Isocitrate dehydrogenase (IDH)–wild-type (WT) high-grade gliomas, especially glioblastomas, are highly aggressive and have an immunosuppressive tumor microenvironment. Although tumor-infiltrating immune cells are known to play a critical role in glioma genesis, their heterogeneity and intercellular interactions remain poorly understood. In this study, we constructed a single-cell transcriptome landscape of immune cells from tumor tissue and matching peripheral blood mononuclear cells (PBMC) from IDH-WT high-grade glioma patients. Our analysis identified two subsets of tumor-associated macrophages (TAM) in tumors with the highest protumorigenesis signatures, highlighting their potential role in glioma progression. We also investigated the T-cell trajectory and identified the aryl hydrocarbon receptor (AHR) as a regulator of T-cell dysfunction, providing a potential target for glioma immunotherapy. We further demonstrated that knockout of AHR decreased chimeric antigen receptor (CAR) T-cell exhaustion and improved CAR T-cell antitumor efficacy both in vitro and in vivo. Finally, we explored intercellular communication mediated by ligand–receptor interactions within the tumor microenvironment and PBMCs and revealed the unique cellular interactions present in the tumor microenvironment. Taken together, our study provides a comprehensive immune landscape of IDH-WT high-grade gliomas and offers potential drug targets for glioma immunotherapy.

Brain tumors require AI-assisted, precise treatments. Methods based on healthcare electronics, such as magnetic resonance imaging (MRI), computed tomography (CT), and gastrointestinal endoscopy, are widely used in hospitals. Maximizing the functionality of these electronic devices is crucial for improving tumor lesion detection and is a challenge in AI-assisted medical applications. Precise classification is vital for effectively planning brain tumor treatments, and accurate classification results offer crucial insights that enable physicians to devise optimal treatment strategies. Therefore, this paper proposes a novel brain tumor classification method named SwinBTC, which is based on the healthcare Internet of Things (HIoT) and integrates a pretrained and fine-tuned Swin transformer model to improve the performance of healthcare electronics. First, a transfer learning (TL)-based brain tumor classification model called SwinBTC is proposed. The general features in the images are used to improve the classification ability of the brain tumor MRI results, further accelerate the model training speed, and avoid overfitting problems. Second, clinical magnetic resonance images obtained from HIoT nodes are used to enrich the dataset, and online and offline data augmentation techniques are used to expand the utilized dataset and increase data diversity, improving the generalizability and reliability of the developed model. Finally, the performance of the proposed approach is evaluated on the CE-MRI and TT-MRI datasets via various classification metrics, and the experimental results reveal that our method outperforms other baselines.

Echocardiographic image segmentation plays a crucial role in analyzing cardiac function and diagnosing cardiovascular diseases. Ultrasound imaging frequently encounters challenges, such as those related to elevated noise levels, diminished spatiotemporal resolution, and the complexity of anatomical structures. These factors significantly hinder the model’s ability to accurately capture and analyze structural relationships and dynamic patterns across various regions of the heart. Mamba, an emerging model, is one of the most cutting-edge approaches that is widely applied to diverse vision and language tasks. It efficiently captures global information with linear complexity and compensates for the shortcomings of convolutional neural networks (CNNs) and conventional transformers. To this end, this article introduces a U-shaped deep learning model incorporating a large-window Mamba scale (LMS) module and a hierarchical feature fusion approach for echocardiographic segmentation. First, a cascaded residual block serves as an encoder and is employed to incrementally extract multiscale detailed features. It addresses the vanishing gradient issue by leveraging a residual structure that ensures stable and rapid convergence throughout the training process. Second, a large-window multiscale mamba module is integrated into the decoder to capture global dependencies across regions and enhance the segmentation capability for complex anatomical structures. Furthermore, our model introduces auxiliary losses at each decoder layer and employs a dual attention mechanism to fuse multilayer features both spatially and across channels. This approach enhances segmentation performance and accuracy in delineating complex anatomical structures. Finally, the experimental results using the EchoNet-Dynamic and CAMUS datasets demonstrate that the model outperforms other methods in terms of both accuracy and robustness. For the segmentation of the left ventricular endocardium ($\text{LV}_{\text{endo}}$), the model achieved optimal values of 95.01 and 93.36, respectively, while for the left ventricular epicardium ($\text{LV}_{\text{epi}}$), values of 87.35 and 87.80, respectively, were achieved. This represents an improvement ranging between 0.54 and 1.11 compared with the best-performing model.

Artificial intelligence (AI)-based medical consumer electronics have attracted increasing attention in the fields of precision medicine and intelligent diagnosis. Accurately predicting echocardiographic parameters, such as the size of the left ventricular (LV) area and the ejection fraction (EF), is crucial for the precise diagnosis and treatment of cardiovascular diseases. However, limited data annotations are typically available for medical data, leading to substantial disparities among the segmentation accuracies obtained by deep learning models across different frames, and varying degrees of overfitting are observed in EF prediction tasks. In this work, a joint mask-augmented adaptive scale alignment (ASA) and periodicity-aware EF prediction method is proposed for cardiac function assessment. This approach enhances the accuracy and reliability of LV segmentation and EF prediction. First, an enhanced strategy is proposed to optimize LV segmentation to address the considerable segmentation accuracy variations across different frames. Second, variants of the ASA method are presented to perform regional alignment in EF prediction tasks, and an existing dataset is extended to address model overfitting. Third, a periodicity-aware method is proposed for EF and keyframe prediction, correcting the unfocused attention of the model for different periods and frames within the same video. Finally, experiments conducted on the EchoNet-Dynamic dataset show that our method achieves segmentation accuracy of 91.51% at end-diastole (ED) and 93.55% at end-systole (ES), surpassing previous methods by 1.12% and 0.59%, respectively. The mean absolute error (MAE) in the EF prediction task is 3.92, and the coefficient of determination (R2) is 0.822, demonstrating that our approach outperforms the current state-of-the-art methods.

The heat-sink effect and thermal damage of conventional thermal ablative technologies can be minimized by irreversible electroporation (IRE), which results in clear ablative boundaries and conservation of blood vessels, facilitating maximal safe surgical resection for glioblastoma. Although much comparative data about the death forms in IRE have been published, the comprehensive genetic regulatory mechanism for apoptosis, among other forms of regulatory cell death (RCD), remains elusive. We investigated the electric field intensity threshold for apoptosis/necrosis (YO-PRO-1/PI co-staining) of the U251 human malignant glioma cell line with stepwise increased uniform field intensity. Time course samples (0–6 h) of apoptosis induction and sham treatment were collected for transcriptome sequencing. Sequencing showed that transcription factor AP-1 and its target gene Bim (Bcl2l11), related to the signaling pathway, played a major role in the apoptosis of glioma after IRE. The sequencing results were confirmed by qPCR and Western blot. We also found that the transcription changes also implicated three other forms of RCD: autophagy, necroptosis, and immunogenic cell death (ICD), in addition to apoptosis. These together imply that IRE possibly mediates apoptosis by the AP-1-Bim pathway, causes mixed RCD simultaneously, and has the potential to aid in the generation of a systemic antitumor immune response.