Search

Abstract Objective It is well known that glucose and lipid metabolism disorders and insulin resistance are common in sepsis, which affect the occurrence and prognosis of multiple organ dysfunction in septic patients. Previous study reported the predictive value of triglyceride-glucose index (TyG), a clinical indicator for insulin resistance, in postoperative delirium patients. However, it remains unclear whether the TyG index is a novel predictive biomarker for sepsis-associated delirium. The aim of this study is to explore the relationship between TyG index and the risk of delirium in patients with sepsis. Methods Adult septic patients were identified from the MIMIC-IV database and divided into four groups based on the mean value of TyG. The primary outcome was the incidence of delirium. The association between TyG and the risk of developing delirium was evaluated by restricted cubic spline (RCS), multivariate logistic regression and subgroup analysis. Propensity Score Matching (PSM) method was used to balance the baseline data. Results A total of 3,331 septic patients were included in the analysis, and further divided into four groups: Q1 (TyG ≤ 8.67), Q2 (8.67 9.61). The RCS curves demonstrated a non-linear positive relationship between TyG index and the risk of developing delirium, and an optimal cut-of value 9.09 was recommended. After balancing the baseline information by PSM, patients in the TyG > 9.09 group had a significant higher incidence of delirium compared with those in the TyG ≤ 9.09 group. In logistic regression analysis, TyG > 9.09 was significantly associated with lower risk of developing delirium in both original cohort (OR 1.54–1.78, all P 0.05). In subgroup analysis, our findings were consistent (all OR > 1 in all subgroups). Conclusion Our study demonstrated an independent association between TyG index and increased risk of delirium in septic patients, indicating that TyG index can serve as a biomarker for delirium in sepsis.

Abstract Background There is no evidence to determine the association between the lactate dehydrogenase to albumin ratio (LAR) and the development of sepsis-associated acute kidney injury (SAKI). We aimed to investigate the predictive impact of LAR for SAKI in patients with sepsis. Methods A total of 4,087 patients with sepsis from the Medical Information Mart for Intensive Care IV (MIMIC IV) database were included. Logistic regression analysis was used to identify the association between LAR and the risk of developing SAKI, and the relationship was visualized using restricted cubic spline (RCS). The clinical predictive value of LAR was evaluated by ROC curve analysis. Subgroup analysis was used to search for interactive factors. Results The LAR level was markedly increased in the SAKI group (p

ObjectiveTo investigate the effect of different single and combined pre-admission antihypertensive drug regimens on the prognosis of critically ill patients.MethodsWe performed a retrospective cohort study using data from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. All initial ICU admission records of patients with hypertension and previous antihypertensive exposure before ICU admission were included. Our primary outcome was 90-day mortality. Propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) were used to balance the distribution of baseline characteristics. Logistic regression analysis and subgroup analysis were performed to determine the independent effect of different single and combined antihypertensive drug regimens on 90-day mortality.ResultsA total of 13,142 patients were included in the final analysis. The 90-day mortality rate in the combined groups is lower than that in the single therapy group (10.94% vs 11.12%), but no statistical significance was found in the original cohort (p = 0.742). After adjustment for potential confounders, the significantly decreased 90-day mortality rate was found in the combined groups (10.78% vs 12.65%, p = 0.004 in PSM; 10.34% vs 11.90%, p = 0.007). Patients who were exposed to either ACEIs or ARBs had a better prognosis than those not exposed (7.19% vs 17.08%, p < 0.001 in single antihypertensive groups; 8.14% vs18.91%, p < 0.001 in combined antihypertensive groups). The results keep robustness in the PSM and IPTW cohorts. In the logistic regression model analysis, combined therapy was associated with a 12%–20% reduced risk of 90-day death after adjusting potential confounders (OR 0.80–0.88, all p < 0.05), while exposure to ACEIs or ARBs was associated with the decreased risk of 90-day death by 52%–62% (OR 0.38–0.48, all p < 0.001) and 40%–62% (OR 0.38–0.60, all p < 0.001) in the single and combined therapy groups, respectively. The results were still robust to subgroup analysis.ConclusionsPre-admission combined antihypertensive therapy is associated with a significantly lower risk of death than exposure to single antihypertensives in critically ill patients. Meanwhile, either ACEIs or ARBs seem to be the optimal candidates for both single and combined therapy. Further high-quality trials are needed to confirm our findings.

Ferroptosis has been observed to play an important role during erythrocyte differentiation (ED). However, the biological gene markers and ferroptosis mechanisms in ED remain unknown. We downloaded the datasets of ED in human umbilical cord blood-derived CD34+ cells from the Gene Expression Omnibus database. Using median differentiation time, the sample was categorized into long and short groups. The differentially expressed ferroptosis-related genes (DE-FRGs) were screened using differential expression analysis. The enrichment analyses and a protein–protein interaction (PPI) network were conducted. To predict the ED stage, a logistic regression model was constructed using the least absolute shrinkage and selection operator (LASSO). Overall, 22 DE-FRGs were identified. Ferroptosis-related pathways were enriched using Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes. Gene Set Enrichment Analysis and Gene Set Variation Analysis revealed the primary involvement of DE-FRGs in JAK-STAT, MAPK, PI3K-AKT-mTORC1, WNT, and NOTCH signaling pathways. Ten-hub DE-FRGs were obtained using PPI analysis. Furthermore, we constructed mRNA-microRNA (miRNA) and mRNA-transcription factor networks. Immune cell infiltration levels differed significantly during ED. LASSO regression analysis established a signature using six DE-FRGs (ATF3, CDH2, CHAC1, DDR2, DPP4, and GDF15) related to the ED stage. Bioinformatic analyses identified ferroptosis-associated genes during ED, which were further validated. Overall, we identified ferroptosis-related genes to predict their correlations in ED. Exploring the underlying mechanisms of ferroptosis may help us better understand pathophysiological changes in ED and provide new evidence for clinical transformation.

Massive Open Online Courses (MOOCs) have gained popularity in the technology-enhanced learning (TEL) domain. To enhance the learning experience in MOOCs, educational recommender systems (ERSs) can play a crucial role by suggesting courses or learning materials that align with students' knowledge states. Thereby, understanding a student's learning needs and predicting knowledge concepts that the student might be interested in are important to provide effective recommendations. Inspired by the superior ability of knowledge graphs (KGs) in modeling the heterogeneous data in MOOCs and Graph Neural Networks (GNNs) in learning on graph-structured data, few works focusing on GNN-based recommendation of knowledge concepts in MOOCs have emerged recently. However, existing approaches in this domain have limitations mainly related to complexity, semantics, and transparency. To address these limitations, in this paper we propose ConceptGCN, an end-to-end framework that combines KGs, Graph Convolutional Networks (GCNs), and pre-trained transformer language model encoders (SBERT) to provide personalized and transparent recommendations of knowledge concepts in the MOOC platform CourseMapper. We conducted extensive offline experiments and an online user study (N=31), demonstrating the benefits of the ConceptGCN-based recommendation approach, in terms of several important user-centric aspects including accuracy, novelty, diversity, usefulness, overall satisfaction, use intentions, and reading intention. In particular, our results indicate that, if SBERT is used for the initial embeddings of items in the KG, a self-connection operation and a semantic similarity-based score function in the aggregation operation of GCN are not necessarily needed.

ObjectiveWe aimed to investigate the association between serum phosphate levels and the risk for developing sepsis associated acute kidney injury (SAKI).MethodsSeptic patients from the Medical Information Mart for Intensive Care IV (MIMIC IV) and the eICU Collaborative Research Database (eICU-CRD) were enrolled. Restricted cubic spline (RCS) was used to visualize the relationship between phosphate levels and the risk of SAKI. Patients were divided into four categories based on their serum phosphate levels. Logistic regression analysis, receiver operating characteristic (ROC) curve and subgroup analysis were performed to evaluate the predictive value of serum phosphate for SAKI.ResultsA total of 9,244 and 2,124 patients from the MIMIC IV and eICU-CRD database were included in the final analysis. RCS curve revealed a non-linear correlation between phosphate levels and the risk of SAKI (p for non-linearity

BackgroundThe causal association between gut microbiota (GM) and the development of diabetic nephropathy (DN) remains uncertain. We sought to explore this potential association using two-sample Mendelian randomization (MR) analysis.MethodsGenome-wide association study (GWAS) data for GM were obtained from the MiBioGen consortium. GWAS data for DN and related phenotypes were collected from the FinngenR9 and CKDGen databases. The inverse variance weighted (IVW) model was used as the primary analysis model, supplemented by various sensitivity analyses. Heterogeneity was assessed using Cochran’s Q test, while horizontal pleiotropy was evaluated through MR-Egger regression and the MR-PRESSO global test. Reverse MR analysis was conducted to identify any reverse causal effects.ResultsOur analysis identified twenty-five bacterial taxa that have a causal association with DN and its related phenotypes (p < 0.05). Among them, only the g_Eubacterium_coprostanoligenes_group showed a significant causal association with type 1 DN (p < Bonferroni-adjusted p-value). Our findings remained consistent regardless of the analytical approach used, with all methods indicating the same direction of effect. No evidence of heterogeneity or horizontal pleiotropy was observed. Reverse MR analysis did not reveal any causal associations.ConclusionsThis study established a causal association between specific GM and DN. Our findings contribute to current understanding of the role of GM in the development of DN, offering potential insights for the prevention and treatment strategies for this condition.

Conclusion: AZD8055 is more effective than rapamycin in inhibiting proliferation and promoting mitochondrial clearance in erythroid differentiation, which might provide us one more therapeutic option other than rapamycin for ineffective erythropoiesis treatment in the future. These findings also provide some preliminary information indicating the roles of mTORC1 and mTORC2 in erythropoiesis, and further studies are necessary to dissect the underlying mechanisms.

Purpose: The aim of present study was to explore the pharmacological mechanisms of Niaoduqing granules on the treatment of podocyte injury in diabetic nephropathy (DN) via network pharmacology and experimental validation.Methods: Active ingredients and related targets of Niaoduqing, as well as related genes of podocyte injury, proteinuria and DN, were obtained from public databases. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction (PPI) network analysis were performed to investigate the potential mechanisms. High glucose (HG) -induced MPC5 cell injury model was treated with the major core active ingredients of Niaoduqing and used to validate the predicted targets and signaling pathways.Results: Totally, 16 potential therapeutic targets were identified by intersecting the targets of Niaoduqing and disease, in which 7 of them were considered as the core targets via PPI network analysis. KEGG enrichment analysis showed that AGE-RAGE signaling pathway was identified as the most crucial signaling pathway. The results of in vitro experiments revealed that the treatment of Niaoduqing active ingredients significantly protected MPC5 cells from HG-induced apoptosis. Moreover, Niaoduqing could significantly attenuate the HG-induced activation of AGE-RAGE signaling pathway, whereas inhibited the over-expression of VEGF-A, ICAM-1, PTGS-2 and ACE in HG-induced MPC5 cells.Conclusion: Niaoduqing might protect against podocyte injury in DN through regulating the activity of AGE/RAGE pathway and expression of multiple genes. Further clinical and animal experimental studies are necessary to confirm present findings.

Background: The molecular mechanisms underlying obstructive sleep apnea (OSA) and its comorbidities may involve mitochondrial dysfunction. However, very little is known about the relationships between mitochondrial dysfunction-related genes and OSA.Methods: Mitochondrial dysfunction-related differentially expressed genes (DEGs) between OSA and control adipose tissue samples were identified using data from the Gene Expression Omnibus database and information on mitochondrial dysfunction-related genes from the GeneCards database. A mitochondrial dysfunction-related signature of diagnostic model was established using least absolute shrinkage and selection operator Cox regression and then verified. Additionally, consensus clustering algorithms were used to conduct an unsupervised cluster analysis. A protein–protein interaction network of the DEGs between the mitochondrial dysfunction-related clusters was constructed using STRING database and the hub genes were identified. Functional analyses, including Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA), were conducted to explore the mechanisms involved in mitochondrial dysfunction in OSA. Immune cell infiltration analyses were conducted using CIBERSORT and single-sample GSEA (ssGSEA).Results: we established mitochondrial dysfunction related four-gene signature of diagnostic model consisted of NPR3, PDIA3, SLPI, ERAP2, and which could easily distinguish between OSA patients and controls. In addition, based on mitochondrial dysfunction-related gene expression, we identified two clusters among all the samples and three clusters among the OSA samples. A total of 10 hub genes were selected from the PPI network of DEGs between the two mitochondrial dysfunction-related clusters. There were correlations between the 10 hub genes and the 4 diagnostic genes. Enrichment analyses suggested that autophagy, inflammation pathways, and immune pathways are crucial in mitochondrial dysfunction in OSA. Plasma cells and M0 and M1 macrophages were significantly different between the OSA and control samples, while several immune cell types, especially T cells (γ/δ T cells, natural killer T cells, regulatory T cells, and type 17 T helper cells), were significantly different among mitochondrial dysfunction-related clusters of OSA samples.Conclusion: A novel mitochondrial dysfunction-related four-gen signature of diagnostic model was built. The genes are potential biomarkers for OSA and may play important roles in the development of OSA complications.

Ferroptosis has been observed to play an important role during erythrocyte differentiation (ED). However, the biological gene markers and ferroptosis mechanisms in ED remain unknown. We downloaded the datasets of ED in human umbilical cord blood-derived CD34+ cells from the Gene Expression Omnibus database. Using median differentiation time, the sample was categorized into long and short groups. The differentially expressed ferroptosis-related genes (DE-FRGs) were screened using differential expression analysis. The enrichment analyses and a protein-protein interaction (PPI) network were conducted. To predict the ED stage, a logistic regression model was constructed using the least absolute shrinkage and selection operator (LASSO). Overall, 22 DE-FRGs were identified. Ferroptosisrelated pathways were enriched using Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes. Gene Set Enrichment Analysis and Gene Set Variation Analysis revealed the primary involvement of DE-FRGs in JAK-STAT, MAPK, PI3K-AKT-mTORC1, WNT, and NOTCH signaling pathways. Ten-hub DE-FRGs were obtained using PPI analysis. Furthermore, we constructed mRNA-microRNA (miRNA) and mRNA-transcription factor networks. Immune cell infiltration levels differed significantly during ED. LASSO regression analysis established a signature using six DE-FRGs (ATF3, CDH2, CHAC1, DDR2, DPP4, and GDF15) related to the ED stage. Bioinformatic analyses identified ferroptosis-associated genes during ED, which were further validated. Overall, we identified ferroptosis-related genes to predict their correlations in ED. Exploring the underlying mechanisms of ferroptosis may help us better understand pathophysiological changes in ED and provide new evidence for clinical transformation. [ABSTRACT FROM AUTHOR]