GLORIA

GEOMAR Library Ocean Research Information Access

X

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • Frontiers Media SA  (9)
  • 2020-2024  (9)
  • 2022  (9)
Material
Publisher
  • Frontiers Media SA  (9)
Person/Organisation
Language
Years
  • 2020-2024  (9)
Year
  • 2022  (9)
  • 1
    Online Resource
    Online Resource
    Table_1.csv
    Frontiers Media SA ; 2022
    In:  Frontiers in Immunology Vol. 13 ( 2022-9-14)
    Details
    In: Frontiers in Immunology, Frontiers Media SA, Vol. 13 ( 2022-9-14)
    Abstract: Dysregulation of immune cell infiltration in the tumor microenvironment contributes to the progression of osteosarcoma (OS). In the present study, we explored genes related to immune cell infiltration and constructed a risk model to predict the prognosis of and guide therapeutic strategies for OS. The gene expression profile of OS was obtained from TARGET and Gene Expression Omnibus, which were set as the discovery and verification cohorts. CIBERSORT and Kaplan survival analyses were used to analyze the effects of immune cells on the overall survival rates of OS in the discovery cohort. Differentially expressed gene (DEG) analysis and protein–protein interaction (PPI) networks were used to analyze genes associated with immune cell infiltration. Cox regression analysis was used to select key genes to construct a risk model that classified OS tissues into high- and low-risk groups. The prognostic value of the risk model for survival and metastasis was analyzed by Kaplan–Meier survival analyses, receiver operating characteristic curves, and immunohistochemical experiments. Immunological characteristics and response effects of immune checkpoint blockade (ICB) therapy in OS tissues were analyzed using the ESTIMATE and Tumor Immune Dysfunction and Exclusion algorithms, while sensitivity for both targeted and chemotherapy drugs was analyzed using the OncoPredict algorithm. It was demonstrated that the high infiltration of resting dendritic cells in OS tissues was associated with poor prognosis. A total of 225 DEGs were found between the high- and low-infiltration groups of OS tissues, while 94 genes interacted with others. Through COX analyses, among these 94 genes, four genes (including AOC3, CDK6, COL22A1, and RNASE6) were used to construct a risk model. This risk model showed a remarkable prognostic value for survival rates and metastasis in both the discovery and verification cohorts. Even though a high microsatellite instability score was observed in the high-risk group, the ICB response in the high-risk group was poor. Furthermore, using OncoPredict, we found that the high-risk group OS tissues were resistant to seven drugs and sensitive to 25 drugs. Therefore, our study indicates that the resting dendritic cell signature constructed by AOC3, CDK6, COL22A1, and RNASE6 may contribute to predicting osteosarcoma prognosis and thus therapy guidance.
    Type of Medium: Online Resource
    ISSN: 1664-3224
    URL: Article
    URL: Article
    DOI: 10.3389/fimmu.2022.1017120
    DOI: 10.3389/fimmu.2022.1017120.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2606827-8
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 2
    Online Resource
    Online Resource
    An application based on bioinformatics and machine learning for risk prediction of sepsis at first clinical presentation using transcriptomic data
    Frontiers Media SA ; 2022
    In:  Frontiers in Genetics Vol. 13 ( 2022-9-2)
    Details
    In: Frontiers in Genetics, Frontiers Media SA, Vol. 13 ( 2022-9-2)
    Abstract: Background: Linking genotypic changes to phenotypic traits based on machine learning methods has various challenges. In this study, we developed a workflow based on bioinformatics and machine learning methods using transcriptomic data for sepsis obtained at the first clinical presentation for predicting the risk of sepsis. By combining bioinformatics with machine learning methods, we have attempted to overcome current challenges in predicting disease risk using transcriptomic data. Methods: High-throughput sequencing transcriptomic data processing and gene annotation were performed using R software. Machine learning models were constructed, and model performance was evaluated by machine learning methods in Python. The models were visualized and interpreted using the Shapley Additive explanation (SHAP) method. Results: Based on the preset parameters and using recursive feature elimination implemented via machine learning, the top 10 optimal genes were screened for the establishment of the machine learning models. In a comparison of model performance, CatBoost was selected as the optimal model. We explored the significance of each gene in the model and the interaction between each gene through SHAP analysis. Conclusion: The combination of CatBoost and SHAP may serve as the best-performing machine learning model for predicting transcriptomic and sepsis risks. The workflow outlined may provide a new approach and direction in exploring the mechanisms associated with genes and sepsis risk.
    Type of Medium: Online Resource
    ISSN: 1664-8021
    URL: Article
    DOI: 10.3389/fgene.2022.979529
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2606823-0
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 3
    Online Resource
    Online Resource
    Data_Sheet_1.pdf
    Frontiers Media SA ; 2022
    In:  Frontiers in Neurology Vol. 13 ( 2022-7-12)
    Details
    In: Frontiers in Neurology, Frontiers Media SA, Vol. 13 ( 2022-7-12)
    Abstract: The objective of this study was to investigate the association between previous stroke and the risk of severe coronavirus disease 2019 (COVID-19). Methods We included 164 (61.8 ± 13.6 years) patients with COVID-19 in a retrospective study. We evaluated the unadjusted and adjusted associations between previous stroke and severe COVID-19, using a Cox regression model. We conducted an overall review of systematic review and meta-analysis to investigate the relationship of previous stroke with the unfavorable COVID-19 outcomes. Results The rate of severe COVID-19 in patients with previous stroke was 28.37 per 1,000 patient days (95% confidence interval [CI]: 10.65–75.59), compared to 3.94 per 1,000 patient days (95% CI: 2.66–5.82) in those without previous stroke ( p & lt; 0.001). Previous stroke was significantly associated with severe COVID-19 using a Cox regression model (unadjusted [hazard ratio, HR]: 6.98, 95% CI: 2.42–20.16, p & lt; 0.001; adjusted HR [per additional 10 years]: 4.62, 95% CI: 1.52–14.04, p = 0.007). An overall review of systematic review and meta-analysis showed that previous stroke was significantly associated with severe COVID-19, mortality, need for intensive care unit admission, use of mechanical ventilation, and an unfavorable composite outcome. Conclusion Previous stroke seems to influence the course of COVID-19 infection; such patients are at high risk of severe COVID-19 and might benefit from early hospital treatment measures and preventive strategies.
    Type of Medium: Online Resource
    ISSN: 1664-2295
    URL: Article
    URL: Article
    DOI: 10.3389/fneur.2022.922936
    DOI: 10.3389/fneur.2022.922936.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2564214-5
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 4
    Online Resource
    Online Resource
    DataSheet1.docx
    Frontiers Media SA ; 2022
    In:  Frontiers in Physiology Vol. 13 ( 2022-10-5)
    Details
    In: Frontiers in Physiology, Frontiers Media SA, Vol. 13 ( 2022-10-5)
    Abstract: Sleep has been related to a variety of health outcomes. However, no association between sleep and asthenozoospermia has been reported. The aim of this study is to first investigate the relationship between sleep status and asthenozoospermia risk. A case-control study, including 540 asthenozoospermia cases and 579 controls, was performed from June 2020 to December 2020 in the infertility clinic from Shengjing Hospital of China Medical University. Data on sleep status were collected by Pittsburgh sleep quality index questionnaires and asthenozoospermia was diagnosed based on the World Health Organization guidelines. Odds ratio (OR) with 95% confidence interval (95% CI) was calculated by logistic regression analysis to assess the aforementioned association. Results of this study demonstrated that compared with total sleep duration of 8–9 h/day, & lt; 8 h/day was related to asthenozoospermia risk (OR: 1.44, 95% CI: 1.05–1.99); compared to good sleep quality, poor sleep quality was associated with asthenozoospermia risk (OR: 1.35; 95% CI: 1.04–1.77). There were multiplicative model interaction effects between sleep quality and tea drinking ( p = 0.04), rotating night shift work ( p & lt; 0.01) on asthenozoospermia risk. However, we failed to detect any associations between night sleep duration, daytime napping duration, night bedtime, wake-up time, sleep pattern and asthenozoospermia risk. In conclusion, short total sleep duration and poor sleep quality might be related to asthenozoospermia risk. Further well-designed prospective studies are warranted to confirm our findings.
    Type of Medium: Online Resource
    ISSN: 1664-042X
    URL: Article
    URL: Article
    DOI: 10.3389/fphys.2022.959009
    DOI: 10.3389/fphys.2022.959009.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2564217-0
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 5
    Online Resource
    Online Resource
    Can Daytime Transcranial Direct Current Stimulation Treatment Change the Sleep Electroencephalogram Complexity of REM Sleep in Depressed Patients? A Double-Blinded, Randomized, Placebo-Controlled Trial
    Frontiers Media SA ; 2022
    In:  Frontiers in Psychiatry Vol. 13 ( 2022-5-18)
    Details
    In: Frontiers in Psychiatry, Frontiers Media SA, Vol. 13 ( 2022-5-18)
    Abstract: The purpose of this study was to determine the effects of daytime transcranial direct current stimulation (tDCS) on sleep electroencephalogram (EEG) in patients with depression. Methods The study was a double-blinded, randomized, controlled clinical trial. A total of 37 patients diagnosed with a major depression were recruited; 19 patients (13 females and 6 males mean age 44.79 ± 15.25 years) received tDCS active stimulation and 18 patients (9 females and 9 males; mean age 43.61 ± 11.89 years) received sham stimulation. Ten sessions of daytime tDCS were administered with the anode over F3 and the cathode over F4. Each session delivered a 2 mA current for 30 min per 10 working days. Hamilton-24 and Montgomery scales were used to assess the severity of depression, and polysomnography (PSG) was used to assess sleep structure and EEG complexity. Eight intrinsic mode functions (IMFs) were computed from each EEG signal in a channel. The sample entropy of the cumulative sum of the IMFs were computed to acquire high-dimensional multi-scale complexity information of EEG signals. Results The complexity of Rapid Eye Movement (REM) EEG signals significantly decreased intrinsic multi-scale entropy (iMSE) (1.732 ± 0.057 vs. 1.605 ± 0.046, P = 0.0004 in the case of the C4 channel, IMF 1:4 and scale 7) after tDCS active stimulation. The complexity of the REM EEG signals significantly increased iMSE (1.464 ± 0.101 vs. 1.611 ± 0.085, P = 0.001 for C4 channel, IMF 1:4 and scale 7) after tDCS sham stimulation. There was no significant difference in the Hamilton-24 ( P = 0.988), Montgomery scale score ( P = 0.726), and sleep structure (N1% P = 0.383; N2% P = 0.716; N3% P = 0.772) between the two groups after treatment. Conclusion Daytime tDCS changed the complexity of sleep in the REM stage, and presented as decreased intrinsic multi-scale entropy, while no changes in sleep structure occurred. This finding indicated that daytime tDCS may be an effective method to improve sleep quality in depressed patients. Trial registration This trial has been registered at the ClinicalTrials.gov (protocol ID: TCHIRB-10409114, in progress).
    Type of Medium: Online Resource
    ISSN: 1664-0640
    URL: Article
    DOI: 10.3389/fpsyt.2022.851908
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2564218-2
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 6
    Online Resource
    Online Resource
    Table_1.DOCX
    Frontiers Media SA ; 2022
    In:  Frontiers in Neurology Vol. 13 ( 2022-5-30)
    Details
    In: Frontiers in Neurology, Frontiers Media SA, Vol. 13 ( 2022-5-30)
    Abstract: This study aims to detect the invisible metabolic abnormality in PET images of patients with anti-leucine-rich glioma-inactivated 1 (LGI1) encephalitis using a multivariate cross-classification method. Methods Participants were divided into two groups, namely, the training cohort and the testing cohort. The training cohort included 17 healthy participants and 17 patients with anti-LGI1 encephalitis whose metabolic abnormality was able to be visibly detected in both the medial temporal lobe and the basal ganglia in their PET images [completely detectable (CD) patients]. The testing cohort included another 16 healthy participants and 16 patients with anti-LGI1 encephalitis whose metabolic abnormality was not able to be visibly detected in the medial temporal lobe and the basal ganglia in their PET images [non-completely detectable (non-CD) patients] . Independent component analysis (ICA) was used to extract features and reduce dimensions. A logistic regression model was constructed to identify the non-CD patients. Results For the testing cohort, the accuracy of classification was 90.63% with 13 out of 16 non-CD patients identified and all healthy participants distinguished from non-CD patients. The patterns of PET signal changes resulting from metabolic abnormalities related to anti-LGI1 encephalitis were similar for CD patients and non-CD patients. Conclusion This study demonstrated that multivariate cross-classification combined with ICA could improve, to some degree, the detection of invisible abnormal metabolism in the PET images of patients with anti-LGI1 encephalitis. More importantly, the invisible metabolic abnormality in the PET images of non-CD patients showed patterns that were similar to those seen in CD patients.
    Type of Medium: Online Resource
    ISSN: 1664-2295
    URL: Article
    URL: Article
    DOI: 10.3389/fneur.2022.812439
    DOI: 10.3389/fneur.2022.812439.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2564214-5
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 7
    Online Resource
    Online Resource
    KRT8 Serves as a Novel Biomarker for LUAD and Promotes Metastasis and EMT via NF-κB Signaling
    Frontiers Media SA ; 2022
    In:  Frontiers in Oncology Vol. 12 ( 2022-5-19)
    Details
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 12 ( 2022-5-19)
    Abstract: Keratin 8 (KRT8) is the major component of the intermediate filament cytoskeleton and aberrant expression in multiple tumors. However, the role of KRT8 in lung adenocarcinoma (LUAD) remains unclear. In the present study, KRT8 expression was found to be upregulated along with prognosis and metastasis in LUAD. Kaplan–Meier analysis presented that the 5-year OS and DSS rates were significantly better among patients with low KRT8 expression compared to those with high expression. Correlation analysis showed that KRT8 expression was significantly associated with gender (P = 0.027), advanced T stage (P = 0.001), advanced N stage (P = 0.048), and advanced pathologic stage (P = 0.025). Univariate Cox analysis demonstrated that KRT8 was a predictor of OS [hazard ratio (HR) = 1.526; 95% confidence interval (CI) 1.141–2.040; P = 0.004] and DSS (HR = 1.625; 95% CI 1.123–2.353; P = 0.010) in the TCGA database. Importantly, downregulation of KRT8 obviously suppressed cell proliferation, cell migration, invasion, and EMT as well as induced cell apoptosis. KRT8 knockdown significantly inhibited NF-κB signaling, suggesting a potential mechanism. Overall, our results indicated that KRT8 could regulate lung carcinogenesis and may serve as a potential target for antineoplastic therapies.
    Type of Medium: Online Resource
    ISSN: 2234-943X
    URL: Article
    DOI: 10.3389/fonc.2022.875146
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2649216-7
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 8
    Online Resource
    Online Resource
    Identification and validation of a potential key gene SGOL1 for poor prognosis in hepatocellular carcinoma based on a bioinformatics approach
    Frontiers Media SA ; 2022
    In:  Frontiers in Oncology Vol. 12 ( 2022-11-9)
    Details
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 12 ( 2022-11-9)
    Abstract: Hepatocellular carcinoma (HCC) is one of the most prevalent types of cancer worldwide. Shugoshin 1 (SGOL1) plays a crucial role in cell mitosis and its aberrant expression level in human tumors has shown to promote chromosomal instability (CIN) and accelerate tumor growth. SGOL1 expression level in HCC cells and tissues, whether it has an influence on HCC patients’ prognosis, and its mechanism of action have not yet been studied. Methods We carried out the bioinformatics analysis of SGOL1 expression level and survival analysis in 8 different malignancies, including HCC. In addition, we analyzed SGOL1 expression level in HCC tissues, as well as HCC patients’ clinical features, enrichment analysis of SGOL1 function and mechanism of action in HCC and tumor immune cells. The effects of SGOL1 expression level and cell viability on HCC were confirmed by in vitro cytological assays. Results It was found that SGOL1 mRNA expression level was significantly higher in several tumor tissues, including HCC, than in corresponding normal tissues, and the elevated SGOL1 expression level was strongly associated with HCC patients’ poor prognosis. It was also revealed that SGOL1 expression level in HCC tissue was positively correlated with disease stage, tumor grade, and tumor size, and the results of multivariate logistic regression analysis showed that SGOL1 was one of the independent influential factors of the prognosis of HCC. Enrichment analysis revealed that SGOL1 expression level in HCC tissue was mainly associated with tumor proliferation, cell cycle, and other factors. The results of the immune infiltration analysis indicated that SGOL1 expression level was associated with immune cell infiltration and immune checkpoints in HCC. In vitro experiments demonstrated the high SGOL1 expression level in HCC tissues and cells, and silencing of SGOL1 resulted in altered cell cycle markers and decreased proliferation, invasion, and migration of HCC cells. Conclusion The findings revealed that SGOL1 is highly expressed in HCC tissues, it is a biomarker of a poor prognosis, which may be related to immune cell infiltration in HCC, and may enhance the proliferation, invasion, and migration of HCC cells. The results may provide new insights into targeted treatment of HCC and improve HCC patients’ prognosis.
    Type of Medium: Online Resource
    ISSN: 2234-943X
    URL: Article
    DOI: 10.3389/fonc.2022.1043161
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2649216-7
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 9
    Online Resource
    Online Resource
    Image_2.tif
    Frontiers Media SA ; 2022
    In:  Frontiers in Immunology Vol. 13 ( 2022-10-6)
    Details
    In: Frontiers in Immunology, Frontiers Media SA, Vol. 13 ( 2022-10-6)
    Abstract: Osteosarcoma is a malignant bone tumor with poor outcomes affecting the adolescents and elderly. In this study, we comprehensively assessed the metabolic characteristics of osteosarcoma patients and constructed a hexosamine biosynthesis pathway (HBP)-based risk score model to predict the prognosis and tumor immune infiltration in patients with osteosarcoma. Methods Gene expression matrices of osteosarcoma were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. GSVA and univariate Cox regression analysis were performed to screen the metabolic features associated with prognoses. LASSO regression analysis was conducted to construct the metabolism-related risk model. Differentially expressed genes (DEGs) were identified and enrichment analysis was performed based on the risk model. CIBERSORT and ESTIMATE algorithms were executed to evaluate the characteristics of tumor immune infiltration. Comparative analyses for immune checkpoints were performed and the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was used to predict immunotherapeutic response. Finally, hub genes with good prognostic value were comprehensive analyzed including drug sensitivity screening and immunohistochemistry (IHC) experiments. Results Through GSVA and survival analysis, the HBP pathway was identified as the significant prognostic related metabolism feature. Five genes in the HBP pathway including GPI, PGM3, UAP1, OGT and MGEA5 were used to construct the HBP-related risk model. Subsequent DEGs and enrichment analyses showed a strong correlation with immunity. Further, CIBERSORT and ESTIMATE algorithms showed differential immune infiltration characteristics correlated with the HBP-related risk model. TIDE algorithms and immune checkpoint analyses suggested poor immunotherapeutic responses with low expression of immune checkpoints in the high-risk group. Further analysis revealed that the UAP1 gene can predict metastasis. IHC experiments suggested that UAP1 expression correlated significantly with the prognosis and metastasis of osteosarcoma patients. When screening for drug sensitivity, high UAP1 expression was suggestive of great sensitivity to antineoplastic drugs including cobimetinib and selumetinib. Conclusion We constructed an HBP-related gene signature containing five key genes (GPI, PGM3, UAP1, OGT, MGEA5) which showed a remarkable prognostic value for predicting prognosis and can guide immunotherapy and targeted therapy for osteosarcoma.
    Type of Medium: Online Resource
    ISSN: 1664-3224
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.3389/fimmu.2022.1028263
    DOI: 10.3389/fimmu.2022.1028263.s001
    DOI: 10.3389/fimmu.2022.1028263.s002
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2606827-8
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...