Cyclical and Patch-Like GDNF Distribution along the Basal Surface of Sertoli Cells in Mouse and Hamster Testes

BACKGROUND AND AIMS: In mammalian spermatogenesis, glial cell line-derived neurotrophic factor (GDNF) is one of the major Sertoli cell-derived factors which regulates the maintenance of undifferentiated spermatogonia including spermatogonial stem cells (SSCs) through GDNF family receptor α1 (GFRα1). It remains unclear as to when, where and how GDNF molecules are produced and exposed to the GFRα1-positive spermatogonia in vivo. METHODOLOGY AND PRINCIPAL FINDINGS: Here we show the cyclical and patch-like distribution of immunoreactive GDNF-positive signals and their close co-localization with a subpopulation of GFRα1-positive spermatogonia along the basal surface of Sertoli cells in mice and hamsters. Anti-GDNF section immunostaining revealed that GDNF-positive signals are mainly cytoplasmic and observed specifically in the Sertoli cells in a species-specific as well as a seminiferous cycle- and spermatogenic activity-dependent manner. In contrast to the ubiquitous GDNF signals in mouse testes, high levels of its signals were cyclically observed in hamster testes prior to spermiation. Whole-mount anti-GDNF staining of the seminiferous tubules successfully visualized the cyclical and patch-like extracellular distribution of GDNF-positive granular deposits along the basal surface of Sertoli cells in both species. Double-staining of GDNF and GFRα1 demonstrated the close co-localization of GDNF deposits and a subpopulation of GFRα1-positive spermatogonia. In both species, GFRα1-positive cells showed a slender bipolar shape as well as a tendency for increased cell numbers in the GDNF-enriched area, as compared with those in the GDNF-low/negative area of the seminiferous tubules. CONCLUSION/SIGNIFICANCE: Our data provide direct evidence of regionally defined patch-like GDNF-positive signal site in which GFRα1-positive spermatogonia possibly interact with GDNF in the basal compartment of the seminiferous tubules

The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.Comment: Technical report of BraSy

An International Standardized Magnetic Resonance Imaging Protocol for Diagnosis and Follow-up of Patients with Multiple Sclerosis

Standardized magnetic resonance imaging (MRI) protocols are important for the diagnosis and monitoring of patients with multiple sclerosis (MS). The Consortium of Multiple Sclerosis Centers (CMSC) convened an international panel of MRI experts to review and update the current guidelines. The objective was to update the standardized MRI protocol and clinical guidelines for diagnosis and follow-up of MS and develop strategies for advocacy, dissemination, and implementation. Conference attendees included neurologists, radiologists, technologists, and imaging scientists with expertise in MS. Representatives from the CMSC, Magnetic Resonance Imaging in MS (MAGNIMS), North American Imaging in Multiple Sclerosis Cooperative, US Department of Veteran Affairs, National Multiple Sclerosis Society, Multiple Sclerosis Association of America, MRI manufacturers, and commercial image analysis companies were present. Before the meeting, CMSC members were surveyed about standardized MRI protocols, gadolinium use, need for diffusion-weighted imaging, and the central vein sign. The panel worked to make the CMSC and MAGNIMS MRI protocols similar so that the updated guidelines could ultimately be accepted by international consensus. Advocacy efforts will promote the importance of standardized MS MRI protocols. Dissemination will include publications, meeting abstracts, educational programming, webinars, “meet the expert” teleconferences, and examination cards. Implementation will require comprehensive and coordinated efforts to make the protocol easy to access and use. The ultimate vision, and goal, is for the guidelines to be universally useful, usable, and used as the standard of care for patients with MS. Int J MS Care. 2020;22:226-232

The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn).

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Outcomes in Newly Diagnosed Atrial Fibrillation and History of Acute Coronary Syndromes: Insights from GARFIELD-AF

BACKGROUND: Many patients with atrial fibrillation have concomitant coronary artery disease with or without acute coronary syndromes and are in need of additional antithrombotic therapy. There are few data on the long-term clinical outcome of atrial fibrillation patients with a history of acute coronary syndrome. This is a 2-year study of atrial fibrillation patients with or without a history of acute coronary syndromes