JAML promotes acute kidney injury mainly through a macrophage-dependent mechanism.

Although macrophages are undoubtedly attractive therapeutic targets for acute kidney injury (AKI) because of their critical roles in renal inflammation and repair, the underlying mechanisms of macrophage phenotype switching and efferocytosis in the regulation of inflammatory responses during AKI are still largely unclear. The present study elucidated the role of junctional adhesion molecule-like protein (JAML) in the pathogenesis of AKI. We found that JAML was significantly upregulated in kidneys from 2 different murine AKI models including renal ischemia/reperfusion injury (IRI) and cisplatin-induced AKI. By generation of bone marrow chimeric mice, macrophage-specific and tubular cell-specific Jaml conditional knockout mice, we demonstrated JAML promoted AKI mainly via a macrophage-dependent mechanism and found that JAML-mediated macrophage phenotype polarization and efferocytosis is one of the critical signal transduction pathways linking inflammatory responses to AKI. Mechanistically, the effects of JAML on the regulation of macrophages were, at least in part, associated with a macrophage-inducible C-type lectin-dependent mechanism. Collectively, our studies explore for the first time to our knowledge new biological functions of JAML in macrophages and conclude that JAML is an important mediator and biomarker of AKI. Pharmacological targeting of JAML-mediated signaling pathways at multiple levels may provide a novel therapeutic strategy for patients with AKI.

Predicting Chronic Myocardial Ischemia Using CCTA-Based Radiomics Machine Learning Nomogram.

BACKGROUND: Coronary computed tomography angiography (CCTA) is a well-established non-invasive diagnostic test for the assessment of coronary artery diseases (CAD). CCTA not only provides information on luminal stenosis but also permits non-invasive assessment and quantitative measurement of stenosis based on radiomics. PURPOSE: This study is aimed to develop and validate a CT-based radiomics machine learning for predicting chronic myocardial ischemia (MIS). METHODS: CCTA and SPECT-myocardial perfusion imaging (MPI) of 154 patients with CAD were retrospectively analyzed and 94 patients were diagnosed with MIS. The patients were randomly divided into two sets: training (n = 107) and test (n = 47). Features were extracted for each CCTA cross-sectional image to identify myocardial segments. Multivariate logistic regression was used to establish a radiomics signature after feature dimension reduction. Finally, the radiomics nomogram was built based on a predictive model of MIS which in turn was constructed by machine learning combined with the clinically related factors. We then validated the model using data from 49 CAD patients and included 18 MIS patients from another medical center. The receiver operating characteristic curve evaluated the diagnostic accuracy of the nomogram based on the training set and was validated by the test and validation set. Decision curve analysis (DCA) was used to validate the clinical practicability of the nomogram. RESULTS: The accuracy of the nomogram for the prediction of MIS in the training, test and validation sets was 0.839, 0.832, and 0.816, respectively. The diagnosis accuracy of the nomogram, signature, and vascular stenosis were 0.824, 0.736 and 0.708, respectively. A significant difference in the number of patients with MIS between the high and low-risk groups was identified based on the nomogram (P < .05). The DCA curve demonstrated that the nomogram was clinically feasible. CONCLUSION: The radiomics nomogram constructed based on the image of CCTA act as a non-invasive tool for predicting MIS that helps to identify high-risk patients with coronary artery disease.

Predicting the progression of Parkinson's disease using conventional MRI and machine learning: An application of radiomic biomarkers in whole-brain white matter.

PURPOSE: This study aimed to develop and validate a radiomics model based on whole-brain white matter and clinical features to predict the progression of Parkinson disease (PD). METHODS: PD patient data from the Parkinson's Progress Markers Initiative (PPMI) database was evaluated. Seventy-two PD patients with disease progression, as measured by the Hoehn-Yahr Scale (HYS) (stage 1-5), and 72 PD patients with stable PD were matched by sex, age, and category of HYS and included in the current study. Each individual's T1 -weighted MRI scans at the baseline timepoint were segmented to isolate whole-brain white matter for radiomics feature extraction. The total dataset was divided into a training and test set according to subject serial number. The size of the training dataset was reduced using the maximum relevance minimum redundancy (mRMR) algorithm to construct a radiomics signature using machine learning. Finally, a joint model was constructed by incorporating the radiomics signature and clinical progression scores. The test data were then used to validate the prediction models, which were evaluated based on discrimination, calibration, and clinical utility. RESULTS: Based on the overall data, the areas under curve (AUCs) of the joint model, signature and Unified Parkinson Disease Rating Scale III PD rating score were 0.836, 0.795, and 0.550, respectively. Furthermore, the sensitivities were 0.805, 0.875, and 0.292, respectively, and the specificities were 0.722, 0.697, and 0.861, respectively. In addition, the predictive accuracy of the model was 0.827, the sensitivity was 0.829 and the specificity was 0.702 for stage-1 PD. For stage-2 PD, the predictive accuracy of the model was 0.854, the sensitivity was 0.960, and the specificity was 0.600. CONCLUSION: Our results provide evidence that conventional structural MRI can predict the progression of PD. This work also supports the use of a simple radiomics signature built from whole-brain white matter features as a useful tool for the assessment and monitoring of PD progression.

Radiomics nomogram based on MRI for predicting white matter hyperintensity progression in elderly adults.

BACKGROUND: White matter hyperintensity (WMH) is widely observed in aging brain and is associated with various diseases. A pragmatic and handy method in the clinic to assess and follow up white matter disease is strongly in need. PURPOSE: To develop and validate a radiomics nomogram for the prediction of WMH progression. STUDY TYPE: Retrospective. POPULATION: Brain images of 193 WMH patients from the Picture Archiving and Communication Systems (PACS) database in the A Medical Center (Zhejiang Provincial People's Hospital). MRI data of 127 WMH patients from the PACS database in the B Medical Center (Zhejiang Lishui People's Hospital) were included for external validation. All of the patients were at least 60 years old. FIELD STRENGTH/SEQUENCE: T1 -fluid attenuated inversion recovery images were acquired using a 3T scanner. ASSESSMENT: WMH was evaluated utilizing the Fazekas scale based on MRI. WMH progression was assessed with a follow-up MRI using a visual rating scale. Three neuroradiologists, who were blinded to the clinical data, assessed the images independently. Moreover, interobserver and intraobserver reproducibility were performed for the regions of interest for segmentation and feature extraction. STATISTICAL TESTS: A receiver operating characteristic (ROC) curve, the area under the curve (AUC) of the ROC was calculated, along with sensitivity and specificity. Also, a Hosmer-Lemeshow test was performed. RESULTS: The AUC of radiomics signature in the primary, internal validation cohort, external validation cohort were 0.886, 0.816, and 0.787, respectively; the specificity were 71.79%, 72.22%, and 81%, respectively; the sensitivity were 92.68%, 87.94% and 78.3%, respectively. The radiomics nomogram in the primary cohort (AUC = 0.899) and the internal validation cohort (AUC = 0.84). The Hosmer-Lemeshow test showed no significant difference between the primary cohort and the internal validation cohort (P > 0.05). The AUC of the radiomics nomogram, radiomics signature, and hyperlipidemia in all patients from the primary and internal validation cohort was 0.878, 0.848, and 0.626, respectively. DATA CONCLUSION: This multicenter study demonstrated the use of a radiomics nomogram in predicting the progression of WMH with elderly adults (an age of at least 60 years) based on conventional MRI. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:535-546.

Correlation of vitamin D receptor gene (ApaI) polymorphism with periodontitis: A meta-analysis of Chinese population.

Many studies have tried to elucidate the connection between vitamin D receptor (VDR) gene (ApaI) polymorphism and periodontitis; however, so far there is no consensus. To further assess the impact of ApaI polymorphism on periodontitis risk, we have conducted a meta-analysis of Chinese population. Relevant literatures were searched according to PubMed and Chinese database in January 2019. The strength of correlation was evaluated by combining odds ratio (ORs) and 95% confidence interval (CIs). Six case-control studies were identified with inclusion criteria, including 734 cases of periodontitis and 687 controls. Based on the overall analysis, the VDR ApaI polymorphism was not due to the risk of periodontitis in all models. Subgroup analysis showed that the risk of periodontitis in North China was significantly reduced. To sum up, the study shows that VDR-ApaI polymorphism may be connected with a lower risk of periodontitis in northern China. It is suggested that inferential studies should be conducted in other ethnic groups.

Consistency of radiologists in identifying pulmonary nodules based on low-dose computed tomography.

BACKGROUND: To study the consistency of radiologists in identifying pulmonary nodules based on low-dose computed tomography (LDCT), and to analyze factors that affect the consistency. METHODS: A total of 750 LDCT cases were collected randomly from three medical centers. Three experienced chest radiologists independently evaluated and detected the pulmonary nodules on 625 cases of LDCT images. The detected nodules were classified into 3 groups: group I (detected by all radiologists); group II (detected by two radiologists); group III (detected by only one radiologist). The consistency with respect to the image features of individual nodules was assessed. RESULTS: A total of 1,206 nodules were identified by the three radiologists. There were 234 (19.4%) nodules in group I, 377 (31.3%) nodules in group II, and 595 (49.3%) nodules in group III. Logistic regression showed that the size, density, and location of the nodules correlated with the detection of nodules. Nodules sized great than or equal to 4 mm were more consistently identified than nodules sized less than 4 mm. Solid and calcified nodules were more consistently identified than sub-solid nodules. Nodules located in the outer zone were more consistently identified than hilar nodules. CONCLUSIONS: There was considerable inter-reader variability with respect to identification of pulmonary nodules in LDCT. Larger nodules, solid or calcified nodules, and nodules located in the outer zone were more consistently identified.