Brain region-specific synaptic function of FUS underlies the FTLD-linked behavioural disinhibition.

Synaptic dysfunction is one of the earliest pathological processes that contribute to the development of many neurological disorders, including Alzheimer's disease and frontotemporal lobar degeneration. However, the synaptic function of many disease-causative genes and their contribution to the pathogenesis of the related diseases remain unclear. In this study, we investigated the synaptic role of fused in sarcoma, an RNA-binding protein linked to frontotemporal lobar degeneration and amyotrophic lateral sclerosis, and its potential pathological role in frontotemporal lobar degeneration using pyramidal neuron-specific conditional knockout mice (FuscKO). We found that FUS regulates the expression of many genes associated with synaptic function in a hippocampal subregion-specific manner, concomitant with the frontotemporal lobar degeneration-linked behavioural disinhibition. Electrophysiological study and molecular pathway analyses further reveal that fused in sarcoma differentially regulates synaptic and neuronal properties in the ventral hippocampus and medial prefrontal cortex, respectively. Moreover, fused in sarcoma selectively modulates the ventral hippocampus-prefrontal cortex projection, which is known to mediate the anxiety-like behaviour. Our findings unveil the brain region- and synapse-specific role of fused in sarcoma, whose impairment might lead to the emotional symptoms associated with frontotemporal lobar degeneration.

Exploring value of CT coronary imaging combined with machine-learning methods to predict myocardial ischemia.

PURPOSE: To establish a machine-learning (ML) model based on coronary computed tomography angiography (CTA) images for evaluating myocardial ischemia in patients diagnosed with coronary atherosclerosis. METHODS: This retrospective analysis includes CTA images acquired from 110 patients. Among them, 58 have myocardial ischemia and 52 have normal myocardial blood supply. The patients are divided into training and test datasets with a ratio 7 : 3. Deep learning model-based CQK software is used to automatically segment myocardium on CTA images and extract texture features. Then, seven ML models are constructed to classify between myocardial ischemia and normal myocardial blood supply cases. Predictive performance and stability of the classifiers are determined by receiver operating characteristic curve with cross validation. The optimal ML model is then validated using an independent test dataset. RESULTS: Accuracy and areas under ROC curves (AUC) obtained from the support vector machine with extreme gradient boosting linear method are 0.821 and 0.777, respectively, while accuracy and AUC achieved by the neural network (NN) method are 0.818 and 0.757, respectively. The naive Bayes model yields the highest sensitivity (0.942), and the random forest model yields the highest specificity (0.85). The k-nearest neighbors model yields the lowest accuracy (0.74). Additionally, NN model demonstrates the lowest relative standard deviations (0.16 for accuracy and 0.08 for AUC) indicating the high stability of this model, and its AUC applying to the independent test dataset is 0.72. CONCLUSION: The NN model demonstrates the best performance in predicting myocardial ischemia using radiomics features computed from CTA images, which suggests that this ML model has promising potential in guiding clinical decision-making.

Enhanced computed tomography radiomics-based machine-learning methods for predicting the Fuhrman grades of renal clear cell carcinoma.

OBJECTIVE: To develop and test an optimal machine learning model based on the enhanced computed tomography (CT) to preoperatively predict pathological grade of clear cell renal cell carcinoma (ccRCC). METHODS: A retrospective analysis of 53 pathologically confirmed cases of ccRCC was performed and 25 consecutive ccRCC cases were selected as a prospective testing set. All patients underwent routine preoperative abdominal CT plain and enhanced scans. Renal tumor lesions were segmented on arterial phase images and 396 radiomics features were extracted. In the training set, seven discrimination classifiers for high- and low-grade ccRCCs were constructed based on seven different machine learning models, respectively, and their performance and stability for predicting ccRCC grades were evaluated through receiver operating characteristic (ROC) analysis and cross-validation. Prediction accuracy and area under ROC curve were used as evaluation indices. Finally, the diagnostic efficacy of the optimal model was verified in the testing set. RESULTS: The accuracies and AUC values achieved by support vector machine with radial basis function kernel (svmRadial), random forest and naïve Bayesian models were 0.860±0.158 and 0.919±0.118, 0.840±0.160 and 0.915±0.138, 0.839±0.147 and 0.921±0.133, respectively, which showed high predictive performance, whereas K-nearest neighborhood model yielded lower accuracy of 0.720±0.188 and lower AUC value of 0.810±0.150. Additionally, svmRadial had smallest relative standard deviation (RSD, 0.13 for AUC, 0.17 for accuracy), which indicates higher stability. CONCLUSION: svmRadial performs best in predicting pathological grades of ccRCC using radiomics features computed from the preoperative CT images, and thus may have high clinical potential in guiding preoperative decision.

Predictive value of bilirubin and serum γ-glutamyltranspeptidase levels in type-2 diabetes mellitus patients with acute coronary syndrome.

BACKGROUND: Cardiovascular disease is a major complication of diabetes mellitus (DM). Type-2 DM (T2DM) is associated with an increased risk of cardiovascular events and mortality, while serum biomarkers may facilitate the prediction of these outcomes. Early differential diagnosis of T2DM complicated with acute coronary syndrome (ACS) plays an important role in controlling disease progression and improving safety. AIM: To investigate the correlation of serum bilirubin and γ-glutamyltranspeptidase (γ-GGT) with major adverse cardiovascular events (MACEs) in T2DM patients with ACS. METHODS: The clinical data of inpatients from January 2022 to December 2022 were analyzed retrospectively. According to different conditions, they were divided into the T2DM complicated with ACS group (T2DM + ACS, n = 96), simple T2DM group (T2DM, n = 85), and simple ACS group (ACS, n = 90). The clinical data and laboratory indices were compared among the three groups, and the correlations of serum total bilirubin (TBIL) levels and serum γ-GGT levels with other indices were discussed. T2DM + ACS patients received a 90-day follow-up after discharge and were divided into event (n = 15) and nonevent (n = 81) groups according to the occurrence of MACEs; Univariate and multivariate analyses were further used to screen the independent influencing factors of MACEs in patients. RESULTS: The T2DM + ACS group showed higher γ-GGT, total cholesterol, low-density lipoprotein cholesterol (LDL-C) and glycosylated hemoglobin (HbA1c) and lower TBIL and high-density lipoprotein cholesterol levels than the T2DM and ACS groups (P < 0.05). Based on univariate analysis, the event and nonevent groups were significantly different in age (t = 3.3612, P = 0.0011), TBIL level (t = 3.0742, P = 0.0028), γ-GGT level (t = 2.6887, P = 0.0085), LDL-C level (t = 2.0816, P = 0.0401), HbA1c level (t = 2.7862, P = 0.0065) and left ventricular ejection fraction (LEVF) levels (t=3.2047, P = 0.0018). Multivariate logistic regression analysis further identified that TBIL level and LEVF level were protective factor for MACEs, and age and γ-GGT level were risk factors (P < 0.05). CONCLUSION: Serum TBIL levels are decreased and γ-GGT levels are increased in T2DM + ACS patients, and the two indices are significantly negatively correlated. TBIL and γ-GGT are independent influencing factors for MACEs in such patients.

Prediction of myocardial ischemia in coronary heart disease patients using a CCTA-Based radiomic nomogram.

Objective: The present study aimed to predict myocardial ischemia in coronary heart disease (CHD) patients based on the radiologic features of coronary computed tomography angiography (CCTA) combined with clinical factors. Methods: The imaging and clinical data of 110 patients who underwent CCTA scan before DSA or FFR examination in Changzhou Second People's Hospital, Nanjing Medical University (90 patients), and The First Affiliated Hospital of Soochow University (20 patients) from March 2018 to January 2022 were retrospectively analyzed. According to the digital subtraction angiography (DSA) and fractional flow reserve (FFR) results, all patients were assigned to myocardial ischemia (n = 58) and normal myocardial blood supply (n = 52) groups. All patients were further categorized into training (n = 64) and internal validation (n = 26) sets at a ratio of 7:3, and the patients from second site were used as external validation. Clinical indicators of patients were collected, the left ventricular myocardium were segmented from CCTA images using CQK software, and the radiomics features were extracted using pyradiomics software. Independent prediction models and combined prediction models were established. The predictive performance of the model was assessed by calibration curve analysis, receiver operating characteristic (ROC) curve and decision curve analysis. Results: The combined model consisted of one important clinical factor and eight selected radiomic features. The area under the ROC curve (AUC) of radiomic model was 0.826 in training set, and 0.744 in the internal validation set. For the combined model, the AUCs were 0.873, 0.810, 0.800 in the training, internal validation, and external validation sets, respectively. The calibration curves demonstrated that the probability of myocardial ischemia predicted by the combined model was in good agreement with the observed values in both training and validation sets. The decision curve was within the threshold range of 0.1-1, and the clinical value of nomogram was higher than that of clinical model. Conclusion: The radiomic characteristics of CCTA combined with clinical factors have a good clinical value in predicting myocardial ischemia in CHD patients.

Long noncoding RNA BS-DRL1 modulates the DNA damage response and genome stability by interacting with HMGB1 in neurons.

Long noncoding RNAs (lncRNAs) are known to regulate DNA damage response (DDR) and genome stability in proliferative cells. However, it remains unknown whether lncRNAs are involved in these vital biological processes in post-mitotic neurons. Here, we report and characterize a lncRNA, termed Brain Specific DNA-damage Related lncRNA1 (BS-DRL1), in the central nervous system. BS-DRL1 is a brain-specific lncRNA and depletion of BS-DRL1 in neurons leads to impaired DDR upon etoposide treatment in vitro. Mechanistically, BS-DRL1 interacts with HMGB1, a chromatin protein that is important for genome stability, and is essential for the assembly of HMGB1 on chromatin. BS-DRL1 mediated DDR exhibits cell-type specificity in the cortex and cerebellum in gamma-irradiated mice and BS-DRL1 knockout mice show impaired motor function and concomitant purkinje cell degeneration. Our study extends the understanding of lncRNAs in DDR and genome stability and implies a protective role of lncRNA against neurodegeneration.

Chronological in vivo imaging reveals endothelial inflammation prior to neutrophils accumulation and lipid deposition in HCD-fed zebrafish.

BACKGROUND AND AIMS: Hyperlipidemia-induced atherosclerosis is the major cause of heart attack and stroke in humans. However, pathological details and molecular mechanisms underlying early atherogenesis remain incompletely characterized. This study explored the early events of atherogenesis in a hypercholesterolemic zebrafish model in vivo. METHODS: We used transparent transgenic zebrafish larvae Tg(lysc:EGFP), Tg(mpx:EGFP), Tg(mpeg1:EGFP), Tg(flk1:EGFP) or Tg(lysc:EGFP/flk1:mCherry), together with fluorescently labeled control and high cholesterol diets (HCD), to dynamically investigate the early development of atherosclerosis with confocal in vivo. Endothelial cells with green fluorescence were sorted by fluorescence-activated cell sorting (FACS) to detect gene expression. Moreover, we treated hypercholesterolemic zebrafish model in vivo or human umbilical vein endothelial cells (HUVEC) in vitro with rosiglitazone, an agonist of peroxisome proliferator-activated receptor γ (PPARγ). RESULTS: We found that HCD-induced endothelial inflammation was an earlier pathological alteration than myeloid cells/neutrophils accumulation and lipid deposition in zebrafish vascular vessels of HCD-fed zebrafish. Endothelial inflammation was characterized by down-regulation of anti-inflammatory PPARγ and upregulation of pro-inflammatory tumor necrosis factor α (TNF-α) and interleukin-1ß (IL-1ß). Pharmacological treatment with rosiglitazone reversed the decrease in the expression of PPARγ and decreased expression of TNF-α and IL-1ß in HCD-fed zebrafish. Moreover, rosiglitazone ameliorated myeloid cells accumulation and lipid deposition in HCD-fed zebrafish in vivo. CONCLUSIONS: Hyperlipidemia-induced endothelial inflammation happens earlier than myeloid cell neutrophils accumulation in vascular vessels, and neutrophils accumulation is prior to lipid deposition during the initial stage of atherosclerosis. Early alleviation of inflammation induced by HCD would have a prophylactic effect for the initial development of atherosclerosis.

Exacerbation of diabetic cardiac hypertrophy in OVE26 mice by angiotensin II is associated with JNK/c-Jun/miR-221-mediated autophagy inhibition.

Both diabetes and angiotensin II (Ang II) excess trigger cardiac remodeling and dysfunction, and diabetic cardiomyopathy. We hypothesized that cardiac hypertrophy associated with the development of diabetic cardiomyopathy is worsened by increased Ang II. Male type 1 diabetic OVE26 and wild-type mice were given Ang II (sc., 1.15 mg/kg, twice a day) for 14 days. Diabetes-induced cardiac dysfunction and hypertrophy was exacerbated by Ang II treatment as determined by echocardiography, wheat germ agglutinin staining and atrial natriuretic peptide. Ang II treatment dramatically exacerbated diabetes-caused decreased LC3-II, a marker of autophagy, and increased p62, an indicator of cytosolic protein clearance. Ang II treatment also augmented diabetes-associated increased phosphorylated levels of c-Jun, JNK, mTOR, and miR-221, and decreased of p27 expression, a direct target of miR-221. Chromatin immunoprecipitation assay showed that Ang II elevated c-Jun binding to the promoter of miR-221 in diabetic mice. These results suggest that Ang II accelerates cardiac hypertrophy in the early stage of murine diabetes, probably through activation of the JKN/c-Jun/miR-221 axis and inhibition of downstream autophagy. Therefore, inhibition of Ang II or miR-221 in diabetic individuals may be a potential approach for delaying the onset and/or reducing the severity of diabetic cardiomyopathy.

Netrin-1 suppresses the MEK/ERK pathway and ITGB4 in pancreatic cancer.

The axon guidance factor netrin-1 promotes tumorigenesis in multiple types of cancers, particularly at their advanced stages. Here, we investigate whether netrin-1 is involved in the in vivo growth of pancreatic adenocarcinoma. We show that netrin-1 is significantly under-expressed in stage-I/II pancreatic ductal adenocarcinoma (PDAC). Netrin-1 over-expression effectively arrests the growth of xenografted PDAC cells without decreasing cell proliferation or increasing apoptosis in two-dimensional cultures in vitro. Integrin-beta4 (ITGB4) expression is significantly reduced, and ITGB4-knockdown mimics the tumor-suppressive effect of netrin-1, implying that ITGB4 is a main target of netrin-1 in constraining PDAC. We further show that netrin-1 signals to UNC5B/FAK to stimulate nitric oxide production, which promotes PP2A-mediated inhibition of the MEK/ERK pathway and decreases phosphorylated-c-Jun recruitment to the ITGB4 promoter. Our findings suggest that netrin-1 can suppress the growth of PDAC and provide a mechanistic insight into this suppression.

Adaptive prospective ECG-triggered sequence coronary angiography in dual-source CT without heart rate control: Image quality and diagnostic performance.

The aim of this study was to evaluate the accuracy of using second generation dual-source CT (DSCT) to obtain high quality images and diagnostic performance and to reduce the radiation dose in adaptive prospective electrocardiography (ECG)-triggered sequence (CorAdSeq) CT coronary angiography (CTCA) without heart rate control. No prescan ß-blockers were administered. Un-enhanced CT and CTCA with adaptive prospective CorAdSeq scanning without heart rate control were performed in 683 consecutive patients divided into two body mass index (BMI) groups: BMI <25 kg/m(2) (group A, n=412) and BMI ≥25 kg/m(2) (group B, n=271). The image quality and quantitative stenosis of all coronary segments with a diameter ≥1 mm were assessed. The mean heart rate (MHR), heart rate variability (HRV) and radiation dose values were recorded. In 426 cases, the diagnostic performance was evaluated using quantitative conventional coronary angiography as the reference standard. Diagnostic image quality was obtained in 98.5% of segments in group A and in 98.8% of segments in group B, with no significant differences between the groups. No correlations were observed between the image quality score and MHR or HRV (P=0.492, P=0.564, respectively). The effective radiation doses in groups A and B were 2.57±1.01 mSv and 6.36±1.88 mSv, respectively. The sensitivities and specificities of diagnosing coronary heart disease per patient were 99.6% and 97.8% in group A and 99.5% and 97.5% in group B, respectively (P>0.05). Adaptive prospective CorAdSeq scanning, without heart rate control, by second generation DSCT had a high image quality and diagnostic performance for coronary artery stenosis with lower radiation doses.