Heterogeneous Interactions Research Articles

TMOD-28. MODELING MICROENVIRONMENTAL DYNAMICS: A NOVEL 3D GLIOMA-NEURON FUSION MODEL

Abstract Gliomas engage in bidirectional interactions with their microenvironment, wherein neuronal activity influences glioma proliferation and gliomas induce neuronal hyperexcitability. Although various models elucidate this complex interplay, a preference for a three-dimensional (3D) cell culture model has emerged to mimic intra-tumoral heterogeneity and microenvironmental interactions influencing glioma invasion, resistance, and recurrence. This study introduces an in vitro, self-assembled, scaffold-free, neuron-glioma spheroid fusion model to investigate the electrophysiological and functional interplay between glioma and its microenvironment. Primary mouse cortical neurons (MCN) were isolated from prenatal brain tissues and were cultured at 500,000 cells/spheroid on ultra-low attachment plates. Glioma organoids (WHO Grade 2-4) were developed from primary patient-derived tissue samples and were allowed to self-assemble for at least two weeks. After spontaneous spiking activity was detected in MCN spheroids, they were combined with glioma organoids in culture and allowed to fuse. A multielectrode array (MEA) was used to characterize the electrophysiological properties of the fusion model. Structural and functional characterizations were performed using immunofluorescence staining of proliferation, microglial, astrocytic, synaptic, and tumor markers. Additionally, the electrophysiological effects of the dual-IDH-mutant inhibitor, Vorasidenib, were assessed in WHO grade 2-4 IDH-mutant gliomas. Electrophysiological analysis of glioma-neuron co-cultures using MEA demonstrated a significantly increased network synchrony and firing rate in the fusion model when compared with the neuron-only condition, consistent with 2D models in the past. In correlation, the fusion model also demonstrated a significant increase in the Ki67 proliferation index across WHO grade 2-4 gliomas when compared to the glioma organoid-only condition. Vorasidenib treatment decreased neuronal firing rate and synchrony. Together, these results offer a high-throughput 3D model for recapitulating the tumor-brain microenvironment for both low and high-grade gliomas. Future studies will focus on the use of this model for drug screening and the exploration of malignant transformation in gliomas.

CNSC-22. GLIOBLASTOMA CELLS EXHIBIT NEURON-LIKE EXCITABILITY IN BOTH ACUTE AND ORGANOTYPIC HUMAN BRAIN SLICES

Abstract Glioblastomas (GBM) are known for their significant intratumor heterogeneity, featuring a variety of plastic cell types that make effective treatment challenging. Recent studies have shown that neuronal-progenitor-like transcriptomic cell states at the leading edge of the tumor receive synaptic input from nearby neurons, which drives disease proliferation. However, the excitability of GBM cells remains controversial, with observations ranging from non-excitable to neuron-like excitability, complicating our understanding of their pathophysiology. In this study, we developed a novel experimental approach to study glioblastoma cells in both acute and cultured brain slices infiltrated with cancer from GBM patients. Using an adeno-associated virus for gene delivery, we selectively expressed fluorescent proteins in specific cell types. Fluorescence-guided whole-cell patch-clamp recordings were then used to analyze the electrophysiological properties of GBM cells and neurons within the tumor microenvironment. Our findings show that GBM cells have distinct electrophysiological properties, including a depolarized resting membrane potential (-31.95 ± 2.18 mV, n = 55 cells) and high membrane resistance (1.27 ± 0.19 GΩ, n = 62 cells). Approximately 56% of GBM cells at the tumor’s leading edge exhibited neuron-like excitability, generating aberrant action potentials (aAPs) upon depolarization. Collectively, our study provides direct evidence of the intrinsic excitability of GBM cells and a detailed characterization of their electrophysiological properties in the cancer-infiltrated human cortex. These insights shed new light on tumor heterogeneity and cell-cell interactions in glioblastoma, potentially guiding the development of targeted therapies.

Dynamics of a single population model with memory and nonlinear boundary condition

In this paper, a heterogeneous single population model with memory effect and nonlinear boundary condition is investigated. By virtue of the implicit function theorem and Lyapunov-Schmidt reduction, spatially nonconstant positive steady state solutions appear from two trivial solutions, respectively. Through the distribution of the eigenvalues and bifurcation analysis, sufficient conditions for the occurrence of the Hopf bifurcation associated with one spatially nonconstant positive steady state are obtained. Results about the stability associated with the other one are also yielded. It is found that with the interaction of memory delay, spatial heterogeneity and nonlinear boundary condition, the Hopf bifurcation will happen in such diffusive model. To be specific, the memory delay could induce a single stability switch from stability to instability. As contrast to the diffusive model only with memory effect, the stability switch is independent of memory delay. The obtained results are applied to some specific models, from which we can find that the theoretical analysis is verified and the results enrich the existing ones.

Global stability for McKean–Vlasov equations on large networks

Abstract We investigate the mean-field dynamics of stochastic McKean differential equations with heterogeneous particle interactions described by large network structures. To express a wide range of graphs, from dense to sparse structures, we incorporate the recently developed graph limit theory of graphops into the limiting McKean–Vlasov equations. Global stability of the splay steady state is proven via a generalised entropy method, leading to explicit graph structure-dependent decay rates. We highlight the robustness of the entropy approach by extending the results to the closely related Sakaguchi–Kuramoto model with intrinsic frequency distributions. We also present central examples of random graphs, such as power law graphs and the spherical graphop, and analyse the limitations of the applied methodology.

Heterointerfacial engineering of N,P-doped carbon nanosheets supported Co/Co2P nanoparticles for boosting oxygen reduction and oxygen evolution reactions towards rechargeable Zn-air battery

Transition metal phosphides (TMPs) with high electrocatalytic activity for the oxygen evolution reaction (OER) are reckoned as a substitution of precious group metals catalysts in rechargeable Zn-air battery. In this work, Co/Co2P heterojunction nanoparticles supported N,P-doped carbon nanosheets (Co/Co2P@NPCNS) were designed and prepared via a facile one-step molten salt-assisted pyrolysis process. Density function theory calculations reveal that the heterogeneous interactions of Co/Co2P effectively enhance the bifunctional electrocatalytic activity for oxygen reduction reaction (ORR) and OER. The synergistic interaction between the Co/Co2P heterojunction nanoparticles with highly exposed active sites and excellent catalytic activity and the two-dimensional doped carbon nanosheets with high conductivity contributes to Co/Co2P@NPCNS exhibiting preeminent bifunctional ORR/OER activity and stability with a high half-wave potential for ORR (0.87 V), a low overpotential for OER (302 mV at 10 mA cm−2) and a low potential gap (0.66 V). The homemade rechargeable Zn-air battery performs high peak power density (187 mW cm−2) and exceptional endurance. This heterogeneous interface tactic of integrating TMPs with heteroatom-doped carbon materials may shed light on the research and development of non-precious metal electrocatalysts.

Interaction regimes of surface water and groundwater in a hyper-arid endorheic watershed on Tibetan Plateau: Insights from multi-proxy data

The interaction between surface water and groundwater is crucial for the management of water resources and the preservation of ecosystems within arid basins, but knowledge on their interaction regimes at the watershed scale remains relatively limited. The present research synthesizes a range of multi-proxy data, including satellite thermal infrared remote sensing, water piezometric level, hydrochemical analyses, and isotopic signatures (2H, 18O and 222Rn), to elucidate the interaction dynamics and patterns between surface water and groundwater within a representative hyper-arid closed basin on Tibetan Plateau. The findings indicate that the water in the basin originates primarily from the glacier/snow melt water and precipitation in the mountainous regions, and would experience multiple but spatially heterogeneous interactions between river and aquifers from the mountain pass to the tail salt lakes after entering the basin. Water interaction in the piedmont alluvial plain is dominated by the pattern of river seepage into aquifers with a water flux of 25.82 m3/s, which drives the development of hierarchical groundwater flow systems in the basin. The interaction pattern converts to groundwater discharge of the local groundwater flow system at the front of alluvial fan plain, which forms the principal spring-fed rivers and the predominant overflow zone in the basin with the groundwater discharge flux of 3.22 m3/s. Most of these discharged groundwaters would infiltrate back into the aquifers in the middle-upper part of the loess plain with the river water leakage flux of 2.89 m3/s. River water and phreatic groundwater present a gradual enrichment of hydrochemcial components and water stable isotopes (2H and 18O) along the flow path due to the intense evaporation in the loess plain. The multiple water interactions between surface water and groundwater lead to the anomalous distribution of fresh water in the middle-lower stream area, which is related to the intermediate groundwater flow system and the local variable groundwater flow system. Water interaction in the lowest salt-marsh plain is in the pattern of regional groundwater flow system discharge into the tail salt lakes under natural condition, but evolves to only discharge into the artificial brine extraction canals rather than the tail salt lakes after decades of brine exploitation. Our findings provide a conceptual and preliminarily quantificational understanding of the interaction regimes between surface water and groundwater in large arid closed basins at the watershed scale.

Enhancing spatial domain detection in spatial transcriptomics with EnSDD

Advancements in spatial transcriptomics have transformed our understanding of organ function and tissue microenvironment. However, accurately identifying spatial domains to depict genome heterogeneity and cellular interactions remains a challenge. In this study, we propose EnSDD (Ensemble-learning for Spatial Domain Detection), a method that ingeniously integrates eight state-of-the-art spatial domain detection methods to automatically identify spatial domains. A key innovation of EnSDD is its dynamic weighting mechanism within the ensemble learning process, which optimizes the contribution of each base model and provides a performance evaluation metric without the need for ground truth data. By leveraging the spatial domains identified through EnSDD, we incorporate the detection of domain-specific spatially variable genes and the spatial distribution of cell types, thereby providing deeper insights into tissue heterogeneity. We validate EnSDD across diverse spatial transcriptomics datasets from various tissue organizational structures. Our results demonstrate that EnSDD significantly enhances spatial domain identification accuracy, identifies genes with spatial expression patterns, and reveals domain-specific cell type enrichment patterns, offering invaluable insights into tissue spatial heterogeneity and regionalization.

CD81-guided heterologous EVs present heterogeneous interactions with breast cancer cells

BackgroundExtracellular vesicles (EVs) are cell-secreted particles conceived as natural vehicles for intercellular communication. The capacity to entrap heterogeneous molecular cargoes and target specific cell populations through EV functionalization promises advancements in biomedical applications. However, the efficiency of the obtained EVs, the contribution of cell-exposed receptors to EV interactions, and the predictability of functional cargo release with potential sharing of high molecular weight recombinant mRNAs are crucial for advancing heterologous EVs in targeted therapy applications.MethodsIn this work, we selected the popular EV marker CD81 as a transmembrane guide for fusion proteins with a C-terminal GFP reporter encompassing or not Trastuzumab light chains targeting the HER2 receptor. We performed high-content imaging analyses to track EV-cell interactions, including isogenic breast cancer cells with manipulated HER2 expression. We validated the functional cargo delivery of recombinant EVs carrying doxorubicin upon EV-donor cell treatment. Then, we performed an in vivo study using JIMT-1 cells commonly used as HER2-refractory, trastuzumab-resistant model to detect a more than 2000 nt length recombinant mRNA in engrafted tumors.ResultsFusion proteins participated in vesicular trafficking dynamics and accumulated on secreted EVs according to their expression levels in HEK293T cells. Despite the presence of GFP, secreted EV populations retained a HER2 receptor-binding capacity and were used to track EV-cell interactions. In time-frames where the global EV distribution did not change between HER2-positive (SK-BR-3) or -negative (MDA-MB-231) breast cancer cell lines, the HER2 exposure in isogenic cells remarkably affected the tropism of heterologous EVs, demonstrating the specificity of antiHER2 EVs representing about 20% of secreted bulk vesicles. The specific interaction strongly correlated with improved cell-killing activity of doxorubicin-EVs in MDA-MB-231 ectopically expressing HER2 and reduced toxicity in SK-BR-3 with a knocked-out HER2 receptor, overcoming the effects of the free drug. Interestingly, the fusion protein-corresponding transcripts present as full-length mRNAs in recombinant EVs could reach orthotopic breast tumors in JIMT-1-xenografted mice, improving our sensitivity in detecting penetrant cargoes in tissue biopsies.ConclusionsThis study highlights the quantitative aspects underlying the creation of a platform for secreted heterologous EVs and shows the limits of single receptor-ligand interactions behind EV-cell engagement mechanisms, which now become the pivotal step to predict functional tropism and design new generations of EV-based nanovehicles.

Heterogeneous Interaction Effects of Environmental and Economic Factors on Green Efficiency of Water Resources in China

Identifying the green efficiency of water resources and its driving factors is paramount for promoting sustainable development in China. The existing research has primarily focused on the spatial heterogeneity of individual factors that impact green efficiency of water resources. However, it has often overlooked the heterogeneity in the interactions between these factors. In this study, we utilized a multiscale geographically weighted regression (MGWR) model to discern the spatial heterogeneity of the individual factors influencing the green efficiency of water resources in China between 2002 and 2016. Subsequently, we demarcated several subregions based on the coefficients derived from the MGWR model. Employing a geographical detector (GD), we quantified the interactive impacts of different factors within these subregions. Our findings unveiled, for the first time, the diverse patterns in the temporal and spatial fluctuations in the factors impacting the eco-friendliness of water resources. The findings underscored that disregarding the spatial heterogeneity of these interactive effects may result in an underestimation of the interactions among factors. Significantly, in 2016, the impact of tertiary industry proportion and completed investment in pollution treatment displayed an enhanced non-linear effect across the entire sample and concurrently demonstrated a bivariate enhanced effect within subregions. These discoveries contribute to a deeper comprehension of the mechanisms influencing these factors, providing valuable insights for policymakers in crafting region-specific water resource policies tailored to the unique developmental requirements of different areas.

Grassland ecosystem service value in the Tibetan Plateau has not recovered during 1995–2015

Grasslands in the Tibetan Plateau (TP) provide substantial ecosystem services (ESs) but face considerable challenges. Previous assessments of grassland ecosystem service value (GESV) have revealed substantial uncertainties due to inconsistencies in GES classifications, land use data, and assessment methods. Here, we propose a new classification system, present merged land use data, and develop integrated comprehensive assessment methods to reassess the GESV in the TP from 1995 to 2015, and then analyzed spatiotemporal changes and driving factors. Our findings indicate that: 1) grasslands dominate the TP, covering approximately 70 % of the land surface, but have been continuously declining, especially during 1995–2000. 2) The value equivalent factor method tends to overestimate GESV, and our reassessment further emphasized the critical role of grasslands. 3) Temporally, GESV exhibited a measured recovery post-2000, though it remained below initial levels. 4) Spatially, GESV declined from southeast to northwest, mainly influenced by precipitation, although this spatial gradient has gradually diminished. 5) Both climatic and anthropogenic factors significantly influenced changes in GESV, but even dual factors had limited explanatory power. Our reassessment sheds light on the spatiotemporal changes and drivers of GESV in the TP over two decades, which is important for grassland management in the region. However, methodological limitations remain, particularly with regard to data accuracy; and further exploration of spatial heterogeneity and multi-factor interactions are also required.

The Construction of Face-to-Face Combination between NiFe-layered Double Hydroxide Nanosheets and Monolayer rGO for Efficient Water Splitting and Oxygen Reduction.

Developing cost-effective and efficient electrocatalysts is essential for advancing a green energy future. Herein, a NiFe-layered double hydroxide loaded on reduced graphene oxide (NiFe-LDHs@rGO) hybrid was synthesized using a straightforward three-step process involving exfoliation tearing, electrostatic self-assembly, and chemical reduction. The face-to-face packing and ultrathin exfoliation enable strong heterogeneous interactions, fully harnessing the potential of these complementary two-dimensional counterparts. Consequently, the resultant catalyst displays outstanding oxygen evolution reaction (OER) catalytic activity and stability, whose overpotential is as low as 241 mV at 30 mA cm-2 and 255 mV at 50 mA cm-2 with a low Tafel slope of 62.1 mV dec-1. Both the experimental results and density functional theory (DFT) calculations reveal that the face-to-face assembly strengthens the electronic interactions between NiFe-LDHs and rGO, which effectively modulates the d-band center of Ni and Fesites and improves the reaction kinetics for OER. Moreover, the resultant NiFe-LDHs@rGO hybrids exhibit excellent multifunctional catalytic performance. Its hydrogen evolution reaction (HER) activity is endowed by Fe-site of NiFe-LDHs and defect states rGO and achieves a low voltage of 1.68 V to drive a current density of 10 mA cm-2 for overall water splitting. The face-to-face heteroassembly also imparts NiFe-LDHs@rGO with superior oxygen reduction reaction (ORR) activity, with a half-wave potential of 0.70 V and a limiting current density of 4.2 mA cm-2. Its ORR primarily follows a four-electron transfer pathway with a minor contribution from a two-electron process. This study establishes the groundwork for optimizing two-dimensional heterogeneous interfaces in LDH@carbon-based materials for advanced energy conversion.

The Distribution of Surface Heat Flow on the Tibetan Plateau Revealed by Data‐Driven Methods

AbstractSurface heat flow (SHF) serves as a vital parameter for assessing the heat transfer from deep Earth to the surface, which can provide crucial insights into internal geodynamic processes. As the “roof of the world,” the Tibetan Plateau and its tectonic evolution are highly important in terms of global climate change and geodynamic study. However, a comprehensive understanding of the SHF distribution across most regions of the Tibetan Plateau is limited due to sparse measurement data. To surmount this limitation, a spatially intelligent approach has been developed: The geographically neural network weighted regression with enhanced interpretability (EI‐GNNWR). This method integrates spatial heterogeneity and nonlinear interactions between geophysical and geological factors to predict the SHF distribution across the Tibetan Plateau. In this study, the EI‐GNNWR model is used to accurately predict SHF across the entire region. After evaluating the effectiveness and interpretability of the EI‐GNNWR model, our results demonstrate that medium to high SHF values are predominantly concentrated in the southern, northeastern, and southeastern sectors of the Tibetan Plateau. These observations suggest that the formation of zones with high SHF values may be strongly influenced by the Moho depth, ridges, topography, and average curvature of satellite gravity gradients. Especially, higher SHF values may indicate more profound geodynamic activities such as collisional orogeny, shear deformation zones, or lithospheric extension. These findings offer novel insights into the spatial patterns of SHF and deepen our understanding of the geothermal formation mechanisms driven by underlying tectonic activities.

Unearthing waste resources: Nano‐adsorbents from complex packaging waste for fluoride detoxification of water

AbstractThis study investigates the synthesis and characterization of Al2O3 nanoparticles derived from multilayered packaging (MLP) waste, specifically postconsumer coffee capsules, and evaluates their efficacy in fluoride adsorption from aqueous solutions. The nanoparticles were synthesized using a facile thermal degradation technique followed by leaching and precipitation processes. Characterization techniques, including inductively coupled plasma‐mass spectroscopy, XRF, Fourier transform infrared spectroscopy, X‐ray diffraction, and transmission electron microscope, confirmed the formation of γ‐Al2O3 nanoparticles with a cubic crystal structure. The adsorption process was found to be endothermic and spontaneous, with a maximum adsorption capacity of 9.60 mg/g. Kinetic studies revealed that the pseudo‐second order model best described the adsorption process, indicating a complex multistep mechanism involving both physisorption in the initial rapid adsorption and chemisorption in the latter slower phase. The Freundlich–Langmuir isotherm provided the best fit for equilibrium data suggesting a complex adsorption mechanism involving both homogeneous and heterogeneous surface interactions. The presence of competing anions, particularly carbonate and phosphate, significantly affected the adsorption efficiency. This research demonstrates the potential of upcycling MLP waste into valuable adsorbents for wastewater treatment applications, offering both environmental and economic benefits.

Family-Wide Photoproximity Profiling of Integrin Protein Social Networks in Cancer.

Integrin family transmembrane receptors mediate dynamic interactions between cells and their extracellular microenvironment. The heterogeneous interaction partners of integrins directly regulate cell adhesion, motility, proliferation, and intracellular signaling. Despite the recognized importance of protein-protein interactions and the formation of signaling hubs around integrins, the ability to detect and quantify these dynamic binding partners with high spatial and temporal resolution remains challenging. Here, we developed an integrin-family-directed quantitative photoproximity protein interaction (PhotoPPI) profiling method to detect and quantify native integrin-centered protein social networks on live cells and tissues without the need for genetic manipulation, antibodies, or non-physiologic cell culture conditions. We drafted quantitative maps of integrin-centered protein social networks, highlighting conserved and unique binding partners between different cell types and cellular microenvironments. Comparison of integrin social networks in cancer cell lines of diverse tissue of origin and disease state identified specific AND-gate binding partners involved cell migration, microenvironmental interactions and proliferation that serve as markers of tumor cell metastatic state. Finally, we identified unique combinations - or barcodes - of integrin-proximal proteins on the surface of pre- and post-metastatic triple negative breast cancer (TNBC) cells whose expression strongly correlate with both positive and negative disease progression and outcomes in TNBC patients. Taken together, these data provide the first family-wide high-resolution maps of native protein interactors on live cells and identify dynamic integrin-centered social networks as potential AND-gate markers of cell identity, microenvironmental context and disease state.

Etching physicochemical adsorption sites of biochar by steam for enhanced hydrogen storage

Enhancing the hydrogen storage capacity of carbon-based materials requires the precise synthesis of strong hydrogen adsorption sites. Consequently, this work suggests improving hydrogen storage capacity by employing H2O low-temperature etching technology. First, the simulation methods investigated the pore structure and oxygen-containing groups’ adsorption properties on hydrogen. The results showed that the oxygen-containing group could increase the adsorption energy of biochar on hydrogen, and the carboxyl group had the greatest effect. A suitable coupling of pores and oxygen-containing groups can improve the microporous region’s hydrogen storage capacity. Moreover, biochar may form a new ultra-microporous region (less than 0.5 nm) by etching it at 700 °C and 30 vol% H2O. After etching 90 min, the surface oxygen concentration remained stable at 14 at.%. In conjunction with results from mass spectrometry, we discovered that the process influencing pore growth was the heterogeneous interaction between CO and H2 to create CH4. At 25 ℃ and −196 ℃, the KSBC-30–60 showed superior hydrogen storage capacity compared to KSB. Its total adsorption capacity was 0.49 wt% and 5.28 wt%, respectively, and increased by 1.32 and 1.2 times. This study proposes a novel approach to improving hydrogen storage by etching porous carbon structures with H2O. The strategy has the potential for large-scale application and can be further utilized in the design and optimization of related carbon adsorbents.

Dark NO2 Reduction on a Graphene Surface with Implications for Soot Aging and HONO Formation.

Soot, primarily composed of elemental carbon (EC) and organic carbon (OC), is ubiquitous in PM2.5. In the atmosphere, the heterogeneous interaction between NO2 and soot is not only an important pathway driving soot aging but also of central importance to nitrous acid (HONO) formation. It is commonly believed that the surface redox reaction between reductive OC and NO2 dominates the night aging of soot and the conversion of NO2 to HONO. However, completely differing from the currently popular explanation, we find here that the redox reaction between EC and NO2 can also drive the conversion of NO2 to HONO during soot aging. By combining in situ experiments with density functional theory (DFT) calculations, we proposed that the surface carbon vacancy defects on graphite/graphene-like EC should be a type of potential primary adsorption and reactive sites inducing the heterogeneous reduction of NO2. We suggested a new mechanism that NO2 is reduced to form HONO on surface vacancy defects through the splitting of H2O molecules, and the carbon atoms adjacent to surface vacancy are simultaneously oxidized to form hydroxyl-functionalized EC. This novel finding provides insights into the chemical mechanism driving the NO2-to-HONO conversion and rapid soot aging, which expands our knowledge of the heterogeneous chemistry of soot in the atmosphere.

Single-Nucleus RNA-Sequencing Reveals a MET+ Oligodendrocyte Subpopulation That Promotes Proliferation of Radiation-Induced Gliomas

PurposeRadiation-induced gliomas (RIGs) are fatal late complications of radiotherapy, with a median survival time of 6–11 months. RIGs demonstrate unique molecular landscape and may originate from a glial lineage distinct from that of primary malignancies or diffuse midline gliomas (DMGs). This study aimed to explore the intratumoral diversity within RIGs to uncover their cellular origin and characteristics, and enhance our understanding of this uncommon tumor type. Methods and MaterialsFormalin-fixed, paraffin-embedded samples were collected from two RIGs and two DMGs for single-nucleus RNA sequencing. A detailed analysis was conducted to assess intratumoral heterogeneity and cellular interactions, including gene set enrichment, pseudotime trajectory, and cell communication analyses. Immunofluorescence staining, proliferation assay, and RNA-seq analysis were also applied to validate our findings. ResultsOur analysis revealed distinct heterogeneity in oligodendrocytes between the DMG and RIG samples. A unique subpopulation of oligodendrocytes in RIGs, which was characterized by gene encoding mesenchymal-epithelial transition factor (c-MET) and therefore termed MET+ ODs, exhibited characteristics typical of cancer cells, such as increased mitotic activity, cancer-related gene expression, and extensive copy number variations. Cell communication studies indicated that MET+ ODs interact vigorously with G1/S and G2/M cycling cells via the neural cell adhesion molecule (NCAM) signaling pathway, potentially enhancing the proliferation of cycling malignant cells. Integrating our results with existing RNA-seq data further supported our hypothesis. The presence of MET+ ODs in RIGs was confirmed by immunostaining, and activation of the NCAM signaling pathway in vitro significantly promoted the proliferation of RIG tumor cells. Moreover, in-vitro radiation induced the transformation of oligodendrocytes to be more similar to MET+ ODs. ConclusionsRIGs are characterized by an oligodendrocyte composition distinct from that of DMGs. A specific subpopulation of MET+ ODs in RIGs may be crucial in tumorigenesis and promote the growth of malignant cells. Identifying MET+ ODs offers a valuable target for future clinical surveillance and therapeutic strategies.

Comprehensive insight into 3D bioprinting technology for brain tumor modeling

Glioblastoma is one of the most common primary malignant brain tumors with a poor prognosis and high mortality rate. Because only small lipophilic molecules can penetrate the blood-brain barrier, chemotherapy and immunotherapy are limited in their ability to effectively treat brain tumor. Three-dimensional (3D) bioprinting has the potential to model the 3D environment of human pathology and diseases directly. In many cancers, 3D bioprinting technology closely mimics the tumor microenvironment, making it a promising tool for drug screening and uncovering the mechanisms of cancer initiation and progression. Recent 3D bioprinting technologies have been developed to recreate the dynamic interactions between the tumor and endothelial cells. Brain tumor models using 3D bioprinting technology can reconstruct the biophysical heterogeneity and immune interactions in the brain. These advanced models regulate the organization of tumor structures for preclinical drug testing and reveal the immunological pathways involved in brain tumor. Here, we highlight 3D bioprinting technologies that can replace conventional in vitro models for brain tumor treatment.

Rapid Identification of Drug Mechanisms with Deep Learning-Based Multichannel Surface-Enhanced Raman Spectroscopy.

Rapid identification of drug mechanisms is vital to the development and effective use of chemotherapeutics. Herein, we develop a multichannel surface-enhanced Raman scattering (SERS) sensor array and apply deep learning approaches to realize the rapid identification of the mechanisms of various chemotherapeutic drugs. By implementing a series of self-assembled monolayers (SAMs) with varied molecular characteristics to promote heterogeneous physicochemical interactions at the interfaces, the sensor can generate diversified SERS signatures for directly high-dimensionality fingerprinting drug-induced molecular changes in cells. We further train the convolutional neural network model on the multidimensional SAM-modulated SERS data set and achieve a discriminatory accuracy toward 99%. We expect that such a platform will contribute to expanding the toolbox for drug screening and characterization and facilitate the drug development process.

HGEE: Learning for Trajectory Prediction with Heterogeneous Graph Interaction and External Embedding of Unmanned Swarm Systems in Adversarial Environment

Trajectory prediction of unmanned swarm systems, serving as the foundation for behavioral and intentional cognition, has attracted extensive attention and made considerable progress in adversarial research. The influence of heterogeneous interaction relationships and external factors is crucial for trajectory prediction. Consequently, this paper proposes the Heterogeneous Graph with External Embedding (HGEE) network. We model the latent variables as multi-layer heterogeneous graphs based on prior knowledge of different interaction relationships and propose a method for calculating edge embeddings for heterogeneous graphs. Furthermore, we introduce a method that combines external environmental feature with historical observational trajectory data as the input for the decoder, enabling the model to learn the impacts of obstacles, targets, and desired formations on trajectories. We demonstrate that our approach surpasses state-of-the-art models in interaction inference and trajectory prediction through experiments on our proposed formation datasets based on consensus theory, across five evaluation metrics.