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.

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.

Clinical/methodological problem: The identification of clinically significant prostate carcinomas while avoiding overdiagnosis of low-malignant tumors is a challenge in routine clinical practice. Standard radiologic procedures: Multiparametric magnetic resonance imaging (MRI) of the prostate acquired and interpreted according to PI-RADS (Prostate Imaging Reporting and Data System Guidelines) is accepted as a clinical standard among urologists and radiologists. Methodological innovations: The PI-RADS guidelines have been newly updated to version 2.1 and, in addition to more precise technical requirements, include individual changes in lesion assessment. Performance: The PI-RADS guidelines have become crucial in the standardization of multiparametric MRI of the prostate and provide templates for structured reporting, facilitating communication with the referring physician. Evaluation: The guidelines, now updated to version 2.1, represent a refinement of the widely used version 2.0. Many aspects of reporting have been clarified, but some previously known limitations remain and require further improvement of the guidelines in future versions.

The human brain has been described as a complex system. Its study by means of neurophysiological signals has revealed the presence of linear and nonlinear interactions. In this context, entropy metrics have been used to uncover brain behavior in the presence and absence of neurological disturbances. Entropy mapping is of great interest for the study of progressive neurodegenerative diseases such as Alzheimer’s disease. The aim of this study was to characterize the dynamics of brain oscillations in such disease by means of entropy and amplitude of low frequency oscillations from Bold signals of the default network and the executive control network in Alzheimer’s patients and healthy individuals, using a database extracted from the Open Access Imaging Studies series. The results revealed higher discriminative power of entropy by permutations compared to low-frequency fluctuation amplitude and fractional amplitude of low-frequency fluctuations. Increased entropy by permutations was obtained in regions of the default network and the executive control network in patients. The posterior cingulate cortex and the precuneus showed differential characteristics when assessing entropy by permutations in both groups. There were no findings when correlating metrics with clinical scales. The results demonstrated that entropy by permutations allows characterizing brain function in Alzheimer’s patients, and also reveals information about nonlinear interactions complementary to the characteristics obtained by calculating the amplitude of low frequency oscillations.

Amyloidosis is a systemic disorder produced by the deposition of insoluble protein fibrils that fold and deposit in the myocardium. Patients with amyloidosis and cardiac involvement have higher mortality than patients without cardiac involvement. The two most prevalent forms of amyloidosis associated with cardiac involvement are AL amyloidosis, due to the deposition of immunoglobulin light chains, and ATTR amyloidosis, due to the deposition of the transthyretin (TTR) protein in mutated or senile form. This article aims to review the different cardiac imaging modalities (echocardiography, cardiac magnetic resonance imaging, nuclear medicine and tomography) that allow to determine the severity of cardiac involvement in patients with amyloidosis, the type of amyloidosis and its prognosis. Finally, we suggest a diagnostic algorithm to determine cardiac involvement in amyloidosis adapted to locally available diagnostic tools, with a practical and clinical approach.

In recent years, ghost imaging has made important progress in the field of remote sensing imaging. In order to promote the application of solar ghost imaging in this field, this paper studies the computational ghost imaging based on the incoherent light of blackbody radiation. Firstly, according to the intensity probability density function of blackbody radiation, the expression of contrast-to-noise ratio (R_CN) describing the quality of computational ghost imaging is obtained, and then the random speckle pattern simulating blackbody radiation is generated by computer with the idea of slice sampling, finally, a digital light projector is used to modulate and generate the random modulated light that simulates the blackbody radiation light source, and this light source is used to realize the computational ghost image of the reflective object in the experiment. The “ghost image” of the object under different measurement frame numbers is reconstructed, and the contrast-to-noise ratio describing the imaging quality is measured. The results show that the image quality is relatively good when the average intensity (gray) of the randomly modulated speckle is about 160. On the other hand, the contrast-to-noise ratio of the image gradually increases from 0.8795 to 1.241, 1.516, 1.755, 2.100 and 2.371 as the number of measurement frames increases from 2,000 to 4,000, 6,000, 8,000, 12,000 and 20,000, respectively. The experimental results are basically consistent with the theoretical analysis. The results are of great significance for the application of ghost imaging with incoherent light, such as sunlight, which is approximately regarded as blackbody radiation, in the field of remote imaging.