Environmental factors drive latitudinal patterns of fine-root architectures of 96 xerophytic species in the dry valleys of Southwest China.

Fine-root architecture is critical feature reflecting root explorative and exploitative strategies for soil resources and soil space occupancy. Yet, studies on the variation of fine-root architecture across different species are scare and little work has been done to integrate the potential drivers on these variations along a biogeographical gradient in arid ecosystems. We measured root branching intensity, topological index, and root branching ratios as well as morphological traits (root diameter and length) in dry valley along a 1000 km latitudinal gradient. Influence of phylogeny, environmental factors on fine-root architecture and trade-offs among root traits were evaluated. With increasing latitude, the topological index and second to third root order branching ratio decreased, whereas first-to-second branching ratio increased. Root branching intensity was associated with short and thin fine roots, but has no significant latitudinal pattern. As a whole, soil microbial biomass was the most important driver in the variation of root branching intensity, and soil texture was the strongest predictor of topological index. Additionally, mean annual temperature was an important factor influencing first-to-second branching ratio. Our results suggest variations in fine-root architectures were more dependent on environmental variables than phylogeny, signifying that fine-root architecture is sensitive to environmental variations.

DualStreamFoveaNet: A Dual Stream Fusion Architecture with Anatomical Awareness for Robust Fovea Localization.

Accurate fovea localization is essential for analyzing retinal diseases to prevent irreversible vision loss. While current deep learning-based methods outperform traditional ones, they still face challenges such as the lack of local anatomical landmarks around the fovea, the inability to robustly handle diseased retinal images, and the variations in image conditions. In this paper, we propose a novel transformer-based architecture called DualStreamFoveaNet (DSFN) for multi-cue fusion. This architecture explicitly incorporates long-range connections and global features using retina and vessel distributions for robust fovea localization. We introduce a spatial attention mechanism in the dual-stream encoder to extract and fuse self-learned anatomical information, focusing more on features distributed along blood vessels and significantly reducing computational costs by decreasing token numbers. Our extensive experiments show that the proposed architecture achieves state-of-the-art performance on two public datasets and one large-scale private dataset. Furthermore, we demonstrate that the DSFN is more robust on both normal and diseased retina images and has better generalization capacity in cross-dataset experiments.

Single-trial interindividual correlation shows semantic and visuospatial networks are fundamental for advanced mathematical learning.

Mathematical learning and ability are crucial for individual and national economic and technological development, but the neural mechanisms underlying advanced mathematical learning remain unclear. The current study used functional magnetic resonance imaging (fMRI) to investigate how brain networks were involved in advanced mathematical learning and transfer. We recorded fMRI data from 24 undergraduate students as they learned the advanced mathematical concept of a commutative mathematical group. After learning, participants were required to complete learning and transfer behavioural tests. Results of single-trial interindividual brain-behaviour correlation analysis found that brain activity in the semantic and visuospatial networks, and the functional connectivity within the semantic network during advanced mathematical learning were positively correlated with learning and transfer effects. Additionally, the functional connectivity between the semantic and visuospatial networks was negatively correlated with the learning and transfer effects. These findings suggest that advanced mathematical learning relies on both semantic and visuospatial networks.

Enhancing substance identification by Raman spectroscopy using deep neural convolutional networks with an attention mechanism.

Raman spectroscopy is widely used for substance identification, providing molecular information from various components along with noise and instrument interference. Consequently, identifying components based on Raman spectra remains challenging. In this study, we collected Raman spectral data of 474 hazardous chemical substances using a portable Raman spectrometer, resulting in a dataset of 59 468 spectra. Our research employed a deep neural convolutional network based on the ResNet architecture, incorporating an attention mechanism called the SE module. By enhancing the weighting of certain spectral features, the performance of the model was significantly improved. We also investigated the classification predictive performance of the model under small-sample conditions, facilitating the addition of new hazardous chemical categories for future deployment on mobile devices. Additionally, we explored the features extracted by the convolutional neural network from Raman spectra, considering both Raman intensity and Raman shift aspects. We discovered that the neural network did not solely rely on intensity or shift for substance classification, but rather effectively combined both aspects. This research contributes to the advancement of Raman spectroscopy applications for hazardous chemical identification, particularly in scenarios with limited data availability. The findings shed light on the significance of spectral features in the model's decision-making process and have implications for broader applications of deep learning techniques in Raman spectroscopy-based substance identification.

Environmental aridity driving latitudinal pattern of biomass allocation fractions in root systems of 63 shrub species in dry valleys.

Fine roots and absorptive roots play key roles in acquiring resources throughout soil profiles and determining plant functions along environmental gradients. Yet, the geographical pattern of carbon allocation in fine roots, particularly in absorptive roots, and their relations with plant sizes and evironment are less understood. We sampled 243 xerophytic shrubs from 63 species distributed along the latitudinal gradient (23°N to 32°N) in dry valleys of southwest China and synthetically measured biomass fractions of plant organs, especially fine roots and absorptive roots (1st to 3rd root order). We identified latitudinal patterns of biomass allocation fractions of organs and their relationships with plant sizes and environmental factors. The latitudinal patterns of both absorptive root and fine-root fractions followed weak unimodal distributions; stem biomass fraction increased with the latitude, while the leaf biomass fraction decreased. The fraction of fine-root biomass had negative relationships with plant height and root depth. The fractions of root, fine root, and absorptive root biomass were largely explained by soil moisture. Furthermore, fraction of fine-root biomass increased in a relatively humid environment. Overall, soil moisture was the most important factor in driving latitudinal patterns of biomass fraction. Our study highlighted that functional redistribution of root system biomass was the critical adaptive strategy along a latitudinal gradient.

How do anxiety and stress affect soccer referees? An ERPs study.

The decision-making of soccer referees is one of the typical forms influenced by factors such as environmental pressure and individual emotions. While previous studies have explored how common factors like personal anxiety and on-field pressure affect the decisions of soccer referees, the mechanisms by which anxiety influences decision-making under pressure remain unclear. This study developed a penalty task based on real soccer match scenarios and recruited 76 experienced soccer referees. These referees were divided into two groups, high anxiety and low anxiety, based on their anxiety levels, to perform decision-making tasks under different pressure environments simulated to mimic real matches. Additionally, this research employed Event-Related Potential (ERP) technology to compare the brain signals of soccer referees with different levels of anxiety when facing foul play under various pressure environments. It was found that referees with high levels of anxiety displayed larger P300 and N400 amplitudes in a low-pressure environment (p = 0.0059, t = 2.9437). However, no significant differences in P300 and N400 amplitudes were observed between referees with high and low levels of anxiety under high-pressure conditions (p = 0.1890, t = 1.3411). This study not only reveals the complex mechanisms of anxiety in the decision-making process of referees but also emphasizes the importance of understanding and managing the psychological state of referees in competitive sports to improve the quality of their decisions. Our findings provide an empirical basis for future efforts to mitigate the impact of anxiety and optimize the decision-making process in similar high-pressure environments.

Exploring the Pathogenesis of Autoimmune Liver Diseases from the Heterogeneity of Target Cells.

The incidence of autoimmune liver diseases (ALDs) and research on their pathogenesis are increasing annually. However, except for autoimmune hepatitis, which responds well to immunosuppression, primary biliary cholangitis and primary sclerosing cholangitis are insensitive to immunosuppressive therapy. Besides the known effects of the environment, genetics, and immunity on ALDs, the heterogeneity of target cells provides new insights into their pathogenesis. This review started by exploring the heterogeneity in the development, structures, and functions of hepatocytes and epithelial cells of the small and large bile ducts. For example, cytokeratin (CK) 8 and CK18 are primarily expressed in hepatocytes, while CK7 and CK19 are primarily expressed in intrahepatic cholangiocytes. Additionally, emerging technologies of single-cell RNA sequencing and spatial transcriptomic are being applied to study ALDs. This review offered a new perspective on understanding the pathogenic mechanisms and potential treatment strategies for ALDs.

Sensitivity of gross primary production to precipitation and the driving factors in China's agricultural ecosystems.

Recent climate warming has significantly affected the sensitivity of Gross Primary Productivity (GPP) to precipitation within China's agricultural ecosystems. Nonetheless, the spatial and temporal nonlinear evolution patterns of GPP-precipitation sensitivity under climate change, as well as the underlying drivers and long-term trends of this sensitivity, are not well understood. This study employs correlation analysis to quantify the sensitivity between GPP and precipitation in China's agricultural ecosystems, and utilizes nonlinear detection algorithms to examine the long-term changes in this sensitivity. Advanced machine learning techniques and frameworks are subsequently applied to analyze the driving factors of GPP-precipitation sensitivity in China's agricultural ecosystems. The findings reveal that approximately 49.00 % of the analyzed pixels exhibit a significant positive correlation between GPP and precipitation. Nonlinear change analysis indicates spatial heterogeneity in GPP-precipitation sensitivity across China's agricultural ecosystems, with patterns showing initial increases followed by decreases accounting for 25.12 %, and patterns of initial decreases followed by increases at 13.27 %. Machine learning analysis identifies temperature, soil moisture, and crop water footprint as the primary factors influencing GPP-precipitation sensitivity in agricultural ecosystems. This study is the first to introduce crop water footprint as a significant factor in the analysis of GPP-precipitation sensitivity. It not only offers new insights into the temporal nonlinear changes and driving factors of GPP-precipitation sensitivity but also underscores the importance of enhancing agricultural water efficiency to maintain agricultural ecosystem health and ensure food security under climate change.

Improving Quantitative Analysis with Cross Instrument-Sparse Bayesian Learning (CI-SBL) Raman Spectroscopy Analysis Algorithm.

Qualitative and quantitative analysis of Raman spectroscopy is a widely used nondestructive analytical technique in many fields. It utilizes the Raman scattering effect of lasers to obtain molecular vibration information on samples. By comparison with the Raman spectra of standard substances, qualitative and quantitative analyses can be achieved on unknown samples. However, current Raman spectroscopy analysis algorithms still have many drawbacks. They struggled to handle quantitative analysis between different instruments. Their prediction accuracy for concentration is generally low, with poor robustness. Therefore, this study addresses these deficiencies by designing the cross instrument-sparse Bayesian learning (CI-SBL) Raman spectroscopy analysis algorithm. CI-SBL can facilitate spectroscopic analysis between different instruments through the cross instrument module. CI-SBL converts data from portable instruments into data from scientific instruments, with high similarity between the converted spectrum and the spectrum from the scientific instruments reaching 98.6%. The similarity between the raw portable instrument spectrum and the scientific instrument spectrum is often lower than 90%. The cross instrument effect of the CI-SBL is remarkable. Moreover, CI-SBL employs sparse Bayesian learning (SBL) as the core module for analysis. Through multiple iterations, the SBL algorithm effectively identified various components within mixtures. In experiments, CI-SBL can achieve a qualitative accuracy of 100% for the majority of binary and multicomponent mixtures. On the other hand, the previous Raman spectroscopy analysis algorithms predominantly yield a qualitative accuracy below 80% for the same data. Additionally, CI-SBL incorporates a quantitative module to calculate the concentration of each component within the mixed samples. In the experiment, the quantification error for all substances was below 3%, with the majority of the substances exhibiting an error of approximately 1%. These experimental results illustrate that CI-SBL significantly enhances the accuracy of qualitative judgment of mixture spectra and the prediction of mixture concentrations compared with previous Raman spectroscopy analysis algorithms. Furthermore, the cross instrument module of CI-SBL allows for a flexible handling of data acquired from different instruments.

Measurable residual disease testing by next generation sequencing is more accurate compared with multiparameter flow cytometry in adults with B-cell acute lymphoblastic leukemia.

Results of measurable residual disease (MRD)-testing by next-generation sequencing (NGS) correlate with relapse risk in adults with B-cell acute lymphoblastic leukemia (ALL) receiving chemotherapy or an allotransplant from a human leukocyte antigen (HLA)-identical relative or HLA-matched unrelated donor. We studied cumulative incidence of relapse (CIR) and survival prediction accuracy using a NGS-based MRD-assay targeting immunoglobulin genes after 2 courses of consolidation chemotherapy cycles in 93 adults with B-cell ALL most receiving HLA-haplotype-matched related transplants. Prediction accuracy was compared with MRD-testing using multi-parameter flow cytometry (MPFC). NGS-based MRD-testing detected residual leukemia in 28 of 65 subjects with a negative MPFC-based MRD-test. In Cox regression multi-variable analyses subjects with a positive NGS-based MRD-test had a higher 3-year CIR (Hazard Ratio [HR] = 3.37; 95 % Confidence Interval [CI], 1.34-8.5; P = 0.01) and worse survival (HR = 4.87 [1.53-15.53]; P = 0.007). Some data suggest a lower CIR and better survival in NGS-MRD-test-positive transplant recipients but allocation to transplant was not random. Our data indicate MRD-testing by NGS is more accurate compared with testing by MPFC in adults with B-cell ALL in predicting CIR and survival. (Registered in the Beijing Municipal Health Bureau Registration N 2007-1007 and in the Chinese Clinical Trial Registry [ChiCTR-OCH-10000940 and ChiCTROPC-14005546]).

Can the return of rural labor improve the physical health of left-behind parents-evidence from rural China.

Objectively In objective terms, the return of rural labor force shortens the spatial distance with parents, leading to changes in caregiving support, emotional support, and financial support for parents, thereby affecting the health status of parents. This article, using data from the Chinese Family Panel Studies, analyzes the characteristics of the health status of parents with and without returning migrant children. By employing multiple linear regression models, PSM models, and IV-2SLS methods to address endogeneity bias, the study preliminarily explores the impact of rural labor force return on parental health. The results show that: (1) among the 5,760 older adult individuals, 1866 of them have returning migrant chil-dren, while the remaining 3,894 do not have returning migrant children. (2) Parents' health status generally follows a normal distribution, with a small proportion of parents having very poor or very good health. The proportions of parents with relatively poor, fair, and relatively good health status range between 20 and 40%. Among parents with returning chil-dren, 40.12% have relatively poor health status, 45.01% have fair health status, and a small proportion have very poor or very good health status. In contrast, among parents without returning children, the proportions of parents with relatively poor, fair, and rela-tively good health status are 21.69, 33.21, and 38.45%, respectively. When parents tran-sition from not having returning children to having returning children, their health status decreases by 0.541 levels, indicating a negative impact of rural labor force return on par-ents' health. Based on the analysis results, this article provides policy recommendations from three aspects: how to increase the income of returning labor force, improve the rural pension system, and enhance the concept of children supporting their parents.

Intrinsic structural covariation links cerebellum subregions to the cerebral cortex.

The human cerebellum is increasingly recognized to be involved in non-motor and higher-order cognitive functions. Yet, its ties with the entire cerebral cortex have not been holistically studied in a whole-brain exploration with a unified analytical framework. Here, we characterized dissociable cortical-cerebellar structural covariation patterns based on regional gray matter volume (GMV) across the brain in n=38,527 UK Biobank participants. Our results invigorate previous observations in that important shares of cortical-cerebellar structural covariation are described as i) a dissociation between the higher-level cognitive system and lower-level sensorimotor system, as well as ii) an anticorrelation between the visual-attention system and advanced associative networks within the cerebellum. We also discovered a novel pattern of ipsilateral, rather than contralateral, cerebral-cerebellar associations. Furthermore, phenome-wide association assays revealed key phenotypes, including cognitive phenotypes, lifestyle, physical properties, and blood assays, associated with each decomposed covariation pattern, helping to understand their real-world implications. This systems neuroscience view paves the way for future studies to explore the implications of these structural covariations, potentially illuminating new pathways in our understanding of neurological and cognitive disorders.

Dietary factors in relation to hypertension: a mendelian randomization study.

BACKGROUND: Observational studies have elucidated the associations between dietary factors and hypertension. Nevertheless, the exploration of these relationships using Mendelian randomization remains scarce currently. METHODS: The Mendelian randomization approach investigated the potential causal relationships between 16 dietary factors and hypertension. To achieve this, we identified genetic variants associated with these dietary factors by utilizing data from European-descent genome-wide association studies with a stringent significance threshold (P < 5 × 10 - 8). Subsequently, we obtained genetic associations with hypertension from the extensive FinnGen Study, encompassing 92,462 cases and 265,626 controls. Our primary analytical method was the inverse variance weighted method, and we also conducted assessments for heterogeneity and pleiotropy to ensure the robustness and reliability of our findings. RESULTS: The study revealed significant associations with hypertension risk for various dietary factors. Specifically, higher weekly alcohol consumption (OR: 1.53, 95% CI: 1.19-1.96) and more frequent alcohol intake (OR: 1.20, 95% CI: 1.08-1.33) were positively correlated with an increased risk of hypertension. Likewise, increased poultry intake (OR: 3.25, 95% CI: 1.83-5.78) and beef intake (OR: 1.80, 95% CI: 1.09-2.97) were also linked to a higher risk of hypertension. Conversely, there were protective factors associated with a decreased risk of hypertension. These included consuming salad and raw vegetables, dried fruits, cheese, and cereals. It is important to note that no evidence of pleiotropy was detected, underscoring the robustness of these findings. CONCLUSIONS: This study uncovered causal relationships between various dietary factors and hypertension risk. Specifically, alcohol consumption in terms of drinks per week and intake frequency, as well as poultry and beef intake, were causally associated with an elevated risk of hypertension. In contrast, consuming salad/raw vegetables, dried fruits, cheese, and cereals demonstrated an inverse causal association with hypertension, suggesting a potential protective effect.

Association between mitochondrial DNA genotype and sperm motility in humans.

The relationship between genetic alterations in mitochondrial DNA (mtDNA) and progressive motility (PR) and rapid progressive motility (grade A) of ejaculated human spermatozoa remains unclear. In this study, we explored the association between human mtDNA genotype and sperm PR and grade A by analyzing mtDNA copy number, loci, haplogroup, rearrangement, deletions, and duplications and sperm motility parameters. Human sperm mtDNA copy number, loci and haplogroups were not associated with human sperm motility PR or A grade. However, the cumulative frequency of human sperm mtDNA rearrangements (including deletions and duplications) in participants with high PR and grade A ratio was higher than in participants with low PR and grade A ratio. Additional studies are needed to understand the relationship between mtDNA genotypes, including deletions and duplications, and human sperm motility.

Effects of Misalignment of c-axis on the Properties of Hydrogenation-Disproportionation-Desorption-Recombination Particles.

Hydrogenation-Disproportionation-Desorption-Recombination (HDDR) Nd2Fe14B particles have excellent magnetic properties, but the magnetic properties of powder are not uniform across different particle sizes. The remanence and maximum magnetic energy products of samples with a particle size of 120 µm are 14.0 kGs and 41.35 MGOe, while the products of samples with a particle size of 60 µm are only 13.3 kGs and 36.31 MGOe. The macroscopic morphology of HDDR Nd2Fe14B particles and the gradient distribution of microstructures in different micro-regions were observed. By modifying the macroscopic morphology of the particles, the poorly oriented clusters on the surface of the particles were precisely eliminated, and the remanence and maximum magnetic energy products of the particles increased to 14.5 kGs and 45 MGOe, respectively. Compared with the original particles, the samples after mechanical grinding had better grain arrangement. The effects of the nanocrystalline c-axis and field misalignment angle θ on the magnetic properties of HDDR Nd2Fe14B particles were investigated through micromagnetic simulation. The targeted removal of macroscopic defects on the particle surface contributed to a 3.6% increase in remanence and an 8.8% increase in the maximum magnetic energy product, offering a promising approach to enhance the microstructure of high-performance HDDR Nd2Fe14B particles.

Effect of CO2 Gasification on Nitrogen Oxide Emission in Oxy-Fuel Combustion.

Due to the cumulative effect of the recycled flue gas in oxy-fuel combustion, the reduction of NO has become the focus of research in which the role of CO2 gasification has a nonnegligible effect. In this article, the heterogeneous gasification mechanisms of CO2 on coal char during oxy-fuel combustion were studied by density functional theory and transition-state theory. The zigzag char model and char(N) model were selected to investigate the CO2 adsorption and gratification reactions at the molecular level. By comparison, the CO2 gasification reaction is preferred to occur on the zigzag char model with a reduction in the energy barrier and an increase in the reaction rate. When considering char nitrogen conversion, the gasification reaction is more prone to releasing NO first, leading to the contraction of the aromatic ring and the generation of CO at high temperatures. Therefore, the study further highlights the promoting role of CO2 gasification on the homogeneous and heterogeneous reduction of NO, which is mainly reflected in the reduction effect of CO. Moreover, it also accelerates the consumption of coal char and NO formation, which is favorable for the reduction reactions to a certain extent.

Insights into the effects of chronic combined chromium-nickel exposure on colon damage in mice through transcriptomic analysis and in vitro gastrointestinal digestion assay.

Heavy metals interact with each other in a coexisting manner to produce complex combined toxicity to organisms. At present, the toxic effects of chronic co-exposure to heavy metals hexavalent chromium [Cr(VI)] and divalent nickel [Ni(II)] on organisms are seldom studied and the related mechanisms are poorly understood. In this study, we explored the mechanism of the colon injury in mice caused by chronic exposure to Cr or/and Ni. The results showed that, compared with the control group, Cr or/and Ni chronic exposure affected the body weight of mice, and led to infiltration of inflammatory cells in the colon, decreased the number of goblet cells, fusion of intracellular mucus particles and damaged cell structure of intestinal epithelial. In the Cr or/and Ni exposure group, the activity of nitric oxide synthase (iNOS) increased, the expression levels of MUC2 were significantly down-regulated, and those of ZO-1 and Occludin were significantly up-regulated. Interestingly, factorial analysis revealed an interaction between Cr and Ni, which was manifested as antagonistic effects on iNOS activity, ZO-1 and MUC2 mRNA expression levels. Transcriptome sequencing further revealed that the expression of genes-related to inflammation, intestinal mucus and tight junctions changed obviously. Moreover, the relative contents of Cr(VI) and Ni(II) in the Cr, Ni and Cr+Ni groups all changed with in-vitro gastrointestinal （IVG）digestion, especially in the Cr+Ni group. Our results indicated that the chronic exposure to Cr or/and Ni can lead to damage to the mice colon, and the relative content changes of Cr(VI) and Ni(II) might be the main reason for the antagonistic effect of Cr+Ni exposure on the colon damage.

Construct Phenylethanoid Glycosides Harnessing Biosynthetic Networks, Protein Engineering and One-Pot Multienzyme Cascades.

Phenylethanoid glycosides (PhGs) exhibit a multitude of structural variations linked to diverse pharmacological activities. Assembling various PhGs via multienzyme cascades represents a concise strategy over traditional synthetic methods. However, the challenge lies in identifying a comprehensive set of catalytic enzymes. This study explores biosynthetic PhG reconstruction from natural precursors, aiming to replicate and amplify their structural diversity. We discovered 12 catalytic enzymes, including four novel 6'-OH glycosyltransferases and three new polyphenol oxidases, revealing the intricate network in PhG biosynthesis. Subsequently, the crystal structure of CmGT3 (2.62 Å) was obtained, guiding the identification of conserved residue 144# as a critical determinant for sugar donor specificity. Engineering this residue in PhG glycosyltransferases (FsGT61, CmGT3, and FsGT6) altered their sugar donor recognition. Finally, a one-pot multienzyme cascade was established, where the combined action of glycosyltransferases and acyltransferases boosted conversion rates by up to 12.6-fold. This cascade facilitated the reconstruction of 26 PhGs with conversion rates ranging from 5-100 %, and 20 additional PhGs detectable by mass spectrometry. PhGs with extra glycosyl and hydroxyl modules demonstrated notable liver cell protection. This work not only provides catalytic tools for PhG biosynthesis, but also serves as a proof-of-concept for cell-free enzymatic construction of diverse natural products.

Deep learning-based Raman spectroscopy qualitative analysis algorithm: A convolutional neural network and transformer approach.

Raman spectroscopy is a general and non-destructive detection technique that can obtain detailed information of the chemical structure of materials. In the past, when using chemometric algorithms to analyze the Raman spectra of mixtures, the challenges of complex spectral overlap and noise often limited the accurate identification of components. The emergence of deep learning has introduced a novel approach to qualitative analysis of mixed Raman spectra. In this paper, we propose a deep learning-based Raman spectroscopy qualitative analysis algorithm (RST) by borrowing the ideas of convolutional neural network and Transformer. By transforming the Raman spectrum into 64 word vectors, the contribution weights of each word vector to the components are obtained. For the 75 spectral data used for validation, the positive identification rate can reach 100.00 %, the recall rate can reach 99.3 %, the average identification score can reach 9.51, and it is applicable to the fields of Raman and surface-enhanced Raman spectroscopy. Furthermore, compared with traditional CNN models, RST has excellent accuracy and robustness in identifying components in complex mixtures. The model's interpretability has been enhanced, aiding in a deeper understanding of spectroscopic learning patterns for future analysis of more complex mixtures.

Nuclear autoantigenic sperm protein facilitates glioblastoma progression and radioresistance by regulating the ANXA2/STAT3 axis.

AIMS: Although radiotherapy is a core treatment modality for various human cancers, including glioblastoma multiforme (GBM), its clinical effects are often limited by radioresistance. The specific molecular mechanisms underlying radioresistance are largely unknown, and the reduction of radioresistance is an unresolved challenge in GBM research. METHODS: We analyzed and verified the expression of nuclear autoantigenic sperm protein (NASP) in gliomas and its relationship with patient prognosis. We also explored the function of NASP in GBM cell lines. We performed further mechanistic experiments to investigate the mechanisms by which NASP facilitates GBM progression and radioresistance. An intracranial mouse model was used to verify the effectiveness of combination therapy. RESULTS: NASP was highly expressed in gliomas, and its expression was negatively correlated with the prognosis of glioma. Functionally, NASP facilitated GBM cell proliferation, migration, invasion, and radioresistance. Mechanistically, NASP interacted directly with annexin A2 (ANXA2) and promoted its nuclear localization, which may have been mediated by phospho-annexin A2 (Tyr23). The NASP/ANXA2 axis was involved in DNA damage repair after radiotherapy, which explains the radioresistance of GBM cells that highly express NASP. NASP overexpression significantly activated the signal transducer and activator of transcription 3 (STAT3) signaling pathway. The combination of WP1066 (a STAT3 pathway inhibitor) and radiotherapy significantly inhibited GBM growth in vitro and in vivo. CONCLUSION: Our findings indicate that NASP may serve as a potential biomarker of GBM radioresistance and has important implications for improving clinical radiotherapy.