Search

Amyotrophic lateral sclerosis (ALS) is an incurable neurodegenerative disease that causes muscle atrophy and primarily targets motor neurons (MNs). Approximately 20% of familial ALS cases are caused by gain-of-function mutations in superoxide dismutase 1 (SOD1), leading to MN degeneration and ion channel dysfunction. Previous studies have shown that n-Butylidenephthalide (BP) delays disease progression and prolongs survival in animal models of ALS. However, no studies have been conducted on models from human sources. Herein, we examined the protective efficacy of BP on MNs derived from induced pluripotent stem cells (iPSCs) of an ALS patient harboring the SOD1G85R mutation as well as on those derived from genetically corrected iPSCs (SOD1G85G). Our results demonstrated that the motor neurons differentiated from iPSC with SOD1G85R mutation exhibited characteristics of neuron degeneration (as indicated by the reduction of neurofilament expression) and ion channel dysfunction (in response to potassium chloride (KCl) and L-glutamate stimulation), in contrast to those derived from the gene corrected iPSC (SOD1G85G). Meanwhile, BP treatment effectively restored calcium ion channel function by reducing the expression of glutamate receptors including glutamate ionotropic receptor AMPA type subunit 3 (GluR3) and glutamate ionotropic receptor NMDA type subunit 1 (NMDAR1). Additionally, BP treatment activated autophagic pathway to attenuate neuron degeneration. Overall, this study supports the therapeutic effects of BP on ALS patient-derived neuron cells, and suggests that BP may be a promising candidate for future drug development. [ABSTRACT FROM AUTHOR]

Simple Summary: This study presented an artificial intelligence-based classification emphasizing error identification and quantification of cellularity and nuclear morphological features in digital pathological images of common astrocytic tumors. The identification of incorrect predictions was essential for the subsequent development of better techniques. Quantifying cellularity and nuclear morphological features brought deeper insights into neoplastic morphology and paved the way for further development of a scoring system for objective classification and precision diagnosis to improve interobserver variations. Interobserver variations in the pathology of common astrocytic tumors impact diagnosis and subsequent treatment decisions. This study leveraged a residual neural network-50 (ResNet-50) in digital pathological images of diffuse astrocytoma, anaplastic astrocytoma, and glioblastoma to recognize characteristic pathological features and perform classification at the patch and case levels with identification of incorrect predictions. In addition, cellularity and nuclear morphological features, including axis ratio, circularity, entropy, area, irregularity, and perimeter, were quantified via a hybrid task cascade (HTC) framework and compared between different characteristic pathological features with importance weighting. A total of 95 cases, including 15 cases of diffuse astrocytoma, 11 cases of anaplastic astrocytoma, and 69 cases of glioblastoma, were collected in Taiwan Hualien Tzu Chi Hospital from January 2000 to December 2021. The results revealed that an optimized ResNet-50 model could recognize characteristic pathological features at the patch level and assist in diagnosis at the case level with accuracies of 0.916 and 0.846, respectively. Incorrect predictions were mainly due to indistinguishable morphologic overlap between anaplastic astrocytoma and glioblastoma tumor cell area, zones of scant vascular lumen with compact endothelial cells in the glioblastoma microvascular proliferation area mimicking the glioblastoma tumor cell area, and certain regions in diffuse astrocytoma with too low cellularity being misrecognized as the glioblastoma necrosis area. Significant differences were observed in cellularity and each nuclear morphological feature among different characteristic pathological features. Furthermore, using the extreme gradient boosting (XGBoost) algorithm, we found that entropy was the most important feature for classification, followed by cellularity, area, circularity, axis ratio, perimeter, and irregularity. Identifying incorrect predictions provided valuable feedback to machine learning design to further enhance accuracy and reduce errors in classification. Moreover, quantifying cellularity and nuclear morphological features with importance weighting provided the basis for developing an innovative scoring system to achieve objective classification and precision diagnosis among common astrocytic tumors. [ABSTRACT FROM AUTHOR]