XAI Driven Intelligent IoMT Secure Data Management Framework.

The Internet of Medical Things (IoMT) has transformed traditional healthcare systems by enabling real-time monitoring, remote diagnostics, and data-driven treatment. However, security and privacy remain significant concerns for IoMT adoption due to the sensitive nature of medical data. Therefore, we propose an integrated framework leveraging blockchain and explainable artificial intelligence (XAI) to enable secure, intelligent, and transparent management of IoMT data. First, the traceability and tamper-proof of blockchain are used to realize the secure transaction of IoMT data, transforming the secure transaction of IoMT data into a two-stage Stackelberg game. The dual-chain architecture is used to ensure the security and privacy protection of the transaction. The main-chain manages regular IoMT data transactions, while the side-chain deals with data trading activities aimed at resale. Simultaneously, the perceptual hash technology is used to realize data rights confirmation, which maximally protects the rights and interests of each participant in the transaction. Subsequently, medical time-series data is modeled using bidirectional simple recurrent units to detect anomalies and cyberthreats accurately while overcoming vanishing gradients. Lastly, an adversarial sample generation method based on local interpretable model-agnostic explanations is provided to evaluate, secure, and improve the anomaly detection model, as well as to make it more explainable and resilient to possible adversarial attacks. Simulation results are provided to illustrate the high performance of the integrated secure data management framework leveraging blockchain and XAI, compared with the benchmarks.

DeepCBA: a deep learning framework for gene expression prediction in maize based on DNA sequence and chromatin interaction.

Chromatin interactions create spatial proximity between distal regulatory elements and target genes in the genome, which has an important impact on gene expression, transcriptional regulation, and phenotypic traits. To date, several methods have been developed for predicting gene expression. However, existing methods do not take into consideration the impact of chromatin interactions on target gene expression, thus potentially reduces the accuracy of gene expression prediction and mining of important regulatory elements. In this study, a highly accurate deep learning-based gene expression prediction model (DeepCBA) based on maize chromatin interaction data was developed. Compared with existing models, DeepCBA exhibits higher accuracy in expression classification and expression value prediction. The average Pearson correlation coefficients (PCC) for predicting gene expression using gene promoter proximal interactions, proximal-distal interactions, and proximal and distal interactions were 0.818, 0.625, and 0.929, respectively, representing an increase of 0.357, 0.16, and 0.469 over the PCC of traditional methods that only use gene proximal sequences. Some important motifs were identified through DeepCBA and were found to be enriched in open chromatin regions and expression quantitative trait loci (eQTL) and have the molecular characteristic of tissue specificity. Importantly, the experimental results of maize flowering-related gene ZmRap2.7 and tillering-related gene ZmTb1 demonstrate the feasibility of DeepCBA in exploring regulatory elements that affect gene expression. Moreover, the promoter editing and verification of two reported genes (ZmCLE7, ZmVTE4) demonstrated new insights of DeepCBA in precise designing of gene expression and even future intelligent breeding. DeepCBA is available at http://www.deepcba.com/ or http://124.220.197.196/.

ParaCPI: A Parallel Graph Convolutional Network for Compound-Protein Interaction Prediction.

Identifying compound-protein interactions (CPIs) is critical in drug discovery, as accurate prediction of CPIs can remarkably reduce the time and cost of new drug development. The rapid growth of existing biological knowledge has opened up possibilities for leveraging known biological knowledge to predict unknown CPIs. However, existing CPI prediction models still fall short of meeting the needs of practical drug discovery applications. A novel parallel graph convolutional network model for CPI prediction (ParaCPI) is proposed in this study. This model constructs feature representation of compounds using a unique approach to predict unknown CPIs from known CPI data more effectively. Experiments are conducted on five public datasets, and the results are compared with current state-of-the-art (SOTA) models under three different experimental settings to evaluate the model's performance. In the three cold-start settings, ParaCPI achieves an average performance gain of 26.75%, 23.84%, and 14.68% in terms of area under the curve compared with the other SOTA models. In addition, the results of the experiments in the case study show ParaCPI's superior ability to predict unknown CPIs based on known data, with higher accuracy and stronger generalization compared with the SOTA models. Researchers can leverage ParaCPI to accelerate the drug discovery process.

An overview of detecting gene-trait associations by integrating GWAS summary statistics and eQTLs.

Detecting genes that affect specific traits (such as human diseases and crop yields) is important for treating complex diseases and improving crop quality. A genome-wide association study (GWAS) provides new insights and directions for understanding complex traits by identifying important single nucleotide polymorphisms. Many GWAS summary statistics data related to various complex traits have been gathered recently. Studies have shown that GWAS risk loci and expression quantitative trait loci (eQTLs) often have a lot of overlaps, which makes gene expression gradually become an important intermediary to reveal the regulatory role of GWAS. In this review, we review three types of gene-trait association detection methods of integrating GWAS summary statistics and eQTLs data, namely colocalization methods, transcriptome-wide association study-oriented approaches, and Mendelian randomization-related methods. At the theoretical level, we discussed the differences, relationships, advantages, and disadvantages of various algorithms in the three kinds of gene-trait association detection methods. To further discuss the performance of various methods, we summarize the significant gene sets that influence high-density lipoprotein, low-density lipoprotein, total cholesterol, and triglyceride reported in 16 studies. We discuss the performance of various algorithms using the datasets of the four lipid traits. The advantages and limitations of various algorithms are analyzed based on experimental results, and we suggest directions for follow-up studies on detecting gene-trait associations.

DER-GCN: Dialog and Event Relation-Aware Graph Convolutional Neural Network for Multimodal Dialog Emotion Recognition.

With the continuous development of deep learning (DL), the task of multimodal dialog emotion recognition (MDER) has recently received extensive research attention, which is also an essential branch of DL. The MDER aims to identify the emotional information contained in different modalities, e.g., text, video, and audio, and in different dialog scenes. However, the existing research has focused on modeling contextual semantic information and dialog relations between speakers while ignoring the impact of event relations on emotion. To tackle the above issues, we propose a novel dialog and event relation-aware graph convolutional neural network (DER-GCN) for multimodal emotion recognition method. It models dialog relations between speakers and captures latent event relations information. Specifically, we construct a weighted multirelationship graph to simultaneously capture the dependencies between speakers and event relations in a dialog. Moreover, we also introduce a self-supervised masked graph autoencoder (SMGAE) to improve the fusion representation ability of features and structures. Next, we design a new multiple information Transformer (MIT) to capture the correlation between different relations, which can provide a better fuse of the multivariate information between relations. Finally, we propose a loss optimization strategy based on contrastive learning to enhance the representation learning ability of minority class features. We conduct extensive experiments on the benchmark datasets, Interactive Emotional Dyadic Motion Capture (IEMOCAP) and Multimodal EmotionLines Dataset (MELD), which verify the effectiveness of the DER-GCN model. The results demonstrate that our model significantly improves both the average accuracy and the F1 value of emotion recognition. Our code is publicly available at https://github.com/yuntaoshou/DER-GCN.

MBB-MOGWO: Modified Boltzmann-Based Multi-Objective Grey Wolf Optimizer.

The primary objective of multi-objective optimization techniques is to identify optimal solutions within the context of conflicting objective functions. While the multi-objective gray wolf optimization (MOGWO) algorithm has been widely adopted for its superior performance in solving multi-objective optimization problems, it tends to encounter challenges such as local optima and slow convergence in the later stages of optimization. To address these issues, we propose a Modified Boltzmann-Based MOGWO, referred to as MBB-MOGWO. The performance of the proposed algorithm is evaluated on multiple multi-objective test functions. Experimental results demonstrate that MBB-MOGWO exhibits rapid convergence and a reduced likelihood of being trapped in local optima. Furthermore, in the context of the Internet of Things (IoT), the quality of web service composition significantly impacts complexities related to sensor resource scheduling. To showcase the optimization capabilities of MBB-MOGWO in real-world scenarios, the algorithm is applied to address a Multi-Objective Problem (MOP) within the domain of web service composition, utilizing real data records from the QWS dataset. Comparative analyses with four representative algorithms reveal distinct advantages of our MBB-MOGWO-based method, particularly in terms of solution precision for web service composition. The solutions obtained through our method demonstrate higher fitness and improved service quality.

Enhancing drug-food interaction prediction with precision representations through multilevel self-supervised learning.

Drug-food interactions (DFIs) crucially impact patient safety and drug efficacy by modifying absorption, distribution, metabolism, and excretion. The application of deep learning for predicting DFIs is promising, yet the development of computational models remains in its early stages. This is mainly due to the complexity of food compounds, challenging dataset developers in acquiring comprehensive ingredient data, often resulting in incomplete or vague food component descriptions. DFI-MS tackles this issue by employing an accurate feature representation method alongside a refined computational model. It innovatively achieves a more precise characterization of food features, a previously daunting task in DFI research. This is accomplished through modules designed for perturbation interactions, feature alignment and domain separation, and inference feedback. These modules extract essential information from features, using a perturbation module and a feature interaction encoder to establish robust representations. The feature alignment and domain separation modules are particularly effective in managing data with diverse frequencies and characteristics. DFI-MS stands out as the first in its field to combine data augmentation, feature alignment, domain separation, and contrastive learning. The flexibility of the inference feedback module allows its application in various downstream tasks. Demonstrating exceptional performance across multiple datasets, DFI-MS represents a significant advancement in food presentations technology. Our code and data are available at https://github.com/kkkayle/DFI-MS.

Can pluripotent/multipotent stem cells reverse Parkinson's disease progression?

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by continuous and selective degeneration or death of dopamine neurons in the midbrain, leading to dysfunction of the nigrostriatal neural circuits. Current clinical treatments for PD include drug treatment and surgery, which provide short-term relief of symptoms but are associated with many side effects and cannot reverse the progression of PD. Pluripotent/multipotent stem cells possess a self-renewal capacity and the potential to differentiate into dopaminergic neurons. Transplantation of pluripotent/multipotent stem cells or dopaminergic neurons derived from these cells is a promising strategy for the complete repair of damaged neural circuits in PD. This article reviews and summarizes the current preclinical/clinical treatments for PD, their efficacies, and the advantages/disadvantages of various stem cells, including pluripotent and multipotent stem cells, to provide a detailed overview of how these cells can be applied in the treatment of PD, as well as the challenges and bottlenecks that need to be overcome in future translational studies.

Dual-View Learning Based on Images and Sequences for Molecular Property Prediction.

The prediction of molecular properties remains a challenging task in the field of drug design and development. Recently, there has been a growing interest in the analysis of biological images. Molecular images, as a novel representation, have proven to be competitive, yet they lack explicit information and detailed semantic richness. Conversely, semantic information in SMILES sequences is explicit but lacks spatial structural details. Therefore, in this study, we focus on and explore the relationship between these two types of representations, proposing a novel multimodal architecture named ISMol. ISMol relies on a cross-attention mechanism to extract information representations of molecules from both images and SMILES strings, thereby predicting molecular properties. Evaluation results on 14 small molecule ADMET datasets indicate that ISMol outperforms machine learning (ML) and deep learning (DL) models based on single-modal representations. In addition, we analyze our method through a large number of experiments to test the superiority, interpretability and generalizability of the method. In summary, ISMol offers a powerful deep learning toolbox for drug discovery in a variety of molecular properties.

Translocation of IGF-1R in endoplasmic reticulum enhances SERCA2 activity to trigger Ca2+ER perturbation in hepatocellular carcinoma.

The well-known insulin-like growth factor 1 (IGF1)/IGF-1 receptor (IGF-1R) signaling pathway is overexpressed in many tumors, and is thus an attractive target for cancer treatment. However, results have often been disappointing due to crosstalk with other signals. Here, we report that IGF-1R signaling stimulates the growth of hepatocellular carcinoma (HCC) cells through the translocation of IGF-1R into the ER to enhance sarco-endoplasmic reticulum calcium ATPase 2 (SERCA2) activity. In response to ligand binding, IGF-1Rß is translocated into the ER by ß-arrestin2 (ß-arr2). Mass spectrometry analysis identified SERCA2 as a target of ER IGF-1Rß. SERCA2 activity is heavily dependent on the increase in ER IGF-1Rß levels. ER IGF-1Rß phosphorylates SERCA2 on Tyr990 to enhance its activity. Mutation of SERCA2-Tyr990 disrupted the interaction of ER IGF-1Rß with SERCA2, and therefore ER IGF-1Rß failed to promote SERCA2 activity. The enhancement of SERCA2 activity triggered Ca2+ER perturbation, leading to an increase in autophagy. Thapsigargin blocked the interaction between SERCA2 and ER IGF-1Rß and therefore SERCA2 activity, resulting in inhibition of HCC growth. In conclusion, the translocation of IGF-1R into the ER triggers Ca2+ER perturbation by enhancing SERCA2 activity through phosphorylating Tyr990 in HCC.

Activation of insulin-like growth factor-1 receptor (IGF-1R) promotes growth of colorectal cancer through triggering the MEX3A-mediated degradation of RIG-I.

Insulin-like growth factor-1 receptor (IGF-1R) has been made an attractive anticancer target due to its overexpression in cancers. However, targeting it has often produced the disappointing results as the role played by cross talk with numerous downstream signalings. Here, we report a disobliging IGF-1R signaling which promotes growth of cancer through triggering the E3 ubiquitin ligase MEX3A-mediated degradation of RIG-I. The active ß-arrestin-2 scaffolds this disobliging signaling to talk with MEX3A. In response to ligands, IGF-1Rß activated the basal ßarr2 into its active state by phosphorylating the interdomain domain on Tyr64 and Tyr250, opening the middle loop (Leu130‒Cys141) to the RING domain of MEX3A through the conformational changes of ßarr2. The models of ßarr2/IGF-1Rß and ßarr2/MEX3A could interpret the mechanism of the activated-IGF-1R in triggering degradation of RIG-I. The assay of the mutants ßarr2Y64A and ßarr2Y250A further confirmed the role of these two Tyr residues of the interlobe in mediating the talk between IGF-1Rß and the RING domain of MEX3A. The truncated-ßarr2 and the peptide ATQAIRIF, which mimicked the RING domain of MEX3A could prevent the formation of ßarr2/IGF-1Rß and ßarr2/MEX3A complexes, thus blocking the IGF-1R-triggered RIG-I degradation. Degradation of RIG-I resulted in the suppression of the IFN-I-associated immune cells in the TME due to the blockade of the RIG-I-MAVS-IFN-I pathway. Poly(I:C) could reverse anti-PD-L1 insensitivity by recovery of RIG-I. In summary, we revealed a disobliging IGF-1R signaling by which IGF-1Rß promoted cancer growth through triggering the MEX3A-mediated degradation of RIG-I.

Predicting examinee performance based on a fuzzy cloud cognitive diagnosis framework in e-learning environment.

The score profiles could be used to measure learners' skills proficiency via cognitive diagnosis models (CDMs) for predicting their performance in the future examination. The prediction results could provide important decision-making supports for personalized e-learning instruction. However, facing the possible complexity of skills, the uncertainty of learners' skill proficiency and the large-scale volume of score profiles, the existing CDMs have limitations in the measurement mechanisms and diagnostic efficiency. In this paper, we proposed an approach based on a fuzzy cloud cognitive diagnosis framework (FC-CDF) to predicting examinees' performance in e-learning environment. In this approach, the normal cloud models (NCMs) are utilized innovatively to measure the expectation, degree of variation and variation frequency of learners' skill proficiency, and each NCM is transformed into an interval fuzzy number to characterize the uncertainty of the skill proficiency for every learner. Combining the educational psychology hypothesis with the parameter estimation method, we could obtain the learners' skill proficiency level and the slip and guess factors relevant to each test item, based on which the learners' scores could be predicted in a future test. Finally, the experiments demonstrate that the proposed approach provides good accuracy and significantly reduces execution time for predicting examinee performance, compared with the existing methods.

Traffic forecasting with graph spatial-temporal position recurrent network.

With the development of social economy and smart technology, the explosive growth of vehicles has caused traffic forecasting to become a daunting challenge, especially for smart cities. Recent methods exploit graph spatial-temporal characteristics, including constructing the shared patterns of traffic data, and modeling the topological space of traffic data. However, existing methods fail to consider the spatial position information and only utilize little spatial neighborhood information. To tackle above limitation, we design a Graph Spatial-Temporal Position Recurrent Network (GSTPRN) architecture for traffic forecasting. We first construct a position graph convolution module based on self-attention and calculate the dependence strengths among the nodes to capture the spatial dependence relationship. Next, we develop approximate personalized propagation that extends the propagation range of spatial dimension information to obtain more spatial neighborhood information. Finally, we systematically integrate the position graph convolution, approximate personalized propagation and adaptive graph learning into a recurrent network (i.e. Gated Recurrent Units). Experimental evaluation on two benchmark traffic datasets demonstrates that GSTPRN is superior to the state-of-art methods.

Graph-generative neural network for EEG-based epileptic seizure detection via discovery of dynamic brain functional connectivity.

Dynamic complexity in brain functional connectivity has hindered the effective use of signal processing or machine learning methods to diagnose neurological disorders such as epilepsy. This paper proposed a new graph-generative neural network (GGN) model for the dynamic discovery of brain functional connectivity via deep analysis of scalp electroencephalogram (EEG) signals recorded from various regions of a patient's scalp. Brain functional connectivity graphs are generated for the extraction of spatial-temporal resolution of various onset epilepsy seizure patterns. Our supervised GGN model was substantiated by seizure detection and classification experiments. We train the GGN model using a clinically proven dataset of over 3047 epileptic seizure cases. The GGN model achieved a 91% accuracy in classifying seven types of epileptic seizure attacks, which outperformed the 65%, 74%, and 82% accuracy in using the convolutional neural network (CNN), graph neural networks (GNN), and transformer models, respectively. We present the GGN model architecture and operational steps to assist neuroscientists or brain specialists in using dynamic functional connectivity information to detect neurological disorders. Furthermore, we suggest to merge our spatial-temporal graph generator design in upgrading the conventional CNN and GNN models with dynamic convolutional kernels for accuracy enhancement.

The antifibrotic effects of the novel compound gorse isoflavone alkaloid on chemical liver injury in rats.

OBJECTIVE: Liver fibrosis is a frequently occurring liver injury which lacks of effective treatment clinically. Here, we investigated the protective effects of a novel compound Gorse isoflavone alkaloid (GIA) against liver fibrosis. METHODS: Totally forty rats were randomly divided into four groups. Then we established a model of liver fibrosis induced by the intragastric administration of carbon tetrachloride (CCl4). This treated group was followed by the intragastric administration of GIA and colchicine. Then the liver index and spleen index, and liver function indexes were detected by kit. Western blotting assay was performed to estimate the expression of Transforming Growth Factor-ß1 (TGF-ß1) and related proteins. Tissue fibrosis was observed by Masson staining. RESULTS: Our results suggested that GIA reduced the deposition of collagen fibres and the fibrosis index hydroxyproline (Hyp) of liver tissue. Furthermore, we found that GIA significantly decreased the expression of Transforming Growth Factor-ß1 (TGF-ß1) and the ratio of p-smad2/3 to smad2/3, enhanced the level of superoxide dismutase (SOD), and decreased the concentration of malonic dialdehyde (MDA) in the liver. CONCLUSIONS: Our findings revealed that GIA has a beneficial effect to resist the liver fibrosis, and could be ideal for potential use in antifibrotic drugs for the liver.

Local Sample-Weighted Multiple Kernel Clustering With Consensus Discriminative Graph.

Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a set of base kernels. Constructing precise and local kernel matrices is proven to be of vital significance in applications since the unreliable distant-distance similarity estimation would degrade clustering performance. Although existing localized MKC algorithms exhibit improved performance compared with globally designed competitors, most of them widely adopt the KNN mechanism to localize kernel matrix by accounting for τ -nearest neighbors. However, such a coarse manner follows an unreasonable strategy that the ranking importance of different neighbors is equal, which is impractical in applications. To alleviate such problems, this article proposes a novel local sample-weighted MKC (LSWMKC) model. We first construct a consensus discriminative affinity graph in kernel space, revealing the latent local structures. Furthermore, an optimal neighborhood kernel for the learned affinity graph is output with naturally sparse property and clear block diagonal structure. Moreover, LSWMKC implicitly optimizes adaptive weights on different neighbors with corresponding samples. Experimental results demonstrate that our LSWMKC possesses better local manifold representation and outperforms existing kernel or graph-based clustering algorithms. The source code of LSWMKC can be publicly accessed from https://github.com/liliangnudt/LSWMKC.

Construction of a Nomogram Model for Predicting Peritoneal Dissemination in Gastric Cancer Based on Clinicopathologic Features and Preoperative Serum Tumor Markers.

Background: Peritoneal dissemination (PD) is the most common mode of metastasis for advanced gastric cancer (GC) with poor prognosis. It is of great significance to accurately predict preoperative PD and develop optimal treatment strategies for GC patients. Our study assessed the diagnostic potential of serum tumor markers and clinicopathologic features, to improve the accuracy of predicting the presence of PD in GC patients. Methods: In our study, 1264 patients with GC at Fudan University Shanghai Cancer Center and Wenzhou people's hospital from 2018 to 2020 were retrospectively analyzed, including 316 cases of PD and 948 cases without PD. All patients underwent enhanced CT scan or magnetic resonance imaging (MRI) before surgery and treatment. Clinicopathological features, including tumor diameter and tumor stage (depth of tumor invasion, nearby lymph node metastasis and distant metastasis), were obtained by imaging examination. The independent risk factors for PD were screened through univariate and multivariate logistic regression analyses, and the results were expressed with 95% confidence intervals (CIs). A model of PD diagnosis and prediction was established by using Cox proportional hazards regression model of training set. Furthermore, the accuracy of the prediction model was verified by ROC curve and calibration plots. Results: Univariate analysis showed that PD in GC was significantly related to tumor diameter (odds ratio (OR)=12.06, p<0.0006), depth of invasion (OR=14.55, p<0.0001), lymph node metastases (OR=5.89, p<0.0001), carcinoembryonic antigen (CEA) (OR=2.50, p<0.0001), CA125 (OR=11.46, p<0.0001), CA72-4 (OR=4.09, p<0.0001), CA19-9 (OR=2.74, p<0.0001), CA50 (OR=5.20, p<0.0001) and CA242 (OR=3.83, p<0.0001). Multivariate analysis revealed that clinical invasion depth and serum marker of CA125 and CA72-4 were independent risk factors for PD. The prediction model was established based on the risk factors using the R program. The area under the curve (AUC) of the receiver operating characteristics (ROC) was 0.931 (95% CI: 0.900-0.960), with the accuracy, sensitivity and specificity values of 90.5%, 86.2% and 82.2%, respectively. Conclusion: The nomogram model constructed using CA125, CA72-4 and depth of invasion increases the accuracy and sensitivity in predicting the incidence of PD in GC patients and can be used as an important tool for preoperative diagnosis.

LAP: Latency-aware automated pruning with dynamic-based filter selection.

Model pruning is widely used to compress and accelerate convolutional neural networks (CNNs). Conventional pruning techniques only focus on how to remove more parameters while ensuring model accuracy. This work not only covers the optimization of model accuracy, but also optimizes the model latency during pruning. When there are multiple optimization objectives, the difficulty of algorithm design increases exponentially. So latency sensitivity is proposed to effectively guide the determination of layer sparsity in this paper. We present the latency-aware automated pruning (LAP) framework which leverages the reinforcement learning to automatically determine the layer sparsity. Latency sensitivity is used as a prior knowledge and involved into the exploration loop. Rather than relying on a single reward signal such as validation accuracy or floating-point operations (FLOPs), our agent receives the feedback on the accuracy error and latency sensitivity. We also provide a novel filter selection algorithm to accurately distinguish important filters within a layer based on their dynamic changes. Compared to the state-of-the-art compression policies, our framework demonstrated superior performances for VGGNet, ResNet, and MobileNet on CIFAR-10, ImageNet, and Food-101. Our LAP allowed the inference latency of MobileNet-V1 to achieve approximately 1.64 times speedup on the Titan RTX GPU, with no loss of ImageNet Top-1 accuracy. It significantly improved the pareto optimal curve on the accuracy and latency trade-off.

VhaAC39-1 regulates gut homeostasis and affects the health span in Drosophila.

Gut homeostasis is a dynamically balanced state to maintain intestinal health. Vacuolar ATPases (V-ATPases) are multi-subunit proton pumps that were driven by ATP hydrolysis. Several subunits of V-ATPases may be involved in the maintenance of intestinal pH and gut homeostasis in Drosophila. However, the specific role of each subunit in this process remains to be elucidated. Here, we knocked down the Drosophila gene VhaAC39-1 encoding the V0d1 subunit of V-ATPases to assess its function in gut homeostasis. Knockdown of VhaAC39-1 resulted in the loss of midgut acidity, the increase of the number of gut microbiota and the impairment of intestinal epithelial integrity in flies. The knockdown of VhaAC39-1 led to the hyperproliferation of intestinal stem cells, increasing the number of enteroendocrine cells, and activated IMD signaling pathway and JAK-STAT signaling pathway, inducing intestinal immune response of Drosophila. In addition, knockdown of VhaAC39-1 caused the disturbance of many physiological indicators such as food intake, triglyceride level and fecundity of flies, which ultimately led to the shortening of the life span of Drosophila. These results shed light on the gut homeostasis mechanisms which would help to identify interventions to promote healthy aging.