Contrastive voxel clustering for multiscale modeling of brain network.

Resting-state functional magnetic resonance imaging (fMRI) provides an efficient way to analyze the functional connectivity between brain regions. A comprehensive understanding of brain functionality requires a unified description of multi-scale layers of neural structure. However, existing brain network modeling methods often simplify this property by averaging Blood oxygen level dependent (BOLD) signals at the brain region level for fMRI-based analysis with the assumption that BOLD signals are homogeneous within each brain region, which ignores the heterogeneity of voxels within each Region of Interest (ROI). This study introduces a novel multi-stage self-supervised learning framework for multiscale brain network analysis, which effectively delineates brain functionality from voxel to ROIs and up to sample level. A Contrastive Voxel Clustering (CVC) module is proposed to simultaneously learn the voxel-level features and clustering assignments, which ensures the retention of informative clustering features at the finest voxel-level and concurrently preserves functional connectivity characteristics. Additionally, based on the extracted features and clustering assignments at the voxel level by CVC, a Brain ROI-based Graph Neural Network (BR-GNN) is built to extract functional connectivity features at the brain ROI-level and used for sample-level prediction, which integrates the functional clustering maps with the pre-established structural ROI maps and creates a more comprehensive and effective analytical tool. Experiments are performed on two datasets, which illustrate the effectiveness and generalization ability of the proposed method by analyzing voxel-level clustering results and brain ROIs-level functional characteristics. The proposed method provides a multiscale modeling framework for brain functional connectivity analysis, which will be further used for other brain disease identification. Code is available at https://github.com/yanliugroup/fmri-cvc.

Development of an electrochemical separation-Rhodopseudomonas palustris electrolysis cell-coupled system for resourceful treatment of mature leachate landfill.

Electrochemical technology is a promising technique for separating ammonia from mature landfill leachate. However, the accompanying migration and transformation of coexisting pollutants and strategies for further high-value resourceful utilization of ammonia have rarely received attention. In this study, an electrochemical separation-Rhodopseudomonas palustris electrolysis cell coupled system was initially constructed for efficient separation and conversion of nitrogen in mature landfill leachate to microbial protein with synchronously tracking the transport and conversion of coexisting heavy metals accompanying the process. The results revealed that ammonia concentration in the cathode increased from 40.3 to 49.8% with increasing the current density from 20 to 40 mA/cm2, with less than 3% of ammonia transformation to NO2--N and NO3--N. During ammonia separation, approximately 95% of HM-DOMs (Cr, Cu, Ni, Pb, and Zn) were released into the anolyte due to humus degradation and further diffused to the cathode. A significant correlation was observed between the releases of HM-DOMs. Cu-DOMs accounted for 70.2% of the total Cu content, which was the highest proportion among the heavy metals (HMs). Among the HMs in anolyte, 57.4% of Pb, 52.5% of Ni, and 50.6% of Zn diffused to the cathode, and most of the HMs were removed in the form of hydroxide precipitations due to heavy alkaline catholyte. Compared with the open-circuit condition, the utilization efficiency of NH4+-N in the R. palustris electrolysis cell increased by 445.1% with 47% and 50% increases in final NH4+-N conversion rate and R. palustris biomass, respectively, due to bio-electrochemical enhanced phototrophic metabolism and acid generation for buffering the strong alkalinity of the electrolyte to maintain suitable growth conditions for R. palustris.

An extensible hierarchical graph convolutional network for early Alzheimer's disease identification.

BACKGROUND AND OBJECTIVE: For early identification of Alzheimer's disease (AD) based on multi-modal magnetic resonance imaging (MRI) data, it is important to make comprehensive use of image features and non-image information to analyze the gray matter atrophy and the structural/functional connectivity abnormalities for different courses of AD. METHODS: In this study, we propose an extensible hierarchical graph convolutional network (EH-GCN) for early AD identification. Based on the extracted image features from multi-modal MRI data using the presented multi-branch residual network (ResNet), the brain regions-of-interests (ROIs) based GCN is built to extract structural and functional connectivity features between different ROIs of the brain. In order to further improve the performance of AD identification, an optimized spatial GCN is proposed as convolution operator in the population-based GCN to avoid rebuilding the graph network and take advantage of relationships between subjects. Finally, the proposed EH-GCN is built by embedding the image features and internal brain connectivity features into the spatial population-based GCN, which provides an extensible way to improve early AD identification performance by adding imaging features and non-image information from multi-modal data. RESULTS: Experiments are performed on two datasets, which illustrate the effectiveness of the extracted structural/functional connectivity features and the high computational efficiency of the proposed method. The classification accuracy of AD vs NC, AD vs MCI and MCI vs NC classification tasks reaches 88.71%, 82.71% and 79.68% respectively. The extracted connectivity features between ROIs indicate that functional abnormalities are earlier than gray matter atrophy and abnormalities of structural connections, which is consistent with the clinical manifestations. The proposed method allows for the addition of other modal image features and non-image information from multi-modal data to continuously improve the performance of clinical data analysis. CONCLUSIONS: The proposed method can help us comprehensively analyze the role of gray matter atrophy, the damage of white matter nerve fiber tracts and the degradation of functional connectivity for different courses of AD, which could be useful for further extraction of clinical biomarkers for early AD identification.

Exosomal miR­152­5p/ARHGAP6/ROCK axis regulates apoptosis and fibrosis in cardiomyocytes.

Acute myocardial infarction (AMI) is a fatal cardiovascular disease with a high mortality rate. The discovery of effective biomarkers is crucial for the diagnosis and treatment of AMI. In the present study, miRNA sequencing and reverse transcription-quantitative polymerase chain reaction techniques revealed that the expression of exosome derived miR-152-5p was significantly downregulated in patients with AMI compared with healthy controls. A series of functional validation experiments were then performed using H9c2 cardiomyocytes. Following transfection of the cardiomyocytes using an miR-152-5p inhibitor, immunofluorescence staining of a-smooth muscle actin revealed a marked increase in fibrosis. Western blotting revealed that the expression levels of the apoptotic protein Bax, TNF-α and collagen-associated proteins were significantly increased, whereas those of the apoptosis-inhibiting factor Bcl-2 and vascular endothelial growth factor A were significantly decreased. Furthermore, the binding of Rho GTPase-activating protein 6 (ARHGAP6) to miR-152-5p was predicted using an online database and verified using a dual-luciferase reporter gene assay. The transfection of cardiomyocytes with miR-152-5p mimics was found to inhibit the activation of ARHGAP6 and Rho-associated coiled-coil containing kinase 2 (ROCK2). These results suggest that miR-152-5p targets ARHGAP6 through the ROCK signaling pathway to inhibit AMI, which implies that miR-152-5p may be a diagnostic indicator and potential target for treatment of myocardial infarction.

A two-phase clustering approach for traffic accident black spots identification: integrated GIS-based processing and HDBSCAN model.

Identifying black spots effectively and accurately is a pivotal and challenging task to improve road traffic safety. A novel black spot identification model is proposed by integrating the GIS-based processing with hierarchical density-based spatial clustering of applications with noise. Additionally, the optimal clustering parameters are determined based on an internal validation indicator called the density-based clustering validation index to minimize the impact of subjectivity in parameter selection. The model is validated by collecting 3536 accident data from 1 August to 31 October 2020 in Hangzhou, China, and eventually identifies 39 black spots. The results show that: (1) The number of accidents contained in black spots account for 75% of all accidents, while the length of network in the black spots only account for 23.26% of the total road network length. (2) Compared with the conventional density-based spatial clustering of applications with noise model and K-means model, the proposed model achieves the best performance with more accidents gathered per unit road length. (3) The sample survey with 6 onsite of the identified black spots indicates that the proposed model has high recognition accuracy and recommend these sites for further investigation.

Preparation of controlled degradation of insulin-like growth factor 1/spider silk protein nanofibrous membrane and its effect on endothelial progenitor cell viability.

The present study aimed to prepare a kind of controlled-releasing insulin-like growth factor 1 (IGF-1)/spider silk protein nanofibrous membrane using a electrostatic spinning method and evaluated its effect on the cell viability of endothelial progenitor cells (EPCs). Recombinant spidroin named as GMCDRSSP-IgF-1 was electro-spun into nanofibrous membrane which can be degraded by protease and be capable of sustained-release of IGF-1. The membrane can be degraded after being treated with thrombin. The release assay results showed that IGF-1 concentration could be maintained at 20 ng/ml for a long time with treatment of Tobacco Etch Virus (TEV) protease. The viability of EPCs on GMCDRSSP-IgF-1 nanofibrous membrane was significantly increased with the presence of TEV protease. The controlled and sustained release of IGF-1 from the nanofibrous membrane could promote the adhesion and viability of EPCs. In summary, the nanofibrous membrane that exhibits controlled degradation and sustained release of IGF-1 was prepared with electrostatic spinning from genetically modified recombinant spider silk protein. The nanofibrous membrane exhibited good blood compatibility and cytocompatibility. With the presence of TEV protease, the sustained-release of IGF-1 significantly promoted the adhesion and viability of EPCs. The new nanofibrous membrane can be potentially used as a scaffold for EPCs culture in vitro and future in vivo studies.

EPC-Derived Exosomal miR-1246 and miR-1290 Regulate Phenotypic Changes of Fibroblasts to Endothelial Cells to Exert Protective Effects on Myocardial Infarction by Targeting ELF5 and SP1.

Myocardial infarction (MI) remains a leading cause of morbidity and mortality worldwide. Endothelial progenitor cell (EPC)-derived exosomes have been found to be effective in alleviating MI, while the detailed mechanisms remain unclear. The present study aimed to determine the protective effects of EPC-derived exosomal miR-1246 and miR-1290 on MI-induced injury and to explore the underlying molecular mechanisms. The exosomes were extracted from EPCs; gene expression levels were determined by quantitative real-time PCR, and protein expression levels were determined by western blot and immunofluorescence staining, respectively. The angiogenesis and proliferation of human cardiac fibroblasts (HCFs) were determined by tube formation assay and immunofluorescence staining of PKH67, respectively. Luciferase reporter, CHIP, and EMSA assays determined the interaction between miR-1246/1290 and the targeted genes (EFL5 and SP1). The protective effects of miR-1246/1290 on MI were evaluated in a rat model of MI. EPC-derived exosomes significantly upregulated miR-1246 and miR-1290 expression and promoted phenotypic changes of fibroblasts to endothelial cells, angiogenesis, and proliferation in HCFs. Exosomes from EPCs with miR-1246 or miR-1290 mimics transfection promoted phenotypic changes of fibroblasts to endothelial cells and angiogenesis in HCFs, while exosomes from EPCs with miR-1246 or miR-1290 knockdown showed opposite effects in HCFs. Mechanistically, miR-1246 and miR-1290 from EPC-derived exosomes induced upregulation of ELF5 and SP1, respectively, by targeting the promoter regions of corresponding genes. Overexpression of both ELF5 and SP1 enhanced phenotypic changes of fibroblasts to endothelial cells and angiogenesis in HCFs pretreated with exosomes from EPCs with miR-1246 or miR-1290 mimics transfection, while knockdown of both EFL5 and SP1 exerted the opposite effects in HCFs. Both ELF5 and SP1 can bind to the promoter of CD31, leading to the upregulation of CD31 in HCFs. Furthermore, in vivo animal studies showed that exosomes from EPCs with miR-1246 or miR-1290 overexpression attenuated the MI-induced cardiac injury in the rats and caused an increase in ELF5, SP1, and CD31 expression, respectively, but suppressed α-SMA expression in the cardiac tissues. In conclusion, our study revealed that miR-1246 and miR-1290 in EPC-derived exosomes enhanced in vitro and in vivo angiogenesis in MI, and these improvements may be associated with amelioration of cardiac injury and cardiac fibrosis after MI.

Post-cardiac injury syndrome in acute myocardial infarction patients undergoing PCI: a case report and literature review.

BACKGROUND: In the era of primary percutaneous coronary intervention (PPCI), the incidence of post-cardiac injury syndrome (PCIS) in patients with acute myocardial infarction (AMI) following PPCI has become less common. However, the intrinsic pathogenesis of this medical condition remains largely uncertain. Unlike the prior reports, the present paper provides new mechanistic clues concerning the pathogenesis of PCI-related PCIS. CASE PRESENTATION: A 45-year-old male with AMI had developed an early onset of PCIS at 3 h after PPCI. A significantly slower TIMI flow (grade ≤ 2) for the culprit arteries was observed through follow-up coronary angiography (CAG); no stent thrombosis or any significant evidence of iatrogenic trauma due the intervention procedures was found. Nevertheless, the the serum level of HsCRP showed similar variation trend as the neutrophil count and troponin T in continuous blood monitoring, which suggested a potential association between PPCI-related coronary microvascular dysfunction (CMD) and pathogenesis of PCIS. CONCLUSIONS: The reported case had excessive inflammatory reaction and CMD resulting from cardiac ischemia-reperfusion injury in an AMI patient with risk factors of endothelial dysfunction. There exists a potential reciprocal causation between PCIS and performance of PPCI in the AMI patient who was susceptible to endothelial damage.