Brain tumors are a primary factor causing cancer-related deaths globally, and their classification remains a significant research challenge due to the variability in tumor intensity, size, and shape, as well as the similar appearances of different tumor types. Accurate differentiation is further complicated by these factors, making diagnosis difficult even with advanced imaging techniques such as magnetic resonance imaging (MRI). Recent techniques in artificial intelligence (AI), in particular deep learning (DL), have improved the speed and accuracy of medical image analysis, but they still face challenges like overfitting and the need for large annotated datasets. This study addresses these challenges by presenting two approaches for brain tumor classification using MRI images. The first approach involves fine-tuning transfer learning cutting-edge models, including SEResNet, ConvNeXtBase, and ResNet101V2, with global average pooling 2D and dropout layers to minimize overfitting and reduce the need for extensive preprocessing. The second approach leverages the Vision Transformer (ViT), optimized with the AdamW optimizer and extensive data augmentation. Experiments on the BT-Large-4C dataset demonstrate that SEResNet achieves the highest accuracy of 97.96%, surpassing ViT’s 95.4%. These results suggest that fine-tuning and transfer learning models are more effective at addressing the challenges of overfitting and dataset limitations, ultimately outperforming the Vision Transformer and existing state-of-the-art techniques in brain tumor classification.

Quantum dot can be seen as an amazing nanotechnological discovery, including inorganic semiconducting nanodots as well as carbon nanodots, like graphene quantum dots. Unlike pristine graphene nanosheet having two dimensional nanostructure, graphene quantum dot is a zero dimensional nanoentity having superior aspect ratio, surface properties, edge effects, and quantum confinement characters. To enhance valuable physical properties and potential prospects of graphene quantum dots, various high-performance nanocomposite nanostructures have been developed using polymeric matrices. In this concern, noteworthy combinations of graphene quantum dots have been reported for a number of thermoplastic polymers, like polystyrene, polyurethane, poly(vinylidene fluoride), poly(methyl methacrylate), poly(vinyl alcohol), and so on. Due to nanostructural compatibility, dispersal, and interfacial aspects, thermoplastics/graphene quantum dot nanocomposites depicted unique microstructure and technically reliable electrical/thermal conductivity, mechanical/heat strength, and countless other physical properties. Precisely speaking, thermoplastic polymer/graphene quantum dot nanocomposites have been reported in the literature for momentous applications in electromagnetic interference shielding, memory devices, florescent diodes, solar cells photocatalysts for environmental remediation, florescent sensors, antibacterial, and bioimaging. To the point, this review article offers an all inclusive and valuable literature compilation of thermoplastic polymer/graphene quantum dot nanocomposites (including design, property, and applied aspects) for field scientists/researchers to carry out future investigations on further novel designs and valued property-performance attributes.

Instant and accurate evaluation of drug resistance in tumors before and during chemotherapy is important for patients with advanced colon cancer and is beneficial for prolonging their progression-free survival time. Here, the possible biomarkers that reflect the drug resistance of colon cancer were investigated using proton magnetic resonance spectroscopy (1H-MRS) in vivo. SW480[5-fluorouracil(5-FU)-responsive] and SW480/5-FU (5-FU-resistant) xenograft models were generated and subjected to in vivo 1H-MRS examinations when the maximum tumor diameter reached 1–1.5 cm. The areas under the peaks for metabolites, including choline (Cho), lactate (Lac), glutamine/glutamate (Glx), and myo-inositol (Ins)/creatine (Cr) in the tumors, were analyzed between two groups. The resistance-related protein expression, cell morphology, necrosis, apoptosis, and cell survival of these tumor specimens were assessed. The content for tCho, Lac, Glx, and Ins/Cr in the tumors of the SW480 group was significantly lower than that of the SW480/5-FU group (P < 0.05). While there was no significant difference in the degree of necrosis and apoptosis rate of tumor cells between the two groups (P > 0.05), the tumor cells of the SW480/5-FU showed a higher cell density and larger nuclei. The expression levels of resistance-related proteins (P-gp, MPR1, PKC) in the SW480 group were lower than those in the SW480/5-FU group (P < 0.01). The survival rate of 5-FU-resistant colon cancer cells was significantly higher than that of 5-FU-responsive ones at 5-FU concentrations greater than 2.5 μg/mL (P < 0.05). These results suggest that alterations in tCho, Lac, Glx1, Glx2, and Ins/Cr detected by 1H-MRS may be used for monitoring tumor resistance to 5-FU in vivo.