ERT-GFAN: A multimodal drug-target interaction prediction model based on molecular biology and knowledge-enhanced attention mechanism.

In drug discovery, precisely identifying drug-target interactions is crucial for finding new drugs and understanding drug mechanisms. Evolving drug/target heterogeneous data presents challenges in obtaining multimodal representation in drug-target prediction(DTI). To deal with this, we propose 'ERT-GFAN', a multimodal drug-target interaction prediction model inspired by molecular biology. Firstly, it integrates bio-inspired principles to obtain structure feature of drugs and targets using Extended Connectivity Fingerprints(ECFP). Simultaneously, the knowledge graph embedding model RotatE is employed to discover the interaction feature of drug-target pairs. Subsequently, Transformer is utilized to refine the contextual neighborhood features from the obtained structure feature and interaction features, and multi-modal high-dimensional fusion features of the three-modal information constructed. Finally, the final DTI prediction results are outputted by integrating the multimodal fusion features into a graphical high-dimensional fusion feature attention network (GFAN) using our innovative multimodal high-dimensional fusion feature attention. This multimodal approach offers a comprehensive understanding of drug-target interactions, addressing challenges in complex knowledge graphs. By combining structure feature, interaction feature, and contextual neighborhood features, 'ERT-GFAN' excels in predicting DTI. Empirical evaluations on three datasets demonstrate our method's superior performance, with AUC of 0.9739, 0.9862, and 0.9667, AUPR of 0.9598, 0.9789, and 0.9750, and Mean Reciprocal Rank(MRR) of 0.7386, 0.7035, and 0.7133. Ablation studies show over a 5% improvement in predictive performance compared to baseline unimodal and bimodal models. These results, along with detailed case studies, highlight the efficacy and robustness of our approach.

Danggui Shaoyao San and disassembled prescription: neuroprotective effects via AMPK/mTOR-mediated autophagy in mice.

BACKGROUND: Danggui Shaoyao San (DSS), a frequently prescribed Chinese medicine formula, has demonstrated clinical efficacy in the treatment of Alzheimer's disease (AD). This study aims to explore the differences in therapeutic effects of DSS and its disassembled prescriptions, Suangan (SG) and Xingan (XG), in treating Alzheimer's Disease and the mechanism of DSS recovering autophagy in AD. METHODS: A network pharmacology strategy was employed to delineate the bioactive constituents, associated targets, and regulatory mechanisms of DSS in AD, encompassing in silico target forecasting, the generation and scrutiny of PPI networks, alongside GO and KEGG-based pathway elucidation. An AD mouse model, induced by intracerebroventricular injection of Aß1-42, was used to evaluate the therapeutic effects of DSS and its disassembled prescriptions on AD. Cognitive function was evaluated using the Morris water maze. Expression levels of inflammatory cytokines were quantified via RT-qPCR and ELISA. Western blotting was used to detect the expression of proteins related to AD pathological markers and the AMPK/mTOR signaling pathway. RESULTS: 50 active compounds and 718 HUB genes were screened from relevant databases and literature. KEGG and GO analyses indicated that DSS's potential mechanisms against AD involved the AMPK/mTOR signaling pathway and mitophagy. In vivo animal model, the results demonstrated that DSS, SG, and XG treatments improved cognitive function and ameliorated neuroinflammation in mice. Additionally, they alleviated the pathological changes of neuronal cells. These treatments also increased the protein level of PSD-95, and decreased levels of APP and p-Tau. Among them, DSS exhibited the best efficacy. Furthermore, DSS, SG, and XG upregulated the expression of LC3, Beclin1, and p-AMPK, while decreasing the expression of P62 and p-mTOR. CONCLUSIONS: DSS, SG, and XG were found to ameliorate AD-related pathological symptoms in Aß1-42-injected mice, likely through the AMPK/mTOR autophagy signaling pathway.

The effect and mechanism of patchouli alcohol on cognitive dysfunction in AD mice induced by Aß1-42 oligomers through AMPK/mTOR pathway.

Alzheimer's disease (AD) is a neurodegenerative brain disorder that progressively impairs long-term and working memory. The function and mechanism of PA(Patchouli alcohol) in improving AD in the external treatment of encephalopathy remain unclear. This study aimed to investigate the therapeutic effect of PA on AD using an Aß1-42 induced AD mouse model with LPS(Lipopolysaccharide) stimulation of BV2 microglial cells. Additionally, we aimed to explore the potential mechanism of PA in enhancing autophagy and reducing neuroinflammation through the AMPK (AMP-activated protein kinase)/mTOR (Mammaliam target of rapamycin) signaling pathway. The Morris water maze was used to assess cognitive function, and cortical and hippocampal tissues were collected for further analysis of the corresponding signaling pathways and inflammatory changes through biological experiments. Our research findings demonstrate that PA has a significant positive impact on cognitive and memory impairments in mice that have been induced with Aß1-42-induced AD. Additionally, PA was also found to revert the activation of microglia induced by LPS. These effects may be attributed to the reduction of neuroinflammation and enhancement of the AMPK/mTOR autophagy pathway. Therefore, PA may serve as an effective therapeutic option to prevent or delay the progression of AD-associated memory dysfunction.

A Comprehensive Review of HER2 in Cancer Biology and Therapeutics.

Human epidermal growth factor receptor 2 (HER2), a targetable transmembrane glycoprotein receptor of the epidermal growth factor receptor (EGFR) family, plays a crucial role in cell proliferation, survival, and differentiation. Aberrant HER2 signaling is implicated in various cancers, particularly in breast and gastric cancers, where HER2 overexpression or amplification correlates with aggressive tumor behavior and poor prognosis. HER2-activating mutations contribute to accelerated tumorigenesis and metastasis. This review provides an overview of HER2 biology, signaling pathways, mechanisms of dysregulation, and diagnostic approaches, as well as therapeutic strategies targeting HER2 in cancer. Understanding the intricate details of HER2 regulation is essential for developing effective targeted therapies and improving patient outcomes.

Integrated analyses reveal the diagnostic and predictive values of COL5A2 and association with immune environment in Crohn's disease.

The pathogenesis of Crohn's disease (CD) involves abnormal immune cell infiltration and dysregulated immune response. Therefore, thorough research on immune cell abnormalities in CD is crucial for improved treatment of this disease. Single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data of CD were obtained from the Gene Expression Omnibus (GEO) database. Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) networks evaluated the proportion of immune infiltrating cells, constructed co-expression network and identified key genes, respectively. Based on the dataset (GSE134809), 15 cell clusters were defined and labeled as different cell types. Among the 11 modules, the yellow module had the closest relationship with plasma cells (cluster 5). Confirmed using RNA sequencing and IHC assay, the expression of COL5A2 in CD samples was higher than that in control samples. Furthermore, the COL5A2 protein expression remarkably decreased in the group of patients who responded to anti-tumor necrosis factor (TNF) treatments, compared to the non-response group. The comprehensive analyses described here provided novel insight into the landscape of CD-associated immune environment. In addition, COL5A2 were identified as potential diagnostic indicators for CD, as well as promising predictive markers for CD patients.

Corrigendum: Mechanotransductive receptor Piezo1 as a promising target in the treatment of fibrosis diseases.

[This corrects the article DOI: 10.3389/fmolb.2023.1270979.].

Integrative approach for predicting drug-target interactions via matrix factorization and broad learning systems.

In the drug discovery process, time and costs are the most typical problems resulting from the experimental screening of drug-target interactions (DTIs). To address these limitations, many computational methods have been developed to achieve more accurate predictions. However, identifying DTIs mostly rely on separate learning tasks with drug and target features that neglect interaction representation between drugs and target. In addition, the lack of these relationships may lead to a greatly impaired performance on the prediction of DTIs. Aiming at capturing comprehensive drug-target representations and simplifying the network structure, we propose an integrative approach with a convolution broad learning system for the DTI prediction (ConvBLS-DTI) to reduce the impact of the data sparsity and incompleteness. First, given the lack of known interactions for the drug and target, the weighted K-nearest known neighbors (WKNKN) method was used as a preprocessing strategy for unknown drug-target pairs. Second, a neighborhood regularized logistic matrix factorization (NRLMF) was applied to extract features of updated drug-target interaction information, which focused more on the known interaction pair parties. Then, a broad learning network incorporating a convolutional neural network was established to predict DTIs, which can make classification more effective using a different perspective. Finally, based on the four benchmark datasets in three scenarios, the ConvBLS-DTI's overall performance out-performed some mainstream methods. The test results demonstrate that our model achieves improved prediction effect on the area under the receiver operating characteristic curve and the precision-recall curve.

Alternative polyadenylation quantitative trait methylation mapping in human cancers provides clues into the molecular mechanisms of APA.

Genetic variants are involved in the orchestration of alternative polyadenylation (APA) events, while the role of DNA methylation in regulating APA remains unclear. We generated a comprehensive atlas of APA quantitative trait methylation sites (apaQTMs) across 21 different types of cancer (1,612 to 60,219 acting in cis and 4,448 to 142,349 in trans). Potential causal apaQTMs in non-cancer samples were also identified. Mechanistically, we observed a strong enrichment of cis-apaQTMs near polyadenylation sites (PASs) and both cis- and trans-apaQTMs in proximity to transcription factor (TF) binding regions. Through the integration of ChIP-signals and RNA-seq data from cell lines, we have identified several regulators of APA events, acting either directly or indirectly, implicating novel functions of some important genes, such as TCF7L2, which is known for its involvement in type 2 diabetes and cancers. Furthermore, we have identified a vast number of QTMs that share the same putative causal CpG sites with five different cancer types, underscoring the roles of QTMs, including apaQTMs, in the process of tumorigenesis. DNA methylation is extensively involved in the regulation of APA events in human cancers. In an attempt to elucidate the potential underlying molecular mechanisms of APA by DNA methylation, our study paves the way for subsequent experimental validations into the intricate biological functions of DNA methylation in APA regulation and the pathogenesis of human cancers. To present a comprehensive catalog of apaQTM patterns, we introduce the Pancan-apaQTM database, available at https://pancan-apaqtm-zju.shinyapps.io/pancanaQTM/.

Edge-Type Hyperintense Intracranial Artery Plaque: A Potential MRI Biomarker of Stroke Recurrence.

BACKGROUND: Identifying patients at high risk of stroke recurrence is important for stroke prevention and treatment. PURPOSE: To explore the characteristics of T1 hyperintense plaques (HIP) and their relationship with stroke recurrence in patients with symptomatic intracranial atherosclerotic stenosis (sICAS). STUDY TYPE: Retrospective. POPULATION: One hundred fifty-seven patients with moderate-to-severe (≥50%) nonocclusive sICAS and MRI studies (42 females and 115 males, mean age 58.69 ± 10.68 years). FIELD STRENGTH/SEQUENCE: 3D higher-resolution black-blood T1-weighted fast-spin-echo sequence at 3.0 T. ASSESSMENT: HIP (signal intensity [SI] of plaque-to-adjacent gray matter >1.0 on non-contrast T1-weighted images) and non-HIP plaques were identified. HIP plaques were categorized as edge type (high SI adjacent to lumen) and non-edge type (high SI within plaque). Clinical and imaging features of different plaque types were compared. Stroke recurrence was assessed through telephone or medical records at 3 and 6 months, and then once a year post-MRI. The relationship between edge type and non-edge types HIP with stroke recurrence was analyzed. STATISTICAL TESTS: Student's t test, Mann-Whitney U-test, chi square test and Fisher's exact test to compare features between plaque types. Kaplan-Meier curves (with log-rank tests) and Cox proportional hazards regression to assess relationship between stroke recurrence and different plaque types. A two-tailed P-value of <0.05 was considered statistically significant. RESULTS: Of 157 culprit lesions, 87 (55%) were HIPs (43 edge type, 44 non-edge type) and 70 (45%) were non-HIPs. Plaque thickness, area, and volume were significantly higher for HIPs than for non-HIPs. Among patients with HIPs, edge type was significantly more likely in the posterior circulation (53.5% vs. 27.3%), and had significantly higher plaque thickness, length, area, volume, plaque burden, and remodeling index than non-edge type. Edge-type HIP was significantly more common than non-edge HIP in patients with diabetes mellitus (51.2% vs. 29.5%) and dyslipidemia (79.1% vs. 54.5%). During median follow-up of 27 months, 33 patients experienced stroke recurrence. Recurrence was associated with edge-type HIP (adjusted hazard ratio = 2.83; 95% confidence interval: 1.40-5.69), both in the overall cohort (34.9% vs. 15.8%) and in patients with HIP (34.9% vs. 9.0%). Age ≥60 years and edge-type HIP had a significant interaction. DATA CONCLUSIONS: Hyperintense plaque may be categorized as edge type or non-edge type. Edge-type HIP may be a potential MRI biomarker of stroke recurrence. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Distinguishing Intracranial Diabetes-Related Atherosclerotic Plaques: A High-Resolution Magnetic Resonance Imaging-Based Radiomics Study.

INTRODUCTION: Diabetes markedly affects the formation and development of intracranial atherosclerosis. The study was aimed at evaluating whether radiomics features can help distinguish plaques primarily associated with diabetes. MATERIALS AND METHODS: We retrospectively analyzed patients who were admitted to our center because of acute ischemic stroke due to intracranial atherosclerosis between 2016 and 2022. Clinical data, blood biomarkers, conventional plaque features, and plaque radiomics features were collected for all patients. Odds ratios (ORs) with 95% confidence intervals (CIs) were determined from logistic regression models. The receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) were used to describe diagnostic performance. The DeLong test was used to compare differences between models. RESULTS: Overall, 157 patients (115 men; mean age, 58.7 ± 10.7 years) were enrolled. Multivariate logistic regression analysis showed that plaque length (OR: 1.17; 95% CI: 1.07-1.28) and area (OR: 1.13; 95% CI: 1.02-1.24) were independently associated with diabetes. On combining plaque length and area as a conventional model, the AUCs of the training and validation cohorts for identifying diabetes patients were 0.789 and 0.720, respectively. On combining radiomics features on T1WI and contrast-enhanced T1WI sequences, a better diagnostic value was obtained in the training and validation cohorts (AUC: 0.889 and 0.861). The DeLong test showed the model combining radiomics and conventional plaque features performed better than the conventional model in both cohorts (p < 0.05). CONCLUSIONS: The use of radiomics features of intracranial plaques on high-resolution magnetic resonance imaging can effectively distinguish culprit plaques with diabetes as the primary pathological cause, which will provide new avenues of research into plaque formation and precise treatment.

ConSpaS: a contrastive learning framework for identifying spatial domains by integrating local and global similarities.

Spatial transcriptomics is a rapidly growing field that aims to comprehensively characterize tissue organization and architecture at single-cell or sub-cellular resolution using spatial information. Such techniques provide a solid foundation for the mechanistic understanding of many biological processes in both health and disease that cannot be obtained using traditional technologies. Several methods have been proposed to decipher the spatial context of spots in tissue using spatial information. However, when spatial information and gene expression profiles are integrated, most methods only consider the local similarity of spatial information. As they do not consider the global semantic structure, spatial domain identification methods encounter poor or over-smoothed clusters. We developed ConSpaS, a novel node representation learning framework that precisely deciphers spatial domains by integrating local and global similarities based on graph autoencoder (GAE) and contrastive learning (CL). The GAE effectively integrates spatial information using local similarity and gene expression profiles, thereby ensuring that cluster assignment is spatially continuous. To improve the characterization of the global similarity of gene expression data, we adopt CL to consider the global semantic information. We propose an augmentation-free mechanism to construct global positive samples and use a semi-easy sampling strategy to define negative samples. We validated ConSpaS on multiple tissue types and technology platforms by comparing it with existing typical methods. The experimental results confirmed that ConSpaS effectively improved the identification accuracy of spatial domains with biologically meaningful spatial patterns, and denoised gene expression data while maintaining the spatial expression pattern. Furthermore, our proposed method better depicted the spatial trajectory by integrating local and global similarities.

Mechanotransductive receptor Piezo1 as a promising target in the treatment of fibrosis diseases.

Fibrosis could happen in every organ, leading to organic malfunction and even organ failure, which poses a serious threat to global health. Early treatment of fibrosis has been reported to be the turning point, therefore, exploring potential correlates in the pathogenesis of fibrosis and how to reverse fibrosis has become a pressing issue. As a mechanism-sensitive cationic calcium channel, Piezo1 turns on in response to changes in the lipid bilayer of the plasma membrane. Piezo1 exerts multiple biological roles, including inhibition of inflammation, cytoskeletal stabilization, epithelial-mesenchymal transition, stromal stiffness, and immune cell mechanotransduction, interestingly enough. These processes are closely associated with the development of fibrotic diseases. Recent studies have shown that deletion or knockdown of Piezo1 attenuates the onset of fibrosis. Therefore, in this paper we comprehensively describe the biology of this gene, focusing on its potential relevance in pulmonary fibrosis, renal fibrosis, pancreatic fibrosis, and cardiac fibrosis diseases, except for the role of drugs (agonists), increased intracellular calcium and mechanical stress using this gene in alleviating fibrosis.

NG-SEM: an effective non-Gaussian structural equation modeling framework for gene regulatory network inference from single-cell RNA-seq data.

Inference of gene regulatory network (GRN) from gene expression profiles has been a central problem in systems biology and bioinformatics in the past decades. The tremendous emergency of single-cell RNA sequencing (scRNA-seq) data brings new opportunities and challenges for GRN inference: the extensive dropouts and complicated noise structure may also degrade the performance of contemporary gene regulatory models. Thus, there is an urgent need to develop more accurate methods for gene regulatory network inference in single-cell data while considering the noise structure at the same time. In this paper, we extend the traditional structural equation modeling (SEM) framework by considering a flexible noise modeling strategy, namely we use the Gaussian mixtures to approximate the complex stochastic nature of a biological system, since the Gaussian mixture framework can be arguably served as a universal approximation for any continuous distributions. The proposed non-Gaussian SEM framework is called NG-SEM, which can be optimized by iteratively performing Expectation-Maximization algorithm and weighted least-squares method. Moreover, the Akaike Information Criteria is adopted to select the number of components of the Gaussian mixture. To probe the accuracy and stability of our proposed method, we design a comprehensive variate of control experiments to systematically investigate the performance of NG-SEM under various conditions, including simulations and real biological data sets. Results on synthetic data demonstrate that this strategy can improve the performance of traditional Gaussian SEM model and results on real biological data sets verify that NG-SEM outperforms other five state-of-the-art methods.

scHOIS: Determining Cell Heterogeneity Through Hierarchical Clustering Based on Optimal Imputation Strategy.

Advances in single-cell RNA sequencing (scRNA-seq) technology provide an unbiased and high-throughput analysis of each cell at single-cell resolution, and further facilitate the development of cellular heterogeneity analysis. Despite the promise of scRNA-seq, the data generated by this method are sparse and noisy because of the presence of dropout events, which can greatly impact downstream analyses such as differential gene expression, cell type annotation, and linage trajectory reconstruction. The development of effective and robust computational methods to address both dropout and clustering are thus urgently needed. In this study, we propose a flexible, accurate two-stage algorithm for single cell heterogeneity analysis via hierarchical clustering based on an optimal imputation strategy, called scHOIS. At the first stage, masked non-negative matrix factorization is applied to approximate the original observed scRNA-seq data, with optimal rank determined by variance analysis. At the second stage, hierarchical clustering is applied to group the imputed cells using Pearson correlation to measure similarity, with the optimal number of clusters determined by integrating three classical indexes. We performed extensive experiments on real-world datasets, which showed that scHOIS effectively and robustly distinguished cellular differences and that the clustering performance of this algorithm was superior to that of other state-of-the-art methods.

Analysis of the spatial-temporal distribution characteristics of hepatitis E in Jiangsu province from 2005 to 2020.

Objective: This study attempts to analyze the spatial clustering and spatial-temporal distribution characteristics of hepatitis E (HE) at the county (city and district) level in Jiangsu province to provide a scientific basis for the prevention and control of HE. Method: The information on HE cases reported in the Chinese Center for Disease Control and Prevention Information System from 2005 to 2020 was collected for spatial autocorrelation analysis and spatial-temporal clustering analysis. Result: From 2005 to 2020, 48,456 HE cases were reported in Jiangsu province, with an average annual incidence rate of 3.87/100,000. Male cases outnumbered female cases (2.46:1), and the incidence was highest in the 30-70 years of age group (80.50%). Farmers accounted for more than half of all cases (59.86%), and in terms of the average annual incidence, the top three cities were all in Zhenjiang city. Spatial autocorrelation analysis showed that Global Moran's I of HE incidence varied from 0.232 to 0.513 for the years. From 2005 to 2020, 31 counties (cities and districts) had high and statistically significant HE incidence, and two clustering areas were detected by spatial-temporal scanning. Conclusion: HE incidence in Jiangsu province from 2005 to 2020 was stable, with age and gender differences, regional clustering, and spatial-temporal clustering. Further investigation of HE clustering areas is necessary to formulate corresponding targeted prevention and control measures.

Eco-friendly simultaneous extraction of pectins and phenolics from passion fruit (Passiflora edulis Sims) peel: Process optimization, physicochemical properties, and antioxidant activity.

The objective of this study was to simultaneously extract passion fruit (Passiflora edulis) peel pectins and phenolics using deep eutectic solvents, to evaluate their physicochemical properties and antioxidant activity. By taking L-proline: citric acid (Pro-CA) as the optimal solvent, the effect of extraction parameters on the yields of extracted passion fruit peel pectins (PFPP) and total phenolic content (TPC) was explored by response surfaces methodology (RSM). A maximum pectin yield (22.63%) and the highest TPC (9.68 mg GAE/g DW) were attained under 90 °C, extraction solvent pH = 2, extraction time of 120 min and L/S ratio of 20 mL/g. In addition, Pro-CA-extracted pectins (Pro-CA-PFPP) and HCl-extracted pectins (HCl-PFPP) were subjected to high performance gel permeation chromatography (HPGPC), Fourier transform infrared spectroscopy (FT-IR), thermogram analysis (TG/DTG) and rheological measurements. Results verified that the Mw and thermal stability of Pro-CA-PFPP were higher than those of HCl-PFPP. The PFPP solutions featured a non-Newtonian behavior, and compared with commercially pectin solution, PFPP solution exhibited a stronger antioxidant activity. Additionally, passion fruit peel extract (PFPE) exhibited stronger antioxidant effects than PFPP. The results of ultra-performance liquid chromatography hybrid triple quadrupole-linear ion trap mass spectrometry (UPLC-Qtrap-MS) and high performance liquid chromatography (HPLC) analysis showed that (-)-epigallocatechin, gallic acid, epicatechin, kaempferol-3-O-rutin and myricetin were the main phenolic compounds in PFPE and PFPP. Our results suggest that Pro-CA can be considered as an eco-friendly solvent for high-efficient extraction of high-value compounds from agricultural by-products.

Breast Cancer Mutations HER2V777L and PIK3CAH1047R Activate the p21-CDK4/6-Cyclin D1 Axis to Drive Tumorigenesis and Drug Resistance.

In metastatic breast cancer, HER2-activating mutations frequently co-occur with mutations in PIK3CA, TP53, or CDH1. Of these co-occurring mutations, HER2 and PIK3CA are the most commonly comutated gene pair, with approximately 40% of HER2-mutated breast cancers also having activating mutations in PIK3CA. To study the effects of co-occurring HER2 and PIK3CA mutations, we generated genetically engineered mice with the HER2V777L; PIK3CAH1047R transgenes (HP mice) and studied the resulting breast cancers both in vivo as well as ex vivo using cancer organoids. HP breast cancers showed accelerated tumor formation in vivo and increased invasion and migration in in vitro assays. HP breast cancer cells were resistant to the pan-HER tyrosine kinase inhibitor, neratinib, but were effectively treated with neratinib plus the HER2-targeted antibody-drug conjugate trastuzumab deruxtecan. Proteomic and RNA-seq analysis of HP breast cancers identified increased gene expression of cyclin D1 and p21WAF1/Cip1 and changes in cell-cycle markers. Combining neratinib with CDK4/6 inhibitors was another effective strategy for treating HP breast cancers, with neratinib plus palbociclib showing a statistically significant reduction in development of mouse HP tumors as compared to either drug alone. The efficacy of both the neratinib plus trastuzumab deruxtecan and neratinib plus palbociclib combinations was validated using a human breast cancer patient-derived xenograft with very similar HER2 and PIK3CA mutations to the HP mice. Further, these two drug combinations effectively treated spontaneous lung metastasis in syngeneic mice transplanted with HP breast cancer organoids. This study provides valuable preclinical data to support the ongoing phase 1 clinical trials of these drug combinations in breast cancer. SIGNIFICANCE: In HER2-mutated breast cancer, PIK3CA mutation activates p21-CDK4/6-cyclin D1 signaling to drive resistance to HER2-targeted therapies, which can be overcome using CDK4/6 inhibitors.

Paeoniflorin, ferulic acid, and atractylenolide III improved LPS-induced neuroinflammation of BV2 microglia cells by enhancing autophagy.

Microglia hyperactivation is an important cause of neuroinflammation in Alzheimer's disease (AD). Paeoniflorin (PF), ferulic acid (FA), and atractylenolide III (ATL) are potent in anti-inflammation and neuroprotection. Multiple components can act on different targets simultaneously to exert synergistic therapeutic effects and exploring the synergistic potential between compounds is an important area of research. We investigated the effects of PF, FA, and ATL, alone or in combination, on LPS-induced neuroinflammation and autophagy in BV2 microglia cells. We found that PF, FA, and ATL, alone or in combination, significantly reduced the production of inflammatory factors such as IL-6, IL-1ß, and TNF-α, especially in the PF + FA + ATL group, which performed the best. In addition, the combination of PF, FA, and ATL significantly increased the expression of autophagy-related proteins p-AMPK, p-ULK1, Beclin1, LC3, and TFEB and decreased the expression of p62. Moreover, the restoration of autophagic flux by the combination of PF, FA, and ATL was abrogated by the addition of the autophagy inhibitor Wortmannin. In conclusion, PF, FA, and ATL have a synergistic effect in reducing LPS-induced inflammatory factor release from BV2 microglia cells, and its protective effect may be through activation of the AMPK/ULK1/TFEB autophagic signaling pathway.

Incremental value of enhanced plaque length for identifying intracranial atherosclerotic culprit plaques: a high-resolution magnetic resonance imaging study.

OBJECTIVES: Besides plaque enhancement grade, the incremental value of enhancement-related high-resolution MRI features in defining culprit plaques needs further evaluation. This study was focused on assessing whether plaque enhancement features contribute to culprit plaque identification and further risk stratification. METHODS: We retrospectively studied patients who experienced an acute ischaemic stroke and transient ischaemic attack due to intracranial atherosclerosis from 2016 to 2022. The enhancement features included enhancement grade, enhanced length, and enhancement quadrant. Associations between plaque enhancement features and culprit plaques, as well as diagnostic value, were investigated using logistic regression and receiver operating characteristic analyses. RESULTS: Overall, 287 plaques were identified, of which 231 (80.5%) and 56 (19.5%) were classified as culprit and non-culprit plaques, respectively. Comparison of the pre- and post-enhancement images revealed enhanced length longer than the plaque length in 46.32% of the culprit plaques. Multivariate logistic regression showed that enhanced length longer than plaque length (OR 6.77; 95% CI 2.47-18.51) and grade II enhancement (OR 7.00; 95% CI 1.69-28.93) were independently associated with culprit plaques. The area under the curve value for the combination of stenosis and plaque enhancement grade for the diagnosis of culprit plaques was 0.787, which increased significantly to 0.825 on the addition of enhanced length longer than the plaque length (p = 0.026 for DeLong's test). CONCLUSIONS: Enhanced length longer than the plaque length and grade II enhancement were independently associated with culprit plaques. The combination of the enhanced plaque features resulted in better culprit plaque identification.

Protective Effect of Ferulic Acid on Lipopolysaccharide-Induced BV2 Microglia Inflammation via AMPK/mTOR Signaling Pathway.

In neurodegenerative diseases, microglial activation and neuroinflammation are essential for the control and progression of neurodegenerative diseases. Mitigating microglium-induced inflammation is one strategy for hindering the progression of neurodegenerative diseases. Ferulic acid (FA) is an effective anti-inflammatory agent, but its potential role and regulation mechanism in neuroinflammatory reactions have not been fully studied. In this study, the neuroinflammation model was established by lipopolysaccharide (LPS), and the inhibitory effect of FA on neuroinflammation of BV2 microglia was studied. The results showed that FA significantly reduced the production and expression of reactive oxygen species (ROS), tumor necrosis factor-α (TNF-α), leukocyte-6 (IL-6) and interleukin-1ß (IL-1ß). We further studied the mechanism of FA's regulation of LPS-induced BV2 neuroinflammation and found that FA can significantly reduce the expression of mTOR in BV2 microglia induced by LPS, and significantly increase the expression of AMPK, indicating that FA may have an anti-inflammatory effect by activating the AMPK/mTOR signaling pathway to regulate the release of inflammatory mediators (such as NLRP3, caspase-1 p20 and IL-1ß). We further added an autophagy inhibitor (3-MA) and an AMPK inhibitor (compound C, CC) for reverse verification. The results showed that FA's inhibitory effects on TNF-α, IL-6 and IL-1ß and its regulatory effect on AMPK/mTOR were destroyed by 3-MA and CC, which further indicated that FA's inhibitory effect on neuroinflammation is related to its activation of the AMPK/mTOR autophagy signaling pathway. In a word, our experimental results show that FA can inhibit LPS-induced neuroinflammation of BV2 microglia by activating the AMPK/mTOR signaling pathway, and FA may be a potential drug for treating neuroinflammatory diseases.