Explore the vast range of titles available.

Erastin was initially discovered as a small molecule compound that selectively kills tumor cells expressing ST and RASV12 and was later widely investigated as an inducer of ferroptosis. Ferroptosis is a recently discovered form of cell death caused by peroxidation induced by the accumulation of intracellular lipid reactive oxygen species (L-ROS) in an iron-dependent manner. Erastin can mediate ferroptosis through a variety of molecules including the cystine-glutamate transport receptor (system XC -), the voltage-dependent anion channel (VDAC), and p53. Erastin is able to enhance the sensitivity of chemotherapy and radiotherapy, suggesting a promising future in cancer therapy. We hope that this review will help to better understand the role of erastin in ferroptosis and lay the foundation for further research and the development of erastin-based cancer therapies in the future.Erastin was initially discovered as a small molecule compound that selectively kills tumor cells expressing ST and RASV12 and was later widely investigated as an inducer of ferroptosis. Ferroptosis is a recently discovered form of cell death caused by peroxidation induced by the accumulation of intracellular lipid reactive oxygen species (L-ROS) in an iron-dependent manner. Erastin can mediate ferroptosis through a variety of molecules including the cystine-glutamate transport receptor (system XC -), the voltage-dependent anion channel (VDAC), and p53. Erastin is able to enhance the sensitivity of chemotherapy and radiotherapy, suggesting a promising future in cancer therapy. We hope that this review will help to better understand the role of erastin in ferroptosis and lay the foundation for further research and the development of erastin-based cancer therapies in the future.

Copper serves as a vital microelement which is widely present in the biosystem, functioning as multi-enzyme active site, including oxidative stress, lipid oxidation and energy metabolism, where oxidation and reduction characteristics are both beneficial and lethal to cells. Since tumor tissue has a higher demand for copper and is more susceptible to copper homeostasis, copper may modulate cancer cell survival through reactive oxygen species (ROS) excessive accumulation, proteasome inhibition and anti-angiogenesis. Therefore, intracellular copper has attracted great interest that multifunctional copper-based nanomaterials can be exploited in cancer diagnostics and antitumor therapy. Therefore, this review explains the potential mechanisms of copper-associated cell death and investigates the effectiveness of multifunctional copper-based biomaterials in the field of antitumor therapy.

One of the primary challenges faced by small rubbing filament machines is the significant variability in material sizes, particularly in the feeding direction. This variability complicates the processing of locally baled straw with a single device. To address this issue, an adjustable feeding and bale-breaking device was developed and tested to enhance the filamentous performance of baled straw. The machine integrates a series of bale-breaking knives along with a pair of feeding and bale-breaking rollers. This paper presents an overview of the machine’s structure and operating principles, alongside an analysis of the forces acting on the straw within the device, which informed the design of key components and devices. A discrete element simulation model suitable for square baled-straw has been established, providing a research foundation for the subsequent optimization of device design parameters. Effects of motor bale-breaking roller rotating speed ( x 1 ), bale-breaking roller height ( x 2 ) and bale-breaking knife quantity ( x 3 ) on the productivity of bonding bond destruction rate ( Y 1 ) and the particle average speed ( Y 2 ) were explored. Three-dimensional quadratic regression orthogonal rotation central combination experiment method combined with response surface method was used to conduct experiments and explore the interaction effects of influence factors on indicators. A regression model of influence factors and evaluation indicators was established through the analysis of variance. The significant factors affecting Y 1 were ordered of x 1 , x 2 , x 3 , and the significant factors affecting Y 2 were ordered of x 2 , x 3 , x 1 . In the interaction of factors, x 1 x 2 and x 2 x 3 had an extremely significant impact, and x 1 x 3 had a significant impact on Y 1 ; x 1 x 2 , x 1 x 3 and x 2 x 3 had a significant impact on Y 2 . The optimal structure and working parameters combination were determined to be 1448 rpm for x 1 , 268 mm for x 2 , and 14 pieces for x 3 . Verification experiments demonstrated that the actual values were 96.95% for the straw rubbing rate and 235.13 kg/(kW·h) for the per unit power productivity. The operation of the adjustable feeding and bale-breaking device developed in this study proved effective in enhancing productivity and breaking performance during the feeding of baled straw. It successfully met the design requirements for the grain size necessary for the comprehensive utilization of straw. Overall, this research establishes a foundational basis for the further development of a small, multipurpose straw rubbing filament machine.

Recent research using 3D printing to create active structures has added an exciting new dimension to 3D printing technology. After being printed, these active, often composite, materials can change their shape over time; this has been termed as 4D printing. In this paper, we demonstrate the design and manufacture of active composites that can take multiple shapes, depending on the environmental temperature. This is achieved by 3D printing layered composite structures with multiple families of shape memory polymer (SMP) fibers – digital SMPs - with different glass transition temperatures ( T g ) to control the transformation of the structure. After a simple single-step thermomechanical programming process, the fiber families can be sequentially activated to bend when the temperature is increased. By tuning the volume fraction of the fibers, bending deformation can be controlled. We develop a theoretical model to predict the deformation behavior for better understanding the phenomena and aiding the design. We also design and print several flat 2D structures that can be programmed to fold and open themselves when subjected to heat. With the advantages of an easy fabrication process and the controllable multi-shape memory effect, the printed SMP composites have a great potential in 4D printing applications.

The impact of time-of-day administration (ToDA) of immune checkpoint inhibitor (ICI) on patient outcomes across multiple cancer types is increasingly being elucidated. Following the results of the CheckMate-649 study, chemotherapy combined with immunotherapy has been established as the standard first-line treatment for advanced gastric cancer (GC). A thorough investigation of the relationship between ICI infusion timing and patient outcomes within this combined regimen holds considerable potential to enhance and optimize clinical treatment strategies. We conducted a retrospective analysis of patients with advanced GC from West China Hospital, Sichuan University, who received first-line chemotherapy combined with ICIs between January 2020 and September 2024. Patients who received fewer than two doses of ICIs were excluded. Follow-up continued until May 2025. The primary endpoint was overall survival (OS), defined as the time from initial ICI infusion to death from any cause. Secondary endpoints included progression-free survival (PFS), objective response rate (ORR), and adverse events (AEs). Propensity score matching (1:2 ratio, caliper width 0.1) mitigated confounding factors. The impact of ICI infusion timing (after 1630h) on OS and PFS was evaluated using Cox proportional hazards regression. ORR and AEs were assessed with either the Chi-square test or Fisher's exact test. To further assess the robustness of our findings, sensitivity analyses were conducted using infusion proportion thresholds of 30%, 40%, and 50% at the fixed time point of 1630h, along with time-point sensitivity analyses at 30-minute intervals across the 1500h to 1700h period. Among 214 patients, 50 patients received ≥20% of ICI after 1630h (Group A), while 164 patients received <20% (Group B). Before propensity score matching, patients in Group B exhibited significantly shorter OS compared to those in Group A (median 15.4 vs. 21.4 months, HR = 1.64, P = 0.014). After matching, the Group B (44 patients) continued to demonstrate significantly shorter OS than Group A (86 patients) (median 15.4 vs. 22.4 months, HR = 1.82, P = 0.013). Multivariable analysis confirmed these findings. No significant differences were found in PFS, ORR, or AEs (all P>0.050). Sensitivity analysis further validated the robustness of the results. Early ToDA of ICIs is associated with longer OS in patients with advanced GC receiving first-line chemotherapy combined with ICIs. Randomized clinical trials are required to validate these findings.

Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) products are widely used in ecology, hydrology, vegetation monitoring, and global circulation models. Compared to the collection-5 (C5) LST products, the newly released collection-6 (C6) LST products have been refined over bare soil pixels. This study aims to evaluate the C6 MODIS 1-km LST product using multi-year in situ data covering barren surfaces. Evaluation using all in situ data shows that the MODIS C6 LSTs are underestimated with a root-mean-square error (RMSE) of 2.59 K for the site in the Gobi area, 3.05 K for the site in the sand desert area, and 2.86 K for the site in the desert steppe area at daytime. For nighttime LSTs, the RMSEs are 2.01 K, 2.88 K, and 1.80 K for the three sites, respectively. Both biases and RMSEs also show strong seasonal signals. Compared to the error of C5 1-km LSTs, the RMSE of C6 1-km LST product is smaller, especially for daytime LSTs, with a value of 2.24 K compared to 3.51 K. The large errors in the sand desert region are presumably due to the lack of global representativeness of the magnitude of emissivity adjustment and misclassification for the barren surface causing error in emissivities. It indicates that the accuracy of the MODIS C6 LST product might be further improved through emissivity adjustment with globally representative magnitude and accurate land cover classification. From this study, the MODIS C6 1-km LST product is recommended for applications.

Achieving optically controlled nanomachine engineering can satisfy the touch-free and non-invasive demands of optoelectronics, nanotechnology, and biology. Traditional optical manipulations are mainly based on optical and photophoresis forces, and they usually drive particles in gas or liquid environments. However, the development of an optical drive in a non-fluidic environment, such as on a strong van der Waals interface, remains difficult. Herein, we describe an efficient 2D nanosheet actuator directed by an orthogonal femtosecond laser, where 2D VSe 2 and TiSe 2 nanosheets deposited on sapphire substrates can overcome the interface van der Waals forces (tens and hundreds of megapascals of surface density) and move on the horizontal surfaces. We attribute the observed optical actuation to the momentum generated by the laser-induced asymmetric thermal stress and surface acoustic waves inside the nanosheets. 2D semimetals with high absorption coefficient can enrich the family of materials suitable to implement optically controlled nanomachines on flat surfaces. Optical manipulation of nanomaterials usually requires fluidic environments or microfibers. Here, the authors report a pulsed laser-induced photoacoustic driving force able to overcome the van der Waals adhesion forces between 2D semimetallic nanosheets and insulating substrates.

New satellite remote sensing and machine learning techniques offer untapped possibilities to monitor global biodiversity with unprecedented speed and precision. These efficiencies promise to reveal novel ecological insights at spatial scales which are germane to the management of populations and entire ecosystems. Here, we present a robust transferable deep learning pipeline to automatically locate and count large herds of migratory ungulates (wildebeest and zebra) in the Serengeti-Mara ecosystem using fine-resolution (38-50 cm) satellite imagery. The results achieve accurate detection of nearly 500,000 individuals across thousands of square kilometers and multiple habitat types, with an overall F1-score of 84.75% (Precision: 87.85%, Recall: 81.86%). This research demonstrates the capability of satellite remote sensing and machine learning techniques to automatically and accurately count very large populations of terrestrial mammals across a highly heterogeneous landscape. We also discuss the potential for satellite-derived species detections to advance basic understanding of animal behavior and ecology. This study presents a deep learning pipeline to automatically locate and count large herds of migratory ungulates (wildebeest and zebra) in the Serengeti-Mara ecosystem using fine resolution satellite imagery. The results achieve accurate detection of nearly 500,000 individuals across thousands of square kilometers and multiple habitat types.

Convolutional neural networks (CNNs) have recently been demonstrated to be able to substantially improve the land cover classification accuracy of hyperspectral images. Meanwhile, the rapidly developing capacity for satellite and airborne image spectroscopy as well as the enormous archives of spectral data have imposed increasing demands on the computational efficiency of CNNs. Here, we propose a novel CNN framework that integrates one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) CNNs to obtain highly accurate and fast land cover classification from airborne hyperspectral images. To achieve this, we first used 3D CNNs to derive both spatial and spectral features from hyperspectral images. Then, we successively utilized a 2D CNN and a 1D CNN to efficiently acquire higher-level representations of spatial or spectral features. Finally, we leveraged the information obtained from the aforementioned steps for land cover classification. We assessed the performance of the proposed method using two openly available datasets (the Indian Pines dataset and the Wuhan University dataset). Our results showed that the overall classification accuracy of the proposed method in the Indian Pines and Wuhan University datasets was 99.65% and 99.85%, respectively. Compared to the state-of-the-art 3D CNN model and HybridSN model, the training times for our model in the two datasets were reduced by an average of 60% and 40%, respectively, while maintaining comparable classification accuracy. Our study demonstrates that the integration of 1D, 2D, and 3D CNNs effectively improves the computational efficiency of land cover classification with hyperspectral images while maintaining high accuracy. Our innovation offers significant advantages in terms of efficiency and robustness for the processing of large-scale hyperspectral images.

Chronic obstructive pulmonary disease (COPD) is the major leading cause of disease with high-mortality worldwide. Cigarette smoke (CS) is a major factor for COPD. CS causes chronic inflammation and oxidative stress, which contributes to lung dysfunction in COPD. Isoliquiritigenin (ILG), a natural flavonoid derived from the root of liquorice, has been reported to possess antiinflammatory and antioxidant activity. In the present study, we tested the mechanism and protective effects of ILG on CS-induced COPD. Mice were exposed to CS for 2 h twice a day for 4 weeks. ILG was given orally 1 h before CS exposure twice a day for 4 weeks. The bronchial alveolar lavage fluid was collected to test the levels of inflammatory cytokines and the number of inflammatory cells. The lung tissues were obtained to evaluate the pathological changes, lung edema, myeloperoxidase (MPO) activity, malondialdehyde (MDA) level, as well as the expression of the nuclear factor-erythroid 2 (Nrf2) and nuclear factor κB (NF-κB) signaling pathways. The results showed that ILG reduced the infiltration of inflammatory cells and the production of inflammatory cytokines. ILG also reversed CS-induced lung pathological injuries, wet/dry ratio, MPO activity, and MDA level. Further research also showed that ILG dose-dependently up-regulated the expression of Nrf2 and down-regulated the expression of NF-κB signaling pathways induced by CS. In conclusion, ILG protected against CS-induced COPD by inhibiting inflammatory and oxidative stress via the regulation of the Nrf2 and NF-κB signaling pathways.