Artificial intelligence-assisted repair of peripheral nerve injury: a new research hotspot and associated challenges

Artificial intelligence can be indirectly applied to the repair of peripheral nerve injury.Specifically,it can be used to analyze and process data regarding peripheral nerve injury and repair,while study findings on peripheral nerve injury and repair can provide valuable data to enrich artificial intelligence algorithms.To investigate advances in the use of artificial intelligence in the diagnosis,rehabilitation,and scientific examination of peripheral nerve injury,we used CiteSpace and VOSviewer software to analyze the relevant literature included in the Web of Science from 1994–2023.We identified the following research hotspots in peripheral nerve injury and repair:(1)diagnosis,classification,and prognostic assessment of peripheral nerve injury using neuroimaging and artificial intelligence techniques,such as corneal confocal microscopy and coherent anti-Stokes Raman spectroscopy;(2)motion control and rehabilitation following peripheral nerve injury using artificial neural networks and machine learning algorithms,such as wearable devices and assisted wheelchair systems;(3)improving the accuracy and effectiveness of peripheral nerve electrical stimulation therapy using artificial intelligence techniques combined with deep learning,such as implantable peripheral nerve interfaces;(4)the application of artificial intelligence technology to brain-machine interfaces for disabled patients and those with reduced mobility,enabling them to control devices such as networked hand prostheses;(5)artificial intelligence robots that can replace doctors in certain procedures during surgery or rehabilitation,thereby reducing surgical risk and complications,and facilitating postoperative recovery.Although artificial intelligence has shown many benefits and potential applications in peripheral nerve injury and repair,there are some limitations to this technology,such as the consequences of missing or imbalanced data,low data accuracy and reproducibility,and ethical issues(e.g.,privacy,data security,research transparency).Future research should address the issue of data collection,as large-scale,high-quality clinical datasets are required to establish effective artificial intelligence models.Multimodal data processing is also necessary,along with interdisciplinary collaboration,medical-industrial integration,and multicenter,large-sample clinical studies.

Artificial intelligence-driven radiomics study in cancer:the role of feature engineering and modeling

Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians.Moreover,some potentially useful quantitative information in medical images,especially that which is not visible to the naked eye,is often ignored during clinical practice.In contrast,radiomics performs high-throughput feature extraction from medical images,which enables quantitative analysis of medical images and prediction of various clinical endpoints.Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis,demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine.However,radiomics remains in a developmental phase as numerous technical challenges have yet to be solved,especially in feature engineering and statistical modeling.In this review,we introduce the current utility of radiomics by summarizing research on its application in the diagnosis,prognosis,and prediction of treatment responses in patients with cancer.We focus on machine learning approaches,for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling.Furthermore,we introduce the stability,reproducibility,and interpretability of features,and the generalizability and interpretability of models.Finally,we offer possible solutions to current challenges in radiomics research.

The future of artificial hibernation medicine:protection of nerves and organs after spinal cord injury

Spinal cord injury is a serious disease of the central nervous system involving irreversible nerve injury and various organ system injuries.At present,no effective clinical treatment exists.As one of the artificial hibernation techniques,mild hypothermia has preliminarily confirmed its clinical effect on spinal cord injury.However,its technical defects and barriers,along with serious clinical side effects,restrict its clinical application for spinal cord injury.Artificial hibernation is a futureoriented disruptive technology for human life support.It involves endogenous hibernation inducers and hibernation-related central neuromodulation that activate particular neurons,reduce the central constant temperature setting point,disrupt the normal constant body temperature,make the body adapt"to the external cold environment,and reduce the physiological resistance to cold stimulation.Thus,studying the artificial hibernation mechanism may help develop new treatment strategies more suitable for clinical use than the cooling method of mild hypothermia technology.This review introduces artificial hibernation technologies,including mild hypothermia technology,hibernation inducers,and hibernation-related central neuromodulation technology.It summarizes the relevant research on hypothermia and hibernation for organ and nerve protection.These studies show that artificial hibernation technologies have therapeutic significance on nerve injury after spinal co rd injury through inflammatory inhibition,immunosuppression,oxidative defense,and possible central protection.It also promotes the repair and protection of res pirato ry and digestive,cardiovascular,locomoto r,urinary,and endocrine systems.This review provides new insights for the clinical treatment of nerve and multiple organ protection after spinal cord injury thanks to artificial hibernation.At present,artificial hibernation technology is not mature,and research fa ces various challenges.Neve rtheless,the effort is wo rthwhile for the future development of medicine.

A data-driven model of drop size prediction based on artificial neural networks using small-scale data sets

An artificial neural network(ANN)method is introduced to predict drop size in two kinds of pulsed columns with small-scale data sets.After training,the deviation between calculate and experimental results are 3.8%and 9.3%,respectively.Through ANN model,the influence of interfacial tension and pulsation intensity on the droplet diameter has been developed.Droplet size gradually increases with the increase of interfacial tension,and decreases with the increase of pulse intensity.It can be seen that the accuracy of ANN model in predicting droplet size outside the training set range is reach the same level as the accuracy of correlation obtained based on experiments within this range.For two kinds of columns,the drop size prediction deviations of ANN model are 9.6%and 18.5%and the deviations in correlations are 11%and 15%.

Application of artificial hibernation technology in acute brain injury

Controlling intracranial pressure,nerve cell regeneration,and microenvironment regulation are the key issues in reducing mortality and disability in acute brain injury.There is currently a lack of effective treatment methods.Hibernation has the characteristics of low temperature,low metabolism,and hibernation rhythm,as well as protective effects on the nervous,cardiovascular,and motor systems.Artificial hibernation technology is a new technology that can effectively treat acute brain injury by altering the body’s metabolism,lowering the body’s core temperature,and allowing the body to enter a state similar to hibernation.This review introduces artificial hibernation technology,including mild hypothermia treatment technology,central nervous system regulation technology,and artificial hibernation-inducer technology.Upon summarizing the relevant research on artificial hibernation technology in acute brain injury,the research results show that artificial hibernation technology has neuroprotective,anti-inflammatory,and oxidative stress-resistance effects,indicating that it has therapeutic significance in acute brain injury.Furthermore,artificial hibernation technology can alleviate the damage of ischemic stroke,traumatic brain injury,cerebral hemorrhage,cerebral infarction,and other diseases,providing new strategies for treating acute brain injury.However,artificial hibernation technology is currently in its infancy and has some complications,such as electrolyte imbalance and coagulation disorders,which limit its use.Further research is needed for its clinical application.

Explainable Artificial Intelligence(XAI)Model for Cancer Image Classification

The use of Explainable Artificial Intelligence(XAI)models becomes increasingly important for making decisions in smart healthcare environments.It is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these algorithms.These models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial intelligence.Nevertheless,the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer images.This research presents an advanced investigation of XAI models to classify cancer images.It describes the different levels of explainability and interpretability associated with XAI models and the challenges faced in deploying them in healthcare applications.In addition,this study proposes a novel framework for cancer image classification that incorporates XAI models with deep learning and advanced medical imaging techniques.The proposed model integrates several techniques,including end-to-end explainable evaluation,rule-based explanation,and useradaptive explanation.The proposed XAI reaches 97.72%accuracy,90.72%precision,93.72%recall,96.72%F1-score,9.55%FDR,9.66%FOR,and 91.18%DOR.It will discuss the potential applications of the proposed XAI models in the smart healthcare environment.It will help ensure trust and accountability in AI-based decisions,which is essential for achieving a safe and reliable smart healthcare environment.

Artificial Intelligence Prediction of One-Part Geopolymer Compressive Strength for Sustainable Concrete

Alkali-activated materials/geopolymer(AAMs),due to their low carbon emission content,have been the focus of recent studies on ecological concrete.In terms of performance,fly ash and slag are preferredmaterials for precursors for developing a one-part geopolymer.However,determining the optimum content of the input parameters to obtain adequate performance is quite challenging and scarcely reported.Therefore,in this study,machine learning methods such as artificial neural networks(ANN)and gene expression programming(GEP)models were developed usingMATLAB and GeneXprotools,respectively,for the prediction of compressive strength under variable input materials and content for fly ash and slag-based one-part geopolymer.The database for this study contains 171 points extracted from literature with input parameters:fly ash concentration,slag content,calcium hydroxide content,sodium oxide dose,water binder ratio,and curing temperature.The performance of the two models was evaluated under various statistical indices,namely correlation coefficient(R),mean absolute error(MAE),and rootmean square error(RMSE).In terms of the strength prediction efficacy of a one-part geopolymer,ANN outperformed GEP.Sensitivity and parametric analysis were also performed to identify the significant contributor to strength.According to a sensitivity analysis,the activator and slag contents had the most effects on the compressive strength at 28 days.The water binder ratio was shown to be directly connected to activator percentage,slag percentage,and calcium hydroxide percentage and inversely related to compressive strength at 28 days and curing temperature.

Advancements in Barrett's esophagus detection:The role of artificial intelligence and its implications

Artificial intelligence(AI)is making significant strides in revolutionizing the detection of Barrett's esophagus(BE),a precursor to esophageal adenocarcinoma.In the research article by Tsai et al,researchers utilized endoscopic images to train an AI model,challenging the traditional distinction between endoscopic and histological BE.This approach yielded remarkable results,with the AI system achieving an accuracy of 94.37%,sensitivity of 94.29%,and specificity of 94.44%.The study's extensive dataset enhances the AI model's practicality,offering valuable support to endoscopists by minimizing unnecessary biopsies.However,questions about the applicability to different endoscopic systems remain.The study underscores the potential of AI in BE detection while highlighting the need for further research to assess its adaptability to diverse clinical settings.

Artificial ground freezing of underground mines in cold regions using thermosyphons with air insulation

Current practice of underground artificial ground freezing(AGF)typically involves huge refrigeration systems of large economic and environmental costs.In this study,a novel AGF technique is proposed deploying available cold wind in cold regions.This is achieved by a static heat transfer device called thermosyphon equipped with an air insulation layer.A refrigeration unit can be optionally integrated to meet additional cooling requirements.The introduction of air insulation isolates the thermosyphon from ground zones where freezing is not needed,resulting in:(1)steering the cooling resources(cold wind or refrigeration)towards zones of interest;and(2)minimizing refrigeration load.This design is demonstrated using well-validated mathematical models from our previous work based on two-phase enthalpy method of the ground coupled with a thermal resistance network for the thermosyphon.Two Canadian mines are considered:the Cigar Lake Mine and the Giant Mine.The results show that our proposed design can speed the freezing time by 30%at the Giant Mine and by two months at the Cigar Lake Mine.Further,a cooling load of 2.4 GWh can be saved at the Cigar Lake Mine.Overall,this study provides mining practitioners with sustainable solutions of underground AGF.

Integrating artificial intelligence and high-throughput phenotyping for crop improvement

Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have revolutionized the field,enabling rapid and accurate assessment of crop traits on a large scale.The integration of AI and machine learning algorithms with HTP data has unlocked new opportunities for crop improvement.AI algorithms can analyze and interpret large datasets,and extract meaningful patterns and correlations between phenotypic traits and genetic factors.These technologies have the potential to revolutionize plant breeding programs by providing breeders with efficient and accurate tools for trait selection,thereby reducing the time and cost required for variety development.However,further research and collaboration are needed to overcome the existing challenges and fully unlock the power of HTP and AI in crop improvement.By leveraging AI algorithms,researchers can efficiently analyze phenotypic data,uncover complex patterns,and establish predictive models that enable precise trait selection and crop breeding.The aim of this review is to explore the transformative potential of integrating HTP and AI in crop improvement.This review will encompass an in-depth analysis of recent advances and applications,highlighting the numerous benefits and challenges associated with HTP and AI.

The Variation of Plankton Community Structure in Artificial Reef Area and Adjacent Waters in Haizhou Bay

Plankton are an important component of marine protected areas(MPAs),and its communities would require much smaller interpatch distances to ensure connection among MPAs.According to the survey from MPAs dominated by artificial reefs and adjacent waters(estuary area(EA),aquaculture area(AA),artificial reef area(ARA),natural area(NA)and comprehensive effect area(CEA))in Haizhou Bay in spring and autumn,we analyzed phyto-zooplankton composition,abundance and biomass,and correlation with hydrologic variables to gain information about the forces that structure the plankton.The results showed that the dominant zooplankton were copepods(spring,98.9%;autumn,94.2%),while the phytoplankton were mainly composed of Bacillariophyta(spring,61.8%;autumn,95.6%).The RDA results showed that temperature,salinity and depth highly associated with the distribution and composition of plankton species among the habitats than other factors in spring;temperature,Chla and DO had the strongest influence in autumn.The zooplankton in the ARA and AA ecosystems basically contained the same species as those in other habitats,and each habitat also exhibited a relatively unique combination of plankton species.The structures of the EA zooplankton in spring and the EA phytoplankton in both seasons were much different than other habitats,which may have been caused by factors such as currents and tides.We concluded that there exists similarity of the plankton community between artificial reef area and adjacent waters,whereas the EAs may be relatively independent systems.Therefore,these interaction between plankton community should be considered when designing MPA networks,and ocean circulations should be considered more than the environmental factors.

Advances in memristor based artificial neuron fabrication-materials,models,and applications

Spiking neural network(SNN),widely known as the third-generation neural network,has been frequently investigated due to its excellent spatiotemporal information processing capability,high biological plausibility,and low energy consumption characteristics.Analogous to the working mechanism of human brain,the SNN system transmits information through the spiking action of neurons.Therefore,artificial neurons are critical building blocks for constructing SNN in hardware.Memristors are drawing growing attention due to low consumption,high speed,and nonlinearity characteristics,which are recently introduced to mimic the functions of biological neurons.Researchers have proposed multifarious memristive materials including organic materials,inorganic materials,or even two-dimensional materials.Taking advantage of the unique electrical behavior of these materials,several neuron models are successfully implemented,such as Hodgkin–Huxley model,leaky integrate-and-fire model and integrate-and-fire model.In this review,the recent reports of artificial neurons based on memristive devices are discussed.In addition,we highlight the models and applications through combining artificial neuronal devices with sensors or other electronic devices.Finally,the future challenges and outlooks of memristor-based artificial neurons are discussed,and the development of hardware implementation of brain-like intelligence system based on SNN is also prospected.

A scoping review of methodologies for applying artificial intelligence to physical activity interventions

Purpose This scoping review aimed to offer researchers and practitioners an understanding of artificial intelligence(AI)applications in physical activity(PA)interventions;introduce them to prevalent machine learning(ML),deep learning(DL),and reinforcement learning(RL)algorithms;and encourage the adoption of AI methodologies.Methods A scoping review was performed in PubMed,Web of Science,Cochrane Library,and EBSCO focusing on AI applications for promoting PA or predicting related behavioral or health outcomes.AI methodologies were summarized and categorized to identify synergies,patterns,and trends informing future research.Additionally,a concise primer on predominant AI methodologies within the realm of PA was provided to bolster understanding and broader application.Results The review included 24 studies that met the predetermined eligibility criteria.AI models were found effective in detecting significant patterns of PA behavior and associations between specific factors and intervention outcomes.Most studies comparing AI models to traditional statistical approaches reported higher prediction accuracy for AI models on test data.Comparisons of different AI models yielded mixed results,likely due to model performance being highly dependent on the dataset and task.An increasing trend of adopting state-of-the-art DL and RL models over standard ML was observed,addressing complex human–machine communication,behavior modification,and decision-making tasks.Six key areas for future AI adoption in PA interventions emerged:personalized PA interventions,real-time monitoring and adaptation,integration of multimodal data sources,evaluation of intervention effectiveness,expanding access to PA interventions,and predicting and preventing injuries.Conclusion The scoping review highlights the potential of AI methodologies for advancing PA interventions.As the field progresses,staying informed and exploring emerging AI-driven strategies is essential for achieving significant improvements in PA interventions and fostering overall well-being.

Spatiotemporal characteristics of seed rain and soil seed bank of artificial Caragana korshinskii Kom. forest in the Tengger Desert, China

Vegetation restoration and reconstruction are effective approaches to desertification control and achieving social and economic sustainability in desert areas.However,the self-succession ability of native plants during the later periods of vegetation restoration remains unclear.Therefore,this study was conducted to bridge the knowledge gap by investigating the regeneration dynamics of artificial forest under natural conditions.The information of seed rain and soil seed bank was collected and quantified from an artificial Caragana korshinskii Kom.forest in the Tengger Desert,China.The germination tests were conducted in a laboratory setting.The analysis of species quantity and diversity in seed rain and soil seed bank was conducted to assess the impact of different durations of sand fixation(60,40,and 20 a)on the progress of vegetation restoration and ecological conditions in artificial C.korshinskii forest.The results showed that the top three dominant plant species in seed rain were Echinops gmelinii Turcz.,Eragrostis minor Host.,and Agropyron mongolicum Keng.,and the top three dominant plant species in soil seed bank were E.minor,Chloris virgata Sw.,and E.gmelinii.As restoration period increased,the density of seed rain and soil seed bank increased first and then decreased.While for species richness,as restoration period increased,it gradually increased in seed rain but decreased in soil seed bank.There was a positive correlation between seed rain density and soil seed bank density among all the three restoration periods.The species similarity between seed rain or soil seed bank and aboveground vegetation decreased with the extension of restoration period.The shape of the seeds,specifically those with external appendages such as spines and crown hair,clearly had an effect on their dispersal,then resulting in lower seed density in soil seed bank.In addition,precipitation was a crucial factor in promoting rapid germination,also resulting in lower seed density in soil seed bank.Our findings provide valuable insights for guiding future interventions during the later periods of artificial C.korshinskii forest,such as sowing and restoration efforts using unmanned aerial vehicles.

Activity Pattern and Habitat Use of Shorebirds in an Artificial Wetland Complex:A Case Study of Breeding Pied Avocet in the Yellow River Delta,China

With the loss of substantial natural wetlands in coastal zones,artificial wetlands provide alternative habitats for many shorebirds.Scientific management of artificial wetlands used by shorebirds plays an important role in maintaining the stability of shorebird population.Satellite tracking technique can obtain high-precision location information of individuals day and night,providing a good technical support for the study of quantitative relationship between waterfowls and their habitats.In this study,satellite tracking method,Remote Sensing(RS)and Geographic Information System(GIS)technology were used to analyze the activity pattern and habitat utilization characteristics of Pied Avocet during breeding period in an artificial wetland complex in the Yellow River Delta(YRD),China.The results showed that the breeding Pied Avocets had a small range of activity,with a total core and main home range of 33.10 km^(2) and 216.30 km^(2),respectively.This species tended to forage in the pond and salt pan during the day and night,respectively,with an unfixed staying time in the breeding ground.The distance between breeding ground and feeding ground was less than 6 km.It is emphasized that in addition to improving the conditions of the remaining natural habitats,effective managing artificial habitats is a priority for shorebird conservation.This research could provide reference for the management of artificial wetlands in coastal zones and supply technique support for the protection of shorebirds and their habitats,and alleviate human-bird conflicts and sustainable development of coastal zones.

Construction of a Cu@hollow TS-1 nanoreactor based on a hierarchical full-spectrum solar light utilization strategy for photothermal synergistic artificial photosynthesis

The artificial photosynthesis technology has been recognized as a promising solution for CO_(2) utilization.Photothermal catalysis has been proposed as a novel strategy to promote the efficiency of artificial photosynthesis by coupling both photochemistry and thermochemistry.However,strategies for maximizing the use of solar spectra with different frequencies in photothermal catalysis are urgently needed.Here,a hierarchical full-spectrum solar light utilization strategy is proposed.Based on this strategy,a Cu@hollow titanium silicalite-1 zeolite(TS-1)nanoreactor with spatially separated photo/thermal catalytic sites is designed to realize high-efficiency photothermal catalytic artificial photosynthesis.The space-time yield of alcohol products over the optimal catalyst reached 64.4μmol g−1 h−1,with the selectivity of CH3CH2OH of 69.5%.This rationally designed hierarchical utilization strategy for solar light can be summarized as follows:(1)high-energy ultraviolet light is utilized to drive the initial and difficult CO_(2) activation step on the TS-1 shell;(2)visible light can induce the localized surface plasmon resonance effect on plasmonic Cu to generate hot electrons for H2O dissociation and subsequent reaction steps;and(3)low-energy near-infrared light is converted into heat by the simulated greenhouse effect by cavities to accelerate the carrier dynamics.This work provides some scientific and experimental bases for research on novel,highly efficient photothermal catalysts for artificial photosynthesis.

Predictive active control of building structures using LQR and artificial intelligence

This study presents a neural network-based model for predicting linear quadratic regulator(LQR)weighting matrices for achieving a target response reduction.Based on the expected weighting matrices,the LQR algorithm is used to determine the various responses of the structure.The responses are determined by numerically analyzing the governing equation of motion using the state-space approach.For training a neural network,four input parameters are considered:the time history of the ground motion,the percentage reduction in lateral displacement,lateral velocity,and lateral acceleration,Output parameters are LQR weighting matrices.To study the effectiveness of an LQR-based neural network(LQRNN),the actual percentage reduction in the responses obtained from using LQRNN is compared with the target percentage reductions.Furthermore,to investigate the efficacy of an active control system using LQRNN,the controlled responses of a system are compared to the corresponding uncontrolled responses.The trained neural network effectively predicts weighting parameters that can provide a percentage reduction in displacement,velocity,and acceleration close to the target percentage reduction.Based on the simulation study,it can be concluded that significant response reductions are observed in the active-controlled system using LQRNN.Moreover,the LQRNN algorithm can replace conventional LQR control with the use of an active control system.

Application of artificial synapse based on all-inorganic perovskite memristor in neuromorphic computing

Artificial synapse inspired by the biological brain has great potential in the field of neuromorphic computing and artificial intelligence.The memristor is an ideal artificial synaptic device with fast operation and good tolerance.Here,we have prepared a memristor device with Au/CsPbBr_(3)/ITO structure.The memristor device exhibits resistance switching behavior,the high and low resistance states no obvious decline after 400 switching times.The memristor device is stimulated by voltage pulses to simulate biological synaptic plasticity,such as long-term potentiation,long-term depression,pair-pulse facilitation,short-term depression,and short-term potentiation.The transformation from short-term memory to long-term memory is achieved by changing the stimulation frequency.In addition,a convolutional neural network was constructed to train/recognize MNIST handwritten data sets;a distinguished recognition accuracy of~96.7%on the digital image was obtained in 100 epochs,which is more accurate than other memristor-based neural networks.These results show that the memristor device based on CsPbBr3 has immense potential in the neuromorphic computing system.

What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?

The present study aimed to explore the potential of artificial intelligence(AI)methodology based on magnetic resonance(MR)images to aid in the management of prostate cancer(PCa).To this end,we reviewed and summarized the studies comparing the diagnostic and predictive performance for PCa between AI and common clinical assessment methods based on MR images and/or clinical characteristics,thereby investigating whether AI methods are generally superior to common clinical assessment methods for the diagnosis and prediction fields of PCa.First,we found that,in the included studies of the present study,AI methods were generally equal to or better than the clinical assessment methods for the risk assessment of PCa,such as risk stratification of prostate lesions and the prediction of therapeutic outcomes or PCa progression.In particular,for the diagnosis of clinically significant PCa,the AI methods achieved a higher summary receiver operator characteristic curve(SROC-AUC)than that of the clinical assessment methods(0.87 vs.0.82).For the prediction of adverse pathology,the AI methods also achieved a higher SROC-AUC than that of the clinical assessment methods(0.86 vs.0.75).Second,as revealed by the radiomics quality score(RQS),the studies included in the present study presented a relatively high total average RQS of 15.2(11.0–20.0).Further,the scores of the individual RQS elements implied that the AI models in these studies were constructed with relatively perfect and standard radiomics processes,but the exact generalizability and clinical practicality of the AI models should be further validated using higher levels of evidence,such as prospective studies and open-testing datasets.

Non-Targeted Metabolomics Reveals the Metabolic Alterations in Response to Artificial Selective Breeding in the Fast-Growing Strains of Pacific Oyster

Pacific oyster(Crassostrea gigas)is one of the most important mollusks cultured all around the world.Selective breeding programs of Pacific oysters in China is initiated since 2006 and developed the genetically improved strain with fast-growing trait.However,little is known about the metabolic signatures of the fast-growing trait.In the present study,the non-targeted metabolomics was performed to analyze the metabolic signatures of adductor muscle tissue in one-year old Pacific oysters from fast-growing strain and the wild population.A total of 7767 and 10174 valid peaks were extracted and quantified in ESI^(+)and ESI^(−)modes,resulting in 399 and 381 annotated metabolites,respectively.PCA and OPLS-DA revealed that considerable separation among samples from fastgrowing strain and wild population,suggesting the differences in metabolic signatures.Meanwhile,81 significantly different metabolites(SDMs)were identified in the comparisons between fast-growing strain and wild population,based on the strict thresholds.It was found that there were highly correlation and conserved coordination among these SDMs.KEGG enrichment analysis indicated that the SDMs were tightly related to pantothenate and CoA biosynthesis,steroid hormone biosynthesis,riboflavin metabolism,and arginine and proline metabolism.Of them,the CoA biosynthesis and metabolism,affected by pantetheine and pantothenic acid,might be important for the growth of Pacific oysters under artificial selective breeding.The study provides the comprehensive views of metabolic signatures in response to artificially selective breeding,and is helpful to better understand the molecular mechanism of fastgrowing traits in Pacific oysters.