Utilizing multicompartmental restriction spectrum magnetic resonance imaging for liver fibrosis characterization in a mouse model.

BACKGROUND: Currently, an advanced imaging method may be necessary for magnetic resonance imaging (MRI) to diagnosis and quantify liver fibrosis (LF). PURPOSE: To evaluate the feasibility of the multicompartmental restriction spectrum imaging (RSI) model to characterize LF in a mouse model. METHODS: Thirty mice with carbon tetrachloride (CCl4)-induced LF and eight control mice were investigated using multi-b-value (ranging from 0 to 2000 s/mm2) diffusion-weighted imaging (DWI) on a 3T scanner. DWI data were processed using RSI model (2-5 compartments) with the Bayesian Information Criterion (BIC) determining the optimal model. Conventional ADC value and signal fraction of each compartment in the optimal RSI model were compared across groups. Receiver operating characteristics (ROC) curve analysis was performed to determine the diagnosis performances of different parameters, while Spearman correlation analysis was employed to investigate the correlation between different tissue compartments and the stage of LF. RESULTS: According to BIC results, a 4-compartment RSI model (RSI4) with optimal ADCs of 0.471 × 10-3, 1.653 × 10-3, 9.487 × 10-3, and > 30 × 10-3, was the optimal model to characterize LF. Significant differences in signal contribution fraction of the C1 and C3 compartments were observed between LF and control groups (P = 0.018 and 0.003, respectively). ROC analysis showed that RSI4-C3 was the most effective single diffusion parameter for characterizing LF (AUC = 0.876, P = 0.003). Furthermore, the combination of ADC values and RSI4-C3 value increased the diagnosis performance significantly (AUC = 0.894, P = 0.002). CONCLUSION: The 4-compartment RSI model has the potential to distinguish LF from the control group based on diffusion parameters. RSI4-C3 showed the highest diagnostic performance among all the parameters. The combination of ADC and RSI4-C3 values further improved the discrimination performance.

Deep Learning Based on ResNet-18 for Classification of Prostate Imaging-Reporting and Data System Category 3 Lesions.

RATIONALE AND OBJECTIVES: To explore the classification and prediction efficacy of the deep learning model for benign prostate lesions, non-clinically significant prostate cancer (non-csPCa) and clinically significant prostate cancer (csPCa) in Prostate Imaging-Reporting and Data System (PI-RADS) 3 lesions. MATERIALS AND METHODS: From January 2015 to December 2021, lesions diagnosed with PI-RADS 3 by multi-parametric MRI or bi-parametric MRI were retrospectively included. They were classified as benign prostate lesions, non-csPCa, and csPCa according to the pathological results. T2-weighted images of the lesions were divided into a training set and a test set according to 8:2. ResNet-18 was used for model training. All statistical analyses were performed using Python open-source libraries. The receiver operating characteristic curve (ROC) was used to evaluate the predictive effectiveness of the model. T-SNE was used for image semantic feature visualization. The class activation mapping was used to visualize the area focused by the model. RESULTS: A total of 428 benign prostate lesion images, 158 non-csPCa images and 273 csPCa images were included. The precision in predicting benign prostate disease, non-csPCa and csPCa were 0.882, 0.681 and 0.851, and the area under the ROC were 0.875, 0.89 and 0.929, respectively. Semantic feature analysis showed strong classification separability between csPCa and benign prostate lesions. The class activation map showed that the deep learning model can focus on the area of the prostate or the location of PI-RADS 3 lesions. CONCLUSION: Deep learning model with T2-weighted images based on ResNet-18 can realize accurate classification of PI-RADS 3 lesions.

Circular RNA DNAH14 molecular mechanism in an experimental model of hepatocellular carcinoma treated with Cobalt chloride to mimic the hypoxia-like response of transcatheter arterial chemoembolization.

Transcatheter arterial chemoembolization (TACE) is the primary local treatment for patients with unresectable hepatocellular carcinoma (HCC). Numerous studies have demonstrated the pivotal role of circular RNAs (circRNAs) in TACE efficacy. This study aimed to investigate the function of circular RNA DNAH14 (circDNAH14) in TACE for HCC and to elucidate its molecular mechanisms. To simulate hypoxia conditions experienced during TACE, HCC cells were treated with cobalt chloride. The expression levels of circDNAH14, microRNA-508-3p (miR-508-3p), and Prothymosin Alpha (PTMA) were modulated via transfection for knockdown or overexpression. Cell Counting Kit-8 and 5-ethynyl-2'-deoxyuridine assays, flow cytometry, and Transwell assays, along with epithelial-mesenchymal transition (EMT) evaluations, were employed to assess cell proliferation, apoptosis, invasion, migration, and EMT. The results indicated that hypoxia treatment downregulated the expression of circDNAH14 and PTMA while upregulating miR-508-3p. Such treatment suppressed HCC cell proliferation, invasion, migration, and EMT, and induced apoptosis. Knockdown of circDNAH14 or PTMA intensified the suppressive effects of hypoxia on the malignant behaviors of HCC cells. Conversely, upregulation of miR-508-3p or PTMA mitigated the effects of circDNAH14 overexpression and knockdown, respectively. Mechanistically, circDNAH14 was found to competitively bind to miR-508-3p, thereby regulating PTMA expression. In vivo, nude mouse xenograft experiments demonstrated that circDNAH14 knockdown augmented the hypoxia-induced suppression of HCC tumor growth. In conclusion, circDNAH14 mitigates the suppressive effects of hypoxia on HCC, both in vitro and in vivo, by competitively binding to miR-508-3p and regulating PTMA expression.

The Role MicroRNA-135a in Suppressing Tumor Growth in Kidney Cancer Through the Regulation of Phosphoprotein Phosphatase2A and the Activation of the AKT and ERK1/2 Signaling Pathways.

Background: Kidney cancer is a frequently occurring malignant tumor in the urinary system, with rising morbidity and mortality rates in recent times. Developing new biomarkers and therapeutic targets is essential to improve the prognosis of patients affected by kidney cancer. In recent years, miRNAs' role in tumorigenesis and development has received growing attention. miRNAs constitute a group of small non-coding RNA molecules that regulate gene expression, affecting various biological processes, including cell proliferation, differentiation, and apoptosis. Of the many miRNAs, miR-135a plays a pivotal role in several cancers. Nevertheless, the precise mechanisms and functions concerning miR-135a in renal cancer remain incompletely understood. Therefore, this study aims to analyze the effects of miR-135a on renal cancer replication and migration and its possible mechanisms, and to provide new strategies for the diagnosis and treatment of renal cancer. Methods: Renal cell lines (ACHN, A498) with stable hyperexpression of miR-135a and reduced expression of miR-135a were constructed by lentivirus packaging. The changes of replication, clone formation and migration ability of overexpressed miR-135a and overexpressed miR-135a in ACHN and A498 renal cell lines were detected. The possible mechanism of miR-135a affecting the replication of kidney cancer was analyzed by target gene prediction, double luciferase test, Western blotting and subcutaneous tumorigenicity assay in nude mice. Results: Hyperexpression of miR-135a can inhibit kidney cancer replication, whereas miR-135a knockdown potentially enhances replication. However, neither hyperexpression nor knockdown of miR-135a affects the migration ability of kidney cancer cells. The protein expression of PP2A-B56-γ, PP2A-Cα and PP2A-Cß in renal cell line decreased after hyperexpression of miR-135a, while the protein expression of PP2A-B56-γ, PP2A-Cα and PP2A-Cß increased after knockdown of miR-135a. In addition, the protein expression of p-Akt and p-ERK1/2 proteins in kidney cancer cells after hyperexpression of miR-135a were down-regulated, while the protein expression of p-Akt and p-ERK1/2 were up-regulated in kidney cancer cells after knockdown of miR-135a. In subcutaneous tumor formation experiments in nude mice, tumor size within nude mice in the miR-135a group was significantly smaller than in the control group. Conclusion: MiR-135a could suppress the replication of kidney cancer by modulating PP2A and AKT, ERK1/2 signaling pathways.