Follow-up study of neuropsychological scores of infant patients with cobalamin C defects and influencing factors of cerebral magnetic resonance imaging characteristics

PurposeThe purpose of this study was to investigate whether baseline cerebral magnetic resonance imaging (MRI) characteristics could predict therapeutic responsiveness in patients with cobalamin C (cblC) defects.Materials and methodsThe cerebral MRI results of 40 patients with cblC defects were evaluated by a neuroradiologist. Neuropsychological scores and imaging data were collected. Neuropsychological tests were performed before and after standardized treatment.ResultsThirty-eight patients initially underwent neuropsychological testing [developmental quotient (DQ)]. CblC defects with cerebellar atrophy, corpus callosum thinning and ventricular dilation had significantly lower DQs than those without (P < 0.05). Through a multivariate linear stepwise regression equation after univariate analysis, ventricular dilation was the most valuable predictor of lower DQs. Thirty-six patients (94.7%) underwent follow-up neuropsychological testing. The pre- and post-treatment DQ values were not significantly different (Z = −1.611, P = 0.107). The post-treatment DQ classification (normal, moderately low, or extremely low) showed nearly no change compared to the pretreatment DQ classification (k = 0.790, P < 0.001).ConclusionVentricular dilation, cerebral atrophy and corpus callosum thinning are the main MRI abnormalities of cblC defects, and these manifestations are significantly correlated with delayed development in children. MRI findings can be considered an important tool for determining the severity of cblC defects

CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark

Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice. With the development of biomedical language understanding benchmarks, AI applications are widely used in the medical field. However, most benchmarks are limited to English, which makes it challenging to replicate many of the successes in English for other languages. To facilitate research in this direction, we collect real-world biomedical data and present the first Chinese Biomedical Language Understanding Evaluation (CBLUE) benchmark: a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification, and an associated online platform for model evaluation, comparison, and analysis. To establish evaluation on these tasks, we report empirical results with the current 11 pre-trained Chinese models, and experimental results show that state-of-the-art neural models perform by far worse than the human ceiling. Our benchmark is released at \url{https://tianchi.aliyun.com/dataset/dataDetail?dataId=95414&lang=en-us}

Hybrid Convolutional Neural Network for Localization of Epileptic Focus Based on iEEG

Epileptic focus localization by analysing intracranial electroencephalogram (iEEG) plays a critical role in successful surgical therapy of resection of the epileptogenic lesion. However, manual analysis and classification of the iEEG signal by clinicians are arduous and time-consuming and excessively depend on the experience. Due to individual differences of patients, the iEEG signal from different patients usually shows very diverse features even if the features belong to the same class. Accordingly, automatic detection of epileptic focus is required to improve the accuracy and to shorten the time for treatment. In this paper, we propose a novel feature fusion-based iEEG classification method, a deep learning model termed Time-Frequency Hybrid Network (TF-HybridNet), in which short-time Fourier transform (STFT) and 1d convolution layers are performed on the input iEEG in parallel to extract features of the time-frequency domain and feature maps. And then, the time-frequency features and feature maps are fused and fed to a 2d convolutional neural network (CNN). We used the Bern-Barcelona iEEG dataset for evaluating the performance of TF-HybridNet, and the experimental results show that our approach is able to differentiate the focal from nonfocal iEEG signal with an average classification accuracy of 94.3% and demonstrates an improved accuracy rate compared to the model using only STFT or one-dimensional convolutional layers as feature extraction

Data_Sheet_1_Follow-up study of neuropsychological scores of infant patients with cobalamin C defects and influencing factors of cerebral magnetic resonance imaging characteristics.XLSX

PurposeThe purpose of this study was to investigate whether baseline cerebral magnetic resonance imaging (MRI) characteristics could predict therapeutic responsiveness in patients with cobalamin C (cblC) defects.Materials and methodsThe cerebral MRI results of 40 patients with cblC defects were evaluated by a neuroradiologist. Neuropsychological scores and imaging data were collected. Neuropsychological tests were performed before and after standardized treatment.ResultsThirty-eight patients initially underwent neuropsychological testing [developmental quotient (DQ)]. CblC defects with cerebellar atrophy, corpus callosum thinning and ventricular dilation had significantly lower DQs than those without (P ConclusionVentricular dilation, cerebral atrophy and corpus callosum thinning are the main MRI abnormalities of cblC defects, and these manifestations are significantly correlated with delayed development in children. MRI findings can be considered an important tool for determining the severity of cblC defects.</p

Data_Sheet_2_Follow-up study of neuropsychological scores of infant patients with cobalamin C defects and influencing factors of cerebral magnetic resonance imaging characteristics.DOCX

PurposeThe purpose of this study was to investigate whether baseline cerebral magnetic resonance imaging (MRI) characteristics could predict therapeutic responsiveness in patients with cobalamin C (cblC) defects.Materials and methodsThe cerebral MRI results of 40 patients with cblC defects were evaluated by a neuroradiologist. Neuropsychological scores and imaging data were collected. Neuropsychological tests were performed before and after standardized treatment.ResultsThirty-eight patients initially underwent neuropsychological testing [developmental quotient (DQ)]. CblC defects with cerebellar atrophy, corpus callosum thinning and ventricular dilation had significantly lower DQs than those without (P ConclusionVentricular dilation, cerebral atrophy and corpus callosum thinning are the main MRI abnormalities of cblC defects, and these manifestations are significantly correlated with delayed development in children. MRI findings can be considered an important tool for determining the severity of cblC defects.</p

Terpenoids from the barks of <i>Magnolia maudiae</i> (Dunn) Figlar

<p>A new germacrenolide (<b>1</b>) and fourteen known terpenoids (<b>2–15</b>) were isolated from the barks of <i>Magnolia maudiae</i> (Dunn) Figlar (Magnoliaceae). The structure of (7<i>α</i>H,11<i>β</i>H)-2<i>α</i>,8<i>α</i>-dihydroxy-4<i>α</i>,5<i>β</i>-epoxy-germacr-1(10)-en-6<i>α</i>,12-olide (<b>1</b>) was elucidated by physical and spectroscopic data analysis, including 1D, 2D NMR and HR-ESI-MS. Lyratol F (<b>9</b>) was isolated from <i>Magnolia</i> for the first time. The structures of known compounds were established by comparing their spectroscopic data with those in literatures.</p