Statistical evaluation of contraceptive methods: use-effectiveness and extended use-effectiveness

Includes bibliographyDocumento disponible en español (85.00

Evaluacion estadistica de metodos anticonceptivos: efectividad de uso y efectividad extendida de uso

Apéndice en inglés contiene instrucciones para el registro de estudios de efectividad de uso de anticonceptivos. Documento disponible en inglés (100.01)Incluye Bibliografí

Properties of rat and mouse [beta]-glucuronidase mRNA and cDNA, including evidence for sequence polymorphism and genetic regulation of mRNA level

cDNA clones containing partial sequences for [beta]-glucuronidase ([beta]G) were constructed from rat preputial gland RNA and identified by their ability to selectively hybridize [beta]G mRNA. One such rat clone was used to isolate several cross-hybridizing clones from a mouse-cDNA library prepared from kidney RNA from androgen-treated animals. Together, the set of mouse clones spans about 2.0 kb of the 2.6-kb [beta]G mRNA. Using these cDNA clones as probes, a genomic polymorphism for DNA restriction fragment size was found that proved to be genetically linked to the [beta]G gene complex. A fragment of [beta]G cDNA was subcloned into a vector carrying an SP6 polymerase promoter to provide a template for the in vitro synthesis of single-stranded RNA complementary to [beta]G mRNA. This provided an extremely sensitive probe for the assay of [beta]G mRNA sequences. Using either nick-translated cDNA or transcribed RNA as a hybridization probe, we found that mouse [beta]G RNA levels are strongly induced by testosterone, and that induction by testosterone is pituitarydependent. During the lag period preceding induction, during the induction period itself, and during deinduction following removal of testosterone, [beta]G mRNA levels paralleled rates of [beta]G synthesis previously measured by in vivo pulse-labelling experiments. Genetic variation in the extent of induction affected either the level of [beta]G mRNA or its efficiency of translation depending on the strain of mice tested.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/25792/1/0000354.pd

Self-associated molecular patterns mediate cancer immune evasion by engaging Siglecs on T cells

© 2018, American Society for Clinical Investigation. This article has been published in final form at https://doi.org/10.1172/JCI120612First-generation immune checkpoint inhibitors, including anti-CTLA-4 and anti-programmed death 1 (anti-PD-1) antibodies, have led to major clinical progress, yet resistance frequently leads to treatment failure. Thus, new targets acting on T cells are needed. CD33-related sialic acid-binding immunoglobulin-like lectins (Siglecs) are pattern-recognition immune receptors binding to a range of sialoglycan ligands, which appear to function as self-associated molecular patterns (SAMPs) that suppress autoimmune responses. Siglecs are expressed at very low levels on normal T cells, and these receptors were not until recently considered as interesting targets on T cells for cancer immunotherapy. Here, we show an upregulation of Siglecs, including Siglec-9, on tumor-infiltrating T cells from non-small cell lung cancer (NSCLC), colorectal, and ovarian cancer patients. Siglec-9-expressing T cells coexpressed several inhibitory receptors, including PD-1. Targeting of the sialoglycan-SAMP/Siglec pathway in vitro and in vivo resulted in increased anticancer immunity. T cell expression of Siglec-9 in NSCLC patients correlated with reduced survival, and Siglec-9 polymorphisms showed association with the risk of developing lung and colorectal cancer. Our data identify the sialoglycan-SAMP/Siglec pathway as a potential target for improving T cell activation for immunotherapy.Peer reviewe

Novel role for thioredoxin reductase-2 in mitochondrial redox adaptations to obesogenic diet and exercise in heart and skeletal muscle

Increased fatty acid availability and oxidative stress are physiological consequences of exercise (Ex) and a high-fat, high-sugar (HFHS) diet. Despite these similarities, the global effects of Ex are beneficial, whereas HFHS diets are largely deleterious to the cardiovascular system. The reasons for this disparity are multifactorial and incompletely understood. We hypothesized that differences in redox adaptations following HFHS diet in comparison to exercise may underlie this disparity, particularly in mitochondria. Our objective in this study was to determine mechanisms by which heart and skeletal muscle (red gastrocnemius, RG) mitochondria experience differential redox adaptations to 12 weeks of HFHS diet and/or exercise training (Ex) in rats. Surprisingly, both HFHS feeding and Ex led to contrasting effects in heart and RG, in that mitochondrial H2O2 decreased in heart but increased in RG following both HFHS diet and Ex, in comparison to sedentary animals fed a control diet. These differences were determined to be due largely to increased antioxidant/anti-inflammatory enzymes in the heart following the HFHS diet, which did not occur in RG. Specifically, upregulation of mitochondrial thioredoxin reductase-2 occurred with both HFHS and Ex in the heart, but only with Ex in RG, and systematic evaluation of this enzyme revealed that it is critical for suppressing mitochondrial H2O2 during fatty acid oxidation. These findings are novel and important in that they illustrate the unique ability of the heart to adapt to oxidative stress imposed by HFHS diet, in part through upregulation of thioredoxin reductase-2. Furthermore, upregulation of thioredoxin reductase-2 plays a critical role in preserving the mitochondrial redox status in the heart and skeletal muscle with exercise.Funding from the National Institutes of Health, United State

Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging.

Deep brain stimulation (DBS) is a highly efficacious treatment option for movement disorders and a growing number of other indications are investigated in clinical trials. To ensure optimal treatment outcome, exact electrode placement is required. Moreover, to analyze the relationship between electrode location and clinical results, a precise reconstruction of electrode placement is required, posing specific challenges to the field of neuroimaging. Since 2014 the open source toolbox Lead-DBS is available, which aims at facilitating this process. The tool has since become a popular platform for DBS imaging. With support of a broad community of researchers worldwide, methods have been continuously updated and complemented by new tools for tasks such as multispectral nonlinear registration, structural/functional connectivity analyses, brain shift correction, reconstruction of microelectrode recordings and orientation detection of segmented DBS leads. The rapid development and emergence of these methods in DBS data analysis require us to revisit and revise the pipelines introduced in the original methods publication. Here we demonstrate the updated DBS and connectome pipelines of Lead-DBS using a single patient example with state-of-the-art high-field imaging as well as a retrospective cohort of patients scanned in a typical clinical setting at 1.5T. Imaging data of the 3T example patient is co-registered using five algorithms and nonlinearly warped into template space using ten approaches for comparative purposes. After reconstruction of DBS electrodes (which is possible using three methods and a specific refinement tool), the volume of tissue activated is calculated for two DBS settings using four distinct models and various parameters. Finally, four whole-brain tractography algorithms are applied to the patient's preoperative diffusion MRI data and structural as well as functional connectivity between the stimulation volume and other brain areas are estimated using a total of eight approaches and datasets. In addition, we demonstrate impact of selected preprocessing strategies on the retrospective sample of 51 PD patients. We compare the amount of variance in clinical improvement that can be explained by the computer model depending on the method of choice. This work represents a multi-institutional collaborative effort to develop a comprehensive, open source pipeline for DBS imaging and connectomics, which has already empowered several studies, and may facilitate a variety of future studies in the field

Interpretable surface-based detection of focal cortical dysplasias:a Multi-centre Epilepsy Lesion Detection study

One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted 'gold-standard' subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy