Practices and ethical concerns regarding preimplantation diagnosis. Who regulates preimplantation genetic diagnosis in Brazil?

Preimplantation genetic diagnosis (PGD) was originally developed to diagnose embryo-related genetic abnormalities for couples who present a high risk of a specific inherited disorder. Because this technology involves embryo selection, the medical, bioethical, and legal implications of the technique have been debated, particularly when it is used to select features that are not related to serious diseases. Although several initiatives have attempted to achieve regulatory harmonization, the diversity of healthcare services available and the presence of cultural differences have hampered attempts to achieve this goal. Thus, in different countries, the provision of PGD and regulatory frameworks reflect the perceptions of scientific groups, legislators, and society regarding this technology. In Brazil, several texts have been analyzed by the National Congress to regulate the use of assisted reproduction technologies. Legislative debates, however, are not conclusive, and limited information has been published on how PGD is specifically regulated. The country requires the development of new regulatory standards to ensure adequate access to this technology and to guarantee its safe practice. This study examined official documents published on PGD regulation in Brazil and demonstrated how little direct oversight of PGD currently exists. It provides relevant information to encourage reflection on a particular regulation model in a Brazilian context, and should serve as part of the basis to enable further reform of the clinical practice of PGD in the country.     

A Stacked Generalization of 3D Orthogonal Deep Learning Convolutional Neural Networks for Improved Detection of White Matter Hyperintensities in 3D FLAIR Images

BACKGROUND AND PURPOSE:Accurate and reliable detection of white matter hyperintensities and their volume quantification can provide valuable clinical information to assess neurologic disease progression. In this work, a stacked generalization ensemble of orthogonal 3D convolutional neural networks, StackGen-Net, is explored for improving automated detection of white matter hyperintensities in 3D T2-FLAIR images.MATERIALS AND METHODS:Individual convolutional neural networks in StackGen-Net were trained on 2.5D patches from orthogonal reformatting of 3D-FLAIR (n = 21) to yield white matter hyperintensity posteriors. A meta convolutional neural network was trained to learn the functional mapping from orthogonal white matter hyperintensity posteriors to the final white matter hyperintensity prediction. The impact of training data and architecture choices on white matter hyperintensity segmentation performance was systematically evaluated on a test cohort (n = 9). The segmentation performance of StackGen-Net was compared with state-of-the-art convolutional neural network techniques on an independent test cohort from the Alzheimer’s Disease Neuroimaging Initiative-3 (n = 20).RESULTS:StackGen-Net outperformed individual convolutional neural networks in the ensemble and their combination using averaging or majority voting. In a comparison with state-of-the-art white matter hyperintensity segmentation techniques, StackGen-Net achieved a significantly higher Dice score (0.76 [SD, 0.08], F1-lesion (0.74 [SD, 0.13]), and area under precision-recall curve (0.84 [SD, 0.09]), and the lowest absolute volume difference (13.3% [SD, 9.1%]). StackGen-Net performance in Dice scores (median = 0.74) did not significantly differ (P = .22) from interobserver (median = 0.73) variability between 2 experienced neuroradiologists. We found no significant difference (P = .15) in white matter hyperintensity lesion volumes from StackGen-Net predictions and ground truth annotations.CONCLUSIONS:A stacked generalization of convolutional neural networks, utilizing multiplanar lesion information using 2.5D spatial context, greatly improved the segmentation performance of StackGen-Net compared with traditional ensemble techniques and some state-of-the-art deep learning models for 3D-FLAIR.

White matter hyperintensities (WMHs) correspond to pathologic features of axonal degeneration, demyelination, and gliosis observed within cerebral white matter.¹ Clinically, the extent of WMHs in the brain has been associated with cognitive impairment, Alzheimer’s disease and vascular dementia, and increased risk of stroke.^2,3 The detection and quantification of WMH volumes to monitor lesion burden evolution and its correlation with clinical outcomes have been of interest in clinical research.^4,5 Although the extent of WMHs can be visually scored,⁶ the categoric nature of such scoring systems makes quantitative evaluation of disease progression difficult. Manually segmenting WMHs is tedious, prone to inter- and intraobserver variability, and is, in most cases, impractical. Thus, there is an increased interest in developing fast, accurate, and reliable computer-aided automated techniques for WMH segmentation.Convolutional neural network (CNN)-based approaches have been successful in several semantic segmentation tasks in medical imaging.⁷ Recent works have proposed using deep learning–based methods for segmenting WMHs using 2D-FLAIR images.^8-11 More recently, a WMH segmentation challenge¹² was also organized (http://wmh.isi.uu.nl/) to facilitate comparison of automated segmentation of WMHs of presumed vascular origin in 2D multislice T2-FLAIR images. Architectures that used an ensemble of separately trained CNNs showed promising results in this challenge, with 3 of the top 5 winners using ensemble-based techniques.¹²Conventional 2D-FLAIR images are typically acquired with thick slices (3–4 mm) and possible slice gaps. Partial volume effects from a thick slice are likely to affect the detection of smaller lesions, both in-plane and out-of-plane. 3D-FLAIR images, with isotropic resolution, have been shown to achieve higher resolution and contrast-to-noise ratio¹³ and have shown promising results in MS lesion detection using 3D CNNs.¹⁴ Additionally, the isotropic resolution enables viewing and evaluation of the images in multiple planes. This multiplanar reformatting of 3D-FLAIR without the use of interpolating kernels is only possible due to the isotropic nature of the acquisition. Network architectures that use information from the 3 orthogonal views have been explored in recent works for CNN-based segmentation of 3D MR imaging data.¹⁵ The use of data from multiple planes allows more spatial context during training without the computational burden associated with full 3D training.¹⁶ The use of 3 orthogonal views simultaneously mirrors how humans approach this segmentation task.Ensembles of CNNs have been shown to average away the variances in the solution and the choice of model- and configuration-specific behaviors of CNNs.¹⁷ Traditionally, the solutions from these separately trained CNNs are combined by averaging or using a majority consensus. In this work, we propose the use of a stacked generalization framework (StackGen-Net) for combining multiplanar lesion information from 3D CNN ensembles to improve the detection of WMH lesions in 3D-FLAIR. A stacked generalization¹⁸ framework learns to combine solutions from individual CNNs in the ensemble. We systematically evaluated the performance of this framework and compared it with traditional ensemble techniques, such as averaging or majority voting, and state-of-the-art deep learning techniques.     

Effect of Electroacupuncture on Spermatogenesis in Rats with Oligozoospermia of Insufficiency of Shen (Kidney) Essence Syndrome

Objective: To assess the effect of electroacupuncture(EA) on expression of cytoskeletal proteins from Sertoli cells(SCs) and spermatogenesis in rats with oligozoospermia of insufficiency of Shen(Kidney)essence syndrome(OIKES).Methods: Twenty healthy male Sprague-Dawley rats were randomly assigned to four groups using a random number table: control,tripterygium glycosides(TG) treatment,sham and EA groups(n=5 in each group).A rat model of OIKES was established by oral gavage with TG.The EA group was treated with TG and received EA at Shenshu(BL 23) and Zusanli(ST 36) acupoints for 20 min,once daily for 30 days,while the sham group received EA at identical acupoints with skin penetration without stimulation.After 30 days,the ?nal body weight and coef?cients for the testis and epididymis were calculated and sperm parameters were measured.Immunohistochemical analyses were performed to detect expression of vimentin and α-tubulin in SCs and proliferating cell nuclear antigen(PCNA) immunoreactivity in germ cells.Apoptosis in germ cells was quanti?ed by the transferase biotin-dUTP nick end labeling assay.Results: Compared with the control group,the final body weight and testis/epididymis coefficients of rats in the TG-treated group were not significantly different,but the sperm count and motility were lower(P0.05).Expressions of vimentin and α-tubulin were also signi?cantly weaker(P0.01).The PCNA immunoreactivity of germ cells was decreased(P=0.059),whereas the apoptotic index of germ cells was increased signi?cantly(P0.01).In contrast,EA at BL 23 and ST 36 acupoints signi?cantly improved the ?nal body weight as well as the sperm count,concentration and motility(P0.01 or P0.05).EA increased expression of vimentin and α-tubulin in SCs markedly,and signi?cantly enhanced PCNA immunoreactivity with decreased apoptosis in germ cells(P0.01 or P0.05).Conclusions: EA at BL 23 and ST 36 acupoints has protective effects on spermatogenesis in rats with OIKES.This effect seems to be achieved by attenuating TG-induced disruption of cytoskeletal protein in SCs.