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08; 95% CI 0.02-0.32). The remembered intensity of pain experienced during the previous half marathon affected the current pain experience directly (
< 0.05) or indirectly (
) by generating pain-related expectancy (bootstrapped point estimate = 0.11; 95% CI 0.01-0.46). The cognitive variables did not influence the memory of pain unpleasantness.
The memory of pain induced by sports activity may change due to cognitive factors; however, further research is needed to investigate their role in shaping the memory of the sensory and affective dimensions of pain.
The memory of pain induced by sports activity may change due to cognitive factors; however, further research is needed to investigate their role in shaping the memory of the sensory and affective dimensions of pain.
Cardiovascular complications are a major cause of death and disability in patients with diabetes mellitus, but how such complications arise is unclear.
Weighted gene correlation network analysis (WGCNA) was performed on gene expression profiles from healthy controls, individuals with diabetes mellitus, and individuals with diabetes mellitus-associated coronary artery disease (DMCAD). Phenotypically related module genes were analyzed for enrichment in Gene Ontology (GO) terms and Kyoto Gene and Genome Encyclopedia (KEGG) pathways. Predicted biological functions were validated using gene set enrichment analysis (GSEA) and ClueGo analysis. Based on the TRRUST v2 database and hypergeometric tests, a global network was built to identify transcription factors (TFs) and downstream target genes potentially involved in DMCAD.
WGCNA identified three modules associated with progression from diabetes mellitus to DMCAD. The module genes were significantly involved in biological processes related to interferon and viral infection, while GSEA of DMCAD samples suggested involvement in viral myocarditis, chemokine signaling and phagosomes. RUNX1 was identified as a potential TF regulating these module genes. Analysis of the global regulatory network of TFs and their targets suggested that CCL3 may be a key regulator in DMCAD.
We found bioinformatic evidence that CCL3 may be a key regulator and RUNX1 a key TF in DMCAD.
We found bioinformatic evidence that CCL3 may be a key regulator and RUNX1 a key TF in DMCAD.This study set out to evaluate quality control within a new in vitro fertilization (IVF) laboratory environment and of new incubators based on the culture results of tripronuclear zygotes. The representative environmental indicators within new and old IVF laboratories were monitored, and tripronuclear zygotes were cultured in the two laboratories; the results were analyzed and compared. Subsequently, tripronuclear (3PN) zygotes were cultured in both new and old incubators and the culture results were compared. No differences were found in embryo development between 3PN zygotes in the old and new laboratories. However, in the quality control test, the degeneration rate and developmental arrest rate in the new incubator early phase group were significantly increased when compared with the old incubators. Moreover, the grade I embryo rate also decreased significantly. Nevertheless, all the above comparisons in the new incubator later phase group showed no statistical significance as compared to those observed in old incubators. Tripronuclear zygotes are sensitive to the environment in IVF laboratories and can be considered useful during quality control trials of new IVF laboratories and new equipment including incubators.
To select variables associated with new-onset postoperative atrial fibrillation (POAF) following isolated coronary artery bypass grafting (CABG) and develop a nomogram for risk prediction in a Chinese population.
The study retrospectively enrolled 4854 consecutive patients undergoing isolated CABG from February 2018 to September 2019, they were divided into derivation cohort and validation cohort with a 31 ratio according to the order of operation date. GSK343 In the derivation cohort, significant variables were selected by use of the multivariate logistic backward stepwise regression analysis and a nomogram model was built on the strength of the results. The model performance was assessed in terms of discrimination and calibration. Besides, we compared the discriminative ability for POAF of the nomogram with established prediction models (CHA2DS2-VASc and HATCH scores) in the two cohorts.
POAF occurred in 1025 (28.2%) out of 3641 patients in the derivation cohort, and in 337 (27.8%) out of 1213 patients in thcy for predicting the risk of POAF following isolated CABG, which might help clinicians predict individual probability of POAF and achieve effective prophylaxis.
Gelsolin (GSN) is the most widely expressed actin-severing protein in humans, which could regulate cell morphology, differentiation, movement and apoptosis. This study aims to explore the GSN as a prognostic biomarker of stomach adenocarcinoma (STAD).
In this study, we used several online databases to comprehensively analyze the role of GSN in STAD. Oncomine and HPA databases were used to explore the GSN expression in various cancer, especially in gastric cancer. Then, UALCAN database was used to evaluate the relationship between GSN expression and promoter methylation in clinical characteristics. Finally, we used TIMER to analyze the correlation between GSN expression and immune infiltrates in gastric cancer.
GSN was down-regulated in gastric cancer, and decreased expression of GSN was related to worse survival. The GSN expression was significantly related to tumor purity in STAD and significantly correlated with infiltrating level of various immune cells, especially the dendritic cells.
Our study proposes that GSN can be served as the biomarker of disease and neoantigen for STAD treatment, which can improve the deficiency of disease-specific targeted therapies currently exist.
Our study proposes that GSN can be served as the biomarker of disease and neoantigen for STAD treatment, which can improve the deficiency of disease-specific targeted therapies currently exist.
To identify the molecular subtypes of glioblastoma multiforme (GBM) related to M2 macrophage-based prognostic genes, then to preliminarily explore their biological functions and construct immunotherapy response gene models.
We used R language to analyze GBM microarray data, and other tools, including xCell and CIBERSORTx, to identify subtypes of GBM that related to M2 macrophages. The process started with the exploration of biological functions of the two subtypes by pathway analyses and GSEA, and continued with a combined procedure of constructing an M2 macrophage-related prognostic gene model and exploring the immune treatment response for GBM.
A high abundance of M2 macrophages in GBM was associated with poor prognosis. According to M2 macrophage-related prognostic genes, GBM was divided into two subtypes (cluster A and cluster B). The differential gene enrichment analysis of the two clusters showed that cluster A was less enriched in M2 macrophages and had immunopotential. The M2score, which was constructed based on M2 macrophage-related prognostic genes, was not only related to the survival and prognosis of patients with GBM, but also predictive of the effectiveness of immunotherapy in these patients. This result has been effectively verified in an external data set.
GBM was successfully divided into two subtypes according to M2-macrophage-related prognostic genes. In GBM, a high M2score may indicate better clinical outcome and enhancement of the immunotherapy response.
GBM was successfully divided into two subtypes according to M2-macrophage-related prognostic genes. In GBM, a high M2score may indicate better clinical outcome and enhancement of the immunotherapy response.
Keloid is a pathological scar type, which invades normal surrounding tissue without self-limiting to cause pain, itching, cosmetic disfigurement, etc. Knowledge of the molecular mechanisms underlying keloid remains unclear. This dilemma leads to no biomarker available for diagnosis. Thus, to seek accurate diagnosis, biomarkers are necessary for keloid diagnosis to help control its incidence.
Gene Expression Omnibus (GEO) database was used to select differentially expressed miRNAs (DE-miRNAs) in GSE113620. miRTarBase miRNA-target tools were used to predict the interactions between miRNAs and their target mRNAs. Target mRNAs that were differentially expressed in keloid were selected by analyzing differentially expressed genes (DEGs) in GSE44270 and GSE92566. PPI network analysis, gene enrichment analysis, cell-specific and tissue-specific expression analyses of DE-target mRNAs were conducted. RT-PCR analysis was conducted to validate our results.
Three novel miRNAs (miR-30b-5p, miR-212-3p, miR-149-5p) and five target mRNAs (
) were identified as potential biomarkers for keloid patients. Additionally, the potential functions of those miRNAs-mRNAs pathways were analyzed.
These findings of keloid-related miRNAs, mRNAs, and miRNA-mRNAs regulatory networks may provide insights into the underlying pathogenesis of keloid and serve as potential biomarkers for keloid diagnosis.
These findings of keloid-related miRNAs, mRNAs, and miRNA-mRNAs regulatory networks may provide insights into the underlying pathogenesis of keloid and serve as potential biomarkers for keloid diagnosis.
As hepatocellular carcinoma (HCC) having the second-highest mortality rate globally, the early diagnosis and prognosis of HCC have always been the focus of various studies. Although PSME4 has been reported to be closely related to several malignancies, its role in HCC remains unclear.
The TCGA-LIHC database and HCC tissues were used to explore the expression of PSME4 in HCC. Gene set enrichment analysis (GSEA) was used to forecast the biological behavior of HCC cells that PSME4 might be involved in regulation. In addition, CCK-8, colony formation and flow cytometry assays were used to explore the effect of PSME4 on HCC cells. Furthermore, the underlying PSME4-related signaling pathways in HCC were further confirmed using GSEA.
We found that the expression of PSME4 in HCC tissues was significantly higher than that in adjacent normal tissues, and patients with high PSME4 expression have a poor prognosis. CCK-8, colony formation and flow cytometry assays shown that knockdown of PSME4 inhibits HCC cell proliferation of HCC cells, promotes cell apoptosis and moves the cell cycle away from the S phase. Mechanistically, PSME4 may promote the development of HCC through mTOR signaling pathway.
The high expression of PSME4 in HCC promotes the proliferation of HCC cells via the mTOR signalling pathway. Therefore, PSME4 is an emerging tumour marker for the early diagnosis and prognosis of HCC.
The high expression of PSME4 in HCC promotes the proliferation of HCC cells via the mTOR signalling pathway. Therefore, PSME4 is an emerging tumour marker for the early diagnosis and prognosis of HCC.
To investigate the liver function indexes and dynamic changes in patients with different clinical types of new coronavirus pneumonia (COVID-19).
A retrospective analysis of 170 COVID-19 patients hospitalized in the Wuxi Fifth People's Hospital was divided into asymptomatic group (13 cases), mild-common group (142 cases) and seriously-critically ill group (15 cases), the clinical data and liver function indexes of the three groups were compared.
A total of 170 patients included 94 males and 76 females, with an average age of 44.7 ± 17.8 years. Seriously-critically ill group was older, and the proportion of patients with diabetes and liver injury at admission was also higher. As the hospitalization time increased, the changes of alanine aminotransferase (ALT) levels in asymptomatic group and mild-common group were not significant (all P > 0.05), while the ALT levels of seriously-critically ill group showed a curve that first flattened and then decreased (degree of freedom 1.809, P = 0.002). Compared with the mild-common group, the daily decrease of ALT was 1.
