Search

Abstract Objectives Patients with rheumatoid arthritis (RA) commonly experience a high prevalence of multiple metabolic diseases (MD), leading to higher morbidity and premature mortality. Here, we aimed to investigate the pathogenesis of MD in RA patients (RA_MD) through an integrated multi-omics approach. Methods Fecal and blood samples were collected from a total of 181 subjects in this study for multi-omics analyses, including 16S rRNA and internally transcribed spacer (ITS) gene sequencing, metabolomics, transcriptomics, proteomics and phosphoproteomics. Spearman’s correlation and protein-protein interaction networks were used to assess the multi-omics data correlations. The Least Absolute Shrinkage and Selection Operator (LASSO) machine learning algorithm were used to identify disease-specific biomarkers for RA_MD diagnosis. Results Our results found that RA_MD was associated with differential abundance of gut microbiota such as Turicibacter and Neocosmospora, metabolites including decreased unsaturated fatty acid, genes related to linoleic acid metabolism and arachidonic acid metabolism, as well as downregulation of proteins and phosphoproteins involved in cholesterol metabolism. Furthermore, a multi-omics classifier differentiated RA_MD from RA with high accuracy (AUC: 0.958). Compared to gouty arthritis and systemic lupus erythematosus, dysregulation of lipid metabolism showed disease-specificity in RA_MD. Conclusions The integration of multi-omics data demonstrates that lipid metabolic pathways play a crucial role in RA_MD, providing the basis and direction for the prevention and early diagnosis of MD, as well as new insights to complement clinical treatment options.

Abstract Background Cervical cancer (CC) is a danger to women’s health, especially in many developing countries. Metabolomics can make the connection between genotypes and phenotypes. It provides a wide spectrum profile of biological processes under pathological or physiological conditions. Method In this study, we conducted plasma metabolomics of healthy volunteers and CC patients and integratively analyzed them with public CC tissue transcriptomics from Gene Expression Omnibus (GEO). Result Here, we screened out a panel of 5 metabolites to precisely distinguish CC patients from healthy volunteers. Furthermore, we utilized multi-omics approaches to explore patients with stage I-IIA1 and IIA2-IV4 CC and comprehensively analyzed the dysregulation of genes and metabolites in CC progression. We identified that plasma levels of trimethylamine N-oxide (TMAO) were associated with tumor size and regarded as a risk factor for CC. Moreover, we demonstrated that TMAO could promote HeLa cell proliferation in vitro. In this study, we delineated metabolic profiling in healthy volunteers and CC patients and revealed that TMAO was a potential biomarker to discriminate between I-IIA1 and IIA2-IV patients to indicate CC deterioration. Conclusion Our study identified a diagnostic model consisting of five metabolites in plasma that can effectively distinguish CC from healthy volunteers. Furthermore, we proposed that TMAO was associated with CC progression and might serve as a potential non-invasive biomarker to predict CC substage. Impact These findings provided evidence of the important role of metabolic molecules in the progression of cervical cancer disease, as well as their ability as potential biomarkers.

Abstract Objectives Rheumatoid arthritis (RA) is a chronic autoimmune disease with complex causes and recurrent attacks that can easily develop into chronic arthritis and eventually lead to joint deformity. Our study aims to elucidate potential mechanism among control, new-onset RA (NORA) and chronic RA (CRA) with multi-omics analysis. Methods A total of 113 RA patients and 75 controls were included in our study. Plasma and stool samples were obtained for 16S rRNA sequencing, internally transcribed spacer (ITS) sequencing and metabolomics analysis. And PBMCs were obtained for RNA sequencing. We used three models, logistic regression, least absolute shrinkage and selection operator (LASSO), and random forest, respectively, to distinguish NORA from CRA, and finally we validated model performance using an external cohort of 26 subjects. Results Our results demonstrated intestinal flora disturbance in RA development, with significantly increased abundance of Escherichia-Shigella and Proteobacteria in NORA. We also found that the diversity was significantly reduced in CRA compared to NORA through fungi analysis. Moreover, we identified 29 differential metabolites between NORA and CRA. Pathway enrichment analysis revealed significant dysregulation of glycerophospholipid metabolism and phenylalanine metabolism pathways in RA patients. Next, we identified 40 differentially expressed genes between NORA and CRA, which acetylcholinesterase (ACHE) was the core gene and significantly enriched in glycerophospholipid metabolism pathway. Correlation analysis showed a strong negatively correlation between glycerophosphocholine and inflammatory characteristics. Additionally, we applied three approaches to develop disease classifier models that were based on plasma metabolites and gut microbiota, which effectively distinguished between new-onset and chronic RA patients in both discovery cohort and external validation cohort. Conclusions These findings revealed that glycerophospholipid metabolism plays a crucial role in the development and progression of RA, providing new ideas for early clinical diagnosis and optimizing treatment strategies.

Abstract Background Rheumatoid arthritis (RA) is a chronic, systemic autoimmune inflammatory disease, the pathogenesis of which is not clear. Clinical remission, or decreased disease activity, is the aim of treatment for RA. However, our understanding of disease activity is inadequate, and clinical remission rates for RA are generally poor. In this study, we used multi-omics profiling to study potential alterations in rheumatoid arthritis with different disease activity levels. Methods Fecal and plasma samples from 131 rheumatoid arthritis (RA) patients and 50 healthy subjects were collected for 16S rRNA sequencing, internally transcribed spacer (ITS) sequencing, and liquid chromatography-tandem mass spectrometry (LC–MS/MS). The PBMCS were also collected for RNA sequencing and whole exome sequencing (WES). The disease groups, based on 28 joints and ESR (DAS28), were divided into DAS28L, DAS28M, and DAS28H groups. Three random forest models were constructed and verified with an external validation cohort of 93 subjects. Results Our findings revealed significant alterations in plasma metabolites and gut microbiota in RA patients with different disease activities. Moreover, plasma metabolites, especially lipid metabolites, demonstrated a significant correlation with the DAS28 score and also associations with gut bacteria and fungi. KEGG pathway enrichment analysis of plasma metabolites and RNA sequencing data demonstrated alterations in the lipid metabolic pathway in RA progression. Whole exome sequencing (WES) results have shown that non-synonymous single nucleotide variants (nsSNV) of the HLA-DRB1 and HLA-DRB5 gene locus were associated with the disease activity of RA. Furthermore, we developed a disease classifier based on plasma metabolites and gut microbiota that effectively discriminated RA patients with different disease activity in both the discovery cohort and the external validation cohort. Conclusion Overall, our multi-omics analysis confirmed that RA patients with different disease activity were altered in plasma metabolites, gut microbiota composition, transcript levels, and DNA. Our study identified the relationship between gut microbiota and plasma metabolites and RA disease activity, which may provide a novel therapeutic direction for improving the clinical remission rate of RA.

Abstract Background To develop and validate a quantitative computed tomography (QCT) based radiomics model for discriminating osteoporosis and osteopenia. Methods A total of 635 patients underwent QCT were retrospectively included from November 2016 to November 2019. The patients with osteopenia or osteoporosis (N = 590) were divided into a training cohort (N = 414) and a test cohort (N = 176). Radiomics features were extracted from the QCT images of the third lumbar vertebra. Minimum redundancy and maximum relevance and least absolute shrinkage and selection operator were used for data dimensional reduction, features selection and radiomics model building. Multivariable logistic regression was applied to construct the combined clinical-radiomic model that incorporated radiomics signatures and clinical characteristics. The performance of the combined clinical-radiomic model was evaluated by the area under the curve of receiver operator characteristic curve (ROC–AUC), accuracy, specificity, sensitivity, positive predictive value, and negative predictive value. Results The patients with osteopenia or osteoporosis were randomly divided into training and test cohort with a ratio of 7:3. Six more predictive radiomics signatures, age, alkaline phosphatase and homocysteine were selected to construct the combined clinical-radiomic model for diagnosis of osteoporosis and osteopenia. The AUC of the combined clinical-radiomic model was 0.96 (95% confidence interval (CI), 0.95 to 0.98) in the training cohort and 0.96 (95% CI 0.92 to 1.00) in the test cohort, which were superior to the clinical model alone (training-AUC = 0.81, test-AUC = 0.79). The calibration curve demonstrated that the radiomics nomogram had good agreement between prediction and observation and decision curve analysis confirmed clinically useful. Conclusions The combined clinical-radiomic model that incorporates the radiomics score and clinical risk factors, can serve as a reliable and powerful tool for discriminating osteoporosis and osteopenia.

Abstract An accurate prediction of prognosis is important for clinical treatments of glioma. In this study, a multiparameter radiomic model is proposed for accurate prognostic prediction of glioma. Three kinds of region of interest were extracted from preoperative postcontrast T1‐weighted images and T2 fluid‐attenuated inversion recovery images acquired from 140 glioma patients. Radiomics score (Radscore) was calculated and the conventional image features and clinical molecular characteristics that may be related to progression‐free survival (PFS) were evaluated. Five uniparameter and various combinations of biparameter and multiparameter models based on above characteristics were built. The performance of these models was evaluated by concordance index (C index), and the nomogram of the multiparameter radiomic model was constructed. The results show that the proposed multiparameter radiomic model has a better prediction performance than other models. In the training and validation sets, the calibration curves of the multiparameter radiomic model for the 1‐, 2‐, and 3‐year PFS probability demonstrate a high consistence between predictions and observations. In conclusion, this study demonstrates that the multiparameter radiomic model based on Radscore, conventional image features and clinical molecular characteristics can improve the prediction accuracy of glioma prognosis, which could be informative for individualized treatments.

IntroductionThe retina represents a critical ocular structure. Of the various ophthalmic afflictions, retinal pathologies have garnered considerable scientific interest, owing to their elevated prevalence and propensity to induce blindness. Among clinical evaluation techniques employed in ophthalmology, optical coherence tomography (OCT) is the most commonly utilized, as it permits non-invasive, rapid acquisition of high-resolution, cross-sectional images of the retina. Timely detection and intervention can significantly abate the risk of blindness and effectively mitigate the national incidence rate of visual impairments.MethodsThis study introduces a novel, efficient global attention block (GAB) for feed forward convolutional neural networks (CNNs). The GAB generates an attention map along three dimensions (height, width, and channel) for any intermediate feature map, which it then uses to compute adaptive feature weights by multiplying it with the input feature map. This GAB is a versatile module that can seamlessly integrate with any CNN, significantly improving its classification performance. Based on the GAB, we propose a lightweight classification network model, GABNet, which we develop on a UCSD general retinal OCT dataset comprising 108,312 OCT images from 4686 patients, including choroidal neovascularization (CNV), diabetic macular edema (DME), drusen, and normal cases.ResultsNotably, our approach improves the classification accuracy by 3.7% over the EfficientNetV2B3 network model. We further employ gradient-weighted class activation mapping (Grad-CAM) to highlight regions of interest on retinal OCT images for each class, enabling doctors to easily interpret model predictions and improve their efficiency in evaluating relevant models.DiscussionWith the increasing use and application of OCT technology in the clinical diagnosis of retinal images, our approach offers an additional diagnostic tool to enhance the diagnostic efficiency of clinical OCT retinal images.

Abstract As COVID-19 spread around the world, epidemic prevention and control policies have been adopted by many countries. This process has prompted online social platforms to become important channels to enable people to socialize and exchange information. The massive use of social media data mining techniques, to analyze the development online of public opinion during the epidemic, is of great significance in relation to the management of public opinion. This paper presents a study that aims to analyze the developmental course of online public opinion in terms of fine-grained emotions presented during the COVID-19 epidemic in China. It is based on more than 45 million Weibo posts during the period from December 1, 2019 to April 30, 2020. A text emotion extraction method based on a dictionary of emotional ontology has been developed. The results show, for example, that a high emotional effect is observed during holidays, such as New Year. As revealed by Internet users, the outbreak of the COVID-19 epidemic and its rapid spread, over a comparatively short period of time, triggered a sharp rise in the emotion “fear”. This phenomenon was noted especially in Wuhan and the immediate surrounding areas. Over the initial 2 months, although this “fear” gradually declined, it remained significantly higher than the more common level of uncertainty that existed during the epidemic’s initial developmental era. Simultaneously, in the main city clusters, the response to the COVID-19 epidemic in central cities, was stronger than that in neighboring cities, in terms of the above emotion. The topics of Weibo posts, the corresponding emotions, and the analysis conclusions can provide auxiliary reference materials for the monitoring of network public opinion under similar major public events.

Summary: Background: Patients with pancreatobiliary tract cancer usually have a poor clinical outcome, with a 5-year overall survival rate below 20%. This is mainly associated with the late diagnosis. In addition, the standard-of-care for patients with malignant biliary stenosis involves a major surgery, the Whipple procedure. An accurate preoperative diagnosis, including differentiation from benign diseases, is critical to avoid unnecessary treatment. Here we developed BileScreen, a sensitive detection modality for the diagnosis of pancreatobiliary tract cancer based on massively parallel sequencing mutation and methylation changes in bile samples. Methods: A total of 338 patients, from five hospitals in China, with pancreatobiliary system disorders were enrolled in this study between November 2018 and October 2020, and 259 were included for the analysis of BileScreen. We profiled 23 gene mutations and 44 genes with methylation modifications in parallel from bile samples, and set up a model for the detection of malignancy based on multi-level biomarkers. Findings: We applied the BileScreen assay in a training cohort (n = 104) to set up the model and algorithm. The model was further evaluated in a validation cohort (n = 105), resulting in 92% sensitivity and 98% specificity. The performance of BileScreen was further assessed in a prospective test cohort (n = 50) of patients diagnosed with suspicious or negative pathology by endoscopic retrograde cholangiopancreatography and were confirmed in follow-up. BileScreen yielded 90% sensitivity and 80% specificity, and outcompeted serum carbohydrate antigen 19-9 in detecting pancreatobiliary tract cancer in all three cohorts, especially in terms of specificity. Interpretation: Taken together, BileScreen has the ability to interrogate mutations and methylation changes in bile samples in parallel, thus rendering it a potentially sensitive detection method to help in the diagnosis of pancreatobiliary tract cancer in a safe, convenient and less-invasive manner. Funding: This study was supported by the Capital's Funds for Health Improvement and Research (2020-2-4025 to S.H.), the National Natural Science Foundation of China (81972311 to H.Z.), CAMS Innovation Fund for Medical Sciences (CIFMS) (2017-12M-4-002 to H.Z.), the CAMS Innovation Fund for Medical Sciences(CIFMS) (2021-I2M-1-066 to CJQ), the Non-profit Central Research Institution Fund of Chinese Academy of Medical Sciences (2019PT310026 to H.Z.) and Sanming Project of Medicine in Shenzhen (SZSM202011010 to H.Z.).

ObjectiveRheumatoid arthritis (RA) is a chronic inflammatory joint disease, which is associated with progressive disability, systemic complications, and early death. But its etiology and pathogenesis are not fully understood. We aimed to investigate the alterations in plasma metabolite profiles, gut bacteria, and fungi and their role of them in the pathogenesis of RA.MethodsMetabolomics profiling of plasma from 363 participants including RA (n = 244), systemic lupus erythematosus (SLE, n = 50), and healthy control (HC, n = 69) were performed using the ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry. The differentially expressed metabolites were selected among groups and used to explore important metabolic pathways. Gut microbial diversity analysis was performed by 16S rRNA sequencing and ITS sequencing (RA = 195, HC = 269), and the specific microbial floras were identified afterward. The diagnosis models were established based on significant differential metabolites and microbial floras, respectively.ResultsThere were 63 differential metabolites discovered between RA and HC groups, mainly significantly enriched in the arginine and proline metabolism, glycine, serine, and threonine metabolism, and glycerophospholipid metabolism between RA and HC groups. The core differential metabolites included L-arginine, creatine, D-proline, ornithine, choline, betaine, L-threonine, LysoPC (18:0), phosphorylcholine, and glycerophosphocholine. The L-arginine and phosphorylcholine were increased in the RA group. The AUC of the predictive model was 0.992, based on the combination of the 10 differential metabolites. Compared with the SLE group, 23 metabolites increased and 61 metabolites decreased in the RA group. However, no significant metabolic pathways were enriched between RA and SLE groups. On the genus level, a total of 117 differential bacteria genera and 531 differential fungal genera were identified between RA and HC groups. The results indicated that three bacteria genera (Eubacterium_hallii_group, Escherichia-Shigella, Streptococcus) and two fungal genera (Candida and Debaryomyces) significantly increased in RA patients. The AUC was 0.80 based on a combination of six differential bacterial genera and the AUC was 0.812 based on a combination of seven differential fungal genera. Functional predictive analysis displayed that differential bacterial and differential fungus both were associated with KEGG pathways involving superpathway of L-serine and glycine biosynthesis I, arginine, ornithine, and proline interconversion.ConclusionThe plasma metabolism profile and gut microbe profile changed markedly in RA. The glycine, serine, and threonine metabolism and arginine and proline metabolism played an important role in RA.

Abstract Accurate prediction of metastasis is an important determinant for selecting appropriate treatment for advanced colorectal cancer (CRC). In this study, 1250 patients in two hospitals from 2014 to 2019 histologically diagnosed with CRC were enrolled. We performed the transcriptome analysis on 141 CRC patients. RNA‐seq analysis revealed that long noncoding RNA (LncRNA) colorectal neoplasia differentially expressed (CRNDE) played an important role in CRC metastasis. The least absolute shrinkage and selection operator regression was used to select features and develop radiomics model. Multivariate logistic regression analysis was used to develop combined model. The radiomics model with 13 filtered radiomics features had good discrimination in predicting expression level of LncRNA CRNDE in training set (receiver operating characteristic [AUC] = 0.809) and testing set (AUC = 0.755). Furthermore, the radiomics model could predict the metastasis of CRC in internal validation set (AUC, 0.665) and in external validation set (AUC = 0.690). The combined model developed with radiomics score and carcinoembryonic antigen had better performance, and the AUC was 0.708, 0.700 in internal validation set and in external validation set, respectively. In conclusion, we proposed a radiomics model and combined model, which could predict the expression level of LncRNA CRNDE and further predict CRC metastasis, thereby helping clinician make treatment decisions.

Optical coherence tomography (OCT) is a new type of tomography that has experienced rapid development and potential in recent years. It is playing an increasingly important role in retinopathy diagnoses. At present, due to the uneven distributions of medical resources in various regions, the uneven proficiency levels of doctors in grassroots and remote areas, and the development needs of rare disease diagnosis and precision medicine, artificial intelligence technology based on deep learning can provide fast, accurate, and effective solutions for the recognition and diagnosis of retinal OCT images. To prevent vision damage and blindness caused by the delayed discovery of retinopathy, a fusion network (FN)-based retinal OCT classification algorithm (FN-OCT) is proposed in this paper to improve upon the adaptability and accuracy of traditional classification algorithms. The InceptionV3, Inception-ResNet, and Xception deep learning algorithms are used as base classifiers, a convolutional block attention mechanism (CBAM) is added after each base classifier, and three different fusion strategies are used to merge the prediction results of the base classifiers to output the final prediction results (choroidal neovascularization (CNV), diabetic macular oedema (DME), drusen, normal). The results show that in a classification problem involving the UCSD common retinal OCT dataset (108,312 OCT images from 4,686 patients), compared with that of the InceptionV3 network model, the prediction accuracy of FN-OCT is improved by 5.3% (accuracy = 98.7%, area under the curve (AUC) = 99.1%). The predictive accuracy and AUC achieved on an external dataset for the classification of retinal OCT diseases are 92 and 94.5%, respectively, and gradient-weighted class activation mapping (Grad-CAM) is used as a visualization tool to verify the effectiveness of the proposed FNs. This finding indicates that the developed fusion algorithm can significantly improve the performance of classifiers while providing a powerful tool and theoretical support for assisting with the diagnosis of retinal OCT.

Background Assessment of colorectal cancer (CRC) lymph node metastasis (LNM) is critical to the decision of surgery, prognosis, and therapy strategy. In this study, we aimed to develop and validate a multiple tumor marker nomogram for predicting LNM in CRC patients. Methods A total of 674 patients who met the inclusion criteria were collected and randomly divided into primary cohort and internal test cohort at a ratio of 7:3. An external test cohort enrolled 178 CRC patients from the West China Hospital. Clinicopathologic variables were obtained from electronic medical records. The least absolute shrinkage and selection operator (LASSO) and interquartile range analysis were carried out for variable dimensionality reduction and feature selection. Multivariate logistic regression analysis was conducted to develop predictive models of LNM. The performance of the established models was evaluated by the receiver operating characteristic (ROC) curve, calibration belt, and clinical usefulness. Results Based on minimum criteria, 18 potential features were reduced to six predictors by LASSO and interquartile range in the primary cohort. The model demonstrated good discrimination and ROC curve (AUC = 0.721 in the internal test cohort, AUC = 0.758 in the external test cohort) in LNM assessment. Good calibration was shown for the probability of CRC LNM in the internal and external test cohorts. Decision curve analysis illustrated that multi-tumor markers nomogram was clinically useful. Conclusions The study proposed a reliable nomogram that could be efficiently and conveniently utilized to facilitate the assessment of individually-tailored LNM in patients with CRC, complementing imaging and biopsy tests.

Diabetic retinopathy (DR) is one of the common chronic complications of diabetes and the most common blinding eye disease. If not treated in time, it might lead to visual impairment and even blindness in severe cases. Therefore, this article proposes an algorithm for detecting diabetic retinopathy based on deep ensemble learning and attention mechanism. First, image samples were preprocessed and enhanced to obtain high quality image data. Second, in order to improve the adaptability and accuracy of the detection algorithm, we constructed a holistic detection model DR-IIXRN, which consists of Inception V3, InceptionResNet V2, Xception, ResNeXt101, and NASNetLarge. For each base classifier, we modified the network model using transfer learning, fine-tuning, and attention mechanisms to improve its ability to detect DR. Finally, a weighted voting algorithm was used to determine which category (normal, mild, moderate, severe, or proliferative DR) the images belonged to. We also tuned the trained network model on the hospital data, and the real test samples in the hospital also confirmed the advantages of the algorithm in the detection of the diabetic retina. Experiments show that compared with the traditional single network model detection algorithm, the auc, accuracy, and recall rate of the proposed method are improved to 95, 92, and 92%, respectively, which proves the adaptability and correctness of the proposed method.

We synthesized a series of quinazolinone derivates as tyrosinase inhibitors and evaluated their inhibition constants. We synthesized 2-(2,6-dimethylhepta-1,5-dien-1-yl)quinazolin-4(3H)-one (Q1) from the natural citral. The concentration, which led to 50% activity loss of Q1, was 103 ± 2 μM (IC50 = 103 ± 2 μM). Furthermore, we considered Q1 to be a mixed-type and reversible tyrosinase inhibitor, and determined the KI and KIS inhibition constants to be 117.07 μM and 423.63 μM, respectively. Our fluorescence experiment revealed that Q1 could interact with the substrates of tyrosine and L-DOPA in addition to tyrosinase. Molecular docking studies showed that the binding of Q1 to tyrosinase was driven by hydrogen bonding and hydrophobicity. Briefly, the current study confirmed a new tyrosinase inhibitor, which is expected to be developed into a novel pigmentation drug.

Human activity area extraction, a popular research topic, refers to mining meaningful location clusters from raw activity data. However, varying densities of large-scale spatial data create a challenge for existing extraction methods. This research proposes a novel area extraction framework (ELV) aimed at tackling the challenge by using clustering with an adaptive distance parameter and a re-segmentation strategy with noise recovery. Firstly, a distance parameter was adaptively calculated to cluster high-density points, which can reduce the uncertainty introduced by human subjective factors. Secondly, the remaining points were assigned according to the spatial characteristics of the clustered points for a more reasonable judgment of noise points. Then, to face the varying density problem, a re-segmentation strategy was designed to segment the appropriate clusters into low- and high-density clusters. Lastly, the noise points produced in the re-segmentation step were recovered to reduce unnecessary noise. Compared with other algorithms, ELV showed better performance on real-life datasets and reached 0.42 on the Silhouette coefficient (SC) indicator, with an improvement of more than 16.67%. ELV ensures reliable clustering results, especially when the density differences of the activity points are large, and can be valuable in some applications, such as location prediction and recommendation.

Coumarin possesses the aromatic group and showed plentiful activities, such as antioxidant, preventing asthma and antisepsis. In addition, coumarin derivatives usually possess good solubility, low cytotoxicity and excellent cell permeability. In our study, we synthesized the compound bridge methylene tacrine (BMT), which has the classical pharmacophore structure of Tacrine (THA). Based on the principle of active substructure splicing, BMT was used as a lead compound and synthesized coumarin–BMT hybrids by introducing coumarin to BMT. In this work, 21 novel hybrids of BMT and coumarin were synthesized and evaluated for their inhibitory activity on AChE. All obtained compounds present preferable inhibition. Compound 8b was the most active compound, with the value of Ki as 49.2 nM, which was higher than Galantamine (GAL) and lower than THA. The result of molecular docking showed that the highest binding free energy was −40.43 kcal/mol for compound 8b, which was an identical trend with the calculated Ki.