EphB2-dependent prefrontal cortex activation promotes long-range social approach and partner responsiveness.

Social behavior starts with dynamic approach prior to the final consummation. The flexible processes ensure mutual feedback across social brains to transmit signals. However, how the brain responds to the initial social stimuli precisely to elicit timed behaviors remains elusive. Here, by using real-time calcium recording, we identify the abnormalities of EphB2 mutant with autism-associated Q858X mutation in processing long-range approach and accurate activity of prefrontal cortex (dmPFC). The EphB2-dependent dmPFC activation precedes the behavioral onset and is actively associated with subsequent social action with the partner. Furthermore, we find that partner dmPFC activity is responsive coordinately to the approaching WT mouse rather than Q858X mutant mouse, and the social defects caused by the mutation are rescued by synchro-optogenetic activation in dmPFC of paired social partners. These results thus reveal that EphB2 sustains neuronal activation in the dmPFC that is essential for the proactive modulation of social approach to initial social interaction.

Transcriptome features of innate immune memory in Drosophila.

Immune memory is the ability of organisms to elicit potentiated immune responses at secondary infection. Current studies have revealed that similar to adaptive immunity, innate immunity exhibits memory characteristics (called "innate immune memory"). Although epigenetic reprogramming plays an important role in innate immune memory, the underlying mechanisms have not been elucidated, especially at the individual level. Here, we established experimental systems for detecting innate immune memory in Drosophila melanogaster. Training infection with low-pathogenic bacteria enhanced the survival rate of the flies at subsequent challenge infection with high-pathogenic bacteria. Among low-pathogenic bacteria, Micrococcus luteus (Ml) and Salmonella typhimurium (St) exerted apparent training effects in the fly but exhibited different mechanisms of action. Ml exerted training effects even after its clearance from flies, while live St persisted in the flies for a prolonged duration. RNA sequencing (RNA-Seq) analysis revealed that Ml training enhanced the expression of the immune-related genes under the challenge condition but not under the non-challenge condition. In contrast, St training upregulated the expression of the immune-related genes independent of challenge. These results suggest that training effects with Ml and St are due to memory and persistence of immune responses, respectively. Furthermore, we searched for the gene involved in immune memory, and identified a candidate gene, Ada2b, which encodes a component of the histone modification complex. The Ada2b mutant suppressed Ml training effects on survival and disrupted the expression of some genes under the training + challenge condition. These results suggest that the gene expression regulated by Ada2b may contribute to innate immune memory in Drosophila.

Pulmonary interstitial lesions in pemphigus mouse model: Verifying pemphigus may not be only limited to skin and mucosa.

Interstitial lung disease (ILD) has been identified as a prevalent complication and significant contributor to mortality in individuals with pemphigus. In this study, a murine model of pemphigus was developed through the subcutaneous administration of serum IgG obtained from pemphigus patients, allowing for an investigation into the association between pemphigus and ILD. Pulmonary interstitial lesions were identified in the lungs of a pemphigus mouse model through histopathology, RT-qPCR and Sircol assay analyses. The severity of these lesions was found to be positively associated with the concentration of IgG in the injected serum. Additionally, DIF staining revealed the deposition of serum IgG in the lung tissue of pemphigus mice, indicating that the subcutaneous administration of human IgG directly impacted the lung tissue of the mice, resulting in damage. This study confirms the presence of pulmonary interstitial lesions in the pemphigus mouse model and establishes a link between pemphigus and ILD.

miR-34a/TAN1/CREB Axis Engages in Alleviating Oligodendrocyte Trophic Factor-Induced Myelin Repair Function and Astrocyte-Dependent Neuroinflammation in the Early Stages of Alzheimer's Disease: The Anti-Neurodegenerative Effect of Treadmill Exercise.

Reduced myelin stability observed in the early stages of Alzheimer's disease leads to spatial learning and memory impairment. Exercise has been shown to protect nerves, reduce the risk of Alzheimer's disease, and strengthen synaptic connectivity. However, the underlying mechanisms of how exercise can promote myelin repair and coordinate inflammation and proliferation are still uncertain. In this study, we conducted histological and biochemical assays of cortical lysates after behavioral testing to detect pathological changes, myelin sheath thickness, and mRNA and protein levels. It is notable that D-galactose model mice exhibited elevated miRNA-34a levels, overactive astrocytes, decreased myelin staining scores, increased apoptosis, and decreased synaptic plasticity in the brain. Significantly, after eight weeks of exercise, we observed improvements in LFB scores, NeuN( +) neuron counts, and myelin basic protein (MBP) expression. Additionally, exercise promoted the expression of oligodendrocyte markers Olig2 and PDFGR-α associated with brain proliferation, and improved spatial cognitive function. Furthermore, it decreased the inflammation caused by astrocyte secretions (TNF-α, Cox-2, CXCL2). Interestingly, we also observed downregulation of miR-34a and activation of the TAN1/PI3K/CREB signaling pathway. Our data shed light on a previously unsuspected mechanism by which exercise reduces miR-34a levels and protects neuronal function and survival by preventing excessive demyelination and inflammatory infiltration in the CNS.

Regulation of Keap1-Nrf2 signaling in health and diseases.

Nuclear factor erythroid 2-related factor 2 (Nrf2) functions as a central regulator in modulating the activities of diverse antioxidant enzymes, maintaining cellular redox balance, and responding to oxidative stress (OS). Kelch-like ECH-associated protein 1 (Keap1) serves as a principal negative modulator in controlling the expression of detoxification and antioxidant genes. It is widely accepted that OS plays a pivotal role in the pathogenesis of various diseases. When OS occurs, leading to inflammatory infiltration of neutrophils, increased secretion of proteases, and the generation of large quantities of reactive oxygen radicals (ROS). These ROS can oxidize or disrupt DNA, lipids, and proteins either directly or indirectly. They also cause gene mutations, lipid peroxidation, and protein denaturation, all of which can result in disease. The Keap1-Nrf2 signaling pathway regulates the balance between oxidants and antioxidants in vivo, maintains the stability of the intracellular environment, and promotes cell growth and repair. However, the antioxidant properties of the Keap1-Nrf2 signaling pathway are reduced in disease. This review overviews the mechanisms of OS generation, the biological properties of Keap1-Nrf2, and the regulatory role of its pathway in health and disease, to explore therapeutic strategies for the Keap1-Nrf2 signaling pathway in different diseases.

PKM2 is a Novel Osteoporosis-Associated Protein in Chinese.

Purpose:Osteoporosis is characterized by low bone mineral density (BMD) and high risk of osteoporotic fracture (OF). Peripheral blood monocytes (PBM) can differentiate into osteoclasts to resorb bone. This study was to identify PBM-expressed proteins significant for osteoporosis in Chinese Han elderly population (>65 years), and focused on two phenotypes of osteoporosis: low BMD and OF. METHODS: Label-free quantitative proteomics was employed to profile PBM proteome and to identify differentially expressed proteins (DEPs) between OF (N=27) vs. non-fractured (NF, N=24) subjects and between low BMD (N=12) vs. high BMD (N=12) subjects in women. Western blotting (WB) was conducted to validate differential expression, and ELISA to evaluate translational value for secretory protein of interest. RESULTS: We discovered 59 DEPs with fold change (FC)>1.3 (P<1×10-5), and validated the significant up-regulation of pyruvate kinase isozyme 2 (PKM2) with osteoporosis (P<0.001). PKM2 protein upregulation with OF was replicated with PBM in men (P=0.04). Plasma PKM2 protein level was significantly elevated with OF in an independent sample (N=100, FC=1.68, P=0.01). Pursuant functional assays showed that extracellular PKM2 protein supplement not only promoted monocyte trans-endothelial migration, growth, and osteoclast differentiation (marker gene expression), but also inhibited osteoblast growth, differentiation (ALP gene expression), and activity. CONCLUSION: The above findings suggest that PKM2 protein is a novel osteoporosis-associated functional protein in Chinese Han elderly population. It may serve as a risk biomarker and drug target for osteoporosis.

Progress on the study of Popeye domain-containing 3 (POPDC3) in malignancies and striated muscle function and homeostasis.

The Popeye domain-containing protein 3 (POPDC3), a transmembrane protein with a unique cyclic adenosine monophosphate (cAMP) binding site, is widely expressed in mammalian tissues, with the highest levels of expression in skeletal muscle. POPDC3 plays a key role in many physiological and pathological processes and is considered a candidate biomarker and potential therapeutic target of cancer. In addition, POPDC3 gene variants have been associated with limb-girdle muscular dystrophy (LGMD) type 26. However, there are only a few studies on the biological role of POPDC3, interacting proteins, potential downstream targets, and regulated signaling pathways. Therefore, this review focuses on the structure of POPDC3 protein, interacting molecules, and the role and mechanism in cancer, and in cardiac and skeletal muscle, and to review the current research progress of POPDC3 and propose possible future study directions.

Re-recognition of innate immune memory as an integrated multidimensional concept.

In the past decade, the concept of immunological memory, which has long been considered a phenomenon observed in the adaptive immunity of vertebrates, has been extended to the innate immune system of various organisms. This de novo immunological memory is mainly called "innate immune memory", "immune priming", or "trained immunity" and has received increased attention because of its potential for clinical and agricultural applications. However, research on different species, especially invertebrates and vertebrates, has caused controversy regarding this concept. Here we discuss the current studies focusing on this immunological memory and summarize several mechanisms underlying it. We propose "innate immune memory" as a multidimensional concept as an integration between the seemingly different immunological phenomena.

Mulberry-like porous-hollow AuPtAg nanorods for electrochemical immunosensing of biomarker myoglobin.

Mulberry-like AuPtAg porous hollow nanorods (PHNR) were facilely synthesized for the first time via a wet chemical method, where Au nanorods (Au NR) behaved as sacrificed template. The anisotropic oriented growth and etching process are involved in this synthesis. Their structural and electronic characteristics were scrutinously examined by TEM, EDS, XPS, and electrochemical techniques. The AuPtAg PHNR provided a large specific surface area and exposed a large number of active sites, showing highly enhanced catalytic activity. On this foundation, a label-free electrochemical immunosensor was developed for myoglobin (Myo) assay based on the AuPtAg PHNR. Further, the built sensor exhibited fast and ultrasensitive responses in a linear range of 0.0001 ~ 1000 ng mL-1 with a low limit of detection (LOD = 0.46 pg mL-1, S/N = 3), and enabled efficient application to human serum samples with acceptable results. Consequently, the developed AuPtAg PHNR-based platform has a broad prospect in practically monitoring Myo and other biomarkers in clinics.

Adaptive Discriminative Regions Learning Network for Remote Sensing Scene Classification.

As an auxiliary means of remote sensing (RS) intelligent interpretation, remote sensing scene classification (RSSC) attracts considerable attention and its performance has been improved significantly by the popular deep convolutional neural networks (DCNNs). However, there are still several challenges that hinder the practical applications of RSSC, such as complex composition of land cover, scale-variation of objects, and redundant and noisy areas for scene classification. In order to mitigate the impact of these issues, we propose an adaptive discriminative regions learning network for RSSC, referred as ADRL-Net briefly, which locates discriminative regions effectively for boosting the performance of RSSC by utilizing a novel self-supervision mechanism. Our proposed ADRL-Net consists of three main modules, including a discriminative region generator, a region discriminator, and a region scorer. Specifically, the discriminative region generator first generates some candidate regions which could be informative for RSSC. Then, the region discriminator evaluates the regions generated by region generator and provides feedback for the generator to update the informative regions. Finally, the region scorer makes prediction scores for the whole image by using the discriminative regions. In such a manner, the three modules of ADRL-Net can cooperate with each other and focus on the most informative regions of an image and reduce the interference of redundant regions for final classification, which is robust to the complex scene composition, object scales, and irrelevant information. In order to validate the efficacy of the proposed network, we conduct experiments on four widely used benchmark datasets, and the experimental results demonstrate that ADRL-Net consistently outperforms other state-of-the-art RSSC methods.

Dupilumab combined with low-dose systemic steroid therapy improves efficacy and safety for bullous pemphigoid.

Routine systemic therapy for bullous pemphigoid (BP) has been challenged due to the inevitably adverse effects. According to the successful applications of dupilumab in BP cases reported, therefore, we investigate the real-life efficacy and safety of dupilumab combined with low-dose oral steroid for BP. A cohort of BP patients who received either dupilumab plus low-dose methylprednisolone (dupilumab group) or merely methylprednisolone (control group) was retrospectively reviewed. The time to disease control was investigated. Additionally, the control dose and cumulative dosage of steroids, Bullous Pemphigoid Disease Area Index (BPDAI) scores, pruritus scores, and adverse events were assessed. A total of 40 patients, with 20 in each group, were retrospectively studied. The time to disease control was shorter in the dupilumab group than the control group (14 days vs. 19 days, p = 0.043). When the disease was controlled, the control dose and cumulative dosage of methylprednisolone in the dupilumab group were substantially lower than those of the control (24.6 mg vs. 48.8 mg, 376.8 mg vs. 985.6 mg, both p < 0.01). Compared with the control, the percentage change from baseline in BPDAI scores and pruritus scores were both significantly reduced, and the adverse events were also less frequent in the dupilumab group. The combination therapy of dupilumab plus low-dose methylprednisolone exhibits superior efficacy and safety in comparison with the current first-line systemic therapy for BP.

Ultrasensitive sandwich-typed electrochemical immunoassay for detection of squamous cell carcinoma antigen based on highly branched PtCo nanocrystals and dendritic mesoporous SiO2@AuPt nanoparticles.

Squamous cell carcinoma antigen (SCCA) is one of the common squamous cell carcinomas (SCC) in women, which usually works as a tumor biomarker for cervical cancer in diagnostic applications. Herein, bimetallic PtCo highly branched nanocrystals (PtCo BNCs) acted as electrode substrates to construct sandwich-typed electrochemical immunosensor for ultrasensitive detection of SCCA, by using dendritic mesoporous SiO2@AuPt nanoparticles (DM-SiO2@AuPt NPs) to adsorb electroactive thionine (Thi) as a signal label. The PtCo BNCs enlarged the loading of the primary antibody (Ab1), showing effective improvement in conductivity and sensitivity. The DM-SiO2 had abundant pores to incorporate more Thi, on which the decorated AuPt NPs created a great number of active sites to immobilize the secondary antibodies (Ab2), thereby obviously amplifying the detection signals. The prepared sensor exhibited a broader linear range (0.001-120 ng mL-1) and a lower detection limit (0.33 pg mL-1, S/N = 3), combined with high reproducibility, a low relative standard deviation (below 2.5%) and acceptable recovery (from 98.5 to 110.0%) even in diluted human serum samples. This research provides a substantial platform for clinical diagnosis of SCCA in practice.

Electrochemical label-free immunoassay of HE4 using 3D PtNi nanocubes assemblies as biosensing interfaces.

Human epididymis protein 4 (HE4) is a vital biomarker for early diagnosis of epithelial ovarian cancer (EOC). Herein, a new label-free biosensor was developed using K3[Fe(CN)6] as the electrochemical probe for ultrasensitive immunoassay of HE4 based on PtNi nanocubes assemblies (NCAs) as efficient biosensing interfaces. The PtNi NCAs were synthesized by a simple solvothermal approach, where N-hexadecyltrimethylammonium chloride (HTAC) and 2,2'-bis(4,5-dimethylimidazole) (BDMM) behaved as co-structuring directors. Under the optimal conditions, the obtained HE4 immunosensor displayed a wide detection range from 0.001 to 100 ng mL-1 and a low detection limit (0.11 pg mL-1, S/N = 3). As a result, the current sensing platform would serve as a useful reference for detecting cancer biomarkers in the clinical assay and diagnosis.

Naturally Occurring Flavonoids and Isoflavonoids and Their Microbial Transformation: A Review.

Flavonoids and isoflavonoids are polyphenolic secondary metabolites usually produced by plants adapting to changing ecological environments over a long period of time. Therefore, their biosynthesis pathways are considered as the most distinctive natural product pathway in plants. Seemingly, the flavonoids and isoflavones from fungi and actinomycetes have been relatively overlooked. In this review, we summarized and classified the isoflavones and flavonoids derived from fungi and actinomycetes and described their biological activities. Increasing attention has been paid to bioactive substances derived from microorganism whole-cell biotransformation. Additionally, we described the utilization of isoflavones and flavonoids as substrates by fungi and actinomycetes for biotransformation through hydroxylation, methylation, halogenation, glycosylation, dehydrogenation, cyclisation, and hydrogenation reactions to obtain rare and highly active biofunctional derivatives. Overall, among all microorganisms, actinomycetes are the main producers of flavonoids. In our review, we also summarized the functional genes involved in flavonoid biosynthesis.

Dual Laplacian regularized matrix completion for microRNA-disease associations prediction.

Since lots of miRNA-disease associations have been verified, it is meaningful to discover more miRNA-disease associations for serving disease diagnosis and prevention of human complex diseases. However, it is not practical to identify potential associations using traditional biological experimental methods since the process is expensive and time consuming. Therefore, it is necessary to develop efficient computational methods to accomplish this task. In this work, we introduced a matrix completion model with dual Laplacian regularization (DLRMC) to infer unknown miRNA-disease associations in heterogeneous omics data. Specifically, DLRMC transformed the task of miRNA-disease association prediction into a matrix completion problem, in which the potential missing entries of the miRNA-disease association matrix were calculated, the missing association can be obtained based on the prediction scores after the completion procedure. Meanwhile, the miRNA functional similarity and the disease semantic similarity were fully exploited to serve the miRNA-disease association matrix completion by using a dual Laplacian regularization term. In the experiments, we conducted global and local Leave-One-Out Cross Validation (LOOCV) and case studies to evaluate the efficacy of DLRMC on the Human miRNA-disease associations dataset obtained from the HMDDv2.0 database. As a result, the AUCs of DLRMC is 0.9174 and 0.8289 in global LOOCV and local LOOCV, respectively, which significantly outperform a variety of previous methods. In addition, in the case studies on four significant diseases related to human health including Colon Neoplasms, Kidney neoplasms, Lymphoma and Prostate neoplasms, 90%, 92%, 92% and 94% out of the top 50 predicted miRNAs has been confirmed, respectively.

Association of Plasma Irisin with Bone Mineral Density in a Large Chinese Population Using an Extreme Sampling Design.

Irisin, a myokine produced by skeletal muscle in response to physical exercise, promotes trans-differentiation of white adipose tissue into brown adipose tissue. Recent evidences suggested that irisin also plays an important role in the control of bone metabolism. This study aimed to ascertain the relationship between plasma irisin and bone mineral density (BMD) in Chinese population by adoption of an extreme sampling method. Based on a large and screened Chinese elderly population (N = 6308), two subgroups with extremely high and low hip BMD were selected for discovery (N = 80, high vs. low BMD = 44:36) and validation (N = 60, high vs. low BMD = 30:30), respectively. Plasma irisin, P1NP, and ß-CTx were measured using commercially available ELISA kits. Other metabolic parameters (e.g., blood glucose, total cholesterol and triglycerides) were collected. Student's t test and Spearman correlation analyses were conducted in SPSS. Significant difference was discovered for plasma irisin between females and age-matched males (N = 80, male vs. female = 42:38, P = 0.002). The plasma irisin levels were significantly higher in high BMD subjects than in low BMD subjects, which was observed in both discovery (P = 0.012) and validation samples (P = 0.022). However, such observation was limited to males only. Further correlation analyses in males showed that plasma irisin was correlated with BMD (r = 0.362, P = 0.025) and triglyceride (r = - 0.354, P = 0.032). Plasma irisin levels were associated with hip BMD in Chinese elderly men. This study represented the first effort of investigating the relationship of plasma irisin and BMD in elderly population. The positive correlation between plasma irisin and BMD hints intrinsic communication between muscle and bone.

Cube is a good form: Hyperspectral band selection via multi-dimensional and high-order structure preserved clustering.

As an effective strategy for reducing the noisy and redundant information for hyperspectral imagery (HSI), hyperspectral band selection intends to select a subset of original hyperspectral bands, which boosts the subsequent different tasks. In this paper, we introduce a multi-dimensional high-order structure preserved clustering method for hyperspectral band selection, referred to as MHSPC briefly. By regarding original hyperspectral images as a tensor cube, we apply the tensor CP (CANDECOMP/PARAFAC) decomposition on it to exploit the multi-dimensional structural information as well as generate a low-dimensional latent feature representation. In order to capture the local geometrical structure along the spectral dimension, a graph regularizer is imposed on the new feature representation in the lower dimensional space. In addition, since the low rankness of HSIs is an important global property, we utilize a nuclear norm constraint on the latent feature representation matrix to capture the global data structure information. Different to most of previous clustering based hyperspectral band selection methods which vectorize each band as a vector without considering the 2-D spatial information, the proposed MHSPC can effectively capture the spatial structure as well as the spectral correlation of original hyperspectral cube in both local and global perspectives. An efficient alternatively updating algorithm with theoretical convergence guarantee is designed to solve the resultant optimization problem, and extensive experimental results on four benchmark datasets validate the effectiveness of the proposed MHSPC over other state-of-the-arts.

Anchor-Sharing and Clusterwise Contrastive Network for Multiview Representation Learning.

Multiview clustering (MVC) has gained significant attention as it enables the partitioning of samples into their respective categories through unsupervised learning. However, there are a few issues as follows: 1) many existing deep clustering methods use the same latent features to achieve the conflict objectives, namely, reconstruction and view consistency. The reconstruction objective aims to preserve view-specific features for each individual view, while the view-consistency objective strives to obtain common features across all views; 2) some deep embedded clustering (DEC) approaches adopt view-wise fusion to obtain consensus feature representation. However, these approaches overlook the correlation between samples, making it challenging to derive discriminative consensus representations; and 3) many methods use contrastive learning (CL) to align the view's representations; however, they do not take into account cluster information during the construction of sample pairs, which can lead to the presence of false negative pairs. To address these issues, we propose a novel multiview representation learning network, called anchor-sharing and clusterwise CL (CwCL) network for multiview representation learning. Specifically, we separate view-specific learning and view-common learning into different network branches, which addresses the conflict between reconstruction and consistency. Second, we design an anchor-sharing feature aggregation (ASFA) module, which learns the sharing anchors from different batch data samples, establishes the bipartite relationship between anchors and samples, and further leverages it to improve the samples' representations. This module enhances the discriminative power of the common representation from different samples. Third, we design CwCL module, which incorporates the learned transition probability into CL, allowing us to focus on minimizing the similarity between representations from negative pairs with a low transition probability. It alleviates the conflict in previous sample-level contrastive alignment. Experimental results demonstrate that our method outperforms the state-of-the-art performance.

[Recent progress of chromatographic techniques for antibody purification].

Antibody drugs are becoming increasingly popular in disease diagnosis, targeted therapy, and immunoprevention owing to their characteristics of high targeting ability, strong specificity, low toxicity, and mild side effects. The demand for antibody drugs is steadily increasing, and their production scale is expanding. Upstream cell culture technology has been greatly improved by the high-capacity production of monoclonal antibodies. However, the downstream purification of antibodies presents a bottleneck in the production process. Moreover, the purification cost of antibodies is extremely high, accounting for approximately 50%-80% of the total cost of antibody production. Chromatographic technology, given its selectivity and high separation efficiency, is the main method for antibody purification. This process usually involves three stages: antibody capture, intermediate purification, and polishing. Different chromatographic techniques, such as affinity chromatography, ion-exchange chromatography, hydrophobic interaction chromatography, mixed-mode chromatography, and temperature-responsive chromatography, are used in each stage. Affinity chromatography, mainly protein A affinity chromatography, is applied for the selective capture and purification of antibodies from raw biofluids or harvested cell culture supernatants. Other chromatographic techniques, such as ion-exchange chromatography, hydrophobic interaction chromatography, and mixed-mode chromatography, are used for intermediate purification and antibody polishing. Affinity biomimetic chromatography and hydrophobic charge-induction chromatography can produce antibodies with purities comparable with those obtained through protein A chromatography, by employing artificial chemical/short peptide ligands with good selectivity, high stability, and low cost. Temperature-responsive chromatography is a promising technique for the separation and purification of antibodies. In this technique, antibody capture and elution is controlled by simply adjusting the column temperature, which greatly eliminates the risk of antibody aggregation and inactivation under acidic elution conditions. The combination of different chromatographic methods to improve separation selectivity and achieve effective elution under mild conditions is another useful strategy to enhance the yield and quality of antibodies. This review provides an overview of recent advances in the field of antibody purification using chromatography and discusses future developments in this technology.

Hierarchical and dynamic graph attention network for drug-disease association prediction.

In the realm of biomedicine, the prediction of associations between drugs and diseases holds significant importance. Yet, conventional wet lab experiments often fall short of meeting the stringent demands for prediction accuracy and efficiency. Many prior studies have predominantly focused on drug and disease similarities to predict drug-disease associations, but overlooking the crucial interactions between drugs and diseases that are essential for enhancing prediction accuracy. Hence, in this paper, a resilient and effective model named Hierarchical and Dynamic Graph Attention Network (HDGAT) has been proposed to predict drug-disease associations. Firstly, it establishes a heterogeneous graph by leveraging the interplay of drug and disease similarities and associations. Subsequently, it harnesses the capabilities of graph convolutional networks and bidirectional long short-term memory networks (Bi-LSTM) to aggregate node-level information within the heterogeneous graph comprehensively. Furthermore, it incorporates a hierarchical attention mechanism between convolutional layers and a dynamic attention mechanism between nodes to learn embeddings for drugs and diseases. The hierarchical attention mechanism assigns varying weights to embeddings learned from different convolutional layers, and the dynamic attention mechanism efficiently prioritizes inter-node information by allocating each node with varying rankings of attention coefficients for neighbour nodes. Moreover, it employs residual connections to alleviate the over-smoothing issue in graph convolution operations. The latent drug-disease associations are quantified through the fusion of these embeddings ultimately. By conducting 5-fold cross-validation, HDGAT's performance surpasses the performance of existing state-of-the-art models across various evaluation metrics, which substantiates the exceptional efficacy of HDGAT in predicting drug-disease associations.