Transmembrane Graphene as an Electron Tunnel to Regulate the Intracellular Redox State.

Cellular redox homeostasis is essential for maintaining cellular activities, such as DNA synthesis and gene expression. Inspired by this, new therapeutic interventions have been rapidly developed to modulate the intracellular redox state using artificial transmembrane electron transport. However, current approaches that rely on external electric field polarization can disrupt cellular functions, limiting their in vivo application. Therefore, it is crucial to develop novel electric-field-free modulation methods. In this work, we for the first time found that graphene could spontaneously insert into living cell membranes and serve as an electron tunnel to regulate intracellular reactive oxygen species and NADH based on the spontaneous bipolar electrochemical reaction mechanism. This work provides a wireless and electric-field-free approach to regulating cellular redox states directly and offers possibilities for biological applications such as cell process intervention and treatment for neurodegenerative diseases.

ROS suppression and oocyte quality restoration: NMN intervention in decabromodiphenyl ether-exposed mice.

Decabromodiphenyl ether (BDE-209) is an organic compound that is widely used in rubber, textile, electronics, plastics and other industries. It has been found that BDE-209 has a destructive effect on the reproductive system of mammals. However, the effect of BDE-209 exposure on oocyte quality and whether there is a viable salvage strategy have not been reported. Here, we report that murine oocytes exposed to BDE-209 produce a series of meiostic defects, including increased fragmentation rates and decreased PBE. Furthermore, exposure of oocytes to BDE-209 hinders mitochondrial function and disrupts mitochondrial integrity. Our observations show that supplementation with NMN successfully alleviated the meiosis impairment caused by BDE-209 and averted oocyte apoptosis by suppressing ROS generation. In conclusion, our findings suggest that NMN supplementation may be able to alleviate the oocyte quality impairment induced by BDE-209 exposure, providing a potential strategy for protecting oocytes from environmental pollutant exposure.

A novel fully automatic segmentation and counting system for metastatic lymph nodes on multimodal magnetic resonance imaging: Evaluation and prognostic implications in nasopharyngeal carcinoma.

BACKGROUND: The number of metastatic lymph nodes (MLNs) is crucial for the survival of nasopharyngeal carcinoma (NPC), but manual counting is laborious. This study aims to explore the feasibility and prognostic value of automatic MLNs segmentation and counting. METHODS: We retrospectively enrolled 980 newly diagnosed patients in the primary cohort and 224 patients from two external cohorts. We utilized the nnUnet model for automatic MLNs segmentation on multimodal magnetic resonance imaging. MLNs counting methods, including manual delineation-assisted counting (MDAC) and fully automatic lymph node counting system (AMLNC), were compared with manual evaluation (Gold standard). RESULTS: In the internal validation group, the MLNs segmentation results showed acceptable agreement with manual delineation, with a mean Dice coefficient of 0.771. The consistency among three counting methods was as follows 0.778 (Gold vs. AMLNC), 0.638 (Gold vs. MDAC), and 0.739 (AMLNC vs. MDAC). MLNs numbers were categorized into three-category variable (1-4, 5-9, > 9) and two-category variable (<4, ≥ 4) based on the gold standard and AMLNC. These categorical variables demonstrated acceptable discriminating abilities for 5-year overall survival (OS), progression-free, and distant metastasis-free survival. Compared with base prediction model, the model incorporating two-category AMLNC-counting numbers showed improved C-indexes for 5-year OS prediction (0.658 vs. 0.675, P = 0.045). All results have been successfully validated in the external cohort. CONCLUSIONS: The AMLNC system offers a time- and labor-saving approach for fully automatic MLNs segmentation and counting in NPC. MLNs counting using AMLNC demonstrated non-inferior performance in survival discrimination compared to manual detection.

LD-UNet: A long-distance perceptual model for segmentation of blurred boundaries in medical images.

The blurriness of boundaries in medical image target regions hinders further improvement in automatic segmentation accuracy and is a challenging problem. To address this issue, we propose a model called long-distance perceptual UNet (LD-UNet), which has a powerful long-|distance perception ability and can effectively perceive the semantic context of an entire image. Specifically, LD-UNet utilizes global and local long-distance induction modules, which endow the model with contextual semantic induction capabilities for long-distance feature dependencies. The modules perform long-distance semantic perception at the high and low stages of LD-UNet, respectively, effectively improving the accuracy of local blurred information assessment. We also propose a top-down deep supervision method to enhance the ability of the model to fit data. Then, extensive experiments on four types of tumor data with blurred boundaries are conducted. The dataset includes nasopharyngeal carcinoma, esophageal carcinoma, pancreatic carcinoma, and colorectal carcinoma. The dice similarity coefficient scores obtained by LD-UNet on the four datasets are 73.35%, 85.93%, 70.04%, and 82.71%. Experimental results demonstrate that LD-UNet is more effective in improving the segmentation accuracy of blurred boundary regions than other methods with long-distance perception, such as transformers. Among all models, LD-UNet achieves the best performance. By visualizing the feature dependency field of the models, we further explore the advantages of LD-UNet in segmenting blurred boundaries.