Multilevel Attention Unet Segmentation Algorithmfor Lung Cancer Based on CT Images

Lung cancer is a malady of the lungs that gravely jeopardizes human health.Therefore,early detection and treatment are paramount for the preservation of human life.Lung computed tomography(CT)image sequences can explicitly delineate the pathological condition of the lungs.To meet the imperative for accurate diagnosis by physicians,expeditious segmentation of the region harboring lung cancer is of utmost significance.We utilize computer-aided methods to emulate the diagnostic process in which physicians concentrate on lung cancer in a sequential manner,erect an interpretable model,and attain segmentation of lung cancer.The specific advancements can be encapsulated as follows:1)Concentration on the lung parenchyma region:Based on 16-bit CT image capturing and the luminance characteristics of lung cancer,we proffer an intercept histogram algorithm.2)Focus on the specific locus of lung malignancy:Utilizing the spatial interrelation of lung cancer,we propose a memory-based Unet architecture and incorporate skip connections.3)Data Imbalance:In accordance with the prevalent situation of an overabundance of negative samples and a paucity of positive samples,we scrutinize the existing loss function and suggest a mixed loss function.Experimental results with pre-existing publicly available datasets and assembled datasets demonstrate that the segmentation efficacy,measured as Area Overlap Measure(AOM)is superior to 0.81,which markedly ameliorates in comparison with conventional algorithms,thereby facilitating physicians in diagnosis.

Colorectal Cancer Segmentation Algorithm Based on Deep Features from Enhanced CT Images

Colorectal cancer,a malignant lesion of the intestines,significantly affects human health and life,emphasizing the necessity of early detection and treatment.Accurate segmentation of colorectal cancer regions directly impacts subsequent staging,treatment methods,and prognostic outcomes.While colonoscopy is an effective method for detecting colorectal cancer,its data collection approach can cause patient discomfort.To address this,current research utilizes Computed Tomography(CT)imaging;however,conventional CT images only capture transient states,lacking sufficient representational capability to precisely locate colorectal cancer.This study utilizes enhanced CT images,constructing a deep feature network from the arterial,portal venous,and delay phases to simulate the physician’s diagnostic process and achieve accurate cancer segmentation.The innovations include:1)Utilizing portal venous phase CT images to introduce a context-aware multi-scale aggregation module for preliminary shape extraction of colorectal cancer.2)Building an image sequence based on arterial and delay phases,transforming the cancer segmentation issue into an anomaly detection problem,establishing a pixel-pairing strategy,and proposing a colorectal cancer segmentation algorithm using a Siamese network.Experiments with 84 clinical cases of colorectal cancer enhanced CT data demonstrated an Area Overlap Measure of 0.90,significantly better than Fully Convolutional Networks(FCNs)at 0.20.Future research will explore the relationship between conventional and enhanced CT to further reduce segmentation time and improve accuracy.

Hyperspectral Image Based Interpretable Feature Clustering Algorithm

Hyperspectral imagery encompasses spectral and spatial dimensions,reflecting the material properties of objects.Its application proves crucial in search and rescue,concealed target identification,and crop growth analysis.Clustering is an important method of hyperspectral analysis.The vast data volume of hyperspectral imagery,coupled with redundant information,poses significant challenges in swiftly and accurately extracting features for subsequent analysis.The current hyperspectral feature clustering methods,which are mostly studied from space or spectrum,do not have strong interpretability,resulting in poor comprehensibility of the algorithm.So,this research introduces a feature clustering algorithm for hyperspectral imagery from an interpretability perspective.It commences with a simulated perception process,proposing an interpretable band selection algorithm to reduce data dimensions.Following this,amulti-dimensional clustering algorithm,rooted in fuzzy and kernel clustering,is developed to highlight intra-class similarities and inter-class differences.An optimized P systemis then introduced to enhance computational efficiency.This system coordinates all cells within a mapping space to compute optimal cluster centers,facilitating parallel computation.This approach diminishes sensitivity to initial cluster centers and augments global search capabilities,thus preventing entrapment in local minima and enhancing clustering performance.Experiments conducted on 300 datasets,comprising both real and simulated data.The results show that the average accuracy(ACC)of the proposed algorithm is 0.86 and the combination measure(CM)is 0.81.

Sanxingdui Cultural Relics Recognition Algorithm Based on Hyperspectral Multi-Network Fusion

Sanxingdui cultural relics are the precious cultural heritage of humanity with high values of history,science,culture,art and research.However,mainstream analytical methods are contacting and detrimental,which is unfavorable to the protection of cultural relics.This paper improves the accuracy of the extraction,location,and analysis of artifacts using hyperspectral methods.To improve the accuracy of cultural relic mining,positioning,and analysis,the segmentation algorithm of Sanxingdui cultural relics based on the spatial spectrum integrated network is proposed with the support of hyperspectral techniques.Firstly,region stitching algorithm based on the relative position of hyper spectrally collected data is proposed to improve stitching efficiency.Secondly,given the prominence of traditional HRNet(High-Resolution Net)models in high-resolution data processing,the spatial attention mechanism is put forward to obtain spatial dimension information.Thirdly,in view of the prominence of 3D networks in spectral information acquisition,the pyramid 3D residual network model is proposed to obtain internal spectral dimensional information.Fourthly,four kinds of fusion methods at the level of data and decision are presented to achieve cultural relic labeling.As shown by the experiment results,the proposed network adopts an integrated method of data-level and decision-level,which achieves the optimal average accuracy of identification 0.84,realizes shallow coverage of cultural relics labeling,and effectively supports the mining and protection of cultural relics.

An Edge Computing Algorithm Based on Multi-Level Star Sensor Cloud

Star sensors are an important means of autonomous navigation and access to space information for satellites.They have been widely deployed in the aerospace field.To satisfy the requirements for high resolution,timeliness,and confidentiality of star images,we propose an edge computing algorithm based on the star sensor cloud.Multiple sensors cooperate with each other to forma sensor cloud,which in turn extends the performance of a single sensor.The research on the data obtained by the star sensor has very important research and application values.First,a star point extraction model is proposed based on the fuzzy set model by analyzing the star image composition,which can reduce the amount of data computation.Then,a mappingmodel between content and space is constructed to achieve low-rank image representation and efficient computation.Finally,the data collected by the wireless sensor is delivered to the edge server,and a differentmethod is used to achieve privacy protection.Only a small amount of core data is stored in edge servers and local servers,and other data is transmitted to the cloud.Experiments show that the proposed algorithm can effectively reduce the cost of communication and storage,and has strong privacy.

Reconstruction and stability of Fe_(3)O_(4)(001)surface:An investigation based on particle swarm optimization and machine learning

Magnetite nanoparticles show promising applications in drug delivery,catalysis,and spintronics.The surface of magnetite plays an important role in these applications.Therefore,it is critical to understand the surface structure of Fe_(3)O_(4)at atomic scale.Here,using a combination of first-principles calculations,particle swarm optimization(PSO)method and machine learning,we investigate the possible reconstruction and stability of Fe_(3)O_(4)(001)surface.The results show that besides the subsurface cation vacancy(SCV)reconstruction,an A layer with Fe vacancy(A-layer-V_(Fe))reconstruction of the(001)surface also shows very low surface energy especially at oxygen poor condition.Molecular dynamics simulation based on the iron–oxygen interaction potential function fitted by machine learning further confirms the thermodynamic stability of the A-layer-V_(Fe)reconstruction.Our results are also instructive for the study of surface reconstruction of other metal oxides.

Design of a photonic crystal fiber polarization beam splitter with simple structure and ultra-wide bandwidth

A novel polarization beam splitter(PBS)based on dual-core photonic crystal fiber(DC-PCF)is proposed in this work.The proposed DC-PCF PBS contains two kinds of lattices and three kinds of air holes to form the asymmetrical elliptic dual-core structure.By using the full-vector finite element method,the propagation characteristics of the proposed DC-PCF PBS are investigated.The simulation results show that the bandwidth of the proposed DC-PCF PBS can reach to 340 nm,which covers the S+C+L+U communication bands,the shortest splitting length is 1.97 mm,and the maximum extinction ratio appears near wavelength 1550 nm.Moreover,the insertion loss of the proposed DC-PCF PBS is very low.It is believed that the proposed DC-PCF PBS has important applications in the field of all-optical communication and network.

Numerical simulation of a truncated cladding negative curvature fiber sensor based on the surface plasmon resonance effect

A refractive index(RI)sensor based on the surface plasmon resonance effect is proposed using a truncated cladding negative curvature fiber(TC-NCF).The influences of the TC-NCF structure parameters on the sensing performances are investigated and compared with the traditional NCF.The simulation results show that the proposed TC-NCF RI sensor has an ultra-wide detection range from 1.16 to 1.43.The maximum wavelength sensitivity reaches 12400 nm/RIU,and the corresponding R^(2)of the polynomial fitting equation is 0.9999.The maximum and minimum resolutions are 2.56×10^(-5)and 8.06×10^(-6),respectively.In addition,the maximum amplitude sensitivity can reach-379.1 RIU^(-1)when the RI is chosen as 1.43.The proposed TC-NCF RI sensor could be useful in biochemical medicine,environmental monitoring,and food safety.

Proton pump inhibitor for non-erosive reflux disease:A meta-analysis

AIM:To evaluate the efficacy,safety and influential factors of proton pump inhibitor(PPI)treatment for non-erosive reflux disease(NERD).METHODS:PubMed,MEDLINE,EMBASE and the Cochrane Library were searched up to April 2013 to identify eligible randomized controlled trials(RCTs)that probed into the efficacy,safety and influential factors of PPI treatment for NERD.The rates of symptomatic relief and adverse events were measured as the outcomes.After RCT selection,assessment and data collection,the pooled RRs and 95%CI were calculated.This meta-analysis was performed using the Stata 12.0 software(Stata Corporation,College Station,Texas,United States).The level of evidence was estimated by the Grading of Recommendations Assessment,Development and Evaluation system.RESULTS:Seventeen RCTs including 6072 patients met the inclusion criteria.The results of the metaanalysis showed that PPI treatment was significantly superior to H2receptor antagonists(H2RA)treatment(RR=1.629,95%CI:1.422-1.867,P=0.000)and placebo(RR=1.903,95%CI:1.573-2.302,P=0.000)for the symptomatic relief of NERD.However,there were no obvious differences between PPI and H2RA(RR=0.928,95%CI:0.776-1.110,P=0.414)or PPI and the placebo(RR=1.000,95%CI:0.896-1.116,P=0.997)regarding the rate of adverse events.The overall rate of symptomatic relief of PPI against NERD was 51.4%(95%CI:0.433-0.595,P=0.000),and relief was influenced by hiatal hernia(P=0.030).The adverse rate of PPI against NERD was 21.0%(95%CI:0.152-0.208,P=0.000),and was affected by hiatal hernia(P=0.081)and drinking(P=0.053).CONCLUSION:PPI overmatched H2RA on symptomatic relief rate but not on adverse rate for NERD.Its relief rate and adverse rate were influenced by hiatal hernia and drinking.

Cooperative inhibitory effect of sinomenine combined with5-fluorouracil on esophageal carcinoma

AIM:To investigate the inhibitory effects of sinomenine(SIN)combined with 5-fluorouracil(5-FU)on esophageal carcinoma in vitro and in vivo.METHODS:Esophageal carcinoma(Eca-109)cells were cultured in DMEM.The single or combined growth inhibition effects of SIN and 5-FU on the Eca-109 cells were examined by measuring the absorbance of CCK-8dye in living cells.Hoechst 33258 staining and an Annexin V/PI apoptosis kit were used to detect the percentage of cells undergoing apoptosis.Western blotting was used to investigate the essential mechanism underlying SIN and 5-FU-induced apoptosis.SIN at 25mg/kg and 5-FU at 12 mg/kg every 3 d,either combined or alone,was injected into nude mice and tumor growth inhibition and side effects of the drug treatment were observed.RESULTS:SIN and 5-FU,both in combination and individually,significantly inhibited the proliferation of Eca-109 cells and induced obvious apoptosis.Furthermore,the combined effects were greater than those of the individual agents(P<0.05).Annexin V/PI staining and Hoechst 33258 staining both indicated that the percentage of apoptotic cells induced by SIN and 5-FU combined or alone were significantly different from the control(P<0.05).The up-regulation of Bax and downregulation of Bcl-2 showed that the essential mechanism of apoptosis induced by SIN and 5-FU occurs via the mitochondrial pathway.SIN and 5-FU alone significantly inhibited the growth of tumor xenografts in vivo,and the combined inhibition rate was even higher(P<0.05).During the course of chemotherapy,no obvious side effects were observed in the liver or kidneys.CONCLUSION:The combined effects of SIN and 5-FU on esophageal carcinoma were superior to those of the individual compounds,and the drug combination did not increase the side effects of chemotherapy.

Overview on metabolomics in traditional Chinese medicine

Metabolomics has been widely used in the modern research of traditional Chinese medicine （TCM）. At the same time, the world is increasingly concerned about TCM, and many studies have been conducted to investigate different aspects of TCM. Among these studies, metabolomic approach has been implemented to facilitate TCM development. The current methods for TCM research are diverse, including nuclear magnetic resonance, gas chromatography-mass spectrometry, and liquid chromatographymass spectrometry. Using these techniques, some advantageous results have been obtained in the studies of TCM, such as diagnosis and treatment, quality control, and mechanisms of action. It is believed that the further development of metabo-lomic analytical techniques is benefcial to the modernization of TCM. This review summarizes potential applications of metabolomics in the area of TCM. Guidelines for good practice for the application of metabolomics in TCM research are also proposed, and the special role of metabolomics in TCM is highlighted.

Multi-View Auxiliary Diagnosis Algorithm for Lung Nodules

Lung is an important organ of human body.More and more people are suffering from lung diseases due to air pollution.These diseases are usually highly infectious.Such as lung tuberculosis,novel coronavirus COVID-19,etc.Lung nodule is a kind of high-density globular lesion in the lung.Physicians need to spend a lot of time and energy to observe the computed tomography image sequences to make a diagnosis,which is inefficient.For this reason,the use of computer-assisted diagnosis of lung nodules has become the current main trend.In the process of computer-aided diagnosis,how to reduce the false positive rate while ensuring a low missed detection rate is a difficulty and focus of current research.To solve this problem,we propose a three-dimensional optimization model to achieve the extraction of suspected regions,improve the traditional deep belief network,and to modify the dispersion matrix between classes.We construct a multi-view model,fuse local three-dimensional information into two-dimensional images,and thereby to reduce the complexity of the algorithm.And alleviate the problem of unbalanced training caused by only a small number of positive samples.Experiments show that the false positive rate of the algorithm proposed in this paper is as low as 12%,which is in line with clinical application standards.

Tunable Supercontinuum Generated in a Yb^3＋-Doped Microstructure Fiber Pumped by Ti：Sapphire Femtosecond Laser

We experimentally demonstrate that a tunable supercontinuum（SC） can be generated in a Yb3＋-doped microstructure fiber by the concept of wavelength conversion with a Ti：sapphire femtosecond（fs） laser as the pump.Experimental results show that an emission light around 1040 nm in an anomalous dispersion region is first generated and amplified by fs pulses in the normal dispersion region. Then, SC spectra from 1100 to 1380 nm and 630 to 840 nm can be achieved by combined effects of higher-order soliton fission and Raman soliton self-frequency shift in the anomalous dispersion region and self-phase modulation, dispersive wave, and four-wave mixing in the normal dispersion region. It is also demonstrated that the 20 nm change of pump results in a 280 nm broadband shift of soliton and the further red-shift of soliton is limited by OH-absorption at 1380 nm.

Oncological outcomes of patients with ductal adenocarcinoma of the prostate receiving radical prostatectomy or radiotherapy

Objective:To evaluate the oncological outcomes of ductal adenocarcinoma of the prostate(DAC)managed with radical prostatectomy(RP)or radiotherapy(RT)and optimize the proper treatment modality to DAC comprehensively.Methods:The cohorts included a total of 528 patients from the Surveillance,Epidemiology and End Results(SEER)database,354 receiving RP and 174 receiving RT.Cox proportional hazards regressions were performed to assess cancer specific mortality(CSM)and overall mortality(OM)between treatment groups.A competing risk analysis was further conducted.Subgroup analyses by age and level of prostate-specific antigen(PSA)were performed.Propensity score matching was implemented.Results:Patients managed with RP had lower risks of CSM and OM compared with RT(before matching:Hazard ratio[HR]=0.24,95%confidence interval[CI]0.13-0.47 and HR=0.26,95%CI 0.17-0.40,respectively;after matching:HR=0.18,95%CI 0.04-0.82 and HR=0.28,95%CI 0.11-0.70,accordingly).Subgroup analyses demonstrated that patients in the middle tertile of the age or with lower tertile PSA level managed with RP took lower risks of OM significantly(HR=0.18,95%CI 0.06-0.57,p<0.01 and HR=0.17,95%CI 0.06-0.54,p<0.01).Conclusion:Among patients with DAC,treatment with RP was associated with better survival outcomes in comparison with RT.Patients with DAC in the middle tertile of the age and with lower tertile PSA level benefited the most from RP.

Single-polarization single-mode hollow-core negative curvature fiber with nested U-type cladding elements

Hollow-core negative curvature fibers(HC-NCFs)have become one of the research hotspots in the field of optical fiber because of their potential applications in the data and energy transmissions.In this work,a new kind of single-polarization single-mode HC-NCF with nested U-type cladding elements is proposed.To achieve the single-polarization single-mode transmission,we use two different silica tubes in thickness,which satisfy the resonance and anti-resonance conditions on the U-type cladding elements and the cladding tubes,respectively.Besides,the elliptical elements are introduced to achieve good single-mode performance.By studying the influences of the structure parameters on the propagation characteristics,the optimized structure parameters are obtained.The simulation results show that when the wavelength is fixed at 1550 nm,the single-polarization single-mode transmission is achieved,with the polarization extinction ratio of 25749 and minimum high-order mode extinction ratio of 174.Furthermore,the confinement loss is only 0.0015 dB/m.

Multi-Directional Reconstruction Algorithm for Panoramic Camera

A panorama can reflect the surrounding scenery because it is an image with a wide angle of view.It can be applied in virtual reality,smart homes and other fields as well.A multi-directional reconstruction algorithm for panoramic camera is proposed in this paper according to the imaging principle of dome camera,as the distortion inevitably exists in the captured panorama.First,parameters of a panoramic image are calculated.Then,a weighting operator with location information is introduced to solve the problem of rough edges by taking full advantage of pixels.Six directions of the mapping model are built,which include up,down,left,right,front and back,according to the correspondence between cylinder and spherical coordinates.Finally,multi-directional image reconstruction can be realized.Various experiments are performed in panoramas(1024×1024)with 30 different shooting scenes.Results show that the azimuth image can be reconstructed quickly and accurately.The fuzzy edge can be alleviated effectively.The rate of pixel utilization can reach 84%,and it is 33%higher than the direct mapping algorithm.Large scale distortion is also further studied.

Comparative assessment of efficacy and safety of different treatment for de novo overactive bladder children: A systematic review and network meta-analysis

Objective:To compare these managements focusing on the efficacy and safety to treat overactive bladder(OAB)in children through network meta-analysis(NMA).Methods:We searched PubMed,Embase,the Cochrane Library Central Register of Controlled Trials(CENTRAL)and the reference lists up to May 1st,2017.Data from eligible randomized controlled trails(RCT)studies including three different treatment options were extracted.The primary outcome was maximal voiding volume(MVV).We performed pairwise metaanalyses by random effects model and NMA by Bayesian model.We used the Grading of Recommendations,Assessment,Development and Evaluations(GRADE)framework to assess the quality of evidence contributing to each network estimate.Results:Six RCTs(462 patients)comparing three different interventions fulfilled the inclusion criteria.A low risk of bias was shown for the majority of the study items.The results of NMA showed that compared with antimuscarinic drugs,Parasacral transcutaneous electrical nerve stimulation was associated with significant improvement in the MVV(mean difference[MD]=58.50,95% confidential interval[CI]:45.95-69.52),followed by urotherapy group(MD=21.03,95%CI:11.85-29.97).When it comes to the constipation,antimuscarinic drugs exerted significant benefit than PTENS(odds ratio[OR]:0.22,95%CI:0.01-0.46).No significant difference was found between other treatments.Conclusion:Compared with antimuscarinic drugs,PTENS was associated with significant better efficacy considering MVV,but more constipation events in de novo OAB children.Antimuscarinic drugs showed remarkably better efficacy considering MVV and comparable safety profile compared with urotherapy.Clinicians should take all known safety and compliance of patients into account when choosing an optimal strategy.

Comparative analysis of chemical profiles of Radix Astragali between ultrafine granular powder and sliced traditional material

Radix Astragali,one of the most popular herbs in traditional Chinese medicine(TCM),is used to strengthen the immune system,protect liver function,fight bacteria and viruses,and treat diabetes,heart failure and seasonal allergies.In recent years,a new form of Radix Astragali material processed by cell wall disrupting technology,namely ultrafine granular powder(UGP)has been introduced into the market.In order to determine chemical consistency and homogeneity of the UGP material prepared from sliced traditional materials(TM)of Radix Astragali,multiple batches of the UGP and TM samples derived from Astragalus membranaceus var.mongholicus were analyzed by UHPLC/DAD-MS using isoflavones and triterpenoid glycosides as marker compounds.The results demonstrated that the chemical profiles of UGP was identical or similar to that of TM,but UGP was highly homogeneous in terms of marker compound contents as assessed,e.g.,by the relative standard deviation values of the nine marker compounds in the range of 8.55%−43.80%for TM2 compared against 1.70%−8.38%for UGP2.Macromolecular component preparation and 1H NMR analyses indicated that TM4 and its corresponding UGP4 produced similar polysaccharides,but the later had approximately two-fold dissolution rate of the polysaccharides when compared to the former(yield 7.22±0.35%vs 3.39±0.20%).This study confirms that UGP of Radix Astragali is chemically consistent and homogenous,supporting its use as a promising material in TCM prescriptions.

Cell softness reveals tumorigenic potential via ITGB8/AKT/glycolysis signaling in a mice model of orthotopic bladder cancer

Background:Bladder cancer,characterized by a high potential of tumor recurrence,has high lifelong monitoring and treatment costs.To date,tumor cells with intrinsic softness have been identified to function as cancer stem cells in several cancer types.Nonetheless,the existence of soft tumor cells in bladder tumors remains elusive.Thus,our study aimed to develop a microbarrier microfluidic chip to efficiently isolate deformable tumor cells from distinct types of bladder cancer cells.Methods:The stiffness of bladder cancer cells was determined by atomic force microscopy(AFM).The modified microfluidic chip was utilized to separate soft cells,and the 3D Matrigel culture system was to maintain the softness of tumor cells.Expression patterns of integrinβ8(ITGB8),protein kinase B(AKT),and mammalian target of rapamycin(mTOR)were determined by Western blotting.Double immunostaining was conducted to examine the interaction between F-actin and tripartite motif containing 59(TRIM59).The stem-cell-like characteristics of soft cells were explored by colony formation assay and in vivo studies upon xenografted tumor models.Results:Using our newly designed microfluidic approach,we identified a small fraction of soft tumor cells in bladder cancer cells.More importantly,the existence of soft tumor cells was confirmed in clinical human bladder cancer specimens,in which the number of soft tumor cells was associated with tumor relapse.Furthermore,we demonstrated that the biomechanical stimuli arising from 3D Matrigel activated the F-actin/ITGB8/TRIM59/AKT/mTOR/glycolysis pathways to enhance the softness and tumorigenic capacity of tumor cells.Simultaneously,we detected a remarkable up-regulation in ITGB8,TRIM59,and phospho-AKT in clinical bladder recurrent tumors compared with their non-recurrent counterparts.Conclusions:The ITGB8/TRIM59/AKT/mTOR/glycolysis axis plays a crucial role in modulating tumor softness and stemness.Meanwhile,the soft tumor cells become more sensitive to chemotherapy after stiffening,that offers new insights for hampering tumor progression and recurrence.

Small molecule metabolites:discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is wellrecognizedhallmarks of diseases. Metabolite signatures that have close proximity to subject’s phenotypic informative dimension,are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers couldlead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity andselectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolicbiomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeuticpotentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathwayschanges as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists haveestablished a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutictarget exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-moleculecandidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening andtreatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.