[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.

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.

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 .

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.

Terrestrial inputs and physical processes control the distributions of potentially toxic elements (PTEs) in the seawater of the large-range Beibu Gulf, the northern South China Sea.

The potentially toxic elements (PTEs), Cu, Pb, Zn, Cd, Cr, Hg and As in the water from the Beibu Gulf, were investigated to reveal the contaminant characteristics and assess the risks to human health. The results showed that the concentration of PTEs in the Beibu Gulf varies significantly both seasonally and spatially, with higher concentrations in summer and in the northern and southern gulf. Terrestrial inputs and local anthropogenic discharge are responsible for the higher level in the northern gulf, and the transportation of water masses is also an important factor for the higher concentrations in the southern gulf. Ecological risk assessment suggested that Hg is the main ecological risk factor. The health risk assessment revealed that dermal exposure to PTEs in the gulf presents potentially carcinogenic health effects for humans. This study provides new insight into the transport of PTEs over a large area of the Beibu Gulf.

Enhanced blue-green response of nanoarray AlGaAs photocathodes for underwater low-light detection.

Underwater optical communication and low-light detection are usually realized via blue-green laser sources and blue-green light-sensitive detectors. Negative-electron-affinity AlGaAs photocathode is an ideal photosensitive material for ocean exploration due to its adjustable spectrum range, long working lifetime, and easy epitaxy of materials. However, compared with other photocathodes, the main problem of AlGaAs photocathode is its low quantum efficiency. Based on Spicer's three-step photoemission model, nanoarray structures are designed on the surface of AlGaAs photocathode to improve its quantum efficiency from two aspects of optical absorption and photoelectron transport. Through simulation, it is concluded that the cylinder with diameter of 120 nm and height of 600 nm is the best nanoarray structure, and its absorptance is always greater than 90% in the 445∼532 nm range. Moreover, the absorptance and quantum efficiency of the cylinder nanoarray AlGaAs photocathode are less affected by the incident angle. When the angle of incident light reaches 70°, the minimum absorptance and quantum efficiency are still 64.6% and 24.9%. In addition, the square or hexagonal arrangement pattern of the nanoarray has little effect on the absorptance, however, a reduction in the overall emission layer thickness will decrease the absorptance near 532 nm.

Segmentation of Portal Vein in Multiphase CTA Image Based on Unsupervised Domain Transfer and Pseudo Label.

BACKGROUND: Clinically, physicians diagnose portal vein diseases on abdominal CT angiography (CTA) images scanned in the hepatic arterial phase (H-phase), portal vein phase (P-phase) and equilibrium phase (E-phase) simultaneously. However, existing studies typically segment the portal vein on P-phase images without considering other phase images. METHOD: We propose a method for segmenting portal veins on multiphase images based on unsupervised domain transfer and pseudo labels by using annotated P-phase images. Firstly, unsupervised domain transfer is performed to make the H-phase and E-phase images of the same patient approach the P-phase image in style, reducing the image differences caused by contrast media. Secondly, the H-phase (or E-phase) image and its style transferred image are input into the segmentation module together with the P-phase image. Under the constraints of pseudo labels, accurate prediction results are obtained. RESULTS: This method was evaluated on the multiphase CTA images of 169 patients. The portal vein segmented from the H-phase and E-phase images achieved DSC values of 0.76 and 0.86 and Jaccard values of 0.61 and 0.76, respectively. CONCLUSION: The method can automatically segment the portal vein on H-phase and E-phase images when only the portal vein on the P-phase CTA image is annotated, which greatly assists in clinical diagnosis.

Nanoparticles for cancer therapy: a review of influencing factors and evaluation methods for biosafety

Nanoparticles are widely used in the biomedical field for diagnostic and therapeutic purposes due to their small size, high carrier capacity, and ease of modification, which enable selective targeting and as contrast agents. Over the past decades, more and more nanoparticles have received regulatory approval to enter the clinic, more nanoparticles have shown potential for clinical translation, and humans have increasing access to them. However, nanoparticles have a high potential to cause unpredictable adverse effects on human organs, tissues, and cells due to their unique physicochemical properties and interactions with DNA, lipids, cells, tissues, proteins, and biological fluids. Currently, issues, such as nanoparticle side effects and toxicity, remain controversial, and these pitfalls must be fully considered prior to their application to body systems. Therefore, it is particularly urgent and important to assess the safety of nanoparticles acting in living organisms. In this paper, we review the important factors influencing the biosafety of nanoparticles in terms of their properties, and introduce common methods to summarize the biosafety evaluation of nanoparticles through in vitro and in body systems (AU)

Multilevel spatial confinement of transition metal selenides porous microcubes for efficient and stable potassium storage.

Recently, potassium-ion batteries (PIBs) have been considered as one of the most promising energy storage systems; however, the slow kinetics and large volume variation induced by the large radius of potassium ions (K+) during chemical reactions lead to inferior structural stability and weak electrochemical activity for most potassium storage anodes. Herein, a multilevel space confinement strategy is proposed for developing zinc-cobalt bimetallic selenide (ZnSe/Co0.85Se@NC@C@rGO) as high-efficient anodes for PIBs by in-situ carbonizing and subsequently selenizing the resorcinol-formaldehyde (RF)-coated zeolitic imidazolate framework-8/zeolitic imidazolate framework-67 (ZIF-8/ZIF-67) encapsulated into 2D graphene. The highly porous carbon microcubes derived from ZIF-8/ZIF-67 and carbon shell arising from RF provide rich channels for ion/electron transfer, present a rigid skeleton to ensure the structural stability, offer space for accommodating the volume change, and minimize the agglomeration of active material during the insertion/extraction of large-radius K+. In addition, the three-dimensional (3D) carbon network composed of graphene and RF-derived carbon-coated microcubes accelerates the electron/ion transfer rate and improves the electrochemical reaction kinetics of the material. As a result, the as-synthesized ZnSe/Co0.85Se@NC@C@rGO as the anode of PIBs possesses the excellent rate capability of 203.9 mA h g-1 at 5 A g-1 and brilliant long-term cycling performance of 234 mA h g-1 after 2,000 cycles at 2 A g-1. Ex-situ X-ray diffraction (Ex-situ XRD) diffraction reveals that the intercalation/de-intercalation of K+ proceeds through the conversion-alloying reaction. The proposed strategy based on the spatial confinement engineering is highly effective to construct high-performance anodes for PIBs.

Nanoparticles for cancer therapy: a review of influencing factors and evaluation methods for biosafety.

Nanoparticles are widely used in the biomedical field for diagnostic and therapeutic purposes due to their small size, high carrier capacity, and ease of modification, which enable selective targeting and as contrast agents. Over the past decades, more and more nanoparticles have received regulatory approval to enter the clinic, more nanoparticles have shown potential for clinical translation, and humans have increasing access to them. However, nanoparticles have a high potential to cause unpredictable adverse effects on human organs, tissues, and cells due to their unique physicochemical properties and interactions with DNA, lipids, cells, tissues, proteins, and biological fluids. Currently, issues, such as nanoparticle side effects and toxicity, remain controversial, and these pitfalls must be fully considered prior to their application to body systems. Therefore, it is particularly urgent and important to assess the safety of nanoparticles acting in living organisms. In this paper, we review the important factors influencing the biosafety of nanoparticles in terms of their properties, and introduce common methods to summarize the biosafety evaluation of nanoparticles through in vitro and in body systems.

Determining effect of seagrass-mediated CO2 flux on the atmospheric cooling potential of a subtropical intertidal seagrass meadow.

Atmospheric greenhouse gas (GHG) emissions from seagrass meadows that determine the ecosystem atmospheric cooling effect have rarely been quantified. This study measured the simultaneous fluxes direct to the atmosphere of three GHGs (CO2, CH4 and N2O) within a Halophila beccarii seagrass meadow and an adjacent unvegetated bare intertidal flat, and their relationships to seagrass abundance and relevant soil parameters. The results showed that seasonal variation in seagrass abundance was strongly linked with the CO2 exchange rate. The CH4 and N2O fluxes were similarly low at both sites and comparable between winter and summer. The global warming potential of CH4 and N2O reduced the ecosystem CO2 uptake by only 5 % at the seagrass site. The results indicated that the H. beccarii meadow had a stronger atmospheric cooling effect than the bare flat and that the seagrass-mediated CO2 flux in this oligotrophic seagrass meadow primarily determined the atmospheric cooling effect.

Proteomics and molecular network analyses reveal that the interaction between the TAT-DCF1 peptide and TAF6 induces an antitumor effect in glioma cells.

Glioblastoma is the most lethal brain cancer in adults. Despite advances in surgical techniques, radiotherapy, and chemotherapy, their therapeutic effect is far from significant, since the detailed underlying pathological mechanism of this cancer is unclear. The establishment of molecular interaction networks has laid the foundation for the exploration of these mechanisms with a view to improving therapy for glioblastoma. In the present study, to further explore the cellular role of DCF1 (dendritic cell-derived factor 1), the proteins bound to TAT-DCF1 (transactivator of transcription-dendritic cell-derived factor 1) were identified, and biosystem analysis was employed. Functional enrichment analyses indicate that TAT-DCF1 induced important biological changes in U251 cells. Furthermore, the established molecular interaction networks indicated that TAT-DCF1 directly interacted with TAF6 in glioma cells and with UBC in HEK293T (human embryonic kidney 293T) cells. In addition, further biological experiments demonstrate that TAT-DCF1 induced the activation of the RPS27A/TOP2A/HMGB2/BCL-2 signaling pathway via interaction with TAF6 in U251 cells. Taken together, these findings suggest that the TAT-DCF1 peptide possesses great potential for the development of glioblastoma therapy through the interaction with TAF6-related pathways and provides further theoretic evidence for the mechanisms underlying the antitumor effects of TAT-DCF1.

The antitumor effect of TAT-DCF1 peptide in glioma cells.

BACKGROUND: Glioblastoma is one of the most malignant brain cancer, thus, establishing an effective therapy is paramount. Our previous results indicate that dendritic cell-derived factor (DCF1) is an attractive candidate for therapy against Glioblastoma, since its overexpression in Glioblastoma U251 cells leads to apoptosis. However, the delivery approach limits its clinical application, in this paper, we expressed TAT-DCF1 fusion protein in E.coli in order to surmount its current delivery problems. METHODS: The coding sequences of the different domains of DCF1 (full length, cytoplasmic, extracellular, 19-amino acid), together with the N-terminal transactivator of transcription (TAT) sequence, were amplified and subcloned into the bacterial expression vector pET30a(+) in order to produce (His)6-tagged fusion proteins. Coomassie blue-stained SDS-PAGE and Western blotting identification showed that purity of the fusion proteins. RESULTS: Immunofluorescence and flow cytometry show that U251 cells were efficiently transduced with the fusion proteins. Cell viability, proliferation, and migration assays suggest that the complete TAT-DCF1 fusion protein significantly decreased U251 proliferation and migration. Flow cytometry further reveals that TAT-DCF1 triggered cellular apoptosis. CONCLUSIONS: In conclusion, these findings suggest that the TAT-DCF1 fusion protein was efficiently transduced into Glioblastoma U251 cells and induced the antitumor effect and support further investigation into specific targeting and side effects of TAT-DCF1 during drug delivery.