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.

The concept of sustainable urban mobility has gained increasing attention in recent years due to the challenges posed by rapid urbanization and environmental degradation. The objective of this study is to explore the role of on-demand transportation in promoting sustainable urban mobility, incorporating insights from customer interests and demands through survey analysis. To fulfill this objective, a mixed-methods approach was employed, combining a systematic literature review with survey analysis of customer interests and demands regarding on-demand transportation services. This study combines a systematic literature review and a targeted survey to provide a comprehensive analysis of sustainable urban mobility, addressing gaps in understanding customer preferences alongside technological and financial considerations. The literature review encompassed various aspects including technological advancements, regulatory frameworks, user preferences, and environmental impacts. The survey analysis involved collecting data on customer preferences, satisfaction levels, and suggestions for improving on-demand transportation services. The findings of the study revealed significant insights into customer interests and demands regarding on-demand transportation services. Analysis of survey data indicated that factors such as convenience, affordability, reliability, and environmental sustainability were key considerations for customers when choosing on-demand transportation options. Additionally, the survey identified specific areas for improvement, including service coverage, accessibility, and integration with existing transportation networks. By providing flexible, efficient, and environmentally friendly transportation options, on-demand services have the potential to reduce congestions, improve air quality, and enhance overall urban livability.

Objective: Standardizing image acquisition protocols and image quality across cameras is an important need in imaging, in particular in multi-center clinical trials and the use of image analysis and machine learning algorithms. The objective of this study was to examine the effect of ordered subset expectation maximization (OSEM) reconstruction parameters on the quantitative image quality of cardiac perfusion SPECT images in different typical SPECT cameras and therefore assess the need to change the parameter values across cameras. Methods: The analysis was carried out by comparing the defect contrast-to-noise ratio (CNR) at 12 OSEM subset-iteration combinations. Eight frames were reconstructed using the SIMIND Monte Carlo Simulation package. An activity of 370 MBq (10mCi) and projection acquisition interval of 20 seconds per projection were used. Attenuation (AC) and scatter corrections (SC) were performed in this study for all images. Results: The 16-2 subset-iteration combination yielded the highest CNR and defect contrast values for both cameras. The difference between CNR values for two cameras was found to be close to 5%. Conclusions: Monte Carlo simulations can be useful to investigate how quantitative image quality behaves with respect to reconstruction parameters and correction algorithms in a controlled environment. In this study, the use of different camera brands did not seem to significantly affect the lesion detectability. Further simulations with more extended range of parameters and camera brands may be conducted in the future to quantify further the variability between different brands of cameras.

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 myoinositol (Ins)/creatine (Cr) in the tumors, were analyzed between two groups. The resistancerelated 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-FUresponsive 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.