ABSTRACT
Cerebral ischemiaâreperfusion injury (CIRI) is a type of secondary brain damage caused by reperfusion after ischemic stroke due to vascular obstruction. In this study, a CIRI diagnostic model was established by identifying hypoxia-related differentially expressed genes (HRDEGs) in patients with CIRI. The ischemiaâreperfusion injury (IRI)-related datasets were downloaded from the Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo ), and hypoxia-related genes in the Gene Cards database were identified. After the datasets were combined, hypoxia-related differentially expressed genes (HRDEGs) expressed in CIRI patients were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the HRDEGs were performed using online tools. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were performed with the combined gene dataset. CIRI diagnostic models based on HRDEGs were constructed via least absolute shrinkage and selection operator (LASSO) regression analysis and a support vector machine (SVM) algorithm. The efficacy of the 9 identified hub genes for CIRI diagnosis was evaluated via mRNAâmicroRNA (miRNA) interaction, mRNA-RNA-binding protein (RBP) network interaction, immune cell infiltration, and receiver operating characteristic (ROC) curve analyses. We then performed logistic regression analysis and constructed logistic regression models based on the expression of the 9 HRDEGs. We next established a nomogram and calibrated the prediction data. Finally, the clinical utility of the constructed logistic regression model was evaluated via decision curve analysis (DCA). This study revealed 9 critical genes with high diagnostic value, offering new insights into the diagnosis and selection of therapeutic targets for patients with CIRI. : Not applicable.
ABSTRACT
Deep learning models have been developed for various predictions in glioma; yet, they were constrained by manual segmentation, task-specific design, or a lack of biological interpretation. Herein, we aimed to develop an end-to-end multi-task deep learning (MDL) pipeline that can simultaneously predict molecular alterations and histological grade (auxiliary tasks), as well as prognosis (primary task) in gliomas. Further, we aimed to provide the biological mechanisms underlying the model's predictions. We collected multiscale data including baseline MRI images from 2776 glioma patients across two private (FAHZU and HPPH, n = 1931) and three public datasets (TCGA, n = 213; UCSF, n = 410; and EGD, n = 222). We trained and internally validated the MDL model using our private datasets, and externally validated it using the three public datasets. We used the model-predicted deep prognosis score (DPS) to stratify patients into low-DPS and high-DPS subtypes. Additionally, a radio-multiomics analysis was conducted to elucidate the biological basis of the DPS. In the external validation cohorts, the MDL model achieved average areas under the curve of 0.892-0.903, 0.710-0.894, and 0.850-0.879 for predicting IDH mutation status, 1p/19q co-deletion status, and tumor grade, respectively. Moreover, the MDL model yielded a C-index of 0.723 in the TCGA and 0.671 in the UCSF for the prediction of overall survival. The DPS exhibits significant correlations with activated oncogenic pathways, immune infiltration patterns, specific protein expression, DNA methylation, tumor mutation burden, and tumor-stroma ratio. Accordingly, our work presents an accurate and biologically meaningful tool for predicting molecular subtypes, tumor grade, and survival outcomes in gliomas, which provides personalized clinical decision-making in a global and non-invasive manner.
ABSTRACT
Drug resistance in cancer cells significantly diminishes treatment efficacy, leading to recurrence and metastasis. A critical factor contributing to this resistance is the epigenetic alteration of gene expression via RNA modifications, such as N6-methyladenosine (m6A), N1-methyladenosine (m1A), 5-methylcytosine (m5C), 7-methylguanosine (m7G), pseudouridine (Ψ), and adenosine-to-inosine (A-to-I) editing. These modifications are pivotal in regulating RNA splicing, translation, transport, degradation, and stability. Governed by "writers," "readers," and "erasers," RNA modifications impact numerous biological processes and cancer progression, including cell proliferation, stemness, autophagy, invasion, and apoptosis. Aberrant RNA modifications can lead to drug resistance and adverse outcomes in various cancers. Thus, targeting RNA modification regulators offers a promising strategy for overcoming drug resistance and enhancing treatment efficacy. This review consolidates recent research on the role of prevalent RNA modifications in cancer drug resistance, with a focus on m6A, m1A, m5C, m7G, Ψ, and A-to-I editing. Additionally, it examines the regulatory mechanisms of RNA modifications linked to drug resistance in cancer and underscores the existing limitations in this field.
Subject(s)
Drug Resistance, Neoplasm , Neoplasms , RNA Processing, Post-Transcriptional , Humans , Drug Resistance, Neoplasm/genetics , Neoplasms/genetics , Neoplasms/drug therapy , Neoplasms/pathology , Neoplasms/metabolism , Animals , Gene Expression Regulation, Neoplastic/drug effects , Epigenesis, Genetic , Antineoplastic Agents/pharmacology , Antineoplastic Agents/therapeutic use , RNA/genetics , RNA/metabolismABSTRACT
This work presents a novel and effective method for fitting multidimensional ellipsoids (i.e., ellipsoids embedded in [Formula: see text]) to scattered data in the contamination of noise and outliers. Unlike conventional algebraic or geometric fitting paradigms that assume each measurement point is a noisy version of its nearest point on the ellipsoid, we approach the problem as a Bayesian parameter estimate process and maximize the posterior probability of a certain ellipsoidal solution given the data. We establish a more robust correlation between these points based on the predictive distribution within the Bayesian framework, i.e., considering each model point as a potential source for generating each measurement. Concretely, we incorporate a uniform prior distribution to constrain the search for primitive parameters within an ellipsoidal domain, ensuring ellipsoid-specific results regardless of inputs. We then establish the connection between measurement point and model data via Bayes' rule to enhance the method's robustness against noise. Due to independent of spatial dimensions, the proposed method not only delivers high-quality fittings to challenging elongated ellipsoids but also generalizes well to multidimensional spaces. To address outlier disturbances, often overlooked by previous approaches, we further introduce a uniform distribution on top of the predictive distribution to significantly enhance the algorithm's robustness against outliers. Thanks to the uniform prior, our maximum a posterior probability coincides with a more tractable maximum likelihood estimation problem, which is subsequently solved by a numerically stable Expectation Maximization (EM) framework. Moreover, we introduce an ε-accelerated technique to expedite the convergence of EM considerably. We also investigate the relationship between our algorithm and conventional least-squares-based ones, during which we theoretically prove our method's superior robustness. To the best of our knowledge, this is the first comprehensive method capable of performing multidimensional ellipsoid-specific fitting within the Bayesian optimization paradigm under diverse disturbances. We evaluate it across lower and higher dimensional spaces in the presence of heavy noise, outliers, and substantial variations in axis ratios. Also, we apply it to a wide range of practical applications such as microscopy cell counting, 3D reconstruction, geometric shape approximation, and magnetometer calibration tasks. In all these test contexts, our method consistently delivers flexible, robust, ellipsoid-specific performance, and achieves the state-of-the-art results.
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The performance of corrosion-induced cracking of reinforced concrete members under transverse constraints was studied. Based on the theory of elastic-plastic mechanics and the hypothesis of uniform corrosion of a steel bar, a three-layer hollow cylinder model was established to predict the critical corrosion of the steel bar at the time of the cracking of the concrete cover. Taking the constraint of stirrups on surrounding concrete into consideration, it can be used to predict the corrosion rate of members with stirrups at the time of the cracking of the concrete cover, which further expands the application range of the corrosion-induced cracking models of concrete. On this basis, the critical corrosion rate of concrete under different stirrup ratios at the time of cracking was measured. The calculated results of the model are in accordance with experimental data. For corner steel bars, when the stirrup spacing is less than 100 mm, the existence of stirrups can effectively delay the occurrence of rust expansion cracks and enhance the durability of the structure. On the basis of this study, the problem of corrosion expansion and cracking of the concrete cover caused by non-uniform corrosion of steel bars along longitudinal and radial directions needs to be further studied in the future.
ABSTRACT
Xiakucao Oral Liquid (XKCOL) has been widely used for treating mammary gland hyperplasia and goiter in China. However, its pharmacokinetic data have been missing to date. To conduct its pharmacokinetic study, we established an LC-tandem mass spectrometry method for the simultaneous determination of eight XKCOL-related compounds in rat plasma. Liquid-liquid extraction was used for the sampling process. Chromatographic separation was performed on a Phenomenon Luna C18 column with a mobile phase of methanol and 2 mM ammonium acetate, using gradient elution at a flow rate of 0.8 mL/min. Detection was performed in the multiple reaction monitoring mode using negative electrospray ionization (ESI-) with optimized MS parameters. Endogenous substances and carryover did not interfere in the detection of analytes. The calibration curves showed a good linear relationship within the linear ranges. The intra- and inter-batch accuracy and precision were 94.8%-110.0% and ≤11.2%, respectively. There was no significant matrix effect and the recovery was reproducible. The dilution of samples did not affect the accuracy and precision. The solution and plasma samples were stable under the various test conditions. The major components of XKCOL absorbed into the blood were salvianic acid A and rosmarinic acid. They demonstrated linear kinetics over the dose range used in this study.
Subject(s)
Drugs, Chinese Herbal , Rats, Sprague-Dawley , Tandem Mass Spectrometry , Animals , Tandem Mass Spectrometry/methods , Rats , Drugs, Chinese Herbal/pharmacokinetics , Drugs, Chinese Herbal/chemistry , Drugs, Chinese Herbal/administration & dosage , Reproducibility of Results , Linear Models , Chromatography, Liquid/methods , Limit of Detection , Male , Liquid-Liquid Extraction/methods , Sensitivity and SpecificityABSTRACT
Globally, grazing activities have profound impacts on the structure and function of ecosystems. This study, based on a 20-year MODIS time series dataset, employs remote sensing techniques and the Seasonal-Trend decomposition using Loess (STL) algorithm to quantitatively assess the stability of alpine grassland ecosystems from multiple dimensions, and to reveal the characteristics of grazing activities and environmental conditions on ecosystem stability. The results indicate that only 5.77% of the area remains undisturbed, with most areas experiencing varying degrees of disturbance. Further analysis shows that grazing activities in high vegetation coverage areas are the main source of interference. In areas with concentrated interference, elevation and slope have a positive correlation with resistance stability, but a negative correlation with recovery stability. Precipitation and landscape diversity have positive effects on both resistance stability and recovery stability. Vegetation coverage and grazing intensity have a negative correlation with resistance stability, but a positive correlation with recovery stability. This highlights the complex interactions between human activities, environmental factors, and ecosystem stability. The findings emphasize the need for targeted conservation and management strategies to mitigate disturbances to ecosystems affected by human activities and enhance their stability.
Subject(s)
Ecosystem , Grassland , Animals , Conservation of Natural Resources , HerbivoryABSTRACT
In the original publication [...].
ABSTRACT
Glioblastoma (GBM) as one of the most lethal brain tumours, remains poor therapeutic index due to its typical characters including heterogeneous, severe immune suppression as well as the existence of blood brain barrier (BBB). Immune sonodynamic (ISD) therapy combines noninvasive sonodynamic therapy with immunotherapy, which has great prospects for the combinational treatment of GBM. Herein, we develop macrophage cell membrane cloaked reactive oxygen species (ROS) responsive biomimetic nanoparticles, co-delivering of sonosensitizer Ce6 and JQ1 (a bromo-domain protein 4 (BRD4) inhibitor which can down-regulate PD-L1) and realizing potent GBM ISD therapy. The ApoE peptide decorated macrophage membrane coating endows these biomimetic nanoparticles with low immunogenicity, efficient BBB permeability, prolonged blood circulation half-live and good biocompatibility. The ROS responsive polymeric inner core could be readily degraded as triggered by excessive ROS under the ultrasound once they accumulated in tumour cells, fast release encapsulated drugs. The generation of ROS not only killed tumour cells via sonodynamic therapy, but also induced immunogenic cell death (ICD) and further activated the anti-tumour immune response. The released JQ1 inhibited tumour cell proliferation and augmented the immune activities by inhibiting the PD-L1 expression on the surface of tumour cells. The cascade sonodynamic and immune therapy resulted in significantly improved median survival time in both orthotopic GL261 and PTEN deficient immunosuppressive CT2A GBM mice models. Therefore, our developed biomimetic nanoparticle platform provides a promising combinational therapy strategy to treat immune suppressive GBM.
Subject(s)
Brain Neoplasms , Glioblastoma , Macrophages , Nanoparticles , Reactive Oxygen Species , Triazoles , Ultrasonic Therapy , Glioblastoma/therapy , Glioblastoma/drug therapy , Glioblastoma/immunology , Animals , Ultrasonic Therapy/methods , Humans , Reactive Oxygen Species/metabolism , Cell Line, Tumor , Brain Neoplasms/therapy , Brain Neoplasms/drug therapy , Brain Neoplasms/immunology , Macrophages/drug effects , Macrophages/immunology , Nanoparticles/chemistry , Triazoles/administration & dosage , Triazoles/chemistry , Triazoles/pharmacology , Cell Membrane/metabolism , Immunotherapy/methods , Mice , Azepines/administration & dosage , Azepines/pharmacology , Azepines/chemistry , Nanomedicine/methods , Biomimetic Materials/chemistry , Female , Antineoplastic Agents/administration & dosage , Antineoplastic Agents/pharmacology , Antineoplastic Agents/therapeutic use , Blood-Brain Barrier/metabolismABSTRACT
AIMS: Although radiotherapy is a core treatment modality for various human cancers, including glioblastoma multiforme (GBM), its clinical effects are often limited by radioresistance. The specific molecular mechanisms underlying radioresistance are largely unknown, and the reduction of radioresistance is an unresolved challenge in GBM research. METHODS: We analyzed and verified the expression of nuclear autoantigenic sperm protein (NASP) in gliomas and its relationship with patient prognosis. We also explored the function of NASP in GBM cell lines. We performed further mechanistic experiments to investigate the mechanisms by which NASP facilitates GBM progression and radioresistance. An intracranial mouse model was used to verify the effectiveness of combination therapy. RESULTS: NASP was highly expressed in gliomas, and its expression was negatively correlated with the prognosis of glioma. Functionally, NASP facilitated GBM cell proliferation, migration, invasion, and radioresistance. Mechanistically, NASP interacted directly with annexin A2 (ANXA2) and promoted its nuclear localization, which may have been mediated by phospho-annexin A2 (Tyr23). The NASP/ANXA2 axis was involved in DNA damage repair after radiotherapy, which explains the radioresistance of GBM cells that highly express NASP. NASP overexpression significantly activated the signal transducer and activator of transcription 3 (STAT3) signaling pathway. The combination of WP1066 (a STAT3 pathway inhibitor) and radiotherapy significantly inhibited GBM growth in vitro and in vivo. CONCLUSION: Our findings indicate that NASP may serve as a potential biomarker of GBM radioresistance and has important implications for improving clinical radiotherapy.
Subject(s)
Annexin A2 , Brain Neoplasms , Glioblastoma , STAT3 Transcription Factor , Animals , Humans , Mice , Annexin A2/genetics , Annexin A2/metabolism , Annexin A2/therapeutic use , Brain Neoplasms/genetics , Brain Neoplasms/radiotherapy , Brain Neoplasms/metabolism , Cell Proliferation/genetics , Glioblastoma/genetics , STAT3 Transcription Factor/genetics , Cell Line, TumorABSTRACT
Fuzi, an effective common herb, is often combined with Gancao to treat disease in clinical practice with enhancing its efficacy and alleviating its toxicity. The major toxic and bioactive compounds in Fuzi and Gancao are aconitine (AC) and glycyrrhizic acid (GL), respectively. This study aims to elucidate detoxification mechanism between AC and GL from pharmacokinetic perspective using physiologically based pharmacokinetic (PBPK) model. In vitro experiments exhibited that AC was mainly metabolized by CYP3A1/2 in rat liver microsomes and transported by P-glycoprotein (P-gp) in Caco-2 cells. Kinetics assays showed that the Km and Vmax of AC towards CYP3A1/2 were 2.38 µM and 57.3 pmol/min/mg, respectively, whereas that of AC towards P-gp was 11.26 µM and 147.1 pmol/min/mg, respectively. GL markedly induced the mRNA expressions of CYP3A1/2 and MDR1a/b in rat primary hepatocytes. In vivo studies suggested that the intragastric and intravenous administration of GL significantly reduced systemic exposure of AC by 27% and 33%, respectively. Drug-drug interaction (DDI) model of PBPK predicted that co-administration of GL would decrease the exposure of AC by 39% and 45% in intragastric and intravenous dosing group, respectively. The consistency between predicted data and observed data confirmed that the upregulation of CYP3A1/2 and P-gp was the crucial detoxification mechanism between AC and GL. Thus, this study provides a demonstration for elucidating the compatibility mechanisms of herbal formula using PBPK modeling and gives support for the clinical co-medication of Fuzi and Gancao.
Subject(s)
ATP Binding Cassette Transporter, Subfamily B, Member 1 , Aconitine , Cytochrome P-450 CYP3A , Glycyrrhizic Acid , Microsomes, Liver , Animals , Cytochrome P-450 CYP3A/metabolism , Cytochrome P-450 CYP3A/genetics , Aconitine/pharmacokinetics , Aconitine/analogs & derivatives , Aconitine/toxicity , Glycyrrhizic Acid/pharmacokinetics , Glycyrrhizic Acid/pharmacology , Humans , Caco-2 Cells , Male , Microsomes, Liver/metabolism , Microsomes, Liver/drug effects , Rats , ATP Binding Cassette Transporter, Subfamily B, Member 1/metabolism , ATP Binding Cassette Transporter, Subfamily B, Member 1/genetics , Rats, Sprague-Dawley , Models, Biological , Inactivation, MetabolicABSTRACT
Interindividual exposure differences have been identified in oral targeted antineoplastic drugs (OADs) owing to the pharmacogenetic background of the patients and their susceptibility to multiple factors, resulting in insufficient efficacy or adverse effects. Therapeutic drug monitoring (TDM) can prevent sub-optimal concentrations of OADs and improve their clinical treatment. This study aimed to develop and validate an LC-MS/MS method for the simultaneous quantification of 11 OADs (gefitinib, imatinib, lenvatinib, regorafenib, everolimus, osimertinib, sunitinib, tamoxifen, lapatinib, fruquintinib and sorafenib) and 2 active metabolites (N-desethyl sunitinib and Z-endoxifen) in human plasma. Protein precipitation was used to extract OADs from the plasma samples. Chromatographic separation was performed using an Eclipse XDB-C18 (4.6 × 150 mm, 5 µm) column with a gradient elution of the mobile phase composed of 2 mM ammonium acetate with 0.1 % formic acid in water (solvent A) and methanol (solvent B) at a flow rate of 0.8 mL/min. Mass analysis was performed using positive ion mode electrospray ionization in multiple-reaction monitoring mode. The developed method was validated following FDA bioanalytical guidelines. The calibration curves were linear over the range of 2-400 ng/mL for gefitinib, imatinib, lenvatinib, regorafenib, and everolimus; 1-200 ng/mL for osimertinib, sunitinib, N-desethyl sunitinib, tamoxifen, and Z-endoxifen; and 5-1000 ng/mL for lapatinib, fruquintinib, and sorafenib, with all coefficients of correlation above 0.99. The intra- and inter-day imprecision was below 12.81 %. This method was successfully applied to the routine TDM of gefitinib, lenvatinib, regorafenib, osimertinib, fruquintinib, and sorafenib to optimize the dosage regimens.
Subject(s)
Acrylamides , Aniline Compounds , Antineoplastic Agents , Indoles , Neoplasms , Phenylurea Compounds , Pyridines , Pyrimidines , Quinolines , Tamoxifen/analogs & derivatives , Humans , Sunitinib , Imatinib Mesylate , Sorafenib , Lapatinib , Chromatography, Liquid/methods , Drug Monitoring/methods , Liquid Chromatography-Mass Spectrometry , Gefitinib , Everolimus , Tandem Mass Spectrometry/methods , Antineoplastic Agents/therapeutic use , Tamoxifen/therapeutic use , Neoplasms/drug therapy , Solvents , Reproducibility of Results , Chromatography, High Pressure Liquid/methodsABSTRACT
Mesh denoising is a crucial technology that aims to recover a high-fidelity 3D mesh from a noise-corrupted one. Deep learning methods, particularly graph convolutional networks (GCNs) based mesh denoisers, have demonstrated their effectiveness in removing various complex real-world noises while preserving authentic geometry. However, it is still a quite challenging work to faithfully regress uncontaminated normals and vertices on meshes with irregular topology. In this paper, we propose a novel pipeline that incorporates two parallel normal-aware and vertex-aware branches to achieve a balance between smoothness and geometric details while maintaining the flexibility of surface topology. We introduce ResGEM, a new GCN, with multi-scale embedding modules and residual decoding structures to facilitate normal regression and vertex modification for mesh denoising. To effectively extract multi-scale surface features while avoiding the loss of topological information caused by graph pooling or coarsening operations, we encode the noisy normal and vertex graphs using four edge-conditioned embedding modules (EEMs) at different scales. This allows us to obtain favorable feature representations with multiple receptive field sizes. Formulating the denoising problem into a residual learning problem, the decoder incorporates residual blocks to accurately predict true normals and vertex offsets from the embedded feature space. Moreover, we propose novel regularization terms in the loss function that enhance the smoothing and generalization ability of our network by imposing constraints on normal consistency. Comprehensive experiments have been conducted to demonstrate the superiority of our method over the state-of-the-art on both synthetic and real-scanned datasets.
ABSTRACT
Isocitrate dehydrogenase gene (IDH) mutation is one of the most important molecular markers of glioma. Accurate detection of IDH status is a crucial step for integrated diagnosis of adult-type diffuse gliomas. Herein, a clustering-based hybrid of a convolutional neural network and a vision transformer deep learning model was developed to detect IDH mutation status from annotation-free hematoxylin and eosin-stained whole slide pathologic images of 2275 adult patients with diffuse gliomas. For comparison, a pure convolutional neural network, a pure vision transformer, and a classic multiple-instance learning model were also assessed. The hybrid model achieved an area under the receiver operating characteristic curve of 0.973 in the validation set and 0.953 in the external test set, outperforming the other models. The hybrid model's ability in IDH detection between difficult subgroups with different IDH status but shared histologic features, achieving areas under the receiver operating characteristic curve ranging from 0.850 to 0.985 in validation and test sets. These data suggest that the proposed hybrid model has a potential to be used as a computational pathology tool for preliminary rapid detection of IDH mutation from whole slide images in adult patients with diffuse gliomas.
Subject(s)
Brain Neoplasms , Glioma , Adult , Humans , Isocitrate Dehydrogenase/genetics , Brain Neoplasms/diagnostic imaging , Brain Neoplasms/genetics , Brain Neoplasms/pathology , Magnetic Resonance Imaging/methods , Glioma/diagnostic imaging , Glioma/genetics , Glioma/pathology , Mutation/genetics , Retrospective StudiesABSTRACT
A large number of 3D spectral descriptors have been proposed in the literature, which act as an essential component for 3D deformable shape matching and related applications. An outstanding descriptor should have desirable natures including high-level descriptive capacity, cheap storage, and robustness to a set of nuisances. It is, however, unclear which descriptors are more suitable for a particular application. This paper fills the gap by comprehensively evaluating nine state-of-the-art spectral descriptors on ten popular deformable shape datasets as well as perturbations such as mesh discretization, geometric noise, scale transformation, non-isometric setting, partiality, and topological noise. Our evaluated terms for a spectral descriptor cover four major concerns, i.e., distinctiveness, robustness, compactness, and computational efficiency. In the end, we present a summary of the overall performance and several interesting findings that can serve as guidance for the following researchers to construct a new spectral descriptor and choose an appropriate spectral feature in a particular application.
ABSTRACT
ETHNOPHARMACOLOGICAL RELEVANCE: Gancao Decoction (GCD) is widely used to treat cholestatic liver injury. However, it is unclear whether is related to prevent hepatocellular necroptosis. AIM OF THE STUDY: The purpose of this study is to clarify the therapeutic effects of GCD against hepatocellular necroptosis induced by cholestasis and its active components. MATERIALS AND METHODS: We induced cholestasis model in wild type mice by ligating the bile ducts or in Nlrp3-/- mice by intragastrical administering Alpha-naphthylisothiocyanate (ANIT). Serum biochemical indices, liver pathological changes and hepatic bile acids (BAs) were measured to evaluate GCD's hepatoprotective effects. Necroptosis was assessed by expression of hallmarkers in mice liver. Moreover, the potential anti-necroptotic effect of components from GCD were investigated and confirmed in ANIT-induced cholestasis mice and in primary hepatocytes from WT mouse stimulated with Tumor Necrosis Factor alpha (TNF-α) and cycloheximide (CHX). RESULTS: GCD dose-dependently alleviated hepatic necrosis, reduced serum aminotranferase activity in both BDL and ANIT-induced cholestasis models. More importantly, the expression of hallmarkers of necroptosis, including MLKL, RIPK1 and RIPK3 phosphorylation (p- MLKL, p-RIPK1, p-RIPK3) were reduced upon GCD treatment. Glycyrrhetinic acid (GA), the main bioactive metabolite of GCD, effectively protected against ANIT-induced cholestasis, with decreased expression of p-MLKL, p-RIPK1 and p-RIPK3. Meanwhile, the expression of Fas-associated death domain protein (FADD), long isoform of cellular FLICE-like inhibitory protein (cFLIPL) and cleaved caspase 8 were upregulated upon GA treatment. Moreover, GA significantly increased the expression of active caspase 8, and reduced that of p-MLKL in TNF-α/CHX induced hepatocytes necroptosis. CONCLUSIONS: GCD substantially inhibits necroptosis in cholestatic liver injury. GA is the main bioactive component responsible for the anti-necroptotic effects, which correlates with upregulation of c-FLIPL and active caspase 8.
Subject(s)
Cholestasis , Drugs, Chinese Herbal , Glycyrrhetinic Acid , Glycyrrhiza , Mice , Animals , Tumor Necrosis Factor-alpha/pharmacology , Caspase 8 , Necroptosis , Liver , Cholestasis/chemically induced , Cholestasis/drug therapy , Cholestasis/pathology , Glycyrrhetinic Acid/pharmacology , 1-Naphthylisothiocyanate/toxicityABSTRACT
Current literature emphasizes surgical complexities and customized resection for managing insular gliomas; however, radiogenomic investigations into prognostic radiomic traits remain limited. We aimed to develop and validate a radiomic model using multiparametric magnetic resonance imaging (MRI) for prognostic prediction and to reveal the underlying biological mechanisms. Radiomic features from preoperative MRI were utilized to develop and validate a radiomic risk signature (RRS) for insular gliomas, validated through paired MRI and RNA-seq data (N = 39), to identify core pathways underlying the RRS and individual prognostic radiomic features. An 18-feature-based RRS was established for overall survival (OS) prediction. Gene set enrichment analysis (GSEA) and weighted gene coexpression network analysis (WGCNA) were used to identify intersectional pathways. In total, 364 patients with insular gliomas (training set, N = 295; validation set, N = 69) were enrolled. RRS was significantly associated with insular glioma OS (log-rank p = 0.00058; HR = 3.595, 95% CI:1.636-7.898) in the validation set. The radiomic-pathological-clinical model (R-P-CM) displayed enhanced reliability and accuracy in prognostic prediction. The radiogenomic analysis revealed 322 intersectional pathways through GSEA and WGCNA fusion; 13 prognostic radiomic features were significantly correlated with these intersectional pathways. The RRS demonstrated independent predictive value for insular glioma prognosis compared with established clinical and pathological profiles. The biological basis for prognostic radiomic indicators includes immune, proliferative, migratory, metabolic, and cellular biological function-related pathways.
Subject(s)
Biological Products , Brain Neoplasms , Glioma , Humans , Brain Neoplasms/diagnostic imaging , Brain Neoplasms/genetics , Brain Neoplasms/metabolism , Reproducibility of Results , Radiomics , Retrospective Studies , Magnetic Resonance Imaging/methods , Glioma/diagnostic imaging , Glioma/genetics , Glioma/metabolism , PrognosisABSTRACT
PURPOSE: Cerebrovascular segmentation and quantification of vascular morphological features in humans and rhesus monkeys are essential for prevention, diagnosis, and treatment of brain diseases. However, current automated whole-brain vessel segmentation methods are often not generalizable to independent datasets, limiting their usefulness in real-world environments with their heterogeneity in participants, scanners, and species. MATERIALS AND METHODS: In this study, we proposed an automated, accurate and generalizable segmentation method for magnetic resonance angiography images called FFCM-MRF. This method integrated fast fuzzy c-means clustering and Markov random field optimization by vessel shape priors and spatial constraints. We used a total of 123 human and 44 macaque MRA images scanned at 1.5 T, 3 T, and 7 T MRI from 9 datasets to develop and validate the method. RESULTS: FFCM-MRF achieved average Dice similarity coefficients ranging from 69.16 % to 89.63 % across multiple independent datasets, with improvements ranging from 3.24 % to 7.3 % compared to state-of-the-art methods. Quantitative analysis showed that FFCM-MRF can accurately segment major arteries in the Circle of Willis at the base of the brain and small distal pial arteries while effectively reducing noise. Test-retest analysis showed that the model yielded high vascular volume and diameter reliability. CONCLUSIONS: Our results have demonstrated that FFCM-MRF is highly accurate and reliable and largely independent of variations in field strength, scanner platforms, acquisition parameters, and species. The macaque MRA data and user-friendly open-source toolbox are freely available at OpenNeuro and GitHub to facilitate studies of imaging biomarkers for cerebrovascular and neurodegenerative diseases.
Subject(s)
Magnetic Resonance Angiography , Magnetic Resonance Imaging , Humans , Animals , Magnetic Resonance Angiography/methods , Macaca mulatta , Reproducibility of Results , Brain/diagnostic imaging , Brain/blood supply , AlgorithmsABSTRACT
INTRODUCTION: The metabotropic glutamate receptor 4 (mGlu4, GRM4) exhibits significant expression within the central nervous system (CNS) and has been implicated to be correlated with a poor prognosis. OBJECTIVE: This study was aimed to elucidate the relationship between the expression profile of GRM4 and the prognosis of glioma patients. METHODS: RNA-sequencing datasets from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and China Glioma Genome Atlas (CGGA) repositories were used to evaluate the potential relationship. The value of clinical prognostic about GRM4 was assessed using clinical survival data from CGGA and TCGA. The GEPIA database was used to select genes like GRM4. PPI network was constructed by the database of (STRING), GO and KEGG analyses were performed. TargetScan, TarBase, miRDB, and starBase were used to explore miRNAs that could regulate GRM4 expression. EWAS Data Hub, MethSurv, and MEXPRESS were used for the analysis and relationship between DNA methylation and GRM4 expression and prognosis in glioma. TIMER2.0 and CAMOIP databases were used to assess the association between immune cell infiltration and GRM4. Human GBM cell lines were used to validate the function of GRM4. RESULTS: Our study shows that GRM4 is under expressed among gliomas and accompanied by poorer OS. Multivariate analysis showed that low mRNA expression of GRM4 was an independent factor of prognostic for shorter OS in all glioma patients. MiR-1262 affects the malignant phenotype of gliomas through GRM4. Methylation of DNA plays an important role in the instruction of GRM4 expression, the methylation level of GRM4 in glioma tissue is higher in comparison to normal tissue, and the higher methylation level was accompanied with the worse prognosis. Further analysis showed that GRM4 mRNA expression in GBM linked negatively with common lymphoid progenitor, Macrophage M1, Macrophage, and T cell CD4+ Th2, but not with the tumor purity. Overexpression of GRM4 prevents the migration of human GBM cell lines in vitro. CONCLUSION: GRM4 may have a substantial impact on the infiltration of immune cells and serve as a valuable prognostic biomarker in gliomas.