[Anti-tumor mechanism of total saponins of Paridis Rhizoma on inducing ferroptosis of breast cancer MCF-7 cells].

This study aims to investigate the mechanism of total saponins of Paridis Rhizoma in inducing the ferroptosis of MCF-7 cells and provide a theoretical basis for the clinical treatment of breast cancer with total saponins of Paridis Rhizoma. The methyl thiazolyl tetrazolium(MTT) assay was employed to examine the effects of different concentrations of total saponins of Paridis Rhizoma on the proliferation of MCF-7 cells. A phase contrast inverted microscope was used to observe the morphological changes of MCF-7 cells. The colony formation assay was employed to test the colony formation of MCF-7 cells. The lactate dehydrogenase(LDH) release test was conducted to determine the cell membrane integrity of MCF-7 cells. The cell scratch assay was employed to examine the migration of MCF-7 cells. After that, the level of reactive oxygen species(ROS) in MCF-7 cells was observed by an inverted fluorescence microscope, and the content of Fe~(2+) in MCF-7 cells was detected by the corresponding kit. Transmission electron microscopy was employed to observe the mitochondrial ultrastructure of MCF-7 cells. Western blot was employed to determine the expression of ferroptosis-related proteins, such as p53, solute carrier family 7 member 11(SLC7A11), glutathione peroxidase 4(GPX4), acyl-CoA synthetase long-chain family member 4(ACSL4), and transferrin receptor protein 1(TFR1) in MCF-7 cells. The results showed that 1.5, 3, 4.5, 6, 7.5, and 9 µg·mL~(-1) total saponins of Paridis Rhizoma significantly inhibited the proliferation of MCF-7 cells, with the IC_(50) of 4.12 µg·mL~(-1). Total saponins of Paridis Rhizoma significantly damaged the morphology of MCF-7 cells, leading to the formation of vacuoles and the gradual shrinkage and detachment of cells. Meanwhile, total saponins of Paridis Rhizoma inhibited the colony formation of MCF-7 cells, destroyed the cell membrane(leading to the release of LDH), and shortened the migration distance of MCF-7 cells. Total saponins of Paridis Rhizoma treatment significantly increased the content of ROS, induced oxidative damage, and led to the accumulation of Fe~(2+) in MCF-7 cells. Furthermore, total saponins of Paridis Rhizoma changed the mitochondrial structure, increased the mitochondrial membrane density, led to the decrease or even disappear of ridges, promoted the expression of p53 protein, down-regulated the expression of SLC7A11 and GPX4, and up-regulated the expression of ACSL4 and TFR1. In summary, total saponins of Paridis Rhizoma can significantly inhibit the proliferation and migration of MCF-7 cells and destroy the cell structure by inducing ferroptosis.

Mixture-of-experts and semantic-guided network for brain tumor segmentation with missing MRI modalities.

Accurate brain tumor segmentation with multi-modal MRI images is crucial, but missing modalities in clinical practice often reduce accuracy. The aim of this study is to propose a mixture-of-experts and semantic-guided network to tackle the issue of missing modalities in brain tumor segmentation. We introduce a transformer-based encoder with novel mixture-of-experts blocks. In each block, four modality experts aim for modality-specific feature learning. Learnable modality embeddings are employed to alleviate the negative effect of missing modalities. We also introduce a decoder guided by semantic information, designed to pay higher attention to various tumor regions. Finally, we conduct extensive comparison experiments with other models as well as ablation experiments to validate the performance of the proposed model on the BraTS2018 dataset. The proposed model can accurately segment brain tumor sub-regions even with missing modalities. It achieves an average Dice score of 0.81 for the whole tumor, 0.66 for the tumor core, and 0.52 for the enhanced tumor across the 15 modality combinations, achieving top or near-top results in most cases, while also exhibiting a lower computational cost. Our mixture-of-experts and sematic-guided network achieves accurate and reliable brain tumor segmentation results with missing modalities, indicating its significant potential for clinical applications. Our source code is already available at https://github.com/MaggieLSY/MESG-Net .

A comprehensive survey on deep active learning in medical image analysis.

Deep learning has achieved widespread success in medical image analysis, leading to an increasing demand for large-scale expert-annotated medical image datasets. Yet, the high cost of annotating medical images severely hampers the development of deep learning in this field. To reduce annotation costs, active learning aims to select the most informative samples for annotation and train high-performance models with as few labeled samples as possible. In this survey, we review the core methods of active learning, including the evaluation of informativeness and sampling strategy. For the first time, we provide a detailed summary of the integration of active learning with other label-efficient techniques, such as semi-supervised, self-supervised learning, and so on. We also summarize active learning works that are specifically tailored to medical image analysis. Additionally, we conduct a thorough comparative analysis of the performance of different AL methods in medical image analysis with experiments. In the end, we offer our perspectives on the future trends and challenges of active learning and its applications in medical image analysis. An accompanying paper list and code for the comparative analysis is available in https://github.com/LightersWang/Awesome-Active-Learning-for-Medical-Image-Analysis.

Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation.

Precise delineation of multiple organs or abnormal regions in the human body from medical images plays an essential role in computer-aided diagnosis, surgical simulation, image-guided interventions, and especially in radiotherapy treatment planning. Thus, it is of great significance to explore automatic segmentation approaches, among which deep learning-based approaches have evolved rapidly and witnessed remarkable progress in multi-organ segmentation. However, obtaining an appropriately sized and fine-grained annotated dataset of multiple organs is extremely hard and expensive. Such scarce annotation limits the development of high-performance multi-organ segmentation models but promotes many annotation-efficient learning paradigms. Among these, studies on transfer learning leveraging external datasets, semi-supervised learning including unannotated datasets and partially-supervised learning integrating partially-labeled datasets have led the dominant way to break such dilemmas in multi-organ segmentation. We first review the fully supervised method, then present a comprehensive and systematic elaboration of the 3 abovementioned learning paradigms in the context of multi-organ segmentation from both technical and methodological perspectives, and finally summarize their challenges and future trends.

Graphene Intermediate Layer for Robust and Spectrum-Extended Cu Photocathode Activated with Cs and O.

Developing an effective method to stably enhance the quantum efficiency (QE) and extend the photoemission threshold of Cu photocathodes beyond the ultraviolet region could benefit the photoinjector for ultrafast electron source applications. The implementation of a 2D material protective layer is considered a promising approach to extending the operating lifetime of photocathodes. We propose that graphene can serve as an intermediate layer at the interface between photocathode material and low-work-function coating. The role of oxygen in the Cs/O activation process on the Cu surface is altered by the graphene interlayer. Besides, the few-layer graphene (FLG) surface could be more likely to induce the formation of Cs2O. Thus, the graphene-Cu composite photocathode can achieve an ultralow surface work function of down to 0.878 eV through Cs/O activation. The photoemission performance of the composite cathode with a FLG interlayer is significantly enhanced. The photocathode has an extended spectral response to the near-infrared region and a higher QE. At 350 nm, its QE is more than twice that of the cesiated bare Cu, reaching 0.247%. After degradation, the graphene-Cu cathode can be fully restored by reactivation, with remarkably enhanced stability. In addition, the composite cathode can be operated reliably under a poor vacuum pressure of over 4 × 10-6 Pa. This study validates a new method for incorporating 2D materials into photocathodes, offering novel approaches to explore robust and spectrum-extended photocathodes.