Identification of potential biomarkers for papillary thyroid carcinoma by comprehensive bioinformatics analysis.

To perform bioinformatics analysis on the papillary thyroid carcinoma (PTC) gene chip dataset to explore new biological markers for PTC. The gene expression profiles of GSE3467 and GSE6004 chip data were collected by GEO2R, and the differentially expressed genes (DEGs) were selected for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Protein-protein interaction (PPI) relationship analysis was achieved using STRING, and the hub genes were obtained using the Cytoscape software. GEPIA was used to validate the expressions of the hub genes in the normal and tumor tissues and to conduct survival analyses. Pertinent genetic pathology results were fetched using the HPA database. Finally, the key genes were clinically verified by reverse transcription-polymerase chain reaction. 97 genes were jointly up-regulated and 107 genes were jointly down-regulated in GSE3467 and GSE6004. GO function enrichment analysis revealed that the DEGs were involved in the regulation of calcium ion transport into cytosol, integrin binding, and cell adhesion molecule binding. KEGG pathway enrichment analysis indicated that the DEGs were chiefly associated with thyroid cancer and non-small cell lung cancer. According to the PPI network, 30 key target genes were identified. Only the expressions of ANK2, TLE1, and TCF4 matched between the normal and tumor tissues, and were associated with disease prognosis. When compared with the normal thyroid tissues, the protein and mRNA expressions of ANK2, TLE1, and TCF4 were down-regulated in PTC. Significant differences exist in overall gene expression between the thyroid tissues of patients with PTC and those of healthy people. Furthermore, the differential genes ANK2, TLE1, and TCF4 are expected to be reliable molecular markers for the mechanism study and diagnosis of PTC.

Bacterial Colonization in the Airways and Intestines of Twin and Singleton Preterm Neonates: A Single-Center Study.

Limited studies have investigated the microbial colonization of the airways and intestines in preterm neonates. We studied the composition of intestinal and airway bacterial colonies in several preterm twin pairs and singletons to explore the dominant bacteria, assess their variability, and predict their phenotypic and metabolic functions. In this descriptive study, we collected sputum and fetal stool specimens from 10 twin pairs (20 cases) and 20 singleton preterm neonates. These specimens were analyzed using 16S rRNA deep sequencing to study the alpha and beta diversities and community structures of airway and intestinal bacteria and predict their metabolic functions. Specimens from twins and singleton neonates had distinct aggregations of intestinal and airway bacteria but showed similarities and high microbial diversities during initial colonization. The top five phyla were Proteobacteria, Firmicutes, Actinobacteriota, Bacteroidota, and Cyanobacteria. The top ten genera were Streptococcus, Acinetobacter, Ralstonia, Staphylococcus, Comamonas, Enterococcus, Stenotrophomonas, Dechlorosoma, Sphingopyxis, and Rothia. Potentially pathogenic and highly stress-tolerant Gram-negative bacteria were predominant in the intestinal flora. A considerable proportion of colonies recovered from the airway and intestines of preterm neonates were functional bacteria. The richness of the intestinal and airway flora was not significantly different between twins and singletons, and the flora clustered together. Both intestinal and airway bacteria of twins and singletons were similar. The species involved in initial colonization were similar but different in proportions; therefore, changes in microbial structure and richness may not be attributed to these species.

Sensitivity enhancement of far-detuned RF field sensing based on Rydberg atoms dressed by a near-resonant RF field.

Rydberg-atom electrometers promise traceable standards for RF electrometry by enabling stable and uniform measurement. In this Letter, we propose an approach to increase the sensitivity of the Rydberg-atom electrometer for far-detuned RF field sensing. The key physical mechanism is the addition of a new ingredient-a local RF field near-resonant with a Rydberg transition-so that the far-detuned field can be detected by the shift of an Autler-Townes (AT) splitting peak, which can be dozens of times larger than the AC Stark shift of the electromagnetic induced transparency (EIT) signal without the near-resonant field. The method enables us to measure far-detuned fields with higher sensitivities, including sub-GHz RF fields (even DC electric fields) which are rarely involved in the existing sensitivity enhancement methods.

Solvent-free mechanochemical preparation of graphene oxide-Fe3 O4 and its application in magnetic dispersive solid-phase extraction of illegal dyes in food samples.

A simple, green, and efficient mechanochemical approach was developed herein to prepare tunable magnetic graphene oxide nanoparticles. The obtained nanoparticles were successfully used as adsorbents in a magnetic dispersive solid-phase extraction method to extract three cationic dyes (i.e., thioflavine T, auramine-O, and basic orange 2) found in food samples. Our proposed approach also utilized high-performance liquid chromatography with ultraviolet detection. Several key variables affecting the extraction recovery were investigated. These included the sample pH, amount of extractant, extraction time, sample volume, elution solvent type and volume, and the stability and reusability of the magnetic graphene oxide nanoparticles. Under optimized conditions, the calibration curve was linear at a concentration range of 0.005-1.0 µg/mL with a correlation coefficient of 0.9992-0.9996. Moreover, the limits of detection were determined at 0.97-1.35 µg/mL. The extraction mechanism was investigated via ultraviolet-visible spectrophotometry and zeta-potential analyses. The developed method was used to analyze the above-mentioned cationic dyes in bean products and yellow fish samples. Notably, satisfactory spiked recoveries ranging from 90.7 to 104.9% were achieved.

Sufficient Forecasting Using Factor Models.

We consider forecasting a single time series when there is a large number of predictors and a possible nonlinear effect. The dimensionality was first reduced via a high-dimensional (approximate) factor model implemented by the principal component analysis. Using the extracted factors, we develop a novel forecasting method called the sufficient forecasting, which provides a set of sufficient predictive indices, inferred from high-dimensional predictors, to deliver additional predictive power. The projected principal component analysis will be employed to enhance the accuracy of inferred factors when a semi-parametric (approximate) factor model is assumed. Our method is also applicable to cross-sectional sufficient regression using extracted factors. The connection between the sufficient forecasting and the deep learning architecture is explicitly stated. The sufficient forecasting correctly estimates projection indices of the underlying factors even in the presence of a nonparametric forecasting function. The proposed method extends the sufficient dimension reduction to high-dimensional regimes by condensing the cross-sectional information through factor models. We derive asymptotic properties for the estimate of the central subspace spanned by these projection directions as well as the estimates of the sufficient predictive indices. We further show that the natural method of running multiple regression of target on estimated factors yields a linear estimate that actually falls into this central subspace. Our method and theory allow the number of predictors to be larger than the number of observations. We finally demonstrate that the sufficient forecasting improves upon the linear forecasting in both simulation studies and an empirical study of forecasting macroeconomic variables.

Innovative application of basalt fibers as biological carrier in constructed wetland-microbial fuel cell for improvement of performance under perfluorooctanoic acid exposure.

Basalt fiber (BF) was filled in constructed wetland-microbial fuel cell (CW-MFC) as bio-carrier for enhancement of operation performance under perfluorooctanoic acid (PFOA) exposure. In this study, although PFOA caused significant decline of ammonium removal by 7.5-7.7 %, slight promotion on nitrogen and phosphorus removal was observed with BF filling, compared to control. PFOA removal also increased by 1.7-3.4 % in BF filling group. Besides, improved electrochemical performance was discovered with BF filling, in which the highest power density increased by 86.6 % than control, even under PFOA stress. Enhanced stability and performance of CW-MFC resulted from stimulation of functional bacteria on electrodes like Dechloromonas, Thauera, Zoogloea, Gemmobacter, and Pseudomonas, which were further enriched on BF carrier. Higher abundance of nitrogen metabolism and related genes on electrodes and BF carrier was also discovered with BF filling. This study offered new findings on application of BF in CW-MFC systems with PFOA exposure.

Natural ventilation cooling effectiveness classification for building design addressing climate characteristics.

The evaluation of natural ventilation potential for effective sustainable options and innovative green building design strategies is of great interest to architects, researchers and governments. From a retrospective review, we found that the potential evaluation of natural ventilation (NV) cooling effectiveness in the same category based on similar meteorological uncertainty, research objectives and objects showed significant differences. Uncertainties added and uncertainty propagation (both model form uncertainties and parameter uncertainties) could result in large discrepancies between simulation outcomes and real scenarios, especially in the design performance modeling (DPM) phase. In this conceptual design stage, a few parameters are available and therefore decisive. It is necessary to review and identify the key performance indicators and explore the extent to which deviations are caused by inconsistencies or biases in model information. As a basis for more concrete research, we propose statistical tests based on quantitative evaluations to explore the rule of natural ventilation potential volatility and identify whether there is a significant potential improvement resulting from the critical parameter enhancement with the optimal relationship. The showcase is applied in China, where there has been a significant amount of criticism regarding the current building climate zoning due to the perceived coarseness of the system and where there has been an active exploration into the possibility of redefining building climate zoning with a view toward improving its accuracy and effectiveness.

Modified basalt fiber filled in constructed wetland-microbial fuel cell: Comparison of performance and microbial impacts under PFASs exposure.

Basalt fiber (BF) with modification of iron (Fe-MBF) and calcium (Ca-MBF) were filled into constructed wetland-microbial fuel cell (CW-MFC) for innovative comparison of improved performance under perfluorooctanoic acid (PFOA) exposure. More enhancement on nitrogen and phosphorus removal was observed by Fe-MBF than Ca-MBF, with significant increase of ammonium (NH4+-N) removal by 3.36-5.66 % (p < 0.05) compared to control, even under PFOA stress. Markedly higher removal efficiency of PFOA by 4.76-8.75 % (p < 0.05) resulted from Fe-MBF, compared to Ca-MBF and control BF groups. Besides, superior electrochemical performance was found in Fe-MBF group, with maximum power density 28.65 % higher than control. Fe-MBF caused higher abundance of dominant microbes on electrodes ranged from phylum to family. Meanwhile, ammonia oxidizing bacteria like Nitrosomonas was more abundant in Fe-MBF group, which was positively correlated to NH4+-N and total nitrogen removal. Some other functional genera involved in denitrification and phosphorus-accumulation were enriched by Fe-MBF on electrodes and MBF carrier, including Dechloromonas, Candidatus_Competibacter, and Pseudomonas. Additionally, there were more biomarkers in Fe-MBF group, like Pseudarcobacter and Acidovorax, conducive to nitrogen and iron cycling. Most functional genes of nitrogen, carbon, and sulfur metabolisms were up-regulated with Fe-MBF filling, causing improvement on nitrogen removal.

Evaluation of CYP2C19 Genetic Variant and Its Lack of Association with Valproic Acid Plasma Concentrations Among Zhuang and Han Schizophrenia Patients in Guangxi.

Purpose: To investigate the CYP2C19 genotype distribution and allelic frequency among the Zhuang and Han schizophrenic populations in Guangxi, examine the correlation between CYP2C19 genetic variants and standardized blood levels of Valproic Acid (VPA) in schizophrenic patients, and evaluate the effects of age, gender, and Body Mass Index (BMI) on standardized VPA blood concentrations. Patients and Methods: Between February and December 2022, 192 Zhuang and Han schizophrenia patients treated with VPA were studied. Steady-state VPA concentrations were determined using homogeneous enzyme immunoassays, and CYP2C19 *1, *2, and *3 loci via q-PCR. CYP2C19 genotype distributions between Zhuang and Han groups in Nanning were compared using chi-square tests and contrasted with other ethnicities. Non-parametric tests analyzed VPA variations, identifying critical factors through multivariate stepwise regression. Results: The study identified five CYP2C19 genotypes at the *2 and *3 loci, with the *3/*3 genotype absent in both cohorts. The CYP2C19 distribution in Guangxi Zhuang and Han mirrors, yet diverges significantly from Hui and Kazakh groups. Among 192 subjects, VPA blood levels remained consistent across metabolic types and ages 18-60 but varied significantly by gender. Multivariate analysis revealed gender and BMI as significant factors, overshadowing CYP2C19 genotype and age. Conclusion: In Guangxi, CYP2C19 genetic variants in Zhuang and Han schizophrenia patients demonstrate statistically indistinguishable allelic and metabolic distributions. Gender and BMI can influence standardized VPA blood concentrations in schizophrenia patients. However, in our study cohort, the CYP2C19 genotype and age are not the primary determinants of standardized VPA blood levels.

Near telomere-to-telomere genome of the model plant Physcomitrium patens.

The model plant Physcomitrium patens has played a pivotal role in enhancing our comprehension of plant evolution and development. However, the current genome harbours numerous regions that remain unfinished and erroneous. To address these issues, we generated an assembly using Oxford Nanopore reads and Hi-C mapping. The assembly incorporates telomeric and centromeric regions, thereby establishing it as a near telomere-to-telomere genome except a region in chromosome 1 that is not fully assembled due to its highly repetitive nature. This near telomere-to-telomere genome resolves the chromosome number at 26 and provides a gap-free genome assembly as well as updated gene models to aid future studies using this model organism.

Challenges of carbon emission reduction by the workshop education pattern.

The COVID-19 pandemic has forced many conferences and educational events to shift from in-person to online, significantly reducing the carbon footprint associated with these activities. Workshops are a common pattern of thematic learning at the university level, usually involving a series of activities, such as gathering, learning, and dining, for participants from different regions. However, unlike a three-day conference, workshops usually last for seven days or more, resulting in a non-negligible carbon footprint. To resolve this challenge, we have developed a model that provides recommendations for minimizing the carbon footprint of workshops. Using data from the DigitalFUTURES International Workshop on architecture education at Tongji University in China, we calculated the carbon footprint of scenarios with varying workshop durations, participation modes, and transportation methods. Our results show that online workshops can reduce the carbon footprint by up to 88% compared to in-person workshops. Hybrid workshops, which combine online and in-person participation, can also lead to significant carbon reductions, with a 46% online participation rate resulting in an 82% reduction in carbon footprint. However, we recommend that in-person participation be maintained to ensure efficient learning and effective communication. Our work provides a sustainable solution for organizing future workshops with a reduced carbon footprint.

A pilot study exploring the association of bronchial bacterial microbiota and recurrent wheezing in infants with atopy.

Background: Differences in bronchial microbiota composition have been found to be associated with asthma; however, it is still unclear whether these findings can be applied to recurrent wheezing in infants especially with aeroallergen sensitization. Objectives: To determine the pathogenesis of atopic wheezing in infants and to identify diagnostic biomarkers, we analyzed the bronchial bacterial microbiota of infants with recurrent wheezing and with or without atopic diseases using a systems biology approach. Methods: Bacterial communities in bronchoalveolar lavage samples from 15 atopic wheezing infants, 15 non-atopic wheezing infants, and 18 foreign body aspiration control infants were characterized using 16S rRNA gene sequencing. The bacterial composition and community-level functions inferred from between-group differences from sequence profiles were analyzed. Results: Both α- and ß-diversity differed significantly between the groups. Compared to non-atopic wheezing infants, atopic wheezing infants showed a significantly higher abundance in two phyla (Deinococcota and unidentified bacteria) and one genus (Haemophilus) and a significantly lower abundance in one phylum (Actinobacteria). The random forest predictive model of 10 genera based on OTU-based features suggested that airway microbiota has diagnostic value for distinguishing atopic wheezing infants from non-atopic wheezing infants. PICRUSt2 based on KEGG hierarchy (level 3) revealed that atopic wheezing-associated differences in predicted bacterial functions included cytoskeleton proteins, glutamatergic synapses, and porphyrin and chlorophyll metabolism pathways. Conclusion: The differential candidate biomarkers identified by microbiome analysis in our work may have reference value for the diagnosis of wheezing in infants with atopy. To confirm that, airway microbiome combined with metabolomics analysis should be further investigated in the future.

The heterogeneous driving forces behind carbon emissions change in 30 selective emerging economies.

Emerging economies are predicted to be future emission hotspots due to expected levels of urbanization and industrialization, and their CO2 emissions are receiving more scrutiny. However, the driving forces underlying dynamic change in emissions are poorly understood, despite their crucial role in developing targeted mitigating pathways. We firstly compile energy-related emissions of 30 selective emerging economies from 2010 to 2018. Then, three growth patterns of emissions in these economies have been identified through emission data, which imply different low-carbon pathways. Most emerging economies saw an increase of varying degrees in emissions, driven by economic growth and partly offset by better energy efficiency and improvements in energy mixes. Furthermore, the industrial structure was another factor that slowed emissions, especially in Latin America and the Caribbean. Our research contributes to the heterogeneous exploration of CO2 emissions produced by energy among sectors and the creation of low-carbon development pathways in emerging economies.

Vision transformer-based weakly supervised histopathological image analysis of primary brain tumors.

Diagnosis of primary brain tumors relies heavily on histopathology. Although various computational pathology methods have been developed for automated diagnosis of primary brain tumors, they usually require neuropathologists' annotation of region of interests or selection of image patches on whole-slide images (WSI). We developed an end-to-end Vision Transformer (ViT) - based deep learning architecture for brain tumor WSI analysis, yielding a highly interpretable deep-learning model, ViT-WSI. Based on the principle of weakly supervised machine learning, ViT-WSI accomplishes the task of major primary brain tumor type and subtype classification. Using a systematic gradient-based attribution analysis procedure, ViT-WSI can discover diagnostic histopathological features for primary brain tumors. Furthermore, we demonstrated that ViT-WSI has high predictive power of inferring the status of three diagnostic glioma molecular markers, IDH1 mutation, p53 mutation, and MGMT methylation, directly from H&E-stained histopathological images, with patient level AUC scores of 0.960, 0.874, and 0.845, respectively.

Induction of activation of the antioxidant response element and stabilization of Nrf2 by 3-(3-pyridylmethylidene)-2-indolinone (PMID) confers protection against oxidative stress-induced cell death.

The antioxidant response elements (ARE) are a cis-acting enhancer sequence located in regulatory regions of antioxidant and detoxifying genes. Nuclear factor (erythroid-derived 2)-like 2 (Nrf2) is a member of the Cap 'n' Collar family of transcription factors that binds to the ARE and regulates the transcription of specific ARE-containing genes. Under oxidative stress, Nrf2/ARE induction is fundamental to defense against reactive oxygen species (ROS) and serves as a key factor in the protection against toxic xenobiotics. 3-(3-Pyridylmethylidene)-2-Indolinone (PMID) is a derivative of 2-indolinone compounds which act as protein kinase inhibitors and show anti-tumor activity. However, the role of PMID in the oxidative stress remains unknown. In the present study, we showed that PMID induced the activation of ARE-mediated transcription, increased the DNA-binding activity of Nrf2 and then up-regulated the expression of antioxidant genes such as HO-1, SOD, and NQO1. The level of Nrf2 protein was increased in cells treated with PMID by a post-transcriptional mechanism. Under CHX treatment, the stability of Nrf2 protein was enhanced by PMID with decreased turnover rate. We showed that PMID reduced the ubiquitination of Nrf2 and disrupted the Cullin3 (Cul3)-Keap1 interaction. Furthermore, cells treated with PMID showed resistance to cytotoxicity by H(2)O(2) and pro-oxidant 6-OHDA. PMID also up-regulated the antioxidant level in BALB/c mice. Taken together, the compound PMID induces the ARE-mediated gene expression through stabilization of Nrf2 protein and activation of Nrf2/ARE pathway and protects against oxidative stress-mediated cell death.

Study on Collision Detection Techniques for the Informed Design of Natural Views in Healthcare Environments.

BACKGROUND: Natural views are an important design strategy for the application of ecological resources in built environments. Numerous clinical studies have indicated that views of nature-for example, plants-can effectively promote patient recovery by relieving their postoperative pains and negative emotions during hospitalization. AIMS: This study demonstrates an intelligent method that develops algorithms of using collision detection techniques in Building Information Modeling to evaluate outdoor plant visibility for patients. METHODS: These algorithms are digitized into a Revit plug-in program, which can be viewed as a design-aided tool for architects with the purpose of informing healthcare environment design in the decision-making process. RESULTS: Its acceptability and effectiveness are evaluated based on the consultations in beta tests. CONCLUSIONS: It is believed that this method can improve the work efficiency of evaluating natural views in wards and help architects implement an informed design of built environments for better health performance. All findings in this study can contribute to the development of computational intelligence and social sustainability in the near future.

Pulmonary lymphomatoid granulomatosis in a 4-year-old girl: A case report.

BACKGROUND: Pulmonary lymphomatoid granulomatosis (PLG) is a lymphoproliferative disease associated with Epstein-Barr viral infection occurring mainly in adults and rarely in children. It is characterized by multiple pulmonary nodules. Its diagnosis depends on lung biopsy findings. Most patients are immunodeficient, and it commonly presents in children undergoing chemotherapy for leukemia. We report the case of a child with PLG caused by a mutation in the macrophage-expressed gene 1 (MPEG1), suggesting possible PLG occurrence in children undergoing treatment for pulmonary nodular lesions. CASE SUMMARY: This study reports a case of PLG without apparent immunodeficiency, suggesting the possibility of this disease occurrence during the treatment of pulmonary nodular lesions in children. Initially, the cause was assumed to be an atypical pathogen. Following conventional anti-infective treatment, chest computed tomography findings revealed that there were still multiple nodules in the lungs. Additionally, the patient was found to be infected with the Epstein-Barr virus. Histopathological examination of the resected lung revealed lymphoproliferative lesions with necrosis. Small lymphocytes, plasma cells, and histiocytes were observed in the background, although Reed-Sternberg cells were absent. Immunohistochemical staining [CD20(+), CD30(+), and CD3(+)] and EBV-encoded small RNA1/2 in situ hybridization of small lymphocytes revealed approximately 200 cells/high-power field. Whole exon sequencing of the patient revealed a mutation in the MPEG1. The patient was eventually diagnosed with PLG and transferred to the Department of Pediatric Oncology for bone marrow transplantation. CONCLUSION: As PLG is rare and fatal, it should be suspected in clinical settings when treatment of initial diagnosis is ineffective.

Bacterial composition and colony structure of the lower respiratory tract in infants and children with recurrent wheezing: a case-control study.

BACKGROUND: The bacterial load of the human lower respiratory tract is at least several times lower than that of the other parts of the body. This study aimed to identify the bacterial composition and colony structure of the lower respiratory tract in infants and children with recurrent wheezing compared with those of children with a bronchial foreign body and clarify whether the length of wheezing in infants can contribute to differences in the lower respiratory tract's bacterial colony structure. METHODS: We collected specimens of alveolar lavage fluid from 48 infants and children who underwent fiberoptic bronchoscopy and were divided into groups: A1 (multiple wheezing: wheezing more than three times in < 1 month), A2 (persistent wheezing: > 1 month), and B (bronchial foreign body; control group). We analyzed the bacterial community structure of alveolar lavage fluid using high-throughput sequencing. The richness and diversity of the microbial communities were assessed by α and ß diversity analyses. RESULTS: A total of 6,644 operational taxonomic units (OTUs) were obtained based on the Illumina Nova sequencing platform and clustered according to those that met the 97% identity threshold, followed by species annotation of the OTU sequences. In the annotation results, 2,608 (39.25%) OTUs were annotated at the genus level. At the genus level, Sphingomonas and Phyllobacterium were significantly higher in group A1 than in group B. There were significantly more Phyllobacterium in group A2 than in group B. Prevotella, Neisseria, and Haemophilus were higher in group B than in groups A1 and A2. The differences in the between-group α and ß diversity analyses were statistically significant. The microbial diversity in groups A1 and A2 was significantly less than that in group B, but there was no statistical difference in bacterial community diversity between groups A1 and A2. CONCLUSION: Recurrent wheezing in infants and children is more likely due to alterations in the overall bacterial microecology and disruption of host respiration and immune homeostasis than the effects of a single bacterium.

New biomarker: the gene HLA-DRA associated with low-grade glioma prognosis.

BACKGROUND: Low-grade gliomas (LGG) are WHO grade II tumors presenting as the most common primary malignant brain tumors in adults. Currently, LGG treatment involves either or a combination of surgery, radiation therapy, and chemotherapy. Despite the knowledge of constitutive genetic risk factors contributing to gliomas, the role of single genes as diagnostic and prognostic biomarkers is limited. The aim of the current study is to discover the predictive and prognostic genetic markers for LGG. METHODS: Transcriptome data and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. We first performed the tumor microenvironment (TME) survival analysis using the Kaplan-Meier method. An analysis was undertaken to screen for differentially expressed genes. The function of these genes was studied by Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Following which a protein-protein interaction network (PPI) was constructed and visualized. Univariate and multivariate COX analyses were performed to obtain the probable prognostic genes. The key genes were selected by an intersection of core and prognostic genes. A clinical correlation analysis of single-gene expression was undertaken. GSEA enrichment analysis was performed to identify the function of key genes. Finally, a single gene-related correlation analysis was performed to identify the core immune cells involved in the development of LGG. RESULTS: A total of 529 transcriptome data and 515 clinical samples were obtained from the TCGA. Immune cells and stromal cells were found to be significantly increased in the LGG microenvironment. The top five core genes intersected with the top 38 prognostically relevant genes and two key genes were identified. Our analysis revealed that a high expression of HLA-DRA was associated with a poor prognosis of LGG. Correlation analysis of immune cells showed that HLA-DRA expression level was related to immune infiltration, positively related to macrophage M1 phenotype, and negatively related to activation of NK cells. CONCLUSIONS: HLA-DRA may be an independent prognostic indicator and an important biomarker for diagnosing and predicting survival in LGG patients. It may also be associated with the immune infiltration phenotype in LGG.

Effect of Different Adjuvants on Immune Responses Elicited by Protein-Based Subunit Vaccines against SARS-CoV-2 and Its Delta Variant.

The global pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become more serious because of the continuous emergence of variants of concern (VOC), thus calling for the development of broad-spectrum vaccines with greater efficacy. Adjuvants play important roles in enhancing the immunogenicity of protein-based subunit vaccines. In this study, we compared the effect of three adjuvants, including aluminum, nanoparticle manganese and MF59, on the immunogenicity of three protein-based COVID-19 vaccine candidates, including RBD-Fc, RBD and S-trimer. We found that the nanoparticle manganese adjuvant elicited the highest titers of SARS-CoV-2 RBD-specific IgG, IgG1 and IgG2a, as well as neutralizing antibodies against infection by pseudotyped SARS-CoV-2 and its Delta variant. What is more, the nanoparticle manganese adjuvant effectively reduced the viral load of the authentic SARS-CoV-2 and Delta variant in the cell culture supernatants. These results suggest that nanoparticle manganese, known to facilitate cGAS-STING activation, is an optimal adjuvant for protein-based COVID-19 subunit vaccines.