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Genetic mouse models of skeletal abnormalities have demonstrated promise in the identification of phenotypes relevant to human skeletal diseases. Traditionally, phenotypes are assessed by manually examining radiographs, a tedious and potentially error-prone process. In response, this study developed a deep learning-based model that streamlines the measurement of murine bone lengths from radiographs in an accurate and reproducible manner. A bone detection and measurement pipeline utilizing the Keypoint R-CNN algorithm with an EfficientNet-B3 feature extraction backbone was developed to detect murine bone positions and measure their lengths. The pipeline was developed utilizing 94 X-ray images with expert annotations on the start and end position of each murine bone. The accuracy of our pipeline was evaluated on an independent dataset test with 592 images, and further validated on a previously published dataset of 21,300 mouse radiographs. The results showed that our model performed comparably to humans in measuring tibia and femur lengths (R2 > 0.92, p-value = 0) and significantly outperformed humans in measuring pelvic lengths in terms of precision and consistency. Furthermore, the model improved the precision and consistency of genetic association mapping results, identifying significant associations between genetic mutations and skeletal phenotypes with reduced variability. This study demonstrates the feasibility and efficiency of automated murine bone length measurement in the identification of mouse models of abnormal skeletal phenotypes.
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Recent advances in foundation models have revolutionized model development in digital pathology, reducing dependence on extensive manual annotations required by traditional methods. The ability of foundation models to generalize well with few-shot learning addresses critical barriers in adapting models to diverse medical imaging tasks. This work presents the Granular Box Prompt Segment Anything Model (GB-SAM), an improved version of the Segment Anything Model (SAM) fine-tuned using granular box prompts with limited training data. The GB-SAM aims to reduce the dependency on expert pathologist annotators by enhancing the efficiency of the automated annotation process. Granular box prompts are small box regions derived from ground truth masks, conceived to replace the conventional approach of using a single large box covering the entire H&E-stained image patch. This method allows a localized and detailed analysis of gland morphology, enhancing the segmentation accuracy of individual glands and reducing the ambiguity that larger boxes might introduce in morphologically complex regions. We compared the performance of our GB-SAM model against U-Net trained on different sizes of the CRAG dataset. We evaluated the models across histopathological datasets, including CRAG, GlaS, and Camelyon16. GB-SAM consistently outperformed U-Net, with reduced training data, showing less segmentation performance degradation. Specifically, on the CRAG dataset, GB-SAM achieved a Dice coefficient of 0.885 compared to U-Net's 0.857 when trained on 25% of the data. Additionally, GB-SAM demonstrated segmentation stability on the CRAG testing dataset and superior generalization across unseen datasets, including challenging lymph node segmentation in Camelyon16, which achieved a Dice coefficient of 0.740 versus U-Net's 0.491. Furthermore, compared to SAM-Path and Med-SAM, GB-SAM showed competitive performance. GB-SAM achieved a Dice score of 0.900 on the CRAG dataset, while SAM-Path achieved 0.884. On the GlaS dataset, Med-SAM reported a Dice score of 0.956, whereas GB-SAM achieved 0.885 with significantly less training data. These results highlight GB-SAM's advanced segmentation capabilities and reduced dependency on large datasets, indicating its potential for practical deployment in digital pathology, particularly in settings with limited annotated datasets.
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Current clustering analysis of spatial transcriptomics data primarily relies on molecular information and fails to fully exploit the morphological features present in histology images, leading to compromised accuracy and interpretability. To overcome these limitations, we have developed a multi-stage statistical method called iIMPACT. It identifies and defines histology-based spatial domains based on AI-reconstructed histology images and spatial context of gene expression measurements, and detects domain-specific differentially expressed genes. Through multiple case studies, we demonstrate iIMPACT outperforms existing methods in accuracy and interpretability and provides insights into the cellular spatial organization and landscape of functional genes within spatial transcriptomics data.
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Perfilación de la Expresión Génica , Transcriptoma , Perfilación de la Expresión Génica/métodos , Humanos , Análisis por Conglomerados , Procesamiento de Imagen Asistido por Computador/métodosRESUMEN
Immunohistochemistry (IHC) is a well-established and commonly used staining method for clinical diagnosis and biomedical research. In most IHC images, the target protein is conjugated with a specific antibody and stained using diaminobenzidine (DAB), resulting in a brown coloration, whereas hematoxylin serves as a blue counterstain for cell nuclei. The protein expression level is quantified through the H-score, calculated from DAB staining intensity within the target cell region. Traditionally, this process requires evaluation by 2 expert pathologists, which is both time consuming and subjective. To enhance the efficiency and accuracy of this process, we have developed an automatic algorithm for quantifying the H-score of IHC images. To characterize protein expression in specific cell regions, a deep learning model for region recognition was trained based on hematoxylin staining only, achieving pixel accuracy for each class ranging from 0.92 to 0.99. Within the desired area, the algorithm categorizes DAB intensity of each pixel as negative, weak, moderate, or strong staining and calculates the final H-score based on the percentage of each intensity category. Overall, this algorithm takes an IHC image as input and directly outputs the H-score within a few seconds, significantly enhancing the speed of IHC image analysis. This automated tool provides H-score quantification with precision and consistency comparable to experienced pathologists but at a significantly reduced cost during IHC diagnostic workups. It holds significant potential to advance biomedical research reliant on IHC staining for protein expression quantification.
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Aprendizaje Profundo , Humanos , Inmunohistoquímica , Hematoxilina/metabolismo , Algoritmos , Núcleo Celular/metabolismoRESUMEN
Spatially resolved transcriptomics (SRT) techniques have revolutionized the characterization of molecular profiles while preserving spatial and morphological context. However, most next-generation sequencing-based SRT techniques are limited to measuring gene expression in a confined array of spots, capturing only a fraction of the spatial domain. Typically, these spots encompass gene expression from a few to hundreds of cells, underscoring a critical need for more detailed, single-cell resolution SRT data to enhance our understanding of biological functions within the tissue context. Addressing this challenge, we introduce BayesDeep, a novel Bayesian hierarchical model that leverages cellular morphological data from histology images, commonly paired with SRT data, to reconstruct SRT data at the single-cell resolution. BayesDeep effectively model count data from SRT studies via a negative binomial regression model. This model incorporates explanatory variables such as cell types and nuclei-shape information for each cell extracted from the paired histology image. A feature selection scheme is integrated to examine the association between the morphological and molecular profiles, thereby improving the model robustness. We applied BayesDeep to two real SRT datasets, successfully demonstrating its capability to reconstruct SRT data at the single-cell resolution. This advancement not only yields new biological insights but also significantly enhances various downstream analyses, such as pseudotime and cell-cell communication.
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Direct CO2 methylation with toluene, as one of the CO2 hydrogenation technologies, exhibits great potential for the CO2 utilization to produce the valuable para-xylene (PX), but the tandem catalysis remains a challenge for low conversion and selectivity due to the competitive side reactions. The thermodynamic analyses and the comparation with two series of catalytic results of direct CO2 methylation are conducted to investigate the product distribution and possible mechanism in adjusting the feasibility of higher conversion and selectivity. Based on the Gibbs energy minimization method, the optimal thermodynamic conditions for direct CO2 methylation are 360-420 °C, 3 MPa with middle CO2/C7H8 ratio (1:1 to 1:4) and high H2 feed (CO2/H2 = 1:3 to 1:6). As a tandem process, the toluene feed would break the thermodynamic limit and has the higher potential of >60% CO2 conversion than that of CO2 hydrogenation without toluene. The direct CO2 methylation route also has advantages over the methanol route with a good prospect for >90% PX selectivity in its isomers due to the dynamic effect of selective catalysis. These thermodynamic and mechanistic analyses would promote the optimal design of bifunctional catalysts for CO2 conversion and product selectivity from the view of reaction pathways of the complex system.
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Dioxins in municipal solid waste incineration fly ash (MSWIFA) can cause significant risks to the environment and human health. In this study, the low-temperature thermal treatment of MSWIFA under industrial conditions was simulated in the laboratory to investigate the process parameters for dioxin degradation and ash discharge stages. Correlation analysis and dioxin fingerprint characterization were used to analyze the degradation and ash discharge processes. The degradation efficiency of low-temperature thermal treatment was influenced by multiple factors. At 400â for 90 min and 1% O2, the dioxin removal rate was 95.80%, the detoxification rate was 91.73%, and the residual dioxin toxicity in MSWIFA was 22.7 ± 17.8 ng I-TEQ/kg, which was in line with the limit value of 50 ng I-TEQ/kg in the "Technical specification for pollution control of fly-ash from municipal solid waste incineration" (HJ1134-2020). The increase in dioxins during ash discharge did not follow a linear relationship with the process parameters. This was assumed to be related to the MSWIFA composition, as some components containing P, Si, and Al at 150 °C may inhibit dioxin formation. The dioxin increased only by 0.79 ± 2.65 ng/kg, an increase in toxicity of 0.42 ± 0.10 ng I-TEQ/kg, when treated at 150 °C for 30 min and 10% O2.
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Dioxinas , Dibenzodioxinas Policloradas , Humanos , Incineración , Residuos Sólidos , Ceniza del Carbón , Temperatura , Dibenzodioxinas Policloradas/análisisRESUMEN
Polyploidy, the duplication of the entire genome within a single cell, is a significant characteristic of cells in many tissues, including the liver. The quantification of hepatic ploidy typically relies on flow cytometry and immunofluorescence (IF) imaging, which are not widely available in clinical settings due to high financial and time costs. To improve accessibility for clinical samples, we developed a computational algorithm to quantify hepatic ploidy using hematoxylin-eosin (H&E) histopathology images, which are commonly obtained during routine clinical practice. Our algorithm uses a deep learning model to first segment and classify different types of cell nuclei in H&E images. It then determines cellular ploidy based on the relative distance between identified hepatocyte nuclei and determines nuclear ploidy using a fitted Gaussian mixture model. The algorithm can establish the total number of hepatocytes and their detailed ploidy information in a region of interest (ROI) on H&E images. This is the first successful attempt to automate ploidy analysis on H&E images. Our algorithm is expected to serve as an important tool for studying the role of polyploidy in human liver disease.
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Aprendizaje Profundo , Humanos , Eosina Amarillenta-(YS) , Hematoxilina , Hígado , Ploidias , PoliploidíaRESUMEN
Microscopic examination of pathology slides is essential to disease diagnosis and biomedical research. However, traditional manual examination of tissue slides is laborious and subjective. Tumor whole-slide image (WSI) scanning is becoming part of routine clinical procedures and produces massive data that capture tumor histologic details at high resolution. Furthermore, the rapid development of deep learning algorithms has significantly increased the efficiency and accuracy of pathology image analysis. In light of this progress, digital pathology is fast becoming a powerful tool to assist pathologists. Studying tumor tissue and its surrounding microenvironment provides critical insight into tumor initiation, progression, metastasis, and potential therapeutic targets. Nucleus segmentation and classification are critical to pathology image analysis, especially in characterizing and quantifying the tumor microenvironment (TME). Computational algorithms have been developed for nucleus segmentation and TME quantification within image patches. However, existing algorithms are computationally intensive and time consuming for WSI analysis. This study presents Histology-based Detection using Yolo (HD-Yolo), a new method that significantly accelerates nucleus segmentation and TME quantification. We demonstrate that HD-Yolo outperforms existing WSI analysis methods in nucleus detection, classification accuracy, and computation time. We validated the advantages of the system on 3 different tissue types: lung cancer, liver cancer, and breast cancer. For breast cancer, nucleus features by HD-Yolo were more prognostically significant than both the estrogen receptor status by immunohistochemistry and the progesterone receptor status by immunohistochemistry. The WSI analysis pipeline and a real-time nucleus segmentation viewer are available at https://github.com/impromptuRong/hd_wsi.
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Neoplasias de la Mama , Aprendizaje Profundo , Humanos , Femenino , Microambiente Tumoral , Algoritmos , Procesamiento de Imagen Asistido por Computador/métodos , Neoplasias de la Mama/patologíaRESUMEN
Dioxin degradation is considered essential for the environmentally sound management of municipal solid waste incineration fly ash (MSWIFA). Among the many degradation techniques, thermal treatment has shown good prospects owing to its high efficiency and wide range of applications. Thermal treatment is divided into high-temperature thermal, microwave thermal, hydrothermal, and low-temperature thermal treatments. High-temperature sintering and melting not only have dioxin degradation rates higher than 95 % but also remove volatile heavy metals, although energy consumption is high. High-temperature industrial co-processing effectively solves the problem of energy consumption, but with a low fly ash (FA) mixture, and the process is limited by location. Microwave thermal treatment and hydrothermal treatment are still in the experimental stage and cannot be used for large-scale processing. The dioxin degradation rate of low-temperature thermal treatment can also be stabilized at higher than 95 %. Compared to other methods, low-temperature thermal treatment is less costly and energy consumption with no restriction on location. This review comprehensively compares the current status of the above-mentioned thermal treatment methods and their ability to dispose of MSWIFA, especially the potential for large-scale processing. Then, the respective characteristics, challenges, and application prospects of different thermal treatment methods were discussed. Finally, based on the goal of low carbon and emission reduction, three possible approaches for improvement were proposed to address the challenges of large-scale processing of low-temperature thermal treatment, namely, adding a catalyst, changing the FA fraction, or supplementing with blockers, providing a reasonable development direction for the degradation of dioxins in MSWIFA.
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Over the past decade, many new cancer treatments have been developed and made available to patients. However, in most cases, these treatments only benefit a specific subgroup of patients, making the selection of treatment for a specific patient an essential but challenging task for oncologists. Although some biomarkers were found to associate with treatment response, manual assessment is time-consuming and subjective. With the rapid developments and expanded implementation of artificial intelligence (AI) in digital pathology, many biomarkers can be quantified automatically from histopathology images. This approach allows for a more efficient and objective assessment of biomarkers, aiding oncologists in formulating personalized treatment plans for cancer patients. This review presents an overview and summary of the recent studies on biomarker quantification and treatment response prediction using hematoxylin-eosin (H&E) stained pathology images. These studies have shown that an AI-based digital pathology approach can be practical and will become increasingly important in improving the selection of cancer treatments for patients.
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Aprendizaje Profundo , Neoplasias , Humanos , Inteligencia Artificial , Medicina de Precisión/métodos , Neoplasias/terapia , Neoplasias/patologíaRESUMEN
Whole slide imaging is becoming a routine procedure in clinical diagnosis. Advanced image analysis techniques have been developed to assist pathologists in disease diagnosis, staging, subtype classification, and risk stratification. Recently, deep learning algorithms have achieved state-of-the-art performances in various imaging analysis tasks, including tumor region segmentation, nuclei detection, and disease classification. However, widespread clinical use of these algorithms is hampered by their performances often degrading due to image quality issues commonly seen in real-world pathology imaging data such as low resolution, blurring regions, and staining variation. Restore-Generative Adversarial Network (GAN), a deep learning model, was developed to improve the imaging qualities by restoring blurred regions, enhancing low resolution, and normalizing staining colors. The results demonstrate that Restore-GAN can significantly improve image quality, which leads to improved model robustness and performance for existing deep learning algorithms in pathology image analysis. Restore-GAN has the potential to be used to facilitate the applications of deep learning models in digital pathology analyses.
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Algoritmos , Patólogos , Humanos , Núcleo Celular , Procesamiento de Imagen Asistido por Computador , Coloración y EtiquetadoRESUMEN
Objectives-To investigate the clinical characteristics, managements, outcome, and evaluate the risk factors of Multisystem (MS) Langerhans Cell Histiocytosis (LCH) with diverse skin lesions as the first sign in four young infants. Methods-Their clinical features, disease progression, therapy, and outcomes were reviewed and analyzed retrospectively. Results-The average onset age of skin lesions was about 2 months. Cases 1 and 2 had risk organs involved (RO+) and a lack of bone lesions, and progression could not be reversed by systemic chemotherapy. They both died eventually. Cases 3 and 4 (RO-) had bone involvement and were given systemic chemotherapy for a prolonged duration. Unluckily, Case 3 had a recurrence 2 years later, while Case 4's recurrence happened nearly one year later, and diabetes insipidus one and a half years later. They both survived and are still in remission. Conclusion-MS-LCH infants with a low age of the first presentation in the skin are prone to dissemination, while RO+ is associated with high mortality. In addition, bone involvement may be a protective factor. Immunohistochemical examination of skin tissue facilitates correct early diagnosis, and adequate follow-up is necessary.
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Improving effector activity of antigen-specific T cells is a major goal in cancer immunotherapy. Despite the identification of several effector T cell (TEFF)-driving transcription factors (TFs), the transcriptional coordination of TEFF biology remains poorly understood. We developed an in vivo T cell CRISPR screening platform and identified a key mechanism restraining TEFF biology through the ETS family TF, Fli1. Genetic deletion of Fli1 enhanced TEFF responses without compromising memory or exhaustion precursors. Fli1 restrained TEFF lineage differentiation by binding to cis-regulatory elements of effector-associated genes. Loss of Fli1 increased chromatin accessibility at ETS:RUNX motifs, allowing more efficient Runx3-driven TEFF biology. CD8+ T cells lacking Fli1 provided substantially better protection against multiple infections and tumors. These data indicate that Fli1 safeguards the developing CD8+ T cell transcriptional landscape from excessive ETS:RUNX-driven TEFF cell differentiation. Moreover, genetic deletion of Fli1 improves TEFF differentiation and protective immunity in infections and cancer.
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Linfocitos T CD8-positivos/citología , Proteína Proto-Oncogénica c-fli-1/metabolismo , Animales , Linfocitos T CD8-positivos/inmunología , Linfocitos T CD8-positivos/metabolismo , Sistemas CRISPR-Cas , Diferenciación Celular , Enfermedad Crónica , Subunidad alfa 3 del Factor de Unión al Sitio Principal/metabolismo , Epigénesis Genética , Redes Reguladoras de Genes , Infecciones/inmunología , Ratones , Neoplasias/inmunologíaRESUMEN
BACKGROUND: To analyze the expression and clinical significance of murine double minute 2 (MDM2), lysosome-associated membrane protein (LAMP1) and P-glycoprotein (P-gp) in children with acute lymphoblastic leukemia (ALL). METHODS: Thirty-three children with ALL who were admitted to our hospital between January 2017 and January 2018 were enrolled as the ALL group. The expression of MDM2, LAMP1 and P-gp was compared between the two groups, as well as between ALL patients with different clinical characteristics. Logistic regression was used to analyze the risk factors that affect the prognosis and survival of ALL patients. Kaplan-Meier survival curves were used to analyze the correlations of MDM2, LAMP1 and P-gp on the prognosis and survival of ALL patients. RESULTS: The expression levels of MDM2, LAMP1 and P-gp in the ALL group were higher than those in the control group (P<0.05). The average survival time of the group with low expression of MDM2 was (34.92±0.56) months, the average survival time of the group with high expression of MDM2 was (31.32±0.42) months, and the difference was statistically significant (P<0.05). The average survival time of the group with low expression of LAMP1 was (36.71±0.55) months, the average survival time of the group with high expression of LAMP1 was (29.87±0.40) months, the difference was statistically significant (P<0.05). The average survival time of the group with low expression of P-gp was (36.29±0.41) months, the average survival time of the group with high expression of P-gp was (26.46±0.37) months, and the difference was statistically significant (P<0.05). CONCLUSIONS: Abnormal expression levels of MDM2, LAMP1 and P-gp protein are related to the occurrence and development of ALL, and are closely related to patient prognosis and survival. Therefore, MDM2, LAMP1and P-gp can serve as molecular markers for predicting the prognosis of children with ALL.
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Cancer cells express high levels of PD-L1, a ligand of the PD-1 receptor on T cells, allowing tumors to suppress T cell activity. Clinical trials utilizing antibodies that disrupt the PD-1/PD-L1 checkpoint have yielded remarkable results, with anti-PD-1 immunotherapy approved as first-line therapy for lung cancer patients. We used CRISPR-based screening to identify regulators of PD-L1 in human lung cancer cells, revealing potent induction of PD-L1 upon disruption of heme biosynthesis. Impairment of heme production activates the integrated stress response (ISR), allowing bypass of inhibitory upstream open reading frames in the PD-L1 5' UTR, resulting in enhanced PD-L1 translation and suppression of anti-tumor immunity. We demonstrated that ISR-dependent PD-L1 translation requires the translation initiation factor eIF5B. eIF5B overexpression, which is frequent in lung adenocarcinomas and associated with poor prognosis, is sufficient to induce PD-L1. These findings illuminate mechanisms of immune checkpoint activation and identify targets for therapeutic intervention.
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Antígeno B7-H1 , Factores Eucarióticos de Iniciación , Neoplasias Pulmonares , Antígeno B7-H1/genética , Factores Eucarióticos de Iniciación/genética , Hemo/biosíntesis , Humanos , Neoplasias Pulmonares/genéticaRESUMEN
BACKGROUND & AIMS: Thirty to 90% of hepatocytes contain whole-genome duplications, but little is known about the fates or functions of these polyploid cells or how they affect development of liver disease. We investigated the effects of continuous proliferative pressure, observed in chronically damaged liver tissues, on polyploid cells. METHODS: We studied Rosa-rtTa mice (controls) and Rosa-rtTa;TRE-short hairpin RNA mice, which have reversible knockdown of anillin, actin binding protein (ANLN). Transient administration of doxycycline increases the frequency and degree of hepatocyte polyploidy without permanently altering levels of ANLN. Mice were then given diethylnitrosamine and carbon tetrachloride (CCl4) to induce mutations, chronic liver damage, and carcinogenesis. We performed partial hepatectomies to test liver regeneration and then RNA-sequencing to identify changes in gene expression. Lineage tracing was used to rule out repopulation from non-hepatocyte sources. We imaged dividing hepatocytes to estimate the frequency of mitotic errors during regeneration. We also performed whole-exome sequencing of 54 liver nodules from patients with cirrhosis to quantify aneuploidy, a possible outcome of polyploid cell divisions. RESULTS: Liver tissues from control mice given CCl4 had significant increases in ploidy compared with livers from uninjured mice. Mice with knockdown of ANLN had hepatocyte ploidy above physiologic levels and developed significantly fewer liver tumors after administration of diethylnitrosamine and CCl4 compared with control mice. Increased hepatocyte polyploidy was not associated with altered regenerative capacity or tissue fitness, changes in gene expression, or more mitotic errors. Based on lineage-tracing experiments, non-hepatocytes did not contribute to liver regeneration in mice with increased polyploidy. Despite an equivalent rate of mitosis in hepatocytes of differing ploidies, we found no lagging chromosomes or micronuclei in mitotic polyploid cells. In nodules of human cirrhotic liver tissue, there was no evidence of chromosome-level copy number variations. CONCLUSIONS: Mice with increased polyploid hepatocytes develop fewer liver tumors following chronic liver damage. Remarkably, polyploid hepatocytes maintain the ability to regenerate liver tissues during chronic damage without generating mitotic errors, and aneuploidy is not commonly observed in cirrhotic livers. Strategies to increase numbers of polypoid hepatocytes might be effective in preventing liver cancer.
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Carcinoma Hepatocelular/genética , Hepatocitos/fisiología , Neoplasias Hepáticas/genética , Regeneración Hepática/genética , Poliploidía , Animales , Tetracloruro de Carbono/toxicidad , Carcinoma Hepatocelular/inducido químicamente , Carcinoma Hepatocelular/patología , Células Cultivadas , Enfermedad Hepática Inducida por Sustancias y Drogas/etiología , Enfermedad Hepática Inducida por Sustancias y Drogas/patología , Dietilnitrosamina/toxicidad , Femenino , Técnicas de Silenciamiento del Gen , Hepatectomía , Hepatocitos/efectos de los fármacos , Humanos , Hígado/citología , Hígado/efectos de los fármacos , Hígado/patología , Cirrosis Hepática/genética , Cirrosis Hepática/patología , Neoplasias Hepáticas/inducido químicamente , Neoplasias Hepáticas/patología , Neoplasias Hepáticas Experimentales/inducido químicamente , Neoplasias Hepáticas Experimentales/genética , Neoplasias Hepáticas Experimentales/patología , Regeneración Hepática/efectos de los fármacos , Masculino , Ratones , Ratones Transgénicos , Proteínas de Microfilamentos/genética , Proteínas de Microfilamentos/metabolismo , Cultivo Primario de Células , Factores Protectores , RNA-Seq , Secuenciación del ExomaRESUMEN
BACKGROUND: Previous studies have estimated that the risk of recurrent stroke was nearly 20% shortly after a transient ischemic attack (TIA) or minor stroke. A missing or hypoplastic (<0.5â¯mm) anterior communicating artery can have deleterious effects on the brain. Our study aimed to investigate the effect of anterior communicating artery flow on neurovascular injury and neurobehavioral outcomes in mice with recurrent stroke and to identify its underlying mechanisms. METHODS: A recurrent stroke model was established by an initial cortical infarction followed by a corticostriatal infarction 3â¯days later. The vascular structure was visualized using synchrotron radiation angiography & magnetic resonance angiography in vivo and transparent endovascular perfusion imaging in vitro. Microvessel perfusion was assessed via fluorescein isothiocyanate perfusion. The infarct volume was measured by magnetic resonance imaging. RESULTS: The finding that anterior communicating artery flow facilitates pial artery patency in the ipsilateral hemisphere in mice with recurrent stroke suggests that compensatory collateral patency contributes to increased regional cerebral blood flow, enhanced microcirculatory perfusion, improved neurological function and reduced infarct volume. CONCLUSIONS: The results of this study demonstrate that anterior communicating artery flow alleviates recurrent stroke-induced neurovascular injury and improves neurobehavioral outcomes by promoting the establishment of collateral circulation.
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Isquemia Encefálica/patología , Circulación Cerebrovascular/fisiología , Ataque Isquémico Transitorio/patología , Animales , Arteria Cerebral Anterior , Encéfalo/fisiopatología , Estenosis Carotídea/patología , Infarto Cerebral/patología , China , Círculo Arterial Cerebral/patología , Circulación Colateral/fisiología , Femenino , Imagen por Resonancia Magnética , Masculino , Ratones , Ratones Endogámicos C57BL , Microcirculación , Accidente Cerebrovascular/fisiopatologíaRESUMEN
BACKGROUND: Functions of astrocytes in the rehabilitation after ischemic stroke, especially their impacts on inflammatory processes, remain controversial. This study uncovered two phenotypes of astrocytes, of which one was helpful, and the other harmful to anoxic neurons after brain ischemia. METHODS: We tested the levels of inflammatory factors including TNF-a, IL-6, IL-10, iNOS, IL-1beta, and CXCL10 in primary astrocytes at 0 h, 6 h, 12 h, 24 h, and 48 h after OGD, grouped the hypoxia astrocytes into iNOS-positive (iNOS(+)) and iNOS-negative (iNOS(-)) by magnetic bead sorting, and then co-cultured the two groups of cells with OGD-treated neurons for 24 h. We further verified the polarization of astrocytes in vivo by detecting the co-localization of iNOS, GFAP, and Iba-1 on MCAO brain sections. Lentivirus overexpressing LCN2 and LCN2 knockout mice (#024630. JAX, USA) were used to explore the role of LCN2 in the functional polarization of astrocytes. 7.0-T MRI scanning and the modified Neurological Severity Score (mNSS) were used to evaluate the neurological outcomes of the mice. RESULTS: After oxygen-glucose deprivation (OGD), iNOS mRNA expression increased to the peak at 6 h in primary astrocytes, but keep baseline expression in LCN2-knockout astrocytes. In mice with transient middle cerebral artery occlusion (tMCAO), LCN2 was proved necessary for astrocyte classical activation. In LCN2 knockout mice with MCAO, no classically activated astrocytes were detected, and smaller infarct volumes and better neurological functions were observed. CONCLUSIONS: The results indicated a novel pattern of astrocyte activation after ischemic stroke and lipocalin-2 (LCN2) plays a key role in polarizing and activating astrocytes.
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Astrocitos/metabolismo , Astrocitos/patología , Isquemia Encefálica/metabolismo , Isquemia Encefálica/patología , Lipocalina 2/deficiencia , Animales , Isquemia Encefálica/genética , Células Cultivadas , Femenino , Lipocalina 2/genética , Masculino , Ratones , Ratones Endogámicos C57BL , Ratones NoqueadosRESUMEN
Phosphatase PP2A expression levels are positively correlated to the clinical severity of systemic lupus erythematosus (SLE) and IL17A cytokine overproduction, indicating a potential role of PP2A in controlling TH17 differentiation and inflammation. By generating a mouse strain with ablation of the catalytic subunit α of PP2A in peripheral mature T cells (PP2A cKO), we demonstrate that the PP2A complex is essential for TH17 differentiation. These PP2A cKO mice had reduced TH17 cell numbers and less severe disease in an experimental autoimmune encephalomyelitis (EAE) model. PP2A deficiency also ablated C-terminal phosphorylation of SMAD2 but increased C-terminal phosphorylation of SMAD3. By regulating the activity of RORγt via binding, the changes in the phosphorylation status of these R-SMADs reduced Il17a gene transcription. Finally, PP2A inhibitors showed similar effects on TH17 cells as were observed in PP2A cKO mice, i.e., decreased TH17 differentiation and relative protection of mice from EAE. Taken together, these data demonstrate that phosphatase PP2A is essential for TH17 differentiation and that inhibition of PP2A could be a possible therapeutic approach to controlling TH17-driven autoimmune diseases.