Quantitative imaging for predicting hematoma expansion in intracerebral hemorrhage: A multimodel comparison.

BACKGROUND: Several studies report that radiomics provides additional information for predicting hematoma expansion in intracerebral hemorrhage (ICH). However, the comparison of diagnostic performance of radiomics for predicting revised hematoma expansion (RHE) remains unclear. METHODS: The cohort comprised 312 consecutive patients with ICH. A total of 1106 radiomics features from seven categories were extracted using Python software. Support vector machines achieved the best performance in both the training and validation datasets. Clinical factors models were constructed to predict RHE. Receiver operating characteristic curve analysis was used to assess the abilities of non-contrast computed tomography (NCCT) signs, radiomics features, and combined models to predict RHE. RESULTS: We finally selected the top 21 features for predicting RHE. After univariate analysis, 4 clinical factors and 5 NCCT signs were selected for inclusion in the prediction models. In the training and validation dataset, radiomics features had a higher predictive value for RHE (AUC = 0.83) than a single NCCT sign and expansion-prone hematoma. The combined prediction model including radiomics features, clinical factors, and NCCT signs achieved higher predictive performances for RHE (AUC = 0.88) than other combined models. CONCLUSIONS: NCCT radiomics features have a good degree of discrimination for predicting RHE in ICH patients. Combined prediction models that include quantitative imaging significantly improve the prediction of RHE, which may assist in the risk stratification of ICH patients for anti-expansion treatments.

Age-related changes after intracerebral hemorrhage: a comparative proteomics analysis of perihematomal tissue.

The risk factors and causes of intracerebral hemorrhage (ICH) and the degree of functional recovery after ICH are distinct between young and elderly patients. The increasing incidence of ICH in young adults has become a concern; however, research on the molecules and pathways involved ICH in subjects of different ages is lacking. In this study, tandem mass tag (TMT)-based proteomics was utilized to examine the protein expression profiles of perihematomal tissue from young and aged mice 24 h after collagenase-induced ICH. Among the 5,129 quantified proteins, ICH induced 108 and 143 differentially expressed proteins (DEPs) in young and aged mice, respectively; specifically, there were 54 common DEPs, 54 unique DEPs in young mice and 89 unique DEPs in aged mice. In contrast, aging altered the expression of 58 proteins in the brain, resulting in 39 upregulated DEPs and 19 downregulated DEPs. Bioinformatics analysis indicated that ICH activated different proteins in complement pathways, coagulation cascades, the acute phase response, and the iron homeostasis signaling pathway in mice of both age groups. Protein-protein interaction (PPI) analysis and ingenuity pathway analysis (IPA) demonstrated that the unique DEPs in the young and aged mice were related to lipid metabolism and carbohydrate metabolism, respectively. Deeper paired-comparison analysis demonstrated that apolipoprotein M exhibited the most significant change in expression as a result of both aging and ICH. These results help illustrate age-related protein expression changes in the acute phase of ICH.

SERPINA3: A novel inflammatory biomarker associated with cerebral small vessel disease burden in ischemic stroke.

BACKGROUND AND OBJECTIVE: Inflammation has emerged as a prominent risk factor for cerebral small vessel disease (CSVD). However, the specific association between various inflammatory biomarkers and the development of CSVD remains unclear. Serine proteinase inhibitor A3 (SERPINA3), Matrix metalloproteinase-9 (MMP-9), Tissue inhibitor metalloproteinase-1 (TIMP-1), Monocyte Chemoattractant Protein-1 (MCP-1) are several inflammatory biomarkers that are potentially involved in the development of CSVD. In this present study, we aimed to investigate the relationship between candidate molecules and CSVD features. METHOD: The concentration of each biomarker was measured in 79 acute ischemic stroke patients admitted within 72 h after symptom onset. The associations between blood levels of inflammatory markers and CSVD score were investigated, as well as each CSVD feature, including white matter hyperintensities (WMH), lacunes, and enlarged perivascular spaces (EPVS). RESULTS: The mean age was 69.0 ± 11.8 years, and 65.8% of participants were male. Higher SERPINA3 level (>78.90 ng/mL) was significantly associated with larger WMH volume and higher scores on Fazekas's scale in all three models. Multiple regression analyses revealed the linear association between absolute WMH burden and SERPINA3 level, especially in model 3 (ß = 0.14; 95% confidence interval [CI], 0.04-0.24 ; p = 0.008 ). Restricted cubic spline regression demonstrated a dose-response relationship between SERPINA3 level and larger WMH volume (p nonlineariy = 0.0366 and 0.0378 in model 2 and mode 3, respectively). Using a receiving operating characteristic (ROC) curve, plasma SERPINA3 level of 64.15 ng/mL distinguished WMH >7.8 mL with the highest sensitivity and specificity (75.92% and 60%, respectively, area under curve [AUC] = 0.668, p = 0.0102). No statistically significant relationship has been found between other candidate biomarkers and CSVD features. CONCLUSION: In summary, among four inflammatory biomarkers that we investigated, SERPINA3 level at baseline was associated with WMH severity, which revealed a novel biomarker for CSVD and validated its relationship with inflammation and endothelial dysfunction.

Multi-time point metabolomics reveals key metabolic features from the ultra-early stage of intracerebral hemorrhage in mice.

Despite decades of intensive research, there are still very limited options for the effective treatment of intracerebral hemorrhage (ICH). Recently, mounting evidence has indicated that the ultra-early stage (<3 h), serving as the primary phase of ICH, plays a pivotal role and may even surpass other stages in terms of its significance. Therefore, uncovering the metabolic alterations induced by ICH in the ultra-early stage is of crucial importance. To investigate this, the collagenase ICH mouse model was employed in this study. ICH or sham-operated mice were euthanized at the ultra-early stage of 3 h and the acute stage of 24 h and 72 h after the operation. Then, the metabolic changes in the perihematomal tissues were detected by liquid chromatography coupled with tandem mass spectrometry. In total, alterations in the levels of 465 metabolites were detected. A total of 136 metabolites were significantly changed at 3 h. At 24 h and 72 h, the amounts were 132 and 126, respectively. Additionally, the key corresponding metabolic pathways for these time points were analyzed through KEGG. To gather additional information, quantitative real-time transcription polymerase chain reaction, enzyme-linked immunosorbent assay and Western blots were performed to validate the metabolic changes. Overall, ICH significantly alters important physiological functions such as cysteine metabolism, purine metabolism, synaptic alterations, the synaptic vesicle cycle, and the ATP-binding cassette transporter system. These might be the key pathologic mechanisms of the ultra-early stage induced by ICH.

Analysis of Age-Dependent Transcriptomic Changes in Response to Intracerebral Hemorrhage in Mice.

Age is a well-known risk factor that is independently associated with poor outcomes after intracerebral hemorrhage (ICH). However, the interrelationship between age and poor outcomes after ICH is not well defined. In this study, we aimed to investigate this relationship based on collagenase-induced ICH mice models. After being assessed neurological deficit 24 h after ICH, mice were euthanized and brain perihematomal tissues were used for RNA-sequencing (RNA-seq). And then the functions of differentially expressed genes (DEGs) identified by RNA-seq were analyzed using Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, Ingenuity Pathway Analysis (IPA) and protein-protein interaction (PPI) analysis. In addition, we performed real-time quantitative polymerase chain reaction (RT-qPCR) for validation of candidate DEGs. In the behavioral tests, aged mice presented significantly worse neurological function than young mice and greater weight loss than aged sham controls 24 h after ICH. In DEGs analysis, ICH affected the expression of more genes in young mice (2,337 DEGs) compared with aged mice (2,005 DEGs). We found aged mice exhibited increased brain inflammatory responses compared with young animals and ICH induced significant activation of the interferon-ß (IFN-ß) and IFN signaling pathways exclusively in aged mice. Moreover, further analysis demonstrated that ICH resulted in the activation of cytosolic DNA-sensing pathway with the production of downstream molecule type I IFN, and the response to type I IFN was more significant in aged mice than in young mice. In agreement with the results of RNA-seq, RT-qPCR indicated that the expression of candidate genes of cyclic GMP-AMP synthase (cGAS), Z-DNA-binding protein 1 (ZBP1), and IFN-ß was significantly altered in aged mice after ICH. Taken together, our study indicated that compared to young animals, aged mice exhibit increased vulnerability to ICH and that the differences in transcriptional response patterns to ICH between young and aged mice. We believe that these findings will facilitate our understanding of ICH pathology and help to translate the results of preclinical studies into a clinical setting.

Integrated Multiomics Analysis Identifies a Novel Biomarker Associated with Prognosis in Intracerebral Hemorrhage.

Existing treatments for intracerebral hemorrhage (ICH) are unable to satisfactorily prevent development of secondary brain injury after ICH and multiple pathological mechanisms are involved in the development of the injury. In this study, we aimed to identify novel genes and proteins and integrated their molecular alternations to reveal key network modules involved in ICH pathology. A total of 30 C57BL/6 male mice were used for this study. The collagenase model of ICH was employed, 3 days after ICH animals were tested neurological. After it, animals were euthanized and perihematomal brain tissues were collected for transcriptome and TMT labeling-based quantitative proteome analyses. Protein-protein interaction (PPI) network, Gene Set Enrichment Analysis (GSEA), and regularized Canonical Correlation Analysis (rCCA) were performed to integrated multiomics data. For validation of hub genes and proteins, qRT-PCR and Western blot were carried out. The candidate biomarkers were further measured by ELISA in the plasma of ICH patients and the controls. A total of 2218 differentially expressed genes (DEGs) and 353 differentially expressed proteins (DEPs) between the ICH model group and control group were identified. GSEA revealed that immune-related gene sets were prominently upregulated and significantly enriched in pathways of inflammasome complex, negative regulation of interleukin-12 production, and pyroptosis during the ICH process. The rCCA network presented two highly connective clusters which were involved in the sphingolipid catabolic process and inflammatory response. Among ten hub genes screened out by integrative analysis, significantly upregulated Itgb2, Serpina3n, and Ctss were validated in the ICH group by qRT-PCR and Western blot. Plasma levels of human SERPINA3 (homologue of murine Serpina3n) were elevated in ICH patients compared with the healthy controls (SERPINA3: 13.3 ng/mL vs. 11.2 ng/mL, p = 0.015). Within the ICH group, higher plasma SERPINA3 levels with a predictive threshold of 14.31 ng/mL (sensitivity = 64.3%; specificity = 80.8%; AUC = 0.742, 95% CI: 0.567-0.916) were highly associated with poor outcome (mRS scores 4-6). Taken together, the results of our study exhibited molecular changes related to ICH-induced brain injury by multidimensional analysis and effectively identified three biomarker candidates in a mouse ICH model, as well as pointed out that Serpina3n/SERPINA3 was a potential biomarker associated with poor functional outcome in ICH patients.