chromosomes of a given pair. • We identify adapted margins of the contrastive loss function for the Siamese training. The detection of structural chromosomal abnormalities (SCA) is crucial for diagnosis, prognosis and management of many genetic diseases and cancers. This detection, done by highly qualified medical experts, is tedious and time-consuming. We propose a highly performing and intelligent method to assist cytogeneticists to screen for SCA. Each chromosome is present in two copies that make up a pair of chromosomes. Usually, SCA are present in only one copy of the pair. Convolutional neural networks (CNN) with Siamese architecture are particularly relevant for evaluating similarities between two images, which is why we used this method to detect abnormalities between both chromosomes of a given pair. As a proof-of-concept, we first focused on a deletion occurring on chromosome 5 (del(5q)) observed in hematological malignancies. Using our dataset, we conducted several experiments without and with data augmentation on seven popular CNN models. Overall, performances obtained were very relevant for detecting deletions, particularly with Xception and InceptionResNetV2 models achieving 97.50 % and 97.01 % of F 1 -score, respectively. We additionally demonstrated that these models successfully recognized another SCA, inversion inv(3), which is one of the most difficult SCA to detect. The performance improved when the training was applied on inversion inv(3) dataset, achieving 94.82 % of F 1 -score. The technique that we propose in this paper is the first highly performing method based on Siamese architecture that allows the detection of SCA. Our code is publicly available at: https://github.com/MEABECHAR/ChromosomeSiameseAD. [ABSTRACT FROM AUTHOR]

Spina bifida is a serious and largely preventable neural tube birth defect and an important cause of mortality and lifelong disability. The People and Organizations United for Spina Bifida and Hydrocephalus (PUSH!) Global Alliance was formed in 2014 to provide a common platform for various organizations worldwide to raise the visibility of spina bifida and hydrocephalus. In its formative phase, the alliance recognized that in order to accelerate surveillance, prevention, and care for these conditions, there was a need to provide an evidence-based assessment of how nations are performing in specific areas. In this paper, we describe the impetus for, and the process of, developing country-level scorecards for spina bifida surveillance, prevention and care. The PUSH! Executive Committee formulated a comprehensive list of six actionable indicators measuring availability of published studies on population-based folate studies; surveillance of prevalence and mortality; prevention-based policies; access to care; and quality of life associated with spina bifida. Rubrics were developed to score each country on the aforementioned indicators. Country scores were pooled across each indicator and the composite scores ranged between zero and three if there was a need for improvement, four and five if they were in good standing, or six for an excellent status. The scorecard included country-specific recommendations assimilated from the literature and published guidelines to aid policy makers in accelerating surveillance and prevention, and improving the care and quality of life indicators. For comparison, country-level scorecards were grouped by WHO-regions. Score cards were made available publicly through the website "www.pu-sh.org". [ABSTRACT FROM AUTHOR]

Vertigo and dizziness are extremely common complaints, related to either peripheral or central nervous system disorders. Among the latter, epilepsy has to be taken into consideration: indeed, vertigo may be part of the initial aura of a focal epileptic seizure in association with other signs/symptoms, or represent the only ictal manifestation, a rare phenomenon known as "vertiginous" or "vestibular" seizure. These ictal symptoms are usually related to a discharge arising from/involving temporal or parietal areas, which are supposed to be a crucial component of the so-called "vestibular cortex". In this paper, we describe three patients suffering from drug-resistant focal epilepsy, symptomatic of malformations of cortical development or perinatal hypoxic/ischemic lesions located in the posterior regions, who presented clusters of vertiginous seizures. The high recurrence rate of such events, recorded during video-EEG monitoring sessions, offered the opportunity to perform an ictal EEG/fMRI study to identify seizure-related hemodynamic changes. The ictal EEG/fMRI revealed the main activation clusters in the temporo-parieto-occipital regions, which are widely recognized to be involved in the processing of vestibular information. Interestingly, ictal deactivation was also detected in the ipsilateral cerebellar hemisphere, suggesting the ictal involvement of cortical-subcortical structures known to be part of the vestibular integration network. [ABSTRACT FROM AUTHOR]

Because observational studies often use imperfect measurements, results are prone to misclassification errors. We used as a motivating example the possible teratogenic risks of antiemetic agents in pregnancy since a large observational study recently showed that first-trimester exposure to doxylamine-pyridoxine was associated with significantly increased risk of congenital malformations as a whole, as well as central nervous system defects, and previous observational studies did not show such associations. A meta-analysis on this issue was carried out with the aim to illustrate how differential exposure and outcome misclassifications may lead to uncertain conclusions. Medline, searched to October 2019 for full text papers in English. Summary Odds Ratios (ORs) with confidence intervals (CIs) were calculated using random-effect models. Probabilistic sensitivity analyses were performed for evaluating the extension of differential misclassification required to account for the exposure-outcome association. Summary ORs were 1.02 (95 % CI, 0.92–1.15), 0.99 (0.82–1.19) and 1.25 (1.08–1.44) for overall congenital, cardiocirculatory, and central nervous system malformations respectively. By assuming exposure and outcome bias factor respectively of 0.95 (i.e., newborns with congenital defects had exposure specificity 5% lower than healthy newborns) and 1.12 (i.e., exposed newborns had outcome sensitivity 12 % higher than unexposed newborns), summary OR of central nervous system defects became 1.13 (95 % CI, 0.99–1.29) and 1.17 (95 % CI, 0.99–1.38). Observational investigations and meta-analyses of observational studies need cautious interpretations. Their susceptibility to several, often sneaky, sources of bias should be carefully evaluated. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel method for magnetic resonance imaging based Alzheimer's disease (AD) or mild cognitive impairment (MCI) diagnosis that systematically integrates voxel-based, region-based, and patch-based approaches into a unified framework. Specifically, we parcellate the brain into predefined regions based on anatomical knowledge (i.e. , templates) and derive complex nonlinear relationships among voxels, whose intensities denote volumetric measurements, within each region. Unlike existing methods that use cubical or rectangular shapes, we consider the anatomical shapes of regions as atypical patches. Using complex nonlinear relationships among voxels in each region learned by deep neural networks, we extract a "regional abnormality representation." We then make a final clinical decision by integrating the regional abnormality representations over the entire brain. It is noteworthy that the regional abnormality representations allow us to interpret and understand the symptomatic observations of a subject with AD or MCI by mapping and visualizing these observations in the brain space. On the baseline MRI dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, our method achieves state-of-the-art performance for four binary classification tasks and one three-class classification task. Additionally, we conducted exhaustive experiments and analysis to validate the efficacy and potential of our method. [ABSTRACT FROM AUTHOR]