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- Palmer, Elizabeth E., et al.
(författare)
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Functional and clinical studies reveal pathophysiological complexity of CLCN4-related neurodevelopmental condition
- 2023
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Ingår i: Molecular Psychiatry. - : SPRINGERNATURE. - 1359-4184 .- 1476-5578. ; 28:2, s. 668-697
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Tidskriftsartikel (refereegranskat)abstract
- Missense and truncating variants in the X-chromosome-linked CLCN4 gene, resulting in reduced or complete loss-of-function (LOF) of the encoded chloride/proton exchanger ClC-4, were recently demonstrated to cause a neurocognitive phenotype in both males and females. Through international clinical matchmaking and interrogation of public variant databases we assembled a database of 90 rare CLCN4 missense variants in 90 families: 41 unique and 18 recurrent variants in 49 families. For 43 families, including 22 males and 33 females, we collated detailed clinical and segregation data. To confirm causality of variants and to obtain insight into disease mechanisms, we investigated the effect on electrophysiological properties of 59 of the variants in Xenopus oocytes using extended voltage and pH ranges. Detailed analyses revealed new pathophysiological mechanisms: 25% (15/59) of variants demonstrated LOF, characterized by a "shift" of the voltage-dependent activation to more positive voltages, and nine variants resulted in a toxic gain-of-function, associated with a disrupted gate allowing inward transport at negative voltages. Functional results were not always in line with in silico pathogenicity scores, highlighting the complexity of pathogenicity assessment for accurate genetic counselling. The complex neurocognitive and psychiatric manifestations of this condition, and hitherto under-recognized impacts on growth, gastrointestinal function, and motor control are discussed. Including published cases, we summarize features in 122 individuals from 67 families with CLCN4-related neurodevelopmental condition and suggest future research directions with the aim of improving the integrated care for individuals with this diagnosis.
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| 2. |
- MacIntosh, Bradley J., et al.
(författare)
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Radiological features of brain hemorrhage through automated segmentation from computed tomography in stroke and traumatic brain injury
- 2023
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Ingår i: Frontiers in Neurology. - 1664-2295. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: Radiological 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. Methods: Non-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. Results: The 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 < 0.01) and evidence of ICH between-site differences. Relative to hand-drawn annotations for one ICH site, the VIOLA-AI segmentation mask achieved a median Dice Similarity Coefficient of 0.82 (interquartile range: 0.78 and 0.83), resulting in a small overestimate in the haematoma volume by a median of 0.47 mL (interquartile range: 0.04 and 1.75 mL). The bleed detection ROC analysis for the whole sample gave a high area-under-the-curve (AUC) of 0.92 (with sensitivity and specificity of 83.28% and 95.41%); however, when considering only the mild head injury site, the TBI-bleed detection gave an AUC of 0.70. Discussion: An 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.
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