MoDAFold: a strategy for predicting the structure of missense mutant protein based on AlphaFold2 and molecular dynamics.

Protein structure prediction is a longstanding issue crucial for identifying new drug targets and providing a mechanistic understanding of protein functions. To enhance the progress in this field, a spectrum of computational methodologies has been cultivated. AlphaFold2 has exhibited exceptional precision in predicting wild-type protein structures, with performance exceeding that of other methods. However, predicting the structures of missense mutant proteins using AlphaFold2 remains challenging due to the intricate and substantial structural alterations caused by minor sequence variations in the mutant proteins. Molecular dynamics (MD) has been validated for precisely capturing changes in amino acid interactions attributed to protein mutations. Therefore, for the first time, a strategy entitled 'MoDAFold' was proposed to improve the accuracy and reliability of missense mutant protein structure prediction by combining AlphaFold2 with MD. Multiple case studies have confirmed the superior performance of MoDAFold compared to other methods, particularly AlphaFold2.

RNAincoder: a deep learning-based encoder for RNA and RNA-associated interaction.

Ribonucleic acids (RNAs) involve in various physiological/pathological processes by interacting with proteins, compounds, and other RNAs. A variety of powerful computational methods have been developed to predict such valuable interactions. However, all these methods rely heavily on the 'digitalization' (also known as 'encoding') of RNA-associated interacting pairs into a computer-recognizable descriptor. In other words, it is urgently needed to have a powerful tool that can not only represent each interacting partner but also integrate both partners into a computer-recognizable interaction. Herein, RNAincoder (deep learning-based encoder for RNA-associated interactions) was therefore proposed to (a) provide a comprehensive collection of RNA encoding features, (b) realize the representation of any RNA-associated interaction based on a well-established deep learning-based embedding strategy and (c) enable large-scale scanning of all possible feature combinations to identify the one of optimal performance in RNA-associated interaction prediction. The effectiveness of RNAincoder was extensively validated by case studies on benchmark datasets. All in all, RNAincoder is distinguished for its capability in providing a more accurate representation of RNA-associated interactions, which makes it an indispensable complement to other available tools. RNAincoder can be accessed at https://idrblab.org/rnaincoder/.

A task-specific encoding algorithm for RNAs and RNA-associated interactions based on convolutional autoencoder.

RNAs play essential roles in diverse physiological and pathological processes by interacting with other molecules (RNA/protein/compound), and various computational methods are available for identifying these interactions. However, the encoding features provided by existing methods are limited and the existing tools does not offer an effective way to integrate the interacting partners. In this study, a task-specific encoding algorithm for RNAs and RNA-associated interactions was therefore developed. This new algorithm was unique in (a) realizing comprehensive RNA feature encoding by introducing a great many of novel features and (b) enabling task-specific integration of interacting partners using convolutional autoencoder-directed feature embedding. Compared with existing methods/tools, this novel algorithm demonstrated superior performances in diverse benchmark testing studies. This algorithm together with its source code could be readily accessed by all user at: https://idrblab.org/corain/ and https://github.com/idrblab/corain/.

Implications of IFNγ SNP rs2069705 in primary Sjögren's syndrome: transcriptional activation and B cell infiltration.

Primary Sjögren's syndrome (pSS) is characterized by its autoimmune nature. This study investigates the role of the IFNγ SNP rs2069705 in modulating the susceptibility to pSS. Differential expression of IFNγ and BAFF was analyzed using the GEO database's mRNA microarray GSE84844. Genotyping of the IFNγ SNP rs2069705 was conducted via the dbSNP website. The JASPAR tool was used for predicting transcription factor bindings. Techniques such as dual-luciferase reporter assays, Chromatin immunoprecipitation, and analysis of a pSS mouse model were applied to study gene and protein interactions. A notable increase in the mutation frequency of IFNγ SNP rs2069705 was observed in MNCs from the exocrine glands of pSS mouse models. Bioinformatics analysis revealed elevated levels of IFNγ and BAFF in pSS samples. The model exhibited an increase in both CD20+ B cells and cells expressing IFNγ and BAFF. Knocking down IFNγ resulted in lowered BAFF expression and less lymphocyte infiltration, with BAFF overexpression reversing this suppression. Activation of the Janus kinase (JAK)/STAT1 pathway was found to enhance transcription in the BAFF promoter region, highlighting IFNγ's involvement in pSS. In addition, rs2069705 was shown to boost IFNγ transcription by promoting interaction between its promoter and STAT4. SNP rs2069705 in the IFNγ gene emerges as a pivotal element in pSS susceptibility, primarily by augmenting IFNγ transcription, activating the JAK/STAT1 pathway, and leading to B-lymphocyte infiltration in the exocrine glands.NEW & NOTEWORTHY The research employed a combination of bioinformatics analysis, genotyping, and experimental models, providing a multifaceted approach to understanding the complex interactions in pSS. We have uncovered that the rs2069705 SNP significantly affects the transcription of IFNγ, leading to altered immune responses and B-lymphocyte activity in pSS.

Large Language Model-Based Natural Language Encoding Could Be All You Need for Drug Biomedical Association Prediction.

Analyzing drug-related interactions in the field of biomedicine has been a critical aspect of drug discovery and development. While various artificial intelligence (AI)-based tools have been proposed to analyze drug biomedical associations (DBAs), their feature encoding did not adequately account for crucial biomedical functions and semantic concepts, thereby still hindering their progress. Since the advent of ChatGPT by OpenAI in 2022, large language models (LLMs) have demonstrated rapid growth and significant success across various applications. Herein, LEDAP was introduced, which uniquely leveraged LLM-based biotext feature encoding for predicting drug-disease associations, drug-drug interactions, and drug-side effect associations. Benefiting from the large-scale knowledgebase pre-training, LLMs had great potential in drug development analysis owing to their holistic understanding of natural language and human topics. LEDAP illustrated its notable competitiveness in comparison with other popular DBA analysis tools. Specifically, even in simple conjunction with classical machine learning methods, LLM-based feature representations consistently enabled satisfactory performance across diverse DBA tasks like binary classification, multiclass classification, and regression. Our findings underpinned the considerable potential of LLMs in drug development research, indicating a catalyst for further progress in related fields.

Evidence linking COVID-19 and the health/well-being of children and adolescents: an umbrella review.

BACKGROUND: Experiences during childhood and adolescence have enduring impacts on physical and mental well-being, overall quality of life, and socioeconomic status throughout one's lifetime. This underscores the importance of prioritizing the health of children and adolescents to establish an impactful healthcare system that benefits both individuals and society. It is crucial for healthcare providers and policymakers to examine the relationship between COVID-19 and the health of children and adolescents, as this understanding will guide the creation of interventions and policies for the long-term management of the virus. METHODS: In this umbrella review (PROSPERO ID: CRD42023401106), systematic reviews were identified from the Cochrane Database of Systematic Reviews; EMBASE (OvidSP); and MEDLINE (OvidSP) from December 2019 to February 2023. Pairwise and single-arm meta-analyses were extracted from the included systematic reviews. The methodological quality appraisal was completed using the AMSTAR-2 tool. Single-arm meta-analyses were re-presented under six domains associated with COVID-19 condition. Pairwise meta-analyses were classified into five domains according to the evidence classification criteria. Rosenberg's FSN was calculated for both binary and continuous measures. RESULTS: We identified 1551 single-arm and 301 pairwise meta-analyses from 124 systematic reviews that met our predefined criteria for inclusion. The focus of the meta-analytical evidence was predominantly on the physical outcomes of COVID-19, encompassing both single-arm and pairwise study designs. However, the quality of evidence and methodological rigor were suboptimal. Based on the evidence gathered from single-arm meta-analyses, we constructed an illustrative representation of the disease severity, clinical manifestations, laboratory and radiological findings, treatments, and outcomes from 2020 to 2022. Additionally, we discovered 17 instances of strong or highly suggestive pairwise meta-analytical evidence concerning long-COVID, pediatric comorbidity, COVID-19 vaccines, mental health, and depression. CONCLUSIONS: The findings of our study advocate for the implementation of surveillance systems to track health consequences associated with COVID-19 and the establishment of multidisciplinary collaborative rehabilitation programs for affected younger populations. In future research endeavors, it is important to prioritize the investigation of non-physical outcomes to bridge the gap between research findings and clinical application in this field.

Unraveling the Electron Transfer Effect of Single-Metal Ce-N4 Sites via Mesopore-Coupling for Boosted Oxygen Reduction Activity.

The development of cerium (Ce) single-atom (SA) electrocatalysts for oxygen reduction reaction (ORR) with high active-site utilization and intrinsic activity has become popular recently but remains challenging. Inspired by an interesting phenomenon that pore-coupling with single-metal cerium sites can accelerate the electron transfer predicted by density functional theory calculations, here, a facile strategy is reported for directional design of a highly active and stable Ce SA catalyst (Ce SA/MC) by the coupling of single-metal Ce-N4 sites and mesopores in nanocarbon via pore-confinement-pyrolysis of Ce/phenanthroline complexes combined with controlling the formation of Ce oxides. This catalyst delivers a comparable ORR catalytic activity with a half-wave potential of 0.845 V versus RHE to the Pt/C catalyst. Also, a Ce SA/MC-based zinc-air battery (ZAB) has exhibited a higher energy density (924 Wh kgZn -1 ) and better long-term cycling durability than a Pt/C-based ZAB. This proposed strategy may open a door for designing efficient rare-earth metal catalysts with single-metal sites coupling with porous structures for next-generation energy devices.

e-TSN: an interactive visual exploration platform for target-disease knowledge mapping from literature.

Target discovery and identification processes are driven by the increasing amount of biomedical data. The vast numbers of unstructured texts of biomedical publications provide a rich source of knowledge for drug target discovery research and demand the development of specific algorithms or tools to facilitate finding disease genes and proteins. Text mining is a method that can automatically mine helpful information related to drug target discovery from massive biomedical literature. However, there is a substantial lag between biomedical publications and the subsequent abstraction of information extracted by text mining to databases. The knowledge graph is introduced to integrate heterogeneous biomedical data. Here, we describe e-TSN (Target significance and novelty explorer, http://www.lilab-ecust.cn/etsn/), a knowledge visualization web server integrating the largest database of associations between targets and diseases from the full scientific literature by constructing significance and novelty scoring methods based on bibliometric statistics. The platform aims to visualize target-disease knowledge graphs to assist in prioritizing candidate disease-related proteins. Approved drugs and associated bioactivities for each interested target are also provided to facilitate the visualization of drug-target relationships. In summary, e-TSN is a fast and customizable visualization resource for investigating and analyzing the intricate target-disease networks, which could help researchers understand the mechanisms underlying complex disease phenotypes and improve the drug discovery and development efficiency, especially for the unexpected outbreak of infectious disease pandemics like COVID-19.

ncRNAInter: a novel strategy based on graph neural network to discover interactions between lncRNA and miRNA.

In recent years, many studies have illustrated the significant role that non-coding RNA (ncRNA) plays in biological activities, in which lncRNA, miRNA and especially their interactions have been proved to affect many biological processes. Some in silico methods have been proposed and applied to identify novel lncRNA-miRNA interactions (LMIs), but there are still imperfections in their RNA representation and information extraction approaches, which imply there is still room for further improving their performances. Meanwhile, only a few of them are accessible at present, which limits their practical applications. The construction of a new tool for LMI prediction is thus imperative for the better understanding of their relevant biological mechanisms. This study proposed a novel method, ncRNAInter, for LMI prediction. A comprehensive strategy for RNA representation and an optimized deep learning algorithm of graph neural network were utilized in this study. ncRNAInter was robust and showed better performance of 26.7% higher Matthews correlation coefficient than existing reputable methods for human LMI prediction. In addition, ncRNAInter proved its universal applicability in dealing with LMIs from various species and successfully identified novel LMIs associated with various diseases, which further verified its effectiveness and usability. All source code and datasets are freely available at https://github.com/idrblab/ncRNAInter.

Comprehensive assessment of deep generative architectures for de novo drug design.

Recently, deep learning (DL)-based de novo drug design represents a new trend in pharmaceutical research, and numerous DL-based methods have been developed for the generation of novel compounds with desired properties. However, a comprehensive understanding of the advantages and disadvantages of these methods is still lacking. In this study, the performances of different generative models were evaluated by analyzing the properties of the generated molecules in different scenarios, such as goal-directed (rediscovery, optimization and scaffold hopping of active compounds) and target-specific (generation of novel compounds for a given target) tasks. In overall, the DL-based models have significant advantages over the baseline models built by the traditional methods in learning the physicochemical property distributions of the training sets and may be more suitable for target-specific tasks. However, both the baselines and DL-based generative models cannot fully exploit the scaffolds of the training sets, and the molecules generated by the DL-based methods even have lower scaffold diversity than those generated by the traditional models. Moreover, our assessment illustrates that the DL-based methods do not exhibit obvious advantages over the genetic algorithm-based baselines in goal-directed tasks. We believe that our study provides valuable guidance for the effective use of generative models in de novo drug design.

Multi-modal chemical information reconstruction from images and texts for exploring the near-drug space.

Identification of new chemical compounds with desired structural diversity and biological properties plays an essential role in drug discovery, yet the construction of such a potential space with elements of 'near-drug' properties is still a challenging task. In this work, we proposed a multimodal chemical information reconstruction system to automatically process, extract and align heterogeneous information from the text descriptions and structural images of chemical patents. Our key innovation lies in a heterogeneous data generator that produces cross-modality training data in the form of text descriptions and Markush structure images, from which a two-branch model with image- and text-processing units can then learn to both recognize heterogeneous chemical entities and simultaneously capture their correspondence. In particular, we have collected chemical structures from ChEMBL database and chemical patents from the European Patent Office and the US Patent and Trademark Office using keywords 'A61P, compound, structure' in the years from 2010 to 2020, and generated heterogeneous chemical information datasets with 210K structural images and 7818 annotated text snippets. Based on the reconstructed results and substituent replacement rules, structural libraries of a huge number of near-drug compounds can be generated automatically. In quantitative evaluations, our model can correctly reconstruct 97% of the molecular images into structured format and achieve an F1-score around 97-98% in the recognition of chemical entities, which demonstrated the effectiveness of our model in automatic information extraction from chemical patents, and hopefully transforming them to a user-friendly, structured molecular database enriching the near-drug space to realize the intelligent retrieval technology of chemical knowledge.

MacFrag: segmenting large-scale molecules to obtain diverse fragments with high qualities.

SUMMARY: Construction of high-quality fragment libraries by segmenting organic compounds is an important part of the drug discovery paradigm. This article presents a new method, MacFrag, for efficient molecule fragmentation. MacFrag utilized a modified version of BRICS rules to break chemical bonds and introduced an efficient subgraphs extraction algorithm for rapid enumeration of the fragment space. The evaluation results with ChEMBL dataset exhibited that MacFrag was overall faster than BRICS implemented in RDKit and modified molBLOCKS. Meanwhile, the fragments acquired through MacFrag were more compliant with the 'Rule of Three'. AVAILABILITY AND IMPLEMENTATION: https://github.com/yydiao1025/MacFrag. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Effects of lianas on forest biogeochemistry during their lives and afterlives.

Climate change and other anthropogenic disturbances are increasing liana abundance and biomass in many tropical and subtropical forests. While the effects of living lianas on species diversity, ecosystem carbon, and nutrient dynamics are receiving increasing attention, the role of dead lianas in forest ecosystems has been little studied and is poorly understood. Trees and lianas coexist as the major woody components of forests worldwide, but they have very different ecological strategies, with lianas relying on trees for mechanical support. Consequently, trees and lianas have evolved highly divergent stem, leaf, and root traits. Here we show that this trait divergence is likely to persist after death, into the afterlives of these organs, leading to divergent effects on forest biogeochemistry. We introduce a conceptual framework combining horizontal, vertical, and time dimensions for the effects of liana proliferation and liana tissue decomposition on ecosystem carbon and nutrient cycling. We propose a series of empirical studies comparing traits between lianas and trees to answer questions concerning the influence of trait afterlives on the decomposability of liana and tree organs. Such studies will increase our understanding of the contribution of lianas to terrestrial biogeochemical cycling, and help predict the effects of their increasing abundance.

Fabrication of Electrode Material for Textile-Based Triboelectric Nanogenerators: Research of the Relationship between Output Performance and Dielectric Material Strain.

In this work, a textile-based triboelectric nanogenerator (TENG) device was developed through electroless plating technology to prepare electrode material. Hydrophilic groups on the fiber surface are able to absorb Ag+, which could play a role in the center of a catalyst to reduce Cu2+ to fabricate Cu-coated cotton toward the fabrication of TENG electrode material. The TENG device established admirable performance and good stabilization, and a maximum voltage at 9.6 V was detected when the stress and strain on the polydimethylsiloxane layer are 82.6 kPa and 5.8%, respectively. In addition, the relationships among device properties and strain/thickness of dielectric materials have been explored in depth as well. The output voltage of the device increases gradually with the enhancement of dielectric strain and stress. As expected, the TENG as-fabricated device was installed to various physical behaviors to illustrate the harvesting of power of knee-jerk movements.

Enhancing Chemical Reaction Monitoring with a Deep Learning Model for NMR Spectra Image Matching to Target Compounds.

In the synthetic laboratory, researchers typically rely on nuclear magnetic resonance (NMR) spectra to elucidate structures of synthesized products and confirm whether they match the desired target compounds. As chemical synthesis technology evolves toward intelligence and continuity, efficient computer-assisted structure elucidation (CASE) techniques are required to replace time-consuming manual analysis and provide the necessary speed. However, current CASE methods typically aim to derive precise chemical structures from spectroscopic data, yet they suffer from drawbacks such as low accuracy, high computational cost, and reliance on chemical libraries. In meticulously designed chemical synthesis reactions, researchers prioritize confirming the attainment of the target product based on NMR spectra, rather than focusing on identifying the specific product obtained. For this purpose, we innovatively developed a binary classification model, termed as MatCS, to directly predict the relationship between NMR spectra image (including 1H NMR and 13C NMR) and the molecular structure of the target compound. After evaluating various feature extraction methods, MatCS employs a combination of the Graph Attention Networks and Graph Convolutional Networks to learn the structural features of molecular graphs and the pretrained ResNet101 network with a Convolutional Block Attention Module to extract features from NMR spectra images. The results show that on a challenging Testsim data set, which poses difficulty in distinguishing spectra of similar molecular structures, MatCS achieves comprehensive evaluation metrics with an F1-score of 0.81 and an AUC value of 0.87. Simultaneously, it exhibited commendable performance on an external SDBS data set containing experimental NMR spectra, showcasing substantial potential for structural verification tasks in real automated chemical synthesis.

Ratiometric electrochemical aptasensor based on split aptamer and Au-rGO for detection of aflatoxin M1.

A novel ratiometric electrochemical aptasensor based on split aptamer and Au-reduced graphene oxide (Au-rGO) nanomaterials was proposed to detect aflatoxin M1 (AFM1). In this work, Au-rGO nanomaterials were coated on the electrode through the electrodeposition method to increase the aptamer enrichment. We split the aptamer of AFM1 into 2 sequences (S1 and S2), where S1 was immobilized on the electrode due to the Au-S bond, and S2 was tagged with methylene blue (MB) and acted as a response signal. A complementary strand to S1 (CS1) labeled with ferrocene (Fc) was introduced as another reporter. In the presence of AFM1, CS1 was released from the electrode surface due to the formation of the S1-AFM1-S2 complex, leading to a decrease in Fc and an increase in the MB signal. The developed ratiometric aptasensor exhibited a linear range of 0.03 µg L-1 to 2.00 µg L-1, with a detection limit of 0.015 µg L-1 for AFM1 detection. The ratiometric aptasensor also showed a linear relationship from 0.2 µg L-1 to 1.00 µg L-1, with a detection limit of 0.05 µg L-1 in natural milk after sample pretreatment, indicating the successful application of the developed ratiometric aptasensor. Our proposed strategy provides a new way to construct aptasensors with high sensitivity and selectivity.

Association between semen parameters and recurrent pregnancy loss: An umbrella review of meta-analyses.

AIM: Recurrent pregnancy loss (RPL) is a common clinical reproductive problem. With research advancements, an increasing number of studies have suggested that male factors play an important role in RPL. However, the evaluation results of male sperm quality in published meta-analyses are inconsistent. We aimed to summarize the evidence of the association between semen factors and RPL and evaluate the level and validity of the evidence. METHODS: We searched PubMed, Cochrane Library, EMBASE, Web of Science, and Scopus databases for systematic reviews or meta-analyses to evaluate the association between male semen parameters and RPL. The methodological quality of the included meta-analyses was assessed, and data and evidence were re-synthesized and stratified using a random-effects model. RESULTS: Seven meta-analyses and nine semen parameters were included in the final analysis. The methodological quality of all publications was considered low or very low. There was highly suggestive evidence for the association between sperm DNA fragmentation (SDF), sperm progressive motility rate, and RPL (class II). The evidence level for the association between sperm concentration, normal sperm morphology, sperm deformity rate, total motility, and RPL was suggestive evidence (class III). The evidence level for the association between sperm volume and sperm count and RPL was weak (class IV). There was no significant association between sperm pH and RPL (class NS). CONCLUSIONS: Our results suggest level II evidence for the association between male SDF and RPL, while the evidence level for the association between conventional semen routine parameters and RPL was low (classes III and IV).

Application of Oral Retractor for Lip Injury Protection in Oral and Maxillofacial Surgeries: A Randomized Controlled Trial.

PURPOSE: Iatrogenic lip injury may occur during oral and maxillofacial surgical procedures. This study aimed to evaluate the effect of oral retractors on iatrogenic lip injury prevention during intraoral procedures of oral and maxillofacial surgery. METHODS: We conducted a randomized controlled trial and included patients who underwent intraoral procedures of oral and maxillofacial surgery. Patients were randomly allocated to receive oral retractor (intervention group) or traditional procedure without lip protection (control group). The incidence of lip injury was the outcome variable. Other study variables included surgical time and satisfaction of patients and surgeons with treatment experience evaluated by visual analog scale (VAS). Student t test and χ 2 test were used to compare both groups' variables and measure the relationship between the predictor variable and the outcome variable. P <0.05 was considered significant for all analyses. RESULTS: A total of 114 patients were included, with 56 allocated to intervention group and 58 to control group. The results showed that the application of an oral retractor did not significantly increase surgical time ( P =0.318). A total of 12 patients had lip injury, with 1 in the intervention group and 11 in the control group ( P =0.003). For the assessment of satisfaction with treatment experience, the intervention group had significantly higher VAS scores for doctors and patients ( P <0.05). CONCLUSIONS: We found that the oral retractor was a good tool for iatrogenic lip injury prevention in oral and maxillofacial surgical procedures and could be considered in clinical treatment.

Binding Free Energy Calculation Based on the Fragment Molecular Orbital Method and Its Application in Designing Novel SHP-2 Allosteric Inhibitors.

The accurate prediction of binding free energy is a major challenge in structure-based drug design. Quantum mechanics (QM)-based approaches show promising potential in predicting ligand-protein binding affinity by accurately describing the behavior and structure of electrons. However, traditional QM calculations face computational limitations, hindering their practical application in drug design. Nevertheless, the fragment molecular orbital (FMO) method has gained widespread application in drug design due to its ability to reduce computational costs and achieve efficient ab initio QM calculations. Although the FMO method has demonstrated its reliability in calculating the gas phase potential energy, the binding of proteins and ligands also involves other contributing energy terms, such as solvent effects, the 'deformation energy' of a ligand's bioactive conformations, and entropy. Particularly in cases involving ionized fragments, the calculation of solvation free energy becomes particularly crucial. We conducted an evaluation of some previously reported implicit solvent methods on the same data set to assess their potential for improving the performance of the FMO method. Herein, we develop a new QM-based binding free energy calculation method called FMOScore, which enhances the performance of the FMO method. The FMOScore method incorporates linear fitting of various terms, including gas-phase potential energy, deformation energy, and solvation free energy. Compared to other widely used traditional prediction methods such as FEP+, MM/PBSA, MM/GBSA, and Autodock vina, FMOScore showed good performance in prediction accuracies. By constructing a retrospective case study, it was observed that incorporating calculations for solvation free energy and deformation energy can further enhance the precision of FMO predictions for binding affinity. Furthermore, using FMOScore-guided lead optimization against Src homology-2-containing protein tyrosine phosphatase 2 (SHP-2), we discovered a novel and potent allosteric SHP-2 inhibitor (compound 8).

Effects of fuel and driving conditions on particle number emissions of China-VI gasoline vehicles: based on corrections to test results.

The increasing number of vehicles are emitting a large amount of particles into the atmosphere, causing serious harm to the ecological environment and human health. This study conducted the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) to investigate the emission characteristics of particle number (PN) of China-VI gasoline vehicles with different gasoline. The gasoline with lower aromatic hydrocarbons and olefins reduced particulate matter (PM) and PN emissions by 24% and 52% respectively. The average PN emission rate of the four vehicles during the first 300 s (the cold start period) was 7.2 times that of the 300 s-1800s. Additionally, because the particle transmission time and instrument response time, the test results of instantaneous emissions of PN were not synchronized with vehicle specific power (VSP). By calculating the Spearman correlation coefficient between pre-average vehicle specific power (PAVSP) and the test results of PN instantaneous emissions, the delay time was determined as 10s. After the PN emissions results were corrected, the PN emissions were found to be more related to VSP. By analyzing the influence of driving status on emission, this study found that vehicles in acceleration mode increased PN emissions by 76% compared to those in constant speed mode.