Pan-cancer analysis identifies the IRF family as a biomarker for survival prognosis and immunotherapy.

IRF family genes have been shown to be crucial in tumorigenesis and tumour immunity. However, information about the role of IRF in the systematic assessment of pan-cancer and in predicting the efficacy of tumour therapy is still unknown. In this work, we performed a systematic analysis of IRF family genes in 33 tumour samples, including expression profiles, genomics and clinical characteristics. We then applied Single-Sample Gene-Set Enrichment Analysis (ssGSEA) to calculate IRF-scores and analysed the impact of IRF-scores on tumour progression, immune infiltration and treatment efficacy. Our results showed that genomic alterations, including SNPs, CNVs and DNA methylation, can lead to dysregulation of IRFs expression in tumours and participate in regulating multiple tumorigenesis. IRF-score expression differed significantly between 12 normal and tumour samples and the impact on tumour prognosis and immune infiltration depended on tumour type. IRF expression was correlated to drug sensitivity and to the expression of immune checkpoints and immune cell infiltration, suggesting that dysregulation of IRF family expression may be a critical factor affecting tumour drug response. Our study comprehensively characterizes the genomic and clinical profile of IRFs in pan-cancer and highlights their reliability and potential value as predictive markers of oncology drug efficacy. This may provide new ideas for future personalized oncology treatment.

DLD is a potential therapeutic target for COVID-19 infection in diffuse large B-cell lymphoma patients.

Since the discovery of copper induces cell death(cuprotosis) in 2022, it has been one of the biggest research hotspots. cuprotosis related genes (CRGs) has been demonstrated to be a potential therapeutic target for cancer, however, the molecular mechanism of CRGs in coronavirus disease 2019 (COVID-19) infected in DLBCL patients has not been reported yet. Therefore, our research objective is first to elucidate the mechanism and role of CRGs in COVID-19. Secondly, we conducted univariate and multivariate analysis and machine learning to screen for CRGs with common expression differences in COVID-19 and DLBCL. Finally, the functional role and immune mechanism of genes in DLBCL were confirmed through cell experiments and immune analysis. The research results show that CRGs play an important role in the occurrence and development of COVID-19. Univariate analysis and machine learning confirm that dihydrolipoamide dehydrogenase (DLD) is the common key gene of COVID-19 and DLBCL. Inhibiting the expression of DLD can significantly inhibit the cycle progression and promote cell apoptosis of DLBCL cells and can target positive regulation of Lysine-specific demethylase 1 (LSD1, also known as KDM1A) to inhibit the proliferation of DLBCL cells and promote cell apoptosis. The immune analysis results show that high-expression of DLD may reduce T cell-mediated anti-tumor immunity by regulating immune infiltration of CD8 + T cells and positively regulating immune checkpoints LAG3 and CD276. Reducing the expression of DLD can effectively enhance T cell-mediated anti-tumor immunity, thereby clearing cancer cells and preventing cancer growth. In conclusion, DLD may be a potential therapeutic target for COVID-19 infection in DLBCL patients. Our research provides a theoretical basis for improving the clinical treatment of COVID-19 infection in DLBCL.

Genetic Control of Alternative Splicing and its Distinct Role in Colorectal Cancer Mechanisms.

BACKGROUND & AIMS: Dysregulation of alternative splicing is implicated in many human diseases, and understanding the genetic variation underlying transcript splicing is essential to dissect the molecular mechanisms of cancers. We aimed to provide a comprehensive functional dissection of splicing quantitative trait loci (sQTLs) in cancer and focus on elucidating its distinct role in colorectal cancer (CRC) mechanisms. METHODS: We performed a comprehensive sQTL analysis to identify genetic variants that control messenger RNA splicing across 33 cancer types from The Cancer Genome Atlas and independently validated in our 154 CRC tissues. Then, large-scale, multicenter, multi-ethnic case-control studies (34,585 cases and 76,023 controls) were conducted to examine the association of these sQTLs with CRC risk. A series of biological experiments in vitro and in vivo were performed to investigate the potential mechanisms of the candidate sQTLs and target genes. RESULTS: The molecular characterization of sQTL revealed its distinct role in cancer susceptibility. Tumor-specific sQTL further showed better response to cancer development. In addition, functionally informed polygenic risk score highlighted the potentiality of sQTLs in the CRC prediction. Complemented by large-scale population studies, we identified that the risk allele (T) of a multi-ancestry-associated sQTL rs61746794 significantly increased the risk of CRC in Chinese (odds ratio, 1.20; 95% CI, 1.12-1.29; P = 8.82 × 10-7) and European (odds ratio, 1.11; 95% CI, 1.07-1.16; P = 1.13 × 10-7) populations. rs61746794-T facilitated PRMT7 exon 16 splicing mediated by the RNA-binding protein PRPF8, thus increasing the level of canonical PRMT7 isoform (PRMT7-V2). Overexpression of PRMT7-V2 significantly enhanced the growth of CRC cells and xenograft tumors compared with PRMT7-V1. Mechanistically, PRMT7-V2 functions as an epigenetic writer that catalyzes the arginine methylation of H4R3 and H3R2, subsequently regulating diverse biological processes, including YAP, AKT, and KRAS pathway. A selective PRMT7 inhibitor, SGC3027, exhibited antitumor effects on human CRC cells. CONCLUSIONS: Our study provides an informative sQTLs resource and insights into the regulatory mechanisms linking splicing variants to cancer risk and serving as biomarkers and therapeutic targets.

Comprehensive genome-wide analysis of wheat xylanase inhibitor protein (XIP) genes: unveiling their role in Fusarium head blight resistance and plant immune mechanisms.

In this comprehensive genome-wide study, we identified and classified 83 Xylanase Inhibitor Protein (XIP) genes in wheat, grouped into five distinct categories, to enhance understanding of wheat's resistance to Fusarium head blight (FHB), a significant fungal threat to global wheat production. Our analysis reveals the unique distribution of XIP genes across wheat chromosomes, particularly at terminal regions, suggesting their role in the evolutionary expansion of the gene family. Several XIP genes lack signal peptides, indicating potential alternative secretion pathways that could be pivotal in plant defense against FHB. The study also uncovers the sequence homology between XIPs and chitinases, hinting at a functional diversification within the XIP gene family. Additionally, the research explores the association of XIP genes with plant immune mechanisms, particularly their linkage with plant hormone signaling pathways like abscisic acid and jasmonic acid. XIP-7A3, in particular, demonstrates a significant increase in expression upon FHB infection, highlighting its potential as a key candidate gene for enhancing wheat's resistance to this disease. This research not only enriches our understanding of the XIP gene family in wheat but also provides a foundation for future investigations into their role in developing FHB-resistant wheat cultivars. The findings offer significant implications for wheat genomics and breeding, contributing to the development of more resilient crops against fungal diseases.

Graph convolutional network-based feature selection for high-dimensional and low-sample size data.

MOTIVATION: Feature selection is a powerful dimension reduction technique which selects a subset of relevant features for model construction. Numerous feature selection methods have been proposed, but most of them fail under the high-dimensional and low-sample size (HDLSS) setting due to the challenge of overfitting. RESULTS: We present a deep learning-based method-GRAph Convolutional nEtwork feature Selector (GRACES)-to select important features for HDLSS data. GRACES exploits latent relations between samples with various overfitting-reducing techniques to iteratively find a set of optimal features which gives rise to the greatest decreases in the optimization loss. We demonstrate that GRACES significantly outperforms other feature selection methods on both synthetic and real-world datasets. AVAILABILITY AND IMPLEMENTATION: The source code is publicly available at https://github.com/canc1993/graces.

Structure-aware protein self-supervised learning.

MOTIVATION: Protein representation learning methods have shown great potential to many downstream tasks in biological applications. A few recent studies have demonstrated that the self-supervised learning is a promising solution to addressing insufficient labels of proteins, which is a major obstacle to effective protein representation learning. However, existing protein representation learning is usually pretrained on protein sequences without considering the important protein structural information. RESULTS: In this work, we propose a novel structure-aware protein self-supervised learning method to effectively capture structural information of proteins. In particular, a graph neural network model is pretrained to preserve the protein structural information with self-supervised tasks from a pairwise residue distance perspective and a dihedral angle perspective, respectively. Furthermore, we propose to leverage the available protein language model pretrained on protein sequences to enhance the self-supervised learning. Specifically, we identify the relation between the sequential information in the protein language model and the structural information in the specially designed graph neural network model via a novel pseudo bi-level optimization scheme. We conduct experiments on three downstream tasks: the binary classification into membrane/non-membrane proteins, the location classification into 10 cellular compartments, and the enzyme-catalyzed reaction classification into 384 EC numbers, and these experiments verify the effectiveness of our proposed method. AVAILABILITY AND IMPLEMENTATION: The Alphafold2 database is available in https://alphafold.ebi.ac.uk/. The PDB files are available in https://www.rcsb.org/. The downstream tasks are available in https://github.com/phermosilla/IEConv\_proteins/tree/master/Datasets. The code of the proposed method is available in https://github.com/GGchen1997/STEPS_Bioinformatics.

HAT: Hypergraph analysis toolbox.

Recent advances in biological technologies, such as multi-way chromosome conformation capture (3C), require development of methods for analysis of multi-way interactions. Hypergraphs are mathematically tractable objects that can be utilized to precisely represent and analyze multi-way interactions. Here we present the Hypergraph Analysis Toolbox (HAT), a software package for visualization and analysis of multi-way interactions in complex systems.

Surface ligand-regulated renal clearance of MRI/SPECT dual-modality nanoprobes for tumor imaging.

BACKGROUND: The general sluggish clearance kinetics of functional inorganic nanoparticles tend to raise potential biosafety concerns for in vivo applications. Renal clearance is a possible elimination pathway for functional inorganic nanoparticles delivered through intravenous injection, but largely depending on the surface physical chemical properties of a given particle apart from its size and shape. RESULTS: In this study, three small-molecule ligands that bear a diphosphonate (DP) group, but different terminal groups on the other side, i.e., anionic, cationic, and zwitterionic groups, were synthesized and used to modify ultrasmall Fe3O4 nanoparticles for evaluating the surface structure-dependent renal clearance behaviors. Systematic studies suggested that the variation of the surface ligands did not significantly increase the hydrodynamic diameter of ultrasmall Fe3O4 nanoparticles, nor influence their magnetic resonance imaging (MRI) contrast enhancement effects. Among the three particle samples, Fe3O4 nanoparticle coated with zwitterionic ligands, i.e., Fe3O4@DMSA, exhibited optimal renal clearance efficiency and reduced reticuloendothelial uptake. Therefore, this sample was further labeled with 99mTc through the DP moieties to achieve a renal-clearable MRI/single-photon emission computed tomography (SPECT) dual-modality imaging nanoprobe. The resulting nanoprobe showed satisfactory imaging capacities in a 4T1 xenograft tumor mouse model. Furthermore, the biocompatibility of Fe3O4@DMSA was evaluated both in vitro and in vivo through safety assessment experiments. CONCLUSIONS: We believe that the current investigations offer a simple and effective strategy for constructing renal-clearable nanoparticles for precise disease diagnosis.

Network dynamics of hypothalamic feeding neurons.

Mutations in the melanocortin 4 receptor (MC4R) result in hyperphagia and obesity and are the most common cause of monogenic obesity in humans. Preclinical rodent studies have determined that the critical role of the MC4R in controlling feeding can be mapped in part to its expression in the paraventricular nucleus of the hypothalamus (paraventricular nucleus [PVN]), where it regulates the activity of anorexic neural circuits. Despite the critical role of PVN MC4R neurons in regulating feeding, the in vivo neuronal activity of these cells remains largely unstudied, and the network activity of PVN MC4R neurons has not been determined. Here, we utilize in vivo single-cell endomicroscopic and mathematical approaches to determine the activity and network dynamics of PVN MC4R neurons in response to changes in energy state and pharmacological manipulation of central melanocortin receptors. We determine that PVN MC4R neurons exhibit both quantitative and qualitative changes in response to fasting and refeeding. Pharmacological stimulation of MC4R with the therapeutic MC4R agonist setmelanotide rapidly increases basal PVN MC4R activity, while stimulation of melanocortin 3 receptor (MC3R) inhibits PVN MC4R activity. Finally, we find that distinct PVN MC4R neuronal ensembles encode energy deficit and energy surfeit and that energy surfeit is associated with enhanced network connections within PVN MC4R neurons. These findings provide valuable insight into the neural dynamics underlying hunger and energy surfeit.

Redistribution of Epithelium and Stroma and Their Relationship With Corneal Curvature After 1-Month Cessation of Overnight Orthokeratology.

OBJECTIVES: To investigate the changes in the thickness of epithelium and stroma and their relationship with corneal curvature following the cessation of overnight orthokeratology for a period of 1 month. METHODS: This prospective study consecutively included 20 juveniles (20 right eyes) who had undergone overnight orthokeratology for a minimum of one year and were willing to discontinue the treatment. The study measured and compared epithelial and corneal curvature using optical coherence tomography and Medmont topographer at the first day of cessation and 1 month after cessation. In addition, changes in uncorrected visual acuity and refractive error before and after the cessation of the treatment were analyzed. RESULTS: The study found a significant increase in the thickness of the epithelium in the central 2-mm area after the cessation of the treatment (t = -4.807, P <0.001). Moreover, the stroma in the paracentral area (2-5 mm) and peripheral area (5-6 mm) showed a general thinning trend ( P =0.016, P =0.016). Regarding the correlation analysis, the change in central epithelial thickness (ΔCET) was significantly correlated with the change in paracentral corneal curvature (ΔPCCC) (r=0.610, P =0.007) and the change in peripheral corneal curvature (ΔPCC) (r=0.597, P =0.009). Similarly, the change in central stromal thickness (ΔCST) was significantly correlated with the change in central corneal curvature (ΔCCC) (r=0.500, P =0.035), ΔPCCC (r=0.700, P =0.001), and ΔPCC (r=0.635, P =0.005). CONCLUSIONS: The study found that the corneal remodeling induced by orthokeratology was reversible after the cessation of the treatment. Specifically, changes in the epithelium were found to be more prominent in the central area, while changes in the stroma were more pronounced in the paracentral and peripheral areas. In addition, the study established a significant correlation between central corneal remodeling and changes in curvature.

[Correlation between methylation of interferon regulatory factor 6 gene promoter in renal tissues and overall survival of patients with Kidney renal clear cell carcinoma].

OBJECTIVE: To assess the prognostic value of methylation of interferon regulatory factor 6 (IRF6) gene promoter in patients diagnosed with Kidney renal clear cell carcinoma (KIRC). METHODS: The primary lesions of fifty KIRC patients who were diagnosed at the First Affiliated Hospital of Nanjing Medical University from January 2016 to January 2020 were collected. The expression of IRF6 protein was determined with an immunohistochemical method. The correlation between the level of IRF6 expression and survival and/or metastasis status was analyzed. The mRNA and protein levels of the IRF6 in KIRC and normal renal tissues were compared by using bioinformatic tools. The difference in the methylation rate of the IRF6 gene promoter between tumor and adjacent tissues was analyzed by searching the online databases. Statistical analysis was carried out for the methylation status of the IRF6 gene promoter region to select those negatively correlated with the overall survival (OS) among the patients. In vitro experiments were conducted with cell lines to verify the correlation between the status of promoter methylation and transcription level of the IRF6 gene. RESULTS: The mRNA and protein levels of the IRF6 gene in KIRC tissues were significantly lower than those of the normal controls, and this was more prominent in patients who had died or developed metastasis. The extent of IRF6 gene promoter methylation in the KIRC tissues was much higher compared with that of the adjacent normal renal tissues. There was a significant negative correlation between the methylation of the IRF6 gene promoter and mRNA level of the IRF6 (R = -0.52). The higher methylation degree in the IRF6 gene promoter regions cg12034118 and cg16030177, the shorter the OS and worse prognosis in the patients. Only twenty CpG sites in cg12034118 were confirmed to be highly methylated in KIRC cell lines. The transcription level of the IRF6 gene was upregulated in a time- and dose-dependent manner after the treatment with demethylation reagent 5-azadeoxycytidine. CONCLUSION: The methylation of IRF6 gene promoter in the renal tissues of KIRC patients is closely correlated with the OS. Cg12034118 may provide a promising biomarker for laboratory detection, and its high methylation rate has certain reference value for the prognosis.

Saturated fatty acid intake, genetic risk and colorectal cancer incidence: A large-scale prospective cohort study.

Previous investigations mainly focused on the associations of dietary fatty acids with colorectal cancer (CRC) risk, which ignored gene-environment interaction and mechanisms interpretation. We conducted a case-control study (751 cases and 3058 controls) and a prospective cohort study (125 021 participants) to explore the associations between dietary fatty acids, genetic risks, and CRC. Results showed that high intake of saturated fatty acid (SFA) was associated with a higher risk of CRC than low SFA intake (HR =1.22, 95% CI:1.02-1.46). Participants at high genetic risk had a greater risk of CRC with the HR of 2.48 (2.11-2.91) than those at low genetic risk. A multiplicative interaction of genetic risk and SFA intake with incident CRC risk was found (PInteraction = 7.59 × 10-20 ), demonstrating that participants with high genetic risk and high SFA intake had a 3.75-fold greater risk of CRC than those with low genetic risk and low SFA intake. Furthermore, incorporating PRS and SFA into traditional clinical risk factors improved the discriminatory accuracy for CRC risk stratification (AUC from 0.706 to 0.731). Multi-omics data showed that exposure to SFA-rich high-fat dietary (HFD) can responsively induce epigenome reprogramming of some oncogenes and pathological activation of fatty acid metabolism pathway, which may contribute to CRC development through changes in gut microbiomes, metabolites, and tumor-infiltrating immune cells. These findings suggest that individuals with high genetic risk of CRC may benefit from reducing SFA intake. The incorporation of SFA intake and PRS into traditional clinical risk factors will help improve high-risk sub-populations in individualized CRC prevention.

Self-enhanced Electrochemiluminescence Luminophore Based on Pd Nanocluster-Anchored Metal Organic Frameworks via Ion Annihilation for Sensitive Cell Assay of Human Lung Cancer.

Electrochemiluminescence (ECL) has attracted significant interest in the analysis of cancer cells, where the ruthenium(II)-based emitter demonstrates urgency and feasibility to improve the ECL efficiency. In this work, the self-enhanced ECL luminophore was prepared by covalent anchoring of Pd nanoclusters on aminated metal organic frameworks (Pd NCs@MOFs), followed by linkage with bis(2,2'-bipyridine)-5-amino-1,10-phenanthroline ruthenium(II) (RuP). The resultant luminophore showed 214-fold self-magnification in the ECL efficiency over RuP alone, combined by promoting the interfacial photoelectron transfer. The enhanced mechanism through ion annihilation was critically proved by controlled experiments and density functional theory (DFT) calculations. Based on the above, a "signal off" ECL biosensor was built by assembly of tyrosine kinase 7 (PTK-7) aptamer (Apt) on the established sensing platform for analysis of human lung cancer cells (A549). The built sensor showed a lower detection limit of 8 cells mL-1, achieving the single-cell detection. This work reported a self-enhanced strategy for synthesis of advanced ECL emitters, combined by exploring the ECL biosensing devices in the single-cell analysis of cancers.

Multimodal Image Fusion Offers Better Spatial Resolution for Mass Spectrometry Imaging.

High-resolution reconstruction has attracted increasing research interest in mass spectrometry imaging (MSI), but it remains a challenging ill-posed problem. In the present study, we proposed a deep learning model to fuse multimodal images to enhance the spatial resolution of MSI data, namely, DeepFERE. Hematoxylin and eosin (H&E) stain microscopy imaging was used to pose constraints in the process of high-resolution reconstruction to alleviate the ill-posedness. A novel model architecture was designed to achieve multi-task optimization by incorporating multi-modal image registration and fusion in a mutually reinforced framework. Experimental results demonstrated that the proposed DeepFERE model is able to produce high-resolution reconstruction images with rich chemical information and a detailed structure on both visual inspection and quantitative evaluation. In addition, our method was found to be able to improve the delimitation of the boundary between cancerous and para-cancerous regions in the MSI image. Furthermore, the reconstruction of low-resolution spatial transcriptomics data demonstrated that the developed DeepFERE model may find wider applications in biomedical fields.

Comparative transcriptomic analysis of wheat cultivars differing in their resistance to Fusarium head blight infection during grain-filling stages reveals unique defense mechanisms at play.

Fusarium head blight (FHB) is a devastating fungal disease that poses a significant threat to wheat production, causing substantial yield losses. Understanding the molecular mechanisms of wheat resistance to FHB is crucial for developing effective disease management strategies. This study aimed to investigate the mechanisms of FHB resistance and the patterns of toxin accumulation in three wheat cultivars, Annong8455, Annong1589, and Sumai3, with different levels of resistance, ranging from low to high respectively, under natural field conditions. Samples were taken at three different grain-filling stages (5, 10, and 15 DPA) for gene expression analysis and phenotypic observation. Results found that toxin concentration was inversely correlated with varietal resistance but not correlated with disease phenotypes, indicating that toxin analysis is a more accurate measure of disease status in wheat ears and grains. Transcriptomic data showed that Sumai3 exhibited a stronger immune response during all stages of grain filling by upregulating genes involved in the active destruction of pathogens and removal of toxins. In contrast, Annong1589 showed a passive prevention of the spread of toxins into cells by the upregulation of genes involved in tyramine biosynthesis at the early stage (5 DPA), which may be involved in cell wall strengthening. Our study demonstrates the complexity of FHB resistance in wheat, with cultivars exhibiting unique and overlapping defense mechanisms, and highlights the importance of considering the temporal and spatial dynamics of gene expression in breeding programs for developing more resistant wheat cultivars.

Distributed twist sensing using frequency-scanning φ-OTDR in a spun fiber.

In this paper, a novel distributed twist sensor based on frequency-scanning phase-sensitive optical time-domain reflectometry (φ-OTDR) in a spun fiber is proposed and demonstrated. Owing to the unique helical structure of the stress rods in the spun fiber, fiber twist gives rise to the variation of the effective refractive index of the transmitting light, which can be quantitatively retrieved through frequency shift using frequency-scanning φ-OTDR. The feasibility of distributed twist sensing has been verified by both simulation and experiment. For proof of concept, distributed twist sensing over a 136 m spun fiber with a 1 m spatial resolution is demonstrated, and the measured frequency shift shows a quadratic fitting dependence on the twist angle. In addition, the responses of both clockwise and counterclockwise twist directions have also been explored and the experiment result indicates that the twist direction can be discriminated since the frequency shift directions are opposite in the correlation spectrum. The proposed twist sensor possesses some outstanding advantages, including high sensitivity, distributed twist measurement and twist direction recognition capability, etc., which is very promising for specific applications in industry, e.g., structural health monitoring, bionic robots, etc.

Benchmarking omics-based prediction of asthma development in children.

BACKGROUND: Asthma is a heterogeneous disease with high morbidity. Advancement in high-throughput multi-omics approaches has enabled the collection of molecular assessments at different layers, providing a complementary perspective of complex diseases. Numerous computational methods have been developed for the omics-based patient classification or disease outcome prediction. Yet, a systematic benchmarking of those methods using various combinations of omics data for the prediction of asthma development is still lacking. OBJECTIVE: We aimed to investigate the computational methods in disease status prediction using multi-omics data. METHOD: We systematically benchmarked 18 computational methods using all the 63 combinations of six omics data (GWAS, miRNA, mRNA, microbiome, metabolome, DNA methylation) collected in The Vitamin D Antenatal Asthma Reduction Trial (VDAART) cohort. We evaluated each method using standard performance metrics for each of the 63 omics combinations. RESULTS: Our results indicate that overall Logistic Regression, Multi-Layer Perceptron, and MOGONET display superior performance, and the combination of transcriptional, genomic and microbiome data achieves the best prediction. Moreover, we find that including the clinical data can further improve the prediction performance for some but not all the omics combinations. CONCLUSIONS: Specific omics combinations can reach the optimal prediction of asthma development in children. And certain computational methods showed superior performance than other methods.

Luminescent Crystalline Carbon- and Nitrogen-Centered Organic Radicals Based on N-Heterocyclic Carbene-Triphenylamine Hybrids.

Developing luminescent radicals with tunable emission is a challenging task due to the limitation of alternative skeletons. Herein, a series of carbene-triphenylamine hybrids were prepared by the direct C2-arylation of N-heterocyclic carbenes with 4-bromo-N,N-bis(4-methoxyphenyl)aniline. These hybrids showed multiple redox-active properties and could be converted to carbon-centered luminescent radicals with blue-to-cyan emissions (λmax : 436-486 nm) or nitrogen-centered luminescent radicals with orange emissions (λmax : 590-623 nm) through chemical reduction or oxidation, respectively. The radical species were characterized by electron paramagnetic resonance spectroscopy, ultraviolet-visible spectroscopy, and single-crystal X-ray diffractometry analysis. Notably, the corresponding nitrogen-centered radicals exhibited good stability in atmospheric air, and their thermal decomposition temperatures were determined to be above 200 °C. In addition, spectral and theoretical calculations indicate that all radicals exhibit anti-Kasha emissions.

Bioinformatics analysis of paravertebral muscles atrophy in adult degenerative scoliosis.

Paravertebral muscles (PVM) act as one of the major dynamic factors to maintain human upright activities and play a remarkable role in maintaining the balance of the trunk. Adult degenerative scoliosis (ADS) has become one of the important causes of disability in the elderly population owing to the changes in spinal biomechanics, atrophy and degeneration of PVM, and imbalance of the spine. Previously, many studies focused on the physical evaluation of PVM degeneration. However, the molecular biological changes are still not completely known. In this study, we established a rat model of scoliosis and performed the proteomic analysis of the PVM of ADS. The results showed that the degree of atrophy, muscle fat deposition, and fibrosis of the PVM of rats positively correlated with the angle of scoliosis. The proteomic results showed that 177 differentially expressed proteins were present in the ADS group, which included 105 upregulated proteins and 72 downregulated proteins compared with the PVM in individuals without spinal deformities. Through the construction of a protein-protein interaction network, 18 core differentially expressed proteins were obtained, which included fibrinogen beta chain, apolipoprotein E, fibrinogen gamma chain, thrombospondin-1, integrin alpha-6, fibronectin-1, platelet factor 4, coagulation factor XIII A chain, ras-related protein Rap-1b, platelet endothelial cell adhesion molecule 1, complement C1q subcomponent subunit A, cathepsin G, myeloperoxidase, von Willebrand factor, integrin beta-1, integrin alpha-1, leukocyte surface antigen CD47, and complement C1q subcomponent subunit B. Further analysis of the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) and immunofluorescence showed that the neutrophil extracellular traps (NETs) formation signaling pathway plays a major role in the pathogenesis of PVM degeneration in ADS. The results of the present study preliminarily laid the molecular biological foundation of PVM atrophy in ADS, which will provide a new therapeutic target for alleviating PVM atrophy and decreasing the occurrence of scoliosis.

Sandaracinobacteroides sayramensis sp. nov., a yellow-pigmented bacterium isolated from lake water.

A Gram-negative, non-motile, facultatively anaerobic, rod-shaped bacterium, designated strain RS1-74T, was isolated from the surface water of Sayram Lake, Xinjiang Uygur Autonomous Region, China. The strain was able to grow optimally at 30 °C and pH 7.0-7.5, and in the presence of 0-0.5 % (v/w) NaCl. Catalase and oxidase activities were present. H2S was produced. Chemotaxonomic analysis showed Q-10 was the sole respiratory quinone. The polar lipids were composed of phosphatidylethanolamine, diphosphatidylglycerol, two glycolipids, phosphatidylglycerol, sphingoglycolipid and two unidentified lipids. Summed feature 3 (C16 : 1 ω7c and/or C16 : 1 ω6c) and summed feature 8 (C18 : 1 ω7c and/or C18 : 1 ω6c) were the predominant fatty acids. Phylogenetic analysis based on 16S rRNA gene sequence showed that strain RS1-74T was closely related to 'Sandaracinobacter neustonicus' JCM 30 734 (98.65 %), 'Sandaracinobacter sibiricus' RB16-17 (98.42 %) and Sandaracinobacteroides hominis SZY PN-1T (97.09%). The genomic DNA G+C content was 66.45 mol%. The average nucleotide identity and DNA-DNA hybridization values among the genomes of strain RS1-74T and 'Sandaracinobacter neustonicus' JCM 30734 and Sandaracinobacteroides hominis SZY PN-1T were 78.2 and 77.22 %, and 22.2 and 20.40 %, respectively. Based on the physiological, biochemical, phylogenetic and genomic data, strain RS1-74T represents a novel species within the genus Sandaracinobacteroides, for which the name Sandaracinobacteroides sayramensis sp. nov. is proposed, with type strain RS1-74T (=KCTC 82674T=MCCC 1K06282T).