Dual-task kidney MR segmentation with transformers in autosomal-dominant polycystic kidney disease.

Autosomal-dominant polycystic kidney disease is a prevalent genetic disorder characterized by the development of renal cysts, leading to kidney enlargement and renal failure. Accurate measurement of total kidney volume through polycystic kidney segmentation is crucial to assess disease severity, predict progression and evaluate treatment effects. Traditional manual segmentation suffers from intra- and inter-expert variability, prompting the exploration of automated approaches. In recent years, convolutional neural networks have been employed for polycystic kidney segmentation from magnetic resonance images. However, the use of Transformer-based models, which have shown remarkable performance in a wide range of computer vision and medical image analysis tasks, remains unexplored in this area. With their self-attention mechanism, Transformers excel in capturing global context information, which is crucial for accurate organ delineations. In this paper, we evaluate and compare various convolutional-based, Transformers-based, and hybrid convolutional/Transformers-based networks for polycystic kidney segmentation. Additionally, we propose a dual-task learning scheme, where a common feature extractor is followed by per-kidney decoders, towards better generalizability and efficiency. We extensively evaluate various architectures and learning schemes on a heterogeneous magnetic resonance imaging dataset collected from 112 patients with polycystic kidney disease. Our results highlight the effectiveness of Transformer-based models for polycystic kidney segmentation and the relevancy of exploiting dual-task learning to improve segmentation accuracy and mitigate data scarcity issues. A promising ability in accurately delineating polycystic kidneys is especially shown in the presence of heterogeneous cyst distributions and adjacent cyst-containing organs. This work contribute to the advancement of reliable delineation methods in nephrology, paving the way for a broad spectrum of clinical applications.

Towards a diagnostic tool for neurological gait disorders in childhood combining 3D gait kinematics and deep learning.

Gait abnormalities are frequent in children and can be caused by different pathologies, such as cerebral palsy, neuromuscular disease, toe walker syndrome, etc. Analysis of the "gait pattern" (i.e., the way the person walks) using 3D analysis provides highly relevant clinical information. This information is used to guide therapeutic choices; however, it is underused in diagnostic processes, probably because of the lack of standardization of data collection methods. Therefore, 3D gait analysis is currently used as an assessment rather than a diagnostic tool. In this work, we aimed to determine if deep learning could be combined with 3D gait analysis data to diagnose gait disorders in children. We tested the diagnostic accuracy of deep learning methods combined with 3D gait analysis data from 371 children (148 with unilateral cerebral palsy, 60 with neuromuscular disease, 19 toe walkers, 60 with bilateral cerebral palsy, 25 stroke, and 59 typically developing children), with a total of 6400 gait cycles. We evaluated the accuracy, sensitivity, specificity, F1 score, Area Under the Curve (AUC) score, and confusion matrix of the predictions by ResNet, LSTM, and InceptionTime deep learning architectures for time series data. The deep learning-based models had good to excellent diagnostic accuracy (ranging from 0.77 to 0.99) for discrimination between healthy and pathological gait, discrimination between different etiologies of pathological gait (binary and multi-classification); and determining stroke onset time. LSTM performed best overall. This study revealed that the gait pattern contains specific, pathology-related information. These results open the way for an extension of 3D gait analysis from evaluation to diagnosis. Furthermore, the method we propose is a data-driven diagnostic model that can be trained and used without human intervention or expert knowledge. Furthermore, the method could be used to distinguish gait-related pathologies and their onset times beyond those studied in this research.

Expansion of non-invasive prenatal screening to the screening of 10 types of chromosomal anomalies: a cost-effectiveness analysis.

OBJECTIVES: To determine the cost-effectiveness of the addition of chromosomal anomalies detectable by non-invasive prenatal screening (NIPS), in a prenatal screening programme targeting common aneuploidies. DESIGN, SETTING AND PARTICIPANTS: A simulation study was conducted to study the addition of chromosomal anomalies detectable by NIPS (sex chromosome aneuploidies, 22q11.2 deletion syndrome, large deletion/duplication >7 Mb and rare autosomal trisomies) to five basic strategies currently aiming the common trisomies: three strategies currently offered by the public healthcare systems in Canada, whose first-tier test is performed with biochemical markers, and two programmes whose first-tier test consists of NIPS-based methods. OUTCOME MEASURES: The total number of cases of chromosomal anomalies detected and the costs related to the consumption of medical services. RESULTS: The most effective and the most cost-effective option in almost all prenatal screening strategies is the option that includes all targeted additional conditions. In the strategies where NIPS is used as first-tier testing, the cost per additional case detected by adding all possible additional anomalies to a programme that currently targets only common trisomies is $C25 710 (95% CI $C25 489 to $C25 934) for massively parallel shotgun sequencing and $C57 711 (95% CI $C57 141 to $C58 292) for targeted massively parallel sequencing, respectively. The acceptability curves show that at a willingness-to-pay of $C50 000 per one additional case detected, the expansion of NIPS-based methods for the detection of all possible additional conditions has a 90% probability of being cost-effective. CONCLUSION: From an economic perspective, in strategies that use NIPS as a first-tier screening test, expanding the programmes to detect any considered chromosomal anomalies other than the three common trisomies would be cost-effective. However, the potential expansion of prenatal screening programmes also requires consideration of societal issues, including ethical ones.

Semi-automatic muscle segmentation in MR images using deep registration-based label propagation.

Fully automated approaches based on convolutional neural networks have shown promising performances on muscle segmentation from magnetic resonance (MR) images, but still rely on an extensive amount of training data to achieve valuable results. Muscle segmentation for pediatric and rare diseases cohorts is therefore still often done manually. Producing dense delineations over 3D volumes remains a time-consuming and tedious task, with significant redundancy between successive slices. In this work, we propose a segmentation method relying on registration-based label propagation, which provides 3D muscle delineations from a limited number of annotated 2D slices. Based on an unsupervised deep registration scheme, our approach ensures the preservation of anatomical structures by penalizing deformation compositions that do not produce consistent segmentation from one annotated slice to another. Evaluation is performed on MR data from lower leg and shoulder joints. Results demonstrate that the proposed few-shot multi-label segmentation model outperforms state-of-the-art techniques.

Decision impact studies, evidence of clinical utility for genomic assays in cancer: A scoping review.

BACKGROUND: Decision impact studies have become increasingly prevalent in cancer prognostic research in recent years. These studies aim to evaluate the impact of a genomic test on decision-making and appear to be a new form of evidence of clinical utility. The objectives of this review were to identify and characterize decision impact studies in genomic medicine in cancer care and categorize the types of clinical utility outcomes reported. METHODS: We conducted a search of four databases, Medline, Embase, Scopus and Web of Science, from inception to June 2022. Empirical studies that reported a "decision impact" assessment of a genomic assay on treatment decisions or recommendations for cancer patients were included. We followed scoping review methodology and adapted the Fryback and Thornbury Model to collect and analyze data on clinical utility. The database searches identified 1803 unique articles for title/abstract screening; 269 articles moved to full-text review. RESULTS: 87 studies met inclusion criteria. All studies were published in the last 12 years with the majority for breast cancer (72%); followed by other cancers (28%) (lung, prostate, colon). Studies reported on the impact of 19 different proprietary (18) and generic (1) assays. Across all four levels of clinical utility, outcomes were reported for 22 discrete measures, including the impact on provider/team decision-making (100%), provider confidence (31%); change in treatment received (46%); patient psychological impacts (17%); and costing or savings impacts (21%). Based on the data synthesis, we created a comprehensive table of outcomes reported for clinical utility. CONCLUSIONS: This scoping review is a first step in understanding the evolution and uses of decision impact studies and their influence on the integration of emerging genomic technologies in cancer care. The results imply that DIS are positioned to provide evidence of clinical utility and impact clinical practice and reimbursement decision-making in cancer care. Systematic review registration: Open Science Framework osf.io/hm3jr.

Assessment and comparison of image quality between two real-time sequences for dynamic MRI of distal joints at 3.0 Tesla.

BACKGROUND: Real-time sequences allow functional evaluation of various joint structures during a continuous motion and help understand the pathomechanics of underlying musculoskeletal diseases. PURPOSE: To assess and compare the image quality of the two most frequently used real-time sequences for joint dynamic magnetic resonance imaging (MRI), acquired during finger and ankle joint motion. MATERIAL AND METHODS: A real-time dynamic acquisition protocol, including radiofrequency (RF)-spoiled and balanced steady-state free precession (bSSFP) sequences, optimized for temporal resolution with similar spatial resolution, was performed using a 3.0-T MRI scanner on 10 fingers and 12 ankles from healthy individuals during active motion. Image quality criteria were evaluated on each time frame and compared between these two sequences. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were determined and compared from regions of interest placed on cortical bone, tendon, fat, and muscle. Visualization of anatomical structures and overall image quality appreciation were rated by two radiologists using a 0-10 grading scale. RESULTS: Mean CNR was significantly higher with bSSFP sequence compared to RF-spoiled sequence. The grading score was in the range of 5-9.3 and was significantly higher with RF-spoiled sequence for bone and joint evaluation and overall image appreciation on the two joints. The standard deviation for SNR, CNR, and grading score during motion was smaller with RF-spoiled sequence for both the joints. The inter-reader reliability was excellent (>0.75) for evaluating anatomical structures in both sequences. CONCLUSION: A RF-spoiled real-time sequence is recommended for the in vivo clinical evaluation of distal joints on a 3.0-T MRI scanner.

Comprehensive personalized ankle joint shape analysis of children with cerebral palsy from pediatric MRI.

Cerebral palsy, a common physical disability in childhood, often causes abnormal patterns of movement and posture. To better understand the pathology and improve rehabilitation of patients, a comprehensive bone shape analysis approach is proposed in this article. First, a group analysis is performed on a clinical MRI dataset using two state-of-the-art shape analysis methods: ShapeWorks and a voxel-based method relying on Advanced Normalization Tools (ANTs) registration. Second, an analysis of three bones of the ankle is done to provide a complete view of the ankle joint. Third, a bone shape analysis is carried out at subject level to highlight variability patterns for personnalized understanding of deformities.

Improving Fetal Fraction of Noninvasive Prenatal Screening Samples Collected in EDTA-Gel Tubes Using Gel Size Selection: A Head-To-Head Comparison of Methods.

The aim of this study was to compare the use of EDTA-gel blood collection tubes with and without size selection to cell-stabilizing collection tubes for remote blood sampling for noninvasive prenatal screening (NIPS). Sixty-one pregnant women at 10 to 14 weeks' gestation undergoing NIPS were recruited. Participants were phlebotomized with Streck and EDTA-gel tubes. EDTA-gel tubes were centrifuged before shipping. Libraries prepared from cell-free DNA (cfDNA) extracted from both types of tubes were sequenced on Illumina NextSeq 500, and fetal fraction was estimated using SeqFF. EDTA-gel tube libraries were size selected on agarose gel to eliminate cfDNA fragments >160 bp and resequenced. The main outcome measure was fetal fraction expressed as percentage of total cfDNA sequenced, calculated from sequence read counts (SeqFF). Streck tube samples showed an average 1% higher fetal fraction than centrifuged EDTA-gel tubes without size selection. This difference increased with temperature. When EDTA-gel samples' libraries were size selected, the mean fetal fraction increased from 7% to 13%, with no sample having fetal fraction <4%. Using EDTA-gel tubes reduces NIPS sampling cost and tube processing time in the laboratory. Also, using EDTA-gel tubes does not lead to cfDNA degradation. Size selection increases fetal fraction, reduces the number of test failures, increases NIPS clinical performance, and may be helpful in situations asking for a higher fetal fraction, such as twin pregnancies or screening for sub-chromosomal imbalances.

User Experience of a Computer-Based Decision Aid for Prenatal Trisomy Screening: Mixed Methods Explanatory Study.

BACKGROUND: Mobile health tools can support shared decision-making. We developed a computer-based decision aid (DA) to help pregnant women and their partners make informed, value-congruent decisions regarding prenatal screening for trisomy. OBJECTIVE: This study aims to assess the usability and usefulness of computer-based DA among pregnant women, clinicians, and policy makers. METHODS: For this mixed methods sequential explanatory study, we planned to recruit a convenience sample of 45 pregnant women, 45 clinicians from 3 clinical sites, and 15 policy makers. Eligible women were aged >18 years and >16 weeks pregnant or had recently given birth. Eligible clinicians and policy makers were involved in prenatal care. We asked the participants to navigate a computer-based DA. We asked the women about the usefulness of the DA and their self-confidence in decision-making. We asked all participants about usability, quality, acceptability, satisfaction with the content of the DA, and collected sociodemographic data. We explored participants' reactions to the computer-based DA and solicited suggestions. Our interview guide was based on the Mobile App Rating Scale. We performed descriptive analyses of the quantitative data and thematic deductive and inductive analyses of the qualitative data for each participant category. RESULTS: A total of 45 pregnant women, 14 clinicians, and 8 policy makers participated. Most pregnant women were aged between 25 and 34 years (34/45, 75%) and White (42/45, 94%). Most clinicians were aged between 35 and 44 years (5/14, 36%) and women (11/14, 79%), and all were White (14/14, 100%); the largest proportion of policy makers was aged between 45 and 54 years (4/8, 50%), women (5/8, 62%), and White (8/8, 100%). The mean usefulness score for preparing for decision-making for women was 80/100 (SD 13), and the mean self-efficacy score was 88/100 (SD 11). The mean usability score was 84/100 (SD 14) for pregnant women, 77/100 (SD 14) for clinicians, and 79/100 (SD 23) for policy makers. The mean global score for quality was 80/100 (SD 9) for pregnant women, 72/100 (SD 12) for clinicians, and 80/100 (SD 9) for policy makers. Regarding acceptability, participants found the amount of information just right (52/66, 79%), balanced (58/66, 88%), useful (38/66, 58%), and sufficient (50/66, 76%). The mean satisfaction score with the content was 84/100 (SD 13) for pregnant women, 73/100 (SD 16) for clinicians, and 73/100 (SD 20) for policy makers. Participants thought the DA could be more engaging (eg, more customizable) and suggested strategies for implementation, such as incorporating it into clinical guidelines. CONCLUSIONS: Pregnant women, clinicians, and policy makers found the DA usable and useful. The next steps are to incorporate user suggestions for improving engagement and implementing the computer-based DA in clinical practice.

An Iterative Centroid Approach for Diffeomorphic Online Atlasing.

Online atlasing, i.e., incrementing an atlas with new images as they are acquired, is key when performing studies on very large, or still being gathered, databases. Regular approaches to atlasing however do not focus on this aspect and impose a complete reconstruction of the atlas when adding images. We propose instead a diffeomorphic online atlasing method that allows gradual updates to an atlas. In this iterative centroid approach, we integrate new subjects in the atlas in an iterative manner, gradually moving the centroid of the images towards its final position. This leads to a computationally cheap approach since it only necessitates one additional registration per new subject added. We validate our approach on several experiments with three main goals: 1- to evaluate atlas image quality of the obtained atlases with sharpness and overlap measures, 2- to assess the deviation in terms of transformations with respect to a conventional atlasing method and 3- to compare its computational time with regular approaches of the literature. We demonstrate that the transformations divergence with respect to a state-of-the-art atlas construction method is small and reaches a plateau, that the two construction methods have the same ability to map subject homologous regions onto a common space and produce images of equivalent quality. The computational time of our approach is also drastically reduced for regular updates. Finally, we also present a direct extension of our method to update spatio-temporal atlases, especially useful for developmental studies.

Effect of preexamination conditions in a centralized-testing model of non-invasive prenatal screening.

OBJECTIVES: Non-invasive prenatal testing requires the presence of fetal DNA in maternal plasma. Understanding how preexamination conditions affect the integrity of cell-free DNA (cfDNA) and fetal fraction (FF) are a prerequisite for test implementation. Therefore, we examined the adjusted effect that EDTA and Streck tubes have on the cfDNA quantity and FF. METHODS: A total of 3,568 maternal blood samples across Canada were collected in either EDTA, or Streck tubes, and processing metrics, maternal body mass index (BMI), gestational age and fetal karyotype and sex were recorded. Plasma samples were sequenced using two different sequencing platforms in separate laboratories. Sequencing data were processed with SeqFF to estimate FF. Linear regression and multivariate imputation by chained equations were used to estimate the adjusted effect of tube type on cfDNA and FF. RESULTS: We found a positive association between cfDNA quantity and blood shipment time in EDTA tubes, which is significantly reduced with the use of Streck tubes. Furthermore, we show the storage of plasma at -80 °C is associated with a 4.4% annual relative decrease in cfDNA levels. FF was not associated with collection tube type when controlling for confounding variables. However, FF was positively associated with gestational age and trisomy 21, while negatively associated with BMI, male fetus, trisomy 18, Turners syndrome and triploidy. CONCLUSIONS: Preexamination, maternal and fetal variables are associated with cfDNA quantity and FF. The consideration of these variables in future studies may help to reduce the number of pregnant women with inconclusive tests as a result of low FF.

Web-Based Training for Nurses on Using a Decision Aid to Support Shared Decision-making About Prenatal Screening: Parallel Controlled Trial.

BACKGROUND: Nurses play an important role in supporting pregnant women making decisions about prenatal screening for Down syndrome. We developed a web-based shared decision-making (SDM) training program for health professionals focusing on Down syndrome screening decisions. OBJECTIVE: In this study, we aim to assess the impact of an SDM training program on nurses' intention to use a decision aid with pregnant women deciding on prenatal screening for Down syndrome. METHODS: In this 2-arm, parallel controlled trial, French-speaking nurses working with pregnant women in the province of Quebec were recruited by a private survey firm. They were allocated by convenience either to the intervention group (web-based SDM course that included prenatal screening) or to the control group (web-based course focusing on prenatal screening alone, with no SDM content). The primary outcome was the intention to use a decision aid. Secondary outcomes were psychosocial variables of intention, knowledge, satisfaction, acceptability, perceived usefulness, and reaction to the pedagogical approach. All outcomes were self-assessed through web-based questionnaires, including the space for written comments. We used 2-tailed Student t test and Fisher exact test to compare continuous and categorical variables between groups, respectively. RESULTS: Of the 57 participants assessed for eligibility, 40 (70%) were allocated to the intervention (n=20) or control group (n=20) and 36 (n=18 in each) completed the courses. The mean age of the participants was 41 (SD 9) years. Most were women (39/40, 98%), White (38/40, 95%), clinical nurses (28/40, 70%), and had completed at least a bachelor's degree (30/40, 75%). After the intervention, the mean score of intention was 6.3 (SD 0.8; 95% CI 5.9-6.7) for the intervention group and 6.0 (SD 1.2; 95% CI 5.42-6.64) for the control group (scale 1-7). The differences in intention and other psychosocial variable scores between the groups were not statistically significant. Knowledge scores for SDM were significantly higher in the intervention group (79%, 95% CI 70-89 vs 64%, 95% CI 57-71; P=.009). The intervention was significantly more acceptable in the intervention group (4.6, 95% CI 4.4-4.8 vs 4.3, 95% CI 4.1-4.5; P=.02), and reaction to the pedagogical approach was also significantly more positive in the intervention group (4.7, 95% CI 4.5-4.8 vs 4.4, 95% CI 4.2-4.5; P=.02). There was no significant difference in overall satisfaction (or in perceived usefulness). Furthermore, 17 participants (9 in the intervention group and 8 in the control group) provided written comments on the intervention. CONCLUSIONS: This study focuses on web-based nursing education and its potential to support pregnant women's decision-making needs. It shows that nurses' intention to use a decision aid to enhance SDM in prenatal care is high, with or without training, but that their knowledge about SDM can be improved with web-based training. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/17878.

Transcriptomic characterization of prurigo nodularis and the therapeutic response to nemolizumab.

BACKGROUND: Prurigo nodularis (PN) is a debilitating, difficult-to-treat, intensely pruritic, chronic inflammatory skin disease characterized by hyperkeratotic skin nodules. The pathogenesis of PN is not well understood but is believed to involve cross talk between sensory nerve fibers, immune cells, and the epidermis. It is centered around the neuroimmune cytokine IL-31, driving an intractable itch-scratch cycle. OBJECTIVE: We sought to provide a comprehensive view of the transcriptomic changes in PN skin and characterize the mechanism of action of the anti-IL-31 receptor inhibitor nemolizumab. METHOD: RNA sequencing of biopsy samples obtained from a cohort of patients treated with the anti-IL-31 receptor inhibitor nemolizumab and taken at baseline and week 12. Generation and integration of patient data with RNA-Seq data generated from reconstructed human epidermis stimulated with IL-31 and other proinflammatory cytokines. RESULTS: Our results demonstrate that nemolizumab effectively decreases IL-31 responses in PN skin, leading to effective suppression of downstream inflammatory responses including TH2/IL-13 and TH17/IL-17 responses. This is accompanied by decreased keratinocyte proliferation and normalization of epidermal differentiation and function. Furthermore, our results demonstrate how transcriptomic changes associated with nemolizumab treatment correlate with improvement in lesions, pruritus, stabilization of extracellular matrix remodeling, and processes associated with cutaneous nerve function. CONCLUSION: These data demonstrate a broad response to IL-31 receptor inhibition with nemolizumab and confirm the critical upstream role of IL-31 in PN pathogenesis.

Gradients of connectivity as graph Fourier bases of brain activity.

The application of graph theory to model the complex structure and function of the brain has shed new light on its organization, prompting the emergence of network neuroscience. Despite the tremendous progress that has been achieved in this field, still relatively few methods exploit the topology of brain networks to analyze brain activity. Recent attempts in this direction have leveraged on the one hand graph spectral analysis (to decompose brain connectivity into eigenmodes or gradients) and the other graph signal processing (to decompose brain activity "coupled to" an underlying network in graph Fourier modes). These studies have used a variety of imaging techniques (e.g., fMRI, electroencephalography, diffusion-weighted and myelin-sensitive imaging) and connectivity estimators to model brain networks. Results are promising in terms of interpretability and functional relevance, but methodologies and terminology are variable. The goals of this paper are twofold. First, we summarize recent contributions related to connectivity gradients and graph signal processing, and attempt a clarification of the terminology and methods used in the field, while pointing out current methodological limitations. Second, we discuss the perspective that the functional relevance of connectivity gradients could be fruitfully exploited by considering them as graph Fourier bases of brain activity.

Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks.

Abdominal anatomy segmentation is crucial for numerous applications from computer-assisted diagnosis to image-guided surgery. In this context, we address fully-automated multi-organ segmentation from abdominal CT and MR images using deep learning. The proposed model extends standard conditional generative adversarial networks. Additionally to the discriminator which enforces the model to create realistic organ delineations, it embeds cascaded partially pre-trained convolutional encoder-decoders as generator. Encoder fine-tuning from a large amount of non-medical images alleviates data scarcity limitations. The network is trained end-to-end to benefit from simultaneous multi-level segmentation refinements using auto-context. Employed for healthy liver, kidneys and spleen segmentation, our pipeline provides promising results by outperforming state-of-the-art encoder-decoder schemes. Followed for the Combined Healthy Abdominal Organ Segmentation (CHAOS) challenge organized in conjunction with the IEEE International Symposium on Biomedical Imaging 2019, it gave us the first rank for three competition categories: liver CT, liver MR and multi-organ MR segmentation. Combining cascaded convolutional and adversarial networks strengthens the ability of deep learning pipelines to automatically delineate multiple abdominal organs, with good generalization capability. The comprehensive evaluation provided suggests that better guidance could be achieved to help clinicians in abdominal image interpretation and clinical decision making.

The influence of biophysical parameters in a biomechanical model of cortical folding patterns.

Abnormal cortical folding patterns, such as lissencephaly, pachygyria and polymicrogyria malformations, may be related to neurodevelopmental disorders. In this context, computational modeling is a powerful tool to provide a better understanding of the early brain folding process. Recent studies based on biomechanical modeling have shown that mechanical forces play a crucial role in the formation of cortical convolutions. However, the effect of biophysical parameters in these models remain unclear. In this paper, we investigate the effect of the cortical growth, the initial geometry and the initial cortical thickness on folding patterns. In addition, we not only use several descriptors of the folds such as the dimensionless mean curvature, the surface-based three-dimensional gyrification index and the sulcal depth, but also propose a new metric to quantify the folds orientation. The results demonstrate that the cortical growth mode does almost not affect the complexity degree of surface morphology; the variation in the initial geometry changes the folds orientation and depth, and in particular, the slenderer the shape is, the more folds along its longest axis could be seen and the deeper the sulci become. Moreover, the thinner the initial cortical thickness is, the higher the spatial frequency of the folds is, but the shallower the sulci become, which is in agreement with the previously reported effects of cortical thickness.

Non-invasive prenatal testing for the prenatal screening of sex chromosome aneuploidies: A systematic review and meta-analysis of diagnostic test accuracy studies.

BACKGROUND: There is little evidence on the performance of non-invasive prenatal testing (NIPT) for the detection of fetal sex chromosomal imbalances. In this review, we aimed to appraise and synthesize the literature on the performance of NIPT for the prenatal detection of fetal sex chromosome aneuploidies. METHODS: We performed our literature search in PubMed, Embase, Cochrane Library, Web of Science, and CADTH. Study selection and data extraction were performed by two reviewers independently. There were no restrictions on the study population. Meta-analyses were performed with "R" software. Pooled sensitivities and specificities with their 95% CI were estimated using a random-effects model. Heterogeneity between studies was assessed by a Q test. RESULTS: Based on 11 studies in high prior risk pregnancies, including 116 affected fetuses in aggregate, Massively Parallel Shotgun Sequencing (MPSS) had a sensitivity of 93.9% (95% CI 84.1%, 97.8%) and a specificity of 99.6% (95% CI 98.7%, 99.9%) for the detection of 45,X. Based on four studies in high-risk pregnancies, with 83 affected fetuses in aggregate, Targeted Massively Parallel Sequencing (TMPS) had a sensitivity of 83.2% (95% CI 49.6%, 96.2%) and specificity was 99.8% (95% CI 98.3%, 100%) for the detection of 45,X. In mixed-risk pregnancies, the sensitivity of TMPS for the detection of 45,X was 90.9% (2 studies; 95% CI 70%, 97.7%) and specificity 99.9% (2 studies; 95% CI 99.4%, 100%); MPSS data were not available in such pregnancies. Based on smaller numbers of studies, and small numbers of affected fetuses in either high-risk or mixed-risk pregnancies (using either MPSS or TMPS), the sensitivities and specificities were equal to or greater than 76.2% for 47,XXX, 47,XXY and 47, XYY. The test failures for SCAs were 0.2% (95% CI 0%, 13.6%) for MPSS and 5.6% (95% CI 3.7%, 8.4%) for TMPS. CONCLUSION: High-quality studies are still desirable in order to estimate the performance of NIPT for the detection of sex chromosome imbalances.

A connectome-based approach to assess motor outcome after neonatal arterial ischemic stroke.

OBJECTIVE: Studies of motor outcome after Neonatal Arterial Ischemic Stroke (NAIS) often rely on lesion mapping using MRI. However, clinical measurements indicate that motor deficit can be different than what would solely be anticipated by the lesion extent and location. Because this may be explained by the cortical disconnections between motor areas due to necrosis following the stroke, the investigation of the motor network can help in the understanding of visual inspection and outcome discrepancy. In this study, we propose to examine the structural connectivity between motor areas in NAIS patients compared to healthy controls in order to define the cortical and subcortical connections that can reflect the motor outcome. METHODS: Thirty healthy controls and 32 NAIS patients with and without Cerebral Palsy (CP) underwent MRI acquisition and manual assessment. The connectome of all participants was obtained from T1-weighted and diffusion-weighted imaging. RESULTS: Significant disconnections in the lesioned and contra-lesioned hemispheres of patients were found. Furthermore, significant correlations were detected between the structural connectivity metric of specific motor areas and manuality assessed by the Box and Block Test (BBT) scores in patients. INTERPRETATION: Using the connectivity measures of these links, the BBT score can be estimated using a multiple linear regression model. In addition, the presence or not of CP can also be predicted using the KNN classification algorithm. According to our results, the structural connectome can be an asset in the estimation of gross manual dexterity and can help uncover structural changes between brain regions related to NAIS.

Investigating the causal role of MRE11A p.E506* in breast and ovarian cancer.

The nuclease MRE11A is often included in genetic test panels for hereditary breast and ovarian cancer (HBOC) due to its BRCA1-related molecular function in the DNA repair pathway. However, whether MRE11A is a true predisposition gene for HBOC is still questionable. We determined to investigate this notion by dissecting the molecular genetics of the c.1516G > T;p.E506* truncating MRE11A variant, that we pinpointed in two unrelated French-Canadian (FC) HBOC patients. We performed a case-control study for the variant in ~ 2500 breast, ovarian, and endometrial cancer patients from the founder FC population of Quebec. Furthermore, we looked for the presence of second somatic alterations in the MRE11A gene in the tumors of the carriers. In summary, these investigations suggested that the identified variant is not associated with an increased risk of developing breast or ovarian cancer. We finally performed a systematic review for all the previously reported MRE11A variants in breast and ovarian cancer. We found that MRE11A germline variants annotated as pathogenic on ClinVar often lacked evidence for such classification, hence misleading the clinical management for affected patients. In summary, our report suggests the lack of clinical utility of MRE11A testing in HBOC, at least in the White/Caucasian populations.

Identification of Predictors of Abnormal Calcium, Magnesium and Phosphorus Blood Levels in the Emergency Department: A Retrospective Cohort Study.

PURPOSE: With rising healthcare costs limiting access to care, the judicious use of diagnostic tests has become a critical issue for many jurisdictions. Calcium, magnesium and phosphorus serum levels are regularly performed tests in the emergency department, but their clinical relevance have come into question. Authors sought to determine risk factors that could predict abnormal calcium, magnesium and phosphorus serum levels, as well as identify patients who may need corrective interventions. METHODS: A retrospective cohort study was conducted in two academic hospitals in Québec City. Demographic and clinical characteristics of 1008 patients who had serum calcium and/or magnesium and/or phosphorus levels drawn by an emergency physician were collected. Multivariate logistic regression models were fitted to obtain adjusted odds ratios for each risk factor for abnormal calcium or magnesium or phosphorus blood levels, and for a required intervention. RESULTS: Among patients for whom calcium, magnesium and phosphorus were tested in the Emergency Department, the most significant risk factors (OR>2) for electrolytic abnormality were as follows: hypocalcemia - respiratory distress, diuretics (excluding loop and thiazide), anti-neoplastic medication, long QTc, chronic kidney disease (CKD); hypercalcemia - bone pain, vitamin D, hallucinations; hypomagnesemia - diabetes, corticosteroids; hypermagnesemia - poor extremity perfusion, CKD, furosemide; hypophosphatemia - seizure; hyperphosphatemia - phosphate-binders, CKD, peripheral vascular atherosclerotic disease. Of all patients tested, 3.4% received a corrective intervention initiated by the emergency physician. Predictors of intervention on an electrolyte abnormality include poor peripheral perfusion, nausea and chronic obstructive pulmonary disease (COPD). CONCLUSION: Emergency physicians can potentially reduce the unnecessary testing of calcium, magnesium and phosphorus blood levels by targeting patients with high-acuity conditions or chronic comorbidities such as CKD, diabetes and COPD.