Contrast-induced acute kidney injury (CI-AKI) is the third most common hospital-acquired AKI after AKI induced by renal perfusion insufficiency and nephrotoxic drugs, taking great adverse effects on the prognosis and increasing hospital stay and medical cost. Diabetes nephropathy (DN) is a common chronic complication of DM (diabetes mellitus), and DN is an independent risk factor for chronic kidney disease (CKD) and CI-AKI. The incidence of CI-AKI significantly increases in patients with renal injury, especially in DM-related nephropathy. The etiology of CI-AKI is not fully clear, and research studies on how DM becomes a facilitated factor of CI-AKI are limited. This review describes the mechanism from three aspects. ① Pathophysiological changes of CI-AKI in kidney under high-glucose status (HGS). HGS can enhance the oxidative stress and increase ROS which next causes stronger vessel constriction and insufficient oxygen supply in kidney via vasoactive substances. HGS also aggravates some ion pump load and the latter increases oxygen consumption. CI-AKI and HGS are mutually causal, making the kidney function continue to decline. ② Immunological changes of DM promoting CI-AKI. Some innate immune cells and pattern recognition receptors (PRRs) in DM and/or DN may respond to some damage-associated molecular patterns (DAMPs) formed by CI-AKI. These effects overlap with some pathophysiological changes in hyperglycemia. ③ Signaling pathways related to both CI-AKI and DM. These pathways involved in CI-AKI are closely associated with apoptosis, inflammation, and ROS production, and some studies suggest that these pathways may be potential targets for alleviating CI-AKI. In conclusion, the pathogenesis of CI-AKI and the mechanism of DM as a predisposing factor for CI-AKI, especially signaling pathways, need further investigation to provide new clinical approaches to prevent and treat CI-AKI.

Schizophrenia (SZ) is a severe psychiatric illness, and it affects around 1% of the general population; however, its reliable diagnosis is challenging. Functional MRI (fMRI) and structural MRI (sMRI) are useful techniques for investigating the functional and structural abnormalities of the human brain, and a growing number of studies have reported that multimodal brain data can improve diagnostic accuracy. Machine learning (ML) is widely used in the diagnosis of neuroscience and neuropsychiatry diseases, and it can obtain high accuracy. However, the conventional ML which concatenated the features into a longer feature vector could not be sufficiently effective to combine different features from different modalities. There are considerable controversies over the use of global signal regression (GSR), and few studies have explored the role of GSR in ML in diagnosing neurological diseases. The current study utilized fMRI and sMRI data to implement a new method named multimodal imaging and multilevel characterization with multiclassifier (M3) to classify SZs and healthy controls (HCs) and investigate the influence of GSR in SZ classification. We found that when we used Brainnetome 246 atlas and without performed GSR, our method obtained a classification accuracy of 83.49%, with a sensitivity of 68.69%, a specificity of 93.75%, and an AUC of 0.8491, respectively. We also got great classification performances with different processing methods (with/without GSR and different brain parcellation schemes). We found that the accuracy and specificity of the models without GSR were higher than that of the models with GSR. Our findings indicate that the M3 method is an effective tool to distinguish SZs from HCs, and it can identify discriminative regions to detect SZ to explore the neural mechanisms underlying SZ. The global signal may contain important neuronal information; it can improve the accuracy and specificity of SZ detection.

Parkinson’s disease (PD) is one of the most common progressive degenerative diseases, and its diagnosis is challenging on clinical grounds. Clinically, effective and quantifiable biomarkers to detect PD are urgently needed. In our study, we analyzed data from two centers, the primary set was used to train the model, and the independent external validation set was used to validate our model. We applied amplitude of low-frequency fluctuation (ALFF)-based radiomics method to extract radiomics features (including first- and high-order features). Subsequently, t-test and least absolute shrinkage and selection operator (LASSO) were harnessed for feature selection and data dimensionality reduction, and grid search method and nested 10-fold cross-validation were applied to determine the optimal hyper-parameter λ of LASSO and evaluate the performance of the model, in which a support vector machine was used to construct the classification model to classify patients with PD and healthy controls (HCs). We found that our model achieved good performance [accuracy = 81.45% and area under the curve (AUC) = 0.850] in the primary set and good generalization in the external validation set (accuracy = 67.44% and AUC = 0.667). Most of the discriminative features were high-order radiomics features, and the identified brain regions were mainly located in the sensorimotor network and lateral parietal cortex. Our study indicated that our proposed method can effectively classify patients with PD and HCs, ALFF-based radiomics features that might be potential biomarkers of PD, and provided further support for the pathological mechanism of PD, that is, PD may be related to abnormal brain activity in the sensorimotor network and lateral parietal cortex.

Resveratrol (Res) is a multi-functional polyphenol compound that has protective functions in acute kidney diseases. Here, we examined whether the resveratrol could ameliorate post-contrast acute kidney injury (PC–AKI) following diabetic nephropathy (DN), and explored any underlying mechanism(s) in vivo and in vitro. Twenty-four rabbits with DN were randomly divided into four groups: control (Cont), resveratrol (Res), iohexol (PC–AKI), and resveratrol plus iohexol (Res+PC–AKI) groups. Functional magnetic resonance imaging, renal histology, blood and urinary biomarkers, silent information regulator l (SIRT1), peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PGC-1α), hypoxia-inducible transcription factor-1α (HIF-1α), and apoptosis-associated protein expression were assessed ex vivo. For in vitro experiments, renal tubular epithelial (HK-2) cells subjected to high glucose conditions were treated with resveratrol, Ex527, an SIRT1 inhibitor, or 2-methoxyestradiol (2-MeOE2), HIF-1α inhibitor, before treatment with iohexol. With regard to the rabbit model of acute renal injury in DN, compared to the PC–AKI group, the Res+PC–AKI group showed decreased levels of cystatin C and urinary neutrophil gelatinase-associated lipocalin, increased pure molecular diffusion (D) and the fraction of water flowing in capillaries (f), a decreased apparent relaxation rate (R2*), renal injury score and apoptosis rate, increased protein expression levels of SIRT1 and PGC-1α, and decreased levels of HIF-1α and apoptosis-associated protein. In addition, iohexol decreased HK-2 cell survival and increased the cell apoptosis rate; results were reversed after treating cells with resveratrol. Resveratrol reduced renal hypoxia, mitochondrial dysfunction and renal tubular cell apoptosis by activating SIRT1–PGC–1α–HIF-1α signaling pathways in PC–AKI with DN.