In silico identification of a novel Cdc2-like kinase 2 (CLK2) inhibitor in triple negative breast cancer.

Dysregulation of RNA splicing processes is intricately linked to tumorigenesis in various cancers, especially breast cancer. Cdc2-like kinase 2 (CLK2), an oncogenic RNA-splicing kinase pivotal in breast cancer, plays a significant role, particularly in the context of triple-negative breast cancer (TNBC), a subtype marked by substantial medical challenges due to its low survival rates. In this study, we employed a structure-based virtual screening (SBVS) method to identify potential CLK2 inhibitors with novel chemical structures for treating TNBC. Compound 670551 emerged as a novel CLK2 inhibitor with a 50% inhibitory concentration (IC50) value of 619.7 nM. Importantly, Compound 670551 exhibited high selectivity for CLK2 over other protein kinases. Functionally, this compound significantly reduced the survival and proliferation of TNBC cells. Results from a cell-based assay demonstrated that this inhibitor led to a decrease in RNA splicing proteins, such as SRSF4 and SRSF6, resulting in cell apoptosis. In summary, we identified a novel CLK2 inhibitor as a promising potential treatment for TNBC therapy.

An ensemble machine learning model generates a focused screening library for the identification of CDK8 inhibitors.

The identification of an effective inhibitor is an important starting step in drug development. Unfortunately, many issues such as the characterization of protein binding sites, the screening library, materials for assays, etc., make drug screening a difficult proposition. As the size of screening libraries increases, more resources will be inefficiently consumed. Thus, new strategies are needed to preprocess and focus a screening library towards a targeted protein. Herein, we report an ensemble machine learning (ML) model to generate a CDK8-focused screening library. The ensemble model consists of six different algorithms optimized for CDK8 inhibitor classification. The models were trained using a CDK8-specific fragment library along with molecules containing CDK8 activity. The optimized ensemble model processed a commercial library containing 1.6 million molecules. This resulted in a CDK8-focused screening library containing 1,672 molecules, a reduction of more than 99.90%. The CDK8-focused library was then subjected to molecular docking, and 25 candidate compounds were selected. Enzymatic assays confirmed six CDK8 inhibitors, with one compound producing an IC50 value of ≤100 nM. Analysis of the ensemble ML model reveals the role of the CDK8 fragment library during training. Structural analysis of molecules reveals the hit compounds to be structurally novel CDK8 inhibitors. Together, the results highlight a pipeline for curating a focused library for a specific protein target, such as CDK8.

Comparative analysis of machine learning algorithms for Alzheimer's disease classification using EEG signals and genetic information.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairments, and behavioral changes. The presence of abnormal beta-amyloid plaques and tau protein tangles in the brain is known to be associated with AD. However, current limitations of imaging technology hinder the direct detection of these substances. Consequently, researchers are exploring alternative approaches, such as indirect assessments involving monitoring brain signals, cognitive decline levels, and blood biomarkers. Recent studies have highlighted the potential of integrating genetic information into these approaches to enhance early detection and diagnosis, offering a more comprehensive understanding of AD pathology beyond the constraints of existing imaging methods. Our study utilized electroencephalography (EEG) signals, genotypes, and polygenic risk scores (PRSs) as features for machine learning models. We compared the performance of gradient boosting (XGB), random forest (RF), and support vector machine (SVM) to determine the optimal model. Statistical analysis revealed significant correlations between EEG signals and clinical manifestations, demonstrating the ability to distinguish the complexity of AD from other diseases by using genetic information. By integrating EEG with genetic data in an SVM model, we achieved exceptional classification performance, with an accuracy of 0.920 and an area under the curve of 0.916. This study presents a novel approach of utilizing real-time EEG data and genetic background information for multimodal machine learning. The experimental results validate the effectiveness of this concept, providing deeper insights into the actual condition of patients with AD and overcoming the limitations associated with single-oriented data.

Utility of polygenic scores across diverse diseases in a hospital cohort for predictive modeling.

Polygenic scores estimate genetic susceptibility to diseases. We systematically calculated polygenic scores across 457 phenotypes using genotyping array data from China Medical University Hospital. Logistic regression models assessed polygenic scores' ability to predict disease traits. The polygenic score model with the highest accuracy, based on maximal area under the receiver operating characteristic curve (AUC), is provided on the GeneAnaBase website of the hospital. Our findings indicate 49 phenotypes with AUC greater than 0.6, predominantly linked to endocrine and metabolic diseases. Notably, hyperplasia of the prostate exhibited the highest disease prediction ability (P value = 1.01 × 10-19, AUC = 0.874), highlighting the potential of these polygenic scores in preventive medicine and diagnosis. This study offers a comprehensive evaluation of polygenic scores performance across diverse human traits, identifying promising applications for precision medicine and personalized healthcare, thereby inspiring further research and development in this field.

Deep learning assists in acute leukemia detection and cell classification via flow cytometry using the acute leukemia orientation tube.

This study aimed to evaluate the sensitivity of AI in screening acute leukemia and its capability to classify either physiological or pathological cells. Utilizing an acute leukemia orientation tube (ALOT), one of the protocols of Euroflow, flow cytometry efficiently identifies various forms of acute leukemia. However, the analysis of flow cytometry can be time-consuming work. This retrospective study included 241 patients who underwent flow cytometry examination using ALOT between 2017 and 2022. The collected flow cytometry data were used to train an artificial intelligence using deep learning. The trained AI demonstrated a 94.6% sensitivity in detecting acute myeloid leukemia (AML) patients and a 98.2% sensitivity for B-lymphoblastic leukemia (B-ALL) patients. The sensitivities of physiological cells were at least 80%, with variable performance for pathological cells. In conclusion, the AI, trained with ResNet-50 and EverFlow, shows promising results in identifying patients with AML and B-ALL, as well as classifying physiological cells.

Predicting ischemic stroke patients' prognosis changes using machine learning in a nationwide stroke registry.

Accurately predicting the prognosis of ischemic stroke patients after discharge is crucial for physicians to plan for long-term health care. Although previous studies have demonstrated that machine learning (ML) shows reasonably accurate stroke outcome predictions with limited datasets, to identify specific clinical features associated with prognosis changes after stroke that could aid physicians and patients in devising improved recovery care plans have been challenging. This study aimed to overcome these gaps by utilizing a large national stroke registry database to assess various prediction models that estimate how patients' prognosis changes over time with associated clinical factors. To properly evaluate the best predictive approaches currently available and avoid prejudice, this study employed three different prognosis prediction models including a statistical logistic regression model, commonly used clinical-based scores, and a latest high-performance ML-based XGBoost model. The study revealed that the XGBoost model outperformed other two traditional models, achieving an AUROC of 0.929 in predicting the prognosis changes of stroke patients followed for 3 months. In addition, the XGBoost model maintained remarkably high precision even when using only selected 20 most relevant clinical features compared to full clinical datasets used in the study. These selected features closely correlated with significant changes in clinical outcomes for stroke patients and showed to be effective for predicting prognosis changes after discharge, allowing physicians to make optimal decisions regarding their patients' recovery.

A novel HDAC8 inhibitor H7E exerts retinoprotective effects against glaucomatous injury via ameliorating aberrant Müller glia activation and oxidative stress.

Glaucoma is considered a neurodegenerative disease characterized by progressive visual field defects that may lead to blindness. Although controlling intraocular pressure (IOP) is the mainstay of glaucoma treatment, some glaucoma patients have unmet needs due to unclear pathogenic mechanisms. Recently, there has been growing evidence that neuroinflammation is a potential target for the development of novel antiglaucoma agents. In this study, we investigated the protective effects and cellular mechanisms of H7E, a novel small molecule inhibits HDAC8, using in vitro and in vivo glaucoma-like models. Importantly, H7E mitigated extracellular MMP-9 activity and MCP-1 levels in glutamate- or S100B-stimulated reactive Müller glia. In addition, H7E inhibited the upregulation of inflammation- and proliferation-related signaling pathways, particularly the ERK and JNK MAPK pathways. Under conditions of oxidative damage, H7E prevents retinal cell death and reduces extracellular glutamate released from stressed Müller glia. In a mouse model of NMDA-induced retinal degeneration, H7E alleviated functional and structural defects within the inner retina as assessed by electroretinography and optical coherence tomography. Our results demonstrated that the newly identified compound H7E protects against glaucoma damage by specifically targeting HDAC8 activity in the retina. This protective effect is attributed to the inhibition of Müller glial activation and the prevention of retinal cell death caused by oxidative stress.

Optimizing ensemble U-Net architectures for robust coronary vessel segmentation in angiographic images.

Automated coronary angiography assessment requires precise vessel segmentation, a task complicated by uneven contrast filling and background noise. Our research introduces an ensemble U-Net model, SE-RegUNet, designed to accurately segment coronary vessels using 100 labeled angiographies from angiographic images. SE-RegUNet incorporates RegNet encoders and squeeze-and-excitation blocks to enhance feature extraction. A dual-phase image preprocessing strategy further improves the model's performance, employing unsharp masking and contrast-limited adaptive histogram equalization. Following fivefold cross-validation and Ranger21 optimization, the SE-RegUNet 4GF model emerged as the most effective, evidenced by performance metrics such as a Dice score of 0.72 and an accuracy of 0.97. Its potential for real-world application is highlighted by its ability to process images at 41.6 frames per second. External validation on the DCA1 dataset demonstrated the model's consistent robustness, achieving a Dice score of 0.76 and an accuracy of 0.97. The SE-RegUNet 4GF model's precision in segmenting blood vessels in coronary angiographies showcases its remarkable efficiency and accuracy. However, further development and clinical testing are necessary before it can be routinely implemented in medical practice.

Impact of the implementation of the Intelligent Antimicrobial System (iAMS) on clinical outcomes among patients with bacteraemia caused by methicillin-resistant Staphylococcus aureus.

OBJECTIVES: This study aimed to investigate the clinical impact of the Intelligent Antimicrobial System (iAMS) on patients with bacteraemia due to methicillin-resistant (MRSA) and methicillin-susceptible Staphylococcus aureus (MSSA). METHODS: A total of 1008 patients with suspected SA infection were enrolled before and after the implementation of iAMS. Among them, 252 with bacteraemia caused by SA, including 118 in the iAMS and 134 in the non-iAMS groups, were evaluated. RESULTS: The iAMS group exhibited a 5.2% (from 55.2% to 50.0%; P = 0.96) increase in the 1-year survival rate. For patients with MRSA and MSSA compared to the non-iAMS group, the 1-year survival rate increased by 17.6% (from 70.9% to 53.3%; P = 0.41) and 7.0% (from 52.3% to 45.3%; P = 0.57), respectively, both surpassing the rate of the non-iAMS group. The iAMS intervention resulted in a higher long-term survival rate (from 70.9% to 52.3%; P = 0.984) for MRSA patients than for MSSA patients. MRSA patients experienced a reduced length of hospital stay (from 23.3% to 35.6%; P = 0.038), and the 45-day discharge rate increased by 20.4% (P = 0.064). Furthermore, the intervention resulted in a significant 97.3% relative decrease in near miss medication incidents reported by pharmacists (P = 0.013). CONCLUSIONS: Implementation of iAMS platform improved long-term survival rates, discharge rates, hospitalization days, and medical cost (although no significant differences were observed) among patients with MRSA bacteraemia. Additionally, it demonstrated significant benefits in ensuring drug safety.

USP24-i-101 targeting of USP24 activates autophagy to inhibit drug resistance acquired during cancer therapy.

Drug resistance in cancer therapy is the major reason for poor prognosis. Addressing this clinically unmet issue is important and urgent. In this study, we found that targeting USP24 by the specific USP24 inhibitors, USP24-i and its analogues, dramatically activated autophagy in the interphase and mitotic periods of lung cancer cells by inhibiting E2F4 and TRAF6, respectively. USP24 functional knockout, USP24C1695A, or targeting USP24 by USP24-i-101 inhibited drug resistance and activated autophagy in gefitinib-induced drug-resistant mice with doxycycline-induced EGFRL858R lung cancer, but this effect was abolished after inhibition of autophagy, indicating that targeting USP24-mediated induction of autophagy is required for inhibition of drug resistance. Genomic instability and PD-L1 levels were increased in drug resistant lung cancer cells and were inhibited by USP24-i-101 treatment or knockdown of USP24. In addition, inhibition of autophagy by bafilomycin-A1 significantly abolished the effect of USP24-i-101 on maintaining genomic integrity, decreasing PD-L1 and inhibiting drug resistance acquired in chemotherapy or targeted therapy. In summary, an increase in the expression of USP24 in cancer cells is beneficial for the induction of drug resistance and targeting USP24 by USP24-i-101 optimized from USP24-i inhibits drug resistance acquired during cancer therapy by increasing PD-L1 protein degradation and genomic stability in an autophagy induction-dependent manner.

Comparing the Long-Term Cardiovascular Outcomes of Lumbo-Peritoneal, Ventriculo-Peritoneal, or Non-Shunting Treatment after Idiopathic Normal Pressure Hydrocephalus: A Nationwide Retrospective Cohort Study.

Purpose: With advances in medical technology, the average lifespan has increased, leading to a growing significance of idiopathic normal pressure hydrocephalus (iNPH), particularly in the elderly population. Most patients with iNPH have been treated either with ventriculo-peritoneal shunts (VPS) or conservative measures. However, lumbo-peritoneal shunts (LPS) have emerged as an alternative treatment option for iNPH in recent decades, extensive research still lacks comparing outcomes with LPS to those with VPS or non-surgical treatment. The aim of the resent study is to disclose the long-term therapeutic outcomes of LPS, VPS, and non-shunting in patients with iNPH. Methods: We used the National Health Insurance Research Database in Taiwan to assess the long-term outcomes of these treatment options. We enrolled 5,537 iNPH patients who received shunting surgery, of which 5,254 were VPS and 283 were LPS. To compare the difference between each group, matching was conducted by propensity score matching using a 1:1 ratio based on LPS patients. Primary outcomes included death and major adverse cardiovascular events (MACEs) Results: Our findings show that VPS resulted in significantly more MACEs than non-surgical treatment (Odds ratio: 1.83, 95% confidence interval: 1.16-2.90). In addition, both VPS and LPS groups had significantly lower overall mortality rates than non-shunting group. Moreover, LPS had lower overall mortality but similar MACEs rates to VPS. Conclusions: Based on these findings, we propose that the LPS is preferable to the VPS, and surgical treatment should be considered the primary choice over conservative treatment unless contraindications are present.

Identification of selective dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) inhibitors and their effects on tau and microtubule.

The overexpression of dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A), commonly observed in neurodegenerative diseases like Alzheimer's disease (AD) and Down syndrome (DS), can induce the formation of neurofibrillary tangles (NFTs) and amyloid plaques. Hence, designing a selective DYRK1A inhibitor would result in a promising small molecule for treating neurodegenerative diseases. Developing selective inhibitors for DYRK1A has been a difficult challenge due to the highly preserved ATP-binding site of protein kinases. In this study, we employed a structure-based virtual screening (SBVS) campaign targeting DYRK1A from a database containing 1.6 million compounds. Enzymatic assays were utilized to verify inhibitory properties, confirming that Y020-3945 and Y020-3957 showed inhibitory activity towards DYRK1A. In particular, the compounds exhibited high selectivity for DYRK1A over a panel of 120 kinases, reduced the phosphorylation of tau, and reversed the tubulin polymerization for microtubule stability. Additionally, treatment with the compounds significantly reduced the secretion of inflammatory cytokines IL-6 and TNF-α activated by DYRK1A-assisted NFTs and Aß oligomers. These identified inhibitors possess promising therapeutic potential for conditions associated with DYRK1A in neurodegenerative diseases. The results showed that Y020-3945 and Y020-3957 demonstrated structural novelty compared to known DYRK1A inhibitors, making them a valuable addition to developing potential treatments for neurodegenerative diseases.

Synthesis and evaluation of potent (iso)ellipticine-based inhibitors of MYLK4 accessed via expeditious synthesis from isoquinolin-5-ol.

The K2S2O8-mediated generation of p-iminoquinone contributed to the regioselective substitution of isoquinolin-5,8-dione. This hydroxyl group-guided substitution was also applied to selected heterocycles and addressed the regioselectivity issue of quinones. This study has provided an expeditious pathway from isoquinolin-5-ol (5) to ellipticine (1) and isoellipticine (2), which benefits the comprehensive comparison of their activity. Compounds 1 and 2 displayed marked MYLK4 inhibitory activity with IC50 values of 7.1 and 6.1 nM, respectively. In the cellular activity of AML cells (MV-4-11 and MOLM-13), compound 1 showed better AML activity than compound 2.

Prediction of methicillin-resistant Staphylococcus aureus and carbapenem-resistant Klebsiella pneumoniae from flagged blood cultures by combining rapid Sepsityper MALDI-TOF mass spectrometry with machine learning.

This study investigated combination of the Rapid Sepsityper Kit and a machine learning (ML)-based matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) approach for rapid prediction of methicillin-resistant Staphylococcus aureus (MRSA) and carbapenem-resistant Klebsiella pneumoniae (CRKP) from positive blood culture bottles. The study involved 461 patients with monomicrobial bloodstream infections. Species identification was performed using the conventional MALDI-TOF MS Biotyper system and the Rapid Sepsityper protocol. The data underwent preprocessing steps, and ML models were trained using preprocessed MALDI-TOF data and corresponding labels. The interpretability of the model was enhanced using SHapely Additive exPlanations values to identify significant features. In total, 44 S. aureus isolates comprising 406 MALDI-TOF MS files and 126 K. pneumoniae isolates comprising 1249 MALDI-TOF MS files were evaluated. This study demonstrated the feasibility of predicting MRSA among S. aureus and CRKP among K. pneumoniae isolates using MALDI-TOF MS and Sepsityper. Accuracy, area under the receiver operating characteristic curve, and F1 score for MRSA/methicillin-susceptible S. aureus were 0.875, 0.898 and 0.904, respectively; for CRKP/carbapenem-susceptible K. pneumoniae, these values were 0.766, 0.828 and 0.795, respectively. In conclusion, the novel ML-based MALDI-TOF MS approach enables rapid identification of MRSA and CRKP from flagged blood cultures within 1 h. This enables earlier initiation of targeted antimicrobial therapy, reducing deaths due to sepsis. The favourable performance and reduced turnaround time of this method suggest its potential as a rapid detection strategy in clinical microbiology laboratories, ultimately improving patient outcomes.

Machine learning approaches for biomarker discovery to predict large-artery atherosclerosis.

Large-artery atherosclerosis (LAA) is a leading cause of cerebrovascular disease. However, LAA diagnosis is costly and needs professional identification. Many metabolites have been identified as biomarkers of specific traits. However, there are inconsistent findings regarding suitable biomarkers for the prediction of LAA. In this study, we propose a new method integrates multiple machine learning algorithms and feature selection method to handle multidimensional data. Among the six machine learning models, logistic regression (LR) model exhibited the best prediction performance. The value of area under the receiver operating characteristic curve (AUC) was 0.92 when 62 features were incorporated in the external validation set for the LR model. In this model, LAA could be well predicted by clinical risk factors including body mass index, smoking, and medications for controlling diabetes, hypertension, and hyperlipidemia as well as metabolites involved in aminoacyl-tRNA biosynthesis and lipid metabolism. In addition, we found that 27 features were present among the five adopted models that could provide good results. If these 27 features were used in the LR model, an AUC value of 0.93 could be achieved. Our study has demonstrated the effectiveness of combining machine learning algorithms with recursive feature elimination and cross-validation methods for biomarker identification. Moreover, we have shown that using shared features can yield more reliable correlations than either model, which can be valuable for future identification of LAA.

Patient and hospital characteristics associated with do-not-resuscitate/do-not-intubate orders: a cross-sectional study based on the Taiwan stroke registry.

BACKGROUND: Previous studies of do-not-resuscitate (DNR) or do-not-intubate (DNI) orders in stroke patients have primarily been conducted in North America or Europe. However, characteristics associated with DNR/DNI orders in stroke patients in Asia have not been reported. METHODS: Based on the Taiwan Stroke Registry, this nationwide cross-sectional study enrolled hospitalized stroke patients from 64 hospitals between 2006 and 2020. We identified characteristics associated with DNR/DNI orders using a two-level random effects model. RESULTS: Among the 114,825 patients, 5531 (4.82%) had DNR/DNI orders. Patients with acute ischemic stroke (AIS) had the highest likelihood of having DNR/DNI orders (adjusted odds ratio [aOR] 1.76, 95% confidence interval [CI] 1.61-1.93), followed by patients with intracerebral hemorrhage (ICH), and patients with subarachnoid hemorrhage (SAH) had the lowest likelihood (aOR 0.53, 95% CI 0.43-0.66). From 2006 to 2020, DNR/DNI orders increased in all three types of stroke. In patients with AIS, women were significantly more likely to have DNR/DNI orders (aOR 1.23, 95% CI 1.15-1.32), while patients who received intravenous alteplase had a lower likelihood (aOR 0.74, 95% CI 0.65-0.84). Patients with AIS who were cared for by religious hospitals (aOR 0.55, 95% CI 0.35-0.87) and patients with SAH who were cared for by medical centers (aOR 0.40, 95% CI 0.17-0.96) were significantly less likely to have DNR/DNI orders. CONCLUSIONS: In Taiwan, DNR/DNI orders increased in stroke patients between 2006 and 2020. Hospital characteristics were found to play a significant role in the use of DNR/DNI orders.

Protective Effects of Statins on Limb and Cardiovascular Outcomes in Patients with Peripheral Artery Disease and End-Stage Renal Disease.

Background: Previous studies have reported that statins have inconsistent and marginal cardiovascular (CV) benefits in patients with end-stage renal disease (ESRD). However, whether statins play a secondary preventive role in patients with peripheral artery disease (PAD) and ESRD remains unclear. Objectives: This study aimed to compare the long-term clinical outcomes between statin users and nonusers with PAD and ESRD. Methods: This retrospective cohort study assessed the long-term protective effects of statins using data from the National Health Insurance Research Database in Taiwan. Propensity score matching was performed according to sex, age, index year, related comorbidities, and medications. The main outcomes were limb events and major adverse CV events (MACEs). Results: The statin user group (n = 4,460) was compared with the propensity score-matched statin nonuser group (n = 4,460). The mean age of the matched patients was 64 years, and 40% of the patients were men. The baseline characteristics of the groups were well-balanced. The overall limb event and MACE rates were not different between the two groups. However, the statin user group had lower rates of limb amputation [adjusted hazard ratio (aHR): 0.85, 95% confidence interval (CI): 0.73-0.99], stroke (aHR: 0.71, 95% CI: 0.62-0.83), CV death (aHR: 0.46, 95% CI: 0.32-0.66), and all-cause death (aHR: 0.45, 95% CI: 0.42-0.48) despite having a higher rate of percutaneous transluminal angioplasty for PAD. Conclusions: This population-based retrospective cohort study demonstrated that statin therapy was associated with a lower risk of limb amputation, nonfatal stroke, CV death, and all-cause death in patients with PAD and ESRD.

Examining Real-World Medication Consultations and Drug-Herb Interactions: ChatGPT Performance Evaluation.

BACKGROUND: Since OpenAI released ChatGPT, with its strong capability in handling natural tasks and its user-friendly interface, it has garnered significant attention. OBJECTIVE: A prospective analysis is required to evaluate the accuracy and appropriateness of medication consultation responses generated by ChatGPT. METHODS: A prospective cross-sectional study was conducted by the pharmacy department of a medical center in Taiwan. The test data set comprised retrospective medication consultation questions collected from February 1, 2023, to February 28, 2023, along with common questions about drug-herb interactions. Two distinct sets of questions were tested: real-world medication consultation questions and common questions about interactions between traditional Chinese and Western medicines. We used the conventional double-review mechanism. The appropriateness of each response from ChatGPT was assessed by 2 experienced pharmacists. In the event of a discrepancy between the assessments, a third pharmacist stepped in to make the final decision. RESULTS: Of 293 real-world medication consultation questions, a random selection of 80 was used to evaluate ChatGPT's performance. ChatGPT exhibited a higher appropriateness rate in responding to public medication consultation questions compared to those asked by health care providers in a hospital setting (31/51, 61% vs 20/51, 39%; P=.01). CONCLUSIONS: The findings from this study suggest that ChatGPT could potentially be used for answering basic medication consultation questions. Our analysis of the erroneous information allowed us to identify potential medical risks associated with certain questions; this problem deserves our close attention.

Urolithin A exhibits a neuroprotective effect against Alzheimer's disease by inhibiting DYRK1A activity.

Alzheimer's disease (AD) is a devastating neurodegenerative disease with more than 50 million people suffer from it. Unfortunately, none of the currently available drugs is able to improve cognitive impairment in AD patients. Urolithin A (UA) is a metabolite obtained from ellagic acid and ellagitannin through the intestinal flora, and it has antioxidant and anti-inflammatory properties. Previous reports found that UA had neuroprotective effects in an AD animal model, but the detailed mechanism still needs to be elucidated. In this study, we performed kinase-profiling to show that dual-specific tyrosine phosphorylation-regulated kinase 1A (DYRK1A) is the main target of UA. Studies showed that the level of DYRK1A in AD patients' brains was higher than that of healthy people, and it was closely related to the occurrence and progression of AD. Our results revealed that UA significantly reduced the activity of DYRK1A, which led to de-phosphorylation of tau and further stabilized microtubule polymerization. UA also provided neuroprotective effects by inhibiting the production of inflammatory cytokines caused by Aß. We further showed that UA significantly improved memory impairment in an AD-like mouse model. In summary, our results indicate that UA is a DYRK1A inhibitor that may provide therapeutic advantages for AD patients.