Targeting BMAL1 reverses drug resistance of acute myeloid leukemia cells and promotes ferroptosis through HMGB1-GPX4 signaling pathway.

PURPOSE: Acute myeloid leukemia (AML) is a refractory hematologic malignancy that poses a serious threat to human health. Exploring alternative therapeutic strategies capable of inducing alternative modes of cell death, such as ferroptosis, holds great promise as a viable and effective intervention. METHODS: We analyzed online database data and collected clinical samples to verify the expression and function of BMAL1 in AML. We conducted experiments on AML cell proliferation, cell cycle, ferroptosis, and chemotherapy resistance by overexpressing/knocking down BMAL1 and using assays such as MDA detection and BODIPY 581/591 C11 staining. We validated the transcriptional regulation of HMGB1 by BMAL1 through ChIP assay, luciferase assay, RNA level detection, and western blotting. Finally, we confirmed the results of our cell experiments at the animal level. RESULTS: BMAL1 up-regulation is an observed phenomenon in AML patients. Furthermore, there existed a strong correlation between elevated levels of BMAL1 expression and inferior prognosis in individuals with AML. We found that knocking down BMAL1 inhibited AML cell growth by blocking the cell cycle. Conversely, overexpressing BMAL1 promoted AML cell proliferation. Moreover, our research results revealed that BMAL1 inhibited ferroptosis in AML cells through BMAL1-HMGB1-GPX4 pathway. Finally, knocking down BMAL1 can enhance the efficacy of certain first-line cancer therapeutic drugs, including venetoclax, dasatinib, and sorafenib. CONCLUSION: Our research results suggest that BMAL1 plays a crucial regulatory role in AML cell proliferation, drug resistance, and ferroptosis. BMAL1 could be a potential important therapeutic target for AML.

A combination of a TLR7/8 agonist and an epigenetic inhibitor suppresses triple-negative breast cancer through triggering anti-tumor immune.

BACKGROUND: Combination therapy involving immune checkpoint blockade (ICB) and other drugs is a potential strategy for converting immune-cold tumors into immune-hot tumors to benefit from immunotherapy. To achieve drug synergy, we developed a homologous cancer cell membrane vesicle (CM)-coated metal-organic framework (MOF) nanodelivery platform for the codelivery of a TLR7/8 agonist with an epigenetic inhibitor. METHODS: A novel biomimetic codelivery system (MCM@UN) was constructed by MOF nanoparticles UiO-66 loading with a bromodomain-containing protein 4 (BRD4) inhibitor and then coated with the membrane vesicles of homologous cancer cells that embedding the 18 C lipid tail of 3M-052 (M). The antitumor immune ability and tumor suppressive effect of MCM@UN were evaluated in a mouse model of triple-negative breast cancer (TNBC) and in vitro. The tumor immune microenvironment was analyzed by multicolor immunofluorescence staining. RESULTS: In vitro and in vivo data showed that MCM@UN specifically targeted to TNBC cells and was superior to the free drug in terms of tumor growth inhibition and antitumor immune activity. In terms of mechanism, MCM@UN blocked BRD4 and PD-L1 to prompt dying tumor cells to disintegrate and expose tumor antigens. The disintegrated tumor cells released damage-associated molecular patterns (DAMPs), recruited dendritic cells (DCs) to efficiently activate CD8+ T cells to mediate effective and long-lasting antitumor immunity. In addition, TLR7/8 agonist on MCM@UN enhanced lymphocytes infiltration and immunogenic cell death and decreased regulatory T-cells (Tregs). On clinical specimens, we found that mature DCs infiltrating tumor tissues of TNBC patients were negatively correlated with the expression of BRD4, which was consistent with the result in animal model. CONCLUSION: MCM@UN specifically targeted to TNBC cells and remodeled tumor immune microenvironment to inhibit malignant behaviors of TNBC.

Machine learning for the early prediction of acute respiratory distress syndrome (ARDS) in patients with sepsis in the ICU based on clinical data.

Background: Acute respiratory distress syndrome (ARDS) is a fatal outcome of severe sepsis. Machine learning models are helpful for accurately predicting ARDS in patients with sepsis at an early stage. Objective: We aim to develop a machine-learning model for predicting ARDS in patients with sepsis in the intensive care unit (ICU). Methods: The initial clinical data of patients with sepsis admitted to the hospital (including population characteristics, clinical diagnosis, complications, and laboratory tests) were used to predict ARDS, and screen out the crucial variables. After comparing eight different algorithms, namely, XG boost, logistic regression, light GBM, random forest, GaussianNB, complement NB, support vector machine (SVM), and K nearest neighbors (KNN), rebuilding a prediction model with the best one. When remodeling with the best algorithm, 10% was randomly selected to test, and the remaining was trained for cross-validation. Using the area under the curve (AUC), sensitivity, accuracy, specificity, positive and negative predictive value, F1 score, kappa value, and clinical decision curve to evaluate the model's performance. Eventually, the application in the model illustrated by the SHAP package. Results: Ten critical features were screened utilizing the lasso method, namely, PaO2/PAO2, A-aDO2, PO2(T), CRP, gender, PO2, RDW, MCH, SG, and chlorine. The prior ranking of variables demonstrated that PaO2/PAO2 was the most significant variable. Among the eight algorithms, the performance of the Gaussian NB algorithm was significantly better than that of the others. After remodeling with the best algorithm, the AUC in the training and validation sets were 0.777 and 0.770, respectively, and the algorithm performed well in the test set (AUC = 0.781, accuracy = 78.6%, sensitivity = 82.4%, F1 score = 0.824). A comparison of the overlap factors with those of previous models revealed that the model we developed performs better. Conclusion: Sepsis-associated ARDS can be accurately predicted early via a machine learning model based on existing clinical data. These findings are helpful for accurate identification and improvement of the prognosis in patients with sepsis-associated ARDS.

Preparation of porous silica materials using a eucalyptus template method and its efficient adsorption of methylene blue.

Methylene blue (MB) is a prevalent pollutant in organic wastewater. For this research, eucalyptus wood was used as a template, into which quartz powder dissolved in NaOH was grown, resulting in a low-cost and efficient porous silica adsorbent material (PSAM). This PSAM successfully replaces expensive materials for MB removal from water. Through the application of Scanning Electron Microscopy (SEM) and Brunauer-Emmett-Teller (BET) analysis, it became evident that PSAM displays a porous slit pore structure characterized by numerous active sites, leading to an impressive maximum specific surface area of 88.05 m²/g. The central objective of this research was to investigate the impact of experimental temperature, initial dye concentration, and pH on the adsorption process. The adsorption kinetics were analyzed using the pseudo-first-order and pseudo-second-order models, as well as the Langmuir model. Remarkably, PSAM exhibited a substantial maximum adsorption capacity of 90.01 mg/g at 293 K, achieving an adsorption rate of over 85% within a mere 10-minute timeframe. The thermodynamic analysis revealed that the adsorption of MB onto PSAM was characterized by spontaneity and accompanied by heat absorption. Fourier Transform Infrared (FTIR) and SEM comparisons of PSAM before and after adsorption indicated that MB adsorption primarily occurred through electrostatic gravitational binding. In comparison to other adsorbents, PSAM exhibited exceptional efficacy in removing MB from water.

Heparin-induced thrombocytopenia associated with low-molecular-weight heparin: clinical feature analysis of cases and pharmacovigilance assessment of the FAERS database.

Background: Unfractionated heparin (UFH) and low-molecular-weight heparin (LMWH) are commonly used anticoagulants for the management of arterial and venous thromboses. However, it is crucial to be aware that LMWH can, in rare cases, lead to a dangerous complication known as heparin-induced thrombocytopenia (HIT). The objective of this study was to evaluate the pharmacovigilance and clinical features of HIT associated with LMWH, as well as identify treatment strategies and risk factors to facilitate prompt management. Methods: We extracted adverse event report data from the FDA Adverse Event Reporting System (FAERS) database for pharmacovigilance assessment. Case reports on LMWH-induced thrombocytopenia dated up to 20 March 2023 were collected for retrospective analysis. Results: Significantly elevated reporting rates of HIT were shown in adverse event (AE) data of LMWHs in the FAERS database, while tinzaparin had a higher proportional reporting ratio (PRR) and reporting odds ratio (ROR) than other LMWHs, indicating a greater likelihood of HIT. Case report analysis indicated that a total of 43 patients showed evidence of LMWH-induced thrombocytopenia with a median onset time of 8 days. Almost half of the events were caused by enoxaparin. LMWHs were mainly prescribed for the treatment of embolism and thromboprophylaxis of joint operation. Patients with a history of diabetes or surgery appeared to be more susceptible to HIT. Clinical symptoms were mostly presented as thrombus, skin lesion, and dyspnea. Almost 90% of the patients experienced a platelet reduction of more than 50% and had a Warkentin 4T score of more than 6, indicating a high likelihood of HIT. In all patients, LMWHs that were determined to be the cause were promptly withdrawn. Following the discontinuation of LMWHs, almost all patients were given alternative anticoagulants and eventually achieved recovery. Conclusion: LMWH-induced thrombocytopenia is rare but serious, with increased risk in patients with diabetes or a surgical history. Prompt recognition and management are crucial for the safe use of LMWHs.

Enhancing Freeze-Thaw Resistance of Alkali-Activated Slag by Metakaolin.

Incorporating metakaolin (MK) into slag to prepare alkali-activated materials can reduce shrinkage and improve the durability of alkali-activated slag (AAS). But its durability under freeze-thaw conditions is unknown. In this paper, the effects of MK content on the freeze-thaw properties of AAS were investigated from the perspective of gel composition and pore solution. The experimental results showed that the addition of MK generates a gel mixture of C-A-S-H and N-A-S-H with a cross-linked structure and decreases the content of bound water and pore water absorption. With the increase of alkali dosage, water absorption decreased to 0.28% and then increased to 0.97%, and the leaching rate of ions was Ca2+ > Al3+ > Na+ > OH-. When the alkali dosage was 8 wt % and the MK content was 30 wt %, the compressive strength loss rate of AAS was 0.58% and the mass loss rate was 0.25% after 50 freeze-thaw cycles.

Bioengineered stem cell membrane functionalized nanoparticles combine anti-inflammatory and antimicrobial properties for sepsis treatment.

BACKGROUND: Sepsis is a syndrome of physiological, pathological and biochemical abnormalities caused by infection. Although the mortality rate is lower than before, many survivors have persistent infection, which means sepsis calls for new treatment. After infection, inflammatory mediators were largely released into the blood, leading to multiple organ dysfunction. Therefore, anti-infection and anti-inflammation are critical issues in sepsis management. RESULTS: Here, we successfully constructed a novel nanometer drug loading system for sepsis management, FZ/MER-AgMOF@Bm. The nanoparticles were modified with LPS-treated bone marrow mesenchymal stem cell (BMSC) membrane, and silver metal organic framework (AgMOF) was used as the nanocore for loading FPS-ZM1 and meropenem which was delivery to the infectious microenvironments (IMEs) to exert dual anti-inflammatory and antibacterial effects. FZ/MER-AgMOF@Bm effectively alleviated excessive inflammatory response and eliminated bacteria. FZ/MER-AgMOF@Bm also played an anti-inflammatory role by promoting the polarization of macrophages to M2. When sepsis induced by cecal ligation and puncture (CLP) challenged mice was treated, FZ/MER-AgMOF@Bm could not only reduce the levels of pro-inflammatory factors and lung injury, but also help to improve hypothermia caused by septic shock and prolong survival time. CONCLUSIONS: Together, the nanoparticles played a role in combined anti-inflammatory and antimicrobial properties, alleviating cytokine storm and protecting vital organ functions, could be a potential new strategy for sepsis management.

Identification of common molecular signatures of SARS-CoV-2 infection and its influence on acute kidney injury and chronic kidney disease.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the main cause of COVID-19, causing hundreds of millions of confirmed cases and more than 18.2 million deaths worldwide. Acute kidney injury (AKI) is a common complication of COVID-19 that leads to an increase in mortality, especially in intensive care unit (ICU) settings, and chronic kidney disease (CKD) is a high risk factor for COVID-19 and its related mortality. However, the underlying molecular mechanisms among AKI, CKD, and COVID-19 are unclear. Therefore, transcriptome analysis was performed to examine common pathways and molecular biomarkers for AKI, CKD, and COVID-19 in an attempt to understand the association of SARS-CoV-2 infection with AKI and CKD. Three RNA-seq datasets (GSE147507, GSE1563, and GSE66494) from the GEO database were used to detect differentially expressed genes (DEGs) for COVID-19 with AKI and CKD to search for shared pathways and candidate targets. A total of 17 common DEGs were confirmed, and their biological functions and signaling pathways were characterized by enrichment analysis. MAPK signaling, the structural pathway of interleukin 1 (IL-1), and the Toll-like receptor pathway appear to be involved in the occurrence of these diseases. Hub genes identified from the protein-protein interaction (PPI) network, including DUSP6, BHLHE40, RASGRP1, and TAB2, are potential therapeutic targets in COVID-19 with AKI and CKD. Common genes and pathways may play pathogenic roles in these three diseases mainly through the activation of immune inflammation. Networks of transcription factor (TF)-gene, miRNA-gene, and gene-disease interactions from the datasets were also constructed, and key gene regulators influencing the progression of these three diseases were further identified among the DEGs. Moreover, new drug targets were predicted based on these common DEGs, and molecular docking and molecular dynamics (MD) simulations were performed. Finally, a diagnostic model of COVID-19 was established based on these common DEGs. Taken together, the molecular and signaling pathways identified in this study may be related to the mechanisms by which SARS-CoV-2 infection affects renal function. These findings are significant for the effective treatment of COVID-19 in patients with kidney diseases.

Machine learning for the prediction of all-cause mortality in patients with sepsis-associated acute kidney injury during hospitalization.

Background: Sepsis-associated acute kidney injury (S-AKI) is considered to be associated with high morbidity and mortality, a commonly accepted model to predict mortality is urged consequently. This study used a machine learning model to identify vital variables associated with mortality in S-AKI patients in the hospital and predict the risk of death in the hospital. We hope that this model can help identify high-risk patients early and reasonably allocate medical resources in the intensive care unit (ICU). Methods: A total of 16,154 S-AKI patients from the Medical Information Mart for Intensive Care IV database were examined as the training set (80%) and the validation set (20%). Variables (129 in total) were collected, including basic patient information, diagnosis, clinical data, and medication records. We developed and validated machine learning models using 11 different algorithms and selected the one that performed the best. Afterward, recursive feature elimination was used to select key variables. Different indicators were used to compare the prediction performance of each model. The SHapley Additive exPlanations package was applied to interpret the best machine learning model in a web tool for clinicians to use. Finally, we collected clinical data of S-AKI patients from two hospitals for external validation. Results: In this study, 15 critical variables were finally selected, namely, urine output, maximum blood urea nitrogen, rate of injection of norepinephrine, maximum anion gap, maximum creatinine, maximum red blood cell volume distribution width, minimum international normalized ratio, maximum heart rate, maximum temperature, maximum respiratory rate, minimum fraction of inspired O2, minimum creatinine, minimum Glasgow Coma Scale, and diagnosis of diabetes and stroke. The categorical boosting algorithm model presented significantly better predictive performance [receiver operating characteristic (ROC): 0.83] than other models [accuracy (ACC): 75%, Youden index: 50%, sensitivity: 75%, specificity: 75%, F1 score: 0.56, positive predictive value (PPV): 44%, and negative predictive value (NPV): 92%]. External validation data from two hospitals in China were also well validated (ROC: 0.75). Conclusions: After selecting 15 crucial variables, a machine learning-based model for predicting the mortality of S-AKI patients was successfully established and the CatBoost model demonstrated best predictive performance.

Autocrine S100B in astrocytes promotes VEGF-dependent inflammation and oxidative stress and causes impaired neuroprotection.

Minimal hepatic encephalopathy (MHE) is strongly associated with neuroinflammation. Nevertheless, the underlying mechanism of the induction of inflammatory response in MHE astrocytes remains not fully understood. In the present study, we investigated the effect and mechanism of S100B, a predominant isoform expressed and released from mature astrocytes, on MHE-like neuropathology in the MHE rat model. We discovered that S100B expressions and autocrine were significantly increased in MHE rat brains and MHE rat brain-derived astrocytes. Furthermore, S100B stimulates VEGF expression via the interaction between TLR2 and RAGE in an autocrine manner. S100B-facilitated VEGF autocrine expression further led to a VEGFR2 and COX-2 interaction, which in turn induced the activation of NFƙB, eventually resulting in inflammation and oxidative stress in MHE astrocytes. MHE astrocytes supported impairment of neuronal survival and growth in a co-culture system. To sum up, a comprehensive understanding of the role of S100B-overexpressed MHE astrocyte in MHE pathogenesis may provide insights into the etiology of MHE.

The role of cuproptosis-related gene in the classification and prognosis of melanoma.

Background: Melanoma, as one of the most aggressive and malignant cancers, ranks first in the lethality rate of skin cancers. Cuproptosis has been shown to paly a role in tumorigenesis, However, the role of cuproptosis in melanoma metastasis are not clear. Studying the correlation beteen the molecular subtypes of cuproptosis-related genes (CRGs) and metastasis of melanoma may provide some guidance for the prognosis of melanoma. Methods: We collected 1085 melanoma samples in The Cancer Genome Atlas(TCGA) and Gene Expression Omnibus(GEO) databases, constructed CRGs molecular subtypes and gene subtypes according to clinical characteristics, and investigated the role of CRGs in melanoma metastasis. We randomly divide the samples into train set and validation set according to the ratio of 1:1. A prognostic model was constructed using data from the train set and then validated on the validation set. We performed tumor microenvironment analysis and drug sensitivity analyses for high and low risk groups based on the outcome of the prognostic model risk score. Finally, we established a metastatic model of melanoma. Results: According to the expression levels of 12 cuproptosis-related genes, we obtained three subtypes of A1, B1, and C1. Among them, C1 subtype had the best survival outcome. Based on the differentially expressed genes shared by A1, B1, and C1 genotypes, we obtained the results of three gene subtypes of A2, B2, and C2. Among them, the B2 group had the best survival outcome. Then, we constructed a prognostic model consisting of 6 key variable genes, which could more accurately predict the 1-, 3-, and 5-year overall survival rates of melanoma patients. Besides, 98 drugs were screened out. Finally, we explored the role of cuproptosis-related genes in melanoma metastasis and established a metastasis model using seven key genes. Conclusions: In conclusion, CRGs play a role in the metastasis and prognosis of melanoma, and also provide new insights into the underlying pathogenesis of melanoma.

Discovering common pathogenetic processes between COVID-19 and sepsis by bioinformatics and system biology approach.

Corona Virus Disease 2019 (COVID-19), an acute respiratory infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has spread rapidly worldwide, resulting in a pandemic with a high mortality rate. In clinical practice, we have noted that many critically ill or critically ill patients with COVID-19 present with typical sepsis-related clinical manifestations, including multiple organ dysfunction syndrome, coagulopathy, and septic shock. In addition, it has been demonstrated that severe COVID-19 has some pathological similarities with sepsis, such as cytokine storm, hypercoagulable state after blood balance is disrupted and neutrophil dysfunction. Considering the parallels between COVID-19 and non-SARS-CoV-2 induced sepsis (hereafter referred to as sepsis), the aim of this study was to analyze the underlying molecular mechanisms between these two diseases by bioinformatics and a systems biology approach, providing new insights into the pathogenesis of COVID-19 and the development of new treatments. Specifically, the gene expression profiles of COVID-19 and sepsis patients were obtained from the Gene Expression Omnibus (GEO) database and compared to extract common differentially expressed genes (DEGs). Subsequently, common DEGs were used to investigate the genetic links between COVID-19 and sepsis. Based on enrichment analysis of common DEGs, many pathways closely related to inflammatory response were observed, such as Cytokine-cytokine receptor interaction pathway and NF-kappa B signaling pathway. In addition, protein-protein interaction networks and gene regulatory networks of common DEGs were constructed, and the analysis results showed that ITGAM may be a potential key biomarker base on regulatory analysis. Furthermore, a disease diagnostic model and risk prediction nomogram for COVID-19 were constructed using machine learning methods. Finally, potential therapeutic agents, including progesterone and emetine, were screened through drug-protein interaction networks and molecular docking simulations. We hope to provide new strategies for future research and treatment related to COVID-19 by elucidating the pathogenesis and genetic mechanisms between COVID-19 and sepsis.

Advancing Prediction of Risk of Intraoperative Massive Blood Transfusion in Liver Transplantation With Machine Learning Models. A Multicenter Retrospective Study.

Background: Liver transplantation surgery is often accompanied by massive blood loss and massive transfusion (MT), while MT can cause many serious complications related to high mortality. Therefore, there is an urgent need for a model that can predict the demand for MT to reduce the waste of blood resources and improve the prognosis of patients. Objective: To develop a model for predicting intraoperative massive blood transfusion in liver transplantation surgery based on machine learning algorithms. Methods: A total of 1,239 patients who underwent liver transplantation surgery in three large grade lll-A general hospitals of China from March 2014 to November 2021 were included and analyzed. A total of 1193 cases were randomly divided into the training set (70%) and test set (30%), and 46 cases were prospectively collected as a validation set. The outcome of this study was an intraoperative massive blood transfusion. A total of 27 candidate risk factors were collected, and recursive feature elimination (RFE) was used to select key features based on the Categorical Boosting (CatBoost) model. A total of ten machine learning models were built, among which the three best performing models and the traditional logistic regression (LR) method were prospectively verified in the validation set. The Area Under the Receiver Operating Characteristic Curve (AUROC) was used for model performance evaluation. The Shapley additive explanation value was applied to explain the complex ensemble learning models. Results: Fifteen key variables were screened out, including age, weight, hemoglobin, platelets, white blood cells count, activated partial thromboplastin time, prothrombin time, thrombin time, direct bilirubin, aspartate aminotransferase, total protein, albumin, globulin, creatinine, urea. Among all algorithms, the predictive performance of the CatBoost model (AUROC: 0.810) was the best. In the prospective validation cohort, LR performed far less well than other algorithms. Conclusion: A prediction model for massive blood transfusion in liver transplantation surgery was successfully established based on the CatBoost algorithm, and a certain degree of generalization verification is carried out in the validation set. The model may be superior to the traditional LR model and other algorithms, and it can more accurately predict the risk of massive blood transfusions and guide clinical decision-making.

Identification and Validation of the Diagnostic Characteristic Genes of Ovarian Cancer by Bioinformatics and Machine Learning.

Background: Ovarian cancer (OC) has a high mortality rate and poses a severe threat to women's health. However, abnormal gene expression underlying the tumorigenesis of OC has not been fully understood. This study aims to identify diagnostic characteristic genes involved in OC by bioinformatics and machine learning. Methods: We utilized five datasets retrieved from the Gene Expression Omnibus (GEO) database, The Cancer Genome Atlas (TCGA) database, and the Genotype-Tissue Expression (GTEx) Project database. GSE12470 and GSE18520 were combined as the training set, and GSE27651 was used as the validation set A. Also, we combined the TCGA database and GTEx database as validation set B. First, in the training set, differentially expressed genes (DEGs) between OC and non-ovarian cancer tissues (nOC) were identified. Next, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO) enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were performed for functional enrichment analysis of these DEGs. Then, two machine learning algorithms, Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were used to get the diagnostic genes. Subsequently, the obtained diagnostic-related DEGs were validated in the validation sets. Then, we used the computational approach (CIBERSORT) to analyze the association between immune cell infiltration and DEGs. Finally, we analyzed the prognostic role of several genes on the KM-plotter website and used the human protein atlas (HPA) online database to analyze the expression of these genes at the protein level. Results: 590 DEGs were identified, including 276 upregulated and 314 downregulated DEGs.The Enrichment analysis results indicated the DEGs were mainly involved in the nuclear division, cell cycle, and IL-17 signaling pathway. Besides, DEGs were also closely related to immune cell infiltration. Finally, we found that BUB1, FOLR1, and PSAT1 have prognostic roles and the protein-level expression of these six genes SFPR1, PSAT1, PDE8B, INAVA and TMEM139 in OC tissue and nOC tissue was consistent with our analysis. Conclusions: We screened nine diagnostic characteristic genes of OC, including SFRP1, PSAT1, BUB1B, FOLR1, ABCB1, PDE8B, INAVA, BUB1, TMEM139. Combining these genes may be useful for OC diagnosis and evaluating immune cell infiltration.

Exploring structural relations among computer self-efficacy, perceived immersion, and intention to use virtual reality training systems.

The use of virtual reality (VR) training systems for education has grown in popularity in recent years. Scholars have reported that self-efficacy and interactivity are important predictors of learning outcomes in virtual learning environments, but little empirical research has been conducted to explain how computer self-efficacy (as a subcategory of self-efficacy) and perceived immersion (as a correlate of interactivity) are connected to the intention to use VR training systems. The present study aims to determine which factors significantly influence behavioral intention when students are exposed to VR training systems via an updated technology acceptance frame by incorporating the constructs of computer self-efficacy and perceived immersion simultaneously. We developed a VR training system regarding circuit connection and a reliable and validated instrument including 9 subscales. The sample data were collected from 124 junior middle school students and 210 senior high school students in two schools located in western China. The samples were further processed into a structural equation model with path analysis and cohort analysis. The results showed that the intention to use VR training systems was indirectly influenced by computer self-efficacy but directly influenced by perceived immersion (ß = 0.451). However, perceived immersion seemed to be influenced mostly by learner interaction (ß = 0.332). Among external variables, learner interaction (ß = 0.149) had the largest total effect on use intention, followed by facilitating conditions (ß = 0.138), computer self-efficacy (ß = 0.104), experimental fidelity (ß = 0.083), and subjective norms (ß = 0.077). The moderating roles of gender differences, grade level, and previous experience in structural relations were also identified. The findings of the present study highlight the ways in which factors and associations are considered in the practical development of VR training systems.

Corrigendum: Machine Learning for the Prediction of Complications in Patients After Mitral Valve Surgery.

[This corrects the article DOI: 10.3389/fcvm.2021.771246.].

[Corrigendum] Anti­fibrotic effect of Sedum sarmentosum Bunge extract in kidneys via the hedgehog signaling pathway.

Subsequently to the publication of the above paper, an interested reader drew to the authors' attention that certain of the data featured in Fig. 1A on p. 740 had already appeared in another publication written by the same authors [Sedum sarmentosum Bunge extract exerts renal anti­fibrotic effects in vivo and in vitro. Bai Y, Lu H, Wu C, Lin C, Liang Y and Chen B. Life Sci: 105, 22­30, 2014]. The authors have been able to re­examine their original data, and realized that certain of the data were misplaced in Fig. 1 in the above paper on account of mishandling their data. The revised version of Fig. 1 in shown on the next page, featuring the corrected data in Fig. 1A for the HE staining SSBE- and Vehicle-UUO experiments, and the Masson staining/SSBE and Vehicle/Sham and UUO experiments (all four data panels), the TGF-ß1 experiments in Fig. 1C (all four data panels) and the four data panels in Fig. 1D. Note that the errors made during the assembly of this figure did not adversely affect the overall conclusions reported in the study. The authors are grateful to the Editor of Molecular Medicine Reports for allowing them the opportunity to publish this corrigendum, and all authors agree to the publication of this corrigendum. Furthermore, they wish to apologize to the readership of the Journal for any inconvenience caused. [Molecular Medicine Reports 16: 737­745, 2017; DOI: 10.3892/mmr.2017.6628].

Corrigendum: Machine Learning for the Prediction of Complications in Patients After Mitral Valve Surgery.

[This corrects the article DOI: 10.3389/fcvm.2021.771246.].

Comprehensive Analysis of Tripterine Anti-Ovarian Cancer Effects Using Weighted Gene Co-Expression Network Analysis and Molecular Docking.

BACKGROUND Ovarian cancer has the highest mortality of gynecological cancers worldwide. The aim of this study was to identify the role of tripterine against ovarian cancer. MATERIAL AND METHODS GSE18520 and GSE12470 data sets were downloaded from the GEO database. WGCNA was used to analyze gene modules and hub genes related to ovarian cancer. These hub genes were intersected with tripterine targets, and GO and KEGG enrichment analyses were performed. HPA and GEPIA determined the expression of tripterine anti-ovarian hub genes in tumor tissues. Kaplan-Meier plotter was used to explore the role of hub genes in ovarian cancer prognosis. AutoDock was used to conduct molecular docking of tripterine and hub genes to observe whether the combination was stable. RESULTS By differential analysis of gene expression and the construction of WGCNA co-expression network, 5 hub genes, ARHGAP11A, MUC1, HBB, RUNX1T1, and FUT8, were screened by module gene screening. Seven biological processes and 20 KEGG-related pathways were obtained by gene enrichment. The expression of tripterine anti-ovarian hub genes ARHGAP11A, MUC1, and FUT8 were obtained by HPA and GEPIA. Using Kaplan-Meier plotter, the survival of ovarian cancer was negatively correlated with ARHGAP11A, MUC1, and FUT8. Molecular docking showed the combination of tripterine and FUT8 was most stable, having the greatest potential role. CONCLUSIONS Tripterine may be involved in megakaryocyte development and platelet production through potential genes ARHGAP11A, MUC1, HBB, RUNX1T1, and FUT8 and may have an anti-ovarian cancer effect in immune factors signaling, transporting and exchanging oxygen pathways, and autophagy pathways, through these 5 key genes.