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Abstract Background: Skin cancer is a common skin disease whose incidence and mortality rates have been showing yearly increases. In this report, we update the most recent data on skin cancer as obtained from GLOBOCAN 2022. Methods: The incidence and mortality rates of skin cancer (melanoma of skin and non-melanoma skin cancer) in GLOBOCAN 2022 were reviewed. These data were analyzed and the characteristics of incidence and mortality across five continents and top five countries and regions in each continent are presented. In addition, correlations between Human Development Index (HDI) and age-standardized incidence and mortality rates of these two skin cancers are described. Results: The GLOBOCAN 2022 data indicated that melanoma was the 17th most common cancer. An estimated 331,722 people were diagnosed with melanoma globally and approximately 58,667 died from this disease. For non-melanoma skin cancer, it ranks as the 5th most common cancer, and an estimated 1,234,533 people were diagnosed with non-melanoma skin cancer globally and approximately 69,416 died from this disease. The incidence of skin cancer varies across geographic regions and countries, with a predominance observed in Oceania, North America, and Europe. Australia was ranked first in terms of incidence, while incidence rates in Africa and Asia were very low. Despite these regional differences in incidence, there was little geographic variation in mortality rates. Currently, the number of deaths from non-melanoma skin cancer exceeds that of melanoma of skin. HDI was positively associated with the incidence of both types of skin cancers, with a positive correlation obtained between HDI and mortality from melanoma of skin and a negative correlation between HDI and mortality from non-melanoma skin cancer. Conclusions: Skin cancer remains a major disease burden worldwide. Substantial variations are observed across countries and regions. Further research on skin cancer will be required to provide a rationale for more effective preventions and treatments of this condition.

Vestibular schwannoma (VS) is a benign tumor originating from Schwann cells, and its molecular pathogenesis remains poorly understood. Increasing evidence suggests oxidative stress (OS) plays a critical role in tumor development, but its involvement in VS is largely unexplored. We analyzed two GEO transcriptomic datasets (GSE54934 and GSE56597) to identify oxidative stress-related differentially expressed genes (OSRDEGs). Functional enrichment, protein-protein interaction (PPI) network construction, hub gene identification, and immune infiltration analyses were performed to uncover potential molecular mechanisms. Fifteen OSRDEGs were identified, and nine hub genes (IL6, CYBB, CAV1, EGFR, SELE, IL18, CDKN2A, ADIPOQ, CDH2) were screened. Enrichment analysis indicated that these genes are mainly involved in apoptosis, reactive oxygen species regulation, and immune-related pathways. Moreover, immune infiltration analysis revealed significant differences in CD8 + T cells and macrophage populations between VS and control tissues. Our study suggests that oxidative stress may contribute to VS progression by influencing immune responses and signaling pathways. These findings provide new insights into the molecular basis of VS and may guide future experimental and therapeutic investigations.

This research delves into the optimization of urbanization spatial patterns in Guizhou Province, China. The findings reveal that with regional coordinated development as the central objective and the optimization of urbanization spatial patterns as the strategic focus, a research framework encompassing “temporal and spatial evolution of urbanization - identification and summation of pain points and difficulties - scenario simulation and optimization - strategic goal selection” is utilized to specifically tackle issues pertaining to urbanization spatial patterns. Through the construction of diverse scenarios and rigorous research analysis, an implementation pathway is derived, advocating for “strengthening the central region of Guizhou, fostering urban agglomeration development, reinforcing developmental support points, and promoting regional coordinated development.” This pathway is then applied to the optimization of urbanization spatial patterns in Guizhou. The study proposes the establishment of a “one body with two wings” urbanization spatial pattern, with Guiyang as the primary core and Zunyi, Liupanshui, Bijie, Anshun, Duyun, and Kaili serving as support points for Guizhou’s primary development zone. Furthermore, urban clusters centered on Xingyi in southwestern Guizhou and Tongren in northeastern Guizhou are projected to gradually emerge. The remaining regions will undergo coordinated development, ultimately contributing to the realization of overall regional coordinated development.

Designing efficient Ruthenium-based catalysts as practical anodes is of critical importance in proton exchange membrane water electrolysis. Here, we develop a self-assembly technique to synthesize 1 nm-thick rutile-structured high-entropy oxides (RuIrFeCoCrO 2 ) from naked metal ions assembly and oxidation at air-molten salt interface. The RuIrFeCoCrO 2 requires an overpotential of 185 mV at 10 m A cm −2 and maintains the high activity for over 1000 h in an acidic electrolyte via the adsorption evolution mechanism. We discuss the role of each element in the RuIrFeCoCrO 2 and find that the Cr, Co, and Ir sites contribute to the catalytic activity, while the Cr atoms weaken the Ru-O bond covalency and improves the catalyst stability. The assembled proton exchange membrane electrolyzer operates stably for more than 600 h at a large current of 1 A cm −2 . The naked ion assembly demonstrated in this work may provide an effective pathway for the controlled synthesis of a diversity of high-entropy materials. Designing efficient ruthenium-based catalysts as practical anodes is important for proton exchange membrane electrolysis. Here, the authors report a self-assembly technique to synthesize atomically thin high-entropy oxides from the assembly of naked metal ions for hydrogen generation.

Studying tissue composition and function in non-human primates (NHPs) is crucial to understand the nature of our own species. Here we present a large-scale cell transcriptomic atlas that encompasses over 1 million cells from 45 tissues of the adult NHP Macaca fascicularis . This dataset provides a vast annotated resource to study a species phylogenetically close to humans. To demonstrate the utility of the atlas, we have reconstructed the cell–cell interaction networks that drive Wnt signalling across the body, mapped the distribution of receptors and co-receptors for viruses causing human infectious diseases, and intersected our data with human genetic disease orthologues to establish potential clinical associations. Our M .  fascicularis cell atlas constitutes an essential reference for future studies in humans and NHPs. A large-scale single-cell transcriptomic atlas of the non-human primate Macaca fascicularis encompasses over 1 million cells from 45 adult tissues.

Enterprises’ green technology innovation is critical to achieving the “win-win” of enterprise competitiveness and environmental protection. The impact of environmental regulation on green technology innovation by enterprises has been widely considered, but the conclusion has not yet been determined, and needs to be studied in detail. To this end, we studied the impact of pollution charge policy on different types of green technology innovation by industrial enterprises in China. We found that (1) the impact of pollution charges on most types of green technology innovation by enterprises has increased significantly over time; (2) the pollution charge policy has a certain inhibition effect on the end-of-pipe technology innovation, but can promote the process improvement of reducing industrial wastewater emissions; (3) there is a U-shaped relationship between the pollution charges and some green technological innovation (e.g., emission intensity of SO2, industrial wastewater emission intensity, and industrial wastewater removal intensity), which is dynamically adjusted over time; and (4) the larger the enterprise’s solid assets, the faster the asset depreciation will inhibit the enterprise from adopting the green process innovation strategy.

Tetraploid black locust (Robinia pseudoacacia L.) is adaptable to salt stress. Here, we compared morphological, physiological, ultrastructural, and proteomic traits of leaves in tetraploid black locust and its diploid relatives under salt stress. The results showed that diploid (2×) plants suffered from greater negative effects than those of tetraploid (4×) plants. After salt treatment, plant growth was inhibited, photosynthesis was reduced, reactive oxygen species, malondialdehyde content, and relative electrolyte leakage increased, and defense-related enzyme activities decreased in 2× compared to those in 4×. In addition, salt stress resulted in distorted chloroplasts, swollen thylakoid membranes, accumulation of plastoglobules, and increased starch grains in 2× compared to those in 4×. However, 4× developed diverse responses under salt stress. A comparative proteomic analysis revealed that 41 and 37 proteins were differentially expressed in 2× and 4×, respectively. These proteins were mainly involved in photosynthesis, stress and defense, energy, metabolism, transcription/translation, and transportation. Distinct patterns of protein changes between 2× and 4× were analyzed. Collectively, our results suggest that the plants showed significantly different responses to salt stress based on ploidy level of the plant. The 4× possessed a better salt protection mechanism than that of 2×, suggesting salt tolerance in the polyploid plant.

Gestational diabetes mellitus (GDM), a high-risk pregnancy complication of great effect on the perinatal health of women and newborns, may cause changes of gut microbiota in mothers and further affect gut microbiota in newborns. This study aimed to investigate the potential effect of mother GDM on newborns' gut microbiota. Meconium DNA was extracted from a total of 34 full-term and C-sectioned newborns, in which 20 newborns had mothers diagnosed with GDM, while 14 had unaffected mothers. Sequencing and bioinformatics analysis of 16S rRNA indicated that the gut microbiota of GDM newborns showed differences compared to control newborns. The taxonomy analyses suggested that the overall bacterial content significantly differed by maternal diabetes status, with the microbiome of the GDM group showing lower alpha-diversity than that of control group. The phyla of Proteobacteria and Actinobacteria in GDM newborns increased, while that of Bacteroidetes significantly reduced (P<0.05). Moreover, several unique gut microbiota in phylum of Proteobacteria, Firmicutes, Actinobacteria, Bacteroidetes, Chloroflexi, Acidobacteria, and Planctomycetes found in control newborns were absent in GDM ones. At genus level, the relative abundance of Prevotella and Lactobacillus significantly decreased (P<0.05) in GDM newborns. Correlation analysis indicated that maternal fasting glucose levels were positively correlated with the relative abundance of phylum Actinobacteria and genus Acinetobacter, while negatively correlated with that of phylum Bacteroidetes and genus Prevotella. However, bacteria in GDM grade A2 (GDM_A2) newborns did not show any statistical variation compared to those from control newborns, which might be attributed to the additional intervention by insulin. The results of this study have important implications for understanding the potential effects of GDM on the gut microbiota of newborns and thus possibly their metabolism at later stages in their lives.

Exosomes are nano-sized biological extracellular vesicles transmitting information between cells and constituting a new intercellular communication mode. Exosomes have many advantages as an ideal drug delivery nanocarrier, including good biocompatibility, permeability, low toxicity, and low immunogenicity. Recently, exosomes have been used to deliver chemotherapeutic agents, natural drugs, nucleic acid drugs, and other antitumor drugs to treat many types of tumors. Due to the limited production of exosomes, synthetic exosome-mimics have been developed as an ideal platform for drug delivery. This review summarizes recent advances in the application of exosomes and exosome-mimics delivering therapeutic drugs in treating cancers.

For the remote sensing classification task, the ability of a single data source to identify the ground objects remains limited due to the lack of feature diversity. As the typical remote sensing data sources, hyperspectral imagery (HSI) and light detection and ranging (LiDAR) data can provide complementary spectral features and elevation information, respectively. To enhance classification ability, a multi-scale Pseudo-Siamese Network with attention mechanism (MA-PSNet) is proposed by fusing HSI and LiDAR data. In the network, two sub-branch networks are designed for extracting the features from HSI and LiDAR, respectively, and the connection is further established between these two branches. Specifically, a multi-scale feature learning module is incorporated, enabling the image features to be fully extracted at different scales. Similarly, a convolutional attention module is also embedded to highlight the saliency information of the objects, which makes the network training can be more targeted, thereby eventually improving the model performance for classification. The evaluation experiments of the proposed model are carried out on an urban dataset from Houston, USA, and a rural dataset from Trento, Italy. The overall accuracy (OA) of the model can reach 95.03% on the Houston data and 99.16% on the Trento data. The experimental results fully demonstrate that the proposed model has competitive performance compared with several state-of-the-art methods.