Multi-modal features-based human-herpesvirus protein-protein interaction prediction by using LightGBM.

The identification of human-herpesvirus protein-protein interactions (PPIs) is an essential and important entry point to understand the mechanisms of viral infection, especially in malignant tumor patients with common herpesvirus infection. While natural language processing (NLP)-based embedding techniques have emerged as powerful approaches, the application of multi-modal embedding feature fusion to predict human-herpesvirus PPIs is still limited. Here, we established a multi-modal embedding feature fusion-based LightGBM method to predict human-herpesvirus PPIs. In particular, we applied document and graph embedding approaches to represent sequence, network and function modal features of human and herpesviral proteins. Training our LightGBM models through our compiled non-rigorous and rigorous benchmarking datasets, we obtained significantly better performance compared to individual-modal features. Furthermore, our model outperformed traditional feature encodings-based machine learning methods and state-of-the-art deep learning-based methods using various benchmarking datasets. In a transfer learning step, we show that our model that was trained on human-herpesvirus PPI dataset without cytomegalovirus data can reliably predict human-cytomegalovirus PPIs, indicating that our method can comprehensively capture multi-modal fusion features of protein interactions across various herpesvirus subtypes. The implementation of our method is available at https://github.com/XiaodiYangpku/MultimodalPPI/.

Deep learning-assisted prediction of protein-protein interactions in Arabidopsis thaliana.

Currently, the experimentally identified interactome of Arabidopsis (Arabidopsis thaliana) is still far from complete, suggesting that computational prediction methods can complement experimental techniques. Motivated by the prosperity and success of deep learning algorithms and natural language processing techniques, we introduce an integrative deep learning framework, DeepAraPPI, allowing us to predict protein-protein interactions (PPIs) of Arabidopsis utilizing sequence, domain and Gene Ontology (GO) information. Our current DeepAraPPI comprises: (i) a word2vec encoding-based Siamese recurrent convolutional neural network (RCNN) model; (ii) a Domain2vec encoding-based multiple-layer perceptron (MLP) model; and (iii) a GO2vec encoding-based MLP model. Finally, DeepAraPPI combines the prediction results of the three individual predictors through a logistic regression model. Compiling high-quality positive and negative training and test samples by applying strict filtering strategies, DeepAraPPI shows superior performance compared with existing state-of-the-art Arabidopsis PPI prediction methods. DeepAraPPI also provides better cross-species predictive ability in rice (Oryza sativa) than traditional machine learning methods, although the overall performance in cross-species prediction remains to be improved. DeepAraPPI is freely accessible at http://zzdlab.com/deeparappi/. In the meantime, we have also made the source code and data sets of DeepAraPPI available at https://github.com/zjy1125/DeepAraPPI.

Azulene-Containing Bis(squaraine) Dyes: Design, Synthesis and Aggregation Behaviors.

The relationship among chemical structure, physicochemical property and aggregation behavior of organic functional material is an important research topic. Here, we designed and synthesized three bis(squaraine) dyes BSQ1, BSQ2 and BSQ3 through the combination of two kinds of unsymmetrical azulenyl squaraine monomers. Their physicochemical properties were investigated in both molecular and aggregate states. Generally, BSQ1 displayed different assembly behaviors from BSQ2 and BSQ3. Upon fabrication into nanoparticles, BSQ1 tend to form J-aggregates while BSQ2 and BSQ3 tend to form H-aggregates in aqueous medium. When in the form of thin films, three bis(squaraine) dyes all adopted J-aggregation packing modes while only BSQ1 presented the most significant rearrangement of aggregate structures as well as the improvement in the carrier mobilities upon thermal annealing. Our research highlights the discrepancy of aggregation behaviors originating from the molecular structure and surrounding circumstances, providing guidance for the molecular design and functional applications of squaraines.

Schistosoma japonicum cystatin alleviates paraquat poisoning caused acute lung injury in mice through activating regulatory macrophages.

BACKGROUND: Paraquat (PQ) is a widely used herbicide that poisons human by accident or intentional ingestion. PQ poisoning causes systemic inflammatory response syndrome (SIRS) resulting in acute lung injury (ALI) with an extremely high mortality rate. Blood trematode Schistosoma japonicum-produced cystatin (Sj-Cys) is a strong immunomodulatory protein that has been experimentally used to treat inflammation related diseases. In this study, Sj-Cys recombinant protein (rSj-Cys) was used to treat PQ-induced lung injury and the immunological mechanism underlying the therapeutic effect was investigated. METHODS: PQ-induced acute lung injury mouse model was established by intraperitoneally injection of 20 mg/kg of paraquat. The poisoned mice were treated with rSj-Cys and the survival rate was observed up to 7 days compared with the group without treatment. The pathological changes of PQ-induced lung injury were observed by examining the histochemical sections of affected lung tissue and the wet to dry ratio of lung as a parameter for inflammation and edema. The levels of the inflammation related cytokines IL-6 and TNF-α and regulatory cytokines IL-10 and TGF-ß were measured in sera and in affected lung tissue using ELISA and their mRNA levels in lung tissue using RT-PCR. The macrophages expressing iNOS were determined as M1 and those expressing Arg-1 as M2 macrophages. The effect of rSj-Cys on the transformation of inflammatory M1 to regulatory M2 macrophages was measured in affected lung tissue in vivo (EKISA and RT-PCR) and in MH-S cell line in vitro (flow cytometry). The expression levels of TLR2 and MyD88 in affected lung tissue were also measured to determine their role in the therapy of rSj-Cys on PQ-induced lung injury. RESULT: We identified that treatment with rSj-Cys significantly improved the survival rate of mice with PQ-induced lung injury from 30 % (untreated) to 80 %, reduced the pathological damage of poisoning lung tissue, associated with significantly reduced levels of proinflammatory cytokines (IL-6 from 1490 to 590 pg/ml, TNF-α from 260 to 150 pg/ml) and increased regulatory cytokines (IL-10 from360 to 550 pg/ml, and TGF-ß from 220 to 410 pg/ml) in both sera (proteins) and affected lung tissue (proteins and mRNAs). The polarization of macrophages from M1to M2 type was found to be involved in the therapeutic effect of rSj-Cys on the PQ-induced acute lung injury, possibly through inhibiting TLR2/MyD88 signaling pathway. CONCLUSIONS: Our study demonstrated the therapeutic effect of rSj-Cys on PQ poisoning caused acute lung injury by inducing M2 macrophage polarization through inhibiting TLR2/MyD88 signaling pathway. The finding in this study provides an alternative approach for the treatment of PQ poisoning and other inflammatory diseases.

Fused Azulenyl Squaraine Derivatives Improve Phototheranostics in the Second Near-Infrared Window by Concentrating Excited State Energy on Non-Radiative Decay Pathways.

The second near-infrared (NIR-II) theranostics offer new opportunities for precise disease phototheranostic due to the enhanced tissue penetration and higher maximum permissible exposure of NIR-II light. However, traditional regimens lacking effective NIR-II absorption and uncontrollable excited-state energy decay pathways often result in insufficient theranostic outcomes. Herein a phototheranostic nano-agent (PS-1 NPs) based on azulenyl squaraine derivatives with a strong NIR-II absorption band centered at 1092 nm is reported, allowing almost all absorbed excitation energy to dissipate through non-radiative decay pathways, leading to high photothermal conversion efficiency (90.98 %) and strong photoacoustic response. Both in vitro and in vivo photoacoustic/photothermal therapy results demonstrate enhanced deep tissue cancer theranostic performance of PS-1 NPs. Even in the 5 mm deep-seated tumor model, PS-1 NPs demonstrated a satisfactory anti-tumor effect in photoacoustic imaging-guided photothermal therapy. Moreover, for the human extracted tooth root canal infection model, the synergistic outcomes of the photothermal effect of PS-1 NPs and 0.5 % NaClO solution resulted in therapeutic efficacy comparable to the clinical gold standard irrigation agent 5.25 % NaClO, opening up possibilities for the expansion of NIR-II theranostic agents in oral medicine.

Current status and future perspectives of computational studies on human-virus protein-protein interactions.

The protein-protein interactions (PPIs) between human and viruses mediate viral infection and host immunity processes. Therefore, the study of human-virus PPIs can help us understand the principles of human-virus relationships and can thus guide the development of highly effective drugs to break the transmission of viral infectious diseases. Recent years have witnessed the rapid accumulation of experimentally identified human-virus PPI data, which provides an unprecedented opportunity for bioinformatics studies revolving around human-virus PPIs. In this article, we provide a comprehensive overview of computational studies on human-virus PPIs, especially focusing on the method development for human-virus PPI predictions. We briefly introduce the experimental detection methods and existing database resources of human-virus PPIs, and then discuss the research progress in the development of computational prediction methods. In particular, we elaborate the machine learning-based prediction methods and highlight the need to embrace state-of-the-art deep-learning algorithms and new feature engineering techniques (e.g. the protein embedding technique derived from natural language processing). To further advance the understanding in this research topic, we also outline the practical applications of the human-virus interactome in fundamental biological discovery and new antiviral therapy development.

HVIDB: a comprehensive database for human-virus protein-protein interactions.

While leading to millions of people's deaths every year the treatment of viral infectious diseases remains a huge public health challenge.Therefore, an in-depth understanding of human-virus protein-protein interactions (PPIs) as the molecular interface between a virus and its host cell is of paramount importance to obtain new insights into the pathogenesis of viral infections and development of antiviral therapeutic treatments. However, current human-virus PPI database resources are incomplete, lack annotation and usually do not provide the opportunity to computationally predict human-virus PPIs. Here, we present the Human-Virus Interaction DataBase (HVIDB, http://zzdlab.com/hvidb/) that provides comprehensively annotated human-virus PPI data as well as seamlessly integrates online PPI prediction tools. Currently, HVIDB highlights 48 643 experimentally verified human-virus PPIs covering 35 virus families, 6633 virally targeted host complexes, 3572 host dependency/restriction factors as well as 911 experimentally verified/predicted 3D complex structures of human-virus PPIs. Furthermore, our database resource provides tissue-specific expression profiles of 6790 human genes that are targeted by viruses and 129 Gene Expression Omnibus series of differentially expressed genes post-viral infections. Based on these multifaceted and annotated data, our database allows the users to easily obtain reliable information about PPIs of various human viruses and conduct an in-depth analysis of their inherent biological significance. In particular, HVIDB also integrates well-performing machine learning models to predict interactions between the human host and viral proteins that are based on (i) sequence embedding techniques, (ii) interolog mapping and (iii) domain-domain interaction inference. We anticipate that HVIDB will serve as a one-stop knowledge base to further guide hypothesis-driven experimental efforts to investigate human-virus relationships.

Development of Yin-Yang ligand for cannabinoid receptors.

Cannabinoid receptors (CBs), including CB1 and CB2, are the key components of a lipid signaling endocannabinoid system (ECS). Development of synthetic cannabinoids has been attractive to modulate ECS functions. CB1 and CB2 are structurally closely related subtypes but with distinct functions. While most efforts focus on the development of selective ligands for single subtype to circumvent the undesired off-target effect, Yin-Yang ligands with opposite pharmacological activities simultaneously on two subtypes, offer unique therapeutic potential. Herein we report the development of a new Yin-Yang ligand which functions as an antagonist for CB1 and concurrently an agonist for CB2. We found that in the pyrazole-cored scaffold, the arm of N1-phenyl group could be a switch, modification of which yielded various ligands with distinct activities. As such, the ortho-morpholine substitution exerted the desired Yin-Yang bifunctionality which, based on the docking study and molecular dynamic simulation, was proposed to be resulted from the hydrogen bonding with S173 and S285 in CB1 and CB2, respectively. Our results demonstrated the feasibility of structure guided ligand evolution for challenging Yin-Yang ligand.

Rigid Gas-Permeable Contact Lens for Visual Rehabilitation in Children Younger Than 12 Years With Penetrative Ocular Trauma.

OBJECTIVES: To observe the clinical outcomes of visual rehabilitation using rigid gas-permeable contact lenses (RGPCLs) after penetrative ocular trauma in children younger than 12 years in China. METHODS: Patients younger than 12 years with penetrative ocular trauma fitted with an RGPCL for visual rehabilitation from 2017 to 2021 were included. In the case cohort, the best-corrected visual acuity (BCVA) with spectacles was measured when the RGPCL was fitted, and the initial BCVA with RGPCL, and the BCVA at the last visit were compared. RESULTS: Fifteen patients, aged 4 to 12 (mean 8.0±2.7) years, who wore an RGPCL for 7 to 53 (mean 20.3±15.7) months, were included. The BCVA was log of minimal angle of resolution 0.4 (0.2-0.7) with spectacles and 0.1 (0.1-0.2) for RGPCL at the initial visit, and 0.0 (0.0-0.1) for BCVA at the last visit, with a statistically significant difference between the three comparisons ( P <0.001). Six of the 15 (40%) children abandoned wearing RGPCL because of discomfort and lens rejection (n=3, 50%), lens loss and inability to replace broken lens because of travel distances and epidemics (n=2, 33%), and cost (n=1, 17%). CONCLUSIONS: Although application is complicated and initial wearing comfort is poor, an RGPCL is still a beneficial, safe tool for postoperative visual rehabilitation in children with open ocular trauma.

Application of the hybrid BOPPPS teaching model in clinical internships in gynecology.

BACKGROUND: The traditional gynecological teaching model is not conducive to the cultivation of trainee doctors' clinical skills, thinking patterns and doctor‒patient communication ability. This study aims to explore the effect of the application of the hybrid BOPPPS (bridge-in, objective, preassessment, participant learning, postassessment, summary) teaching model in clinical internships in gynecology. METHODS: This observational study was conducted among final-year undergraduate medical trainee doctors at Jiaxing Maternity and Child Health Care Hospital from September 2020 to June 2022. Members of the control group were introduced to the traditional teaching model, while members of the experimental group were introduced to the hybrid BOPPPS teaching model. Trainee doctors' final examination scores and teaching satisfaction were compared. RESULTS: The control group consisted of 114 students who entered the university to pursue undergraduate degrees in 2017, and the experimental group consisted of 121 students who entered the university to pursue undergraduate degrees in 2018. The final examination scores attained by trainee doctors in the experimental group were higher than those attained by trainee doctors in the control group (P < 0.05). The final theoretical exam scores attained by members of the control group were significantly higher than their preassessment scores (P < 0.01). The scores differed significantly between female and male subjects before the internship (p<0.05) but not after the internship (p>0.05). In total, 93.4% of trainee doctors in the experimental group thought that the hybrid BOPPPS teaching model helped them improve their case analysis ability, and the difference in this measure between the experimental and control groups was statistically significant (P < 0.05). A total of 89.3% of trainee doctors in the experimental group supported the promotion and application of the hybrid BOPPPS model in practice in other disciplines. CONCLUSION: The hybrid BOPPPS teaching model helps improve trainee doctors' learning environment, stimulate their interest and initiative in learning, enhance their clinical practice ability and increase their satisfaction; therefore, this model is worth promoting and applying in practice in other disciplines.

Transfer learning via multi-scale convolutional neural layers for human-virus protein-protein interaction prediction.

MOTIVATION: To complement experimental efforts, machine learning-based computational methods are playing an increasingly important role to predict human-virus protein-protein interactions (PPIs). Furthermore, transfer learning can effectively apply prior knowledge obtained from a large source dataset/task to a small target dataset/task, improving prediction performance. RESULTS: To predict interactions between human and viral proteins, we combine evolutionary sequence profile features with a Siamese convolutional neural network (CNN) architecture and a multi-layer perceptron. Our architecture outperforms various feature encodings-based machine learning and state-of-the-art prediction methods. As our main contribution, we introduce two transfer learning methods (i.e. 'frozen' type and 'fine-tuning' type) that reliably predict interactions in a target human-virus domain based on training in a source human-virus domain, by retraining CNN layers. Finally, we utilize the 'frozen' type transfer learning approach to predict human-SARS-CoV-2 PPIs, indicating that our predictions are topologically and functionally similar to experimentally known interactions. AVAILABILITY AND IMPLEMENTATION: The source codes and datasets are available at https://github.com/XiaodiYangCAU/TransPPI/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Transition-Linker Containing Detergents for Membrane Protein Studies.

It is a pressing need, but still challenging to explore the structure and function of membrane proteins (MPs). One of the main obstacles is the limited availability of matched detergents for the handling of specific MPs. We describe herein the design of new detergents by incorporation of a transition linker between the hydrophilic head and the hydrophobic tail. This design allows a gradual change of hydrophobicity between the outside and inside of micelles, in contrast to the abrupt switch in conventional detergents. Notably, many of these detergents assembled into micelles in while retaining low critical micelle concentrations. Meanwhile, thermal stabilizing evaluation identified superior detergents for representative MPs, including G protein-coupled receptors and a transporter protein. Among them, further improved the NMR study of MPs. We anticipate these that results will encourage future detergent expansion through new remodeling on the traditional detergent scaffold.

Azulene-Embedded [n]Helicenes (n=5, 6 and 7).

Azulene is a non-benzenoid aromatic building block with unique chemical structure and physicochemical properties. By using the "bottom-up" synthetic strategy, we synthesized three azulene-embedded [n]helicenes ([n]AzHs, n=5, 6 and 7), in which one terminal azulene subunit was fused with n-2 benzene rings. P- and M-enantiomers were observed in the packing diagrams of [5]-, and [6]AzHs. However, P- and M-[7]AzHs could be isolated by recrystallization of the racemic mixture. These [n]AzHs were endowed with new properties through the azulene moiety such as low-lying first electric state (S1 ), small optical energy gap and anti-Kasha emission. [6]-, and [7]AzHs exhibit strong chiroptical responses with high absorption dissymmetry factor (gabs ) maxima of about 0.02, which is among the highest |gabs | values of helicenes in the visible range. These azulene-embedded [n]helicenes contribute to the non-benzenoid helicene library and allow the structure-property relationships to be better understood.

TRIM11 promotes lymphomas by activating the ß-catenin signaling and Axin1 ubiquitination degradation.

OBJECTIVE: Lymphoma, a malignant tumor, is mainly characterized by painless lymph node enlargement and hepatosplenomegaly. At present, lymphoma is mainly treated by radiation, chemical drugs, bone marrow transplantation and surgery. However, due to the high degree of heterogeneity, lymphomas are highly different in terms of treatment intensity and prognosis. This study is designed to investigate the function of tripartite motif-containing 11 (TRIM11) in lymphomas. METHODS: The expression of TRIM11 in lymphoma tissues and multiple lymphoma cell lines was respectively detected by microarray immunohistochemistry, real-time PCR and Western blotting. After TRIM11 knockdown, overexpression, or ß-catenin inhibitor XAV939 treatment, proliferation, apoptosis and cell cycle progression, as well as expression of related-genes were detected. Next, Co-Immunoprecipitation (Co-IP) and ubiquitination detection were performed. RESULTS: Elevated expression of tripartite motif-containing 11 (TRIM11) was observed in lymphoma tissues and multiple lymphoma cell lines (Raji, Jurkat, U937 and Hut78). Knockdown of TRIM11 in lymphoma cells significantly suppressed cell proliferation and prevented cell cycle progression from entering S or G2 phase. Concurrently, the expression of ß-catenin, Cyclin D1 and c-Myc proteins in TRIM11-silenced lymphoma cells was decreased, while Axin1 was increased. In addition, TRIM11 overexpression had an opposite effect to TRIM11 knockdown, and a ß-catenin inhibitor, XAV939, potently attenuated the induction of TRIM11 on lymphoma cells. Co-IP assay showed the interaction of TRIM11 and Axin1, and TRIM11 knockdown inhibited Axin1 ubiquitination degradation. CONCLUSIONS: Together all, the results suggested that TRIM11 may be an oncogene in lymphomas, which involving the activation of the ß-catenin signaling and the ubiquitination degradation of Axin1.

Serum levels of novel brominated flame retardants (NBFRs) in residents of a major BFR-producing region: Occurrence, impact factors and the relationship to thyroid and liver function.

Five currently used novel brominated flame retardants (NBFRs) were determined in 172 serum samples collected from nonoccupational residents of a major BFR-producing region. All the 5 NBFRs presented high detection frequencies (DFs, >90%), and decabromodiphenyl ethane (DBDPE), a substitute of decabrominated diphenyl ethers (deca-BDE), was the most abundant NBFR. The levels of DBDPE were from

Association between lifestyle factors and depressive symptoms among Chinese middle school students: a cross-sectional study.

This study aimed to identify the independent contributions of lifestyle factors to depressive symptoms among Chinese middle school students, with a focus on gender differences. A cross-sectional study of 3081 middle school students was conducted in Ganzhou City, Jiangxi Province, China. Students were asked to complete a questionnaire including socio-demographics, lifestyle factors, the Pittsburgh Sleep Quality Index and the Chinese Secondary School Students Depression Scale. The total prevalence of depressive symptoms was 19.9%. Poor quality of sleep, smoking, drinking and longer mobile phone use time were related to increased prevalence of depressive symptoms after adjusting for potential confounders. A significant interaction between gender and quality of sleep on the depressive symptoms was found (P = 0.014). The gender-stratified analysis showed that quality of sleep was significantly associated with depressive symptoms in both genders. However, the effect in males was greater than that in females.

TIPE1 impairs stemness maintenance in colorectal cancer through directly targeting ß-catenin.

TIPE1 (tumor necrosis factor-α-induced protein 8-like 1) contributes to cell death in diverse cancers. However, the expression and biological functions of TIPE1 in colon cancer remain unclear. In the present study, we report that TIPE1 was downregulated in colon cancer tissues and positively correlates with prognosis of colon cancer patients. TIPE1 overexpression significantly inhibits colon cancer cell growth both in vitro and in vivo through impairing stemness, accompanied with downregulation of the stemness-related markers, ALDH, CD133, CD44 and SOX-9. Mechanically, TIPE1 directly targets ß-catenin and promotes ß-catenin degradation in a protease-dependent manner, and Wnt/ß-catenin signaling plays a crucial role during TIPE1-mediated stemness inhibition in colon cancer. These findings reveal that TIPE1 exerts anti-tumor effects in colon cancer and suggest that TIPE1 would be a therapeutic target for cancers.

PlaPPISite: a comprehensive resource for plant protein-protein interaction sites.

BACKGROUND: Protein-protein interactions (PPIs) play very important roles in diverse biological processes. Experimentally validated or predicted PPI data have become increasingly available in diverse plant species. To further explore the biological functions of PPIs, understanding the interaction details of plant PPIs (e.g., the 3D structural contexts of interaction sites) is necessary. By integrating bioinformatics algorithms, interaction details can be annotated at different levels and then compiled into user-friendly databases. In our previous study, we developed AraPPISite, which aimed to provide interaction site information for PPIs in the model plant Arabidopsis thaliana. Considering that the application of AraPPISite is limited to one species, it is very natural that AraPPISite should be evolved into a new database that can provide interaction details of PPIs in multiple plants. DESCRIPTION: PlaPPISite (http://zzdlab.com/plappisite/index.php) is a comprehensive, high-coverage and interaction details-oriented database for 13 plant interactomes. In addition to collecting 121 experimentally verified structures of protein complexes, the complex structures of experimental/predicted PPIs in the 13 plants were also constructed, and the corresponding interaction sites were annotated. For the PPIs whose 3D structures could not be modelled, the associated domain-domain interactions (DDIs) and domain-motif interactions (DMIs) were inferred. To facilitate the reliability assessment of predicted PPIs, the source species of interolog templates, GO annotations, subcellular localizations and gene expression similarities are also provided. JavaScript packages were employed to visualize structures of protein complexes, protein interaction sites and protein interaction networks. We also developed an online tool for homology modelling and protein interaction site annotation of protein complexes. All data contained in PlaPPISite are also freely available on the Download page. CONCLUSION: PlaPPISite provides the plant research community with an easy-to-use and comprehensive data resource for the search and analysis of protein interaction details from the 13 important plant species.

Azulene-Based BN-Heteroaromatics.

Azulene, a nonalternant bicyclic aromatic hydrocarbon, has unique chemical and physical properties and is considered to be a promising building block for constructing novel polycyclic aromatic hydrocarbons (PAHs) and heteroaromatics. We present here the first two azulene-based BN-heteroaromatics Az-BN-1 and Az-BN-2. The chemical structures and optical and electrochemical properties of both compounds have been investigated, as well as their sensing behavior in response to fluoride ion. Az-BN-1 and Az-BN-2 show different photophysical properties from other reported BN-embedded PAHs, such as lower band gaps and unusual fluorescence. In addition, Az-BN-1 and Az-BN-2 exhibit unexpected deboronization upon addition of trifluoroacetic acid, which distinguishes them from other reported BN-heteroaromatics and can be ascribed to the unique property of the azulene unit.

Novel nanoarchitecture of arginine-glycine-aspartate conjugated gold nanoparticles: a sensitive and selective platform for detecting arachidonic acid.

A novel electrochemical approach for determination of arachidonic acid (ARA) was developed based on the linear arginine-glycine-aspartic-Au (RGD-Au) nanomaterial modified on glassy carbon electrode (GCE). The prepared material was characterized by transmission electron microscopy (TEM), field emission scanning electron microscopy (FESEM), ultraviolet-visible spectroscopy (UV-vis), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). The electrochemical signal was obtained from the reduction of 1,4-naphthoquinone and ARA served as a proton source. Under the optimum experimental conditions, the RGD-Au-based electrode was used to analyze ARA. Meanwhile, the electrochemical characteristics were also studied by cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and linear sweep voltammetry (LSV). The sensor showed a wider linear range from 0.5 to 100 µM and the linear fitting equation was Ip (µA) = 0.0721 c + 2.4583 (R2 = 0.9987) with a detection limit of 80 nM. The application of the sensor in real samples was tested and compared with that of LC-MS/MS. This sensor would be a promising platform for detection of ARA in blood plasma. Graphical abstract.