Postnatal development of rat retina:a continuous observation and comparison between the organotypic retinal explant model and in vivo development

The organotypic retinal explant culture has been established for more than a decade and offers a range of unique advantages compared with in vivo experiments and cell cultures.However,the lack of systematic and continuous comparison between in vivo retinal development and the organotypic retinal explant culture makes this model controversial in postnatal retinal development studies.Thus,we aimed to verify the feasibility of using this model for postnatal retinal development studies by comparing it with the in vivo retina.In this study,we showed that postnatal retinal explants undergo normal development,and exhibit a consistent structure and timeline with retinas in vivo.Initially,we used SOX2 and PAX6 immunostaining to identify retinal progenitor cells.We then examined cell proliferation and migration by immunostaining with Ki-67 and doublecortin,respectively.Ki-67-and doublecortin-positive cells decreased in both in vivo and explants during postnatal retinogenesis,and exhibited a high degree of similarity in abundance and distribution between groups.Additionally,we used Ceh-10 homeodomain-containing homolog,glutamate-ammonia ligase(glutamine synthetase),neuronal nuclei,and ionized calcium-binding adapter molecule 1 immunostaining to examine the emergence of bipolar cells,Müller glia,mature neurons,and microglia,respectively.The timing and spatial patterns of the emergence of these cell types were remarkably consistent between in vivo and explant retinas.Our study showed that the organotypic retinal explant culture model had a high degree of consistency with the progression of in vivo early postnatal retina development.The findings confirm the accuracy and credibility of this model and support its use for long-term,systematic,and continuous observation.

Multiparameter magnetic resonance imaging-based radiomics model for the prediction of rectal cancer metachronous liver metastasis

BACKGROUND The liver,as the main target organ for hematogenous metastasis of colorectal cancer,early and accurate prediction of liver metastasis is crucial for the diagnosis and treatment of patients.Herein,this study aims to investigate the application value of a combined machine learning(ML)based model based on the multiparameter magnetic resonance imaging for prediction of rectal metachronous liver metastasis(MLM).AIM To investigate the efficacy of radiomics based on multiparametric magnetic resonance imaging images of preoperative first diagnosed rectal cancer in predicting MLM from rectal cancer.METHODS We retrospectively analyzed 301 patients with rectal cancer confirmed by surgical pathology at Jingzhou Central Hospital from January 2017 to December 2023.All participants were randomly assigned to the training or validation queue in a 7:3 ratio.We first apply generalized linear regression model(GLRM)and random forest model(RFM)algorithm to construct an MLM prediction model in the training queue,and evaluate the discriminative power of the MLM prediction model using area under curve(AUC)and decision curve analysis(DCA).Then,the robustness and generalizability of the MLM prediction model were evaluated based on the internal validation set between the validation queue groups.RESULTS Among the 301 patients included in the study,16.28%were ultimately diagnosed with MLM through pathological examination.Multivariate analysis showed that carcinoembryonic antigen,and magnetic resonance imaging radiomics were independent predictors of MLM.Then,the GLRM prediction model was developed with a comprehensive nomogram to achieve satisfactory differentiation.The prediction performance of GLRM in the training and validation queue was 0.765[95%confidence interval(CI):0.710-0.820]and 0.767(95%CI:0.712-0.822),respectively.Compared with GLRM,RFM achieved superior performance with AUC of 0.919(95%CI:0.868-0.970)and 0.901(95%CI:0.850-0.952)in the training and validation queue,respectively.The DCA indicated that the predictive ability and net profit of clinical RFM were improved.CONCLUSION By combining multiparameter magnetic resonance imaging with the effectiveness and robustness of ML-based predictive models,the proposed clinical RFM can serve as an insight tool for preoperative assessment of MLM risk stratification and provide important information for individual diagnosis and treatment of rectal cancer patients.

SIZHD17 is involved in the control of chlorophyll and carotenoid metabolism in tomato fruit

Chlorophylls and carotenoids are essential and bene fi cial substances for both plant and human health.Identifying the regulatory network of these pigments is necessary for improving fruit quality.In a previous study,we identi fi ed an R2R3-MYB transcription factor,SlMYB72,that plays an important role in chlorophyll and carotenoid metabolism in tomato fruit.Here,we demonstrated that the SlMYB72-interacting protein SlZHD17,which belongs to the zinc-fi nger homeodomain transcription factor family,also functions in chlorophyll and carotenoid metabolism.Silencing SIZHD 17 in tomato improved multiple bene fi cial agronomic traits,including dwar fi sm,accelerated fl owering,and earlier fruit harvest.More importantly,downregulating SIZHD17 in fruits resulted in larger chloroplasts and a higher chlorophyll content.Dual-luciferase,yeast one-hybrid and electrophoretic mobility shift assays clari fi ed that SlZHD17 regulates the chlorophyll biosynthesis gene SIPOR-B and chloroplast developmental regulator SITKN2 in a direct manner.Chlorophyll degradation and plastid transformation were also retarded after suppression of SIZHD17 in fruits,which was caused by the inhibition of SISGR1,a crucial factor in chlorophyll degradation.On the other hand,the expression of the carotenoid biosynthesis genes SIPSY1 and SIZISO was also suppressed and directly regulated by SlZHD17,which induced uneven pigmentation and decreased the lycopene content in fruits with SIZHD17 suppression at the ripe stage.Furthermore,the protein-protein interactions between SlZHD17 and other pigment regulators,including SlARF4,SlBEL11,and SlTAGL1,were also presented.This study provides new insight into the complex pigment regulatory network and provides new options for breeding strategies aiming to improve fruit quality.

A comprehensive proteomic analysis of elaioplasts from citrus fruits reveals insights into elaioplast biogenesis and function

Elaioplasts of citrus peel are colorless plastids which accumulate significant amounts of terpenes.However,other functions of elaioplasts have not been fully characterized to date.Here,a LC–MS/MS shotgun technology was applied to identify the proteins from elaioplasts that were highly purified from young fruit peel of kumquat.A total of 655 putative plastid proteins were identified from elaioplasts according to sequence homology in silico and manual curation.Based on functional classification via Mapman,~50%of the identified proteins fall into six categories,including protein metabolism,transport,and lipid metabolism.Of note,elaioplasts contained ATP synthase and ADP,ATP carrier proteins at high abundance,indicating important roles for ATP generation and transport in elaioplast biogenesis.Additionally,a comparison of proteins between citrus chromoplast and elaioplast proteomes suggest a high level of functional conservation.However,some distinctive protein profiles were also observed in both types of plastids notably for isoprene biosynthesis in elaioplasts,and carotenoid metabolism in chromoplasts.In conclusion,this comprehensive proteomic study provides new insights into the major metabolic pathways and unique characteristics of elaioplasts and chromoplasts in citrus fruit.

国家自然科学基金面上项目的遴选标准及其规范化探讨

同行评议的决策机制中,获取同行专家提供的评议意见很重要,管理者如何基于评议意见做出科学决策也同样不可忽视。国家自然科学基金项目管理者面对大量的项目,且每个项目包含有多个同行专家的评议意见,如何汇总项目的评议意见并从众多项目中优中选优开展项目遴选,倘若没有科学且可把握的遴选标准,遴选过程的不规范、不科学必然事实上将对获取的同行专家评议意见打折扣。因此,建立科学、规范的项目遴选标准,减少遴选过程中可能存在的随意性,避免其中人为因素干扰,是确保同行评议制度公正性的重要环节。

Hypoglycemic mechanism of Tegillarca granosa polysaccharides on type 2 diabetic mice by altering gut microbiota and regulating the PI3K-akt signaling pathwaye

Type 2 diabetes mellitus(T2DM)is a complex metabolic disease threatening human health.We investigated the effects of Tegillarca granosa polysaccharide(TGP)and determined its potential mechanisms in a mouse model of T2DM established through a high-fat diet and streptozotocin.TGP(5.1×10^(3) Da)was composed of mannose,glucosamine,rhamnose,glucuronic acid,galactosamine,glucose,galactose,xylose,and fucose.It could significantly alleviate weight loss,reduce fasting blood glucose levels,reverse dyslipidemia,reduce liver damage from oxidative stress,and improve insulin sensitivity.RT-PCR and Western blotting indicated that TGP could activate the phosphatidylinositol-3-kinase/protein kinase B signaling pathway to regulate disorders in glucolipid metabolism and improve insulin resistance.TGP increased the abundance of Allobaculum,Akkermansia,and Bifidobacterium,restored the microbiota abundance in the intestinal tracts of mice with T2DM,and promoted short-chain fatty acid production.This study provides new insights into the antidiabetic effects of TGP and highlights its potential as a natural hypoglycemic nutraceutical.

A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

Evaluating the stability and volumetric flowback rate of proppant packs in hydraulic fractures using the lattice Boltzmann-discrete element coupling method

The stability and mobility of proppant packs in hydraulic fractures during hydrocarbon production are numerically investigated by the lattice Boltzmann-discrete element coupling method(LB-DEM).This study starts with a preliminary proppant settling test,from which a solid volume fraction of 0.575 is calibrated for the proppant pack in the fracture.In the established workflow to investigate proppant flowback,a displacement is applied to the fracture surfaces to compact the generated proppant pack as well as further mimicking proppant embedment under closure stress.When a pressure gradient is applied to drive the fluid-particle flow,a critical aperture-to-diameter ratio of 4 is observed,above which the proppant pack would collapse.The results also show that the volumetric proppant flowback rate increases quadratically with the fracture aperture,while a linear variation between the particle flux and the pressure gradient is exhibited for a fixed fracture aperture.The research outcome contributes towards an improved understanding of proppant flowback in hydraulic fractures,which also supports an optimised proppant size selection for hydraulic fracturing operations.

Cross-Dimension Attentive Feature Fusion Network for Unsupervised Time-Series Anomaly Detection

Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series data.Due to the challenges associated with annotating anomaly events,time series reconstruction has become a prevalent approach for unsupervised anomaly detection.However,effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time series.In this paper,we propose a cross-dimension attentive feature fusion network for time series anomaly detection,referred to as CAFFN.Specifically,a series and feature mixing block is introduced to learn representations in 1D space.Additionally,a fast Fourier transform is employed to convert the time series into 2D space,providing the capability for 2D feature extraction.Finally,a cross-dimension attentive feature fusion mechanism is designed that adaptively integrates features across different dimensions for anomaly detection.Experimental results on real-world time series datasets demonstrate that CAFFN performs better than other competing methods in time series anomaly detection.

科技支撑云南种业高质量创新发展的对策建议

种子是农业的“芯片”。2020年12月,中央经济工作会议将“解决好种子问题”列入八大重点任务之一,提出“要开展种源‘卡脖子’技术攻关,立志打一场种业翻身仗”。作为世界粮食生产大国,我国在育种技术领域创新能力明显不足,种业安全战略面临严峻挑战。本文在分析国内外作物育种技术发展历程、产业现状、技术热点及发展趋势的基础上,针对云南省作物育种现状及存在问题,研究提出了科技支撑云南种业高质量创新发展的相关建议。

Stability mechanism and control of the pumpable supports in longwall recovery room

The load-bearing performance(LBP)of pumpable supports(PPS)is crucial for the stability of longwall pre-driven recovery room(PRR)surrounding rock.However,the unbalanced bearing coefficient(UBC)of the PPS(undertaking unequal load along the mining direction)has not been investigated.A mechanical model of the PRR was established,considering the main roof cantilever beam structure,to derive an assessment formula for the load,the failure criteria,and the UBC of the PPS.Subsequently,the generation mechanisms,and influencing factors of the UBC were revealed.Global sensitivity analysis shows that the main roof hanging length(l_(2))and the spacing between the PPS(r)significantly impact the UBC.A novel design of the PPS and the coupling control technology were proposed and applied to reduce the UBC of the PPS in the adjacent longwall PRR.Monitor results showed no failure of the PPS at the test site,with the UBC(ζ)reduced to 1.1 consistent with the design value(1.15)basically,fully utilizing the collaborative LBP of the PPS.Finally,the maximum roof-to-floor convergence of the PRR was 234 mm,effectively controlling the stability of the surrounding rock of the PRR and ensuring the mining equipment recovery.

Efficient stabilization of dredged sludge with high water content using an improved bio-carbonation of reactive magnesia cement method

This study proposed an improved bio-carbonation of reactive magnesia cement(RMC)method for dredged sludge stabilization using the urea pre-hydrolysis strategy.Based on unconfined compression strength(UCS),pickling-drainage,and scanning electron microscopy(SEM)tests,the effects of prehydrolysis duration(T),urease activity(UA)and curing age(CA)on the mechanical properties and microstructural characteristics of bio-carbonized samples were systematically investigated and analyzed.The results demonstrated that the proposed method could significantly enhance urea hydrolysis and RMC bio-carbonation to achieve efficient stabilization of dredged sludge with 80%high water content.A significant strength increment of up to about 1063.36 kPa was obtained for the bio-carbonized samples after just 7 d of curing,which was 2.64 times higher than that of the 28-day cured ordinary Portland cement-reinforced samples.Both elevated T and UA could notably increase urea utilization ratio and carbonate ion yield,but the resulting surge in supersaturation also affected the precipitation patterns of hydrated magnesia carbonates(HMCs),which weakened the cementation effect of HMCs on soil particles and further inhibited strength enhancement of bio-carbonized samples.The optimum formula was determined to be the case of T?24 h and UA?10 U/mL for dredged sludge stabilization.A 7-day CA was enough for bio-carbonized samples to obtain stable strength,albeit slightly affected by UA.The benefits of high efficiency and water stability presented the potential of this method in achieving dredged sludge stabilization and resource utilization.This investigation provides informative ideas and valuable insights on implementing advanced bio-geotechnical techniques to achieve efficient stabilization of soft soil,such as dredged sludge.

Long-Term Exposure to Low-Level Ambient Air Pollution and Mortality among 0.3 Million Chinese Older Adults

Objective Evidence that long-term exposure to ambient air pollution increases mortality among older adults,particularly those residing in low-level air pollution locations,remains scarce.This study investigated the potential links between long-term low-level air pollution exposure and mortality among Chinese older adults.Methods A population-based study with 317,464 individuals aged≥65 years was conducted in Shenzhen,China during 2018 and 2020.Logistic regression models were used to analyze the associations between long-term exposure to air pollution and all-cause mortality,as the primary outcome,as well as non-accidental,cancer and cardiovascular mortality.Results Significant associations of PM1,PM_(2.5),PM_(10),SO_(2),CO,and O3 exposures with a higher risk of all-cause mortality were found.Adjusted odds ratio(OR)for each 1μg/m^(3) increment was 1.49[95%confidence interval(CI):1.46,1.53]for PM1,1.30(1.27,1.32)for PM_(2.5),1.05(1.04,1.06)for PM_(10),5.84(5.39,6.32)for SO_(2),1.04(1.04,1.05)for CO,and 1.02(1.00,1.03)for O3,respectively.Long-term PM1,PM_(2.5),PM_(10),SO_(2),and CO exposures also elevated the risks of non-accidental,cancer and cardiovascular mortality.Conclusion Long-term low-level air pollution exposure was associated with an increased mortality risk among Chinese older adults.

Learning to Branch in Combinatorial Optimization With Graph Pointer Networks

Traditional expert-designed branching rules in branch-and-bound(B&B) are static, often failing to adapt to diverse and evolving problem instances. Crafting these rules is labor-intensive, and may not scale well with complex problems.Given the frequent need to solve varied combinatorial optimization problems, leveraging statistical learning to auto-tune B&B algorithms for specific problem classes becomes attractive. This paper proposes a graph pointer network model to learn the branch rules. Graph features, global features and historical features are designated to represent the solver state. The graph neural network processes graph features, while the pointer mechanism assimilates the global and historical features to finally determine the variable on which to branch. The model is trained to imitate the expert strong branching rule by a tailored top-k Kullback-Leibler divergence loss function. Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed branching rules. It also outperforms state-of-the-art machine-learning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances. In addition, the model can generalize to unseen instances and scale to larger instances.

Numerical simulation of melt flow and temperature field during DC casting 2024 aluminium alloy under different casting conditions

Casting speed,casting temperature and secondary cooling water flow rate are the main process parameters affecting the DC casting process.These parameters significantly influence the flow and temperature fields during casting,which are crucial for the quality of the ingot and can determine the success or failure of the casting operation.Numerical simulation,with the advantages of low cost,rapid execution,and visualized results,is an important method to study and optimize the DC casting process.In the present work,a simulation model of DC casting 2024 aluminum alloy was established,and the reliability of the model was verified.Then,the influence of casting parameters on flow field and temperature field was studied in detail by numerical simulation method.Results show that with the increase of casting speed,the melt flow becomes faster,the depths of slurry zone and mushy zone increase,and the variation of slurry zone depth is greater than that of mushy zone.With an increase in casting temperature,the melt flow rate increases,the depth of the slurry zone becomes shallower,and the depth of the mushy zone experiences only minor changes.The simulation results further indicate that the increase of the flow rate of the secondary cooling water slightly reduces the depths of both slurry and mushy zone.

Biomimetic Integrated Nanozyme for Flare and Recurrence of Gouty Arthritis

Flare and multiple recurrences pose significant challenges in gouty arthritis.Traditional treatments provide temporary relief from inflammation but fail to promptly alleviate patient pain or effectively prevent subsequent recurrences.It should also be noted that both anti-inflammation and metabolism of uric acid are necessary for gouty arthritis,calling for therapeutic systems to achieve these two goals simultaneously.In this study,we propose a biomimetic integrated nanozyme,HMPB-Pt@MM,comprising platinum nanozyme and hollow Prussian blue.It demonstrates anti-inflammatory properties by eliminating reactive oxygen species and reducing infiltration of inflammatory macrophages.Additionally,it rapidly targets inflamed ankles through the camouflage of macrophage membranes.Furthermore,HMPB-Pt@MM exhibits urate oxidase-like capabilities,continuously metabolizing locally elevated uric acid concentrations,ultimately inhibiting multiple recurrences of gouty arthritis.In summary,HMPB-Pt@MM integrates ROS clearance with uric acid metabolism,offering a promising platform for the treatment of gouty arthritis.

Amphipathic Janus Nanofibers Aerogel for Efficient Solar Steam Generation

Solar steam generation is a promising water purification technology due to its low-cost and environmentally friendly applications in water purification and desalination.However,hydrophilic or hydrophobic materials alone are insufficient in achieving necessary characteristics for constructing highquality solar steam generators with good comprehensive properties.Herein,novel hydrophile/hydrophobe amphipathic Janus nanofibers aerogel is designed and used as a host material for preparing solar steam generators.The product consists of an internal cubic aerogel and an external layer of photothermal materials.The internal aerogel is composed of electrospun amphipathic Janus nanofibers.Owing to the unique composition and structure,the prepared solar steam generator integrates the features of high water evaporation rate(2.944 kg m^(-2)h^(-1)under 1 kW m^(-2)irradiation),selffloating,salt-resisting,and fast performance recovery after flipping.Moreover,the product also exhibits excellent properties on desalination and removal of organic pollutants.Compared with traditional hydrophilic aerogel host material,the amphipathic Janus nanofibers aerogel brings much higher water evaporation rate and salt resistance.

In Situ-Constructed Li_(x)MoS_(2)with Highly Exposed Interface Boosting High-Loading and Long-Life Cathode for All-Solid-State Li-S Batteries

As the persistent concerns regarding sluggish reaction kinetics and insufficient conductivities of sulfur cathodes in all-solid-state Li-S batteries(ASSLSBs),numerous carbon additives and solid-state electrolytes(SSEs)have been incorporated into the cathode to facilitate ion/electron pathways around sulfur.However,this has resulted in a reduced capacity and decomposition of SSEs.Therefore,it is worth exploring neotype sulfur hosts with electronic/ionic conductivity in the cathode.Herein,we present a hybrid cathode composed of few-layered S/MoS_(2)/C nanosheets(<5 layers)that exhibits high-loading and long-life performance without the need of additional carbon additives in advanced ASSLSBs.The multifunctional MoS_(2)/C host exposes the abundant surface for intimate contacting sites,in situ-formed LixMoS_(2)during discharging as mixed ion/electron conductive network improves the S/Li2S conversion,and contributes extra capacity for the part of active materials.With a high active material content(S+MoS_(2)/C)of 60 wt%in the S/MoS_(2)/C/Li_(6)PS_(5)Cl cathode composite(the carbon content is only~3.97 wt%),the S/MoS_(2)/C electrode delivers excellent electrochemical performance,with a high reversible discharge capacity of 980.3 mAh g^(-1)(588.2 mAh g^(-1)based on the whole cathode weight)after 100 cycles at 100 mA g^(-1).The stable cycling performance is observed over 3500 cycles with a Coulombic efficiency of 98.5%at 600 mA g^(-1),while a high areal capacity of 10.4 mAh cm^(-2)is achieved with active material loading of 12.8 mg cm^(-2).

Understanding Bonding Nature of α-Keggin Polyoxometalates[XW_(12)O_(40)]^(n−)(X=Al,Si,P,S):A Generalized Superatomic Perspective

α-Keggin polyoxometalates(POMs)[XW_(12)O_(40)]^(n−)(X=Al,Si,P,S)are widely used in batteries owing to their remarkable redox activity.However,the mechanism underlying the applications appears inconsistent with the widely accepted covalent bonding nature.Here,first-principles calculations show that XW_(12)are core–shell structures composed of a shell and an XO_(4)^(n−)core,both are stabilized by covalent interactions.Interestingly,owing to the presence of a substantial number of electrons in W_(12)O_(36)shell,the frontier molecular orbitals of XW_(12)are not only strongly delocalized but also exhibit superatomic properties with high-angular momentum electrons that do not conform to the Jellium model.Detailed analysis indicates that energetically high lying filled molecular orbitals(MOs)have reached unusually high-angular momentum characterized by quantum number K or higher,allowing for the accommodation of numerous electrons.This attribute confers strong electron acceptor ability and redox activity to XW_(12).Moreover,electrons added to XW_(12)still occupy the K orbitals and will not cause rearrangement of the MOs,thereby maintaining the stability of these structures.Our findings highlight the structure–activity relationship and provide a direction for tailor-made POMs with specific properties at atomic level.

Washed microbiota transplantation for Crohn’s disease:A metagenomic,metatranscriptomic,and metabolomic-based study

BACKGROUND Fecal microbiota transplantation(FMT)is a promising therapeutic approach for treating Crohn’s disease(CD).The new method of FMT,based on the automatic washing process,was named as washed microbiota transplantation(WMT).Most existing studies have focused on observing the clinical phenomena.However,the mechanism of action of FMT for the effective management of CD-particularly in-depth multi-omics analysis involving the metagenome,metatranscriptome,and metabolome-has not yet been reported.AIM To assess the efficacy of WMT for CD and explore alterations in the microbiome and metabolome in response to WMT.METHODS We conducted a prospective,open-label,single-center clinical study.Eleven CD patients underwent WMT.Their clinical responses(defined as a decrease in their CD Activity Index score of>100 points)and their microbiome(metagenome,metatranscriptome)and metabolome profiles were evaluated three months after the procedure.RESULTS Seven of the 11 patients(63.6%)showed an optimal clinical response three months post-WMT.Gut microbiome diversity significantly increased after WMT,consistent with improved clinical symptoms.Comparison of the metagenome and metatranscriptome analyses revealed consistent alterations in certain strains,such as Faecalibac-terium prausnitzii,Roseburia intestinalis,and Escherichia coli.In addition,metabolomics analyses demonstrated that CD patients had elevated levels of various amino acids before treatment compared to the donors.However,levels of vital amino acids that may be associated with disease progression(e.g.,L-glutamic acid,gamma-glutamyl-leucine,and prolyl-glutamine)were reduced after WMT.CONCLUSION WMT demonstrated therapeutic efficacy in CD treatment,likely due to the effective reconstruction of the patient’s microbiome.Multi-omics techniques can effectively help decipher the potential mechanisms of WMT in treating CD.