CapsNet-LDA: predicting lncRNA-disease associations using attention mechanism and capsule network based on multi-view data.

Cumulative studies have shown that many long non-coding RNAs (lncRNAs) are crucial in a number of diseases. Predicting potential lncRNA-disease associations (LDAs) can facilitate disease prevention, diagnosis and treatment. Therefore, it is vital to develop practical computational methods for LDA prediction. In this study, we propose a novel predictor named capsule network (CapsNet)-LDA for LDA prediction. CapsNet-LDA first uses a stacked autoencoder for acquiring the informative low-dimensional representations of the lncRNA-disease pairs under multiple views, then the attention mechanism is leveraged to implement an adaptive allocation of importance weights to them, and they are subsequently processed using a CapsNet-based architecture for predicting LDAs. Different from the conventional convolutional neural networks (CNNs) that have some restrictions with the usage of scalar neurons and pooling operations. the CapsNets use vector neurons instead of scalar neurons that have better robustness for the complex combination of features and they use dynamic routing processes for updating parameters. CapsNet-LDA is superior to other five state-of-the-art models on four benchmark datasets, four perturbed datasets and an independent test set in the comparison experiments, demonstrating that CapsNet-LDA has excellent performance and robustness against perturbation, as well as good generalization ability. The ablation studies verify the effectiveness of some modules of CapsNet-LDA. Moreover, the ability of multi-view data to improve performance is proven. Case studies further indicate that CapsNet-LDA can accurately predict novel LDAs for specific diseases.

An Unsupervised Classification Algorithm for Heterogeneous Cryo-EM Projection Images Based on Autoencoders.

Heterogeneous three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy (cryo-EM) is an important but very challenging technique for recovering the conformational heterogeneity of flexible biological macromolecules such as proteins in different functional states. Heterogeneous projection image classification is a feasible solution to solve the structural heterogeneity problem in single-particle cryo-EM. The majority of heterogeneous projection image classification methods are developed using supervised learning technology or require a large amount of a priori knowledge, such as the orientations or common lines of the projection images, which leads to certain limitations in their practical applications. In this paper, an unsupervised heterogeneous cryo-EM projection image classification algorithm based on autoencoders is proposed, which only needs to know the number of heterogeneous 3D structures in the dataset and does not require any labeling information of the projection images or other a priori knowledge. A simple autoencoder with multi-layer perceptrons trained in iterative mode and a complex autoencoder with residual networks trained in one-pass learning mode are implemented to convert heterogeneous projection images into latent variables. The extracted high-dimensional features are reduced to two dimensions using the uniform manifold approximation and projection dimensionality reduction algorithm, and then clustered using the spectral clustering algorithm. The proposed algorithm is applied to two heterogeneous cryo-EM datasets for heterogeneous 3D reconstruction. Experimental results show that the proposed algorithm can effectively extract category features of heterogeneous projection images and achieve high classification and reconstruction accuracy, indicating that the proposed algorithm is effective for heterogeneous 3D reconstruction in single-particle cryo-EM.

Hyb4mC: a hybrid DNA2vec-based model for DNA N4-methylcytosine sites prediction.

BACKGROUND: DNA N4-methylcytosine is part of the restrictive modification system, which works by regulating some biological processes, for example, the initiation of DNA replication, mismatch repair and inactivation of transposon. However, using experimental methods to detect 4mC sites is time-consuming and expensive. Besides, considering the huge differences in the number of 4mC samples among different species, it is challenging to achieve a robust multi-species 4mC site prediction performance. Hence, it is of great significance to develop effective computational tools to identify 4mC sites. RESULTS: This work proposes a flexible deep learning-based framework to predict 4mC sites, called Hyb4mC. Hyb4mC adopts the DNA2vec method for sequence embedding, which captures more efficient and comprehensive information compared with the sequence-based feature method. Then, two different subnets are used for further analysis: Hyb_Caps and Hyb_Conv. Hyb_Caps is composed of a capsule neural network and can generalize from fewer samples. Hyb_Conv combines the attention mechanism with a text convolutional neural network for further feature learning. CONCLUSIONS: Extensive benchmark tests have shown that Hyb4mC can significantly enhance the performance of predicting 4mC sites compared with the recently proposed methods.

RSAP-Net: joint optic disc and cup segmentation with a residual spatial attention path module and MSRCR-PT pre-processing algorithm.

BACKGROUND: Glaucoma can cause irreversible blindness to people's eyesight. Since there are no symptoms in its early stage, it is particularly important to accurately segment the optic disc (OD) and optic cup (OC) from fundus medical images for the screening and prevention of glaucoma. In recent years, the mainstream method of OD and OC segmentation is convolution neural network (CNN). However, most existing CNN methods segment OD and OC separately and ignore the a priori information that OC is always contained inside the OD region, which makes the segmentation accuracy of most methods not high enough. METHODS: This paper proposes a new encoder-decoder segmentation structure, called RSAP-Net, for joint segmentation of OD and OC. We first designed an efficient U-shaped segmentation network as the backbone. Considering the spatial overlap relationship between OD and OC, a new Residual spatial attention path is proposed to connect the encoder-decoder to retain more characteristic information. In order to further improve the segmentation performance, a pre-processing method called MSRCR-PT (Multi-Scale Retinex Colour Recovery and Polar Transformation) has been devised. It incorporates a multi-scale Retinex colour recovery algorithm and a polar coordinate transformation, which can help RSAP-Net to produce more refined boundaries of the optic disc and the optic cup. RESULTS: The experimental results show that our method achieves excellent segmentation performance on the Drishti-GS1 standard dataset. In the OD and OC segmentation effects, the F1 scores are 0.9752 and 0.9012, respectively. The BLE are 6.33 pixels and 11.97 pixels, respectively. CONCLUSIONS: This paper presents a new framework for the joint segmentation of optic discs and optic cups, called RSAP-Net. The framework mainly consists of a U-shaped segmentation skeleton and a residual space attention path module. The design of a pre-processing method called MSRCR-PT for the OD/OC segmentation task can improve segmentation performance. The method was evaluated on the publicly available Drishti-GS1 standard dataset and proved to be effective.

MAGCNSE: predicting lncRNA-disease associations using multi-view attention graph convolutional network and stacking ensemble model.

BACKGROUND: Many long non-coding RNAs (lncRNAs) have key roles in different human biologic processes and are closely linked to numerous human diseases, according to cumulative evidence. Predicting potential lncRNA-disease associations can help to detect disease biomarkers and perform disease analysis and prevention. Establishing effective computational methods for lncRNA-disease association prediction is critical. RESULTS: In this paper, we propose a novel model named MAGCNSE to predict underlying lncRNA-disease associations. We first obtain multiple feature matrices from the multi-view similarity graphs of lncRNAs and diseases utilizing graph convolutional network. Then, the weights are adaptively assigned to different feature matrices of lncRNAs and diseases using the attention mechanism. Next, the final representations of lncRNAs and diseases is acquired by further extracting features from the multi-channel feature matrices of lncRNAs and diseases using convolutional neural network. Finally, we employ a stacking ensemble classifier, consisting of multiple traditional machine learning classifiers, to make the final prediction. The results of ablation studies in both representation learning methods and classification methods demonstrate the validity of each module. Furthermore, we compare the overall performance of MAGCNSE with that of six other state-of-the-art models, the results show that it outperforms the other methods. Moreover, we verify the effectiveness of using multi-view data of lncRNAs and diseases. Case studies further reveal the outstanding ability of MAGCNSE in the identification of potential lncRNA-disease associations. CONCLUSIONS: The experimental results indicate that MAGCNSE is a useful approach for predicting potential lncRNA-disease associations.

Metallothionein 2A (MT2A) controls cell proliferation and liver metastasis by controlling the MST1/LATS2/YAP1 signaling pathway in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is one of the three major cancers in the world and is the cancer with the most liver metastasis. The present study aimed to investigate the role of metallothionein 2A (MT2A) in the modulation of CRC cell proliferation and liver metastasis, as well as its molecular mechanisms. METHODS: The expression profile of metallothionein 2A (MT2A) in colorectal cancer retrieved from TCGA, GEO and Oncomine database. The biological effect of MT2A overexpression was investigated mainly involving cell proliferation and migration in CRC cells as well as growth and metastasis in CRC animal models. To explore the specific mechanism of MT2A metastasis in CRC, transcriptome sequencing was used to compare the overall expression difference between the control group and the MT2A overexpression group. RESULTS: Metallothionein 2A (MT2A) was downregulated in the tumor tissues of patients with CRC compared to adjacent normal tissues and was related to the tumor M stage of patients. MT2A overexpression inhibited CRC cell proliferation and migration in cells, as well as growth and metastasis in CRC animal models. While knockdown of MT2A had the opposite effect in cells. Western blotting confirmed that MT2A overexpression promoted the phosphorylation of MST1, LAST2 and YAP1, thereby inhibiting the Hippo signaling pathway. Additionally, specific inhibitors of MST1/2 inhibited MT2A overexpression-mediated phosphorylation and relieved the inhibition of the Hippo signaling pathway, thus promoting cell proliferation. Immunohistochemistry in subcutaneous grafts and liver metastases further confirmed this result. CONCLUSIONS: Our results suggested that MT2A is involved in CRC growth and liver metastasis. Therefore, MT2A and MST1 may be potential therapeutic targets for patients with CRC, especially those with liver metastases.

Multiple Target Tracking Based on Multiple Hypotheses Tracking and Modified Ensemble Kalman Filter in Multi-Sensor Fusion.

In multi-sensor fusion (MSF), the integration of multi-sensor observation data with different observation errors to achieve more accurate positioning of the target has always been a research focus. In this study, a modified ensemble Kalman filter (EnKF) is presented to substitute the traditional Kalman filter (KF) in the multiple hypotheses tracking (MHT) to deal with the high nonlinearity that always shows up in multiple target tracking (MTT) problems. In addition, the multi-source observation data fusion is also realized by using the modified EnKF, which enables the low-precision observation data to be corrected by high-precision observation data, and the accuracy of the corrected data can be calibrated by the statistical information provided by the EnKF. Numerical studies are given to demonstrate the effectiveness of our proposed method and the results show that the MHT-EnKF method can achieve remarkable enhancement in dealing with nonlinear movement variation and positioning accuracy for MTT problems in MSF scenario.

Portal hypertension predicts short-term and long-term outcomes after hepatectomy in hepatocellular carcinoma patients.

BACKGROUND AND AIMS: The impact of portal hypertension (PH) on postoperative short-term outcomes and long-term survival in hepatocellular carcinoma (HCC) patients has lately been discussed controversially. This study aimed to explore the influence of PH on postoperative outcomes in HCC patients undergoing surgical resection. METHODS: Patients undergoing hepatectomy for HCC from 2010 to 2014 were enrolled. The impact of PH on postoperative complications, posthepatectomy liver failure (PHLF) and overall survival (OS) was evaluated. RESULTS: A total of 355 HCC patients were enrolled; 129 (36.3%) experienced postoperative complications and 21 (5.9%) developed PHLF. PH was identified as an independent predictor of PHLF. Patients with PH experienced a higher incidence of complications and PHLF than patients without PH. On the Cox proportional hazards regression model, PH was verified as a risk factor of OS for BCLC stage 0/A and B patients. Patients without PH had significantly better long-term survival compared to patients with PH both in the total cohort and in cirrhosis subgroup. CONCLUSION: Liver resection in HCC patients with PH showed a significantly increased postoperative complications and PHLF, and revealed a decreasing long-term survival than non-PH patients. Besides, tumor burden also played an important role in determining the OS. However, due to the improvement in surgical technique and perioperative management, surgery was feasible in carefully selected HCC patients with PH.

Ability of the ALBI grade to predict posthepatectomy liver failure and long-term survival after liver resection for different BCLC stages of HCC.

BACKGROUND: Underlying liver function is a major concern when applying surgical resection for hepatocellular carcinoma (HCC). We aimed to explore the capability of the albumin-bilirubin (ALBI) grade to predict post-hepatectomy liver failure (PHLF) and long-term survival after hepatectomy for HCC patients with different Barcelona Clinic Liver Cancer (BCLC) stages. METHODS: Between January 2010 and December 2014, 338 HCC patients who were treated with liver resection were enrolled. The predictive accuracy of ALBI grade system for PHLF and long-term survival across different BCLC stages was examined. RESULTS: A total of 26 (7.7%) patients developed PHLF. Patients were divided into BCLC 0/A and BCLC B/C categories. ALBI score was found to be a strong independent predictor of PHLF across different BCLC stages by multivariate analysis. In terms of overall survival (OS), it exhibited high discriminative power in the total cohort and in BCLC 0/A subgroup. However, differences in OS between ALBI grade 1 and 2 patients in BCLC B/C subgroup were not significant (P = 0.222). CONCLUSION: The ALBI grade showed good predictive ability for PHLF in HCC patients across different BCLC stages. However, the ALBI grade was only a significant predictor of OS in BCLC stage 0/A patients and failed to predict OS in BCLC stage B/C patients.

LollipopE: Bi-centered lollipop embedding for complex logic query on knowledge graph.

Answering complex First-Order Logic (FOL) query plays a vital role in multi-hop knowledge graph (KG) reasoning. Geometric methods have emerged as a promising category of approaches in this context. However, existing best-performing geometric query embedding (QE) model is still up against three-fold potential problems: (i) underutilization of embedding space, (ii) overreliance on angle information, (iii) uncaptured hierarchy structure. To bridge the gap, we propose a lollipop-like bi-centered query embedding method named LollipopE. To fully utilize embedding space, LollipopE employs learnable centroid positions to represent multiple entities distributed along the same axis. To address the potential overreliance on angular metrics, we design an angular-based and centroid-based metric. This involves calculating both an angular distance and a centroid-based geodesic distance, which empowers the model to make more informed selections of relevant answers from a wider perspective. To effectively capture the hierarchical relationships among entities within the KG, we incorporate dynamic moduli, which allows for the representation of the hierarchical structure among entities. Extensive experiments demonstrate that LollipopE surpasses the state-of-the-art geometric methods. Especially, on more hierarchical datasets, LollipopE achieves the most significant improvement.

MAG-Net : Multi-fusion network with grouped attention for retinal vessel segmentation.

Retinal vessel segmentation plays a vital role in the clinical diagnosis of ophthalmic diseases. Despite convolutional neural networks (CNNs) excelling in this task, challenges persist, such as restricted receptive fields and information loss from downsampling. To address these issues, we propose a new multi-fusion network with grouped attention (MAG-Net). First, we introduce a hybrid convolutional fusion module instead of the original encoding block to learn more feature information by expanding the receptive field. Additionally, the grouped attention enhancement module uses high-level features to guide low-level features and facilitates detailed information transmission through skip connections. Finally, the multi-scale feature fusion module aggregates features at different scales, effectively reducing information loss during decoder upsampling. To evaluate the performance of the MAG-Net, we conducted experiments on three widely used retinal datasets: DRIVE, CHASE and STARE. The results demonstrate remarkable segmentation accuracy, specificity and Dice coefficients. Specifically, the MAG-Net achieved segmentation accuracy values of 0.9708, 0.9773 and 0.9743, specificity values of 0.9836, 0.9875 and 0.9906 and Dice coefficients of 0.8576, 0.8069 and 0.8228, respectively. The experimental results demonstrate that our method outperforms existing segmentation methods exhibiting superior performance and segmentation outcomes.

MGFmiRNAloc:predicting miRNA Subcellular Localization Using Molecular Graph Feature and Convolutional Block Attention Module.

MiRNA has distinct physiological functions at various cellular locations. However, few effective computational methods for predicting the subcellular location of miRNA exist, thereby leaving considerable room for improvement. Accordingly, our study proposes the MGFmiRNAloc simplified molecular input line entry system (SMILES) format as a new approach for predicting the subcellular localization of miRNA. Additionally, the graphical convolutional network (GCN) technique was employed to extract the atomic nodes and topological structure of a single base, thereby constructing RNA sequence molecular map features. Subsequently, the channel attention and spatial attention mechanisms (CBAM) were designed to mine deeper for more efficient information. Finally, the prediction module was used to detect the subcellular localization of miRNA. The 10-fold cross-validation and independent test set experiments demonstrate that MGFmiRNAloc outperforms the most sophisticated methods. The results indicate that the new atomic level feature representation proposed in this study could overcome the limitations of small samples and short miRNA sequences, accurately predict the subcellular localization of miRNAs, and be extended to the subcellular localization of other sequences.

Surfactant-Free Synthesis of Crystalline Mesoporous Metal Oxides by a Seeds/ NaCl-Mediated Growth Strategy.

Transitional metal oxides (TMOs) with ultra-high specific surface areas (SSAs), large pore volume, and tailored exposed facets appeal to significant interests in heterogeneous catalysis. Nevertheless, synthesizing the metal oxides with all the above features is challenging. Herein, the so-called seeds/NaCl-mediated growth method is successfully developed based on a bottom-up route. First, the (Brunauer-Emmett-Teller) BET SSAs of TMOs prepared with this method are significantly higher, where the BET SSAs of CeO2 , SnO2 , Nb2 O5 , Fe3 O4 , Mn3 O4 , Mg(OH)2 , and ZrO2 reached 187, 275, 518, 212, 147, 186, and 332 m2  g-1 , respectively. Second, these TMOs exhibit unique mesoporous structures, generated mainly by the aggregation of rod-like or other aspherical primary nanoparticles. More importantly, no environmental-unfriendly organic surfactants or expensive metal alkoxides are involved in this method. Therefore, the entire synthesis protocol fully fitted the "green synthesis" definition, and the corresponding TMOs prepares displayed excellent catalytic performance.

Multiorgan dysfunction syndrome due to high-dose cantharidin poisoning: A case report.

BACKGROUND: This report delves into the diagnostic and therapeutic journey undertaken by a patient with high-dose cantharidin poisoning and multiorgan dysfunction syndrome (MODS). Particular emphasis is placed on the comprehensive elucidation of the clinical manifestations of high-dose cantharidin poisoning, the intricate path to diagnosis, and the exploration of potential underlying mechanisms. CASE SUMMARY: A patient taking 10 g of cantharidin powder orally subsequently developed MODS. The patient was treated with supportive care, fluid hydration and antibiotics, and hemoperfusion and hemofiltration therapy for 24 h and successfully recovered 8 d after hospital admission. Cantharidin poisoning can cause life-threatening MODS and is rare clinically. This case underscores the challenge in diagnosis and highlights the need for early clinical differentiation to facilitate accurate assessment and prompt intervention. CONCLUSION: This article has reported and analyzed the clinical data, diagnosis, treatment, and prognosis of a case of high-dose cantharidin poisoning resulting in MODS and reviewed the relevant literature to improve the clinical understanding of this rare condition.

Silane coupling agent in biomedical materials.

Medical devices are becoming more and more significant in our daily life. For implantable medical devices, good biocompatibility is required for further use in vivo. Thus, surface modification of medical devices is really important, which gives a wide application scene for a silane coupling agent. The silane coupling agent is able to form a durable bond between organic and inorganic materials. The dehydration process provides linking sites to achieve condensation of two hydroxyl groups. The forming covalent bond brings excellent mechanical properties among different surfaces. Indeed, the silane coupling agent is a popular component in surface modification. Metals, proteins, and hydrogels are using silane coupling agent to link parts commonly. The mild reaction environment also brings advantages for the spread of the silane coupling agent. In this review, we summarize two main methods of using the silane coupling agent. One is acting as a crosslinker mixed in the whole system, and the other is to provide a bridge between different surfaces. Moreover, we introduce their applications in biomedical devices.

Tackling higher-order relations and heterogeneity: Dynamic heterogeneous hypergraph network for spatiotemporal activity prediction.

Spatiotemporal activity prediction aims to predict user activities at a particular time and location, which is applicable in city planning, activity recommendations, and other domains. The fundamental endeavor in spatiotemporal activity prediction is to model the intricate interaction patterns among users, locations, time, and activities, which is characterized by higher-order relations and heterogeneity. Recently, graph-based methods have gained popularity due to the advancements in graph neural networks. However, these methods encounter two significant challenges. Firstly, higher-order relations and heterogeneity are not adequately modeled. Secondly, the majority of established methods are designed around the static graph structures that rely solely on co-occurrence relations, which can be imprecise. To overcome these challenges, we propose DyH2N, a dynamic heterogeneous hypergraph network for spatiotemporal activity prediction. Specifically, to enhance the capacity for modeling higher-order relations, hypergraphs are employed in lieu of graphs. Then we propose a set representation learning-inspired heterogeneous hyperedge learning module, which models higher-order relations and heterogeneity in spatiotemporal activity prediction using a non-decomposable manner. To improve the encoding of heterogeneous spatiotemporal activity hyperedges, a knowledge representation-regularized loss is introduced. Moreover, we present a hypergraph structure learning module to update the hypergraph structures dynamically. Our proposed DyH2N model has been extensively tested on four real-world datasets, proving to outperform previous state-of-the-art methods by 5.98% to 27.13%. The effectiveness of all framework components is demonstrated through ablation experiments.

CRBSP:Prediction of CircRNA-RBP Binding Sites Based on Multimodal Intermediate Fusion.

Circular RNA (CircRNA) is widely expressed and has physiological and pathological significance, regulating post-transcriptional processes via its protein-binding activity. However, whereas much work has been done on linear RNA and RNA binding protein (RBP), little is known about the binding sites of CircRNA. The current report is on the development of a medium-term multimodal data fusion strategy, CRBSP, to predict CircRNA-RBP binding sites. CRBSP represents the CircRNA trinucleotide semantic, location, composition and frequency information as the corresponding coding methods of Word to vector (Word2vec), Position-specific trinucleotide propensity (PSTNP), Pseudo trinucleotide composition (PseTNC) and Trinucleotide nucleotide composition (TNC), respectively. CNN (Convolution Neural Networks) was used to extract global information and BiLSTM (bidirectional Long- and Short-Term Memory network) encoder and LSTM (Long- and Short-Term Memory network) decoder for local sequence information. Enhancement of the contributions of key features by the self-attention mechanism was followed by mid-term fusion of the four enhanced features. Logistic Regression (LR) classifier showed that CRBSP gives a mean AUC value of 0.9362 through 5-fold Cross Validation of all 37 datasets, a performance which is superior to five current state-of-the-art models. Similar evaluation of linear RNA-RBP binding sites gave an AUC value of 0.7615 which is also higher than other prediction methods, demonstrating the robustness of CRBSP.

STDP-based adaptive graph convolutional networks for automatic sleep staging.

Automatic sleep staging is important for improving diagnosis and treatment, and machine learning with neuroscience explainability of sleep staging is shown to be a suitable method to solve this problem. In this paper, an explainable model for automatic sleep staging is proposed. Inspired by the Spike-Timing-Dependent Plasticity (STDP), an adaptive Graph Convolutional Network (GCN) is established to extract features from the Polysomnography (PSG) signal, named STDP-GCN. In detail, the channel of the PSG signal can be regarded as a neuron, the synapse strength between neurons can be constructed by the STDP mechanism, and the connection between different channels of the PSG signal constitutes a graph structure. After utilizing GCN to extract spatial features, temporal convolution is used to extract transition rules between sleep stages, and a fully connected neural network is used for classification. To enhance the strength of the model and minimize the effect of individual physiological signal discrepancies on classification accuracy, STDP-GCN utilizes domain adversarial training. Experiments demonstrate that the performance of STDP-GCN is comparable to the current state-of-the-art models.

A Review of Cu-Ni-Sn Alloys: Processing, Microstructure, Properties, and Developing Trends.

Cu-Ni-Sn alloys have been widely used in the aerospace industry, the electronics industry, and other fields due to their excellent electrical and thermal conductivity, high strength, corrosion and wear resistance, etc., which make Cu-15Ni-8Sn alloys the perfect alternative to Cu-Be alloys. This paper begins with how Cu-Ni-Sn alloys are prepared. Then, the microstructural features, especially the precipitation order of each phase, are described. In addition, the influence of alloying elements, such as Si, Ti, and Nb, on its microstructure and properties is discussed. Finally, the effects of plastic deformation and heat treatment on Cu-Ni-Sn alloys are discussed. This review is able to provide insight into the development of novel Cu-Ni-Sn alloys with a high performance.

Causal Association Between Tea Consumption and Gout: A Mendelian Randomization Study.

OBJECTIVE: Evidence from prospective studies on the consumption of tea and risk of gout is conflicting and limited. We aimed to investigate the potential causal effects of tea intake on gout using Mendelian randomization (MR). METHODS: Genome-wide association studies in UK Biobank included 349 376 individuals and successfully discovered single-nucleotide polymorphisms linked to consumption of one cup of tea per day. Summary statistics from the Chronic Kidney Disease Genetics consortium included 13 179 cases and 750 634 controls for gout. Two-sample MR analyses were used to evaluate the relationship between tea consumption and gout risk. The inverse-variance weighted (IVW) method was used for primary analysis, and sensitivity analyses were also conducted to validate the potential causal effect. RESULTS: In this study, the genetically predicted increase in tea consumption per cup was associated with a lower risk of gout in the IVW method (OR: 0.90; 95% CI: 0.82-0.98). Similar results were found in weighted median methods (OR: 0.88; 95% CI: 0.78-1.00), while no significant associations were found in MR-Egger (OR: 0.89; 95% CI: 0.71-1.11), weighted mode (OR: 0.80; 95% CI: 0.65-0.99), and simple mode (OR: 1.01; 95% CI: 0.75-1.36). In addition, no evidence of pleiotropy was detected by MR-Egger regression (P=0.95) or MR-PRESSO analysis (P=0.07). CONCLUSION: This study provides evidence for the daily consumption of an extra cup of tea to reduce the risk of gout.