Cell membrane coated nanoparticles as a biomimetic drug delivery platform for enhancing cancer immunotherapy.

Cancer immunotherapy, a burgeoning modality for cancer treatment, operates by activating the autoimmune system to impede the growth of malignant cells. Although numerous immunotherapy strategies have been employed in clinical cancer therapy, the resistance of cancer cells to immunotherapeutic medications and other apprehensions impede the attainment of sustained advantages for most patients. Recent advancements in nanotechnology for drug delivery hold promise in augmenting the efficacy of immunotherapy. However, the efficacy is currently constrained by the inadequate specificity of delivery, low rate of response, and the intricate immunosuppressive tumor microenvironment. In this context, the investigation of cell membrane coated nanoparticles (CMNPs) has revealed their ability to perform targeted delivery, immune evasion, controlled release, and immunomodulation. By combining the advantageous features of natural cell membranes and nanoparticles, CMNPs have demonstrated their unique potential in the realm of cancer immunotherapy. This review aims to emphasize recent research progress and elucidate the underlying mechanisms of CMNPs as an innovative drug delivery platform for enhancing cancer immunotherapy. Additionally, it provides a comprehensive overview of the current immunotherapeutic strategies involving different cell membrane types of CMNPs, with the intention of further exploration and optimization.

Recent Advances in Nanomaterials for the Treatment of Acute Kidney Injury.

Acute kidney injury (AKI) is a serious clinical syndrome with high morbidity, elevated mortality, and poor prognosis, commonly considered a "sword of Damocles" for hospitalized patients, especially those in intensive care units. Oxidative stress, inflammation, and apoptosis, caused by the excessive production of reactive oxygen species (ROS), play a key role in AKI progression. Hence, the investigation of effective and safe antioxidants and inflammatory regulators to scavenge overexpressed ROS and regulate excessive inflammation has become a promising therapeutic option. However, the unique physiological structure and complex pathological alterations in the kidneys render traditional therapies ineffective, impeding the residence and efficacy of most antioxidant and anti-inflammatory small molecule drugs within the renal milieu. Recently, nanotherapeutic interventions have emerged as a promising and prospective strategy for AKI, overcoming traditional treatment dilemmas through alterations in size, shape, charge, and surface modifications. This Review succinctly summarizes the latest advancements in nanotherapeutic approaches for AKI, encompassing nanozymes, ROS scavenger nanomaterials, MSC-EVs, and nanomaterials loaded with antioxidants and inflammatory regulator. Following this, strategies aimed at enhancing biocompatibility and kidney targeting are introduced. Furthermore, a brief discussion on the current challenges and future prospects in this research field is presented, providing a comprehensive overview of the evolving landscape of nanotherapeutic interventions for AKI.

MFF-Net: Multiscale feature fusion semantic segmentation network for intracranial surgical instruments.

BACKGROUND: In robot-assisted surgery, automatic segmentation of surgical instrument images is crucial for surgical safety. The proposed method addresses challenges in the craniotomy environment, such as occlusion and illumination, through an efficient surgical instrument segmentation network. METHODS: The network uses YOLOv8 as the target detection framework and integrates a semantic segmentation head to achieve detection and segmentation capabilities. A concatenation of multi-channel feature maps is designed to enhance model generalisation by fusing deep and shallow features. The innovative GBC2f module ensures the lightweight of the network and the ability to capture global information. RESULTS: Experimental validation of the intracranial glioma surgical instrument dataset shows excellent performance: 94.9% MPA score, 89.9% MIoU value, and 126.6 FPS. CONCLUSIONS: According to the experimental results, the segmentation model proposed in this study has significant advantages over other state-of-the-art models. This provides a valuable reference for the further development of intelligent surgical robots.

InstrumentNet: An integrated model for real-time segmentation of intracranial surgical instruments.

In robot-assisted surgery, precise surgical instrument segmentation technology can provide accurate location and pose data for surgeons, helping them perform a series of surgical operations efficiently and safely. However, there are still some interfering factors, such as surgical instruments being covered by tissue, multiple surgical instruments interlacing with each other, and instrument shaking during surgery. To better address these issues, an effective surgical instrument segmentation network called InstrumentNet is proposed, which adopts YOLOv7 as the object detection framework to achieve a real-time detection solution. Specifically, a multiscale feature fusion network is constructed, which aims to avoid problems such as feature redundancy and feature loss and enhance the generalization ability. Furthermore, an adaptive feature-weighted fusion mechanism is introduced to regulate network learning and convergence. Finally, a semantic segmentation head is introduced to integrate the detection and segmentation functions, and a multitask learning loss function is specifically designed to optimize the surgical instrument segmentation performance. The proposed segmentation model is validated on a dataset of intracranial surgical instruments provided by seven experts from Beijing Tiantan Hospital and achieved an mAP score of 93.5 %, Dice score of 82.49 %, and MIoU score of 85.48 %, demonstrating its universality and superiority. The experimental results demonstrate that the proposed model achieves good segmentation performance on surgical instruments compared to other advanced models and can provide a reference for developing intelligent medical robots.