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Ferroptosis is an iron-dependent form of regulated cell death that is triggered by the toxic build-up of lipid peroxides on cellular membranes. In recent years, ferroptosis has garnered enormous interest in cancer research communities, partly because it is a unique cell death modality that is mechanistically and morphologically different from other forms of cell death, such as apoptosis, and therefore holds great potential for cancer therapy. In this Review, we summarize the current understanding of ferroptosis-inducing and ferroptosis defence mechanisms, dissect the roles and mechanisms of ferroptosis in tumour suppression and tumour immunity, conceptualize the diverse vulnerabilities of cancer cells to ferroptosis, and explore therapeutic strategies for targeting ferroptosis in cancer.In recent years, research in the field of ferroptosis in cancer has risen steeply in part owing to its potential to be targeted. In this Review, Lei et al. provide an up-to-date synthesis of the roles and mechanisms of ferroptosis in tumour growth and progression, including its function in tumour immunity, highlighting it as a vulnerability that can be exploited for cancer therapy.

The cystine/glutamate antiporter SLC7A11 (also commonly known as xCT) functions to import cystine for glutat hione biosynthesis and antioxidant defense and is overexpressed in multiple human cancers. Recent studies revealed that SLC7A11 overexpression promotes tumor growth partly through suppressing ferroptosis, a form of regulated cell death induced by excessive lipid peroxidation. However, cancer cells with high expression of SLC7A11 (SLC7A11 high) also have to endure the significant cost associated with SLC7A11-mediated metabolic reprogramming, leading to glucoseand glutamine-dependency in SLC7A11 high cancer cells, which presents potential metabolic vulnerabilities for therapeutic targeting in SLC7A11 high cancer. In this review, we summarize diverse regulatory mechanisms of SLC7A11 in cancer, discuss ferroptosis-dependent and-independent functions of SLC7A11 in promoting tumor development, explore the mechanistic basis of SLC7A11-induced nutrient dependency in cancer cells, and conceptualize therapeutic strategies to target SLC7A11 in cancer treatment. This review will provide the foundation for further understanding SLC7A11 in ferroptosis, nutrient dependency, and tumor biology and for developing novel effective cancer therapies.

Septic acute kidney injury (AKI) is characterized by inflammation. Pyroptosis often occurs during AKI and is associated with the development of septic AKI. This study found that induction of insulin-like growth factor 2 mRNA binding protein 1 (IGF2BP1) to a higher level can induce pyroptosis in renal tubular cells. Meanwhile, macrophage migration inhibitory factor (MIF), a subunit of NLRP3 inflammasomes, was essential for IGF2BP1-induced pyroptosis. A putative m6A recognition site was identified at the 3'-UTR region of E2F transcription factor 1 (E2F1) mRNA via bioinformatics analyses and validated using mutation and luciferase experiments. Further actinomycin D (Act D) chase experiments showed that IGF2BP1 stabilized E2F1 mRNA dependent on m6A. Electrophoretic mobility shift assay (EMSA) and chromatin immunoprecipitation (ChIP) indicated that E2F1 acted as a transcription factor to promote MIF expression. Thus, IGF2BP1 upregulated MIF through directly upregulating E2F1 expression via m6A modification. Experiments on mice with cecum ligation puncture (CLP) surgery verified the relationships between IGF2BP1, E2F1, and MIF and demonstrated the significance of IGF2BP1 in MIF-associated pyroptosis . In conclusion, IGF2BP1 was a potent pyroptosis inducer in septic AKI through targeting the MIF component of NLRP3 inflammasomes. Inhibiting IGF2BP1 could be an alternate pyroptosis-based treatment for septic AKI.

With the continuous development of deep learning technology, object detection has received extensive attention across various computer fields as a fundamental task of computational vision. Effective detection of objects in remote sensing images is a key challenge, owing to their small size and low resolution. In this study, a remote sensing image detection (RSI-YOLO) approach based on the YOLOv5 target detection algorithm is proposed, which has been proven to be one of the most representative and effective algorithms for this task. The channel attention and spatial attention mechanisms are used to strengthen the features fused by the neural network. The multi-scale feature fusion structure of the original network based on a PANet structure is improved to a weighted bidirectional feature pyramid structure to achieve more efficient and richer feature fusion. In addition, a small object detection layer is added, and the loss function is modified to optimise the network model. The experimental results from four remote sensing image datasets, such as DOTA and NWPU-VHR 10, indicate that RSI-YOLO outperforms the original YOLO in terms of detection performance. The proposed RSI-YOLO algorithm demonstrated superior detection performance compared to other classical object detection algorithms, thus validating the effectiveness of the improvements introduced into the YOLOv5 algorithm.

Cancer cells often upregulate nutrient transporters to fulfill their increased biosynthetic and bioenergetic needs, and to maintain redox homeostasis. One nutrient transporter frequently overexpressed in human cancers is the cystine/glutamate antiporter solute carrier family 7 member 11 (SLC7A11; also known as xCT). SLC7A11 promotes cystine uptake and glutathione biosynthesis, resulting in protection from oxidative stress and ferroptotic cell death. Recent studies have unexpectedly revealed that SLC7A11 also plays critical roles in glutamine metabolism and regulates the glucose and glutamine dependency of cancer cells. This review discusses the roles of SLC7A11 in regulating the antioxidant response and nutrient dependency of cancer cells, explores our current understanding of SLC7A11 regulation in cancer metabolism, and highlights key open questions for future studies in this emerging research area. A deeper understanding of SLC7A11 in cancer metabolism may identify new therapeutic opportunities to target this important amino acid transporter for cancer treatment.

Over the past few decades, an increasing number of isotopic studies (Re–Os, Lu–Hf, Sm–Nd, etc [...]

Aiming to address the currently low accuracy of domestic industrial defect detection, this paper proposes a Two-Stage Industrial Defect Detection Framework based on Improved-YOLOv5 and Optimized-Inception-ResnetV2, which completes positioning and classification tasks through two specific models. In order to make the first-stage recognition more effective at locating insignificant small defects with high similarity on the steel surface, we improve YOLOv5 from the backbone network, the feature scales of the feature fusion layer, and the multiscale detection layer. In order to enable second-stage recognition to better extract defect features and achieve accurate classification, we embed the convolutional block attention module (CBAM) attention mechanism module into the Inception-ResnetV2 model, then optimize the network architecture and loss function of the accurate model. Based on the Pascal Visual Object Classes 2007 (VOC2007) dataset, the public dataset NEU-DET, and the optimized dataset Enriched-NEU-DET, we conducted multiple sets of comparative experiments on the Improved-YOLOv5 and Inception-ResnetV2. The testing results show that the improvement is obvious. In order to verify the superiority and adaptability of the two-stage framework, we first test based on the Enriched-NEU-DET dataset, and further use AUBO-i5 robot, Intel RealSense D435 camera, and other industrial steel equipment to build actual industrial scenes. In experiments, a two-stage framework achieves the best performance of 83.3% mean average precision (mAP), evaluated on the Enriched-NEU-DET dataset, and 91.0% on our built industrial defect environment.

Fringe projection profilometry (FPP) has been widely applied in industrial 3D measurement due to its high precision and non-contact advantages. However, FPP often encounters measurement problems with high-dynamic-range objects, consequently impacting phase computation. In this paper, an adaptive exposure time selection method is proposed to calculate the optimal number of exposures and exposure time by using an improved clustering method to divide the region with different reflection degrees. Meanwhile, the phase order sharing strategy is adopted in the phase unwrapping stage, and the same set of complementary Gray code patterns is used to calculate the phase orders under different exposure times. The experimental results demonstrate that the measurement error of the method described in this paper was reduced by 25.4% under almost the same exposure times.

Ferroptosis, an iron-dependent form of regulated cell death driven by peroxidative damages of polyunsaturated-fatty-acid-containing phospholipids in cellular membranes, has recently been revealed to play an important role in radiotherapy-induced cell death and tumor suppression, and to mediate the synergy between radiotherapy and immunotherapy. In this review, we summarize known as well as putative mechanisms underlying the crosstalk between radiotherapy and ferroptosis, discuss the interactions between ferroptosis and other forms of regulated cell death induced by radiotherapy, and explore combination therapeutic strategies targeting ferroptosis in radiotherapy and immunotherapy. This review will provide important frameworks for future investigations of ferroptosis in cancer therapy.

A bstract Gluino-SUGRA ( g ~ SUGRA), which is an economical extension of the predictive mSUGRA, adopts much heavier gluino mass parameter than other gauginos mass parameters and universal scalar mass parameter at the unification scale. It can elegantly reconcile the experimental results on the Higgs boson mass, the muon g − 2, the null results in search for supersymmetry at the LHC and the results from B-physics. In this work, we propose several new ways to generate large gaugino hierarchy (i.e. M 3 » M 1 , M 2 ) for g ~ SUGRA model building and then discuss in detail the implications of the new muon g − 2 results with the updated LHC constraints on such g ~ SUGRA scenarios. We obtain the following observations: (i) for the most interesting M 1 = M 2 case at the GUT scale with a viable bino-like dark matter, the g ~ SUGRA can explain the muon g − 2 anomaly at 1 σ level and be consistent with the updated LHC constraints for 6 ≤ M 3 /M 1 ≤ 9 at the GUT scale; (ii) For M 1 : M 2 = 5 : 1 at the GUT scale with wino-like dark matter, the g ~ SUGRA model can explain the muon g − 2 anomaly at 2 σ level and be consistent with the updated LHC constraints for 3 ≤ M 3 /M 1 ≤ 3 . 2 at the GUT scale; (iii) For M 1 : M 2 = 3 : 2 at the GUT scale with mixed bino-wino dark matter, the g ~ SUGRA model can explain the muon g − 2 anomaly at 1 σ level and be consistent with the updated LHC constraints for 6 . 9 ≤ M 3 /M 1 ≤ 7 . 5 at the GUT scale. Although the choice of heavy gluino will always increase the FT involved, some of the 1 σ/ 2 σ survived points of ∆ a μ combine can still allow low EWFT of order several hundreds and be fairly natural. Constraints from (dimension-five operator induced) proton decay are also discussed.