Advancements and challenges in pharmacokinetic and pharmacodynamic research on the traditional Chinese medicine saponins: a comprehensive review.

Recent research on traditional Chinese medicine (TCM) saponin pharmacokinetics has revealed transformative breakthroughs and challenges. The multicomponent nature of TCM makes it difficult to select representative indicators for pharmacokinetic studies. The clinical application of saponins is limited by their low bioavailability and short half-life, resulting in fluctuating plasma concentrations. Future directions should focus on novel saponin compounds utilizing colon-specific delivery and osmotic pump systems to enhance oral bioavailability. Optimizing drug combinations, such as ginsenosides with aspirin, shows therapeutic potential. Rigorous clinical validation is essential for practical applications. This review emphasizes a transformative era in saponin research, highlighting the need for clinical validation. TCM saponin pharmacokinetics, guided by traditional principles, are in development, utilizing multidisciplinary approaches for a comprehensive understanding. This research provides a theoretical basis for new clinical drugs and supports rational clinical medication.

Machine learning for predicting intrahospital mortality in ST-elevation myocardial infarction patients with type 2 diabetes mellitus.

In an era of increasing need for precision medicine, machine learning has shown promise in making accurate acute myocardial infarction outcome predictions. The accurate assessment of high-risk patients is a crucial component of clinical practice. Type 2 diabetes mellitus (T2DM) complicates ST-segment elevation myocardial infarction (STEMI), and currently, there is no practical method for predicting or monitoring patient prognosis. The objective of the study was to compare the ability of machine learning models to predict in-hospital mortality among STEMI patients with T2DM. We compared six machine learning models, including random forest (RF), CatBoost classifier (CatBoost), naive Bayes (NB), extreme gradient boosting (XGBoost), gradient boosting classifier (GBC), and logistic regression (LR), with the Global Registry of Acute Coronary Events (GRACE) risk score. From January 2016 to January 2020, we enrolled patients aged > 18 years with STEMI and T2DM at the Affiliated Hospital of Zunyi Medical University. Overall, 438 patients were enrolled in the study [median age, 62 years; male, 312 (71%); death, 42 (9.5%]). All patients underwent emergency percutaneous coronary intervention (PCI), and 306 patients with STEMI who underwent PCI were enrolled as the training cohort. Six machine learning algorithms were used to establish the best-fit risk model. An additional 132 patients were recruited as a test cohort to validate the model. The ability of the GRACE score and six algorithm models to predict in-hospital mortality was evaluated. Seven models, including the GRACE risk model, showed an area under the curve (AUC) between 0.73 and 0.91. Among all models, with an accuracy of 0.93, AUC of 0.92, precision of 0.79, and F1 value of 0.57, the CatBoost model demonstrated the best predictive performance. A machine learning algorithm, such as the CatBoost model, may prove clinically beneficial and assist clinicians in tailoring precise management of STEMI patients and predicting in-hospital mortality complicated by T2DM.

A novel prognostic N7-methylguanosine-related long non-coding RNA signature in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is regulated by methylation modifications and long noncoding RNAs (lncRNAs). However, knowledge of N7-methylguanosine (m7G)-related lncRNAs that predict ccRCC prognosis remains insufficient. A prognostic multi-lncRNA signature was created using LASSO regression to examine the differential expression of m7G-related lncRNAs in ccRCC. Furthermore, we performed Kaplan-Meier analysis and area under the curve (AUC) analysis for diagnosis. In all, a model based on five lncRNAs was developed. Principal component analysis (PCA) indicated that the risk model precisely separated the patients into different groups. The IC50 value for drug sensitivity divided patients into two risk groups. High-risk group of patients was more susceptible to A.443654, A.770041, ABT.888, AMG.706, and AZ628. Moreover, a lower tumor mutation burden combined with low-risk scores was associated with a better prognosis of ccRCC. Quantitative real-time polymerase chain reaction (qRT-PCR) exhibited that the expression levels of LINC01507, AC093278.2 were very high in all five ccRCC cell lines, AC084876.1 was upregulated in all ccRCC cell lines except 786-O, and the levels of AL118508.1 and DUXAP8 were upregulated in the Caki-1 cell line. This risk model may be promising for the clinical prediction of prognosis and immunotherapeutic responses in patients with ccRCC.

Association of LDL-C level with neoatherosclerosis and plaque vulnerability in patients with late restenosis: an optical coherence tomography study.

Neoatherosclerosis (NA) is a significant contributor to late stent failure; however, predictors of late in-stent restenosis (ISR) with NA have not been systematically reported. This study aimed to identify predictors of NA incidence and plaque vulnerability in patients with late ISR and the role of low-density lipoprotein cholesterol (LDL-C) levels in this process. A total of 216 patients with 216 lesions who underwent optical coherence tomography (OCT) before interventional procedure for late drug-eluting stent ISR were enrolled and divided into NA and non-NA groups based on OCT findings. Results showed that higher LDL-C levels were associated with NA, thin-cap fibroatheroma (TCFA), intimal disruption, plaque erosion, and thrombosis. Multivariate regression analysis revealed that the LDL-C level was an independent risk factor for NA and TCFA. The LDL-C levels exhibited a significant predictive value for NA and TCFA, surpassing other factors such as stent age and other lipid types. In conclusion, a high LDL-C level is an independent predictor of NA incidence and plaque vulnerability in patients with late ISR.

Machine learning-based construction of a ferroptosis and necroptosis associated lncRNA signature for predicting prognosis and immunotherapy response in hepatocellular cancer.

Introduction: Liver hepatocellular carcinoma (LIHC), one of the most common malignancies worldwide, occurs with high incidence and mortality. Ferroptosis and necroptosis are critically associated with LIHC prognosis. Some long non-coding RNAs (lncRNAs) have been found to induce ferroptosis and necroptosis in hepatocellular carcinoma cells. Methods: Cox regression analysis was used to construct a risk model for LIHC based on differentially expressed ferroptosis and necroptosis related lncRNAs (F-NLRs), and their expression in SMMC7721, HepG2 and WRL68 cells was detected by qPCR. Results: Five F-NLRs were associated with LIHC prognosis, including KDM4A-AS1, ZFPM2-AS1, AC099850.3, MKLN1-AS, and BACE1-AS. Kaplan-Meier survival analysis indicated that patients with LIHC in the high-risk group were associated with poor prognosis. The combined F-NLR signature model demonstrated a prognostic AUC value of 0.789 and was more accurate than standard clinical variables for predicting LIHC prognosis. T cell functions and immunotherapy responses differed significantly between patients in the low- and high-risk groups. Additionally, immune checkpoints and m6A-related genes were differentially expressed between patients in the two risk groups. Furthermore, proteins encoded by the five F-NLRs were overexpressed in four liver cancer cell lines compared to that in human liver cell line WRL68. Pan-cancer examination revealed that expression levels of the five F-NLRs differed between most common tumor types and normal tissues. Conclusion: F-NLRs identified in this study provide a predictive signature representing ferroptosis and necroptosis in LIHC, which correlated well with patient prognosis, clinicopathological characteristics, and immunotherapy responses. The study findings help to elucidate the mechanisms of F-NLRs in LIHC and provide further guidance for the selection and development of immunotherapeutic agents for LIHC.

Serum lipidomics reveals phosphatidylethanolamine and phosphatidylcholine disorders in patients with myocardial infarction and post-myocardial infarction-heart failure.

BACKGROUND: Myocardial infarction (MI) and post-MI-heart failure (pMIHF) are a major cause of death worldwide, however, the underlying mechanisms of pMIHF from MI are not well understood. This study sought to characterize early lipid biomarkers for the development of pMIHF disease. METHODS: Serum samples from 18 MI and 24 pMIHF patients were collected from the Affiliated Hospital of Zunyi Medical University and analyzed using lipidomics with Ultra High Performance Liquid Chromatography and Q-Exactive High Resolution Mass Spectrometer. The serum samples were tested by the official partial least squares discriminant analysis (OPLS-DA) to find the differential expression of metabolites between the two groups. Furthermore, the metabolic biomarkers of pMIHF were screened using the subject operating characteristic (ROC) curve and correlation analysis. RESULTS: The average age of the 18 MI and 24 pMIHF participants was 57.83 ± 9.28 and 64.38 ± 10.89 years, respectively. The B-type natriuretic peptide (BNP) level was 328.5 ± 299.842 and 3535.96 ± 3025 pg/mL, total cholesterol(TC) was 5.59 ± 1.51 and 4.69 ± 1.13 mmol/L, and blood urea nitrogen (BUN) was 5.24 ± 2.15 and 7.20 ± 3.49 mmol/L, respectively. In addition, 88 lipids, including 76 (86.36%) down-regulated lipids, were identified between the patients with MI and pMIHF. ROC analysis showed that phosphatidylethanolamine (PE) (12:1e_22:0) (area under the curve [AUC] = 0.9306) and phosphatidylcholine (PC) (22:4_14:1) (AUC = 0.8380) could be potential biomarkers for the development of pMIHF. Correlation analysis showed that PE (12:1e_22:0) was inversely correlated with BNP and BUN, but positively correlated with TC. In contrast, PC (22:4_14:1) was positively associated with both BNP and BUN, and was negatively associated with TC. CONCLUSIONS: Several lipid biomarkers were identified that could potentially be used to predict and diagnose patients with pMIHF. PE (12:1e_22:0) and PC (22:4_14:1) could sufficiently differentiate between patients with MI and pMIHF.

Identification and Validation of Cuproptosis-Related LncRNA Signatures in the Prognosis and Immunotherapy of Clear Cell Renal Cell Carcinoma Using Machine Learning.

(1) Objective: We aimed to mine cuproptosis-related LncRNAs with prognostic value and construct a corresponding prognostic model using machine learning. External validation of the model was performed in the ICGC database and in multiple renal cancer cell lines via qPCR. (2) Methods: TCGA and ICGC cohorts related to renal clear cell carcinoma were included. GO and KEGG analyses were conducted to determine the biological significance of differentially expressed cuproptosis-related LncRNAs (CRLRs). Machine learning (LASSO), Kaplan-Meier, and Cox analyses were conducted to determine the prognostic genes. The tumor microenvironment and tumor mutation load were further studied. TIDE and IC50 were used to evaluate the response to immunotherapy, a risk model of LncRNAs related to the cuproptosis genes was established, and the ability of this model was verified in an external independent ICGC cohort. LncRNAs were identified in normal HK-2 cells and verified in four renal cell lines via qPCR. (3) Results: We obtained 280 CRLRs and identified 66 LncRNAs included in the TCGA-KIRC cohort. Then, three hub LncRNAs (AC026401.3, FOXD2-AS1, and LASTR), which were over-expressed in the four ccRCC cell lines compared with the human renal cortex proximal tubule epithelial cell line HK-2, were identified. In the ICGC database, the expression of FOXD2-AS1 and LASTR was consistent with the qPCR and TCGA-KIRC. The results also indicated that patients with low-risk ccRCC-stratified by tumor-node metastasis stage, sex, and tumor grade-had significantly better overall survival than those with high-risk ccRCC. The predictive algorithm showed that, according to the three CRLR models, the low-risk group was more sensitive to nine target drugs (A.443654, A.770041, ABT.888, AG.014699, AMG.706, ATRA, AP.24534, axitinib, and AZ628), based on the estimated half-maximal inhibitory concentrations. In contrast, the high-risk group was more sensitive to ABT.263 and AKT inhibitors VIII and AS601245. Using the CRLR models, the correlation between the tumor immune microenvironment and cancer immunotherapy response revealed that high-risk patients are more likely to respond to immunotherapy than low-risk patients. In terms of immune marker levels, there were significant differences between the high- and low-risk groups. A high TMB score in the high-risk CRLR group was associated with worse survival, which could be a prognostic factor for KIRC. (4) Conclusions: This study elucidates the core cuproptosis-related LncRNAs, FOXD2-AS1, AC026401.3, and LASTR, in terms of potential predictive value, immunotherapeutic strategy, and outcome of ccRCC.

Methyltransferase like 3-mediated N6-methylatidin methylation inhibits vascular smooth muscle cells phenotype switching via promoting phosphatidylinositol 3-kinase mRNA decay.

N6-methylatidine (m6A) is involved in post-transcriptional metabolism and a variety of pathological processes. However, little is known about the role of m6A in vascular proliferative diseases, particularly in vascular smooth muscle cells (VSMCs) phenotype switching-induced neointimal hyperplasia. In the current study, we discovered that methyltransferase like 3 (METTL3) is a critical candidate for catalyzing a global increase in m6A in response to carotid artery injury and various VSMCs phenotype switching. The inhibited neointimal hyperplasia was obtained after in vivo gene transfer to knock-down Mettl3. In vitro overexpression of Mettl3 resulted in increased VSMC proliferation, migration, and reduced contractile gene expression with a global elevation of m6A modification. In contrast, Mettl3 knockdown reversed this facilitated phenotypic switch in VSMCs, as demonstrated by downregulated m6A, decreased proliferation, migration, and increased expression of contractile genes. Mechanistically, Mettl3 knock-down was found to promote higher phosphatidylinositol 3-kinase (Pi3k) mRNA decay thus inactivating the PI3K/AKT signal to inhibit VSMCs phenotype switching. Overall, our findings highlight the importance of METTL3-mediated m6A in VSMCs phenotype switching and offer a novel perspective on targeting METTL3 as a therapeutic option for VSMCs phenotype switching modulated pathogenesis, including atherosclerosis and restenosis.

Pharmacological profiles and therapeutic applications of pachymic acid (Review).

Poria cocos is a saprophytic fungus that grows in diverse species of Pinus. Its sclerotium, called fu-ling or hoelen, has been used in various traditional Chinese medicines and health foods for thousands of years, and in several modern proprietary traditional Chinese medicinal products. It has extensive clinical indications, including sedative, diuretic, and tonic effects. Pachymic acid (PA) is the main lanostane-type triterpenoid in Poria cocos. Evidence suggests that PA has various biological properties such as cytotoxic, anti-inflammatory, antihyperglycemic, antiviral, antibacterial, sedative-hypnotic, and anti-ischemia/reperfusion activities. Although considerable advancements have been made, some fundamental and intricate issues remain unclear, such as the underlying mechanisms of PA. The present study aimed to summarize the biological properties and therapeutic potential of PA. The biosynthetic, pharmacokinetic, and metabolic pathways of PA, and its underlying mechanisms were also comprehensively summarized.

A Novel Prognostic Ferroptosis-Related Long Noncoding RNA Signature in Clear Cell Renal Cell Carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common primary malignancy of renal cancer in adults. Ferroptosis is critically associated with the prognosis of ccRCC. However, knowledge of long noncoding RNA- (lncRNA-) related ferroptosis that affects the prognosis of ccRCC is still insufficient. Using the LASSO regression, we created a risk model based on differentially expressed ferroptosis-related lncRNAs (FRLRS) in ccRCC. The analysis of Kaplan-Meier for survival, area under the curve (AUC) for diagnosis, nomogram for predicting overall survival, and gene expression for immune checkpoints were performed based on the screened independent prognostic factors. Nine lncRNAs were found to be associated with ccRCC prognosis. Furthermore, the prognostic AUC of the FRLRS signature was 0.78, demonstrating its usefulness in predicting ccRCC prognosis. The lncRNA risk model outperformed the standard clinical variables in predicting ccRCC prognosis. Finally, The Cancer Genome Atlas revealed that T cell functions, such as cytolytic activity, human leukocyte antigen activity, inflammation regulation, and type II interferon response coordination, are significantly different between two different risk levels of ccRCC. Immune checkpoints were also expressed differently in programmed cell death 1 receptor, inducible T cell costimulator, cytotoxic T-lymphocyte antigen-4, and leukocyte-associated immunoglobulin-like receptor 1. The nine FRLRS signature models may affect the prognosis of ccRCC.

Diagnosis of Lung Cancer by ATR-FTIR Spectroscopy and Chemometrics.

Lung cancer is the leading cause of cancer-related death in the world. Early diagnosis has great significance for the survival of patients with lung cancer. In this paper, attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy combined with chemometrics was used to study the serum samples from patients with lung cancer and healthy people. The results of spectral band area comparison showed that the concentrations of protein, lipid and nucleic acids molecules in serum of patients with lung cancer were increased compared with those in healthy people. The original spectra were preprocessed to improve the accuracy of principal component regression (PCR) and partial least squares-discriminant analysis (PLS-DA) models. PLS-DA results for first derivative spectral data in nucleic acids (1250-1000cm-1) band showed 80% sensitivity, 91.89% specificity and 87.10% accuracy with high R c 2 of 0.8949 and R v 2 of 0.8153, low RMSEC of 0.3136 and RMSEV of 0.4180. It is shown that ATR-FTIR spectroscopy combined with chemometrics might be developed as a simple method for clinical screening and diagnosis of lung cancer.

Nomogram for the Prediction of Intrahospital Mortality Risk of Patients with ST-Segment Elevation Myocardial Infarction Complicated with Hyperuricemia: A Multicenter Retrospective Study.

PURPOSE: This study aimed to establish an accurate and easy predictive model for ST-segment elevation myocardial infarction (STEMI) patients with hyperuricemia, using readily available features to estimate intrahospital mortality risk. PATIENTS AND METHODS: This was a multicenter retrospective study involving the development of risk prediction models for intrahospital mortality among all STEMI patients with hyperuricemia from Zunyi Medical University Chest Pain Center's specialized alliance between January 1, 2016 and June 30, 2020. The primary outcome was intrahospital mortality. A total of 48 candidate variables were considered from demographic and clinical data. The least absolute shrinkage and selection operator (LASSO) was used to develop a nomogram. Concordance index values, decision curve analysis, the area under the curve (AUC), and clinical impact curves were examined. In this study, 489 patients with STEMI were included in the training dataset and an additional 209 patients from the 44 chest pain centers were included in the test cohort. B-type natriuretic peptides, α-hydroxybutyrate dehydrogenase (α-HBDH), cystatin C, out-of-hospital cardiac arrest (OHCA), shock index, and neutrophil-to-lymphocyte ratio were associated with intrahospital mortality and included in the nomogram. RESULTS: The model showed good discrimination power, and the AUC generated to predict survival in the training set was 0.875 (95% confidence interval, 0.825-0.925). In the validation set, the AUC of survival predictions was 0.87 (95% confidence interval, 0.792-0.947). Calibration plots and decision curve analysis showed good model performance in both datasets. A web-based calculator (https://bzxzmu.shinyapps.io/STEMI-with-Hyperuricemia-intrahospital-mortality/) was established based on the nomogram model, which was used to measure the levels of OHCA, neutrophil-to-lymphocyte ratio, shock index, α-HBDH, cystatin C, and B-type natriuretic peptides. CONCLUSION: For practical applications, this model may prove clinically useful for personalized therapy management in patients with STEMI with hyperuricemia.

Development of a machine learning model to predict the risk of late cardiogenic shock in patients with ST-segment elevation myocardial infarction.

BACKGROUND: The in-hospital mortality of patients with ST-segment elevation myocardial infarction (STEMI) increases to more than 50% following a cardiogenic shock (CS) event. This study highlights the need to consider the risk of delayed calculation in developing in-hospital CS risk models. This report compared the performances of multiple machine learning models and established a late-CS risk nomogram for STEMI patients. METHODS: This study used logistic regression (LR) models, least absolute shrinkage and selection operator (LASSO), support vector regression (SVM), and tree-based ensemble machine learning models [light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost)] to predict CS risk in STEMI patients. The models were developed based on 1,598 and 684 STEMI patients in the training and test datasets, respectively. The models were compared based on accuracy, the area under the curve (AUC), recall, precision, and Gini score, and the optimal model was used to develop a late CS risk nomogram. Discrimination, calibration, and the clinical usefulness of the predictive model were assessed using C-index, calibration plotd, and decision curve analyses. RESULTS: A total of 2282 STEMI patients recruited between January 1, 2016 and May 31, 2020, were included in the complete dataset. The linear models built using LASSO and LR showed the highest overall predictive power, with an average accuracy over 0.93 and an AUC above 0.82. With a C-index of 0.811 [95% confidence interval (CI): 0.769-0.853], the LASSO nomogram showed good differentiation and proper calibration. In internal validation tests, a high C-index value of 0.821 was achieved. Decision curve analysis (DCA) and clinical impact curve (CIC) examination showed that compared with the previous score-based models, the LASSO model showed superior clinical relevance. CONCLUSIONS: In this study, five machine learning methods were developed for in-hospital CS prediction. The LASSO model showed the best predictive performance. This nomogram could provide an accurate prognostic prediction for CS risk in patients with STEMI.

Clinical Feature-Based Machine Learning Model for 1-Year Mortality Risk Prediction of ST-Segment Elevation Myocardial Infarction in Patients with Hyperuricemia: A Retrospective Study.

Accurate risk assessment of high-risk patients is essential in clinical practice. However, there is no practical method to predict or monitor the prognosis of patients with ST-segment elevation myocardial infarction (STEMI) complicated by hyperuricemia. We aimed to evaluate the performance of different machine learning models for the prediction of 1-year mortality in STEMI patients with hyperuricemia. We compared five machine learning models (logistic regression, k-nearest neighbor, CatBoost, random forest, and XGBoost) with the traditional global (GRACE) risk score for acute coronary event registrations. We registered patients aged >18 years diagnosed with STEMI and hyperuricemia at the Affiliated Hospital of Zunyi Medical University between January 2016 and January 2020. Overall, 656 patients were enrolled (average age, 62.5 ± 13.6 years; 83.6%, male). All patients underwent emergency percutaneous coronary intervention. We evaluated the performance of five machine learning classifiers and the GRACE risk model in predicting 1-year mortality. The area under the curve (AUC) of the six models, including the GRACE risk model, ranged from 0.75 to 0.88. Among all the models, CatBoost had the highest predictive accuracy (0.89), AUC (0.87), precision (0.84), and F1 value (0.44). After hybrid sampling technique optimization, CatBoost had the highest accuracy (0.96), AUC (0.99), precision (0.95), and F1 value (0.97). Machine learning algorithms, especially the CatBoost model, can accurately predict the mortality associated with STEMI complicated by hyperuricemia after a 1-year follow-up.

Hypoxia-reoxygenation induces macrophage polarization and causes the release of exosomal miR-29a to mediate cardiomyocyte pyroptosis.

To investigate the mechanism by which hypoxia-reoxygenation (HR) mediates macrophage polarization to the M1 phenotype and then mediates cardiomyocyte (CM) pyroptosis through exosome release. Mouse bone marrow macrophages and CMs were cultured in vitro under hypoxia for 12 h and reoxygenation for 6 h to establish an HR cell model. qPCR was used to detect the M1 or M2 macrophage markers IL-1ß, TNF-α, MR, and Arg, and a macrophage and CM coculture system was then established. Macrophages were transfected with an exosome-CD63-red fluorescent protein (RFP) lentivirus, allowing secretion of exosomes expressing RFP, and GW4869 was used to inhibit exosome release by macrophages. qPCR detected miR-29 expression in macrophage-derived exosomes, and macrophages were transfected with miR-29a inhibitors to obtain exosomes with low miR-29a expression (siR-exos). Pyroptosis indicators were detected by Western blot and ELISA. Importantly, LPS induced bone marrow macrophage polarization to the M1 type as a positive control to further verify that these exosomes (LPS-exos) regulated CM pyroptosis by delivering miR29a. Dual luciferase reporter and Western blot assays were adopted to analyze the miR-29a and MCL-1 target relationship. In addition, MCL-1 overexpression was used as a rescue experiment to determine whether miR-29a regulates pyroptosis in CM by targeting MCL-1. Macrophages expressed the M1 macrophage markers IL-1ß and TNF-α after HR exposure. After CM coculture, RFP expression was significantly higher in the HR group than in the normal (Nor) group but significantly reduced in the GW4869 group. Immunofluorescence showed that caspase-1 mRNA and protein expression in the HR group was significantly higher than that in the Nor group (P < 0.05). Caspase-1 expression was significantly decreased in the GW4869 group compared with the HR group (P < 0.05). Western blotting showed that the pyrolysis-related NLRP3 and ASC protein expression levels were significantly upregulated in the HR group compared with the control (Ctr) and Nor groups (P < 0.05). However, GW4869 effectively inhibited pyroptosis-related protein expression (P < 0.05). In addition, ELISA showed that the expression of the inflammation indicators IL-1ß and IL-18 was significantly increased in the HR group compared to the Ctr group (P < 0.05) but decreased in the GW4869 group (P < 0.05). qPCR showed that miR-29a was upregulated in the HR group compared to the Nor group. Moreover, HR-induced exosomes (HR-exos) from macrophages exacerbated HR-induced CM pyroptosis, while inhibition of miR-29a in exosomes partially offset CM pyroptosis induction. LPS-exos promoted pyroptosis-related protein expression, as the IL-1ß and IL-18 concentrations were increased in the LPS-exos group. However, pyroptosis-related proteins were observably decreased, and IL-1ß and IL-18 were also significantly decreased after miR-29a inhibition when compared with that in the HR-exos and LPS-exos groups. Mcl-1 overexpression reversed miR-29a-mediated CM pyroptosis in an HR environment. HR treatment induced macrophage polarization towards the M1 phenotype, which mediated CM pyroptosis through exosomal miR-29a transfer by targeting MCL-1.

[High-salt exposure induces macrophage polarization to promote proliferation and phenotypic transformation of co-cultured renal fibroblasts].

OBJECTIVE: To investigate high-salt exposure-induced polarization of mononuclear macrophages and the changes in proliferation and phenotypic transformation of renal fibroblasts in a co-culture system. METHODS: Cultured mononuclear macrophages were exposed to high salt (161 mmol/L Na +) for 2 h and the surface markers of M0, M1 and M2-type macrophages were detected with RT-qPCR. The culture medium of the macrophages in normal and high-salt groups was collected for detection of the mRNA and protein levels of IL-6 and TGF-ß1 using RT-qPCR and ELISA. A co-culture system of high salt-exposed macrophages and renal fibroblasts (NRK-49F) was established using a Transwell chamber, and the changes in proliferation and migration of NRK-49F cells were examined using EdU assay and Transwell assay, respectively. Western blotting was performed to detect the expressions of collagen I, collagen III and collagen α-SMA in NRK-49F cells. RESULTS: The high salt-exposed macrophages showed significantly increased mRNA levels of M2-type macrophage surface markers mannose receptor and arginase (P < 0.05). The results of EdU and Transwell assays showed that NRK-49F cells co-cultured with high salt-exposed macrophages exhibited significantly increased proliferation and migration ability (P < 0.05). Co-culture with high salt-exposed macrophages resulted in significantly enhanced protein expressions of collagen I, collagen III and α-SMA in NRK-49F cells (P < 0.05) and significantly increased levels of IL-6 and TGF-ß1 in the culture medium (P < 0.05). CONCLUSIONS: High-salt exposure induces polarization of mononuclear macrophages into M2-type macrophages and promotes secretion of IL-6 and TGF-ß1 by the macrophages to induce the proliferation and phenotypic transformation of NRK-49F cells.

[Role of TGF-ß1/ILK/FSP1 signaling pathway in cyclosporin A-induced epithelialmesenchymal transition in cultured renal tubular epithelial cells].

OBJECTIVE: To explore the role of transforming growth factor-ß1/integrin-linked kinase/fibroblast-specific protein 1 (TGF- ß1/ILK/FSP1) signaling pathway in cyclosporine A (CsA)-induced renal tubular epithelial cell transdifferentiation. METHODS: Rat renal tubular epithelial NRK-52E cells were induced with 1 mg/L CsA, treated with TGF-ß1 inhibitor (SB431542, 10 µmol/L), or transfected with the ILK-RNAi lentiviral expression vector (ILKshRNA) or a negative control vector before CsA induction. The expressions of TGF-ß1, ILK and FSP-1 mRNAs and proteins in the cells were detected using real-time PCR and Western blotting. The positive cells for α-SMA expression were detected by immunohistochemistry. RESULTS: Compared with the blank control cells, the cells treated with CsA showed significantly increased levels of TGF-ß1, ILK and FSP-1 mRNAs and proteins (P < 0.05). The expressions of TGF-ß1, ILK and FSP-1 were significantly lower in TGF-ß1 inhibitor group than in CsA group (P < 0.05). The levels of ILK and FSP-1 were significantly decreased after shRNA-mediated ILK silencing (P < 0.05). The number of positive cells for α-SMA was significantly lower in cells treated with SB431542 and in cells with ILK silencing than in the cells treated with CsA alone (P < 0.05). CONCLUSIONS: The activation of TGF-ß1/ILK/FSP-1 signaling pathway is an important mechanism for CsA-induced transdifferentiation in rat renal tubular epithelial cells. ILK participates in CsA-induced epithelialmesenchymal transition of renal tubular epithelial cells.