This study aims at analyzing the separability of acute cerebral infarction lesions which were invisible in CT. 38 patients, who were diagnosed with acute cerebral infarction and performed both CT and MRI, and 18 patients, who had no positive finding in either CT or MRI, were enrolled. Comparative studies were performed on lesion and symmetrical regions, normal brain and symmetrical regions, lesion, and normal brain regions. MRI was reconstructed and affine transformed to obtain accurate lesion position of CT. Radiomic features and information gain were introduced to capture efficient features. Finally, 10 classifiers were established with selected features to evaluate the effectiveness of analysis. 1301 radiomic features were extracted from candidate regions after registration. For lesion and their symmetrical regions, there were 280 features with information gain greater than 0.1 and 2 features with information gain greater than 0.3. The average classification accuracy was 0.6467, and the best classification accuracy was 0.7748. For normal brain and their symmetrical regions, there were 176 features with information gain greater than 0.1, 1 feature with information gain greater than 0.2. The average classification accuracy was 0.5414, and the best classification accuracy was 0.6782. For normal brain and lesions, there were 501 features with information gain greater than 0.1 and 1 feature with information gain greater than 0.5. The average classification accuracy was 0.7480, and the best classification accuracy was 0.8694. In conclusion, the study captured significant features correlated with acute cerebral infarction and confirmed the separability of acute lesions in CT, which established foundation for further artificial intelligence‐assisted CT diagnosis.

Immune clearance and insufficient targeting have limited the efficacy of existing therapeutic strategies for cancer. Toxic side effects and individual differences in response to treatment have further limited the benefits of clinical treatment for patients. Biomimetic cancer cell membrane-based nanotechnology has provided a new approach for biomedicine to overcome these obstacles. Biomimetic nanoparticles exhibit various effects (e.g., homotypic targeting, prolonging drug circulation, regulating the immune system, and penetrating biological barriers) after encapsulation by cancer cell membranes. The sensitivity and specificity of diagnostic methods will also be improved by utilizing the properties of cancer cell membranes. In this review, different properties and functions of cancer cell membranes are presented. Utilizing these advantages, nanoparticles can exhibit unique therapeutic capabilities in various types of diseases, such as solid tumors, hematological malignancies, immune system diseases, and cardiovascular diseases. Furthermore, cancer cell membrane-encapsulated nanoparticles show improved effectiveness and efficiency in combination with current diagnostic and therapeutic methods, which will contribute to the development of individualized treatments. This strategy has promising clinical translation prospects, and the associated challenges are discussed.

The multimodal magnetic resonance imaging (MRI) technique has been extensively studied over the past few years since it offers complementary information that can increase diagnostic accuracy. Simple methods to synthesize contrast agents are necessary for the development of multimodal MRI. Herein, uniformly distributed Fe₃O₄/Gd₂O₃ nanocubes for T₁–T₂ dual-mode MRI contrast agents were successfully designed and synthesized. In order to increase hydrophilicity and biocompatibility, the nanocubes were coated with nontoxic 3,4-dihydroxyhydrocinnamic acid (DHCA). The results show that iron (Fe) and gadolinium (Gd) were homogeneously distributed throughout the Fe₃O₄/Gd₂O₃-DHCA (FGDA) nanocubes. Relaxation time analysis was performed on the images obtained from the 3.0 T scanner. The results demonstrated that r₁ and r₂ maximum values were 67.57 ± 6.2 and 24.2 ± 1.46 mM⁻¹·s⁻¹, respectively. In vivo T₁- and T₂-weighted images showed that FGDA nanocubes act as a dual-mode contrast agent enhancing MRI quality. Overall, these experimental results suggest that the FGDA nanocubes are interesting tools that can be used to increase MRI quality, enabling accurate clinical diagnostics.

Hepatocellular carcinoma (HCC) is the sixth most common malignant tumor and the third leading cause of cancer death worldwide. Most patients are diagnosed at an advanced stage, and systemic chemotherapy is the preferred treatment modality for advanced HCC. Curcumin (CUR) is a polyphenolic antineoplastic drug with low toxicity obtained from plants. However, its low bioavailability and poor solubility limit its functionality. In this study, radiofrequency- (RF) enhanced responsive nanoflowers (NFs), containing superparamagnetic ferric oxide nanoclusters (Fe₃O₄ NCs), – CUR layer, – and MnO₂ (CUR-Fe@MnO₂ NFs), were verified to have a thermal therapeutic effect. Transmission electron microscopy was used to characterize the CUR-Fe@MnO₂ NFs, which appeared flower-like with a size of 96.27 nm. The in vitro experimental data showed that RF enhanced the degradation of CUR-Fe@MnO₂ NFs to release Mn²⁺ and CUR. The cytotoxicity test results indicated that after RF heating, the CUR-Fe@MnO₂ NFs significantly suppressed HCC cell proliferation. Moreover, CUR-Fe@MnO₂ NFs were effective T₁/T₂ contrast agents for molecular magnetic resonance imaging due to the release of Mn²⁺ and Fe₃O₄ NCs.