Performance assessment of an artificial intelligence-based coronary artery calcium scoring algorithm in non-gated chest CT scans of different slice thickness.

Background: The coronary artery calcium score (CACS) has been shown to be an independent predictor of cardiovascular events. The traditional coronary artery calcium scoring algorithm has been optimized for electrocardiogram (ECG)-gated images, which are acquired with specific settings and timing. Therefore, if the artificial intelligence-based coronary artery calcium score (AI-CACS) could be calculated from a chest low-dose computed tomography (LDCT) examination, it could be valuable in assessing the risk of coronary artery disease (CAD) in advance, and it could potentially reduce the occurrence of cardiovascular events in patients. This study aimed to assess the performance of an AI-CACS algorithm in non-gated chest scans with three different slice thicknesses (1, 3, and 5 mm). Methods: A total of 135 patients who underwent both LDCT of the chest and ECG-gated non-contrast enhanced cardiac CT were prospectively included in this study. The Agatston scores were automatically derived from chest CT images reconstructed at slice thicknesses of 1, 3, and 5 mm using the AI-CACS software. These scores were then compared to those obtained from the ECG-gated cardiac CT data using a conventional semi-automatic method that served as the reference. The correlations between the AI-CACS and electrocardiogram-gated coronary artery calcium score (ECG-CACS) were analyzed, and Bland-Altman plots were used to assess agreement. Risk stratification was based on the calculated CACS, and the concordance rate was determined. Results: A total of 112 patients were included in the final analysis. The correlations between the AI-CACS at three different thicknesses (1, 3, and 5 mm) and the ECG-CACS were 0.973, 0.941, and 0.834 (all P<0.01), respectively. The Bland-Altman plots showed mean differences in the AI-CACS for the three thicknesses of -6.5, 15.4, and 53.1, respectively. The risk category agreement for the three AI-CACS groups was 0.868, 0.772, and 0.412 (all P<0.01), respectively. While the concordance rates were 91%, 84.8%, and 62.5%, respectively. Conclusions: The AI-based algorithm successfully calculated the CACS from LDCT scans of the chest, demonstrating its utility in risk categorization. Furthermore, the CACS derived from images with a slice thickness of 1 mm was more accurate than those obtained from images with slice thicknesses of 3 and 5 mm.

Loss of microRNA-15a/16-1 function promotes neuropathological and functional recovery in experimental traumatic brain injury.

The diffuse axonal damage in white matter and neuronal loss, along with excessive neuroinflammation, hinder long-term functional recovery after traumatic brain injury (TBI). MicroRNAs (miRs) are small noncoding RNAs that negatively regulate protein-coding target genes in a posttranscriptional manner. Recent studies have shown that loss of function of the miR-15a/16-1 cluster reduced neurovascular damage and improved functional recovery in ischemic stroke and vascular dementia. However, the role of the miR-15a/16-1 cluster in neurotrauma is poorly explored. Here, we report that genetic deletion of the miR-15a/16-1 cluster facilitated the recovery of sensorimotor and cognitive functions, alleviated white matter/gray matter lesions, reduced cerebral glial cell activation, and inhibited infiltration of peripheral blood immune cells to brain parenchyma in a murine model of TBI when compared with WT controls. Moreover, intranasal delivery of the miR-15a/16-1 antagomir provided similar brain-protective effects conferred by genetic deletion of the miR-15a/16-1 cluster after experimental TBI, as evidenced by showing improved sensorimotor and cognitive outcomes, better white/gray matter integrity, and less inflammatory responses than the control antagomir-treated mice after brain trauma. miR-15a/16-1 genetic deficiency and miR-15a/16-1 antagomir also significantly suppressed inflammatory mediators in posttrauma brains. These results suggest miR-15a/16-1 as a potential therapeutic target for TBI.

Automated detection and classification of coronary atherosclerotic plaques on coronary CT angiography using deep learning algorithm.

Background: Coronary artery disease (CAD) is the leading cause of mortality worldwide. Recent advances in deep learning technology promise better diagnosis of CAD and improve assessment of CAD plaque buildup. The purpose of this study is to assess the performance of a deep learning algorithm in detecting and classifying coronary atherosclerotic plaques in coronary computed tomographic angiography (CCTA) images. Methods: Between January 2019 and September 2020, CCTA images of 669 consecutive patients with suspected CAD from Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine were included in this study. There were 106 patients included in the retrospective plaque detection analysis, which was evaluated by a deep learning algorithm and four independent physicians with varying clinical experience. Additionally, 563 patients were included in the analysis for plaque classification using the deep learning algorithm, and their results were compared with those of expert radiologists. Plaques were categorized as absent, calcified, non-calcified, or mixed. Results: The deep learning algorithm exhibited higher sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy {92% [95% confidence interval (CI): 89.5-94.1%], 87% (95% CI: 84.2-88.5%), 79% (95% CI: 76.1-82.4%), 95% (95% CI: 93.4-96.3%), and 89% (95% CI: 86.9-90.0%)} compared to physicians with ≤5 years of clinical experience in CAD diagnosis for the detection of coronary plaques. The algorithm's overall sensitivity, specificity, PPV, NPV, accuracy, and Cohen's kappa for plaque classification were 94% (95% CI: 92.3-94.7%), 90% (95% CI: 88.8-90.3%), 70% (95% CI: 68.3-72.1%), 98% (95% CI: 97.8-98.5%), 90% (95% CI: 89.8-91.1%) and 0.74 (95% CI: 0.70-0.78), indicating strong performance. Conclusions: The deep learning algorithm has demonstrated reliable and accurate detection and classification of coronary atherosclerotic plaques in CCTA images. It holds the potential to enhance the diagnostic capabilities of junior radiologists and junior intervention cardiologists in the CAD diagnosis, as well as to streamline the triage of patients with acute coronary symptoms.

Role of regulatory non-coding RNAs in traumatic brain injury.

Traumatic brain injury (TBI) is a potentially fatal health event that cannot be predicted in advance. After TBI occurs, it can have enduring consequences within both familial and social spheres. Yet, despite extensive efforts to improve medical interventions and tailor healthcare services, TBI still remains a major contributor to global disability and mortality rates. The prompt and accurate diagnosis of TBI in clinical contexts, coupled with the implementation of effective therapeutic strategies, remains an arduous challenge. However, a deeper understanding of changes in gene expression and the underlying molecular regulatory processes may alleviate this pressing issue. In recent years, the study of regulatory non-coding RNAs (ncRNAs), a diverse class of RNA molecules with regulatory functions, has been a potential game changer in TBI research. Notably, the identification of microRNAs (miRNAs), long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), and other ncRNAs has revealed their potential as novel diagnostic biomarkers and therapeutic targets for TBI, owing to their ability to regulate the expression of numerous genes. In this review, we seek to provide a comprehensive overview of the functions of regulatory ncRNAs in TBI. We also summarize regulatory ncRNAs used for treatment in animal models, as well as miRNAs, lncRNAs, and circRNAs that served as biomarkers for TBI diagnosis and prognosis. Finally, we discuss future challenges and prospects in diagnosing and treating TBI patients in the clinical settings.

[Effects of Different Carbon Inputs on Soil Stoichiometry in Tianshan Mountains].

Soil C, N, and P elements are important components of the forest ecosystem. Studying the influence of exogenous carbon input change on the stoichiometry of the forest soil can reveal the element recycling process and the balanced feedback mechanism of the forest ecosystem. In this study, using the research object of a spruce forest in Tianshan Mountain, the short-term effect of exogenous carbon input on soil C, N, and P in the soil was analyzed through Detritus Input and Removal Treatment (DIRT), and then the interrelationship between soil stoichiometry and other soil physicochemical factors under different treatments was discussed. The results showed that:① the soil C, N, and P contents in most soil layers were the highest double litter (DL) treatment, soil ω(C) by soil depth from shallow to deep was 168.92, 119.88, 103.33, and 64.23 g·kg-1; soil ω(N) was 10.60, 9.32, 8.78, and 8.07 g·kg-1; soil ω(P) was 0.50, 0.45, 0.37, and 0.36 g·kg-1; in the no input (NI) treatment, soil ω(C) by soil depth from shallow to deep was 104.56, 89.24, 48.08, and 43.96 g·kg-1; soil ω(N) was 6.83, 2.60, 2.63, and 2.22 g·kg-1; soil ω(P) was 0.40, 0.34, 0.32, and 0.22 g·kg-1; and a decreased trend was shown with the deepening of the soil layer. Except in the NI treatment, C:N was 0-10 cm and significantly higher than that in other soils (P<0.05), NL soil C:P at 30-50 cm was significantly higher than that in other soils, and NI soil N:P was 0-10 cm and significantly higher than that in other soils (P<0.05). ② Microbial carbon, nitrogen, and phosphorus were significantly higher from 0-10 cm than that in other soil layers (P<0.05). ③ Redundancy analysis results showed that soluble organic carbon and microbial nitrogen at different carbon input levels were important factors affecting the stoichiometric characteristics of soil C, N, and P.

Interleukin-4 mitigates anxiety-like behavior and loss of neurons and fiber tracts in limbic structures in a microglial PPARγ-dependent manner after traumatic brain injury.

Traumatic brain injury (TBI) is commonly followed by intractable psychiatric disorders and long-term changes in affect, such as anxiety. The present study sought to investigate the effect of repetitive intranasal delivery of interleukin-4 (IL-4) nanoparticles on affective symptoms after TBI in mice. Adult male C57BL/6 J mice (10-12 weeks of age) were subjected to controlled cortical impact (CCI) and assessed by a battery of neurobehavioral tests up to 35 days after CCI. Neuron numbers were counted in multiple limbic structures, and the integrity of limbic white matter tracts was evaluated using ex vivo diffusion tensor imaging (DTI). As STAT6 is a critical mediator of IL-4-specific transcriptional activation, STAT6 knockout mice were used to explore the role of endogenous IL-4/STAT6 signaling axis in TBI-induced affective disorders. We also employed microglia/macrophage (Mi/Mϕ)-specific PPARγ conditional knockout (mKO) mice to test if Mi/Mϕ PPARγ critically contributes to IL-4-afforded beneficial effects. We observed anxiety-like behaviors up to 35 days after CCI, and these measures were exacerbated in STAT6 KO mice but mitigated by repetitive IL-4 delivery. We discovered that IL-4 protected against neuronal loss in limbic structures, such as the hippocampus and the amygdala, and improved the structural integrity of fiber tracts connecting the hippocampus and amygdala. We also observed that IL-4 boosted a beneficial Mi/Mϕ phenotype (CD206+/Arginase 1+/PPARγ+ triple-positive) in the subacute injury phase, and that the numbers of Mi/Mϕ appositions with neurons were robustly correlated with long-term behavioral performances. Remarkably, PPARγ-mKO completely abolished IL-4-afforded protection. Thus, CCI induces long-term anxiety-like behaviors in mice, but these changes in affect can be attenuated by transnasal IL-4 delivery. IL-4 prevents the long-term loss of neuronal somata and fiber tracts in key limbic structures, perhaps due to a shift in Mi/Mϕ phenotype. Exogenous IL-4 therefore holds promise for future clinical management of mood disturbances following TBI.

Genetic deletion of Krüppel-like factor 11 aggravates traumatic brain injury.

BACKGROUND: The long-term functional recovery of traumatic brain injury (TBI) is hampered by pathological events, such as parenchymal neuroinflammation, neuronal death, and white matter injury. Krüppel-like transcription factor 11 (KLF 11) belongs to the zinc finger family of transcription factors and actively participates in various pathophysiological processes in neurological disorders. Up to now, the role and molecular mechanisms of KLF11 in regulating the pathogenesis of brain trauma is poorly understood. METHODS: KLF11 knockout (KO) and wild-type (WT) mice were subjected to experimental TBI, and sensorimotor and cognitive functions were evaluated by rotarod, adhesive tape removal, foot fault, water maze, and passive avoidance tests. Brain tissue loss/neuronal death was examined by MAP2 and NeuN immunostaining, and Cresyl violet staining. White matter injury was assessed by Luxol fast blue staining, and also MBP/SMI32 and Caspr/Nav1.6 immunostaining. Activation of cerebral glial cells and infiltration of blood-borne immune cells were detected by GFAP, Iba-1/CD16/32, Iba-1/CD206, Ly-6B, and F4/80 immunostaining. Brian parenchymal inflammatory cytokines were measured with inflammatory array kits. RESULTS: Genetic deletion of KLF11 worsened brain trauma-induced sensorimotor and cognitive deficits, brain tissue loss and neuronal death, and white matter injury in mice. KLF11 genetic deficiency in mice also accelerated post-trauma astrocytic activation, promoted microglial polarization to a pro-inflammatory phenotype, and increased the infiltration of peripheral neutrophils and macrophages into the brain parenchyma. Mechanistically, loss-of-KLF11 function was found to directly increase the expression of pro-inflammatory cytokines in the brains of TBI mice. CONCLUSION: KLF11 acts as a novel protective factor in TBI. KLF11 genetic deficiency in mice aggravated the neuroinflammatory responses, grey and white matter injury, and impaired long-term sensorimotor and cognitive recovery. Elucidating the functional importance of KLF11 in TBI may lead us to discover novel pharmacological targets for the development of effective therapies against brain trauma.

Iodine-based extracellular volume for evaluating myocardial status in patients undergoing percutaneous coronary intervention for acute myocardial infarction by using dual-layer spectral detector computed tomography: a comparison study with magnetic resonance.

Background: The myocardial status of patients who undergo percutaneous coronary intervention (PCI) must be evaluated accurately to enable treatment plans to be made for potential complications such as abrupt vessel closure, stent deformation, and myocardial chronic ischemia. This study examined the modality and clinical feasibility of iodine-based extracellular volume (ECV) assessment of the myocardium versus cardiovascular magnetic resonance (CMR) imaging in patients undergoing PCI. Methods: In all, 21 patients who underwent PCI were prospectively enrolled in the study. All patients underwent follow-up cardiac dual-layer spectral detector computed tomography (SDCT) and CMR imaging after PCI. Myocardial ECV was quantified by either computed tomography (ECVCT) or magnetic resonance (ECVMR) using iodine or T1-weighted mapping, respectively. The quality of SDCT and CMR images was independently assessed by two radiologists using a 4-point scale (1= poor and 4= excellent). Any patient with an image quality (IQ) score <2 was excluded. Consistency between radiologists was evaluated using intraclass correlation coefficients (ICC). Correlations between ECVCT and ECVMR values were analyzed using Pearson's test, and consistency was analyzed with Bland-Altman plots. Results: Nineteen of 21 patients completed both cardiac CT and CMR examinations, while three patients were excluded after IQ assessment (two with poor CMR IQ; one with a discontinuous coronary artery on CT images). The mean (±SD) IQ scores for CT and CMR images were 3.81±0.40 and 3.25±0.58, respectively, and interobserver agreement was good (ICC =0.93 and 0.92 for CT and CMR, respectively). The mean (±SD) ECVCT and ECVMR values were 35.93%±9.73% and 33.89%±7.51%, respectively, with good correlation (r=0.79, P<0.001). Bland-Altman analysis showed a difference of 2.04% (95% CI: -9.56%, 13.64%) between the ECVCT and ECVMR values. Conclusions: There is high correlation between iodine-based ECVCT and ECVMR values, which indicates that ECVCT is clinically feasible for evaluating the status of myocardial recovery in patients undergoing PCI.

Clinical feasibility of using effective atomic number maps derived from non-contrast spectral computed tomography to identify non-calcified atherosclerotic plaques: a preliminary study.

Background: To assess the clinical feasibility of using effective atomic number (Zeff) maps derived from non-contrast-enhanced computed tomography (NCECT) scans obtained by dual-layer spectral computed tomography (DLCT) to identify non-calcified atherosclerotic plaques. Methods: A total of 37 patients with 86 non-calcified atherosclerotic plaques confirmed by contrast-enhanced CT (CECT) were enrolled in this retrospective study. Both spectral-based-images (SBI) and conventional images (CI) were reconstructed from NCECT and CECT scans. The presence of plaques on NCECT Zeff maps and CIs were independently assessed by 2 radiologists. In CECT scans, plaques and regions of interest (ROIs) in vessel lumens were assessed with CT attenuation and Zeff values, and the proportion of plaques was determined as Area (plaque)/Area (vessel). The CT and Zeff values for plaques and blood were recorded from both CECT and NCECT scans. Contrast-to-noise ratios (CNRs) of the plaques were calculated and compared using CT attenuation and Zeff values. Finally, interobserver agreement was evaluated. Results: A total of 47 of the 86 (54.7%) plaques were identified on Zeff map images derived from the NCECT scans while only 7 (8.1%) plaques were identified on the CI. There was no significant difference between the mean vessel ROI area measured on CIs and that measured on Zeff map images (502.19 vs. 498.14 mm2; P=0.28), while the mean plaque ROI area was larger (81.45 vs. 75.46 mm2). The observer consensus of vessel and plaque ROI area measurements using both methods was excellent, with interclass correlation coefficients (ICCs) of 0.99 and 0.94, respectively. For the 7 plaques detected both by NCECT CI and Zeff mapping, the CT attenuation and Zeff blood values were both larger than the plaque values [42.00 vs. 25.67 Hounsfield unit (HU); 7.33 vs. 7.19 HU; both P<0.05]; the plaque ROI area measurement on the NCE Zeff map was smaller than that on the CE CI (48.73 vs. 77.76 mm2), but was much larger than that on the NCE CI (18.39 mm2). For all 47 plaques detected by NCE Zeff mapping, the CT attenuation and Zeff values of blood and plaques on the NCECT images showed no significant differences (42.53 vs. 35.14 HU; P=0.18; 7.32 vs. 7.31, P=0.71); however, the CNR of Zeff was significantly higher than the CT attenuation value (1.69 vs. 1.12; P<0.05) derived from the NCECT scans. Inter-reviewer agreement was good (ICC =0.78). Conclusions: Zeff map images derived from NCECT SBI with DLCT provide a potentially feasible approach for identifying non-calcified atherosclerotic plaques, which might be clinically useful for the screening of asymptomatic at-risk patients.

Genetic Deficiency of MicroRNA-15a/16-1 Confers Resistance to Neuropathological Damage and Cognitive Dysfunction in Experimental Vascular Cognitive Impairment and Dementia.

Chronic cerebral hypoperfusion-derived brain damage contributes to the progression of vascular cognitive impairment and dementia (VCID). Cumulative evidence has shown that microRNAs (miRs) are emerging as novel therapeutic targets for CNS disorders. In this study, it is sought to determine the regulatory role of miR-15a/16-1 in VCID. It is found that miR-15a/16-1 knockout (KO) mice exhibit less cognitive and sensorimotor deficits following VCID. Genetic deficiency of miR-15a/16-1 in VCID mice also mitigate myelin degeneration, axonal injury, and neuronal loss. Mechanistically, miR-15a/16-1 binds to the 3'-UTR of AKT3 and IL-10RA. Genetic deletion of miR-15a/16-1 increases AKT3 and IL-10RA expression in VCID brains, and intranasal delivery of AKT3 and IL-10RA siRNA-loaded nanoparticles partially reduce brain protection and cognitive recovery in miR-15a/16-1 KO mice after VCID. In conclusion, the miR-15a/16-1-IL/10RA/AKT3 axis plays a critical role in regulating vascular brain damage and cognitive decline after VCID. Targeting miR-15a/16-1 is a novel therapeutic approach for the treatment of VCID.

Non-coding RNAs in the regulation of blood-brain barrier functions in central nervous system disorders.

The blood-brain barrier (BBB) is an essential component of the neurovascular unit that controls the exchanges of various biological substances between the blood and the brain. BBB damage is a common feature of different central nervous systems (CNS) disorders and plays a vital role in the pathogenesis of the diseases. Non-coding RNAs (ncRNAs), such as microRNAs (miRNAs), long non-coding RNA (lncRNAs), and circular RNAs (circRNAs), are important regulatory RNA molecules that are involved in almost all cellular processes in normal development and various diseases, including CNS diseases. Cumulative evidences have demonstrated ncRNA regulation of BBB functions in different CNS diseases. In this review, we have summarized the miRNAs, lncRNAs, and circRNAs that can be served as diagnostic and prognostic biomarkers for BBB injuries, and demonstrated the involvement and underlying mechanisms of ncRNAs in modulating BBB structure and function in various CNS diseases, including ischemic stroke, hemorrhagic stroke, traumatic brain injury (TBI), spinal cord injury (SCI), multiple sclerosis (MS), Alzheimer's disease (AD), vascular cognitive impairment and dementia (VCID), brain tumors, brain infections, diabetes, sepsis-associated encephalopathy (SAE), and others. We have also discussed the pharmaceutical drugs that can regulate BBB functions via ncRNAs-related signaling cascades in CNS disorders, along with the challenges, perspective, and therapeutic potential of ncRNA regulation of BBB functions in CNS diseases.

Calcium Scoring at Coronary CT Angiography Using Deep Learning.

Background Separate noncontrast CT to quantify the coronary artery calcium (CAC) score often precedes coronary CT angiography (CTA). Quantifying CAC scores directly at CTA would eliminate the additional radiation produced at CT but remains challenging. Purpose To quantify CAC scores automatically from a single CTA scan. Materials and Methods In this retrospective study, a deep learning method to quantify CAC scores automatically from a single CTA scan was developed on training and validation sets of 292 patients and 73 patients collected from March 2019 to July 2020. Virtual noncontrast scans obtained with a spectral CT scanner were used to develop the algorithm to alleviate tedious manual annotation of calcium regions. The proposed method was validated on an independent test set of 240 CTA scans collected from three different CT scanners from August 2020 to November 2020 using the Pearson correlation coefficient, the coefficient of determination, or r2, and the Bland-Altman plot against the semiautomatic Agatston score at noncontrast CT. The cardiovascular risk categorization performance was evaluated using weighted κ based on the Agatston score (CAC score risk categories: 0-10, 11-100, 101-400, and >400). Results Two hundred forty patients (mean age, 60 years ± 11 [standard deviation]; 146 men) were evaluated. The positive correlation between the automatic deep learning CTA and semiautomatic noncontrast CT CAC score was excellent (Pearson correlation = 0.96; r2 = 0.92). The risk categorization agreement based on deep learning CTA and noncontrast CT CAC scores was excellent (weighted κ = 0.94 [95% CI: 0.91, 0.97]), with 223 of 240 scans (93%) categorized correctly. All patients who were miscategorized were in the direct neighboring risk groups. The proposed method's differences from the noncontrast CT CAC score were not statistically significant with regard to scanner (P = .15), sex (P = .051), and section thickness (P = .67). Conclusion A deep learning automatic calcium scoring method accurately quantified coronary artery calcium from CT angiography images and categorized risk. © RSNA, 2021 See also the editorial by Goldfarb and Cao et al in this issue.

Nitro-oleic acid-mediated blood-brain barrier protection reduces ischemic brain injury.

Nitro-oleic acid (OA-NO2), a nitroalkene formed in nitric oxide-dependent oxidative reactions, has been found in human plasma and is thought to regulate pathophysiological functions. Recently, accumulating evidence suggests that OA-NO2 may function as an anti-inflammatory mediator, and ameliorate the progression of diabetes and cardiovascular diseases. However, the role of OA-NO2 in ischemic brain injury remains unexplored. In this study, C57BL/6 mice were subjected to 1 h transient middle cerebral artery occlusion (MCAO) and followed by 1- 7 days of reperfusion. These mice were treated with vehicle, OA, or OA-NO2 (10 mg/kg) via tail vein injection at 2 h after the onset of MCAO. Our results show that intravenous administration of OA-NO2 led to reduced BBB leakage in ischemic brains, reduced brain infarct, and improved sensorimotor functions in response to ischemic insults when compared to OA and vehicle controls. Also, OA-NO2 significantly reduced BBB leakage-triggered infiltration of neutrophils and macrophages in the ischemic brains. Moreover, OA-NO2 treatment reduced the M1-type microglia and increased M2-type microglia. Mechanistically, OA-NO2 alleviated the decline of mRNA and protein level of major endothelial TJs including ZO-1 in stroke mice. Treatment of OA-NO2 also significantly inhibited stroke-induced inflammatory mediators, iNOS, E-selectin, P-selectin, and ICAM1, in mouse brains. In conclusion, OA-NO2 preserves BBB integrity and confers neurovascular protection in ischemic brain damage. OA-NO2-mediated brain protection may help us to develop a novel therapeutic strategy for the treatment of ischemic stroke.

Long non-coding RNAs mediate cerebral vascular pathologies after CNS injuries.

Central nervous system (CNS) injuries are one of the leading causes of morbidity and mortality worldwide, accompanied with high medical costs and a decreased quality of life. Brain vascular disorders are involved in the pathological processes of CNS injuries and might play key roles for their recovery and prognosis. Recently, increasing evidence has shown that long non-coding RNAs (lncRNAs), which comprise a very heterogeneous group of non-protein-coding RNAs greater than 200 nucleotides, have emerged as functional mediators in the regulation of vascular homeostasis under pathophysiological conditions. Remarkably, lncRNAs can regulate gene transcription and translation, thus interfering with gene expression and signaling pathways by different mechanisms. Hence, a deeper insight into the function and regulatory mechanisms of lncRNAs following CNS injury, especially cerebrovascular-related lncRNAs, could help in establishing potential therapeutic strategies to improve or inhibit neurological disorders. In this review, we highlight recent advancements in understanding of the role of lncRNAs and their application in mediating cerebrovascular pathologies after CNS injury.

Genetic deletion of endothelial microRNA-15a/16-1 promotes cerebral angiogenesis and neurological recovery in ischemic stroke through Src signaling pathway.

Cerebral angiogenesis is tightly controlled by specific microRNAs (miRs), including the miR-15a/16-1 cluster. Recently, we reported that endothelium-specific conditional knockout of the miR-15a/16-1 cluster (EC-miR-15a/16-1 cKO) promotes post-stroke angiogenesis and improves long-term neurological recovery by increasing protein levels of VEGFA, FGF2, and their respective receptors VEGFR2 and FGFR1. Herein, we further investigated the underlying signaling mechanism of these pro-angiogenic factors after ischemic stroke using a selective Src family inhibitor AZD0530. EC-miR-15a/16-1 cKO and age- and sex-matched wild-type littermate (WT) mice were subjected to 1 h middle cerebral artery occlusion (MCAO) and 28d reperfusion. AZD0530 was administered daily by oral gavage to both genotypes of mice 3-21d after MCAO. Compared to WT, AZD0530 administration exacerbated spatial cognitive impairments and brain atrophy in EC-miR-15a/16-1 cKO mice following MCAO. AZD0530 also attenuated long-term recovery of blood flow and inhibited the formation of new microvessels, including functional vessels with blood circulation, in the penumbra of stroked cKO mice. Moreover, AZD0530 blocked the Src signaling pathway by downregulating phospho-Src and its downstream mediators (p-Stat3, p-Akt, p-FAK, p-p44/42 MAPK, p-p38 MAPK) in post-ischemic brains. Collectively, our data demonstrated that endothelium-targeted deletion of the miR-15a/16-1 cluster promotes post-stroke angiogenesis and improves long-term neurological recovery via activating Src signaling pathway.

Noncoding RNAs in Cardiovascular Disease: Current Knowledge, Tools and Technologies for Investigation, and Future Directions: A Scientific Statement From the American Heart Association.

BACKGROUND: The discovery that much of the non-protein-coding genome is transcribed and plays a diverse functional role in fundamental cellular processes has led to an explosion in the development of tools and technologies to investigate the role of these noncoding RNAs in cardiovascular health. Furthermore, identifying noncoding RNAs for targeted therapeutics to treat cardiovascular disease is an emerging area of research. The purpose of this statement is to review existing literature, offer guidance on tools and technologies currently available to study noncoding RNAs, and identify areas of unmet need. METHODS: The writing group used systematic literature reviews (including MEDLINE, Web of Science through 2018), expert opinion/statements, analyses of databases and computational tools/algorithms, and review of current clinical trials to provide a broad consensus on the current state of the art in noncoding RNA in cardiovascular disease. RESULTS: Significant progress has been made since the initial studies focusing on the role of miRNAs (microRNAs) in cardiovascular development and disease. Notably, recent progress on understanding the role of novel types of noncoding small RNAs such as snoRNAs (small nucleolar RNAs), tRNA (transfer RNA) fragments, and Y-RNAs in cellular processes has revealed a noncanonical function for many of these molecules. Similarly, the identification of long noncoding RNAs that appear to play an important role in cardiovascular disease processes, coupled with the development of tools to characterize their interacting partners, has led to significant mechanistic insight. Finally, recent work has characterized the unique role of extracellular RNAs in mediating intercellular communication and their potential role as biomarkers. CONCLUSIONS: The rapid expansion of tools and pipelines for isolating, measuring, and annotating these entities suggests that caution in interpreting results is warranted until these methodologies are rigorously validated. Most investigators have focused on investigating the functional role of single RNA entities, but studies suggest complex interaction between different RNA molecules. The use of network approaches and advanced computational tools to understand the interaction of different noncoding RNA species to mediate a particular phenotype may be required to fully comprehend the function of noncoding RNAs in mediating disease phenotypes.

Endothelium-targeted deletion of the miR-15a/16-1 cluster ameliorates blood-brain barrier dysfunction in ischemic stroke.

The blood-brain barrier (BBB) maintains a stable brain microenvironment. Breakdown of BBB integrity during cerebral ischemia initiates a devastating cascade of events that eventually leads to neuronal loss. MicroRNAs are small noncoding RNAs that suppress protein expression, and we previously showed that the miR-15a/16-1 cluster is involved in the pathogenesis of ischemic brain injury. Here, we demonstrated that when subjected to experimentally induced stroke, mice with an endothelial cell (EC)-selective deletion of miR-15a/16-1 had smaller brain infarcts, reduced BBB leakage, and decreased infiltration of peripheral immune cells. These mice also showed reduced infiltration of proinflammatory M1-type microglia/macrophage in the peri-infarct area without changes in the number of resolving M2-type cells. Stroke decreases claudin-5 abundance, and we found that EC-selective miR-15a/16-1 deletion enhanced claudin-5 mRNA and protein abundance in ischemic mouse brains. In cultured mouse brain microvascular ECs (mBMECs), the miR-15a/16-1 cluster directly bound to the 3' untranslated region (3'UTR) of Claudin-5, and lentivirus-mediated ablation of miR-15a/16-1 diminished oxygen-glucose deprivation (OGD)-induced down-regulation of claudin-5 mRNA and protein abundance and endothelial barrier dysfunction. These findings suggest that genetic deletion of endothelial miR-15a/16-1 suppresses BBB pathologies after ischemic stroke. Elucidating the molecular mechanisms of miR-15a/16-1-mediated BBB dysfunction may enable the discovery of new therapies for ischemic stroke.

Endothelium-targeted overexpression of Krüppel-like factor 11 protects the blood-brain barrier function after ischemic brain injury.

Microvascular endothelial cell (EC) injury and the subsequent blood-brain barrier (BBB) breakdown are frequently seen in many neurological disorders, including stroke. We have previously documented that peroxisome proliferator-activated receptor gamma (PPARγ)-mediated cerebral protection during ischemic insults needs Krüppel-like factor 11 (KLF11) as a critical coactivator. However, the role of endothelial KLF11 in cerebrovascular function and stroke outcome is unclear. This study is aimed at investigating the regulatory role of endothelial KLF11 in BBB preservation and neurovascular protection after ischemic stroke. EC-targeted overexpression of KLF11 significantly mitigated BBB leakage in ischemic brains, evidenced by significantly reduced extravasation of BBB tracers and infiltration of peripheral immune cells, and less brain water content. Endothelial cell-selective KLF11 transgenic (EC-KLF11 Tg) mice also exhibited smaller brain infarct and improved neurological function in response to ischemic insults. Furthermore, EC-targeted transgenic overexpression of KLF11 preserved cerebral tight junction (TJ) levels and attenuated the expression of pro-inflammatory factors in mice after ischemic stroke. Mechanistically, we demonstrated that KLF11 directly binds to the promoter of major endothelial TJ proteins including occludin and ZO-1 to promote their activities. Our data indicate that KLF11 functions at the EC level to preserve BBB structural and functional integrity, and therefore, confers brain protection in ischemic stroke. KLF11 may be a novel therapeutic target for the treatment of ischemic stroke and other neurological conditions involving BBB breakdown and neuroinflammation.