A National Spinal Muscular Atrophy Registry for Real-World Evidence.

BACKGROUND: Spinal muscular atrophy (SMA) is a devastating rare disease that affects individuals regardless of ethnicity, gender, and age. The first-approved disease-modifying therapy for SMA, nusinursen, was approved by Health Canada, as well as by American and European regulatory agencies following positive clinical trial outcomes. The trials were conducted in a narrow pediatric population defined by age, severity, and genotype. Broad approval of therapy necessitates close follow-up of potential rare adverse events and effectiveness in the larger real-world population. METHODS: The Canadian Neuromuscular Disease Registry (CNDR) undertook an iterative multi-stakeholder process to expand the existing SMA dataset to capture items relevant to patient outcomes in a post-marketing environment. The CNDR SMA expanded registry is a longitudinal, prospective, observational study of patients with SMA in Canada designed to evaluate the safety and effectiveness of novel therapies and provide practical information unattainable in trials. RESULTS: The consensus expanded dataset includes items that address therapy effectiveness and safety and is collected in a multicenter, prospective, observational study, including SMA patients regardless of therapeutic status. The expanded dataset is aligned with global datasets to facilitate collaboration. Additionally, consensus dataset development aimed to standardize appropriate outcome measures across the network and broader Canadian community. Prospective outcome studies, data use, and analyses are independent of the funding partner. CONCLUSION: Prospective outcome data collected will provide results on safety and effectiveness in a post-therapy approval era. These data are essential to inform improvements in care and access to therapy for all SMA patients

An Evolutionarily Conserved Enhancer Regulates Bmp4 Expression in Developing Incisor and Limb Bud

To elucidate the transcriptional regulation of Bmp4 expression during organogenesis, we used phylogenetic footprinting and transgenic reporter analyses to identify Bmp4 cis-regulatory modules (CRMs). These analyses identified a regulatory region located ∼46 kb upstream of the mouse Bmp4 transcription start site that had previously been shown to direct expression in lateral plate mesoderm. We refined this regulatory region to a 396-bp minimal enhancer, and show that it recapitulates features of endogenous Bmp4 expression in developing mandibular arch ectoderm and incisor epithelium during the initiation-stage of tooth development. In addition, this enhancer directs expression in the apical ectodermal ridge (AER) of the developing limb and in anterior and posterior limb mesenchyme. Transcript profiling of E11.5 mouse incisor dental lamina, together with protein binding microarray (PBM) analyses, allowed identification of a conserved DNA binding motif in the Bmp4 enhancer for Pitx homeoproteins, which are also expressed in the developing mandibular and incisor epithelium. In vitro electrophoretic mobility shift assays (EMSA) and in vivo transgenic reporter mutational analyses revealed that this site supports Pitx binding and that the site is necessary to recapitulate aspects of endogenous Bmp4 expression in developing craniofacial and limb tissues. Finally, Pitx2 chromatin immunoprecipitation (ChIP) demonstrated direct binding of Pitx2 to this Bmp4 enhancer site in a dental epithelial cell line. These results establish a direct molecular regulatory link between Pitx family members and Bmp4 gene expression in developing incisor epithelium

Demethylation of Methylmercury in Bird, Fish, and Earthworm

International audienceToxicity of methylmercury (MeHg) to wildlife and humans results from its binding to cysteine residues of proteins, forming MeHg-cysteinate (MeHgCys) complexes that hinder biological functions. MeHgCys complexes can be detoxified in vivo, yet how this occurs is unknown. We report that MeHgCys complexes are transformed into selenocysteinate [Hg(Sec)4] complexes in multiple animals from two phyla (a waterbird, freshwater fish, and earthworms) sampled in different geographical areas and contaminated by different Hg sources. In addition, high energy-resolution X-ray absorption spectroscopy (HR-XANES) and chromatography-inductively coupled plasma mass spectrometry of the waterbird liver support the binding of Hg(Sec)4 to selenoprotein P and biomineralization of Hg(Sec)4 to chemically inert nanoparticulate mercury selenide (HgSe). The results provide a foundation for understanding mercury detoxification in higher organisms and suggest that the identified MeHgCys to Hg(Sec)4 demethylation pathway is common in nature

Table_1_Radiological features of brain hemorrhage through automated segmentation from computed tomography in stroke and traumatic brain injury.DOCX

IntroductionRadiological assessment is necessary to diagnose spontaneous intracerebral hemorrhage (ICH) and traumatic brain injury intracranial hemorrhage (TBI-bleed). Artificial intelligence (AI) deep learning tools provide a means for decision support. This study evaluates the hemorrhage segmentations produced from three-dimensional deep learning AI model that was developed using non-contrast computed tomography (CT) imaging data external to the current study.MethodsNon-contrast CT imaging data from 1263 patients were accessed across seven data sources (referred to as sites) in Norway and Sweden. Patients were included based on ICH, TBI-bleed, or mild TBI diagnosis. Initial non-contrast CT images were available for all participants. Hemorrhage location frequency maps were generated. The number of estimated haematoma clusters was correlated with the total haematoma volume. Ground truth expert annotations were available for one ICH site; hence, a comparison was made with the estimated haematoma volumes. Segmentation volume estimates were used in a receiver operator characteristics (ROC) analysis for all samples (i.e., bleed detected) and then specifically for one site with few TBI-bleed cases.ResultsThe hemorrhage frequency maps showed spatial patterns of estimated lesions consistent with ICH or TBI-bleed presentations. There was a positive correlation between the estimated number of clusters and total haematoma volume for each site (correlation range: 0.45–0.74; each p-value DiscussionAn open-source segmentation tool was used to visualize hemorrhage locations across multiple data sources and revealed quantitative hemorrhage site differences. The automated total hemorrhage volume estimate correlated with a per-participant hemorrhage cluster count. ROC results were moderate-to-high. The VIOLA-AI tool had promising results and might be useful for various types of intracranial hemorrhage.</p

Image_1_Radiological features of brain hemorrhage through automated segmentation from computed tomography in stroke and traumatic brain injury.TIFF

IntroductionRadiological assessment is necessary to diagnose spontaneous intracerebral hemorrhage (ICH) and traumatic brain injury intracranial hemorrhage (TBI-bleed). Artificial intelligence (AI) deep learning tools provide a means for decision support. This study evaluates the hemorrhage segmentations produced from three-dimensional deep learning AI model that was developed using non-contrast computed tomography (CT) imaging data external to the current study.MethodsNon-contrast CT imaging data from 1263 patients were accessed across seven data sources (referred to as sites) in Norway and Sweden. Patients were included based on ICH, TBI-bleed, or mild TBI diagnosis. Initial non-contrast CT images were available for all participants. Hemorrhage location frequency maps were generated. The number of estimated haematoma clusters was correlated with the total haematoma volume. Ground truth expert annotations were available for one ICH site; hence, a comparison was made with the estimated haematoma volumes. Segmentation volume estimates were used in a receiver operator characteristics (ROC) analysis for all samples (i.e., bleed detected) and then specifically for one site with few TBI-bleed cases.ResultsThe hemorrhage frequency maps showed spatial patterns of estimated lesions consistent with ICH or TBI-bleed presentations. There was a positive correlation between the estimated number of clusters and total haematoma volume for each site (correlation range: 0.45–0.74; each p-value DiscussionAn open-source segmentation tool was used to visualize hemorrhage locations across multiple data sources and revealed quantitative hemorrhage site differences. The automated total hemorrhage volume estimate correlated with a per-participant hemorrhage cluster count. ROC results were moderate-to-high. The VIOLA-AI tool had promising results and might be useful for various types of intracranial hemorrhage.</p

Ultrafast collective oxygen-vacancy flow in Ca-doped BiFeO3

The ultrafast motion of oxygen vacancies in solids is crucial for various future applications, such as oxide electrolytes. Visualization and quantification can offer unforeseen opportunities to probe the collective dynamics of defects in crystalline solids, but little research has been conducted on oxygen vacancy electromigration using these approaches. Here, we visualize electric-field-induced creation and propagation of oxygen-vacancy-rich and -poor competing phases and their interface with optical contrast in Ca-substituted BiFeO that contains a high density of mobile oxygen vacancies. We quantitatively determined the drift velocity of collective migration to be on the order of 100 μm s with an activation barrier of 0.79 eV, indicating a significantly large ionic mobility of 2 × 10 cm s V at a remarkably low temperature of 390 °C. In addition, visualization enables direct observation of fluidic behavior, such as the enhancement of conduction at channel edges, which results in U-shaped viscous propagation of the phase boundary and turbulence under a reverse electric field. All of these results provide new insights into the collective motion of defects. 3 −1 −6 2 −1 −

Tissue-Specific Oncogenic Activity of KRASA146T

KRAS is the most frequently mutated oncogene. The incidence of specific KRAS alleles varies between cancers from different sites, but it is unclear whether allelic selection results from biological selection for specific mutant KRAS proteins. We used a cross-disciplinary approach to compare KRASG12D, a common mutant form, and KRASA146T, a mutant that occurs only in selected cancers. Biochemical and structural studies demonstrated that KRASA146T exhibits a marked extension of switch 1 away from the protein body and nucleotide binding site, which activates KRAS by promoting a high rate of intrinsic and guanine nucleotide exchange factor– induced nucleotide exchange. Using mice genetically engineered to express either allele, we found that KRASG12D and KRASA146T exhibit distinct tissue-specific effects on homeostasis that mirror mutational frequencies in human cancers. These tissue-specific phenotypes result from allele-specific signaling properties, demonstrating that context-dependent variations in signaling downstream of different KRAS mutants drive the KRAS mutational pattern seen in cancer. SIGNIFICANCE: Although epidemiologic and clinical studies have suggested allele-specific behaviors for KRAS, experimental evidence for allele-specific biological properties is limited. We combined structural biology, mass spectrometry, and mouse modeling to demonstrate that the selection for specific KRAS mutants in human cancers from different tissues is due to their distinct signaling properties.National Institutes of Health (Grant U01CA215798