Hard-Carbon Negative Electrodes from Biomasses for Sodium-Ion Batteries.

With the development of high-performance electrode materials, sodium-ion batteries have been extensively studied and could potentially be applied in various fields to replace the lithium-ion cells, owing to the low cost and natural abundance. As the key anode materials of sodium-ion batteries, hard carbons still face problems, such as poor cycling performance and low initial Coulombic efficiency. Owning to the low synthesis cost and the natural presence of heteroatoms of biomasses, biomasses have positive implications for synthesizing the hard carbons for sodium-ion batteries. This minireview mainly explains the research progress of biomasses used as the precursors to prepare the hard-carbon materials. The storage mechanism of hard carbons, comparisons of the structural properties of hard carbons prepared from different biomasses, and the influence of the preparation conditions on the electrochemical properties of hard carbons are introduced. In addition, the effect of doping atoms is also summarized to provide an in-depth understanding and guidance for the design of high-performance hard carbons for sodium-ion batteries.

Cytidine-rich hydrogel as an electrochemical signal amplification strategy for microRNA detection.

Signal amplification strategies increase the complexities of biosensors while improving the response signals. Herein, a novel electrochemical biosensor was developed based on a DNA hydrogel for sensitive analysis using microRNA-21 (miRNA-21) as a detection model. Poly C sequences combined with C-Ag(I)-C hydrogel formed a DNA hydrogel by the unique interaction between the cytosines and silver ions. Thus, with a three-way conjunction structure of DNA, this C-Ag(I)-C hydrogel was constructed as a novel biosensor for the detection of miRNAs. With the assistance of this hydrogel, numerous silver ions gathered around DNA strands, which would amplify the signal. Under these conditions, the silver ions produced distinct square wave voltammetry oxidation peak currents. This electrochemical biosensor we designed exhibited a great linear relationship for the logarithm of the concentration of miRNA-21 from 1 fM to 100 pM with a detection limit of 0.117 fM. Furthermore, our sensors were able to differentiate miRNA-21 from its homologous family with satisfactory responsiveness in the dilute bovine serum system.

Highly selective and sensitive detection of glutamate by an electrochemical aptasensor.

An electrochemical aptamer-based sensor was developed for glutamate, the major excitatory neurotransmitter in the central nervous system. Determining glutamic acid release and glutamic acid levels is crucial for studying signal transmission and for diagnosing pathological conditions in the brain. Glutamic acid-selective oligonucleotides were isolated from an ssDNA library using the Capture-SELEX protocol in complex medium. The selection permitted the isolation of an aptamer 1d04 with a dissociation constant of 12 µM. The aptamer sequence was further used in the development of an electrochemical aptamer sensor. For this purpose, a truncated aptamer sequence named glu1 was labelled with a ferrocene redox tag at the 3'-end and immobilized on a gold electrode surface via Au-thiol bonds. Using 6-mercapto-1-hexanol as the backfill, the sensor performance was characterized by alternating current voltammetry. The glu1 aptasensor showed a limit of detection of 0.0013 pM, a wide detection range between 0.01 pM and 1 nM, and good selectivity for glutamate in tenfold diluted human serum. With this enzyme-free aptasensor, the highly selective and sensitive detection of glutamate was demonstrated, which possesses great potential for implementation in microelectrodes and for in vitro as well as in vivo monitoring of neurotransmitter release.

Efficacy assessment of ticagrelor versus clopidogrel in Chinese patients with acute coronary syndrome undergoing percutaneous coronary intervention by data mining and machine-learning decision tree approaches.

WHAT IS KNOWN AND OBJECTIVE: Although ticagrelor has been well-known to improve clinical outcomes in patients undergoing percutaneous coronary intervention (PCI), and its effectiveness and safety have not been well evaluated in Chinese patients. This study aimed to evaluate the effectiveness and safety of ticagrelor in Chinese patients. In order to find potential effect modifiers on the drug effects, a decision tree method was performed to detect interactions between treatment and patient characteristics in an automatic and systematic manner. METHODS: This retrospective study included acute coronary syndrome (ACS) patients who underwent PCI and received either ticagrelor (N = 250) or clopidogrel (N = 291) while hospitalized between August 2014 and August 2015. After propensity score matching, Kaplan-Meier analysis was used to study the event-free survival against major adverse cardiovascular events (MACE, primary efficacy outcome, defined as the composite of cardiac death, non-fatal myocardial infarction [MI], stroke, restenosis and target vessel revascularization [TVR]), re-hospitalization, the need for urgent re-PCI (secondary efficacy outcome) and bleeding events (safety outcome) within 12 months of the PCI date. To search for effect modifiers of the two antiplatelet therapies, a machine-learning decision tree algorithm was conducted to predict re-hospitalization status. RESULTS: After propensity score matching (N = 442), ticagrelor and clopidogrel had no significant difference in MACE, re-hospitalization and bleeding. The decision tree analysis showed that the number of diseased vessels modulated the effect of ticagrelor and clopidogrel on re-hospitalization rates. In single-vessel disease (SVD) patients, ticagrelor was associated with lower hazards than clopidogrel for all efficacy outcomes: MACE (HR = 0.190, 95% CI: 0.042-0.866), re-hospitalization (HR = 0.296, 95% CI: 0.108-0.808), urgent re-PCI (HR = 0.249, 95% CI: 0.069-0.895), bleeding (HR = 1.006, 95% CI: 0.063-16.129). However, in multi-vessel disease (MVD) patients, the two treatments did not show significant difference. WHAT IS NEW AND CONCLUSION: In the general patient population, there was no significant difference between ticagrelor and clopidogrel on the hazard of MACE. However, ticagrelor achieved a better effectiveness than clopidogrel in patients with SVD. This pilot study provides scientific basis to call for a large-scale prospective study in this population.

Fibrinogen-templated gold nanoclusters for fluorometric determination of cysteine and mercury(II).

Gold nanoclusters (Au NCs) using fibrinogen (FBG) protein as template are fabricated via one-pot reduction strategy, and applied for fluorometric detections of cysteine (Cys) and mercury(II). The modified FBG-Au NCs exhibit red fluorescence, with excitation/emission maxima at 360/620 nm, a 7% quantum yield, and a 2.2 µs decay time. The fluorescence of the nanoprobe is quenched by Cys and Hg(II). Cys can be determined by fluorometry in the 0.01 to 150 µmol L-1 concentration range and with a detection limit of 0.79 µmol L-1. Due to the oxidation of Hg(II), it can be detected in the 0.01 to 10 µmol L-1 concentration range. The properties of the FBG-Au NCs and the analytical performance are comparable with previously reported peptide/protein-templated Au NCs, supplying a promising candidate for Au NCs nanoprobes synthesis and applications. Graphical abstractSchematic representation of the preparation of gold nanoclusters (Au NCs) using fibrinogen (FBG) as the template. The modified Au NCs were applied to the fluorometric detection of cysteine (Cys) and mercury ion (Hg(II)).

The efficacy and safety of cilostazol as an alternative to aspirin in Chinese patients with aspirin intolerance after coronary stent implantation: a combined clinical study and computational system pharmacology analysis.

Dual antiplatelet therapy (DAT) with aspirin and clopidogrel is the standard regimen to achieve rapid platelet inhibition and prevent thrombotic events. Currently, little information is available regarding alternative antiplatelet therapy in patients with an allergy or intolerance to aspirin. Although cilostazol is already a common alternative to aspirin in clinical practice in China, its efficacy and safety remain to be determined. We retrospectively analyzed 613 Chinese patients who had undergone primary percutaneous coronary intervention (PCI). Among them, 405 patients received standard DAT (aspirin plus clopidogrel) and 205 patients were identified with intolerance to aspirin and received alternative DAT (cilostazol plus clopidogrel). There were no significant differences between the two groups in their baseline clinical characteristics. The main outcomes of the study included major adverse cardiac events (MACEs) and bleeding events during 12 months of follow-up. The MACEs endpoint was reached in 10 of 205 patients treated with cilostazol (4.9%) and in 34 of 408 patients treated with aspirin (8.3%). No statistically significant difference was observed in MACEs between the two groups. However, patients in the cilostazol group had less restenosis than did patients in the aspirin group (1.5% vs 4.9%, P=0.035). The occurrence of bleeding events tended to be lower in the cilostazol group (0.49% vs 2.7%, P=0.063). These clinical observations were further analyzed using network system pharmacology analysis, and the outcomes were consistent with clinical observations and preclinical data reports. We conclude that in Chinese patients with aspirin intolerance undergoing coronary stent implantation, the combination of clopidogrel with cilostazol may be an efficacious and safe alternative to the standard DAT regimen.

Discovery of Novel Ligands for TNF-α and TNF Receptor-1 through Structure-Based Virtual Screening and Biological Assay.

Tumor necrosis factor α (TNF-α) is overexpressed in various diseases, and it has been a validated therapeutic target for autoimmune diseases. All therapeutics currently used to target TNF-α are biomacromolecules, and limited numbers of TNF-α chemical inhibitors have been reported, which makes the identification of small-molecule alternatives an urgent need. Recent studies have mainly focused on identifying small molecules that directly bind to TNF-α or TNF receptor-1 (TNFR1), inhibit the interaction between TNF-α and TNFR1, and/or regulate related signaling pathways. In this study, we combined in silico methods with biophysical and cell-based assays to identify novel antagonists that bind to TNF-α or TNFR1. Pharmacophore model filtering and molecular docking were applied to identify potential TNF-α antagonists. In regard to TNFR1, we constructed a three-dimensional model of the TNF-α-TNFR1 complex and carried out molecular dynamics simulations to sample the conformations. The residues in TNF-α that have been reported to play important roles in the TNF-α-TNFR1 complex were removed to form a pocket for further virtual screening of TNFR1-binding ligands. We obtained 20 virtual hits and tested them using surface plasmon resonance-based assays, which resulted in one ligand that binds to TNFR1 and four ligands with different scaffolds that bind to TNF-α. T1 and R1, the two most active compounds with Kd values of 11 and 16 µM for TNF-α and TNFR1, respectively, showed activities similar to those of known antagonists. Further cell-based assays also demonstrated that T1 and R1 have similar activities compared to the known TNF-α antagonist C87. Our work has not only produced several TNF-α and TNFR1 antagonists with novel scaffolds for further structural optimization but also showcases the power of our in silico methods for TNF-α- and TNFR1-based drug discovery.

Population pharmacokinetic modeling of sotatercept in healthy participants and patients with pulmonary arterial hypertension.

Sotatercept is a breakthrough, first-in-class biologic, that is FDA-approved for the treatment of pulmonary arterial hypertension (PAH). A population pharmacokinetic (PopPK) model was developed using data from two phase 1 studies in healthy participants, and two phase 2 studies and one phase 3 study in participants with PAH. The pooled sotatercept PK data encompassed single intravenous (IV) or subcutaneous (SC) doses ranging from 0.01 to 3.0 mg/kg, as well as multiple SC doses ranging from 0.03 to 1.0 mg/kg, with PK samples collected up to a maximum of ~150 weeks following Q3W and Q4W dosing regimens. The final PopPK analysis included 350 participants, with 30 and 320 participants receiving sotatercept IV and SC, respectively. A two-compartment model with a first-order absorption rate constant and a linear disposition from central compartment well-described sotatercept PK. The estimated bioavailability is ~66%; bioavailability, clearance (CL), and central volume (VC) have low to moderate inter-individual variability. Time-varying body weight and baseline albumin concentration were statistically significant predictors of PK; CL and VC were predicted to increase with increasing body weight, while CL was predicted to decrease with increasing baseline albumin concentration. However, the magnitude of covariate effects is not predicted to meaningfully alter the disposition of sotatercept. Altogether, the PopPK modeling results demonstrate favorable PK characteristics (low to moderate variability and typical bioavailability), supporting sotatercept as a SC biological agent for the treatment of patients with PAH.

Truncated Electrochemical Aptasensor with Enhanced Antifouling Capability for Highly Sensitive Serotonin Detection.

Accurate determination of serotonin (ST) provides insight into neurological processes and enables applications in clinical diagnostics of brain diseases. Herein, we present an electrochemical aptasensor based on truncated DNA aptamers and a polyethylene glycol (PEG) molecule-functionalized sensing interface for highly sensitive and selective ST detection. The truncated aptamers have a small size and adopt a stable stem-loop configuration, which improves the accessibility of the aptamer for the analyte and enhances the sensitivity of the aptasensor. Upon target binding, these aptamers perform a conformational change, leading to a variation in the Faraday current of the redox tag, which was recorded by square wave voltammetry (SWV). Using PEG as blocking molecules minimizes nonspecific adsorption of other interfering molecules and thus endows an enhanced antifouling ability. The proposed electrochemical aptamer sensor showed a wide range of detection lasting from 0.1 nM to 1000 nM with a low limit of detection of 0.14 nM. Owing to the unique properties of aptamer receptors, the aptasensor also exhibits high selectivity and stability. Furthermore, with the reduced unspecific adsorption, assaying of ST in human serum and artificial cerebrospinal fluid (aCSF) showed excellent performance. The reported strategy of utilizing antifouling PEG describes a novel approach to building antifouling aptasensors and holds great potential for neurochemical investigations and clinical diagnosis.

A Combined Plasmonic and Electrochemical Aptasensor Based on Gold Nanopit Arrays for the Detection of Human Serum Albumin.

Electrochemical and optical platforms are commonly employed in designing biosensors. However, one signal readout can easily lead to inaccuracies due to the effect of nonstandard test procedures, different operators, and experimental environments. We have developed a dual-signal protocol that combined two transducer principles in one aptamer-based biosensor by simultaneously performing electrochemical- and extraordinary optical transmission (EOT)-based plasmonic detection using gold nanopit arrays (AuNpA). Compared with full hole structures, we found that nanopits, that did not fully penetrate the gold film, not only exhibited a better plasmonic bandwidth and refractive index sensitivity both in the finite-difference time-domain simulation and in experiments by shielding the gold/quartz mode but also enlarged the electrochemical active surface area. Therefore, the periodic non-fully penetrating AuNpA were modified with ferrocene-labeled human serum albumin aptamer receptors. The formation of the receptor layer and human serum albumin binding complex induced a conformational change, which resulted in variation in the electron transfer between the electro-active ferrocene units and the AuNpA surface. Simultaneously, the binding event caused a surface plasmon polaritons wavelength shift corresponding to a change in the surface refractive index. Interestingly, although both transducers recorded the same binding process, they led to different limits of detection, dynamic ranges, and sensitivities. The electrochemical transducer showed a dynamic detection range from 1 nM to 600 µM, while the optical transducer covered high concentrations from 100 µM to 600 µM. This study not only provides new insights into the design of plasmonic nanostructures but also potentially opens an exciting avenue for dual-signal disease diagnosis and point-of-care testing applications.

A reusable neurotransmitter aptasensor for the sensitive detection of serotonin.

Serotonin is one of the important neurotransmitters in human nervous system and associated with central nervous system diseases. Herein, we have prepared a novel electrochemical aptasensor for rapid and sensitive detection of serotonin by using the pre-designed and prepared DNA aptamers. In the absence of serotonin, the electron transfer rate on the aptasensor was faster than that in the presence of serotonin due to the hairpin structure of the aptamer was loose and MB could be closer to the electrode surface. While in the presence of serotonin, the hairpin structure of the aptamer was extended and MB was far away from the electrode surface. The effect of MB labeled sites on analytical performances of the proposed aptasensors was discussed by comparing sensitivity of the aptasensors that MB labeled in the intermediate of the aptamer with that MB labeled at the 3' end of the aptamer. It was found that sensitivity of the intermediate-labeled aptasensor was much higher than the terminal-labeled aptasensor due to the specific conformational changes before and after aptamer binding to serotonin. The developed aptasensors exhibits a rapid electrochemical response and high sensitivity for the determination of serotonin. Under the optimal experimental conditions, the linear range for serotonin concentrations by the intermediate-labeled aptasensor was 1 pM-10 nM with a detection limit of 0.017 fM (S/N = 3). Moreover, the proposed aptasensor is reusable and shows good reproducibility and selectivity for the detection of serotonin in 100-fold diluted rat cerebrospinal fluid, suggesting a good application prospect in the detection of serotonin in real samples.

Signal-on electrochemical aptasensors with different target-induced conformations for prostate specific antigen detection.

Prostate specific antigen (PSA) has become a potential biomarker for detecting prostate cancer (PCa) in the early stage. Herein, we report a target-induced resolution for the detection of PSA sensitively and specifically by amperometric electrochemical measurements. To meet a satisfactory performance, three conformations of pre-design DNA aptamers including two stem-loop structures and a double strand structure have been investigated and compared. All of them are immobilized on gold electrode as capture probes with redox-active molecular. The mechanism of signal transduction depends on molecular recognition events involving aptamer conformational changes, thus influencing the charge transfer. A short, single-stranded DNA (ssDNA) pseudoknot forming two stem-loop structural aptamers with labeled MB at the 3' -terminus was found to posse the highest signal variation than other structure when induced by PSA due to the strong conformational change. With the optimized capture strand, the aptasensor showed the peak current increase of MB by the binding relationship between PSA and the sensor over a wide concentration range of 4 magnitude orders. The proposed aptasensor exhibited a wide detection range from 10 pg/mL to 500 ng/mL with a low detection limit of 1.24 pg/mL (S/N = 3). Moreover, the electrochemical aptasensor demonstrated good reproducibility, sensitivity, selectivity, and reliability for the detection of PSA. We also found the aptasensor had a good response in the human serum samples, making this device easy to operate for the detection of the PSA physiological concentration.

Analysis of substance use and its outcomes by machine learning I. Childhood evaluation of liability to substance use disorder.

BACKGROUND: Substance use disorder (SUD) exacts enormous societal costs in the United States, and it is important to detect high-risk youths for prevention. Machine learning (ML) is the method to find patterns and make prediction from data. We hypothesized that ML identifies the health, psychological, psychiatric, and contextual features to predict SUD, and the identified features predict high-risk individuals to develop SUD. METHOD: Male (N = 494) and female (N = 206) participants and their informant parents were administered a battery of questionnaires across five waves of assessment conducted at 10-12, 12-14, 16, 19, and 22 years of age. Characteristics most strongly associated with SUD were identified using the random forest (RF)algorithm from approximately 1000 variables measured at each assessment. Next, the complement of features was validated, and the best models were selected for predicting SUD using seven ML algorithms. Lastly, area under the receiver operating characteristic curve (AUROC) evaluated accuracy of detecting individuals who develop SUD+/- up to thirty years of age. RESULTS: Approximately thirty variables strongly predict SUD. The predictors shift from psychological dysregulation and poor health behavior in late childhood to non-normative socialization in mid to late adolescence. In 10-12-year-old youths, the features predict SUD+/- with 74% accuracy, increasing to 86% at 22 years of age. The RF algorithm optimally detects individuals between 10-22 years of age who develop SUD compared to other ML algorithms. CONCLUSION: These findings inform the items required for inclusion in instruments to accurately identify high risk youths and young adults requiring SUD prevention.

Analysis of substance use and its outcomes by machine learning: II. Derivation and prediction of the trajectory of substance use severity.

BACKGROUND: This longitudinal study explored the utility of machine learning (ML) methodology in predicting the trajectory of severity of substance use from childhood to thirty years of age using a set of psychological and health characteristics. DESIGN: Boys (N = 494) and girls (N = 206) were recruited using a high-risk paradigm at 10-12 years of age and followed up at 12-14, 16, 19, 22, 25 and 30 years of age. MEASUREMENTS: At each visit, the subjects were administered a comprehensive battery to measure psychological makeup, health status, substance use and psychiatric disorder, and their overall harmfulness of substance consumption was quantified according to the multidimensional criteria (physical, dependence, and social) developed by Nutt et al. (2007). Next, high- and low- substance use severity trajectories were derived differentially associated with probability of segueing to substance use disorder (SUD). ML methodology was employed to predict trajectory membership. FINDINGS: The high-severity trajectory group had a higher probability of leading to SUD than the low-severity trajectory (89.0% vs 32.4%; odds ratio = 16.88, p < 0.0001). Thirty psychological and health status items at each of the six visits predict membership in the high- or low-severity trajectory, with 71% accuracy at 10-12 years of age, increasing to 93% at 22 years of age. CONCLUSION: These findings demonstrate the applicability of the machine learning methodology for detecting membership in a substance use trajectory with high probability of culminating in SUD, potentially informing primary and secondary prevention.

Growing prospects of DNA nanomaterials in novel biomedical applications.

As an important genetic material for life, DNA has been investigated widely in recent years, especially in interdisciplinary fields crossing nanomaterials and biomedical applications. It plays an important role because of its extraordinary molecular recognition capability and novel conformational polymorphism. DNA is also a powerful and versatile building block for the fabrication of nanostructures and nanodevices. Such DNA-based nanomaterials have also been successfully applied in various aspects ranging from biosensors to biomedicine and special logic gates, as well as in emerging molecular nanomachines. In this present mini-review, we briefly overview the recent progress in these fields. Furthermore, some challenges are also discussed in the conclusions and perspectives section, which aims to stimulate broader scientific interest in DNA nanotechnology and its biomedical applications.

DNA conformational polymorphism for biosensing applications.

In this mini review, we will briefly introduce the rapid development of DNA conformational polymorphism in biosensing field, including canonical DNA duplex, triplex, quadruplex, DNA origami, as well as more functionalized DNAs (aptamer, DNAzyme etc.). Various DNA structures are adopted to play important roles in sensor construction, through working as recognition receptor, signal reporter or linking staple for signal motifs, etc. We will mainly summarize their recent developments in DNA-based electrochemical and fluorescent sensors. For the electrochemical sensors, several types will be included, e.g. the amperometric, electrochemical impedance, electrochemiluminescence, as well as field-effect transistor sensors. For the fluorescent sensors, DNA is usually modified with fluorescent molecules or novel nanomaterials as report probes, excepting its core recognition function. Finally, general conclusion and future perspectives will be discussed for further developments.

Chemogenomics Systems Pharmacology Mapping of Potential Drug Targets for Treatment of Traumatic Brain Injury.

Traumatic brain injury (TBI) is associated with high mortality and morbidity. Though the death rate of initial trauma has dramatically decreased, no drug has been developed to effectively limit the progression of the secondary injury caused by TBI. TBI appears to be a predisposing risk factor for Alzheimer's disease (AD), whereas the molecular mechanisms remain unknown. In this study, we have conducted a research investigation of computational chemogenomics systems pharmacology (CSP) to identify potential drug targets for TBI treatment. TBI-induced transcriptional profiles were compared with those induced by genetic or chemical perturbations, including drugs in clinical trials for TBI treatment. The protein-protein interaction network of these predicted targets were then generated for further analyses. Some protein targets when perturbed, exhibit inverse transcriptional profiles in comparison with the profiles induced by TBI, and they were recognized as potential therapeutic targets for TBI. Drugs acting on these targets are predicted to have the potential for TBI treatment if they can reverse the TBI-induced transcriptional profiles that lead to secondary injury. In particular, our results indicated that TRPV4, NEUROD1, and HPRT1 were among the top therapeutic target candidates for TBI, which are congruent with literature reports. Our analyses also suggested the strong associations between TBI and AD, as perturbations on AD-related genes, such as APOE, APP, PSEN1, and MAPT, can induce similar gene expression patterns as those of TBI. To the best of our knowledge, this is the first CSP-based gene expression profile analyses for predicting TBI-related drug targets, and the findings could be used to guide the design of new drugs targeting the secondary injury caused by TBI.

Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era.

Over the last decade, deep learning (DL) methods have been extremely successful and widely used to develop artificial intelligence (AI) in almost every domain, especially after it achieved its proud record on computational Go. Compared to traditional machine learning (ML) algorithms, DL methods still have a long way to go to achieve recognition in small molecular drug discovery and development. And there is still lots of work to do for the popularization and application of DL for research purpose, e.g., for small molecule drug research and development. In this review, we mainly discussed several most powerful and mainstream architectures, including the convolutional neural network (CNN), recurrent neural network (RNN), and deep auto-encoder networks (DAENs), for supervised learning and nonsupervised learning; summarized most of the representative applications in small molecule drug design; and briefly introduced how DL methods were used in those applications. The discussion for the pros and cons of DL methods as well as the main challenges we need to tackle were also emphasized.

Correction to: Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era.

The name of the corresponding author should be 'Xiang-Qun Xie', rather than 'Xiang-Qun Sean Xie'.

Synergism of antihypertensives and cholinesterase inhibitors in Alzheimer's disease.

INTRODUCTION: We investigated the effect of antihypertensive (aHTN) medications and cholinesterase inhibitors (ChEIs) on the cognitive decline in patients with Alzheimer's disease (AD) and analyzed synergism by chemogenomics systems pharmacology mapping. METHODS: We compared the effect of aHTN drugs on Mini-Mental State Examination scores in 617 AD patients with hypertension, and studied the synergistic effects. RESULTS: The combination of diuretics, calcium channel blockers, and renin-angiotensin-aldosterone system blockers showed slower cognitive decline compared with other aHTN groups (Δß = +1.46, P < .0001). aHTN medications slow down cognitive decline in ChEI users (Δß = +0.56, P = .006), but not in non-ChEI users (Δß = -0.31, P = .53). DISCUSSION: aHTN and ChEI drugs showed synergistic effects. A combination of diuretics, renin-angiotensin-aldosterone system blockers, and calcium channel blockers had the slowest cognitive decline. The chemogenomics systems pharmacology-identified molecular targets provide system pharmacology interpretation of the synergism of the drugs in clinics. The results suggest that improving vascular health is essential for AD treatment and provide a novel direction for AD drug development.