Introducing a chaotic component in the control system of soil respiration

Chaos theory has been proved to be of great significance in a series of critical applications although, until now, its applications in analyzing soil respiration have not been addressed. This study aims to introduce a chaotic component in the control system of soil respiration and explain control complexity of this nonlinear chaotic system. This also presents a theoretical framework for better understanding chaotic components of soil respiration in arid land. A concept model of processes and mechanisms associated with subterranean CO2 evolution are developed, and dynamics of the chaotic system is characterized as an extended Riccati equation. Controls of soil respiration and kinetics of the chaotic system are interpreted and as a first attempt, control complexity of this nonlinear chaotic system is tackled by introducing a period-regulator in partitioning components of soil respiration

Luteolin inhibits GPVI-mediated platelet activation, oxidative stress, and thrombosis

Introduction: Luteolin inhibits platelet activation and thrombus formation, but the mechanisms are unclear. This study investigated the effects of luteolin on GPVI-mediated platelet activation in vitro and explored the effect of luteolin on thrombosis, coagulation, and platelet production in vivo.Methods: Washed human platelets were used for aggregation, membrane protein expression, ATP, Ca2+, and LDH release, platelet adhesion/spreading, and clot retraction experiments. Washed human platelets were used to detect collagen and convulxin-induced reactive oxygen species production and endogenous antioxidant effects. C57BL/6 male mice were used for ferric chloride-induced mesenteric thrombosis, collagen-epinephrine induced acute pulmonary embolism, tail bleeding, coagulation function, and luteolin toxicity experiments. The interaction between luteolin and GPVI was analyzed using solid phase binding assay and surface plasmon resonance (SPR).Results: Luteolin inhibited collagen- and convulxin-mediated platelet aggregation, adhesion, and release. Luteolin inhibited collagen- and convulxin-induced platelet ROS production and increased platelet endogenous antioxidant capacity. Luteolin reduced convulxin-induced activation of ITAM and MAPK signaling molecules. Molecular docking simulation showed that luteolin forms hydrogen bonds with GPVI. The solid phase binding assay showed that luteolin inhibited the interaction between collagen and GPVI. Surface plasmon resonance showed that luteolin bonded GPVI. Luteolin inhibited integrin αIIbβ3-mediated platelet activation. Luteolin inhibited mesenteric artery thrombosis and collagen- adrenergic-induced pulmonary thrombosis in mice. Luteolin decreased oxidative stress in vivo. Luteolin did not affect coagulation, hemostasis, or platelet production in mice.Discussion: Luteolin may be an effective and safe antiplatelet agent target for GPVI. A new mechanism (decreased oxidative stress) for the anti-platelet activity of luteolin has been identified

The Expanded Vermiculite Was Quickly Prepared by the Catalytic Action of Manganese Dioxide on Hydrogen Peroxide and Its Adsorption Properties to Cd

The structure and activity of vermiculite can be maintained by expanding vermiculite (Vrm) with hydrogen peroxide. However, it is time-consuming. In past studies, little attention has been paid to the catalytic properties of manganese dioxide on hydrogen peroxide to improve the swelling efficiency of vermiculite. In this experiment, this catalytic effect was utilized to swell Vrm in a short time. The samples were then used to adsorb Cd from the solution. Through a series of characterization tests. The results showed that the exothermic rate was 1960.42–2089.164 J/min and the total exothermic heat was 39,208.4–41,783.28 J when expanding 10 gVrm, which could have a good expansion effect. The expansion was completed in about 40 min. Compared with Vrm, the adsorption of Cd is enhanced by about 30%. It is consistent with the proposed secondary kinetic adsorption model. This study provides a new perspective and theoretical guidance for improving the efficiency of Vrm stripping by hydrogen peroxide. A kind of expanded Vrm with better Cd adsorption efficiency was also prepared

Serum ferritin and neutrophil-to-lymphocyte ratio predict all-cause mortality in patients receiving maintenance hemodialysis: a prospective study

Introduction: Maintenance hemodialysis is an effective treatment for end-stage renal disease patients. A critical factor contributing to the deterioration and death of maintenance hemodialysis patients is inflammation. Therefore, we focused on two inflammatory markers, serum ferritin and neutrophil-to-lymphocyte ratio, to speculate whether they could predict the prognosis of maintenance hemodialysis patients.Patients and methods: We followed 168 patients with maintenance hemodialysis from July 2019 to July 2022 with the endpoint of all-cause death or follow-up completion. Receiver operating characteristic curves were plotted to assess the values of serum ferritin, neutrophil-to-lymphocyte ratio and serum ferritin combined with neutrophil-to-lymphocyte ratio to predict the outcomes of maintenance hemodialysis patients. Kaplan-Meier survival curves were constructed to compare survival rates over time.Results: Receiver operating characteristic curves demonstrated that the best cut-off value of serum ferritin for predicting the prognosis of maintenance hemodialysis patients was 346.05 μg/L, and that of neutrophil-to-lymphocyte ratio was 3.225. Furthermore, a combination of both had a more excellent predicting value than either index (p < 0.05). Kaplan-Meier survival curve analyses revealed that low serum ferritin levels and low neutrophil-to-lymphocyte ratio had a higher probability of survival than high ferritin levels and high neutrophil-to-lymphocyte ratio, separately.Conclusion: Elevated serum ferritin and neutrophil-to-lymphocyte ratio are closely related to all-cause mortality among maintenance hemodialysis patients, for which they may be predictors of all-cause mortality. Additionally, the combination of the two has a much higher predictor value for the prognosis of maintenance hemodialysis patients

Interpretable prediction of 3-year all-cause mortality in patients with chronic heart failure based on machine learning

Abstract Background The goal of this study was to assess the effectiveness of machine learning models and create an interpretable machine learning model that adequately explained 3-year all-cause mortality in patients with chronic heart failure. Methods The data in this paper were selected from patients with chronic heart failure who were hospitalized at the First Affiliated Hospital of Kunming Medical University, from 2017 to 2019 with cardiac function class III-IV. The dataset was explored using six different machine learning models, including logistic regression, naive Bayes, random forest classifier, extreme gradient boost, K-nearest neighbor, and decision tree. Finally, interpretable methods based on machine learning, such as SHAP value, permutation importance, and partial dependence plots, were used to estimate the 3-year all-cause mortality risk and produce individual interpretations of the model's conclusions. Result In this paper, random forest was identified as the optimal aools lgorithm for this dataset. We also incorporated relevant machine learning interpretable tand techniques to improve disease prognosis, including permutation importance, PDP plots and SHAP values for analysis. From this study, we can see that the number of hospitalizations, age, glomerular filtration rate, BNP, NYHA cardiac function classification, lymphocyte absolute value, serum albumin, hemoglobin, total cholesterol, pulmonary artery systolic pressure and so on were important for providing an optimal risk assessment and were important predictive factors of chronic heart failure. Conclusion The machine learning-based cardiovascular risk models could be used to accurately assess and stratify the 3-year risk of all-cause mortality among CHF patients. Machine learning in combination with permutation importance, PDP plots, and the SHAP value could offer a clear explanation of individual risk prediction and give doctors an intuitive knowledge of the functions of important model components

Integrated analysis and validation of ferroptosis-related genes and immune infiltration in acute myocardial infarction

Abstract Background Acute myocardial infarction (AMI) is indeed a significant cause of mortality and morbidity in individuals with coronary heart disease. Ferroptosis, an iron-dependent cell death, is characterized by the accumulation of intracellular lipid peroxides, which is implicated in cardiomyocyte injury. This study aims to identify biomarkers that are indicative of ferroptosis in the context of AMI, and to examine their potential roles in immune infiltration. Methods Firstly, the GSE59867 dataset was used to identify differentially expressed ferroptosis-related genes (DE-FRGs) in AMI. We then performed gene ontology (GO) and functional enrichment analysis on these DE-FRGs. Secondly, we analyzed the GSE76591 dataset and used bioinformatic methods to build ceRNA networks. Thirdly, we identified hub genes in protein–protein interaction (PPI) network. After obtaining the key DE-FRGs through the junction of hub genes with ceRNA and least absolute shrinkage and selection operator (LASSO). ImmucellAI was applied to estimate the immune cell infiltration in each sample and examine the relationship between key DE-FRGs and 24 immunocyte subsets. The diagnostic performance of these genes was further evaluated using the receiver operating characteristic (ROC) curve analysis. Ultimately, we identified an immune-related ceRNA regulatory axis linked to ferroptosis in AMI. Results Among 56 DE-FRGs identified in AMI, 41 of them were integrated into the construction of competitive endogenous RNA (ceRNA) networks. TLR4 and PIK3CA were identified as key DE-FRGs and PIK3CA was confirmed as a diagnostic biomarker for AMI. Moreover, CD4_native cells, nTreg cells, Th2 cells, Th17 cells, central-memory cells, effector-memory cells, and CD8_T cells had higher infiltrates in AMI samples compared to control samples. In contrast, exhausted cells, iTreg cells, and Tfh cells had lower infiltrates in AMI samples. Spearman analysis confirmed the correlation between 24 immune cells and PIK3CA/TLR4. Ultimately, we constructed an immune-related regulatory axis involving XIST and OIP5-AS1/miR-216a/PIK3CA. Conclusion Our comprehensive analysis has identified PIK3CA as a robust and promising biomarker for this condition. Moreover, we have also identified an immune-related regulatory axis involving XIST and OIP5-AS1/miR-216a/PIK3CA, which may play a key role in regulating ferroptosis during AMI progression

New developments in non-exosomal and exosomal ncRNAs in coronary artery disease. Supplementary material.

Supplementary figures 1 and 2    Supplementary material Table S1. Findings of miRNAs in coronary artery disease    Supplementary material Table S2. Findings of lncRNAs in coronary artery disease    Supplementary material Table S3. Findings of circRNAs in coronary artery disease    Supplementary material Table S4. Findings of exosomal-derived ncRNAs in coronary artery disease</p

DataSheet2_Integrated bioinformatics analysis for novel miRNAs markers and ceRNA network in diabetic retinopathy.xlsx

In order to seek a more outstanding diagnosis and treatment of diabetic retinopathy (DR), we predicted the miRNA biomarkers of DR and explored the pathological mechanism of DR through bioinformatics analysis.Method: Based on public omics data and databases, we investigated ncRNA (non-coding RNA) functions based on the ceRNA hypothesis.Result: Among differentially expressed miRNAs (DE-miRNAs), hsa-miR-1179, -4797-3p and -665 may be diagnosis biomarkers of DR. Functional enrichment analysis revealed differentially expressed mRNAs (DE-mRNAs) enriched in mitochondrial transport, cellular respiration and energy derivation. 18 tissue/organ-specific expressed genes, 10 hub genes and gene cluster modules were identified. The ceRNA networks lncRNA FBXL19-AS1/miR-378f/MRPL39 and lncRNA UBL7-AS1/miR-378f/MRPL39 might be potential RNA regulatory pathways in DR.Conclusion: Differentially expressed hsa-miR-1179, -4797-3p and -665 can be used as powerful markers for DR diagnosis, and the ceRNA network: lncRNA FBXL19-AS1/UBL7-AS1-miR-378f-MRPL39 may represent an important regulatory role in DR progression.</p