Search Results (331)

Oral squamous cell carcinoma (OSCC), an aggressive and poorly prognosed subtype of head and neck squamous cell carcinoma (HNSCC), has prompted urgent calls for innovative therapeutic approaches. Evodiamine (EVO), a natural alkaloid extracted from the Chinese herb Evodia rutaecarpa, has demonstrated significant potential in curbing tumor cell proliferation and slowing tumor expansion. However, its specific effects on cell senescence within the context of OSCC have remained shrouded in uncertainty. This study delves into the mechanisms of EVO’s impact on OSCC by harnessing databases such as the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and CellAge to pinpoint potential targets and carry out in-depth bioinformatics analysis. The findings reveal that EVO can markedly enhance the expression of the androgen receptor (AR) in OSCC cells, inducing cellular senescence and thereby inhibiting tumor progression. Furthermore, the research indicates that AR expression is considerably lower in OSCC tissues than in normal tissues. This low expression of AR in tumor tissues is closely associated with advanced clinical stages and unfavorable prognoses in HNSCC patients. These discoveries open up new avenues for therapeutic strategies, and suggest that AR holds promise as a potential therapeutic target for OSCC, and EVO may amplify its antitumor effects by enhancing AR-mediated cellular senescence in the treatment of OSCC. Full article

With the continuous development of research on natural medicines, quinone compounds have become increasingly important in the research field of chemical constituents of natural treatments. However, there is a lack of in-depth and systematic collation of their types, distribution, pharmacological activities, and potential toxicities. This article comprehensively reviews the structural types, biogenetic pathways, extraction and separation methods, structural identification techniques, pharmacological activities, and toxicities of quinone compounds. It is found that the main difficulties in the research of quinone compounds lie in the cumbersome traditional separation and structural identification processes, as well as the insufficient in-depth studies on the mechanisms of their activities and toxicities. This review aims to provide a reference for research on quinone compounds in natural products and offer ideas and suggestions for subsequent in-depth exploration of the pharmacological activities of quinone compounds, prevention and control of their toxicities, and the realization of rational drug use. Full article

To investigate the role of potassium in the regulation of potato growth, dynamic changes in starch–sugar metabolism, and processing quality. In this study, “Gannong Potato No. 9” was used as the test material, and five potassium concentration treatments of 0, 9.4, 23.5, 28.5, and 37.6 mmol/L were set. The results showed that moderate application of potassium (23.5 mmol/L) significantly enhanced plant height, stem thickness, and tuber yield. It also promotes starch accumulation in all tissues and reduces sucrose, fructose, and glucose content, thus optimizing processing quality. Dynamic analyses showed that potassium affects carbohydrate transport and partitioning among tissues by regulating the direction of carbon partitioning and the rate of conversion. Correlation analysis confirmed the synergistic effect of starch–sugar metabolism among tissues, forming a spatio-temporally linked carbon allocation network. This study reveals the pivotal role of potash in potato starch–sugar metabolism and provides a theoretical basis for precision potassium application and quality improvement. Full article

High-sensitivity fiber scintillator detectors are the key to achieving high signal-to-noise ratio and high contrast imaging in X-ray Compton backscattering technology. We established a simulation model of wavelength-shifting fiber (WSF) scintillator detectors based on Geant4. The influences of ray source energy, detection area, number of WSFs, and coupling mechanism on detection efficiency were simulated. By using the epoxy resin coupling method, the transmission efficiency between the WSF and scintillator was increased from 4.56% to 19.79%. Based on the simulation data, we developed an X-ray WSFs scintillator detector, built an X-ray backscattering imaging experimental system, obtained high-contrast backscattering images, and verified the performance of the detector. Full article

This study proposes a novel unified constitutive model that systematically integrates the microstructure evolution and macroscopic stress–strain response during the hot deformation of a Ni-based superalloy. The proposed model incorporates a suite of microstructural variables, including damage fraction, recrystallization fraction, δ phase content, average grain size, and dislocation density. Furthermore, the model explicitly considers critical macroscopic stress state parameters, specifically the magnitude and orientation of maximum principal stress, hydrostatic stress component, and Mises equivalent stress. A comparative analysis of rheological curves derived from uniaxial tension and compression experiments reveals that the prediction errors of the proposed model are less than 3%. The model is subsequently implemented to investigate the evolution characteristics of the damage accumulation fraction and δ phase content under varying stress directions and initial δ phase contents. An advanced computational framework integrating the finite element method with the proposed constitutive model is established through customized subroutines. The framework exhibits exceptional predictive accuracy across critical regions of disk forging, as evidenced by a close agreement between computational and experimental results. Specifically, the relative errors for predicting recrystallization fraction and average grain size remain consistently below 8% under varying stress–strain conditions. Testing results from four representative regions demonstrate a good alignment of high-temperature tensile properties with the macroscopic stress–strain distributions and microstructure characteristics, thereby confirming the model’s reliability in simulating and optimizing the forging process. Full article

Compared with traditional grain starch sources, duckweed (Spirodela polyrhiza, S. polyrhiza for simple) does not require soil to produce starch, and the process is less affected by the external environment. Moreover, it produces high levels of starch under certain conditions. This study investigated the patterns and mechanisms of starch accumulation in S. polyrhiza ZH0196 under nutrient stress by determining the changes in starch content, photosynthesis, and amylase activity at different stress induction times. Under nutrient stress, the culture solution was replaced with deionized water. The starch content increased from 1.95% to 41.71% (dry weight) after 2 days of nutrient stress induction. Short-term nutrient-stress treatment had little effect on frond photosynthesis, enhanced the activity of starch synthesis-related enzyme, and weakened the activity of degradation-related enzymes. The transcriptome results further indicated that the key genes and metabolic patterns of starch synthesis promoted starch accumulation in S. polyrhiza ZH0196 fronds by accelerating the response to CO₂ fixation via the Calvin cycle, promoting straight-chain starch synthesis, and decreasing starch degradation after short-term oligotrophic treatment. This study suggests that nutrient stress is a green and efficient method of increasing the starch yield of duckweed, which represents an important insight for developing duckweed starch resources. Full article

Precise monitoring of the leaf area index (LAI) and soil–plant analysis development (SPAD, which represents chlorophyll content) at the field level is crucial for enhancing crop yield and formulating agricultural management strategies. Currently, most studies use multispectral sensors mounted on unmanned aerial vehicles (UAVs) to obtain images, whereby the spectral information is utilized to estimate rice growth parameters. Considering the cost of multispectral sensors and factors influencing rice growth parameters, this study integrated satellite remote sensing images with UAV visible-light images to obtain high-resolution multispectral images during key rice growth stages, thereby determining the rice LAI and SPAD on the same day. The vegetation indices and textural features most correlated with rice LAI and SPAD were selected using Pearson correlation analysis, and based on vegetation indices, textural features, and their combinations, regression models were established. The results indicate the following: (1) The fusion of satellite and UAV images, combined with spectral information and textural features, can significantly improve the estimation accuracy of LAI and SPAD compared to using only spectral information or textural features. (2) Sparrow search algorithm-optimized extreme gradient boosting (SSA-XGBoost) regression achieved the highest accuracy, with R² and RMSE of 0.904 and 0.183 in LAI estimation and 0.857 and 0.882 in SPAD estimation, respectively. This demonstrates that integrating satellite and UAV images, combined with vegetation indices and texture features, can effectively establish rice LAI and SPAD estimation models, using the SSA-optimized XGBoost method, as an effective and feasible solution for precise monitoring of rice growth parameters. Full article

Upper tract urothelial carcinoma (UTUC) presents aggressive features and a tumor microenvironment with T cell depletion. However, the role of tumor-associated neutrophils in UTUC remains unclear. This study aimed to investigate how UTUC tumor-derived factors modulate neutrophils and their impact on T cell immune responses. Our findings demonstrate that UTUC secreted tumor-derived factors, with apolipoprotein A1 (Apo-A1) being the predominant factor, which upregulated arginase-1 expression in neutrophils. STAT3 activation was responsible for the upregulation of arginase-1 in neutrophils. Blocking the interactions between Apo-A1 and its receptors reduced arginase-1 expression in neutrophils treated with tumor tissue culture supernatant (TTCS). Moreover, both CD4⁺ T and CD8⁺ T cell proliferation were inhibited by neutrophils treated with Apo-A1 or TTCS. Importantly, blocking Apo-A1 signaling in neutrophils reversed the inhibitory effects on T cells. In UTUC patients, the neutrophil-to-lymphocyte ratio was higher than that in healthy subjects. The expression of arginase-1 in neutrophils and the level of Apo-A1 within UTUC tumors were negatively correlated with tumor-infiltrating CD4⁺ T cells. Additionally, neutrophils from UTUC patients showed increased expression of arginase-1 and exhibited inhibitory effects of T cell functions. These findings suggest that UTUC orchestrates an immune-suppressive microenvironment through Apo-A1-mediated upregulation of arginase-1 in neutrophils, ultimately leading to the inhibition of T cell proliferation. Full article

Background: Living donor liver transplantation (LDLT) has become a widely accepted alternative to deceased donor liver transplantation (DDLT). Nevertheless, the available meta-analyses shed light on a perplexing issue regarding which transplant is better. Therefore, we performed an umbrella review to summarize and evaluate the evidence from current meta-analyses. Methods: Two independent reviewers conducted a search of PubMed, Embase, Web of Science, and the Cochrane Database of Systematic Reviews from inception to 1 June 2024. The methodological quality of each included meta-analysis was evaluated using AMSTAR2 (A Measurement Tool to Assess Systematic Reviews). Results: The search identified 10 meta-analyses from 486 individual articles, including cohort studies and observational studies. Regrettably, the quality of these meta-analyses ranged from critically low to moderate. Receipt of LDLT offers a survival advantage to the patients with HCC compared with DDLT but with a higher complication rate. However, high-quality studies are required in the future to validate our assertions owing to the low certainty of the evidence. Conclusions: Despite the complication risks, LDLT remains a cost-effective option without compromising patient and graft survival, especially for HCC patients. Extensive, well-designed studies are essential to validate these conclusions. Full article

This study investigated the response of watermelon seedlings to drought stress by assessing the growth, physiological, and biochemical indices using a pot-based continuous drought method. Drought stress indices, phenotypic plasticity indices, and membership function values were calculated, followed by a correlation analysis, principal component analysis, and cluster analysis, to comprehensively evaluate the drought resistance of 13 watermelon genotypes. The results revealed that drought stress significantly reduced the fresh and dry weights, root length, root area, root volume, root tips, and forks of watermelon seedlings. Additionally, drought significantly reduced the relative water content of leaves and increased the levels of osmotic-adjustment substances (soluble sugars, soluble proteins, proline, and starch). Persistent drought also modulated the activities of antioxidant enzymes (SOD, POD, and CAT), leading to oxidative stress through the accumulation of H₂O₂. Membrane damage, indicated by a significant increase in the MDA content and relative conductivity, was observed, adversely affecting seedling growth. Phenotypic plasticity indices indicated that watermelon exhibits strong adaptability to drought. Cluster analysis categorized the 13 genotypes into four groups: highly drought-resistant (14X5), drought-resistant (LK13, JLR, HXF1, 14X4, 14X1, and 14X6), low drought-resistant (21F05, JH1, JR3, 14X7, and 16F02), and drought-sensitive (16C07). This study provides valuable genetic resources for breeding drought-resistant watermelon varieties. Full article

Background and Objective: Using echocardiogram data for cardiovascular disease (CVD) can lead to difficulties due to imbalanced datasets, leading to biased predictions. Machine learning models can enhance prognosis accuracy, but their effectiveness is influenced by optimal feature selection and robust classification techniques. This study introduces an event-based self-similarity approach to enhance automatic feature selection approach for imbalanced echocardiogram data. Critical features correlated with disease progression were identified by leveraging self-similarity patterns. This study used an echocardiogram dataset, visual presentations of high-frequency sound wave signals, and data of patients with heart disease who are treated using three treatment methods: catheter ablation, ventricular defibrillator, and drug control—over the course of three years. Methods: The dataset was classified into nine categories and Recursive Feature Elimination (RFE) was applied to identify the most relevant features, reducing model complexity while maintaining diagnostic accuracy. Machine learning classification models, including XGBoost and CATBoost, were trained and evaluated. Results: Both models achieved comparable accuracy values, 84.3% and 88.4%, respectively, under different normalization techniques. To further optimize performance, the models were combined into a voting ensemble, improving feature selection and predictive accuracy. Four essential features—age, aorta (AO), left ventricular (LV), and left atrium (LA)—were identified as critical for prognosis and were found in Random Forest (RF)-voting ensemble classifier. The results underscore the importance of feature selection techniques in handling imbalanced datasets, improving classification robustness, and reducing bias in automated prognosis systems. Conclusions: Our findings highlight the potential of machine learning-driven echocardiogram analysis to enhance patient care by providing accurate, data-driven assessments. Full article

Callose plays an important role in plant development and in response to a wide range of biotic and abiotic stresses. However, the systematic identification of callose synthase (CalS), the major enzyme for callose biosynthesis, has been delayed in crops, especially in Solanaceae. In the current research, 18 CalS genes (NtCalS1–NtCalS18) were identified in Nicotiana tabacum and classified into four subfamilies. A comprehensive analysis of their physicochemical properties, gene structure, and evolutionary history highlighted their evolutionary conservation. We also identified a number of NtCalSs that responded to the infection with Phytophthora nicotianae and Ralstonia solanacearum, as well as to drought and cold treatments, by analyzing RNA-seq data. NtCalS1 and NtCalS12, a highly homologous gene pair, were selected to create mutants using the CRISPR-Cas9 technology for their drastic response to Phytophthora nicotianae infection as well as the strong expression levels in roots. The mutants with the simultaneous knockout of NtCalS1 and NtCalS12, compared with the control plants, displayed more resistance to tobacco black shank caused by Phytophthora nicotianae. Furthermore, the real-time quantitative PCR (qRT-PCR) assay showed that the knockout of NtCalS1 and NtCalS12 activated the signaling pathways mediated by plant hormones salicylic acid (SA), jasmonic acid (JA), and ethylene (ET) before and after the infection of Phytophthora nicotianae and thus may have contributed to tobacco immunity against black shank. These findings contribute valuable information for further understanding the roles of CalS genes in tobacco stress responses and provide alternative genes for resistance improvement. Full article

Clustering algorithms are widely used in statistical data analysis as a form of unsupervised machine learning, playing a crucial role in big data mining research for Maritime Intelligent Transportation Systems. While numerous studies have explored methods for optimizing ship trajectory clustering, such as narrowing dynamic time windows to prevent errors in time warp calculations or employing the Mahalanobis distance, these methods enhance DBSCAN (Density-Based Spatial Clustering of Applications with Noise) by leveraging trajectory similarity features for clustering. In recent years, machine learning research has rapidly accumulated, and multiple studies have shown that HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) outperforms DBSCAN in achieving accurate and efficient clustering results due to its hierarchical density-based clustering processing technique, particularly in big data mining. This study focuses on the area near Taichung Port in central Taiwan, a crucial maritime shipping route where ship trajectories naturally exhibit a complex and intertwined distribution. Using ship coordinates and heading, the experiment normalized and transformed them into three-dimensional spatial features, employing the HDBSCAN algorithm to obtain optimal clustering results. These results provided a more nuanced analysis compared to human visual observation. This study also utilized O notation and execution time to represent the performance of various methods, with the literature review indicating that HDBSCAN has the same time complexity as DBSCAN but outperforms K-means and other methods. This research involved approximately 293,000 real historical data points and further employed the Silhouette Coefficient and Davies–Bouldin Index to objectively analyze the clustering results. The experiment generated eight clusters with a noise ratio of 12.7%, and the evaluation results consistently demonstrate that HDBSCAN outperforms other methods for big data analysis of ship trajectory clustering. Full article

Adaptive changes encompass physiological, morphological, or behavioral modifications occurring in organisms in response to specific environmental conditions. These modifications may become established within a population through natural selection. While adaptive changes can influence individuals or populations over short timeframes, evolution involves the inheritance and accumulation of these changes over extended periods under environmental pressures through natural selection. At present, addressing climate change, emerging infectious diseases, and food security are the main challenges faced by scientists. A comprehensive and profound understanding of the mechanisms of adaptive evolution is of great significance for solving these problems. The genetic basis of these adaptations can be examined through classical genetics, which includes stochastic gene mutations and chromosomal instability, as well as epigenetics, which involves DNA methylation and histone modifications. These mechanisms not only govern the rate and magnitude of adaptive changes but also affect the transmission of adaptive traits to subsequent generations. In the study of adaptive changes under controlled conditions, short-term controlled experiments are commonly utilized in microbial and animal research to investigate long-term evolutionary trends. However, the application of this approach in plant research remains limited. This review systematically compiles the findings on adaptive changes and their genetic foundations in organisms within controlled environments. It aims to provide valuable insights into fundamental evolutionary processes, offering novel theoretical frameworks and research methodologies for future experimental designs, particularly in the field of plant studies. Full article

This paper looks into the impact of China’s new rural land reform, the three rights separation policy (TRSP), on Chinese farmers’ income. Based on data collected from 360 rural households in Anhui Province, China, 2021, this paper constructed the influence pathways of the TRSP on household income and estimated the effects along different pathways using the structural equation model (SEM) model. It showed that through expanding the planting scale and promoting resource-use efficiency, the new land tenure system can indirectly increase transfer-in household income. However, the TRSP has a significant negative direct effect on transfer-out households’ income, and only a slight impact on transferring rural labor to other industries or relaxing the liquidity constraint. In short, the TRSP’s effect on income gains is more prominent in transfer-in households than transfer-out ones, which in the long run would lead to an increased income gap, more so if transfer-out households lack easy access to non-farm employment. Our findings suggest that public authorities should respect farmers’ autonomy in land transfer decisions and pay special attention to labor transfer in poverty alleviation. Meanwhile, widening income disparities among different groups should be heeded while implementing local governments’ service roles. Full article