Nerve guidance conduits (NGCs) represent a promising strategy to support axonal growth and orientation during peripheral nerve regeneration. Polycaprolactone (PCL) offers suitable mechanical and biodegradable properties for NGC fabrication. To enhance its functionality, carbon quantum dots (CQDs) can improve physical and chemical properties, while Moringa oleifera extract contributes bioactive compounds that support neurogenesis and nerve repair. In this study, electrospun aligned nanofiber scaffolds composed of PCL, CQDs, and Moringa extract were fabricated and evaluated. Scaffolds containing both CQDs and Moringa showed improved mechanical strength, hydrophilicity, and degradation profile. The release of Moringa extract was quantified, and biocompatibility was confirmed via cell viability, adhesion, and differentiation assays using B65 and PC12 cells. These scaffolds significantly enhanced cell proliferation, neurite outgrowth, and neural differentiation, as demonstrated by DAPI staining, SEM, MTT, qRT-PCR, and immunocytochemistry. In vivo, a sciatic nerve transection model in rats was used to assess the regenerative potential of the conduits. After 12 weeks, improvements in sciatic functional index, electrophysiology, muscle recovery, and nerve histology were observed. Immunohistochemistry, qRT-PCR, and TEM further confirmed myelin sheath regeneration and neuromuscular recovery. These findings suggest that NGCs incorporating CQDs and Moringa extract provide a synergistic platform for promoting nerve regeneration and functional recovery, offering a novel and effective approach for the treatment of peripheral nerve injuries (PNI).

Gliomas are known to have different sub-regions within the tumor, including the edema, necrotic, and active tumor regions. Segmenting of these regions is very important for glioma treatment decisions and management. This paper aims to demonstrate the application of U-Net and pre-trained U-Net backbone networks in glioma semantic segmentation, utilizing different magnetic resonance imaging (MRI) image weights. The data used in this study for network training, validation, and testing is the Multimodal Brain Tumor Segmentation (BraTS) 2021 challenge. In this study, we applied the U-Net and different pre-trained Backbone U-Net for the semantic segmentation of glioma regions. The ResNet, Inception, and VGG networks, which are pre-trained using the ImageNet dataset, have been used as the Backbone in the U-Net architecture. The Accuracy (ACC) and Intersection over Union (IoU) were employed to assess the performance of the networks. The most prominent finding to emerge from this study is that trained ResNet-U-Net with T1 post-contrast enhancement (T1Gd) has the highest ACC and IoU for the necrotic and active tumor regions semantic segmentation in glioma. It was also demonstrated that a trained ResNet-U-Net with T2 Fluid-Attenuated Inversion Recovery (T2-FLAIR) is a suitable combination for edema segmentation in glioma. Our study further validates that the proposed framework’s architecture and modules are scientifically grounded and practical, enabling the extraction and aggregation of valuable semantic information to enhance glioma semantic segmentation capability. It demonstrates how useful the ResNet-U-Net will be for physicians to extract glioma regions automatically. Keywords Glioma, Artificial intelligence, Segmentation, Magnetic resonance imaging

Abstract We aimed to investigate the utility of peritumoral edema-derived radiomic features from magnetic resonance imaging (MRI) image weights and fused MRI sequences for enhancing the performance of machine learning-based glioma grading. The present study utilized the Multimodal Brain Tumor Segmentation Challenge 2023 (BraTS 2023) dataset. Laplacian Redecomposition (LRD) was employed to fuse multimodal MRI sequences. The fused image quality was evaluated using the Entropy, standard deviation (STD), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) metrics. A comprehensive set of radiomic features was subsequently extracted from peritumoral edema regions using PyRadiomics. The Boruta algorithm was applied for feature selection, and an optimized classification pipeline was developed using the Tree-based Pipeline Optimization Tool (TPOT). Model performance for glioma grade classification was evaluated based on accuracy, precision, recall, F1-score, and area under the curve (AUC) parameters. Analysis of fused image quality metrics confirmed that the LRD method produces high-quality fused images. From 851 radiomic features extracted from peritumoral edema regions, the Boruta algorithm selected different sets of informative features in both standard MRI and fused images. Subsequent TPOT automated machine learning optimization analysis identified a fine-tuned Stochastic Gradient Descent (SGD) classifier, trained on features from T1Gd+FLAIR fused images, as the top-performing model. This model achieved superior performance in glioma grade classification (Accuracy = 0.96, Precision = 1.0, Recall = 0.94, F1-Score = 0.96, AUC = 1.0). Radiomic features derived from peritumoral edema in fused MRI images using the LRD method demonstrated distinct, grade-specific patterns and can be utilized as a non-invasive, accurate, and rapid glioma grade classification method.

فصل نامه اختصاصی، علمی، فرهنگی، اجتماعی دانشکده فن آوری های نوین پزشکی

نشست صمیمی دانشجویان با رئیس دانشکده

بازدید اعضای بورد هوش مصنوعی از دانشکده فن‌آوری‌های نوین علوم پزشکی اصفهان

برگزاری مجموعه برنامه‌های تخصصی دانشکده فناوری‌های نوین پزشکی به مناسبت هفته پژوهش

🎓 کارگاه تخصصی روند اخذ مجوز اداره تجهیزات پزشکی به مناسبت هفته پژوهش

در بازدید اعضای بورد تخصصی وزارت بهداشت از دانشکده فن‌آوری‌های نوین علوم پزشکی اصفهان، حمایت همه‌جانبه استانی از رشته هوش مصنوعی در علوم پزشکی آشکار شد. در جلسه ای که در دفتر ریاست دانشگاه با حضور جناب آقای دکتر شیرانی، اعضای بورد هوش مصنوعی در پزشکی، رئیس و اعضای هیئت علمی دانشکده فناوری های نوین پزشکی برگزار شد، تأکید گردید که با وجود کادر متخصص و تجهیزات کامل در دانشکده فناوری هاین نوین پزشکی و همچنین وجود "میز هوش مصنوعی" در استانداری اصفهان، بسترهای لازم برای پشتیبانی از این رشته و راه اندازی آن فراهم است.

اطلاعیه جلسه دفاعیه دکتری تخصصی - آقای حسین محمدی