Identify connectome between genotypes and brain network phenotypes via deep self-reconstruction sparse canonical correlation analysis.

MOTIVATION: As a rising research topic, brain imaging genetics aims to investigate the potential genetic architecture of both brain structure and function. It should be noted that in the brain, not all variations are deservedly caused by genetic effect, and it is generally unknown which imaging phenotypes are promising for genetic analysis. RESULTS: In this work, genetic variants (i.e. the single nucleotide polymorphism, SNP) can be correlated with brain networks (i.e. quantitative trait, QT), so that the connectome (including the brain regions and connectivity features) of functional brain networks from the functional magnetic resonance imaging data is identified. Specifically, a connection matrix is firstly constructed, whose upper triangle elements are selected to be connectivity features. Then, the PageRank algorithm is exploited for estimating the importance of different brain regions as the brain region features. Finally, a deep self-reconstruction sparse canonical correlation analysis (DS-SCCA) method is developed for the identification of genetic associations with functional connectivity phenotypic markers. This approach is a regularized, deep extension, scalable multi-SNP-multi-QT method, which is well-suited for applying imaging genetic association analysis to the Alzheimer's Disease Neuroimaging Initiative datasets. It is further optimized by adopting a parametric approach, augmented Lagrange and stochastic gradient descent. Extensive experiments are provided to validate that the DS-SCCA approach realizes strong associations and discovers functional connectivity and brain region phenotypic biomarkers to guide disease interpretation. AVAILABILITY AND IMPLEMENTATION: The Matlab code is available at https://github.com/meimeiling/DS-SCCA/tree/main. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Combined strategy of knowledge-based rule selection and historical data percentile-based range determination to improve an autoverification system for clinical chemistry test results.

BACKGROUND: Current autoverification, which is only knowledge-based, has low efficiency. Regular historical data analysis may improve autoverification range determination. We attempted to enhance autoverification by selecting autoverification rules by knowledge and ranges from historical data. This new system was compared with the original knowledge-based system. METHODS: New types of rules, extreme values, and consistency checks were added and the autoverification workflow was rearranged to construct a framework. Criteria for creating rules for extreme value ranges, limit checks, consistency checks, and delta checks were determined by analyzing historical Zhongshan laboratory data. The new system's effectiveness was evaluated using pooled data from 20 centers. Efficiency improvement was assessed by a multicenter process. RESULTS: Effectiveness was evaluated by the true positive rate, true negative rate, and overall consistency rate, as compared to manual verification, which were 77.55%, 78.53%, and 78.3%, respectively for the new system. The original overall consistency rate was 56.2%. The new pass rates, indicating efficiency, were increased by 19%-51% among hospitals. Further customization using individualized data increased this rate. CONCLUSIONS: The improved system showed a comparable effectiveness and markedly increased efficiency. This transferable system could be further improved and popularized by utilizing historical data from each hospital.

Discovering network phenotype between genetic risk factors and disease status via diagnosis-aligned multi-modality regression method in Alzheimer's disease.

MOTIVATION: Neuroimaging genetics is an emerging field to identify the associations between genetic variants [e.g. single-nucleotide polymorphisms (SNPs)] and quantitative traits (QTs) such as brain imaging phenotypes. However, most of the current studies focus only on the associations between brain structure imaging and genetic variants, while neglecting the connectivity information between brain regions. In addition, the brain itself is a complex network, and the higher-order interaction may contain useful information for the mechanistic understanding of diseases [i.e. Alzheimer's disease (AD)]. RESULTS: A general framework is proposed to exploit network voxel information and network connectivity information as intermediate traits that bridge genetic risk factors and disease status. Specifically, we first use the sparse representation (SR) model to build hyper-network to express the connectivity features of the brain. The network voxel node features and network connectivity edge features are extracted from the structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (fMRI), respectively. Second, a diagnosis-aligned multi-modality regression method is adopted to fully explore the relationships among modalities of different subjects, which can help further mine the relation between the risk genetics and brain network features. In experiments, all methods are tested on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The experimental results not only verify the effectiveness of our proposed framework but also discover some brain regions and connectivity features that are highly related to diseases. AVAILABILITY AND IMPLEMENTATION: The Matlab code is available at http://ibrain.nuaa.edu.cn/2018/list.htm.

DNA methylation markers for diagnosis and prognosis of common cancers.

The ability to identify a specific cancer using minimally invasive biopsy holds great promise for improving the diagnosis, treatment selection, and prediction of prognosis in cancer. Using whole-genome methylation data from The Cancer Genome Atlas (TCGA) and machine learning methods, we evaluated the utility of DNA methylation for differentiating tumor tissue and normal tissue for four common cancers (breast, colon, liver, and lung). We identified cancer markers in a training cohort of 1,619 tumor samples and 173 matched adjacent normal tissue samples. We replicated our findings in a separate TCGA cohort of 791 tumor samples and 93 matched adjacent normal tissue samples, as well as an independent Chinese cohort of 394 tumor samples and 324 matched adjacent normal tissue samples. The DNA methylation analysis could predict cancer versus normal tissue with more than 95% accuracy in these three cohorts, demonstrating accuracy comparable to typical diagnostic methods. This analysis also correctly identified 29 of 30 colorectal cancer metastases to the liver and 32 of 34 colorectal cancer metastases to the lung. We also found that methylation patterns can predict prognosis and survival. We correlated differential methylation of CpG sites predictive of cancer with expression of associated genes known to be important in cancer biology, showing decreased expression with increased methylation, as expected. We verified gene expression profiles in a mouse model of hepatocellular carcinoma. Taken together, these findings demonstrate the utility of methylation biomarkers for the molecular characterization of cancer, with implications for diagnosis and prognosis.

[Analysis of a pedigree affected with hereditary coagulation factor XII deficiency due to a homozygous 252delAsn deletion of F12 gene].

OBJECTIVE: To analyze the clinical phenotype and genetic basis of a consanguineous pedigree affected with hereditary coagulation factor XII (FXII) deficiency. METHODS: Following extraction of genomic DNA, all exons and flanking regions of F12 gene were subjected to PCR amplification and Sanger sequencing. ClustalX-2.1-win and MutationTaster software was used to analyze the conservation and impact of the variants on protein function. RESULTS: DNA sequencing showed that the proband carried a homozygous g.6753-6755delACA deletion (p.252delAsn) in exon 9 of the F12 gene, for which her father, mother and brother were heterozygous carriers. The same deletion was not found in her sister. CONCLUSION: The homozygous p.252delAsn deletion probably underlies the hereditary FXII deficiency in this pedigree.

Identification of associations between genotypes and longitudinal phenotypes via temporally-constrained group sparse canonical correlation analysis.

MOTIVATION: Neuroimaging genetics identifies the relationships between genetic variants (i.e., the single nucleotide polymorphisms) and brain imaging data to reveal the associations from genotypes to phenotypes. So far, most existing machine-learning approaches are widely used to detect the effective associations between genetic variants and brain imaging data at one time-point. However, those associations are based on static phenotypes and ignore the temporal dynamics of the phenotypical changes. The phenotypes across multiple time-points may exhibit temporal patterns that can be used to facilitate the understanding of the degenerative process. In this article, we propose a novel temporally constrained group sparse canonical correlation analysis (TGSCCA) framework to identify genetic associations with longitudinal phenotypic markers. RESULTS: The proposed TGSCCA method is able to capture the temporal changes in brain from longitudinal phenotypes by incorporating the fused penalty, which requires that the differences between two consecutive canonical weight vectors from adjacent time-points should be small. A new efficient optimization algorithm is designed to solve the objective function. Furthermore, we demonstrate the effectiveness of our algorithm on both synthetic and real data (i.e., the Alzheimer's Disease Neuroimaging Initiative cohort, including progressive mild cognitive impairment, stable MCI and Normal Control participants). In comparison with conventional SCCA, our proposed method can achieve strong associations and discover phenotypic biomarkers across multiple time-points to guide disease-progressive interpretation. AVAILABILITY AND IMPLEMENTATION: The Matlab code is available at https://sourceforge.net/projects/ibrain-cn/files/ . CONTACT: dqzhang@nuaa.edu.cn or shenli@iu.edu.

Circulating tumour DNA methylation markers for diagnosis and prognosis of hepatocellular carcinoma.

An effective blood-based method for the diagnosis and prognosis of hepatocellular carcinoma (HCC) has not yet been developed. Circulating tumour DNA (ctDNA) carrying cancer-specific genetic and epigenetic aberrations may enable a noninvasive 'liquid biopsy' for diagnosis and monitoring of cancer. Here, we identified an HCC-specific methylation marker panel by comparing HCC tissue and normal blood leukocytes and showed that methylation profiles of HCC tumour DNA and matched plasma ctDNA are highly correlated. Using cfDNA samples from a large cohort of 1,098 HCC patients and 835 normal controls, we constructed a diagnostic prediction model that showed high diagnostic specificity and sensitivity (P < 0.001) and was highly correlated with tumour burden, treatment response, and stage. Additionally, we constructed a prognostic prediction model that effectively predicted prognosis and survival (P < 0.001). Together, these findings demonstrate in a large clinical cohort the utility of ctDNA methylation markers in the diagnosis, surveillance, and prognosis of HCC.

SVR Rates of HCV-infected population under PEG-IFN-α/R treatment in Northwest China.

BACKGROUND: Chronic HCV Patients taking PEG-IFN-α/R from different ethnic groups have different probabilities of reaching a sustained viral response (SVR). There are many influence factors, such as HCV genotype, IL-28B single-nucleotide polymorphisms (SNP), Fibrosis 4 index (FIB-4), and aspartate aminotransferase-to-platelet ratio index (APRI) score. But the baseline factors in relation to treatment outcome was still not much clear. METHODS: We evaluated data from 231 chronic HCV patients with or without liver fibrosis and their antiviral efficacy after treatment with pegylated interferon plus ribavirin (PEG-IFN-α/R) for 24-48 weeks. IL-28B SNP and HCV genotypes were analyzed with genome sequencing using pyrosequencing. RESULTS: Sustained viral response (SVR) rates of patients with HCV 1b and 2a genotypes were 52.25% (58/111) and 75.28% (67/89) (P < 0.01). SVR rates of patients with IL-28B rs8099917 TT, rs12979860 CC and rs12980275 AA were 92.41% (25/27), 92.86% (26/28) and 88.89% (24/27) separately. We found that SVR rates in HCV 1b and 2a patients were only 31.0 and 39.4% if their FIB-4 > 3.25. In addition, when their APRI > 2, only 30.3% of HCV 1b patients and 50.2% of HCV 2a patients could obtain SVR. CONCLUSIONS: There were high proportion of HCV genotype 1b and 2a in Northwest China. In both HCV 1b and 2a genotypes, patients with protective-genotype of IL-28B were more likely to obtain SVR. However, those with significant fibrosis or cirrhosis were less likely, no matter their genotype. Combined factors of HCV genotype, IL-28B genotype, FIB-4 and ARPI may indicate high prediction and clinical value regarding treatment with PEG-IFN-α/R and prognostic evaluation of chronic hepatitis C patients.

Differential expression of microRNAs in postoperative radiotherapy sensitive and resistant patients with glioblastoma multiforme.

Glioblastoma multiforme (GBM) is the most malignant primary brain tumor and more resistant to radiotherapy. However, hetero-radiosensitivity occurs in different patients. MicroRNAs (miRNAs) play important roles in the initiation and progression of a multitude of tumors. The study aims to examine the different microRNAs expression profiles of postoperative radiotherapy sensitive and resistant patients with GBM, to make an inquiry about their potential role and discover a certain set of radio-sensitivity markers. Three paired samples from six GBM patients who had only been treated with postoperative radiotherapy were selected, and then, they were divided into radiotherapy sensitive group and resistant group according to their overall survivals, local recurrence rates, and Karnofsky Performance Scale scores. Expression profiles of miRNAs in these two groups were determined by the method of microarray assay. Comparing with resistant patients, 13 miRNAs were significantly upregulated and 10 miRNAs were greatly downregulated in sensitive group. Among them, four miRNAs were validated by quantitative RT-PCR. The differentially expressed miRNAs and their putative target genes were revealed by bioformatic analysis to play a role in cell signaling, proliferation, aging, and death. High-enrichment pathway analysis identified that some classical pathways participated in numerous metabolic processes, especially in cell cycle regulation, such as mTOR, MAPK, TGF-beta, and PI3K-Akt signaling pathways. Our research will contribute to identifying clinical diagnostic markers and therapeutic targets in the treatment of GBM by postoperative radiotherapy.

The effect of thalassemia on erythrocyte reference intervals in a representative Han Chinese adult population.

BACKGROUND: The data from apparently healthy individuals with thalassemia has been demonstrated to have an effect on the reference intervals for the erythrocyte indices in areas with a high incidence of thalassemia. METHODS: Six clinical centers screened apparently healthy individuals using a questionnaire and a physical examination. Then, the qualified reference individuals were selected by hematological indices and a genotypic thalassemia diagnosis. Statistical comparisons were conducted for the erythrocyte reference intervals in the Chinese population with and without thalassemia. The constituent ratios and the mean (SD) of erythrocyte indices according to the thalassemia genotype were calculated. The relationship between the MCV values and the thalassemia genotype was also estimated. RESULTS: 4,636 reference individuals were included using hematological indices and genotypic thalassemia screening. The results of the erythrocyte reference intervals for individuals in Guangzhou with thalassemia demonstrated that the RBC, MCV, and MCH values significantly differed by gender compared with other regions (p < 0.01). In contrast, for individuals without thalassemia, the results tended to be similar and clinically acceptable. In addition, the results of the erythrocyte indices revealed significant differences among α-thalassemia patients, ß-thalassemia patients, and the control group. CONCLUSIONS: Apparently healthy individuals with thalassemia in the high prevalence zone of thalassemia could not be excluded by the questionnaire, physical examination or laboratory indices (Fe < 6 µmol/L, Hb < 90 g/L). The screening of genotypic thalassemia based on the MCV or MCH values to exclude unqualified individuals is the most effective way to obtain accurate and reliable reference intervals for the erythrocyte indices.

Diagnostic accuracy of intracellular mycobacterium tuberculosis detection for tuberculous meningitis.

RATIONALE: Early diagnosis and treatment of tuberculous meningitis saves lives, but current laboratory diagnostic tests lack sensitivity. OBJECTIVES: We investigated whether the detection of intracellular bacteria by a modified Ziehl-Neelsen stain and early secretory antigen target (ESAT)-6 in cerebrospinal fluid leukocytes improves tuberculous meningitis diagnosis. METHODS: Cerebrospinal fluid specimens from patients with suspected tuberculous meningitis were stained by conventional Ziehl-Neelsen stain, a modified Ziehl-Neelsen stain involving cytospin slides with Triton processing, and an ESAT-6 immunocytochemical stain. Acid-fast bacteria and ESAT-6-expressing leukocytes were detected by microscopy. All tests were performed prospectively in a central laboratory by experienced technicians masked to the patients' final diagnosis. MEASUREMENTS AND MAIN RESULTS: Two hundred and eighty patients with suspected tuberculous meningitis were enrolled. Thirty-seven had Mycobacterium tuberculosis cultured from cerebrospinal fluid; 40 had a microbiologically confirmed alternative diagnosis; the rest had probable or possible tuberculous meningitis according to published criteria. Against a clinical diagnostic gold standard the sensitivity of conventional Ziehl-Neelsen stain was 3.3% (95% confidence interval, 1.6-6.7%), compared with 82.9% (95% confidence interval, 77.4-87.3%) for modified Ziehl-Neelsen stain and 75.1% (95% confidence interval, 68.8-80.6%) for ESAT-6 immunostain. Intracellular bacteria were seen in 87.8% of the slides positive by the modified Ziehl-Neelsen stain. The specificity of modified Ziehl-Neelsen and ESAT-6 stain was 85.0% (95% confidence interval, 69.4-93.8%) and 90.0% (95% confidence interval, 75.4-96.7%), respectively. CONCLUSIONS: Enhanced bacterial detection by simple modification of the Ziehl-Neelsen stain and an ESAT-6 intracellular stain improve the laboratory diagnosis of tuberculous meningitis.

Reference Intervals of Serum Sodium, Potassium, and Chlorine in Chinese Han Population and Comparison of Two ISE Methods.

BACKGROUND: Currently there are no reference intervals (RIs) of sodium (Na), potassium (K), and chlorine (Cl) on Chinese population. Two kinds of ion-selective electrode (ISE) methods were commonly used to determine K, Na, and Cl levels in China, the difference between these two methods needs to be evaluated. METHODS: A total of 4,524 healthy participants (1,916 males and 2,608 females) between 20-79 years old from six cities in China were selected by strict criteria. Serum K, Na, and CL were tested on Roche Modular analyzers in six assigned laboratories. According to EP-9A2, using Roche Modular analyzer (indirect ISE) as comparative method, Olympus AU 5400 analyzer (indirect ISE) and Johnson&Johnson Fusion 5.1 analyzer (direct ISE) were evaluated. RESULTS: In Chinese population, the RIs for K, Na, and CL are 3.6-5.2, 136-146, and 99-110 mmol/l, respectively. Compared to the Roche indirect ISE method, Johnson direct ISE method showed a positive bias; and Olympus indirect ISE method just showed a very slight bias. CONCLUSION: The RIs of K, Na, and Cl of Han Chinese healthy adult population were found to be smaller than those provided by the manufacturer. By a criteria of biological variations for CV, the differences of Na and K between Roche analyzer and Johnson analyzer were not acceptable for clinical application, while the differences of Na, K, and Cl between Roche and Olympus analyzers were acceptable for clinical application.

Clinical application and evaluation of Sysmex XE-5000 analyser for detecting nucleated red blood cells in peripheral blood.

BACKGROUND: To evaluate the performance of Sysmex XE-5000 analyser for detecting nucleated red blood cells (NRBCs) in peripheral blood and investigate the clinical application of this analyser. METHODS: The absolute NRBC counts (NRBC#) and percentage (NRBC%) of 137 blood specimens (NRBC-positive according to the DIFF channel of the analyser) were determined in the NRBC channel of the analyser. The intra-assay imprecision, carryover rate, and linear range of the analyser were evaluated. The NRBC% of the blood sample was detected with a microscope, and the difference between two methods was analysed. RESULTS: The intra-assay imprecision of the analyser for detecting NRBC# in specimens with high, moderate, and low Q-flag values were 2.10%, 3.26%, and 11.62%, respectively, and the imprecision for detecting NRBC% were 3.79%, 5.80%, and 13.33%, respectively. The carryover rates of the analyser for detecting NRBC# and NRBC% were 0.51% and 0.26%, respectively. CONCLUSIONS: Sysmex XE-5000 analyser had good linearity in NRBC# (i.e., 0/L to 18 x 10(9)/L). The NRBC%s of the two methods did not significantly differ (p = 0.716). The analyser can completely replace the traditional microscope for clinically classifying and counting NRBCs.

Ionic liquid foam floatation coupled with ionic liquid dispersive liquid-liquid microextraction for the separation and determination of estrogens in water samples by high-performance liquid chromatography with fluorescence detection.

An ionic liquid foam floatation coupled with ionic liquid dispersive liquid-liquid microextraction method was proposed for the extraction and concentration of 17-α-estradiol, 17-ß-estradiol-benzoate, and quinestrol in environmental water samples by high-performance liquid chromatography with fluorescence detection. 1-Hexyl-3-methylimidazolium tetrafluoroborate was applied as foaming agent in the foam flotation process and dispersive solvent in microextraction. The introduction of the ion-pairing and salting-out agent NH4 PF6 was beneficial to the improvement of recoveries for the hydrophobic ionic liquid phase and analytes. Parameters of the proposed method including concentration of 1-hexyl-3-methylimidazolium tetrafluoroborate, flow rate of carrier gas, floatation time, types and concentration of ionic liquids, salt concentration in samples, extraction time, and centrifugation time were evaluated. The recoveries were between 98 and 105% with relative standard deviations lower than 7% for lake water and well water samples. The isolation of the target compounds from the water was found to be efficient, and the enrichment factors ranged from 4445 to 4632. This developing method is free of volatile organic solvents compared with regular extraction. Based on the unique properties of ionic liquids, the application of foam floatation, and dispersive liquid-liquid microextraction was widened.

Hypergraph convolutional network for longitudinal data analysis in Alzheimer's disease.

Alzheimer's disease (AD) is an irreversible and progressive neurodegenerative disease. Longitudinal structural magnetic resonance imaging (sMRI) data have been widely used for tracking AD pathogenesis and diagnosis. However, existing methods tend to treat each time point equally without considering the temporal characteristics of longitudinal data. In this paper, we propose a weighted hypergraph convolution network (WHGCN) to use the internal correlations among different time points and leverage high-order relationships between subjects for AD detection. Specifically, we construct hypergraphs for sMRI data at each time point using the K-nearest neighbor (KNN) method to represent relationships between subjects, and then fuse the hypergraphs according to the importance of the data at each time point to obtain the final hypergraph. Subsequently, we use hypergraph convolution to learn high-order information between subjects while performing feature dimensionality reduction. Finally, we conduct experiments on 518 subjects selected from the Alzheimer's disease neuroimaging initiative (ADNI) database, and the results show that the WHGCN can get higher AD detection performance and has the potential to improve our understanding of the pathogenesis of AD.

Metabolic profiling in tissues and urine of patients with prostatic lesions and the diagnostic value of urine extracellular vesicles metabolites in prostate cancer.

BACKGROUND: Prostate cancer (PCa) lacks convenient and highly specific diagnostic markers. Although the value of extracellular vesicles (EV) in oncology is widely recognized, the diagnostic value of EV metabolites requires further exploration. This study aimed to explore the diagnostic value of urine EV (u-EV) metabolomics in PCa. METHODS: We first detected metabolites in paired tissues cells (cells), tissue EV (t-EVs), u-EVs, and urine samples in cohort 1 (8 PCa vs. 5 benign prostatic hypertrophy, BPH) to prob the feasibility of EV metabolites as diagnostic markers. We then analyzed the value of u-EVs as markers for PCa diagnosis and typing in the expanded sample cohort (60 PCa vs. 40 BPH). RESULTS: U-EV metabolites were more consistent with those in tissue-derived samples (cells and t-EVs) than those in urine, and more differential metabolites between BPH and PCa were identified in u-EV. Subsequently, we used a random forest model to construct a panel of six metabolites for PCa, which showed an area under the curve (AUC) of 0.833 in training cohort and 0.844 in validation cohort. We also found significantly differentially expressed metabolites between PCa subtypes (Gleason ≤ 7 vs. Gleason > 7 and localized vs. metastasis), demonstrating the value of EV metabolites in PCa typing and prognostic assessment. CONCLUSION: Metabolomic analysis of u-EVs is a promising source of noninvasive markers for PCa diagnosis.

Influential factors on urine EV DNA methylation detection and its diagnostic potential in prostate cancer.

The value of Extracellular vesicles (EVs) diagnostic markers is widely recognized. However, current research on EV DNA remains limited. This study investigates the biological properties, preprocessing factors, and diagnostic potential of EV DNA. We found that DNA positive vesicles account for 23.3% ± 6.7% of the urine total EV, with a large amount of DNA attached to the outside. EV DNA fragments are large, there is no significant effect on uEV DNA when store urine less than 6 h at 4°C. In addition, the influence of different EV extraction methods on methylation detection is also minor. More importantly, RASSF1A methylation in urine total EV DNA can distinguish between PCa and BPH, with an AUC of 0.874. Our results suggest the potential of urine EV DNA as a novel marker for PCa diagnosis. This provides a new idea for the study of urinary tumor markers.

Neutrophil extracellular trap-mediated impairment of meningeal lymphatic drainage exacerbates secondary hydrocephalus after intraventricular hemorrhage.

Rationale: Hydrocephalus is a substantial complication after intracerebral hemorrhage (ICH) or intraventricular hemorrhage (IVH) that leads to impaired cerebrospinal fluid (CSF) circulation. Recently, brain meningeal lymphatic vessels (mLVs) were shown to serve as critical drainage pathways for CSF. Our previous studies indicated that the degradation of neutrophil extracellular traps (NETs) after ICH/IVH alleviates hydrocephalus. However, the mechanisms by which NET degradation exerts beneficial effects in hydrocephalus remain unclear. Methods: A mouse model of hydrocephalus following IVH was established by infusing autologous blood into both wildtype and Cx3cr1-/- mice. By studying the features and processes of the model, we investigated the contribution of mLVs and NETs to the development and progression of hydrocephalus following secondary IVH. Results: This study observed the widespread presence of neutrophils, fibrin and NETs in mLVs following IVH, and the degradation of NETs alleviated hydrocephalus and brain injury. Importantly, the degradation of NETs improved CSF drainage by enhancing the recovery of lymphatic endothelial cells (LECs). Furthermore, our study showed that NETs activated the membrane protein CX3CR1 on LECs after IVH. In contrast, the repair of mLVs was promoted and the effects of hydrocephalus were ameliorated after CX3CR1 knockdown and in Cx3cr1-/- mice. Conclusion: Our findings indicated that mLVs participate in the development of brain injury and secondary hydrocephalus after IVH and that NETs contribute to acute LEC injury and lymphatic thrombosis. CX3CR1 is a key molecule in NET-induced LEC damage and meningeal lymphatic thrombosis, which leads to mLV dysfunction and exacerbates hydrocephalus and brain injury. NETs may be a critical target for preventing the obstruction of meningeal lymphatic drainage after IVH.

Neutrophil Extracellular Traps (NETs): A New Therapeutic Target for Neuroinflammation and Microthrombosis After Subarachnoid Hemorrhage?

Neutrophil extracellular traps (NETs) play a major role in intrinsic immunity by limiting and killing pathogens. Recently, a series of studies have confirmed that NETs are closely associated with vascular injury and microthrombosis. Furthermore, NETs play an important role in neuroinflammation after ischemic and hemorrhagic stroke. Neuroinflammation and microthrombosis after subarachnoid hemorrhage are key pathophysiological processes associated with poor prognosis, but their crucial formation mechanisms and interventions remain to be elucidated. Could NETs, as an emerging and important pathogenesis, be a new therapeutic target after subarachnoid hemorrhage?

miR-92a-1-5p enriched prostate cancer extracellular vesicles regulate osteoclast function via MAPK1 and FoxO1.

BACKGROUND: We have previously reported that extracellular vesicles (EVs) derived from osteoblastic, osteoclastic and mixed prostate cancer cells promote osteoclast differentiation and inhibit osteoblast differentiation via transferring miR-92a-1-5p. In the present study, we focused on engineering miR-92a-1-5p into EVs and determining any therapeutic roles and mechanisms of the engineered EVs. METHODS: A stable prostate cancer cell line (MDA PCa 2b) overexpressing miR-92a-1-5p was constructed by lentivirus, and EVs were isolated by ultracentrifugation. The overexpression of miR-92a-1-5p in both cells and EVs was tested using qPCR. Osteoclast function was evaluated by Trap staining, mRNA expression of osteoclastic markers ctsk and trap, immunolabeling of CTSK and TRAP and microCT using either in vitro and in vivo assays. Target gene of miR-92a-1-5p was proved by a dual-luciferase reporter assay system. siRNAs were designed and used for transient expression in order to determine the role of downstream genes on osteoclast differentiation. RESULTS: Stable overexpression cells of miRNA-92a-5p was associated with EVs upregulating this microRNA, as confirmed by qPCR. Further, miR-92a-1-5p enriched EVs promote osteoclast differentiation in vitro by reducing MAPK1 and FoxO1 expression, associated with increased osteoclast function as shown by TRAP staining and mRNA expression of osteoclast functional genes. siRNA targeting MAPK1 or FoxO1 resulted in similar increase in osteoclast function. In vivo, the miR-92a-1-5p enriched EVs given via i.v. injection promote osteolysis, which was associated with reduction of MAPK1 and FoxO1 expression in bone marrow. CONCLUSION: These experiments suggest that miR-92a-1-5p enriched EVs regulate osteoclast function via reduction of MAPK1 and FoxO1.