Search Results (27)

Depression is now one of the most common mental health concerns in the digital era, calling for powerful computational tools for its detection and its level of severity estimation. A multi-level depression severity detection framework in the Reddit social media network is proposed in this study, and posts are classified into four levels: minimum, mild, moderate, and severe. We take a dual approach using classical machine learning (ML) algorithms and recent Transformer-based architectures. For the ML track, we build ten classifiers, including Logistic Regression, SVM, Naive Bayes, Random Forest, XGBoost, Gradient Boosting, K-NN, Decision Tree, AdaBoost, and Extra Trees, with two recently proposed embedding methods, Word2Vec and GloVe embeddings, and we fine-tune them for mental health text classification. Of these, XGBoost yields the highest F1-score of 94.01 using GloVe embeddings. For the deep learning track, we fine-tune ten Transformer models, covering BERT, RoBERTa, XLM-RoBERTa, MentalBERT, BioBERT, RoBERTa-large, DistilBERT, DeBERTa, Longformer, and ALBERT. The highest performance was achieved by the MentalBERT model, with an F1-score of 97.31, followed by RoBERTa (96.27) and RoBERTa-large (96.14). Our results demonstrate that, to the best of the authors’ knowledge, domain-transferred Transformers outperform non-Transformer-based ML methods in capturing subtle linguistic cues indicative of different levels of depression, thereby highlighting their potential for fine-grained mental health monitoring in online settings. Full article

Colorectal cancer (CRC) ranks among the most frequently diagnosed malignancies and is associated with a significantly high mortality rate. In recent years, increasing attention has been directed toward naturally derived substances with anticancer properties. In our study, we focused on determining the biological and antibacterial effects of selected essential oils (EOs)—peppermint, oregano, tea tree, lemon, lavender, frankincense, and oil blends (Zengest and OnGuard). Analyses were performed on human colon carcinoma cell lines (HCT-116 and HT-29). The cytotoxic (MTT assay), genotoxic effects (comet assay), and reactive oxygen species levels (ROS-Glo™ H₂O₂ Assay) of EOs and oil blends were determined. In our study, we found that all of the studied oils have the potential cyto/genotoxic effects on CRC cell lines after 24 h exposure. The results revealed that oregano, Zengest, and frankincense showed statistically the highest cytotoxic effects [IC₅₀ 0.05 µg/mL] compared to the other studied oils. These oils induced DNA damage and also increased ROS levels. On the other hand, peppermint was shown to have the lowest cytotoxic effect [IC₅₀ 0.67 µg/mL] on the HT-29 cell line. We also evaluated the antibacterial effects of oregano, tea tree, and the OnGuard blend, determining their impact on the viability of beneficial bacteria models, including Lacticaseibacillus rhamnosus, Lactiplantibacillus plantarum, Lacticaseibacillus paracasei, Lactobacillus brevis, Lactobacillus pentosus, and Weizmannia coagulans. Oregano exhibited strong antibacterial activity, with an inhibition zone of 31 mm, while tea tree and OnGuard showed inhibition zones ranging from 12 to 15 mm. The EOs (oregano, tea tree, OnGuard) demonstrated antibacterial effects, with MICs ranging from 0.05 to 0.5 µg/mL. Peppermint, lemon, lavender, frankincense, and the Zengest blend did not inhibit the growth of lactic acid bacteria or W. coagulans, and thus did not impact bacterial survival. On the other hand, they demonstrated potential anticancer effects. Full article

In natural language processing (NLP) tasks, computing semantic textual similarity (STS) is crucial for capturing nuanced semantic differences in text. Traditional word vector methods, such as Word2Vec and GloVe, as well as deep learning models like BERT, face limitations in handling context dependency and polysemy and present challenges in computational resources and real-time processing. To address these issues, this paper introduces two novel methods. First, a sentence embedding generation method based on Kullback–Leibler Divergence (KLD) optimization is proposed, which enhances semantic differentiation between sentence vectors, thereby improving the accuracy of textual similarity computation. Second, this study proposes a framework incorporating RoBERTa knowledge distillation, which integrates the deep semantic insights of the RoBERTa model with prior methodologies to enhance sentence embeddings while preserving computational efficiency. Additionally, the study extends its contributions to sentiment analysis tasks by leveraging the enhanced embeddings for classification. The sentiment analysis experiments, conducted using a Stochastic Gradient Descent (SGD) classifier on the ACL IMDB dataset, demonstrate the effectiveness of the proposed methods, achieving high precision, recall, and F1 score metrics. To further augment model accuracy and efficacy, a feature selection approach is introduced, specifically through the Dynamic Principal Component Selection (DPCS) algorithm. The DPCS method autonomously identifies and prioritizes critical features, thus enriching the expressive capacity of sentence vectors and significantly advancing the accuracy of similarity computations. Experimental results demonstrate that our method outperforms existing methods in semantic similarity computation on the SemEval-2016 dataset. When evaluated using cosine similarity of average vectors, our model achieved a Pearson correlation coefficient (τ) of 0.470, a Spearman correlation coefficient (ρ) of 0.481, and a mean absolute error (MAE) of 2.100. Compared to traditional methods such as Word2Vec, GloVe, and FastText, our method significantly enhances similarity computation accuracy. Using TF-IDF-weighted cosine similarity evaluation, our model achieved a τ of 0.528, ρ of 0.518, and an MAE of 1.343. Additionally, in the cosine similarity assessment leveraging the Dynamic Principal Component Smoothing (DPCS) algorithm, our model achieved a τ of 0.530, ρ of 0.518, and an MAE of 1.320, further demonstrating the method’s effectiveness and precision in handling semantic similarity. These results indicate that our proposed method has high relevance and low error in semantic textual similarity tasks, thereby better capturing subtle semantic differences between texts. Full article

Alveolar echinococcosis (AE) stands as a perilous zoonotic affliction caused by the larvae of Echinococcus multilocularis. There is an imperative need to explore novel therapeutic agents or lead compounds for the treatment of AE. Asparagusic acid, characterized by its low toxicity and possessing antimicrobial, antioxidant, and anti-parasitic attributes, emerges as a promising candidate. The aim of this study was to investigate the in vivo and in vitro efficacy of asparagusic acid against E. multilocularis. Morphological observations, scanning electron microscopy, ROS assays, mitochondrial membrane potential assays, and Western blot were used to evaluate the in vitro effects of asparagusic acid on protoscoleces. The effects of asparagusic acid on vesicles were assessed via PGI release, γ-GGT release, and transmission electron microscopy observations. CellTiter-Glo assays, Caspase3 activity assays, flow cytometry, and Western blot were used for an evaluation of the effect of asparaginic acid on the proliferation and apoptosis of germinal cells. The in vivo efficacy of asparagusic acid was evaluated in a murine AE model. Asparagusic acid exhibited a pronounced killing effect on the protoscoleces post-treatment. Following an intervention with asparagusic acid, there was an increase in ROS levels and a decline in mitochondrial membrane potential in the protoscolex. Moreover, asparagusic acid treatment resulted in the upregulation of PGI and γ-GGT release in metacestode vesicles, concomitant with the inhibition of germinal cell viability. Furthermore, asparagusic acid led to an enhanced relative expression of Caspase3 in the culture supernatant of both the protoscoleces and germinal cells, accompanied by an increase in the proportion of apoptotic germinal cells. Notably, asparagusic acid induced an augmentation in Bax and Caspase3 protein expression while reducing Bcl2 protein expression in both the protoscoleces and germinal cells. In vitro cytotoxicity assessments demonstrated the low toxicity of asparagusic acid towards normal human hepatocytes and HFF cells. Additionally, in vivo experiments revealed that asparagusic acid administration at doses of 10 mg/kg and 40 mg/kg significantly reduced metacestode wet weight. A histopathological analysis displayed the disruption of the germinal layer structure within lesions post-asparagusic acid treatment, alongside the preservation of laminated layer structures. Transmission electron microscopy further revealed mitochondrial swelling and heightened cell necrosis subsequent to the asparagusic acid treatment. Furthermore, asparagusic acid promoted Caspase3 and Bax protein expression while decreasing Bcl2 protein expression in perilesional tissues. Subsequently, it inhibited the expression of Ki67, MMP2, and MMP9 proteins in the perilesional tissues and curbed the activation of the PI3K/Akt signaling pathway within the lesion-host microenvironmental tissues. Asparagusic acid demonstrated a pronounced killing effect on E. multilocularis, suggesting its potential as a promising therapeutic agent for the management of AE. Full article

Recently, generative artificial intelligence (GAI) has impressed the world with its ability to create text, images, and videos. However, there are still areas in which GAI produces undesirable or unintended results due to being “uncertain”. Before wider use of AI-generated content, it is important to identify concepts where GAI is uncertain to ensure the usage thereof is ethical and to direct efforts for improvement. This study proposes a general pipeline to automatically quantify uncertainty within GAI. To measure uncertainty, the textual prompt to a text-to-image model is compared to captions supplied by four image-to-text models (GIT, BLIP, BLIP-2, and InstructBLIP). Its evaluation is based on machine translation metrics (BLEU, ROUGE, METEOR, and SPICE) and word embedding’s cosine similarity (Word2Vec, GloVe, FastText, DistilRoBERTa, MiniLM-6, and MiniLM-12). The generative AI models performed consistently across the metrics; however, the vector space models yielded the highest average similarity, close to 80%, which suggests more ideal and “certain” results. Suggested future work includes identifying metrics that best align with a human baseline to ensure quality and consideration for more GAI models. The work within can be used to automatically identify concepts in which GAI is “uncertain” to drive research aimed at increasing confidence in these areas. Full article

Numerous studies have reported the possibility of enhancing the properties of materials by incorporating foreign elements within their crystal lattice. In this context, while magnetite has widely known properties that have been used for various biomedical applications, the introduction of other metals within its structure could prospectively enhance its effectiveness. Specifically, zinc and cerium have demonstrated their biomedical potential through significant antioxidant, anticancer, and antimicrobial features. Therefore, the aim of the present study was to develop a series of zinc and/or cerium-substituted magnetite nanoparticles that could further be used in the medical sector. The nanostructures were synthesized through the co-precipitation method and their morpho-structural characteristics were evaluated through X-ray diffraction (XRD), inductively coupled plasma mass spectrometry (ICP-MS), X-ray photoelectron spectroscopy (XPS), dynamic light scattering (DLS), zeta potential, scanning electron microscopy (SEM), and energy dispersive X-ray spectroscopy (EDX) analyses. Furthermore, the nanostructures were subjected to a ROS-Glo H₂O₂ assay for assessing their antioxidant potential, MTT assay for determining their anticancer effects, and antimicrobial testing against S. aureus, P. aeruginosa, and C. albicans strains. Results have proven promising for future biomedical applications, as the nanostructures inhibit oxidative stress in normal cells, with between two- and three-fold reduction and cell proliferation in tumor cells; a two-fold decrease in cell viability and microbial growth; an inhibition zone diameter of 4–6 mm and minimum inhibitory concentration (MIC) of 1–2 mg/mL. Full article

Efforts have been dedicated by researchers in the field of natural language processing (NLP) to detecting and combating fake news using an assortment of machine learning (ML) and deep learning (DL) techniques. In this paper, a review of the existing studies is conducted to understand and curtail the dissemination of fake news. Specifically, we conducted a benchmark study using a wide range of (1) classical ML algorithms such as logistic regression (LR), support vector machines (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), XGBoost (XGB) and an ensemble learning method of such algorithms, (2) advanced ML algorithms such as convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent units (BiGRU), CNN-BiLSTM, CNN-BiGRU and a hybrid approach of such techniques and (3) DL transformer-based models such as BERT${}_{base}$ and RoBERTa${}_{base}$. The experiments are carried out using different pretrained word embedding methods across four well-known real-world fake news datasets—LIAR, PolitiFact, GossipCop and COVID-19—to examine the performance of different techniques across various datasets. Furthermore, a comparison is made between context-independent embedding methods (e.g., GloVe) and the effectiveness of BERT${}_{base}$—contextualised representations in detecting fake news. Compared with the state of the art’s results across the used datasets, we achieve better results by solely relying on news text. We hope this study can provide useful insights for researchers working on fake news detection. Full article

Background: Mantle cell lymphoma (MCL) is a rare and aggressive subtype of B-cell non-Hodgkin lymphoma that remains incurable with standard therapy. Statins are well-tolerated, inexpensive, and widely prescribed as cholesterol-lowering agents to treat hyperlipidemia and to prevent cardiovascular diseases through the blockage of the mevalonate metabolic pathway. These drugs have also shown promising anti-cancer activity through pleiotropic effects including the induction of lymphoma cell death. However, their potential use as anti-MCL agents has not been evaluated so far. Aim: The present study aimed to investigate the activity of simvastatin on MCL cells. Methods: We evaluated the cytotoxicity of simvastatin in MCL cell lines by CellTiter-Glo and lactate dehydrogenase (LDH) release assays. Cell proliferation and mitotic index were assessed by direct cell recounting and histone H3-pSer10 immunostaining. Apoptosis induction and reactive oxygen species (ROS) generation were evaluated by flow cytometry. Cell migration and invasion properties were determined by transwell assay. The antitumoral effect of simvastatin in vivo was evaluated in a chick embryo chorioallantoic membrane (CAM) MCL xenograft model. Results: We show that treatment with simvastatin induced a 2 to 6-fold LDH release, inhibited more than 50% of cell proliferation, and enhanced the caspase-independent ROS-mediated death of MCL cells. The effective impairment of MCL cell survival was accompanied by the inhibition of AKT and mTOR phosphorylation. Moreover, simvastatin strongly decreased MCL cell migration and invasion ability, leading to a 55% tumor growth inhibition and a consistent diminution of bone marrow and spleen metastasis in vivo. Conclusion: Altogether, these data provide the first preclinical insight into the effect of simvastatin against MCL cells, suggesting that this agent might be considered for repurpose as a precise MCL therapy. Full article

Since cancer incidence is constantly increasing, novel and more efficient treatment methods that overcome the current limitations of chemotherapy are continuously explored. In this context, the aim of the present study was to investigate the potential of two types of magnetite microspheres as drug delivery vehicles for the controlled release of rosmarinic acid (RA) in anticancer therapies. The magnetite microspheres were obtained through the solvothermal method by using polyethylene glycol (PEG) with two different molecular weights as the surfactant. The physicochemical characterization of the so-obtained drug delivery carriers involved X-ray diffraction (XRD) coupled with Rietveld refinement, scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), dynamic light scattering (DLS) and zeta potential, and UV–Vis spectrophotometry. The magnetite-based anticancer agents were biologically evaluated through the ROS-Glo H₂O₂ and MTT assays. Results proved the formation of magnetite spheres with submicronic sizes and the effective RA loading and controlled release, while the biological assays demonstrated the anticancer potential of the present systems. Thus, this study successfully developed a promising drug delivery alternative based on magnetite that could be used in the continuous fight against cancer. Full article

As one of the most toxic elements, mercury (Hg) is a widespread toxicant in aquatic environments. Crayfish are considered suitable for indicating the impact of heavy metals on aquatic crustaceans. Nevertheless, Hg toxicity on Procambarus clarkii is largely unknown. In this research, the acute Hg-induced alterations of biochemical responses, histopathology, hepatopancreatic transcriptome, and intestinal microbiome of Procambarus clarkii were studied. Firstly, Hg induced significant changes in reactive oxygen species (ROS) and malonaldehyde (MDA) content as well as antioxidant enzyme activity. Secondly, Hg exposure caused structural damage to the hepatopancreas (e.g., vacuolization of the epithelium and dilatation of the lumen) as well as to the intestines (e.g., dysregulation of lamina epithelialises and extension of lamina proprias). Thirdly, after treatment with three different concentrations of Hg, RNA-seq assays of the hepatopancreas revealed a large number of differentially expressed genes (DEGs) linked to a specific function. Among the DEGs, a lot of redox metabolism- (e.g., ACOX3, SMOX, GPX3, GLO1, and P4HA1), ion transport- (e.g., MICU3, MCTP, PYX, STEAP3, and SLC30A2), drug metabolism- (e.g., HSP70, HSP90A, CYP2L1, and CYP9E2), immune response- (e.g., SMAD4, HDAC1, and DUOX), and apoptosis-related genes (e.g., CTSL, CASP7, and BIRC2) were identified, which suggests that Hg exposure may perturb the redox equilibrium, disrupt the ion homeostasis, weaken immune response and ability, and cause apoptosis. Fourthly, bacterial 16S rRNA gene sequencing showed that Hg exposure decreased bacterial diversity and dysregulated intestinal microbiome composition. At the phylum level, there was a marked decrease in Proteobacteria and an increase in Firmicutes after exposure to high levels of Hg. With regards to genus, abundances of Bacteroides, Dysgonomonas, and Arcobacter were markedly dysregulated after Hg exposures. Our findings elucidate the mechanisms involved in Hg-mediated toxicity in aquatic crustaceans at the tissue, cellular, molecular as well as microbial levels. Full article

Although previous genome-wide association studies (GWASs) on post-traumatic stress disorder (PTSD) have identified multiple risk loci, how these loci confer risk of PTSD remains unclear. Through the FUSION pipeline, we integrated two human brain proteome reference datasets (ROS/MAP and Banner) with the PTSD GWAS dataset, respectively, to conduct a proteome-wide association study (PWAS) analysis. Then two transcriptome reference weights (Rnaseq and Splicing) were applied to a transcriptome-wide association study (TWAS) analysis. Finally, the PWAS and TWAS results were investigated through brain imaging analysis. In the PWAS analysis, 8 and 13 candidate genes were identified in the ROS/MAP and Banner reference weight groups, respectively. Examples included ADK (p_PWAS-ROS/MAP = 3.00 × 10⁻⁵) and C3orf18 (p_PWAS-Banner = 7.07 × 10⁻³¹). Moreover, the TWAS also detected multiple candidate genes associated with PTSD in two different reference weight groups, including RIMS2 (p_{TWAS-Splicing} = 3.84 × 10⁻²), CHMP1A (p_TWAS-Rnaseq = 5.09 × 10⁻⁴), and SIRT5 (p_{TWAS-Splicing} = 4.81 × 10⁻³). Further comparison of the PWAS and TWAS results in different populations detected the overlapping genes: MADD (p_PWAS-Banner = 4.90 × 10⁻², p_{TWAS-Splicing} = 1.23 × 10⁻²) in the total population and GLO1(p_PWAS-Banner = 4.89 × 10⁻³, p_TWAS-Rnaseq = 1.41 × 10⁻³) in females. Brain imaging analysis revealed several different brain imaging phenotypes associated with MADD and GLO1 genes. Our study identified multiple candidate genes associated with PTSD in the proteome and transcriptome levels, which may provide new clues to the pathogenesis of PTSD. Full article

Thymol affects various types of tumor cell lines, including colorectal cancer cells. However, the hydrophobic properties of thymol prevent its wider use. Therefore, new derivatives (acetic acid thymol ester, thymol β-D-glucoside) have been synthesized with respect to hydrophilic properties. The cytotoxic effect of the new derivatives on the colorectal cancer cell lines HT-29 and HCT-116 was assessed via MTT assay. The genotoxic effect was determined by comet assay and micronucleus analysis. ROS production was evaluated using ROS-Glo™ H₂O₂ Assay. We confirmed that one of the thymol derivatives (acetic acid thymol ester) has the potential to have a cyto/genotoxic effect on colorectal cancer cells, even at much lower (IC₅₀~0.08 μg/mL) concentrations than standard thymol (IC₅₀~60 μg/mL) after 24 h of treatment. On the other side, the genotoxic effect of the second studied derivative—thymol β-D-glucoside was observed at a concentration of about 1000 μg/mL. The antiproliferative effect of studied derivatives of thymol on the colorectal cancer cell lines was found to be both dose- and time-dependent at 100 h. Moreover, thymol derivative-treated cells did not show any significantly increased rate of micronuclei formation. New derivatives of thymol significantly increased ROS production too. The results confirmed that the effect of the derivative on tumor cells depends on its chemical structure, but further detailed research is needed. However, thymol and its derivatives have great potential in the prevention and treatment of colorectal cancer, which remains one of the most common cancers in the world. Full article

Inflammation caused by oxidative stress (ROS) demonstrates an essential mechanism in the pathogenesis of acute pancreatitis (AP). Important sources for ROS comprise the reactive compound methylglyoxal (MGO) itself and the MGO-derived formation of advanced glycation end-products (AGEs). AGEs bind to the transmembrane receptor RAGE and activate NF-κB, and lead to the production of pro-inflammatory cytokines. MGO is detoxified by glyoxalase-I (Glo-I). The importance of Glo-I was shown in different models of inflammation and carcinogenesis. Nevertheless, the role of Glo-I and MGO in AP has not been evaluated so far. This study analyzed Glo-I in cerulein-(CN)-induced AP and determined the effects of Glo-I knockdown, overexpression and pharmacological modulation. Methods: AP was induced in C57BL6/J mice by i.p. injection of CN. Glo-I was analyzed in explanted pancreata by Western Blot, qRT-PCR and immunohistochemistry. AR42J cells were differentiated by dexamethasone and stimulated with 100 nM of CN. Cells were simultaneously treated with ethyl pyruvate (EP) or S-p-bromobenzylglutathione-cyclopentyl-diester (BrBz), two Glo-I modulators. Knockdown and overexpression of Glo-I was achieved by transient transfection with Glo-I siRNA and pEGFP-N1-Glo-I-Vector. Amylase secretion, TNF-α production (ELISA) and expression of Glo-I, RAGE and NF-κB were measured. Results: Glo-I was significantly upregulated on protein and mRNA levels in CN-treated mice and AR42J cells. Dexamethasone-induced differentiation of AR42J cells increased the expression of Glo-I and RAGE. Treatment of AR42J cells with CN and EP or BrBz resulted in a significant reduction of CN-induced amylase secretion, NF-κB, RAGE and TNF-α. Overexpression of Glo-I led to a significant reduction of CN-induced amylase levels, NF-κB expression and TNF-α, whereas Glo-I knockdown revealed only slight alterations. Measurements of specific Glo-I activity and MGO levels indicated a complex regulation in the model of CN-induced AP. Conclusion: Glo-I is overexpressed in a model of CN-induced AP. Pharmacological modulation and overexpression of Glo-I reduced amylase secretion and the release of pro-inflammatory cytokines in AP in vitro. Targeting Glo-I in AP seems to be an interesting approach for future in vivo studies of AP. Full article

Saudi Arabian flora have a history of use as folklore remedies, although such properties have yet to be explored rigorously, and the safety of such remedies should be assessed. This study determined the anti-proliferative, cytotoxic, and antioxidant properties of extracts of the following five plants indigenous to Saudi Arabia: Aizoon canariense, Citrullus colocynthis, Maerua crassifolia, Rhazya stricta, and Tribulus macropterus. The aerial parts of the five plants were collected from various locations of the western and northern regions of Saudi Arabia and used to prepare methanolic extracts. Three approaches were used to determine the proliferation and cytotoxicity effects using HaCaT cells: MTT, FACS, and confocal microscopy. Meanwhile, two approaches were used to study the antioxidant potential: DPPH (acellular) and RosGlo (cellular, using HaCaT cells). C. colocynthis possessed anti-proliferative activity against HaCaT cells, showing a significant decrease in cell proliferation from 24 h onwards, while R. stricta showed significant inhibition of cell growth at 120 and 168 h. The IC₅₀ values were determined for both plant extracts for C. colocynthis, with 17.32 and 16.91 µg/mL after five and seven days of treatment, respectively, and for R. stricta, with 175 and 105.3 µg/mL after five and seven days of treatment. R. stricta and M. crassifolia exhibited the highest capacities for scavenging the DPPH radical with IC₅₀ values of 335 and 448 µg/mL, respectively. The subsequent ROS-Glo H₂O₂ assay confirmed these findings. The R. stricta and M. crassifolia extracts showed potent antioxidant activity in both acellular and cellular models. The C. colocynthis extract also demonstrated significant anti-proliferation and cytotoxic activity, as did the R. stricta extract. These properties support their usage in folk medicine and also indicate a further potential for development for holistic medicinal use or as sources of new active compounds. Full article

The glyoxal-lysine dimer (GOLD), which is a glyoxal (GO)-derived advanced glycation end product (AGE), is produced by the glycation reaction. In this study, we evaluated the effect of GOLD on the oxidative damage and inflammatory response in SV40 MES 13 mesangial cells. GOLD significantly increased the linkage with the V-type immunoglobulin domain of RAGE, a specific receptor of AGE. We found that GOLD treatment increased RAGE expression and reactive oxygen species (ROS) production in mesangial cells. GOLD remarkably regulated the protein and mRNA expression of nuclear factor erythroid 2-related factor 2 (NRF2) and glyoxalase 1 (GLO1). In addition, mitochondrial deterioration and inflammation occurred via GOLD-induced oxidative stress in mesangial cells. GOLD regulated the mitogen-activated protein kinase (MAPK) and the release of proinflammatory cytokines associated with the inflammatory mechanism of mesangial cells. Furthermore, oxidative stress and inflammatory responses triggered by GOLD were suppressed through RAGE inhibition using RAGE siRNA. These results demonstrate that the interaction of GOLD and RAGE plays an important role in the function of mesangial cells. Full article