Explore the vast range of titles available.

Apigenin is a common dietary flavonoid that is abundantly present in many fruits, vegetables and Chinese medicinal herbs and serves multiple physiological functions, such as strong anti-inflammatory, antioxidant, antibacterial and antiviral activities and blood pressure reduction. Therefore, apigenin has been used as a traditional medicine for centuries. Recently, apigenin has been widely investigated for its anti-cancer activities and low toxicity. Apigenin was reported to suppress various human cancers in vitro and in vivo by multiple biological effects, such as triggering cell apoptosis and autophagy, inducing cell cycle arrest, suppressing cell migration and invasion, and stimulating an immune response. In this review, we focus on the most recent advances in the anti-cancer effects of apigenin and their underlying mechanisms, and we summarize the signaling pathways modulated by apigenin, including the PI3K/AKT, MAPK/ERK, JAK/STAT, NF-κB and Wnt/β-catenin pathways. We also discuss combinatorial strategies to enhance the anti-cancer effect of apigenin on various cancers and its use as an adjuvant chemotherapeutic agent to overcome cancer drug resistance or to alleviate other adverse effects of chemotherapy. The functions of apigenin against cancer stem cells are also summarized and discussed. These data demonstrate that apigenin is a promising reagent for cancer therapy. Apigenin appears to have the potential to be developed either as a dietary supplement or as an adjuvant chemotherapeutic agent for cancer therapy.

YOLOv8, as an efficient object detection method, can swiftly and precisely identify objects within images. However, traditional algorithms encounter difficulties when detecting small objects in remote sensing images, such as missing information, background noise, and interactions among multiple objects in complex scenes, which may affect performance. To tackle these challenges, we propose an enhanced algorithm optimized for detecting small objects in remote sensing images, named HP-YOLOv8. Firstly, we design the C2f-D-Mixer (C2f-DM) module as a replacement for the original C2f module. This module integrates both local and global information, significantly improving the ability to detect features of small objects. Secondly, we introduce a feature fusion technique based on attention mechanisms, named Bi-Level Routing Attention in Gated Feature Pyramid Network (BGFPN). This technique utilizes an efficient feature aggregation network and reparameterization technology to optimize information interaction between different scale feature maps, and through the Bi-Level Routing Attention (BRA) mechanism, it effectively captures critical feature information of small objects. Finally, we propose the Shape Mean Perpendicular Distance Intersection over Union (SMPDIoU) loss function. The method comprehensively considers the shape and size of detection boxes, enhances the model’s focus on the attributes of detection boxes, and provides a more accurate bounding box regression loss calculation method. To demonstrate our approach’s efficacy, we conducted comprehensive experiments across the RSOD, NWPU VHR-10, and VisDrone2019 datasets. The experimental results show that the HP-YOLOv8 achieves 95.11%, 93.05%, and 53.49% in the mAP@0.5 metric, and 72.03%, 65.37%, and 38.91% in the more stringent mAP@0.5:0.95 metric, respectively.

Metabolic syndrome (MS), a chronic and non‐communicable pathological condition, is characterized by a constellation of clinical manifestations including insulin resistance, abdominal adiposity, elevated blood pressure, and perturbations in lipid metabolism. The prevalence of MS has increased dramatically in both developed and developing countries and has now become a truly global problem. Excessive energy intake and concomitant obesity are the main drivers of this syndrome. Mitophagy, in which cells degrade damaged mitochondria through a selective form of autophagy, assumes a crucial position in the regulation of mitochondrial integrity and maintenance. Abnormal mitochondrial quality could result in a spectrum of pathological conditions related to metabolic dysfunction, including metabolic syndrome, cardiovascular ailments, and neoplasms. Recently, there has been a proliferation of research pertaining to the process of mitophagy in the context of MS, and there are various regulatory pathways in MS, including pathways like the ubiquitin‐dependent mechanism and receptor‐mediated mechanisms, among others. Furthermore, studies have uncovered that the process of mitophagy serves a defensive function in the advancement of Metabolic Syndrome, and inhibition of mitophagy exacerbates the advancement of MS. As a result, the regulation of mitophagy holds great promise as a therapeutic approach in the management of Metabolic Syndrome. In this comprehensive analysis, the authors present a synthesis of the diverse regulatory pathways involved in mitophagy in the context of Metabolic Syndrome, as well as its modes of action in metabolic disorders implicated in the development of MS, Including obesity, insulin resistance (IR), and type 2 diabetes mellitus (T2DM), offering novel avenues for the prophylaxis and therapeutic management of MS.

Recent advances in MXene (Ti 3 C 2 T x ) fibers, prepared from electrically conductive and mechanically strong MXene nanosheets, address the increasing demand of emerging yet promising electrode materials for the development of textile-based devices and beyond. However, to reveal the full potential of MXene fibers, reaching a balance between electrical conductivity and mechanical property is still the fundamental challenge, mainly due to the difficulties to further compact the loose MXene nanosheets. In this work, we demonstrate a continuous and controllable route to fabricate ultra-compact MXene fibers with an in-situ generated protective layer via the synergy of interfacial interactions and thermal drawing-induced stresses. The resulting ultra-compact MXene fibers with high orientation and low porosity exhibit not only excellent tensile strength and ultra-high toughness, but also high electrical conductivity. Then, we construct meter-scale MXene textiles using these ultra-compact fibers to achieve high-performance electromagnetic interference shielding and personal thermal management, accompanied by the high mechanical durability and stability even after multiple washing cycles. The demonstrated generic strategy can be applied to a broad range of nanostructured materials to construct functional fibers for large-scale applications in both space and daily lives. Forming compact layered nanostructures is key to achieving continuous MXene fibers with electrical and mechanical properties. Here, authors demonstrate ultra-compact high-performance MXene fibers via a controllable synergy of interfacial interactions and thermal drawing-induced stresses.

Creating micro/nanostructures on fibers is beneficial for extending the application range of fiber-based devices. To achieve this using thermal fiber drawing is particularly important for the mass production of longitudinally uniform fibers up to tens of kilometers. However, the current thermal fiber drawing technique can only fabricate one-directional micro/nano-grooves longitudinally due to structure elongation and polymer reflow. Here, we develop a direct imprinting thermal drawing (DITD) technique to achieve arbitrarily designed surface patterns on entire fiber surfaces with high resolution in all directions. Such a thermal imprinting process is simulated and confirmed experimentally. Key process parameters are further examined, showing a process feature size as small as tens of nanometers. Furthermore, nanopatterns are fabricated on fibers as plasmonic metasurfaces, and double-sided patterned fibers are produced to construct self-powered wearable touch sensing fabric, revealing the bright future of the DITD technology in multifunctional fiber-based devices, wearable electronics, and smart textiles. Creating micro/nanostructures on fibers is beneficial to many fiber-based devices, which remains a challenge in large-scale fabrication due to elongation and reflow. Here, the authors demonstrate a method for generating high-resolution, arbitrarily designed surface patterns on fiber during the thermal drawing process.

Currently, how to deal with the massive garbage produced by various human activities is a hot topic all around the world. In this paper, a preliminary and essential step is to classify the garbage into different categories. However, the mainstream waste classification mode relies heavily on manual work, which consumes a lot of labor and is very inefficient. With the rapid development of deep learning, convolutional neural networks (CNN) have been successfully applied to various application fields. Therefore, some researchers have directly adopted CNNs to classify garbage through their images. However, compared with other images, the garbage images have their own characteristics (such as inter-class similarity, intra-class variance and complex background). Thus, neglecting these characteristics would impair the classification accuracy of CNN. To overcome the limitations of existing garbage image classification methods, a Depth-wise Separable Convolution Attention Module (DSCAM) is proposed in this paper. In DSCAM, the inherent relationships of channels and spatial positions in garbage image features are captured by two attention modules with depth-wise separable convolutions, so that our method could only focus on important information and ignore the interference. Moreover, we also adopt a residual network as the backbone of DSCAM to enhance its discriminative ability. We conduct the experiments on five garbage datasets. The experimental results demonstrate that the proposed method could effectively classify the garbage images and that it outperforms some classical methods.

Mitochondrial fusion and fission dynamics fine-tune cellular calcium homeostasis, ATP production capacity and ROS production and play important roles in cell proliferation and migration. Dysregulated mitochondrial dynamics is closely related to tumor development, but the mechanism of mitochondrial dynamics dysregulation and its role in the development of lung cancer remains unclear. Here, we demonstrate that the DNA sensor protein absent in melanoma 2 (AIM2) is highly expressed in non-small cell lung cancer (NSCLC) cells and that high AIM2 expression is associated with poor prognosis in patients with NSCLC. High expression of AIM2 contributes to tumor cell growth and proliferation independent of inflammasome activation in vitro and in vivo. Further studies have shown that AIM2 colocalizes with mitochondria in NSCLC cells and that AIM2 knockdown leads to enhanced mitochondrial fusion and decreased cell proliferation. Mechanistic studies have shown that AIM2 downregulation promotes MFN2 upregulation, thereby enhancing mitochondrial fusion. Moreover, we found that mitochondrial fusion driven by AIM2 knockdown leads to a decrease of cellular reactive oxygen species (ROS) production, which further causes inactivation of the MAPK/ERK signaling pathway. Together, we discovered a novel function of AIM2 in promoting NSCLC development by regulating mitochondrial dynamics and revealed its underlying mechanism. Our work provides new intervention targets for the treatment of NSCLC.

Precipitation alteration and nitrogen (N) deposition caused by anthropogenic activities could profoundly affect the structure and functioning of plant communities in arid ecosystems. However, the plant community impacts conferred by large temporal changes in precipitation, especially with a concurrent increase in N deposition, remain unclear. To address this uncertainty, from 2016 to 2017, an in situ field experiment was conducted to examine the effects of five precipitation levels, two N levels and their interaction on the plant community function and composition in a desert steppe in northern China. Above‐ground net primary production (ANPP) and plant community‐weighted mean (CWM) height significantly increased with increasing precipitation, and both were well fitted with a positive linear model, but with a higher slope under N addition. The ANPP increase was primarily driven by the increase in Artemisia capillaris, a companion forb sensitive to precipitation variation. The plant community composition shifted with precipitation enhancement—from a community dominated by Stipa tianschanica, a perennial grass, to a community dominated by Artemisia capillaris. Synthesis. The findings imply that the ecosystem sensitivity to future changes in precipitation variability will be mediated by two potential mechanisms: concurrent N deposition and plant community‐level change. It is suggested that we should consider the vegetation compositional shift and multiple resource colimitation in assessing the sensitivity of terrestrial ecosystems to climate change. Nitrogen deposition magnified the sensitivity of above‐ground net primary production (ANPP) to precipitation enhancement, with the plant community composition shifted at plant functional groups (PFGs) level—the dominant species shifted from a native perennial grass, Stipa tianschanica, to a water‐sensitive companion forb, Artemisia capillaris. This implies that the ecosystem sensitivity to future changes in precipitation variability will be mediated by two potential mechanisms: concurrent nitrogen deposition and plant community‐level change.

ObjectiveThe significance of the liver-microbiome axis has been increasingly recognised as a major modulator of autoimmunity. The aim of this study was to take advantage of a large well-defined corticosteroids treatment-naïve group of patients with autoimmune hepatitis (AIH) to rigorously characterise gut dysbiosis compared with healthy controls.DesignWe performed a cross-sectional study of individuals with AIH (n=91) and matched healthy controls (n=98) by 16S rRNA gene sequencing. An independent cohort of 28 patients and 34 controls was analysed to validate the results. All the patients were collected before corticosteroids therapy.ResultsThe gut microbiome of steroid treatment-naïve AIH was characterised with lower alpha-diversity (Shannon and observed operational taxonomic units, both p<0.01) and distinct overall microbial composition compared with healthy controls (p=0.002). Depletion of obligate anaerobes and expansion of potential pathobionts including Veillonella were associated with disease status. Of note, Veillonella dispar, the most strongly disease-associated taxa (p=8.85E–8), positively correlated with serum level of aspartate aminotransferase and liver inflammation. Furthermore, the combination of four patients with AIH-associated genera distinguished AIH from controls with an area under curves of approximately 0.8 in both exploration and validation cohorts. In addition, multiple predicted functional modules were altered in the AIH gut microbiome, including lipopolysaccharide biosynthesis as well as metabolism of amino acids that can be processed by bacteria to produce immunomodulatory metabolites.ConclusionOur study establishes compositional and functional alterations of gut microbiome in AIH and suggests the potential for using gut microbiota as non-invasive biomarkers to assess disease activity.

Cancer remains a significant global public health concern. Numerous challenges still remain in its treatment. Recently, a novel two-dimensional material—violet phosphorene nanosheets (VPNS)—has shown considerable application potential in the biomedical field due to its unique physicochemical properties. The VPNS with a concentration of 41.00 μg/mL has been demonstrated to exhibit significant anti-cancer effects through the induction of apoptosis. The treatment of VPNS was revealed by cell metabolomics analysis to a marked down-regulation of succinate hemialdehyde expression and an up-regulation of pyridoxine levels in cancer cells. These differentially expressed metabolites are closely associated with the vitamin B6 metabolic pathway. In addition, the VPNS has also been demonstrated to exert excellent anti-cancer effects within a living organism by in vivo animal experiments.