Altered splicing of the BIN1 muscle-specific exon in humans and dogs with highly progressive centronuclear myopathy

Amphiphysin 2, encoded by BIN1, is a key factor for membrane sensing and remodelling in different cell types. Homozygous BIN1 mutations in ubiquitously expressed exons are associated with autosomal recessive centronuclear myopathy (CNM), a mildly progressive muscle disorder typically showing abnormal nuclear centralization on biopsies. In addition, misregulation of BIN1 splicing partially accounts for the muscle defects in myotonic dystrophy (DM). However, the muscle-specific function of amphiphysin 2 and its pathogenicity in both muscle disorders are not well understood. In this study we identified and characterized the first mutation affecting the splicing of the muscle-specific BIN1 exon 11 in a consanguineous family with rapidly progressive and ultimately fatal centronuclear myopathy. In parallel, we discovered a mutation in the same BIN1 exon 11 acceptor splice site as the genetic cause of the canine Inherited Myopathy of Great Danes (IMGD). Analysis of RNA from patient muscle demonstrated complete skipping of exon 11 and BIN1 constructs without exon 11 were unable to promote membrane tubulation in differentiated myotubes. Comparative immunofluorescence and ultrastructural analyses of patient and canine biopsies revealed common structural defects, emphasizing the importance of amphiphysin 2 in membrane remodelling and maintenance of the skeletal muscle triad. Our data demonstrate that the alteration of the muscle-specific function of amphiphysin 2 is a common pathomechanism for centronuclear myopathy, myotonic dystrophy, and IMGD. The IMGD dog is the first faithful model for human BIN1-related CNM and represents a mammalian model available for preclinical trials of potential therapies

Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models

Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM) 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model’s structure and in silico “experimental” data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation

Neurite Orientation Dispersion and Density Imaging Color Maps to Characterize Brain Diffusion in Neurologic Disorders

Purpose: Neurite orientation dispersion and density imaging (NODDI) has recently been developed to overcome diffusion technique limitations in modeling biological systems. This manuscript reports a preliminary investigation into the use of a single color-coded map to represent NODDI-derived information. Materials and methods: An optimized diffusion-weighted imaging protocol was acquired in several clinical neurological contexts including demyelinating disease, neoplastic process, stroke, and toxic/metabolic disease. The NODDI model was fitted to the diffusion datasets. NODDI is based on a three-compartment diffusion model and provides maps that quantify the contributions to the total diffusion signal in each voxel. The NODDI compartment maps were combined into a single 4-dimensional volume visualized as RGB image (red for anisotropic Gaussian diffusion, green for non-Gaussian anisotropic diffusion, and blue for isotropic Gaussian diffusion), in which the relative contributions of the different microstructural compartments can be easily appreciated. Results: The NODDI color maps better describe the heterogeneity of neoplastic as well inflammatory lesions by identifying different tissue components within areas apparently homogeneous on conventional imaging. Moreover, NODDI color maps seem to be useful for identifying vasogenic edema differently from tumor-infiltrated edema. In multiple sclerosis, the NODDI color maps enable a visual assessment of the underlying microstructural changes, possibly highlighting an increased inflammatory component, within lesions and potentially in normal-appearing white matter. Conclusion: The NODDI color maps could make this technique valuable in a clinical setting, providing comprehensive and accessible information in normal and pathological brain tissues in different neurological pathologies

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Interaction of PLP with GFP-MAL2 in the Human Oligodendroglial Cell Line HOG

The velocity of the nerve impulse conduction of vertebrates relies on the myelin sheath, an electrically insulating layer that surrounds axons in both the central and peripheral nervous systems, enabling saltatory conduction of the action potential. Oligodendrocytes are the myelin-producing glial cells in the central nervous system. A deeper understanding of the molecular basis of myelination and, specifically, of the transport of myelin proteins, will contribute to the search of the aetiology of many dysmyelinating and demyelinating diseases, including multiple sclerosis. Recent investigations suggest that proteolipid protein (PLP), the major myelin protein, could reach myelin sheath by an indirect transport pathway, that is, a transcytotic route via the plasma membrane of the cell body. If PLP transport relies on a transcytotic process, it is reasonable to consider that this myelin protein could be associated with MAL2, a raft protein essential for transcytosis. In this study, carried out with the human oligodendrocytic cell line HOG, we show that PLP colocalized with green fluorescent protein (GFP)-MAL2 after internalization from the plasma membrane. In addition, both immunoprecipitation and immunofluorescence assays, indicated the existence of an interaction between GFP-MAL2 and PLP. Finally, ultrastructural studies demonstrated colocalization of GFP-MAL2 and PLP in vesicles and tubulovesicular structures. Taken together, these results prove for the first time the interaction of PLP and MAL2 in oligodendrocytic cells, supporting the transcytotic model of PLP transport previously suggested

Glioblastoma Surgery Imaging-Reporting and Data System: Validation and Performance of the Automated Segmentation Task

For patients with presumed glioblastoma, essential tumor characteristics are determined from preoperative MR images to optimize the treatment strategy. This procedure is time-consuming and subjective, if performed by crude eyeballing or manually. The standardized GSI-RADS aims to provide neurosurgeons with automatic tumor segmentations to extract tumor features rapidly and objectively. In this study, we improved automatic tumor segmentation and compared the agreement with manual raters, describe the technical details of the different components of GSI-RADS, and determined their speed. Two recent neural network architectures were considered for the segmentation task: nnU-Net and AGU-Net. Two preprocessing schemes were introduced to investigate the tradeoff between performance and processing speed. A summarized description of the tumor feature extraction and standardized reporting process is included. The trained architectures for automatic segmentation and the code for computing the standardized report are distributed as open-source and as open-access software. Validation studies were performed on a dataset of 1594 gadolinium-enhanced T1-weighted MRI volumes from 13 hospitals and 293 T1-weighted MRI volumes from the BraTS challenge. The glioblastoma tumor core segmentation reached a Dice score slightly below 90%, a patientwise F1-score close to 99%, and a 95th percentile Hausdorff distance slightly below 4.0 mm on average with either architecture and the heavy preprocessing scheme. A patient MRI volume can be segmented in less than one minute, and a standardized report can be generated in up to five minutes. The proposed GSI-RADS software showed robust performance on a large collection of MRI volumes from various hospitals and generated results within a reasonable runtime

Glioblastoma surgery imaging—reporting and data system: Standardized reporting of tumor volume, location, and resectability based on automated segmentations

Treatment decisions for patients with presumed glioblastoma are based on tumor characteristics available from a preoperative MR scan. Tumor characteristics, including volume, location, and resectability, are often estimated or manually delineated. This process is time consuming and subjective. Hence, comparison across cohorts, trials, or registries are subject to assessment bias. In this study, we propose a standardized Glioblastoma Surgery Imaging Reporting and Data System (GSI-RADS) based on an automated method of tumor segmentation that provides standard reports on tumor features that are potentially relevant for glioblastoma surgery. As clinical validation, we determine the agreement in extracted tumor features between the automated method and the current standard of manual segmentations from routine clinical MR scans before treatment. In an observational consecutive cohort of 1596 adult patients with a first time surgery of a glioblastoma from 13 institutions, we segmented gadolinium-enhanced tumor parts both by a human rater and by an automated algorithm. Tumor features were extracted from segmentations of both methods and compared to assess differences, concordance, and equivalence. The laterality, contralateral infiltration, and the laterality indices were in excellent agreement. The native and normalized tumor volumes had excellent agreement, consistency, and equivalence. Multifocality, but not the number of foci, had good agreement and equivalence. The location profiles of cortical and subcortical structures were in excellent agreement. The expected residual tumor volumes and resectability indices had excellent agreement, consistency, and equivalence. Tumor probability maps were in good agreement. In conclusion, automated segmentations are in excellent agreement with manual segmentations and practically equivalent regarding tumor features that are potentially relevant for neurosurgical purposes. Standard GSI-RADS reports can be generated by open access software

Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection

Multi-class glioma segmentation on real-world data with missing MRI sequences: comparison of three deep learning algorithms

This study tests the generalisability of three Brain Tumor Segmentation (BraTS) challenge models using a multi-center dataset of varying image quality and incomplete MRI datasets. In this retrospective study, DeepMedic, no-new-Unet (nn-Unet), and NVIDIA-net (nv-Net) were trained and tested using manual segmentations from preoperative MRI of glioblastoma (GBM) and low-grade gliomas (LGG) from the BraTS 2021 dataset (1251 in total), in addition to 275 GBM and 205 LGG acquired clinically across 12 hospitals worldwide. Data was split into 80% training, 5% validation, and 15% internal test data. An additional external test-set of 158 GBM and 69 LGG was used to assess generalisability to other hospitals’ data. All models’ median Dice similarity coefficient (DSC) for both test sets were within, or higher than, previously reported human inter-rater agreement (range of 0.74–0.85). For both test sets, nn-Unet achieved the highest DSC (internal = 0.86, external = 0.93) and the lowest Hausdorff distances (10.07, 13.87 mm, respectively) for all tumor classes (p < 0.001). By applying Sparsified training, missing MRI sequences did not statistically affect the performance. nn-Unet achieves accurate segmentations in clinical settings even in the presence of incomplete MRI datasets. This facilitates future clinical adoption of automated glioma segmentation, which could help inform treatment planning and glioma monitoring

In vivo migration of labeled autologous natural killer cells to liver metastases in patients with colon carcinoma

BACKGROUND: Besides being the effectors of native anti-tumor cytotoxicity, NK cells participate in T-lymphocyte responses by promoting the maturation of dendritic cells (DC). Adherent NK (A-NK) cells constitute a subset of IL-2-stimulated NK cells which show increased expression of integrins and the ability to adhere to solid surface and to migrate, infiltrate, and destroy cancer. A critical issue in therapy of metastatic disease is the optimization of NK cell migration to tumor tissues and their persistence therein. This study compares localization to liver metastases of autologous A-NK cells administered via the systemic (intravenous, i.v.) versus locoregional (intraarterial, i.a.) routes. PATIENTS AND METHODS: A-NK cells expanded ex-vivo with IL-2 and labeled with (111)In-oxine were injected i.a. in the liver of three colon carcinoma patients. After 30 days, each patient had a new preparation of (111)In-A-NK cells injected i.v. Migration of these cells to various organs was evaluated by SPET and their differential localization to normal and neoplastic liver was demonstrated after i.v. injection of (99m)Tc-phytate. RESULTS: A-NK cells expressed a donor-dependent CD56(+)CD16(+)CD3(- )(NK) or CD56(+)CD16(+)CD3(+ )(NKT) phenotype. When injected i.v., these cells localized to the lung before being visible in the spleen and liver. By contrast, localization of i.a. injected A-NK cells was virtually confined to the spleen and liver. Binding of A-NK cells to liver neoplastic tissues was observed only after i.a. injections. CONCLUSION: This unique study design demonstrates that A-NK cells adoptively transferred to the liver via the intraarterial route have preferential access and substantial accumulation to the tumor site