Emerging evidence indicates that the water channel protein aquaporin 4 (AQP4) plays an essential role in water homeostasis and is implicated in the pathogenesis of brain edema. This study aimed to understand the physiological role of AQP4 in hypoxia–ischemia‐mediated cytotoxic brain edema. We specifically knocked down AQP4 expression by intracerebral injection of a plasmid containing AQP4 siRNA into a neonatal piglet model. The success of the hypoxia–ischemia‐induced piglet model was confirmed by conventional magnetic resonance imaging and diffusion‐weighted imaging. AQP4 knockdown led to reduced brain edema accompanied by a higher apparent diffusion coefficient value, compared to the control group injected with a plasmid containing scrambled siRNA. Real‐time polymerase chain reaction and immunohistochemical analysis confirmed that AQP4 siRNA significantly reduced AQP4 mRNA and protein expression. Finally, neurological function analysis revealed that AQP4 knockdown significantly improved neurobehavioral manifestation of the piglets after exposure to hypoxia‐ischemia. Taken together, these results indicate that AQP4 plays an important role in mediating brain edema in hypoxic‐ischemic encephalopathy. Therefore, AQP4 could be a therapeutic target to ameliorate early‐stage brain edema. © 2015 IUBMB Life, 67(3):182–190, 2015

Background: Intravoxel incoherent motion diffusion‐weighted imaging (IVIM‐DWI) and dynamic contrast‐enhanced MRI (DCE‐MRI) are sensitive imaging modalities for detecting liver lesions, but their value in evaluating cirrhosis‐related nodules remains unclear.Purpose: To investigate whether IVIM‐DWI and DCE‐MRI can differentiate different types of cirrhosis‐related nodules, and whether these modalities can monitor changes in cell density and angiogenesis during the malignant transformation of cirrhosis‐related nodules in a rat modelStudy Type: Prospective.Animal Model: Thirty‐five male Sprague–Dawley rats with 106 cirrhosis‐related nodules (19 regenerative nodules [RNs], 47 dysplastic nodules [DNs], and 40 hepatocellular carcinomas [HCCs]).Field Strength/Sequence: IVIM‐DWI and DCE sequence at 3.0T MRI.Assessment: IVIM‐DWI parameters (D, D*, f, and apparent diffusion coefficient [ADC]) and DCE‐MRI parameters (K^trans, K_ep, and V_e) were calculated by two radiologists using postprocessing software. The “cell density” and “unpaired arterial ratio” were analyzed with a microscope by two pathologists.Statistical Tests: MRI parameters were compared among the different types of nodules by one‐way analysis of variance or the Kruskal–Wallis test. The Pearson correlation test was used to analyze the correlation of MRI parameters with the pathological types of nodules, cell density, and unpaired arterial ratio.Results: The K^trans, K_ep, and V_e values of HCCs were significantly higher than those of DNs and RNs. D and ADC values were significantly lower in HCCs than in DNs and RNs. There were moderate positive correlations of K^trans with the pathological types of nodules and the unpaired arterial ratio. Moderate negative correlations were observed among D, ADC, and the pathological types of nodules, between D and cell density, and between ADC and cell density.Data Conclusion: IVIM‐DWI and DCE‐MRI are valuable in differentiating different types of cirrhotic‐related nodules. D and ADC are correlated with changes in cell density during the malignant transformation of cirrhosis‐related nodules, while K^trans is correlated with increased angiogenesis.Level of Evidence: 1Technical Efficacy: Stage 1J. Magn. Reson. Imaging 2020;51:812–823.

Background: Performing biopsy for intermediate lesions with PI-RADS 3 has always been controversial. Moreover, it is difficult to differentiate prostate cancer (PCa) and benign prostatic hyperplasia (BPH) nodules in PI-RADS 3 lesions by conventional scans, especially for transition zone (TZ) lesions. The purpose of this study is sub-differentiation of transition zone (TZ) PI-RADS 3 lesions using intravoxel incoherent motion (IVIM), stretched exponential model, and diffusion kurtosis imaging (DKI) to aid the biopsy decision process.Methods: A total of 198 TZ PI-RADS 3 lesions were included. 149 lesions were BPH, while 49 lesions were PCa, including 37 non-clinical significant PCa (non-csPCa) lesions and 12 clinical significant PCa (csPCa) lesions. Binary logistic regression analysis was used to examine which parameters could predict PCa in TZ PI-RADS 3 lesions. The ROC curve was used to test diagnostic efficiency in distinguishing PCa from TZ PI-RADS 3 lesions, while one-way ANOVA analysis was used to examine which parameters were statistically significant among BPH, non-csPCa and csPCa.Results: The logistic model was statistically significant (χ2 = 181.410, p<0.001) and could correctly classify 89.39% of the subjects. Parameters of fractional anisotropy (FA) (p=0.004), mean diffusion (MD) (p=0.005), mean kurtosis (MK) (p=0.015), diffusion coefficient (D) (p=0.001), and distribute diffusion coefficient (DDC) (p=0.038) were statistically significant in the model. ROC analysis showed that AUC was 0.9197 (CI 95%: 0.8736-0.9659). Sensitivity, specificity, positive predictive value and negative predictive value were 92.1%, 80.4%, 93.9% and 75.5%, respectively. FA and MK of csPCa were higher than those of non-csPCa (all p<0.05), while MD, ADC, D, and DDC of csPCa were lower than those of non-csPCa (all p<0.05).Conclusion: FA, MD, MK, D, and DDC can predict PCa in TZ PI-RADS 3 lesions and inform the decision-making process of whether or not to perform a biopsy. Moreover, FA, MD, MK, D, DDC, and ADC may have ability to identify csPCa and non-csPCa in TZ PI-RADS 3 lesions.