59 research outputs found

    A clinical and molecular characterisation of CRB1-associated maculopathy

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    To date, over 150 disease-associated variants in CRB1 have been described, resulting in a range of retinal disease phenotypes including Leber congenital amaurosis and retinitis pigmentosa. Despite this, no genotype–phenotype correlations are currently recognised. We performed a retrospective review of electronic patient records to identify patients with macular dystrophy due to bi-allelic variants in CRB1. In total, seven unrelated individuals were identified. The median age at presentation was 21 years, with a median acuity of 0.55 decimalised Snellen units (IQR = 0.43). The follow-up period ranged from 0 to 19 years (median = 2.0 years), with a median final decimalised Snellen acuity of 0.65 (IQR = 0.70). Fundoscopy revealed only a subtly altered foveal reflex, which evolved into a bull’s-eye pattern of outer retinal atrophy. Optical coherence tomography identified structural changes—intraretinal cysts in the early stages of disease, and later outer retinal atrophy. Genetic testing revealed that one rare allele (c.498_506del, p.(Ile167_Gly169del)) was present in all patients, with one patient being homozygous for the variant and six being heterozygous. In trans with this, one variant recurred twice (p.(Cys896Ter)), while the four remaining alleles were each observed once (p.(Pro1381Thr), p.(Ser478ProfsTer24), p.(Cys195Phe) and p.(Arg764Cys)). These findings show that the rare CRB1 variant, c.498_506del, is strongly associated with localised retinal dysfunction. The clinical findings are much milder than those observed with bi-allelic, loss-of-function variants in CRB1, suggesting this in-frame deletion acts as a hypomorphic allele. This is the most prevalent disease-causing CRB1 variant identified in the non-Asian population to date

    Prospective study on the mismatch concept in acute stroke patients within the first 24 h after symptom onset - 1000Plus study

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    <p>Abstract</p> <p>Background</p> <p>The mismatch between diffusion weighted imaging (DWI) lesion and perfusion imaging (PI) deficit volumes has been used as a surrogate of ischemic penumbra. This pathophysiology-orientated patient selection criterion for acute stroke treatment may have the potential to replace a fixed time window. Two recent trials - DEFUSE and EPITHET - investigated the mismatch concept in a multicenter prospective approach. Both studies randomized highly selected patients (n = 74/n = 100) and therefore confirmation in a large consecutive cohort is desirable. We here present a single-center approach with a 3T MR tomograph next door to the stroke unit, serving as a bridge from the ER to the stroke unit to screen all TIA and stroke patients. Our primary hypothesis is that the prognostic value of the mismatch concept is depending on the vessel status. Primary endpoint of the study is infarct growth determined by imaging, secondary endpoints are neurological deficit on day 5-7 and functional outcome after 3 months.</p> <p>Methods and design</p> <p>1000Plus is a prospective, single centre observational study with 1200 patients to be recruited. All patients admitted to the ER with the clinical diagnosis of an acute cerebrovascular event within 24 hours after symptom onset are screened. Examinations are performed on day 1, 2 and 5-7 with neurological examination including National Institute of Health Stroke Scale (NIHSS) scoring and stroke MRI including T2*, DWI, TOF-MRA, FLAIR and PI. PI is conducted as dynamic susceptibility-enhanced contrast imaging with a fixed dosage of 5 ml 1 M Gadobutrol. For post-processing of PI, mean transit time (MTT) parametric images are determined by deconvolution of the arterial input function (AIF) which is automatically identified. Lesion volumes and mismatch are measured and calculated by using the perfusion mismatch analyzer (PMA) software from ASIST-Japan. Primary endpoint is the change of infarct size between baseline examination and day 5-7 follow up.</p> <p>Discussions</p> <p>The aim of this study is to describe the incidence of mismatch and the predictive value of PI for final lesion size and functional outcome depending on delay of imaging and vascular recanalization. It is crucial to standardize PI for future randomized clinical trials as for individual therapeutic decisions and we expect to contribute to this challenging task.</p> <p>Trial Registration</p> <p>clinicaltrials.gov NCT00715533</p

    BRCA1-mutated and basal-like breast cancers have similar aCGH profiles and a high incidence of protein truncating TP53 mutations

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    <p>Abstract</p> <p>Background</p> <p>Basal-like breast cancers (BLBC) are aggressive breast cancers for which, so far, no targeted therapy is available because they typically lack expression of hormone receptors and HER2. Phenotypic features of BLBCs, such as clinical presentation and early age of onset, resemble those of breast tumors from <it>BRCA1</it>-mutation carriers. The genomic instability of <it>BRCA1</it>-mutated tumors can be effectively targeted with DNA-damaging agents and poly-(ADP-ribose) polymerase 1 (PARP1) inhibitors. Molecular similarities between BLBCs and <it>BRCA1</it>-mutated tumors may therefore provide predictive markers for therapeutic response of BLBCs.</p> <p>Methods</p> <p>There are several known molecular features characteristic for <it>BRCA1</it>-mutated breast tumors: 1) increased numbers of genomic aberrations, 2) a distinct pattern of genomic aberrations, 3) a high frequency of <it>TP53 </it>mutations and 4) a high incidence of complex, protein-truncating <it>TP53 </it>mutations. We compared the frequency of <it>TP53 </it>mutations and the pattern and amount of genomic aberrations between <it>BRCA1</it>-mutated breast tumors, BLBCs and luminal breast tumors by <it>TP53 </it>gene sequencing and array-based comparative genomics hybridization (aCGH) analysis.</p> <p>Results</p> <p>We found that the high incidence of protein truncating <it>TP53 </it>mutations and the pattern and amount of genomic aberrations specific for BRCA1-mutated breast tumors are also characteristic for BLBCs and different from luminal breast tumors.</p> <p>Conclusions</p> <p>Complex, protein truncating TP53 mutations in BRCA1-mutated tumors may be a direct consequence of genomic instability caused by BRCA1 loss, therefore, the presence of these types of TP53 mutations in sporadic BLBCs might be a hallmark of BRCAness and a potential biomarker for sensitivity to PARP inhibition. Also, our data suggest that a small subset of genomic regions may be used to identify BRCA1-like BLBCs. BLBCs share molecular features that were previously found to be specific for BRCA1-mutated breast tumors. These features might be useful for the identification of tumors with increased sensitivity to (high-dose or dose-dense) alkylating agents and PARP inhibitors.</p

    Data Descriptor: An open resource for transdiagnostic research in pediatric mental health and learning disorders

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    Technological and methodological innovations are equipping researchers with unprecedented capabilities for detecting and characterizing pathologic processes in the developing human brain. As a result, ambitions to achieve clinically useful tools to assist in the diagnosis and management of mental health and learning disorders are gaining momentum. To this end, it is critical to accrue large-scale multimodal datasets that capture a broad range of commonly encountered clinical psychopathology. The Child Mind Institute has launched the Healthy Brain Network (HBN), an ongoing initiative focused on creating and sharing a biobank of data from 10,000 New York area participants (ages 5–21). The HBN Biobank houses data about psychiatric, behavioral, cognitive, and lifestyle phenotypes, as well as multimodal brain imaging (resting and naturalistic viewing fMRI, diffusion MRI, morphometric MRI), electroencephalography, eyetracking, voice and video recordings, genetics and actigraphy. Here, we present the rationale, design and implementation of HBN protocols. We describe the first data release (n =664) and the potential of the biobank to advance related areas (e.g., biophysical modeling, voice analysis
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