Comparison of intestinal toxicity in enhancing intestinal permeability and in causing ROS production of six PPD quinones in Caenorhabditis elegans.

As the derivatives of p-phenylenediamines (PPDs), PPD quinones (PPDQs) have received increasing attention due to their possible exposure risk. We compared the intestinal toxicity of six PPDQs (6-PPDQ, 77PDQ, CPPDQ, DPPDQ, DTPDQ and IPPDQ) in Caenorhabditis elegans. In the range of 0.01-10 µg/L, only 77PDQ (10 µg/L) moderately induced the lethality. All the examined PPDQs at 0.01-10 µg/L did not affect intestinal morphology. Different from this, exposure to 6-PPDQ (1-10 µg/L), 77PDQ (0.1-10 µg/L), CPPDQ (1-10 µg/L), DPPDQ (1-10 µg/L), DTPDQ (1-10 µg/L), and IPPDQ (10 µg/L) enhanced intestinal permeability to different degrees. Meanwhile, exposure to 6-PPDQ (0.1-10 µg/L), 77PDQ (0.01-10 µg/L), CPPDQ (0.1-10 µg/L), DPPDQ (0.1-10 µg/L), DTPDQ (1-10 µg/L), and IPPDQ (1-10 µg/L) resulted in intestinal reactive oxygen species (ROS) production and activation of both SOD-3::GFP and GST-4::GFP. In 6-PPDQ, 77PDQ, CPPDQ, DPPDQ, DTPDQ, and/or IPPDQ exposed nematodes, the ROS production was strengthened by RNAi of genes (acs-22, erm-1, hmp-2, and pkc-3) governing functional state of intestinal barrier. Additionally, expressions of acs-22, erm-1, hmp-2, and pkc-3 were negatively correlated with intestinal ROS production in nematodes exposed to 6-PPDQ, 77PDQ, CPPDQ, DPPDQ, DTPDQ, and/or IPPDQ. Therefore, exposure to different PPDQs differentially induced the intestinal toxicity on nematodes. Our data highlighted potential exposure risk of PPDQs at low concentrations to organisms by inducing intestinal toxicity.

Deep learning-based label-free imaging of lymphatics and aqueous veins in the eye using optical coherence tomography.

We demonstrate an adaptation of deep learning for label-free imaging of the micro-scale lymphatic vessels and aqueous veins in the eye using optical coherence tomography (OCT). The proposed deep learning-based OCT lymphangiography (DL-OCTL) method was trained, validated and tested, using OCT scans (23 volumetric scans comprising 19,736 B-scans) from 11 fresh ex vivo porcine eyes with the corresponding vessel labels generated by a conventional OCT lymphangiography (OCTL) method based on thresholding with attenuation compensation. Compared to conventional OCTL, the DL-OCTL method demonstrates comparable results for imaging lymphatics and aqueous veins in the eye, with an Intersection over Union value of 0.79 ± 0.071 (mean ± standard deviation). In addition, DL-OCTL mitigates the imaging artifacts in conventional OCTL where the OCT signal modelling was corrupted by the tissue heterogeneity, provides ~ 10 times faster processing based on a rough comparison and does not require OCT-related knowledge for correct implementation as in conventional OCTL. With these favorable features, DL-OCTL promises to improve the practicality of OCTL for label-free imaging of lymphatics and aqueous veins for preclinical and clinical imaging applications.

Cancer-Alterome: a literature-mined resource for regulatory events caused by genetic alterations in cancer.

It is vital to investigate the complex mechanisms underlying tumors to better understand cancer and develop effective treatments. Metabolic abnormalities and clinical phenotypes can serve as essential biomarkers for diagnosing this challenging disease. Additionally, genetic alterations provide profound insights into the fundamental aspects of cancer. This study introduces Cancer-Alterome, a literature-mined dataset that focuses on the regulatory events of an organism's biological processes or clinical phenotypes caused by genetic alterations. By proposing and leveraging a text-mining pipeline, we identify 16,681 thousand of regulatory events records encompassing 21K genes, 157K genetic alterations and 154K downstream bio-concepts, extracted from 4,354K pan-cancer literature. The resulting dataset empowers a multifaceted investigation of cancer pathology, enabling the meticulous tracking of relevant literature support. Its potential applications extend to evidence-based medicine and precision medicine, yielding valuable insights for further advancements in cancer research.

Human emissions of size-resolved fluorescent bioaerosols in control situations.

Human bioaerosols contribute significantly to indoor air quality. This study used a Wideband Integrated Bioaerosol Sensor (WIBS-4A) instrument for real-time measurement of particle size distribution and count to differentiate fluorescent bioaerosols from non-fluorescent aerosols. Through an experiment involving 12 subjects (six men and six women) wearing standard cotton clothing in a 2 m × 2 m × 2 m environmental chamber, we established a quantitative method to obtain the bioaerosol emission rate of a single subject, aiming to explore the effects of masks and sex on bioaerosol emissions from different individuals. The mean emission rates of fluorescent bioaerosols in the particle size ranges of 0.5-2.5 µm and 2.5-10 µm were 3.192±2.11×104 counts/(person·h) and 13.98±9.34×104 counts/(person·h), respectively. A comparison between those wearing and not wearing masks revealed no significant differences in the emissions of fluorescent bioaerosols. This suggests respiratory sources may not significantly impact the emissions of fluorescent bioaerosols from individuals under seated breathing conditions. Significant disparities in the fluorescent bioaerosol emission rates of different biological sexes were observed through independent sample analysis. Males exhibited 41 % and 15 % higher emission rates than females for particle size ranges of 0.5-2.5 µm and 2.5-10 µm, respectively, possibly because of different metabolic rates. A significant correlation between metabolic rates and fluorescent bioaerosols (sig = 0.044 < 0.05) was observed in all the subjects. These findings underscore the individual variations that affect bioaerosol emission rates. The data provided by this study will facilitate further analysis of the on-site measured data and source analysis.

A systematic evaluation of computation methods for cell segmentation.

Cell segmentation is a fundamental task in analyzing biomedical images. Many computational methods have been developed for cell segmentation, but their performances are not well understood in various scenarios. We systematically evaluated the performance of 18 segmentation methods to perform cell nuclei and whole cell segmentation using light microscopy and fluorescence staining images. We found that general-purpose methods incorporating the attention mechanism exhibit the best overall performance. We identified various factors influencing segmentation performances, including training data and cell morphology, and evaluated the generalizability of methods across image modalities. We also provide guidelines for choosing the optimal segmentation methods in various real application scenarios. We developed Seggal, an online resource for downloading segmentation models already pre-trained with various tissue and cell types, which substantially reduces the time and effort for training cell segmentation models.

Combined-task deep network based on LassoNet feature selection for predicting the comorbidities of acute coronary syndrome.

Acute coronary syndrome (ACS) is a multifaceted cardiovascular condition frequently accompanied by multiple comorbidities, which can have significant implications for patient outcomes and treatment approaches. Precisely predicting these comorbidities is crucial for providing personalized care and making well-informed clinical decisions. However, there is a shortage of research investigating the identification of risk factors associated with ACS comorbidities and accurately predicting their likelihood of occurrence beyond heart failure. In this study, an approach called Combined-task Deep Network based on LassoNet feature selection (CDNL) is presented for predicting ACS comorbidities, including hypertension, diabetes, hyperlipidemia, and heart failure. In order to identify crucial biomarkers associated with ACS comorbidities, the proposed framework first incorporates LassoNet, which extends Lasso regression to the deep network by adding a skip (residual) layer. Additionally, a correlation score calculation method across tasks is introduced based on measuring the overlap of identified biomarkers and their assigned importance. This method enables the development of an optimal combined-task prediction model for each ACS comorbidity, addressing the challenge of limited representations in traditional multi-task learning. Our evaluation, conducted through a meticulous cross-sectional study at a tertiary hospital in China, involved a dataset of 2941 samples with 42 clinical features. The results demonstrate that CDNL facilitates the identification of significant biomarkers and achieves an average improvement in AUC of 4.93% and 8.58% compared to deep learning multi-layer neural network (DNN) and SVM, respectively. Additionally, it shows an average improvement of 2.64% and 1.92% compared to two state-of-the-art multi-task models.

Synthesis, characterization and discovery of multiple anticancer mechanisms of dibutyltin complexes based on salen-like ligands.

A series of novel dibutyltin complexes based on salen-like ligands (S01-S03) were synthesized and characterized using ultraviolet-visible spectra，infrared spectra, 1H, 13C, and 119Sn nuclear magnetic resonance, high-resolution mass spectrometry, X-ray crystallography, and thermogravimetric analysis. Complex S03 had excellent anticancer activity in vitro (IC50 = 1.5 ± 0.2 µM in CAL-27 cell lines), which highly activated ROS expression levels and induced apoptosis and cell cycle arrest at the G2/M phase. Interestingly, complex S03 induced cancer cell death through multiple mechanisms (mitochondrial pathway, ER-stress pathway, and DNA damage pathway). This study reveals new mechanisms of organotin complexes and provides new insights into the development of organotin metal complexes as anticancer drugs in the future, and compounds with multiple anticancer mechanisms may be a new strategy for delaying or overcoming drug resistance to chemotherapy and target therapy.

TNFAIP3 Derived from Skeletal Stem Cells Alleviated Rat Osteoarthritis by Inhibiting the Necroptosis of Subchondral Osteoblasts.

Recent investigations have shown that the necroptosis of tissue cells in joints is important in the development of osteoarthritis (OA). This study aimed to investigate the potential effects of exogenous skeletal stem cells (SSCs) on the necroptosis of subchondral osteoblasts in OA. Human SSCs and subchondral osteoblasts isolated from human tibia plateaus were used for Western blotting, real-time PCR, RNA sequencing, gene editing, and necroptosis detection assays. In addition, the rat anterior cruciate ligament transection OA model was used to evaluate the effects of SSCs on osteoblast necroptosis in vivo. The micro-CT and pathological data showed that intra-articular injections of SSCs significantly improved the microarchitecture of subchondral trabecular bones in OA rats. Additionally, SSCs inhibited the necroptosis of subchondral osteoblasts in OA rats and necroptotic cell models. The results of bulk RNA sequencing of SSCs stimulated or not by tumor necrosis factor α suggested a correlation of SSCs-derived tumor necrosis factor α-induced protein 3 (TNFAIP3) and cell necroptosis. Furthermore, TNFAIP3-derived from SSCs contributed to the inhibition of the subchondral osteoblast necroptosis in vivo and in vitro. Moreover, the intra-articular injections of TNFAIP3-overexpressing SSCs further improved the subchondral trabecular bone remodeling of OA rats. Thus, we report that TNFAIP3 from SSCs contributed to the suppression of the subchondral osteoblast necroptosis, which suggests that necroptotic subchondral osteoblasts in joints may be possible targets to treat OA by stem cell therapy.

The association between chronic sinonasal inflammation and nasopharyngeal carcinoma - A systematic review and meta-analysis.

PURPOSE: There has been mounting evidence that inflammation is a key risk factor towards the development of certain cancers. Past studies have shown associations between nasopharyngeal carcinoma (NPC) and sinonasal tract inflammation. We aim to conduct a review and meta-analysis on the association between NPC and chronic sinus inflammation. MATERIALS AND METHODS: We conducted a meta-analysis, searching 4 international databases from 1 January 1973 to 28 March 2022 for studies reporting on sinonasal inflammation and NPC in adult patients (>18 years old). We included cohort, case-control or cross-sectional studies. These studies must examine the association between a prior history of sinonasal inflammation and the risk of developing NPC. The outcome is the incidence of NPC in patients who had prior sinonasal inflammation. RESULTS: 8 studies (8245 NPC; 1,036,087 non-NPC) were included. The overall odds ratio (OR) of patients having NPC after reporting sinonasal inflammation was 1.81 (95 % CI 1.73-1.89). Of note, chronic rhinosinusitis (CRS) (OR of 1.78 (95 %-CI: 1.68-1.90)) was more closely associated with an increased risk of NPC, as compared to allergic rhinitis (AR) (OR of 1.60 (95 %-CI: 1.52-1.68)). CONCLUSION: Chronic sinonasal inflammation is significantly associated with NPC in this systemic review and meta-analysis. The true cause-effect relationship and the potential effects of targeted screening need to be explored thoroughly with large scale prospective studies.

The neutrophil percentage-to-albumin ratio is associated with all-cause mortality in patients with chronic heart failure.

BACKGROUND: In this study, we evaluated the predictive utility of neutrophil percentage-to-albumin ratio (NPAR) for all-cause mortality in patients with chronic heart failure (CHF). METHODS: Patients diagnosed as CHF enrolled in this retrospective cohort study were from Beijing Chaoyang Hospital, capital medical university. Admission NPAR was calculated as neutrophil percentage divided by serum albumin. The endpoints of this study were defined as 90-day, 1-year and 2-year all-cause mortality. Multivariable Cox proportional hazard regression model was performed to confirm the association between NPAR and all-cause mortality. Receiver operating characteristics (ROC) curves were used to evaluate the ability for NPAR to predict all-cause mortality. RESULTS: The 90-day (P = 0.009), 1-year (P < 0.001) and 2-year (P < 0.001) all-cause mortality in 622 patients with CHF were increased as admission NPAR increased. Multivariable Cox regression analysis found the higher NPAR value was still independently associated with increased risk of 90-day (Group III versus Group I: HR, 95% CI: 2.21, 1.01-4.86, P trend = 0.038), 1-year (Group III versus Group I: HR, 95% CI:2.13, 1.30-3.49, P trend = 0.003), and 2-year all-cause mortality (Group III versus Group I: HR, 95% CI:2.06, 1.37-3.09, P trend = 0.001), after adjustments for several confounders. ROC curves revealed that NPAR had a better ability to predict all-cause mortality in patients with CHF, than either albumin or the neutrophil percentage alone. CONCLUSIONS: NPAR was independently correlated with 90-day, 1-year, and 2-year all-cause mortality in patients with CHF.

GeneSegNet: a deep learning framework for cell segmentation by integrating gene expression and imaging.

When analyzing data from in situ RNA detection technologies, cell segmentation is an essential step in identifying cell boundaries, assigning RNA reads to cells, and studying the gene expression and morphological features of cells. We developed a deep-learning-based method, GeneSegNet, that integrates both gene expression and imaging information to perform cell segmentation. GeneSegNet also employs a recursive training strategy to deal with noisy training labels. We show that GeneSegNet significantly improves cell segmentation performances over existing methods that either ignore gene expression information or underutilize imaging information.

Feasibility of deep learning-based polarization-sensitive optical coherence tomography angiography for imaging cutaneous microvasculature.

Polarization-sensitive optical coherence tomography (PS-OCT) measures the polarization states of the backscattered light from tissue that can improve angiography based on conventional optical coherence tomography (OCT). We present a feasibility study on PS-OCT integrated with deep learning for PS-OCT angiography (PS-OCTA) imaging of human cutaneous microvasculature. Two neural networks were assessed for PS-OCTA, including the residual dense network (RDN), which previously showed superior performance for angiography with conventional OCT and the upgraded grouped RDN (GRDN). We also investigated different protocols to process the multiple signal channels provided by the Jones matrices from the PS-OCT system to achieve optimal PS-OCTA performance. The training and testing of the deep learning-based PS-OCTA were performed using PS-OCT scans collected from 18 skin locations comprising 16,600 B-scan pairs. The results demonstrated a moderately improved performance of GRDN over RDN, and of the use of the combined signal from the Jones matrix elements over the separate use of the elements, as well as a similar image quality to that provided by speckle decorrelation angiography. GRDN-based PS-OCTA also showed ∼2-3 times faster processing and improved mitigation of tissue motion as compared to speckle decorrelation angiography, and enabled fully automatic processing. Deep learning-based PS-OCTA can be used for imaging cutaneous microvasculature, which may enable easy adoption of PS-OCTA for preclinical and clinical applications.

Gene based message passing for drug repurposing.

The medicinal effect of a drug acts through a series of genes, and the pathological mechanism of a disease is also related to genes with certain biological functions. However, the complex information between drug or disease and a series of genes is neglected by traditional message passing methods. In this study, we proposed a new framework using two different strategies for gene-drug/disease and drug-disease networks, respectively. We employ long short-term memory (LSTM) network to extract the flow of message from series of genes (gene path) to drug/disease. Incorporating the resulting information of gene paths into drug-disease network, we utilize graph convolutional network (GCN) to predict drug-disease associations. Experimental results showed that our method GeneDR (gene-based drug repurposing) makes better use of the information in gene paths, and performs better in predicting drug-disease associations.

Marker-Free Isoelectric Focusing Patterns for Identification of Meat Samples via Deep Learning.

Isoelectric focusing (IEF) is a powerful tool for resolving complex protein samples, which generates IEF patterns consisting of multiplex analyte bands. However, the interpretation of IEF patterns requires the careful selection of isoelectric point (pI) markers for profiling the pH gradient and a trivial process of pI labeling, resulting in low IEF efficiency. Here, we for the first time proposed a marker-free IEF method for the efficient and accurate classification of IEF patterns by using a convolutional neural network (CNN) model. To verify our method, we identified 21 meat samples whose IEF patterns comprised different bands of meat hemoglobin, myoglobin, and their oxygen-binding variants but no pI marker. Thanks to the high throughput and short assay time of the microstrip IEF, we efficiently collected 1449 IEF patterns to construct the data set for model training. Despite the absence of pI markers, we experimentally introduced the severe pH gradient drift into 189 IEF patterns in the data set, thereby omitting the need for profiling the pH gradient. To enhance the model robustness, we further employed data augmentation during the model training to mimic pH gradient drift. With the advantages of simple preprocessing, a rapid inference of 50 ms, and a high accuracy of 97.1%, the CNN model outperformed the traditional algorithm for simultaneously identifying meat species and cuts of meat of 105 IEF patterns, suggesting its great potential of being combined with microstrip IEF for large-scale IEF analyses of complicated protein samples.

Human osteoarthritic articular cartilage stem cells suppress osteoclasts and improve subchondral bone remodeling in experimental knee osteoarthritis partially by releasing TNFAIP3.

BACKGROUND: Though articular cartilage stem cell (ACSC)-based therapies have been demonstrated to be a promising option in the treatment of diseased joints, the wide variety of cell isolation, the unknown therapeutic targets, and the incomplete understanding of the interactions of ACSCs with diseased microenvironments have limited the applications of ACSCs. METHODS: In this study, the human ACSCs have been isolated from osteoarthritic articular cartilage by advantage of selection of anatomical location, the migratory property of the cells, and the combination of traumatic injury, mechanical stimuli and enzymatic digestion. The protective effects of ACSC infusion into osteoarthritis (OA) rat knees on osteochondral tissues were evaluated using micro-CT and pathological analyses. Moreover, the regulation of ACSCs on osteoarthritic osteoclasts and the underlying mechanisms in vivo and in vitro were explored by RNA-sequencing, pathological analyses and functional gain and loss experiments. The one-way ANOVA was used in multiple group data analysis. RESULTS: The ACSCs showed typical stem cell-like characteristics including colony formation and committed osteo-chondrogenic capacity. In addition, intra-articular injection into knee joints yielded significant improvement on the abnormal subchondral bone remodeling of osteoarthritic rats. Bioinformatic and functional analysis showed that ACSCs suppressed osteoarthritic osteoclasts formation, and inflammatory joint microenvironment augmented the inhibitory effects. Further explorations demonstrated that ACSC-derived tumor necrosis factor alpha-induced protein 3 (TNFAIP3) remarkably contributed to the inhibition on osteoarhtritic osteoclasts and the improvement of abnormal subchondral bone remodeling. CONCLUSION: In summary, we have reported an easy and reproducible human ACSC isolation strategy and revealed their effects on subchondral bone remodeling in OA rats by releasing TNFAIP3 and suppressing osteoclasts in a diseased microenvironment responsive manner.

The impact of green finance on the optimization of industrial structure: Evidence from China.

Green finance promotes the optimization of industrial structure and continuous improvement of ecological environment by supporting the development of green industries. Based on the panel data of 30 provinces in China from 2012 to 2020, this paper uses the entropy weight TOPSIS method to measure the development level of green finance and the level of industrial structure optimization in China, and constructs a panel data model to empirically test the impact of green finance on the upgrading of China's industrial structure. The study finds that there is still an imbalance and insufficiency in the development of green finance and industrial structure optimization in China. From 2012 to 2020, the development level of green finance and the level of industrial structure optimization in China have been continuously rising, but there is obvious heterogeneity, showing an eastern>central>western spatial pattern. Empirical analysis results show that at the significance level of 1‰, the development of green finance has a significant promoting effect on the rationalization and upgrading of the industrial structure. However, there is significant heterogeneity in the impact of green finance on industrial structure optimization. In terms of regional heterogeneity, at the significance level of 1‰, the role of green finance in promoting the optimization of industrial structure in central and western China is higher than that in eastern China, and the impact of green finance on China's industrial structure shows a spatial pattern of western>central>eastern China. In terms of industry heterogeneity, at the significance level of 1‰, green finance has a significant promoting effect on the development of green industries, and a significant inhibiting effect on the development of high-energy-consuming industries. Specifically, in the green industry, green finance has the greatest promoting effect on the communication and other electronic equipment manufacturing industry; in the high-energy-consuming industry, green finance has the greatest inhibiting effect on the black metal smelting and rolling processing industry, and the smallest impact on the petroleum, coal and other fuel processing industry. Finally, based on this, policy suggestions for green finance to support the optimization of industrial structure are proposed from two dimensions: government and financial institutions.

Deciphering the connection between upstream obstacles, wake structures, and root signals in seal whisker array sensing using interpretable neural networks.

This study presents a novel method that combines a computational fluid-structure interaction model with an interpretable deep-learning model to explore the fundamental mechanisms of seal whisker sensing. By establishing connections between crucial signal patterns, flow characteristics, and attributes of upstream obstacles, the method has the potential to enhance our understanding of the intricate sensing mechanisms. The effectiveness of the method is demonstrated through its accurate prediction of the location and orientation of a circular plate placed in front of seal whisker arrays. The model also generates temporal and spatial importance values of the signals, enabling the identification of significant temporal-spatial signal patterns crucial for the network's predictions. These signal patterns are further correlated with flow structures, allowing for the identification of important flow features relevant for accurate prediction. The study provides insights into seal whiskers' perception of complex underwater environments, inspiring advancements in underwater sensing technologies.

Paeoniflorin increases the survival of Pseudomonas aeruginosa infected Caenorhabditis elegans at the immunosuppression stage by activating PMK-1, BAR-1, and EGL-1 signals.

Paeoniflorin is the major active compound of total glycoside of paeony in Paeonia lactiflora Pall. Although several aspects of beneficial effects of paeoniflorin have been described, whether the paeoniflorin treatment is helpful for inhibiting the pathogen infection-induced immunosuppression remains largely unclear. Using the immunosuppression model in Caenorhabditis elegans induced by Pseudomonas aeruginosa infection, we here examined the beneficial effect of paeoniflorin treatment against the immunosuppression induced by bacterial pathogen infection. In this immunosuppression model, we observed that the survival rate of P. aeruginosa infected nematodes at the immunosuppression stage could be significantly increased by 25-100 mg/L paeoniflorin treatment. P. aeruginosa accumulation in intestinal lumen of nematodes at the immunosuppression stage was reduced by paeoniflorin treatment. Paeoniflorin could activate the expressions of antimicrobial genes (lys-1 and lys-8) in nematodes at the immunosuppression stage. Moreover, at the immunosuppression stage, paeoniflorin treatment increased the expressions of bar-1, pmk-1, and egl-1 required for the control of innate immunity against bacterial infection. Meanwhile, RNAi of bar-1, pmk-1, and egl-1 inhibited the beneficial effect of paeoniflorin treatment in increasing the survival, reducing the P. aeruginosa accumulation in intestinal lumen, and activating the expressions of antimicrobial genes (lys-1 and lys-8) in nematodes at the immunosuppression stage. Therefore, paeoniflorin treatment could effectively inhibit the immunosuppression induced by bacterial pathogen infection in the hosts.

Andrographolide protects against atrial fibrillation by alleviating oxidative stress injury and promoting impaired mitochondrial bioenergetics. / ç©¿å¿ƒèŽ²å† é ¯é€šè¿‡ç¼“è§£æ°§åŒ–åº”æ¿€å’Œä¿ƒè¿›çº¿ç²’ä½“èƒ½é‡åˆæˆå¯¹å¿ƒæˆ¿é¢¤åŠ¨å‘æŒ¥é¢„é˜²ä½œç”¨.

Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia seen in clinical settings, which has been associated with substantial rates of mortality and morbidity. However, clinically available drugs have limited efficacy and adverse effects. We aimed to investigate the mechanisms of action of andrographolide (Andr) with respect to AF. We used network pharmacology approaches to investigate the possible therapeutic effect of Andr. To define the role of Andr in AF, HL-1 cells were pro-treated with Andr for 1 h before rapid electronic stimulation (RES) and rabbits were pro-treated for 1 d before rapid atrial pacing (RAP). Apoptosis, myofibril degradation, oxidative stress, and inflammation were determined. RNA sequencing (RNA-seq) was performed to investigate the relevant mechanism. Andr treatment attenuated RAP-induced atrial electrophysiological changes, inflammation, oxidative damage, and apoptosis both in vivo and in vitro. RNA-seq indicated that oxidative phosphorylation played an important role. Transmission electron microscopy and adenosine triphosphate (ATP) content assay respectively validated the morphological and functional changes in mitochondria. The translocation of nuclear factor erythroid 2-related factor 2 (Nrf2) to the nucleus and the molecular docking suggested that Andr might exert a therapeutic effect by influencing the Keap1-Nrf2 complex. In conclusions, this study revealed that Andr is a potential preventive therapeutic drug toward AF via activating the translocation of Nrf2 to the nucleus and the upregulation of heme oxygenase-1 (HO-1) to promote mitochondrial bioenergetics.