Rupture-Related Features of Cerebral Arteriovenous Malformations and Their Utility in Predicting Hemorrhage.

BACKGROUND: Evaluating rupture risk in cerebral arteriovenous malformations currently lacks quantitative hemodynamic and angioarchitectural features necessary for predicting subsequent hemorrhage. We aimed to derive rupture-related hemodynamic and angioarchitectural features of arteriovenous malformations and construct an ensemble model for predicting subsequent hemorrhage. METHODS: This retrospective study included 3 data sets, as follows: training and test data sets comprising consecutive patients with untreated cerebral arteriovenous malformations who were admitted from January 2015 to June 2022 and a validation data set comprising patients with unruptured arteriovenous malformations who received conservative treatment between January 2009 and December 2014. We extracted rupture-related features and developed logistic regression (clinical features), decision tree (hemodynamic features), and support vector machine (angioarchitectural features) models. These 3 models were combined into an ensemble model using a weighted soft-voting strategy. The performance of the models in discriminating ruptured arteriovenous malformations and predicting subsequent hemorrhage was evaluated with confusion matrix-related metrics in the test and validation data sets. RESULTS: A total of 896 patients (mean±SD age, 28±14 years; 404 women) were evaluated, with 632, 158, and 106 patients in the training, test, and validation data sets, respectively. From the training set, 9 clinical, 10 hemodynamic, and 2912 pixel-based angioarchitectural features were extracted. A logistic regression model was built using 4 selected clinical features (age, nidus size, location, and venous aneurysm), whereas a decision-tree model was constructed from 4 hemodynamic features (outflow time, stasis index, cerebral blood flow, and outflow volume ratio). A support vector machine model was designed using 5 pixel-based angioarchitectural features. In the validation data set, the accuracy, sensitivity, specificity, and area under the curve of the ensemble model for predicting subsequent hemorrhages were 0.840, 0.889, 0.823, and 0.911, respectively. CONCLUSIONS: The ensemble model incorporating clinical, hemodynamic, and angioarchitectural features showed favorable performance in predicting subsequent hemorrhage of cerebral arteriovenous malformations.

MEKK3-TGFß crosstalk regulates inward arterial remodeling.

Arterial remodeling is an important adaptive mechanism that maintains normal fluid shear stress in a variety of physiologic and pathologic conditions. Inward remodeling, a process that leads to reduction in arterial diameter, plays a critical role in progression of such common diseases as hypertension and atherosclerosis. Yet, despite its pathogenic importance, molecular mechanisms controlling inward remodeling remain undefined. Mitogen-activated protein kinases (MAPKs) perform a number of functions ranging from control of proliferation to migration and cell-fate transitions. While the MAPK ERK1/2 signaling pathway has been extensively examined in the endothelium, less is known about the role of the MEKK3/ERK5 pathway in vascular remodeling. To better define the role played by this signaling cascade, we studied the effect of endothelial-specific deletion of its key upstream MAP3K, MEKK3, in adult mice. The gene's deletion resulted in a gradual inward remodeling of both pulmonary and systematic arteries, leading to spontaneous hypertension in both vascular circuits and accelerated progression of atherosclerosis in hyperlipidemic mice. Molecular analysis revealed activation of TGFß-signaling both in vitro and in vivo. Endothelial-specific TGFßR1 knockout prevented inward arterial remodeling in MEKK3 endothelial knockout mice. These data point to the unexpected participation of endothelial MEKK3 in regulation of TGFßR1-Smad2/3 signaling and inward arterial remodeling in artery diseases.

A Bayesian multivariate mixture model for high throughput spatial transcriptomics.

High throughput spatial transcriptomics (HST) is a rapidly emerging class of experimental technologies that allow for profiling gene expression in tissue samples at or near single-cell resolution while retaining the spatial location of each sequencing unit within the tissue sample. Through analyzing HST data, we seek to identify sub-populations of cells within a tissue sample that may inform biological phenomena. Existing computational methods either ignore the spatial heterogeneity in gene expression profiles, fail to account for important statistical features such as skewness, or are heuristic-based network clustering methods that lack the inferential benefits of statistical modeling. To address this gap, we develop SPRUCE: a Bayesian spatial multivariate finite mixture model based on multivariate skew-normal distributions, which is capable of identifying distinct cellular sub-populations in HST data. We further implement a novel combination of Pólya-Gamma data augmentation and spatial random effects to infer spatially correlated mixture component membership probabilities without relying on approximate inference techniques. Via a simulation study, we demonstrate the detrimental inferential effects of ignoring skewness or spatial correlation in HST data. Using publicly available human brain HST data, SPRUCE outperforms existing methods in recovering expertly annotated brain layers. Finally, our application of SPRUCE to human breast cancer HST data indicates that SPRUCE can distinguish distinct cell populations within the tumor microenvironment. An R package spruce for fitting the proposed models is available through The Comprehensive R Archive Network.

High-resolution magnetic resonance imaging-based radiomic features aid in selecting endovascular candidates among patients with cerebral venous sinus thrombosis.

OBJECTIVES: Cerebral venous sinus thrombosis (CVST) can cause sinus obstruction and stenosis, with potentially fatal consequences. High-resolution magnetic resonance imaging (HRMRI) can diagnose CVST qualitatively, although quantitative screening methods are lacking for patients refractory to anticoagulation therapy and who may benefit from endovascular treatment (EVT). Thus, in this study, we used radiomic features (RFs) extracted from HRMRI to build machine learning models to predict response to drug therapy and determine the appropriateness of EVT. MATERIALS AND METHODS: RFs were extracted from three-dimensional T1-weighted motion-sensitized driven equilibrium (MSDE), T2-weighted MSDE, T1-contrast, and T1-contrast MSDE sequences to build radiomic signatures and support vector machine (SVM) models for predicting the efficacy of standard drug therapy and the necessity of EVT. RESULTS: We retrospectively included 53 patients with CVST in a prospective cohort study, among whom 14 underwent EVT after standard drug therapy failed. Thirteen RFs were selected to construct the RF signature and CVST-SVM models. In the validation dataset, the sensitivity, specificity, and area under the curve performance for the RF signature model were 0.833, 0.937, and 0.977, respectively. The radiomic score was correlated with days from symptom onset, history of dyslipidemia, smoking, fibrin degradation product, and D-dimer levels. The sensitivity, specificity, and area under the curve for the CVST-SVM model in the validation set were 0.917, 0.969, and 0.992, respectively. CONCLUSIONS: The CVST-SVM model trained with RFs extracted from HRMRI outperformed the RF signature model and could aid physicians in predicting patient responses to drug treatment and identifying those who may require EVT.

IRIS-FGM: an integrative single-cell RNA-Seq interpretation system for functional gene module analysis.

SUMMARY: Single-cell RNA-Seq (scRNA-Seq) data is useful in discovering cell heterogeneity and signature genes in specific cell populations in cancer and other complex diseases. Specifically, the investigation of condition-specific functional gene modules (FGM) can help to understand interactive gene networks and complex biological processes in different cell clusters. QUBIC2 is recognized as one of the most efficient and effective biclustering tools for condition-specific FGM identification from scRNA-Seq data. However, its limited availability to a C implementation restricted its application to only a few downstream analysis functionalities. We developed an R package named IRIS-FGM (Integrative scRNA-Seq Interpretation System for Functional Gene Module analysis) to support the investigation of FGMs and cell clustering using scRNA-Seq data. Empowered by QUBIC2, IRIS-FGM can effectively identify condition-specific FGMs, predict cell types/clusters, uncover differentially expressed genes and perform pathway enrichment analysis. It is noteworthy that IRIS-FGM can also take Seurat objects as input, facilitating easy integration with the existing analysis pipeline. AVAILABILITY AND IMPLEMENTATION: IRIS-FGM is implemented in the R environment (as of version 3.6) with the source code freely available at https://github.com/BMEngineeR/IRISFGM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Wolframin is a novel regulator of tau pathology and neurodegeneration.

Selective neuronal vulnerability to protein aggregation is found in many neurodegenerative diseases including Alzheimer's disease (AD). Understanding the molecular origins of this selective vulnerability is, therefore, of fundamental importance. Tau protein aggregates have been found in Wolframin (WFS1)-expressing excitatory neurons in the entorhinal cortex, one of the earliest affected regions in AD. The role of WFS1 in Tauopathies and its levels in tau pathology-associated neurodegeneration, however, is largely unknown. Here we report that WFS1 deficiency is associated with increased tau pathology and neurodegeneration, whereas overexpression of WFS1 reduces those changes. We also find that WFS1 interacts with tau protein and controls the susceptibility to tau pathology. Furthermore, chronic ER stress and autophagy-lysosome pathway (ALP)-associated genes are enriched in WFS1-high excitatory neurons in human AD at early Braak stages. The protein levels of ER stress and autophagy-lysosome pathway (ALP)-associated proteins are changed in tau transgenic mice with WFS1 deficiency, while overexpression of WFS1 reverses those changes. This work demonstrates a possible role for WFS1 in the regulation of tau pathology and neurodegeneration via chronic ER stress and the downstream ALP. Our findings provide insights into mechanisms that underpin selective neuronal vulnerability, and for developing new therapeutics to protect vulnerable neurons in AD.

IRIS3: integrated cell-type-specific regulon inference server from single-cell RNA-Seq.

A group of genes controlled as a unit, usually by the same repressor or activator gene, is known as a regulon. The ability to identify active regulons within a specific cell type, i.e., cell-type-specific regulons (CTSR), provides an extraordinary opportunity to pinpoint crucial regulators and target genes responsible for complex diseases. However, the identification of CTSRs from single-cell RNA-Seq (scRNA-Seq) data is computationally challenging. We introduce IRIS3, the first-of-its-kind web server for CTSR inference from scRNA-Seq data for human and mouse. IRIS3 is an easy-to-use server empowered by over 20 functionalities to support comprehensive interpretations and graphical visualizations of identified CTSRs. CTSR data can be used to reliably characterize and distinguish the corresponding cell type from others and can be combined with other computational or experimental analyses for biomedical studies. CTSRs can, therefore, aid in the discovery of major regulatory mechanisms and allow reliable constructions of global transcriptional regulation networks encoded in a specific cell type. The broader impact of IRIS3 includes, but is not limited to, investigation of complex diseases hierarchies and heterogeneity, causal gene regulatory network construction, and drug development. IRIS3 is freely accessible from https://bmbl.bmi.osumc.edu/iris3/ with no login requirement.

A comprehensive model including preoperative peripheral blood inflammatory markers for prediction of the prognosis of diffuse spinal cord astrocytoma following surgery.

PURPOSE: Due to the rarity of diffuse spinal cord astrocytoma, an effective model is still lacking to stratify their prognosis. Here, we aimed to establish a prognostic model through comprehensively evaluating clinicopathological features and preoperative peripheral blood inflammatory markers in 89 cases. METHODS: We performed univariate and multivariate Cox regression to identify prognosis factors. The Kaplan-Meier curves and ROC curves were employed to compare the prognostic value of selected factors. RESULTS: In addition to clinicopathological factors, we revealed the preoperative peripheral blood leukocyte count, neutrophils-to-lymphocytes ratio (NLR), and platelet-to-lymphocyte ratio (PLR) were also significantly correlated with overall survival of spinal cord astrocytoma in univariate Cox regression, and NLR was still significant in multivariate Cox analysis. Further, we demonstrated that NLR ≤ 3.65 and preoperative McCormick score (MMS) ≤ 3 were independently correlated with better survival of WHO grade IV tumors. Meanwhile, Ki-67 < 10% and resection extent ≥ 90% were independent prognostic factors in WHO grade II/III tumors. Finally, we developed a prognostic model that had better predictive efficiencies than WHO grade and histological grade for 1-year (AUC = 76.6), 2- year (AUC = 80.9), and 3-year (AUC = 80.3) survival. This model could classify tumors into 4 classifications with increasingly poor prognosis: 1, WHO grade II/III, with Ki-67 < 10% and resection extent ≥ 90%; 2, WHO grade II/III, Ki-67 ≥ 10% or resection < 90%; 3, WHO grade IV, NLR ≤ 3.65 and MMS ≤ 3; 4, WHO grade IV, with NRL > 3.65 or MMS = 4. CONCLUSION: We successfully constructed a comprehensive prognostic model including preoperative peripheral blood inflammatory markers, which can stratify diffuse spinal cord astrocytoma into 4 subgroups.

Systematically characterize the clinical and biological significances of 1p19q genes in 1p/19q non-codeletion glioma.

1p/19q codeletion, which leads to the abnormal expression of 1p19q genes in oligodendroglioma, is associated with chemosensitivity and favorable prognosis. Here, we aimed to explore the clinical implications of 1p19q gene expression in 1p/19q non-codel gliomas. We analyzed expression of 1p19q genes in 668 1p/19q non-codel gliomas obtained from The Cancer Genome Atlas (n = 447) and the Chinese Glioma Genome Atlas (n = 221) for training and validation, respectively. The expression of 1p19q genes was significantly correlated with the clinicopathological features and overall survival of 1p/19q non-codel gliomas. Then, we derived a risk signature of 25 selected 1p19q genes that not only had prognosis value in total 1p/19q non-codel gliomas but also had prognosis value in stratified gliomas. The prognosis value of the risk signature was superior than known clinicopathological features in 1p/19q non-codel gliomas and was also highly associated with the following features: loss of CDKN2A/B copy number in mutant-IDH-astrocytoma; telomerase reverse transcriptase (TERT) promoter mutation, combined chromosome 7 gain/chromosome 10 loss and epidermal growth factor receptor amplification in wild-type-IDH-astrocytoma; classical and mesenchymal subtypes in glioblastoma. Furthermore, genes enriched in the biological processes of cell division, extracellular matrix, angiogenesis significantly correlated to the signature risk score, and this is also supported by the immunohistochemistry and cell biology experiments. In conclusion, the expression profile of 1p19q genes is highly associated with the malignancy and prognosis of 1p/19q non-codel gliomas. A 25-1p19q-gene signature has powerfully predictive value for both malignant molecular pathological features and prognosis across distinct subgroups of 1p/19q non-codel gliomas.

Immunomodulatory activity of Ganoderma lucidum immunomodulatory protein via PI3K/Akt and MAPK signaling pathways in RAW264.7 cells.

Ganoderma lucidum immunomodulatory protein (FIP-glu) is an active ingredient with potential immunoregulatory functions. The study was conducted to explore the immunomodulatory activities of recombinant FIP-glu (rFIP-glu) and its possible mechanism in macrophage RAW264.7 cells. In vitro assays of biological activity indicated that rFIP-glu significantly activated RAW264.7 cells and possessed proinflammatory and anti-inflammatory abilities. RNA sequencing analysis and Western blot analysis showed that macrophage activation involved PI3K/Akt and MAPK pathways. Furthermore, real-time quantitative polymerase chain reaction indicated that the PI3K inhibitor LY294002 blocked the messenger RNA (mRNA) levels of MCP-1 (CCL-2), the MEK1/2 inhibitor U0126 reduced the mRNA levels of TNF-α and MCP-1 (CCL-2), and the JNK1/2/3 inhibitor SP600125 prevented the upregulation of inducible nitric oxide synthase mRNA in rFIP-glu-induced cells. rFIP-glu did not mediate these inflammatory effects through a general pathway but rather through a different pathway for a different inflammatory mediator. These data imply that rFIP-glu possessed immunomodulatory activity in macrophages, which was mediated through PI3K/Akt and MAPK pathways.

Systematically profiling the expression of eIF3 subunits in glioma reveals the expression of eIF3i has prognostic value in IDH-mutant lower grade glioma.

BACKGROUND: Abnormal expression of the eukaryotic initiation factor 3 (eIF3) subunits plays critical roles in tumorigenesis and progression, and also has potential prognostic value in cancers. However, the expression and clinical implications of eIF3 subunits in glioma remain unknown. METHODS: Expression data of eIF3 for patients with gliomas were obtained from the Chinese Glioma Genome Atlas (CGGA) (n = 272) and The Cancer Genome Atlas (TCGA) (n = 595). Cox regression, the receiver operating characteristic (ROC) curves and Kaplan-Meier analysis were used to study the prognostic value. Gene oncology (GO) and gene set enrichment analysis (GSEA) were utilized for functional prediction. RESULTS: In both the CGGA and TCGA datasets, the expression levels of eIF3d, eIF3e, eIF3f, eIF3h and eIF3l highly were associated with the IDH mutant status of gliomas. The expression of eIF3b, eIF3i, eIF3k and eIF3m was increased with the tumor grade, and was associated with poorer overall survival [All Hazard ratio (HR) > 1 and P < 0.05]. By contrast, the expression of eIF3a and eIF3l was decreased in higher grade gliomas and was associated with better overall survival (Both HR < 1 and P < 0.05). Importantly, the expression of eIF3i (located on chromosome 1p) and eIF3k (Located on chromosome 19q) were the two highest risk factors in both the CGGA [eIF3i HR = 2.068 (1.425-3.000); eIF3k HR = 1.737 (1.166-2.588)] and TCGA [eIF3i HR = 1.841 (1.642-2.064); eIF3k HR = 1.521 (1.340-1.726)] databases. Among eIF3i, eIF3k alone or in combination, the expression of eIF3i was the more robust in stratifying the survival of glioma in various pathological subgroups. The expression of eIF3i was an independent prognostic factor in IDH-mutant lower grade glioma (LGG) and could also predict the 1p/19q codeletion status of IDH-mutant LGG. Finally, GO and GSEA analysis showed that the elevated expression of eIF3i was significantly correlated with the biological processes of cell proliferation, mRNA processing, translation, T cell receptor signaling, NF-κB signaling and others. CONCLUSIONS: Our study reveals the expression alterations during glioma progression, and highlights the prognostic value of eIF3i in IDH-mutant LGG.

Downregulated miR-27b promotes keratinocyte proliferation by targeting PLK2 in oral lichen planus.

BACKGROUND: MicroRNA-27b (miR-27b) was recently found to be significantly downregulated in oral lichen planus (OLP). However, evidence of the function of miR-27b in OLP remains limited. METHODS: Initially, miR-27b expression in OLP was verified using the quantitative real-time polymerase chain reaction (qRT-PCR). Functionally, gain-/loss-of-function studies were then conducted using miR-27b mimics/inhibitor to investigate cell growth in human oral keratinocytes (HOKs). Mechanistically, subsequent miRNA target analyses including a starBase database analysis and a luciferase reporter assay were performed to predict and validate the direct target, respectively. In addition, overexpression/knockdown assays of target(s) of miR-27b were performed to investigate its functional significance and qRT-PCR and western blotting were used to evaluate the target(s) of miR-27b mRNA and protein levels, respectively. RESULTS: MicroRNA-27b was significantly downregulated in OLP tissues when compared with healthy control tissues. Bioinformatics predicted that Polo Like Kinase 2 (PLK2) might be a potential target of miR-27b, while the luciferase reporter assay results showed the direct inhibition of the plk2-3'untranslated region by miR-27b. Moreover, functional analysis indicated that downregulated miR-27b caused an increase in cell growth in HOKs, and correspondingly, overexpression of PLK2 promoted HOK proliferation. CONCLUSIONS: There were aberrant expressions of miR-27b and PLK2 in OLP tissues. Decreased miR-27b may have induced cell proliferation by increasing the levels of PLK2 in HOKs, which provides a new perspective into the potential mechanisms underlying OLP development.

Prediction of Venous Trans-Stenotic Pressure Gradient Using Shape Features Derived From Magnetic Resonance Venography in Idiopathic Intracranial Hypertension Patients.

OBJECTIVE: Idiopathic intracranial hypertension (IIH) is a condition of unknown etiology associated with venous sinus stenosis. This study aimed to develop a magnetic resonance venography (MRV)-based radiomics model for predicting a high trans-stenotic pressure gradient (TPG) in IIH patients diagnosed with venous sinus stenosis. MATERIALS AND METHODS: This retrospective study included 105 IIH patients (median age [interquartile range], 35 years [27-42 years]; female:male, 82:23) who underwent MRV and catheter venography complemented by venous manometry. Contrast enhanced-MRV was conducted under 1.5 Tesla system, and the images were reconstructed using a standard algorithm. Shape features were derived from MRV images via the PyRadiomics package and selected by utilizing the least absolute shrinkage and selection operator (LASSO) method. A radiomics score for predicting high TPG (≥ 8 mmHg) in IIH patients was formulated using multivariable logistic regression; its discrimination performance was assessed using the area under the receiver operating characteristic curve (AUROC). A nomogram was constructed by incorporating the radiomics scores and clinical features. RESULTS: Data from 105 patients were randomly divided into two distinct datasets for model training (n = 73; 50 and 23 with and without high TPG, respectively) and testing (n = 32; 22 and 10 with and without high TPG, respectively). Three informative shape features were identified in the training datasets: least axis length, sphericity, and maximum three-dimensional diameter. The radiomics score for predicting high TPG in IIH patients demonstrated an AUROC of 0.906 (95% confidence interval, 0.836-0.976) in the training dataset and 0.877 (95% confidence interval, 0.755-0.999) in the test dataset. The nomogram showed good calibration. CONCLUSION: Our study presents the feasibility of a novel model for predicting high TPG in IIH patients using radiomics analysis of noninvasive MRV-based shape features. This information may aid clinicians in identifying patients who may benefit from stenting.

A contrastive learning approach to integrate spatial transcriptomics and histological images.

The rapid growth of spatially resolved transcriptomics technology provides new perspectives on spatial tissue architecture. Deep learning has been widely applied to derive useful representations for spatial transcriptome analysis. However, effectively integrating spatial multi-modal data remains challenging. Here, we present ConGcR, a contrastive learning-based model for integrating gene expression, spatial location, and tissue morphology for data representation and spatial tissue architecture identification. Graph convolution and ResNet were used as encoders for gene expression with spatial location and histological image inputs, respectively. We further enhanced ConGcR with a graph auto-encoder as ConGaR to better model spatially embedded representations. We validated our models using 16 human brains, four chicken hearts, eight breast tumors, and 30 human lung spatial transcriptomics samples. The results showed that our models generated more effective embeddings for obtaining tissue architectures closer to the ground truth than other methods. Overall, our models not only can improve tissue architecture identification's accuracy but also may provide valuable insights and effective data representation for other tasks in spatial transcriptome analyses.

Graph Fourier transform for spatial omics representation and analyses of complex organs.

Spatial omics technologies are capable of deciphering detailed components of complex organs or tissue in cellular and subcellular resolution. A robust, interpretable, and unbiased representation method for spatial omics is necessary to illuminate novel investigations into biological functions, whereas a mathematical theory deficiency still exists. We present SpaGFT (Spatial Graph Fourier Transform), which provides a unique analytical feature representation of spatial omics data and elucidates molecular signatures linked to critical biological processes within tissues and cells. It outperformed existing tools in spatially variable gene prediction and gene expression imputation across human/mouse Visium data. Integrating SpaGFT representation into existing machine learning frameworks can enhance up to 40% accuracy of spatial domain identification, cell type annotation, cell-to-spot alignment, and subcellular hallmark inference. SpaGFT identified immunological regions for B cell maturation in human lymph node Visium data, characterized secondary follicle variations from in-house human tonsil CODEX data, and detected extremely rare subcellular organelles such as Cajal body and Set1/COMPASS. This new method lays the groundwork for a new theoretical model in explainable AI, advancing our understanding of tissue organization and function.

Spatial and functional targeting of intratumoral Tregs reverses CD8+ T cell exhaustion and promotes cancer immunotherapy.

Intratumoral regulatory T cells (Tregs) are key mediators of cancer immunotherapy resistance, including anti-PD-(L)1 immune checkpoint blockade (ICB). The mechanisms driving Treg infiltration into the tumor microenvironment (TME) and the consequence on CD8+ T cell exhaustion remains elusive. Herein, we report that heat shock protein gp96 (GRP94) is indispensable for Treg tumor infiltration, primarily through gp96's roles in chaperoning integrins. Among various gp96-dependent integrins, we found that only LFA-1 (αL integrin) but not αV, CD103 (αE) or ß7 integrin was required for Treg tumor homing. Loss of Treg infiltration into the TME by genetically deleting gp96/LFA-1 potently induces rejection of multiple ICB-resistant murine cancer models in a CD8+ T cell-dependent manner without loss of self-tolerance. Moreover, gp96 deletion impeded Treg activation primarily by suppressing IL-2/STAT5 signaling, which also contributes to tumor regression. By competing for intratumoral IL-2, Tregs prevent activation of CD8+ tumor-infiltrating lymphocytes (TILs), drive TOX induction and induce bona fide CD8+ T cell exhaustion. By contrast, Treg ablation leads to striking CD8+ T cell activation without TOX induction, demonstrating clear uncoupling of the two processes. Our study reveals that the gp96/LFA-1 axis plays a fundamental role in Treg biology and suggests that Treg-specific gp96/LFA-1 targeting represents a valuable strategy for cancer immunotherapy without inflicting autoinflammatory conditions.

Epstein-Barr Virus Orchestrates Spatial Reorganization and Immunomodulation within the Classic Hodgkin Lymphoma Tumor Microenvironment.

Classic Hodgkin Lymphoma (cHL) is a tumor composed of rare malignant Hodgkin and Reed-Sternberg (HRS) cells nested within a T-cell rich inflammatory immune infiltrate. cHL is associated with Epstein-Barr Virus (EBV) in 25% of cases. The specific contributions of EBV to the pathogenesis of cHL remain largely unknown, in part due to technical barriers in dissecting the tumor microenvironment (TME) in high detail. Herein, we applied multiplexed ion beam imaging (MIBI) spatial pro-teomics on 6 EBV-positive and 14 EBV-negative cHL samples. We identify key TME features that distinguish between EBV-positive and EBV-negative cHL, including the relative predominance of memory CD8 T cells and increased T-cell dysfunction as a function of spatial proximity to HRS cells. Building upon a larger multi-institutional cohort of 22 EBV-positive and 24 EBV-negative cHL samples, we orthogonally validated our findings through a spatial multi-omics approach, coupling whole transcriptome capture with antibody-defined cell types for tu-mor and T-cell populations within the cHL TME. We delineate contrasting transcriptomic immunological signatures between EBV-positive and EBV-negative cases that differently impact HRS cell proliferation, tumor-immune interactions, and mecha-nisms of T-cell dysregulation and dysfunction. Our multi-modal framework enabled a comprehensive dissection of EBV-linked reorganization and immune evasion within the cHL TME, and highlighted the need to elucidate the cellular and molecular fac-tors of virus-associated tumors, with potential for targeted therapeutic strategies.

Dimension-agnostic and granularity-based spatially variable gene identification.

Identifying spatially variable genes (SVGs) is critical in linking molecular cell functions with tissue phenotypes. Spatially resolved transcriptomics captures cellular-level gene expression with corresponding spatial coordinates in two or three dimensions and can be used to infer SVGs effectively. However, current computational methods may not achieve reliable results and often cannot handle three-dimensional spatial transcriptomic data. Here we introduce BSP (big-small patch), a spatial granularity-guided and non-parametric model to identify SVGs from two or three-dimensional spatial transcriptomics data in a fast and robust manner. This new method has been extensively tested in simulations, demonstrating superior accuracy, robustness, and high efficiency. BSP is further validated by substantiated biological discoveries in cancer, neural science, rheumatoid arthritis, and kidney studies with various types of spatial transcriptomics technologies.

Recent advances of bioactive proteins/polypeptides in the treatment of breast cancer.

Proteins do not only serve as nutrients to fulfill the demand for food, but also are used as a source of bioactive proteins/polypeptides for regulating physical functions and promoting physical health. Female breast cancer has the highest incidence in the world and is a serious threat to women's health. Bioactive proteins/polypeptides exert strong anti-tumor effects and exhibit inhibition of multiple breast cancer cells. This review discussed the suppressing effects of bioactive proteins/polypeptides on breast cancer in vitro and in vivo, and their mechanisms of migration and invasion inhibition, apoptosis induction, and cell cycle arrest. This may contribute to providing a basis for the development of bioactive proteins/polypeptides for the treatment of breast cancer.

Quantitative evaluation of the subsequent hemorrhage with arteriography-derived hemodynamic features in patients with untreated cerebral arteriovenous malformation.

Background: Patients with untreated cerebral arteriovenous malformations (AVMs) are at risk of intracerebral hemorrhage. However, treatment to prevent AVM hemorrhage carries risks. Objective: This study aimed to analyze the AVM nidus-related hemodynamic features and identify the risk factors for subsequent hemorrhage. Methods: We retrospectively identified patients with untreated AVMs who were assessed at our institution between March 2010 and March 2021. Patients with ≥6 months of treatment-free and hemorrhage-free follow-up after diagnosed by digital subtraction angiography were included in subsequent examinations. The hemodynamic features were extracted from five contrast flow-related parameter maps. The Kaplan-Meier analyses and Cox proportional hazards regression models were used to find the potential risk factors for subsequent hemorrhage. Results: Overall, 104 patients with a mean follow-up duration of 3.37 years (median, 2.42 years; range, 6-117 months) were included in study, and the annual risk of rupture was 3.7%. Previous rupture (hazard ratio [HR], 4.89; 95% confidence interval [CI], 1.16-20.72), deep AVM location (HR, 4.02; 95% CI, 1.01-15.99), higher cerebral blood volume (HR, 3.35; 95% CI, 1.15-9.74) in the nidus, and higher stasis index (HR, 1.54; 95% CI, 1.06-2.24) in the nidus were associated with subsequent hemorrhage in untreated AVMs. Conclusion: Higher cerebral blood volume and stasis index in the nidus suggest increased blood inflow and stagnant blood drainage. The combination of these factors may cause subsequent hemorrhage of AVMs.