NH2+ implantations induced superior hemocompatibility of carbon nanotubes

NH(2)(+) implantation was performed on multiwalled carbon nanotubes (MWCNTs) prepared by chemical vapor deposition. The hemocompatibility of MWCNTs and NH(2)(+)-implanted MWCNTs was evaluated based on in vitro hemolysis, platelet adhesion, and kinetic-clotting tests. Compared with MWCNTs, NH(2)(+)-implanted MWCNTs displayed more perfect platelets and red blood cells in morphology, lower platelet adhesion rate, lower hemolytic rate, and longer kinetic blood-clotting time. NH(2)(+)-implanted MWCNTs with higher fluency of 1 × 10(16) ions/cm(2) led to the best thromboresistance, hence desired hemocompatibility. Fourier transfer infrared and X-ray photoelectron spectroscopy analyses showed that NH(2)(+) implantation caused the cleavage of some pendants and the formation of some new N-containing functional groups. These results were responsible for the enhanced hemocompatibility of NH(2)(+)-implanted MWCNTs

The Controversy, Challenges, and Potential Benefits of Putative Female Germline Stem Cells Research in Mammals

The conventional view is that female mammals lose their ability to generate new germ cells after birth. However, in recent years, researchers have successfully isolated and cultured a type of germ cell from postnatal ovaries in a variety of mammalian species that have the abilities of self-proliferation and differentiation into oocytes, and this finding indicates that putative germline stem cells maybe exist in the postnatal mammalian ovaries. Herein, we review the research history and discovery of putative female germline stem cells, the concept that putative germline stem cells exist in the postnatal mammalian ovary, and the research progress, challenge, and application of putative germline stem cells in recent years

SIRT5 promotes IDH2 desuccinylation and G6PD deglutarylation to enhance cellular antioxidant defense

Abstract Excess in mitochondrial reactive oxygen species (ROS) is considered as a major cause of cellular oxidative stress. NADPH, the main intracellular reductant, has a key role in keeping glutathione in its reduced form GSH, which scavenges ROS and thus protects the cell from oxidative damage. Here, we report that SIRT5 desuccinylates and deglutarylates isocitrate dehydrogenase 2 (IDH2) and glucose‐6‐phosphate dehydrogenase (G6PD), respectively, and thus activates both NADPH‐producing enzymes. Moreover, we show that knockdown or knockout of SIRT5 leads to high levels of cellular ROS. SIRT5 inactivation leads to the inhibition of IDH2 and G6PD, thereby decreasing NADPH production, lowering GSH, impairing the ability to scavenge ROS, and increasing cellular susceptibility to oxidative stress. Our study uncovers a SIRT5‐dependent mechanism that regulates cellular NADPH homeostasis and redox potential by promoting IDH2 desuccinylation and G6PD deglutarylation

Altitudinal Patterns in Adaptive Evolution of Genome Size and Inter-Genome Hybridization Between Three Elymus Species From the Qinghai–Tibetan Plateau

Genome size variation and hybridization occur frequently within or between plant species under diverse environmental conditions, which enrich species diversification and drive the evolutionary process. Elymus L. is the largest genus in Triticeae with five recognized basic genomes (St, H, P, W, and Y). However, the data on population cytogenetics of Elymus species are sparse, especially whether genome hybridization and chromosomal structure can be affected by altitude are still unknown. In order to explore the relationship between genome sizes, we studied interspecific hybridization and altitude of Elymus species at population genetic and cytological levels. Twenty-seven populations at nine different altitudes (2,800–4,300 m) of three Elymus species, namely, hexaploid E. nutans (StHY, 2n = 6x = 42), tetraploid E. burchan-buddae (StY, 2n = 4x = 28), and E. sibiricus (StH, 2n = 4x = 28), were sampled from the Qinghai–Tibetan Plateau (QTP) to estimate whether intraspecific variation could affect the genomic relationships by genomic in situ hybridization (GISH), and quantify the genome size of Elymus among different altitude ecological groups by flow cytometry. The genome size of E. nutans, E. burchan-buddae, and E. sibiricus varied from 12.38 to 22.33, 8.81 to 18.93, and 11.46 to 20.96 pg/2C with the averages of 19.59, 12.39, and 16.85 pg/2C, respectively. The curve regression analysis revealed a strong correlation between altitude and nuclear DNA content in three Elymus species. In addition, the chromosomes of the St and Y genomes demonstrated higher polymorphism than that of the H genome. Larger genome size variations occurred in the mid-altitude populations (3,900–4,300 m) compared with other-altitude populations, suggesting a notable altitudinal pattern in genome size variation, which shaped genome evolution by altitude. This result supports our former hypothesis that genetic richness center at medium altitude is useful and valuable for species adaptation to highland environmental conditions, germplasm utilization, and conservation

Multi-omics analysis reveals the prognostic and tumor micro-environmental value of lumican in multiple cancer types

Background: Lumican (LUM), a proteoglycan of the extracellular matrix, has been reported to be involved in the regulation of immune escape processes, but the data supporting this phenomenon are not sufficient. In this study, we aimed to explore the links among LUM expression, survival, tumor microenvironment (TME), and immunotherapy in 33 cancer types.Methods: Data from several databases, such as UCSC Xena, GTEx, UALCAN, HPA, GEPIA2, TISIDB, PrognoScan, TIMER2, and GEO, as well as published studies, were used to determine the relationship between LUM expression and clinical features, TME, heterogeneity, and tumor stemness.Results: The expression of LUM was statistically different in most tumors versus normal tissues, both at the RNA and protein expression levels. High expression of LUM was typically associated with a poor prognosis in tumors. Additionally, immune scores, six immune cells, four immunosuppressive cells, cancer-associated fibroblasts (CAFs)-associated and immunosuppressive factors, tumor mutation burden (TMB), microsatellite instability (MSI), DNAss, and RNAss were all significantly associated with LUM. Among them, LUM expression displayed a significant positive correlation with CAFs and their factors, and exhibited immunosuppressive effects in six independent immunotherapy cohorts.Conclusion: Multi-omics analysis suggests that LUM may have been a prognostic marker, contributed to immunosuppression in the TME, and decreased the effectiveness of immune checkpoint inhibitors

The +2 oxidation state of Cr incorporated into the crystal lattice of UO2

Doping by Cr is used to improve the performance of uranium dioxide (UO2)-based nuclear fuel. However, the mechanism of structural incorporation of Cr remains unclear. Here, in order to understand this process on the atomic scale and the redox state of Cr in UO2-based nuclear fuel, we performed intensive ab initio atomistic simulations of the Cr doped UO2 matrix. We unexpectedly found that Cr in UO2 exists as Cr2+ species and not as the widely claimed Cr3+. We re-evaluated previously published x-ray absorption near edge structure spectroscopy data and confirmed the computed redox state of Cr. Thermodynamic consideration shows that the favorable structural arrangement of Cr in UO2 is given by a pair of associated Cr2+ and oxygen vacancy. The realism of this doping mechanism is further demonstrated by a match to the measured maximum Cr solubility and small lattice contraction

Mendelian randomization and transcriptomic analysis reveal an inverse causal relationship between Alzheimer’s disease and cancer

Abstract Background Alzheimer’s disease (AD) and cancer are common age-related diseases, and epidemiological evidence suggests an inverse relationship between them. However, investigating the potential mechanism underlying their relationship remains insufficient. Methods Based on genome-wide association summary statistics for 42,034 AD patients and 609,951 cancer patients from the GWAS Catalog using the two-sample Mendelian randomization (MR) method. Moreover, we utilized two-step MR to identify metabolites mediating between AD and cancer. Furthermore, we employed colocalization analysis to identify genes whose upregulation is a risk factor for AD and demonstrated the genes’ upregulation to be a favorable prognostic factor for cancer by analyzing transcriptomic data for 33 TCGA cancer types. Results Two-sample MR analysis revealed a significant causal influence for increased AD risk on reduced cancer risk. Two-step MR analysis identified very low-density lipoprotein (VLDL) as a key mediator of the negative cause-effect relationship between AD and cancer. Colocalization analysis uncovered PVRIG upregulation to be a risk factor for AD. Transcriptomic analysis showed that PVRIG expression had significant negative correlations with stemness scores, and positive correlations with antitumor immune responses and overall survival in pan-cancer and multiple cancer types. Conclusion AD may result in lower cancer risk. VLDL is a significant intermediate variable linking AD with cancer. PVRIG abundance is a risk factor for AD but a protective factor for cancer. This study demonstrates a causal influence for AD on cancer and provides potential molecular connections between both diseases

Identification Method of Wheat Grain Phenotype Based on Deep Learning of ImCascade R-CNN

ObjectiveWheat serves as the primary source of dietary carbohydrates for the human population, supplying 20% of the required caloric intake. Currently, the primary objective of wheat breeding is to develop wheat varieties that exhibit both high quality and high yield, ensuring an overall increase in wheat production. Additionally, the consideration of phenotype parameters, such as grain length and width, holds significant importance in the introduction, screening, and evaluation of germplasm resources. Notably, a noteworthy positive association has been observed between grain size, grain shape, and grain weight. Simultaneously, within the scope of wheat breeding, the occurrence of inadequate harvest and storage practices can readily result in damage to wheat grains, consequently leading to a direct reduction in both emergence rate and yield. In essence, the integrity of wheat grains directly influences the wheat breeding process. Nevertheless, distinguishing between intact and damaged grains remains challenging due to the minimal disparities in certain characteristics, thereby impeding the accurate identification of damaged wheat grains through manual means. Consequently, this study aims to address this issue by focusing on the detection of wheat kernel integrity and completing the attainment of grain phenotype parameters.MethodsThis study presented an enhanced approach for addressing the challenges of low detection accuracy, unclear segmentation of wheat grain contour, and missing detection. The proposed strategy involves utilizing the Cascade Mask R-CNN model and replacing the backbone network with ResNeXt to mitigate gradient dispersion and minimize the model's parameter count. Furthermore, the inclusion of Mish as an activation function enhanced the efficiency and versatility of the detection model. Additionally, a multilayer convolutional structure was introduced in the detector to thoroughly investigate the latent features of wheat grains. The Soft-NMS algorithm was employed to identify the candidate frame and achieve accurate segmentation of the wheat kernel adhesion region. Additionally, the ImCascade R-CNN model was developed. Simultaneously, to address the issue of low accuracy in obtaining grain contour parameters due to disordered grain arrangement, a grain contour-based algorithm for parameter acquisition was devised. Wheat grain could be approximated as an oval shape, and the grain edge contour could be obtained according to the mask, the distance between the farthest points could be iteratively obtained as the grain length, and the grain width could be obtained according to the area. Ultimately, a method for wheat kernel phenotype identification was put forth. The ImCascade R-CNN model was utilized to analyze wheat kernel images, extracting essential features and determining the integrity of the kernels through classification and boundary box regression branches. The mask generation branch was employed to generate a mask map for individual wheat grains, enabling segmentation of the grain contours. Subsequently, the number of grains in the image was determined, and the length and width parameters of the entire wheat grain were computed.Results and DiscussionsIn the experiment on wheat kernel phenotype recognition, a comparison and improvement were conducted on the identification results of the Cascade Mask R-CNN model and the ImCascade R-CNN model across various modules. Additionally, the efficacy of the model modification scheme was verified. The comparison of results between the Cascade Mask R-CNN model and the ImCascade R-CNN model served to validate the proposed model's ability to significantly decrease the missed detection rate. The effectiveness and advantages of the ImCascade R-CNN model were verified by comparing its loss value, P-R value, and mAP_50 value with those of the Cascade Mask R-CNN model. In the context of wheat grain identification and segmentation, the detection results of the ImCascade R-CNN model were compared to those of the Cascade Mask R-CNN and Deeplabv3+ models. The comparison confirmed that the ImCascade R-CNN model exhibited superior performance in identifying and locating wheat grains, accurately segmenting wheat grain contours, and achieving an average accuracy of 90.2% in detecting wheat grain integrity. These findings serve as a foundation for obtaining kernel contour parameters. The grain length and grain width exhibited average error rates of 2.15% and 3.74%, respectively, while the standard error of the aspect ratio was 0.15. The statistical analysis and fitting of the grain length and width, as obtained through the proposed wheat grain shape identification method, yielded determination coefficients of 0.9351 and 0.8217, respectively. These coefficients demonstrated a strong agreement with the manually measured values, indicating that the method is capable of meeting the demands of wheat seed testing and providing precise data support for wheat breeding.ConclusionsThe findings of this study can be utilized for the rapid and precise detection of wheat grain integrity and the acquisition of comprehensive grain contour data. In contrast to current wheat kernel recognition technology, this research capitalizes on enhanced grain contour segmentation to furnish data support for the acquisition of wheat kernel contour parameters. Additionally, the refined contour parameter acquisition algorithm effectively mitigates the impact of disordered wheat kernel arrangement, resulting in more accurate parameter data compared to existing kernel appearance detectors available in the market, providing data support for wheat breeding and accelerating the cultivation of high-quality and high-yield wheat varieties