Walking Dead Macrophage-Based Positive Enhancement MRI for Ultrahighly Efficient Diagnosis of Nephritis.

Nephritis is an inflammatory condition of the glomerulus, and the clinical gold standard for its diagnosis is a kidney biopsy. However, obtaining biopsy results can take several days, which does not meet the requirement of rapid diagnosis, especially for rapidly progressive types. To achieve an effective and noninvasive diagnosis, we propose a nephritis-specific, positive magnetic resonance imaging (MRI) contrast agent based on Gd3+ anchored walking dead macrophage Gd-RAW. Gd-RAW exhibits high selectivity for inflammatory renal parenchyma and provides comparable results to histopathology methods. The Gd-RAW-based MRI contrast agent reduces the diagnostic time of nephritis from 14 days of biopsy to 1 h. Furthermore, in a unilateral nephritis model constructed by increasing the glycerol concentration, the T1WI of renal parenchyma exhibits an increased signal-to-noise ratio, which is crucial for evaluating nephritic severity. This work promotes rapid diagnosis of nephritis and potentially provides sufficient evidence for clinicians to offer timely treatment to patients. The methodology of paramagnetic ion-anchored macrophage corpse also opens up new prospects for designing more specific and biosafe MRI contrast agents.

Hydrogen-Bonded Organic Frameworks Chelated Manganese for Precise Magnetic Resonance Imaging Diagnosis of Cancers.

Magnetic resonance imaging (MRI) is an important tool in the diagnosis of many cancers. However, clinical gadolinium (Gd)-based MRI contrast agents have limitations, such as large doses and potential side effects. To address these issues, we developed a hydrogen-bonded organic framework-based MRI contrast agent (PFC-73-Mn). Due to the hydrogen-bonded interaction of water molecules and the restricted rotation of manganese ions, PFC-73-Mn exhibits high longitudinal relaxation r1 (5.03 mM-1 s-1) under a 3.0 T clinical MRI scanner. A smaller intravenous dose (8 µmol of Mn/kg) of PFC-73-Mn can provide strong contrast and accurate diagnosis in multiple kinds of cancers, including breast tumor and ultrasmall orthotopic glioma. PFC-73-Mn represents a prospective new approach in tumor imaging, especially in early-stage cancer.

Translation-invariant context-retentive wavelet reflection removal network.

It has been widely investigated for images taken through glass to remove unwanted reflections in deep learning. However, none of these methods have bad effects, but they all remove reflections in specific situations, and validate the results with their own datasets, e.g., several local places with strong reflections. These limitations will result in situations where real reflections in the world cannot be effectively eliminated. In this study, a novel Translation-invariant Context-retentive Wavelet Reflection Removal Network is proposed to address this issue. In addition to context and background, low-frequency sub-images still have a small amount of reflections. To enable background context retention and reflection removal, the low-frequency sub-images at each level are performed on the Context Retention Subnetwork (CRSn) after wavelet transform. Novel context level blending and inverse wavelet transform are proposed to remove reflections in low frequencies and retain background context recursively, which is of great help in restoring clean images. High-frequency sub-images with reflections are performed on the Detail-enhanced Reflection layer removal Subnetwork to complete reflection removal. In addition, in order to further separate the reflection layer and the transmission layer better, we also propose Detail-enhanced Reflection Information Transmission, through which the extracted features of reflection layer in high-frequency images can help the CRSn effectively separate the transmission layer and the reflection layer, so as to achieve the effects of removing reflection. The quantitative and visual experimental results on benchmark datasets demonstrate that the proposed method performs better than the state-of-the-art approaches.

Using machine learning to predict one-year cardiovascular events in patients with severe dilated cardiomyopathy.

PURPOSE: Dilated cardiomyopathy (DCM) is a common form of cardiomyopathy and it is associated with poor outcomes. A poor prognosis of DCM patients with low ejection fraction has been noted in the short-term follow-up. Machine learning (ML) could aid clinicians in risk stratification and patient management after considering the correlation between numerous features and the outcomes. The present study aimed to predict the 1-year cardiovascular events in patients with severe DCM using ML, and aid clinicians in risk stratification and patient management. MATERIALS AND METHODS: The dataset used to establish the ML model was obtained from 98 patients with severe DCM (LVEF < 35%) from two centres. Totally 32 features from clinical data were input to the ML algorithm, and the significant features highly relevant to the cardiovascular events were selected by Information gain (IG). A naive Bayes classifier was built, and its predictive performance was evaluated using the area under the curve (AUC) of the receiver operating characteristics by 10-fold cross-validation. RESULTS: During the 1-year follow-up, a total of 22 patients met the criterion of the study end-point. The top features with IG > 0.01 were selected for ML model, including left atrial size (IG = 0.240), QRS duration (IG = 0.200), and systolic blood pressure (IG = 0.151). ML performed well in predicting cardiovascular events in patients with severe DCM (AUC, 0.887 [95% confidence interval, 0.813-0.961]). CONCLUSIONS: ML effectively predicted risk in patients with severe DCM in 1-year follow-up, and this may direct risk stratification and patient management in the future.

The comparison of short-term prognostic value of T1 mapping with feature tracking by cardiovascular magnetic resonance in patients with severe dilated cardiomyopathy.

To evaluate and compare the prognostic value of T1 mapping with feature tracking cardiovascular magnetic resonance (FT-CMR) imaging in patients with severe dilated cardiomyopathy (DCM) during short-term follow-up. A total of 46 patients with severe DCM (LVEF < 35%) underwent 3.0-T CMR with T1 mapping and FT-CMR analysis. The study end-point was defined as a combination of cardiac death, heart transplantation, and hospitalization due to cardiovascular events. The significance of the risk factors was mainly evaluated by univariate and multivariate Cox model analyses. During the median follow-up of 13 months (interquartile range 7-17 months), two patients died of heart failure, one received a heart transplantation, and six were hospitalized for heart failure. In the univariate analysis, extracellular volume fraction (ECV) showed significant predictive association with cardiovascular events (hazard ratio [HR] 1.35; 95% confidence interval [CI] 1.13-1.62; P = 0.001). No strain parameters in FT-CMR differed significantly between patients with or without events (all P > 0.05). In the multivariate analyses, ECV was the sole independent predictor of cardiovascular events (HR, 1.48; 95% CI 1.13-1.94; P = 0.005). The area under the curve of the time-dependent receiver operating characteristic in leave-one-out cross-validation (all > 0.70) further confirmed the predictive significance of ECV. In patients with severe DCM, ECV was not only a strong independent predictor of adverse cardiovascular events but also provided prognostic value prior to strain parameters of the FT-CMR in the short term.