Nuclear autoantigenic sperm protein facilitates glioblastoma progression and radioresistance by regulating the ANXA2/STAT3 axis.

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.

Physiologically based pharmacokinetic modeling for confirming the role of CYP3A1/2 and P-glycoprotein in detoxification mechanism between glycyrrhizic acid and aconitine in rats.

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.

Simultaneous determination of 11 oral targeted antineoplastic drugs and 2 active metabolites by LC-MS/MS in human plasma and its application to therapeutic drug monitoring in cancer patients.

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.

ResGEM: Multi-scale Graph Embedding Network for Residual Mesh Denoising.

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.

Computational Pathology for Prediction of Isocitrate Dehydrogenase Gene Mutation from Whole Slide Images in Adult Patients with Diffuse Glioma.

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.

Gancao decoction attenuates hepatic necroptosis via activating caspase 8 in cholestatic liver injury.

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.

Spectral Descriptors for 3D Deformable Shape Matching: A Comparative Survey.

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.

Radiomic profiling for insular diffuse glioma stratification with distinct biologic pathway activities.

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.

Low expression of GRM4 is associated with poor prognosis and tumor immune infiltration in glioma.

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.

FFCM-MRF: An accurate and generalizable cerebrovascular segmentation pipeline for humans and rhesus monkeys based on TOF-MRA.

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.

Leveraging a disulfidptosis­related lncRNAs signature for predicting the prognosis and immunotherapy of glioma.

BACKGROUND: Gliomas, a prevalent form of primary brain tumors, are linked with a high mortality rate and unfavorable prognoses. Disulfidptosis, an innovative form of programmed cell death, has received scant attention concerning disulfidptosis-related lncRNAs (DRLs). The objective of this investigation was to ascertain a prognostic signature utilizing DRLs to forecast the prognosis and treatment targets of glioma patients. METHODS: RNA-seq data were procured from The Cancer Genome Atlas database. Disulfidptosis-related genes were compiled from prior research. An analysis of multivariate Cox regression and the least absolute selection operator was used to construct a risk model using six DRLs. The risk signature's performance was evaluated via Kaplan-Meier survival curves and receiver operating characteristic curves. Additionally, functional analysis was carried out using GO, KEGG, and single-sample GSEA to investigate the biological functions and immune infiltration. The research also evaluated tumor mutational burden, therapeutic drug sensitivity, and consensus cluster analysis. Reverse transcription quantitative PCR was conducted to validate the expression level of DRLs. RESULTS: A prognostic signature comprising six DRLs was developed to predict the prognosis of glioma patients. High-risk patients had significantly shorter overall survival than low-risk patients. The robustness of the risk model was validated by receiver operating characteristic curves and subgroup survival analysis. Risk model was used independently as a prognostic indicator for the glioma patients. Notably, the low-risk patients displayed a substantial decrease in the immune checkpoints, the proportion of immune cells, ESTIMATE and immune score. IC50 values from the different risk groups allowed us to discern three drugs for the treatment of glioma patients. Lastly, the potential clinical significance of six DRLs was determined. CONCLUSIONS: A novel six DRLs signature was developed to predict prognosis and may provide valuable insights for patients with glioma seeking novel immunotherapy and targeted therapy.

Comparison of detection methods for carbonation depth of concrete.

This paper presents comprehensive research of the advantages and applicability of various concrete carbonation detection methods. Employing a combination of Phenolphthalein indicator (PI), Thermogravimetric analysis (TGA), X-ray phase analysis (XRD), Fourier transform infrared spectroscopy (FTIR), and Quantitative calcium carbonate analysis (CA), a detailed comparison to determine the carbonation depth in the partial carbonation zone of concrete specimens is conducted. Among the quantitative analysis methods, CA measures CaCO3 content based on chemical reactions, while TGA obtains the concentration distribution of Ca(OH)2 and CaCO3. Among qualitative analysis methods, XRD tested the intensity distribution of Ca(OH)2 and CaCO3, while FTIR traced the characteristic peaks of C-O functional groups in a specific spectral range to determine the depth of carbonation of concrete. Results indicate that the depth of carbonation values measured by CA, TGA, XRDA, and FTIR are 2-3 times higher than those measured by PI. This research may provide valuable insights for the design of carbonation detection in concrete.

Glycyrrhetinic acid attenuates endoplasmic reticulum stress-induced hepatocyte apoptosis via CHOP/DR5/Caspase 8 pathway in cholestasis.

Bile acid (BA)-induced apoptosis is a common pathologic feature of cholestatic liver injury. Glycyrrhetinic acid (GA) is the hepatoprotective constituent of licorice. In the present study, the anti-apoptotic potential of GA was investigated in wild type and macrophage-depleted C57BL/6 mice challenged with alpha-naphthyl isothiocyanate (ANIT), and hepatocytes stimulated with Taurocholic acid (TCA) or Tumor necrosis factor-alpha (TNF-α). Apoptosis was determined by TUNEL positive cells and expression of executioner caspases. Firstly, we found that GA markedly alleviated liver injury, accompanied with reduced positive TUNEL-staining cells, and expression of caspases 3, 8 and 9 in mice modeled with ANIT. Secondly, GA mitigated apoptosis in macrophage-depleted mice with exacerbated liver injury and augmented cell apoptosis. In vitro study, pre-treatment with GA reduced the expression of activated caspases 3 and 8 in hepatocytes stimulated with TCA, but not TNF-α. The ability of GA to ameliorate apoptosis was abolished in the presence of Tauroursodeoxycholic Acid (TUDCA), a chemical chaperon against Endoplasmic reticulum stress (ER stress). Furthermore, GA attenuated the over-expression of Glucose regulated protein 78 (GRP78), and blocked all three branches of Unfolded protein reaction (UPR) in cholestatic livers of mice induced by ANIT. GA also downregulated C/EBP homologous protein (CHOP) expression, accompanied with reduced expression of Death receptor 5 (DR5) and activation of caspase 8 in both ANIT-modeled mice and TCA-stimulated hepatocytes. The results indicate that GA inhibits ER stress-induced hepatocyte apoptosis in cholestasis, which correlates with blocking CHOP/DR5/Caspase 8 pathway.

Biological underpinnings of radiomic magnetic resonance imaging phenotypes for risk stratification in IDH wild-type glioblastoma.

BACKGROUND: To develop and validate a conventional MRI-based radiomic model for predicting prognosis in patients with IDH wild-type glioblastoma (GBM) and reveal the biological underpinning of the radiomic phenotypes. METHODS: A total of 801 adult patients (training set, N = 471; internal validation set, N = 239; external validation set, N = 91) diagnosed with IDH wild-type GBM were included. A 20-feature radiomic risk score (Radscore) was built for overall survival (OS) prediction by univariate prognostic analysis and least absolute shrinkage and selection operator (LASSO) Cox regression in the training set. GSEA and WGCNA were applied to identify the intersectional pathways underlying the prognostic radiomic features in a radiogenomic analysis set with paired MRI and RNA-seq data (N = 132). The biological meaning of the conventional MRI sequences was revealed using a Mantel test. RESULTS: Radscore was demonstrated to be an independent prognostic factor (P < 0.001). Incorporating the Radscore into a clinical model resulted in a radiomic-clinical nomogram predicting survival better than either the Radscore model or the clinical model alone, with better calibration and classification accuracy (a total net reclassification improvement of 0.403, P < 0.001). Three pathway categories (proliferation, DNA damage response, and immune response) were significantly correlated with the prognostic radiomic phenotypes. CONCLUSION: Our findings indicated that the prognostic radiomic phenotypes derived from conventional MRI are driven by distinct pathways involved in proliferation, DNA damage response, and immunity of IDH wild-type GBM.

Multimodal evaluation of the bloodstream alteration before and after combined revascularization for Moyamoya disease.

Objective: This study aimed to explore the hemodynamic changes before and after anastomosis in patients with Moyamoya disease (MMD) using multiple models. Methods: We prospectively enrolled 42 MMD patients who underwent combined revascularization. Intraoperative FLOW800 was performed before and after anastomosis, and parameters was collected, including maximum intensity, delay time, rise time, slope, blood flow index, and microvascular transit time (MVTT). Additionally, preoperative and postoperative hemodynamic parameters were measured using color Doppler ultrasonography (CDUS), including peak systolic velocity, end-diastolic velocity, resistance index (RI), pulsatility index (PI), and flow volume. Subsequently, the correlation between FLOW800 and CDUS parameters was explored. Results: A total of 42 participants took part with an average age of 46.5 years, consisting of 19 men and 23 women. The analysis of FLOW800 indicated that both the delay time and rise time experienced a substantial decrease in both the recipient artery and vein. Additionally, the MVTT was found to be significantly reduced after the surgery (5.7 ± 2.2 s vs. 4.9 ± 1.6, p = 0.021). However, no statistically significant differences were observed among the other parameters. Similarly, all postoperative parameters in CDUS hemodynamics exhibited significant alterations in comparison to the preoperative values. The correlation analysis between FLOW800 and CDUS parameters indicated a significant association between MVTT and RI and PI, no significant relationships were found among the other parameters in the two groups. Conclusion: The hemodynamic outcomes of the donor and recipient arteries demonstrated significant changes following bypass surgery. The parameter of time appears to be more precise and sensitive in assessing hemodynamics using FLOW800. Multiple evaluations of hemodynamics could offer substantial evidence for perioperative management.

Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images.

Current diagnosis of glioma types requires combining both histological features and molecular characteristics, which is an expensive and time-consuming procedure. Determining the tumor types directly from whole-slide images (WSIs) is of great value for glioma diagnosis. This study presents an integrated diagnosis model for automatic classification of diffuse gliomas from annotation-free standard WSIs. Our model is developed on a training cohort (n = 1362) and a validation cohort (n = 340), and tested on an internal testing cohort (n = 289) and two external cohorts (n = 305 and 328, respectively). The model can learn imaging features containing both pathological morphology and underlying biological clues to achieve the integrated diagnosis. Our model achieves high performance with area under receiver operator curve all above 0.90 in classifying major tumor types, in identifying tumor grades within type, and especially in distinguishing tumor genotypes with shared histological features. This integrated diagnosis model has the potential to be used in clinical scenarios for automated and unbiased classification of adult-type diffuse gliomas.

Radiomic features from multiparametric magnetic resonance imaging predict molecular subgroups of pediatric low-grade gliomas.

BACKGROUND: We aimed to develop machine learning models for prediction of molecular subgroups (low-risk group and intermediate/high-risk group) and molecular marker (KIAA1549-BRAF fusion) of pediatric low-grade gliomas (PLGGs) based on radiomic features extracted from multiparametric MRI. METHODS: 61 patients with PLGGs were included in this retrospective study, which were divided into a training set and an internal validation set at a ratio of 2:1 based on the molecular subgroups or the molecular marker. The patients were classified into low-risk and intermediate/high-risk groups, BRAF fusion positive and negative groups, respectively. We extracted 5929 radiomic features from multiparametric MRI. Thereafter, we removed redundant features, trained random forest models on the training set for predicting the molecular subgroups or the molecular marker, and validated their performance on the internal validation set. The performance of the prediction model was verified by 3-fold cross-validation. RESULTS: We constructed the classification model differentiating low-risk PLGGs from intermediate/high-risk PLGGs using 4 relevant features, with an AUC of 0.833 and an accuracy of 76.2% in the internal validation set. In the prediction model for predicting KIAA1549-BRAF fusion using 4 relevant features, an AUC of 0.818 and an accuracy of 81.0% were achieved in the internal validation set. CONCLUSIONS: The current study demonstrates that MRI radiomics is able to predict molecular subgroups of PLGGs and KIAA1549-BRAF fusion with satisfying sensitivity. TRIAL REGISTRATION: This study was retrospectively registered at clinicaltrials.gov (NCT04217018).

Ferritin light chain promotes the reprogramming of glioma immune microenvironment and facilitates glioma progression.

Background: Tumor-associated macrophages (TAMs), the most abundant non-tumor cell population in the glioma microenvironment, play a crucial role in immune evasion and immunotherapy resistance of glioblastoma (GBM). However, the regulatory mechanism of the immunosuppressive TME of GBM remains unclear. Methods: Bioinformatics were used to analyse the potential role of ferritin light chain (FTL) in GBM immunology and explore the effects of FTL on the reprogramming of the GBM immune microenvironment and GBM progression. Results: The FTL gene was found to be upregulated in TAMs of GBM at both the bulk and single-cell RNA-seq levels. FTL contributed to the protumor microenvironment by promoting M2 polarization in TAMs via inhibiting the expression of iPLA2ß to facilitate the ferroptosis pathway. Inhibition of FTL in TAMs attenuated glioma angiogenesis, promoted the recruitment of T cells and sensitized glioma to anti-PD1 therapy. Conclusion: Our study suggested that FTL promoted the development of an immunosuppressive TME by inducing M2 polarization in TAMs, and inhibition of FTL in TAMs reprogrammed the TME and sensitized glioma to anti-PD1 therapy, providing a new strategy for improving the therapeutic effect of anti-PD1.

Study on the Effect of Main Chain Molecular Structure on Adsorption, Dispersion, and Hydration of Polycarboxylate Superplasticizers.

Polycarboxylate ether (PCE) with different main chain structures was prepared by aqueous solution free radical polymerization using unsaturated acids containing sulfonic acid groups, acrylamide groups, and carboxyl groups and isoprenyl polyoxyethylene ether (IPEG). The molecular structure was characterized by infrared spectroscopy and gel chromatography, while adsorption, dispersion, and hydration properties were studied using a total organic carbon analyzer, rheometer, and isothermal microcalorimeter, respectively. The results show that the adsorption process of PCE on cement particles is spontaneous physical adsorption. The adsorption forces are mainly electrostatic interaction, and hydrogen bonding. The introduction of sulfonic acid groups and polycarboxylic acid groups reduces the initial adsorption amount of PCE but can accelerate the adsorption rate of PCE on cement and increase the adsorption amount at the adsorption equilibrium. The introduction of acrylamide groups in the PCE main chain is beneficial to the initial dispersion of PCE and can reduce the plastic viscosity of cement slurry. PCE can delay the hydration of cement. The introduction of acrylamide groups and dicarboxylic acid groups in the PCE main chain helps prolong the induction period of cement hydration, while the introduction of sulfonic acid groups is not conducive to its retarding effect.