VOx Matrix Confinement Approach to Generate Sub-3 nm L10-Pt-Based Intermetallic Catalysts for Fuel Cell Cathode.

Pt-based intermetallic compounds (IMCs) are considered as a class of promising fuel cell electrocatalysts, owing to their outstanding intrinsic activity and durability. However, the synthesis of uniformly dispersed IMCs with small sizes presents a formidable challenge during the essential high-temperature annealing process. Herein, a facile and generally applicable VOx matrix confinement strategy is demonstrated for the controllable synthesis of ordered L10-PtM (M = Fe, Co, and Mn) nanoparticles, which not only enhances the dispersion of intermetallic nanocrystals, even at high loading (40 wt%), but also simplifies the oxide removal and acid-washing procedures. Taking intermetallic PtCo as an example, the as-prepared catalyst displays a high-performance oxygen reduction activity (mass activity of 1.52 A mgPt -1) and excellent stability in the membrane electrode assemblies (MEAs) (the ECSA has just 7% decay after durability test). This strategy provides an economical and scalable route for the controlled synthesis of Pt-based intermetallic catalysts, which can pave a way for the commercialization of fuel cell technologies.

A Dielectric MXene-Induced Self-Built Electric Field in Polymer Electrolyte Triggering Fast Lithium-Ion Transport and High-Voltage Cycling Stability.

Quasi-solid polymer electrolyte (QPE) lithium (Li)-metal battery holds significant promise in the application of high-energy-density batteries, yet it suffers from low ionic conductivity and poor oxidation stability. Herein, a novel self-built electric field (SBEF) strategy is proposed to enhance Li+ transportation and accelerate the degradation dynamics of carbon-fluorine bond cleavage in LiTFSI by optimizing the termination of MXene. Among them, the SBEF induced by dielectric Nb4C3F2 MXene effectively constructs highly conductive LiF-enriched SEI and CEI stable interfaces, moreover, enhances the electrochemical performance of the QPE. The related Li-ion transfer mechanism and dual-reinforced stable interface are thoroughly investigated using ab initio molecular dynamics, COMSOL, XPS depth profiling, and ToF-SIMS. This comprehensive approach results in a high conductivity of 1.34 mS cm-1, leading to a small polarization of approximately 25 mV for Li//Li symmetric cell after 6000 h. Furthermore, it enables a prolonged cycle life at a high voltage of up to 4.6 V. Overall, this work not only broadens the application of MXene for QPE but also inspires the great potential of the self-built electric field in QPE-based high-voltage batteries.

Role of crosstalk between synovial cells and chondrocytes in osteoarthritis (Review).

Osteoarthritis (OA) is a low-grade, nonspecific inflammatory disease that affects the entire joint. This condition is characterized by synovitis, cartilage erosion, subchondral bone defects, and subpatellar fat pad damage. There is mounting evidence demonstrating the significance of crosstalk between synovitis and cartilage destruction in the development of OA. To comprehensively explore the phenotypic alterations of synovitis and cartilage destruction, it is important to elucidate the crosstalk mechanisms between chondrocytes and synovial cells. Furthermore, the updated iteration of single-cell sequencing technology reveals the interaction between chondrocyte and synovial cells. In the present review, the histological and pathological alterations between cartilage and synovium during OA progression are described, and the mode of interaction and molecular mechanisms between synovial cells and chondrocytes in OA, both of which affect the OA process mainly by altering the inflammatory environment and cellular state, are elucidated. Finally, the current OA therapeutic approaches are summarized and emerging therapeutic targets are reviewed in an attempt to provide potential insights into OA treatment.

Li ions traffic controller on thin lithium metal anode: Regulating deposition, optimizing and protecting solid electrolyte interphase.

The performance of thin lithium metal anodes is affected due to issues that weaken the electrode-electrolyte interphase. In this work, a coating layer serving as a Li+ traffic controller based on hexadecyl trimethyl ammonium bis(trifluoromethanesulphonyl)imide ([CTA][TFSI]) and poly (vinylidene difluoride co-hexafluoropropylene) (P(VDF-HFP)) is used to stabilize the thin lithium metal interface. The CTA+ ions in the coating layer can effectively regulate the distribution of Li+ concentration to promote uniform deposition of lithium. The anion of [CTA][TFSI] can optimize solid electrolyte interphase (SEI) with inorganic-rich components, which improve the ionic conductivity and reaction kinetics. Furthermore, the flexible polymer skeleton can fortify the fragile SEI, facilitating the consistent operation of the battery. Due to these improvements, a thin Li metal anode (4 mAh cm-2) with a coating layer in a Li||Li symmetric cell demonstrates a lifespan of 600 h at 1 mA cm-2 and 1 mAh cm-2. Notably, full cells with an ultra-low negative electrode/positive electrode = 1 (N/P = 1) demonstrate a stable performance over 200 cycles and 90 cycles at 0.5C and 1C (1C = 170 mA g-1), respectively.

Identification of DNA-protein binding residues through integration of Transformer encoder and Bi-directional Long Short-Term Memory.

DNA-protein binding is crucial for the normal development and function of organisms. The significance of accurately identifying DNA-protein binding sites lies in its role in disease prevention and the development of innovative approaches to disease treatment. In the present study, we introduce a precise and robust identifier for DNA-protein binding residues. In the context of protein representation, we combine the evolutionary information of the protein, represented by its position-specific scoring matrix, with the spatial information of the protein's secondary structure, enriching the overall informational content. This approach initially employs a combination of Bi-directional Long Short-Term Memory and Transformer encoder to jointly extract the interdependencies among residues within the protein sequence. Subsequently, convolutional operations are applied to the resulting feature matrix to capture local features of the residues. Experimental results on the benchmark dataset demonstrate that our method exhibits a higher level of competitiveness when compared to contemporary classifiers. Specifically, our method achieved an MCC of 0.349, SP of 96.50%, SN of 44.03% and ACC of 94.59% on the PDNA-41 dataset.

Contribution of ApoB-100/SORT1-Mediated Immune Microenvironment in Regulating Oxidative Stress, Inflammation, and Ferroptosis After Spinal Cord Injury.

This study aims to explore the impacts of ApoB-100/SORT1-mediated immune microenvironment during acute spinal cord injury (SCI), and to investigate the potential mechanism. CB57BL/6 mice underwent moderate thoracic contusion injury to establish the SCI animal model, and received ApoB-100 lentivirus injection to interfere ApoB-100 level. Functional recovery was assessed using the Basso, Beattie, and Bresnahan (BBB) score and footprint analysis. Transmission electron microscopy was applied to observe the ultrastructure of the injured spinal cord tissue. Hematoxylin-eosin (HE) staining and Perls staining were conducted to assess histological changes and iron deposition. Biochemical factor and cytokines were detected using their commercial kits. M1/M2 macrophage markers were detected by immunofluorescence assay in vivo and by flow cytometry in vitro. HT22 neurons were simulated by lipopolysaccharide (LPS), followed by incubation with polarized macrophage medium to simulate the immune microenvironment of injured spinal cord in vitro. The local immune microenvironment is changed in SCI mice, accompanied with the occurrence of oxidative stress and the elevation of both M1 and M2 macrophages. Knockdown of ApoB-100 ameliorates oxidative stress and lipid disorder, and inhibits inflammation and ferroptosis in SCI mice. Importantly, knockdown of ApoB-100 can partly restrict M1 macrophages but does not change M2 macrophage proportion in SCI mice. Further, M1 macrophages are observed to attenuate the inflammatory response, oxidative stress, and ferroptosis levels of LPS-induced HT22 cells, which is further strengthened by SORT1 knockdown. Blockage of ApoB-100/SORT1-mediated immune microenvironment plays a protective role against SCI via inhibiting oxidative stress, inflammation, lipid disorders, and ferroptosis, providing novel insights of the targeted therapy of SCI.

Stabilizing Highly Active Ru Sites by Electron Reservoir in Acidic Oxygen Evolution.

Proton exchange membrane water electrolysis is hindered by the sluggish kinetics of the anodic oxygen evolution reaction. RuO2 is regarded as a promising alternative to IrO2 for the anode catalyst of proton exchange membrane water electrolyzers due to its superior activity and relatively lower cost compared to IrO2. However, the dissolution of Ru induced by its overoxidation under acidic oxygen evolution reaction (OER) conditions greatly hinders its durability. Herein, we developed a strategy for stabilizing RuO2 in acidic OER by the incorporation of high-valence metals with suitable ionic electronegativity. A molten salt method was employed to synthesize a series of high-valence metal-substituted RuO2 with large specific surface areas. The experimental results revealed that a high content of surface Ru4+ species promoted the OER intrinsic activity of high-valence doped RuO2. It was found that there was a linear relationship between the ratio of surface Ru4+/Ru3+ species and the ionic electronegativity of the dopant metals. By regulating the ratio of surface Ru4+/Ru3+ species, incorporating Re, with the highest ionic electronegativity, endowed Re0.1Ru0.9O2 with exceptional OER activity, exhibiting a low overpotential of 199 mV to reach 10 mA cm-2. More importantly, Re0.1Ru0.9O2 demonstrated outstanding stability at both 10 mA cm-2 (over 300 h) and 100 mA cm-2 (over 25 h). The characterization of post-stability Re0.1Ru0.9O2 revealed that Re promoted electron transfer to Ru, serving as an electron reservoir to mitigate excessive oxidation of Ru sites during the OER process and thus enhancing OER stability. We conclude that Re, with the highest ionic electronegativity, attracted a mass of electrons from Ru in the pre-catalyst and replenished electrons to Ru under the operating potential. This work spotlights an effective strategy for stabilizing cost-effective Ru-based catalysts for acidic OER.

Application of radiomics model based on lumbar computed tomography in diagnosis of elderly osteoporosis.

A metabolic bone disease characterized by decreased bone formation and increased bone resorption is osteoporosis. It can cause pain and fracture of patients. The elderly are prone to osteoporosis and are more vulnerable to osteoporosis. In this study, radiomics are extracted from computed tomography (CT) images to screen osteoporosis in the elderly. Collect the plain scan CT images of lumbar spine, cut the region of interest of the image and extract radiomics features, use Lasso regression to screen variables and adjust complexity, use python language to model random forests, support vector machines, K nearest neighbor, and finally use receiver operating characteristic curve to evaluate the performance of the model, including precision, recall, accuracy and area under the curve (AUC). For the model, 14 radiolomics features were selected. The diagnosis performance of random forest model and support vector machine is good, all around 0.9. The AUC of K nearest neighbor model in training set and test set is 0.828 and 0.796, respectively. We selected the plain scan CT images of the elderly lumbar spine to build radiomics features model, which has good diagnostic performance and can be used as a tool to assist the diagnosis of osteoporosis in the elderly.

Hf and Co Dual Single Atoms Co-Doped Carbon Catalyst Enhance the Oxygen Reduction Performance.

Single-atom metal-doped M-N-C (M═Fe, Co, Mn, or Ni) catalysts exhibit excellent catalytic activity toward oxygen reduction reactions (ORR). However, their performance still has a large gap considering the demand for their practical applications. This study reports a high-performance dual single-atom doped carbon catalyst (HfCo-N-C), which is prepared by pyrolyzing Co and Hf co-doped ZIF-8 . Co and Hf are atomically dispersed in the carbon framework and coordinated with N to form Co-N4 and Hf-N4 active moieties. The synergetic effect between Co-N4 and Hf-N4 significantly enhance the catalytic activity and durability of the catalyst. In an acidic medium, the ORR half-wave potential (E1/2 ) of the catalyst is up to 0.82 V , which is much higher than that of the Co-N-C catalyst without Hf co-doping (0.80 V). The kinetic current density of the catalyst is up to 2.49 A cm-2 at 0.85 V , which is 1.74 times that of the Co-N-C catalyst without Hf co-doping. Moreover, the catalyst exhibits excellent cathodic performance in single proton exchange membrane fuel cells and Zn-air batteries. Furthermore, Hf co-doping can effectively suppress the formation of H2 O2 , resulting in significantly improved stability and durability.

Constructing a Topologically Adaptable Solid Electrolyte Interphase for a Highly Reversible Zinc Anode.

The performance of aqueous zinc metal batteries is significantly compromised by the stability of the solid electrolyte interphase (SEI), which is intimately linked to the structure of the electrical double layer (EDL) between the zinc anode and electrolyte. Furthermore, understanding the mechanical behavior of SEI is crucial, as it governs its response to stress induced by volume changes, fracture, or deformation. In this study, we introduce l-glutamine (Gln) as an additive to regulate the adsorbed environment of the EDL and in situ produce a hybrid SEI consisting of ZnS and Gln-related species. The results of the nanoindentation test indicate that the hybrid SEI exhibits a low modulus and low hardness, alongside exceptional shape recovery capability, which effectively limits side reactions and enables topological adaptation to volume fluctuations in zinc anodes during zinc ion plating/stripping, thereby enabling Zn//Zn symmetric cells to exhibit an ultralong cycle life of 4000 h in coin cells and a high cumulative capacity of 18,000 mA h in pouch cells. More importantly, the superiority of the formulated strategy is further demonstrated in Zn//NH4V4O10 full cells at different N/P ratios of 5.2, 4.9, 3.5, and 2.4. This provides a promising approach for future interfacial modulation in aqueous battery chemistry.

GTAMP-DTA: Graph transformer combined with attention mechanism for drug-target binding affinity prediction.

Drug target affinity prediction (DTA) is critical to the success of drug development. While numerous machine learning methods have been developed for this task, there remains a necessity to further enhance the accuracy and reliability of predictions. Considerable bias in drug target binding prediction may result due to missing structural information or missing information. In addition, current methods focus only on simulating individual non-covalent interactions between drugs and proteins, thereby neglecting the intricate interplay among different drugs and their interactions with proteins. GTAMP-DTA combines special Attention mechanisms, assigning each atom or amino acid an attention vector. Interactions between drug forms and protein forms were considered to capture information about their interactions. And fusion transformer was used to learn protein characterization from raw amino acid sequences, which were then merged with molecular map features extracted from SMILES. A self-supervised pre-trained embedding that uses pre-trained transformers to encode drug and protein attributes is introduced in order to address the lack of labeled data. Experimental results demonstrate that our model outperforms state-of-the-art methods on both the Davis and KIBA datasets. Additionally, the model's performance undergoes evaluation using three distinct pooling layers (max-pooling, mean-pooling, sum-pooling) along with variations of the attention mechanism. GTAMP-DTA shows significant performance improvements compared to other methods.

AttentionMGT-DTA: A multi-modal drug-target affinity prediction using graph transformer and attention mechanism.

The accurate prediction of drug-target affinity (DTA) is a crucial step in drug discovery and design. Traditional experiments are very expensive and time-consuming. Recently, deep learning methods have achieved notable performance improvements in DTA prediction. However, one challenge for deep learning-based models is appropriate and accurate representations of drugs and targets, especially the lack of effective exploration of target representations. Another challenge is how to comprehensively capture the interaction information between different instances, which is also important for predicting DTA. In this study, we propose AttentionMGT-DTA, a multi-modal attention-based model for DTA prediction. AttentionMGT-DTA represents drugs and targets by a molecular graph and binding pocket graph, respectively. Two attention mechanisms are adopted to integrate and interact information between different protein modalities and drug-target pairs. The experimental results showed that our proposed model outperformed state-of-the-art baselines on two benchmark datasets. In addition, AttentionMGT-DTA also had high interpretability by modeling the interaction strength between drug atoms and protein residues. Our code is available at https://github.com/JK-Liu7/AttentionMGT-DTA.

VIRMA promotes neuron apoptosis via inducing m6A methylation of STK10 in spinal cord injury animal models.

BACKGROUND: Spinal cord injury (SCI) occurs as a devastating neuropathic disease. The role of serine-threonine kinase 10 (STK10) in the development of SCI remains unclear. OBJECTIVE: This study aimed to investigate the action of m6A methylation on STK10 in the apoptosis of spinal cord neurons in the pathogenesis of SCI and the possible underlying mechanisms. METHODS: Rat model of SCI was established and subsequently evaluated for motor function, pathological conditions, and apoptosis of spinal cord neurons. And the effects of overexpression of STK10 on neuronal cells in animal models of spinal cord injury and glyoxylate deprivation (OGD) cell models were evaluated. m6A2Target database and SRAMP database were used to predict the m6A methylation sites of STK10. The methylation kits were used to detect overall m6A methylation. Finally, the interaction between STK10 and vir like m6A methyltransferase associated (VIRMA) was explored in animal and cellular models. RESULTS: STK10 is markedly decreased in spinal cord injury models and overexpression of STK10 inhibits neuronal apoptosis. VIRMA can induce m6A methylation of STK10. VIRMA is over-expressed in spinal cord injury models and negatively regulates the expression of STK10. m6A methylation and apoptosis of neuronal cells are reduced by the knockdown of VIRMA and STK10 shRNA have shown the opposite effects. CONCLUSIONS: VIRMA promotes neuronal apoptosis in spinal cord injury by regulating STK10 m6A methylation.

DTR-Net: Dual-Space 3D Tooth Model Reconstruction From Panoramic X-Ray Images.

In digital dentistry, cone-beam computed tomography (CBCT) can provide complete 3D tooth models, yet suffers from a long concern of requiring excessive radiation dose and higher expense. Therefore, 3D tooth model reconstruction from 2D panoramic X-ray image is more cost-effective, and has attracted great interest in clinical applications. In this paper, we propose a novel dual-space framework, namely DTR-Net, to reconstruct 3D tooth model from 2D panoramic X-ray images in both image and geometric spaces. Specifically, in the image space, we apply a 2D-to-3D generative model to recover intensities of CBCT image, guided by a task-oriented tooth segmentation network in a collaborative training manner. Meanwhile, in the geometric space, we benefit from an implicit function network in the continuous space, learning using points to capture complicated tooth shapes with geometric properties. Experimental results demonstrate that our proposed DTR-Net achieves state-of-the-art performance both quantitatively and qualitatively in 3D tooth model reconstruction, indicating its potential application in dental practice.

A progressive framework for tooth and substructure segmentation from cone-beam CT images.

BACKGROUND: Accurate segmentation of individual tooth and their substructures including enamel, pulp, and dentin from cone-beam computed tomography (CBCT) images is essential for dental diagnosis and treatment planning in digital dentistry. Existing methods for tooth segmentation based on CBCT images have achieved substantial progress; however, techniques for further segmentation into substructures are yet to be developed. PURPOSE: We aim to propose a novel three-stage progressive deep-learning-based framework for automatically segmenting 3D tooth from CBCT images, focusing on finer substructures, i.e., enamel, pulp, and dentin. METHODS: In this paper, we first detect each tooth using its centroid by a clustering scheme, which efficiently determines each tooth detection by applying learned displacement vectors from the foreground tooth region. Next, guided by the detected centroid, each tooth proposal, combined with the corresponding tooth map, is processed through our tooth segmentation network. We also present an attention-based hybrid feature fusion mechanism, which provides intricate details of the tooth boundary while maintaining the global tooth shape, thereby enhancing the segmentation process. Additionally, we utilize the skeleton of the tooth as a guide for subsequent substructure segmentation. RESULTS: Our algorithm is extensively evaluated on a collected dataset of 314 patients, and the extensive comparison and ablation studies demonstrate superior segmentation results of our approach. CONCLUSIONS: Our proposed method can automatically segment tooth and finer substructures from CBCT images, underlining its potential applicability for clinical diagnosis and surgical treatment.

Decreased FoxO1 expression contributes to facet joint osteoarthritis pathogenesis by impairing chondrocyte migration and extracellular matrix synthesis.

Facet joint osteoarthritis (FJOA), a condition commonly observed in individuals of middle to old age, has been relatively under-researched compared to other subtypes of osteoarthritis (OA). This study investigated the role of transcription factor FoxO1 in FJOA using a Col2a1-creERT knock-in mouse model. It was found that FoxO1 deletion led to severe osteoarthritic changes, indicating that FoxO1 played a critical role in cartilage homeostasis. Transcriptome sequencing was performed on degenerated cartilage from FoxO1-deleted mice. This process identified differentially expressed genes (DEGs), offering insights into the molecular mechanisms underlying FJOA. Bioinformatics analysis, including Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA) and protein-protein interaction (PPI) network analysis, identified Itgb3, Itga1, Itga6, Itga7, Itga8, Itga10, Col1a1, and Il6, as potential key contributors to FJOA after FoxO1 deletion. Importantly, overexpression of Itgb3 and inhibition of Il6 counteracted FoxO1 knockdown-induced impairments in chondrocyte migration and extracellular matrix synthesis, respectively. This study discovered FoxO1 as a key regulator of the pathogenesis of FJOA, helped unravel the complex molecular mechanisms underlying FJOA, and contributed to the development of promising therapeutic avenues toward FJOA.

Tissue Segmentation of Thick-Slice Fetal Brain MR Scans With Guidance From High-Quality Isotropic Volumes.

Accurate tissue segmentation of thick-slice fetal brain magnetic resonance (MR) scans is crucial for both reconstruction of isotropic brain MR volumes and the quantification of fetal brain development. However, this task is challenging due to the use of thick-slice scans in clinically-acquired fetal brain data. To address this issue, we propose to leverage high-quality isotropic fetal brain MR volumes (and also their corresponding annotations) as guidance for segmentation of thick-slice scans. Due to existence of significant domain gap between high-quality isotropic volume (i.e., source data) and thick-slice scans (i.e., target data), we employ a domain adaptation technique to achieve the associated knowledge transfer (from high-quality "source" volumes to thick-slice "target" scans). Specifically, we first register the available high-quality isotropic fetal brain MR volumes across different gestational weeks to construct longitudinally-complete source data. To capture domain-invariant information, we then perform Fourier decomposition to extract image content and style codes. Finally, we propose a novel Cycle-Consistent Domain Adaptation Network (C 2 DA-Net) to efficiently transfer the knowledge learned from high-quality isotropic volumes for accurate tissue segmentation of thick-slice scans. Our C 2 DA-Net can fully utilize a small set of annotated isotropic volumes to guide tissue segmentation on unannotated thick-slice scans. Extensive experiments on a large-scale dataset of 372 clinically acquired thick-slice MR scans demonstrate that our C 2 DA-Net achieves much better performance than cutting-edge methods quantitatively and qualitatively. Our code is publicly available at https://github.com/sj-huang/C2DA-Net.

Regulating Ir-O Covalency to Boost Acidic Oxygen Evolution Reaction.

The unsatisfactory oxygen evolution reaction (OER) activity of IrO2 has intensively raised the cost and energy consumption of hydrogen generation from proton exchange membrane water electrolyzers. Here, the acidic OER activity of the rutile IrO2 is significantly enhanced by the incorporation of trivalent metals (e.g., Gd, Nd, and Pr) to increase the Ir-O covalency, while the high-valence (pentavalent or higher) metal incorporation decreases the Ir-O covalency resulting in worse OER activity. Experimental and theoretical analyses indicate that enhanced Ir-O covalency activates lattice oxygen and triggers lattice oxygen-mediated mechanism to enhance OER kinetics, which is verified by the finding of a linear relationship between the natural logarithm of intrinsic activity and Ir-O covalency described by charge transfer energy. By regulating the Ir-O covalency, the obtained Gd-IrO2-δ merely needs 260 mV of overpotential to reach 10 mA cm-2 and shows impressive stability during a 200-h test in 0.5 м H2 SO4 . This work provides an effective strategy for significantly enhancing the OER activity of the widely used IrO2 electrocatalysts through the rational regulation of Ir-O covalency.

Ammonia-Induced FCC Ru Nanocrystals for Efficient Alkaline Hydrogen Electrocatalysis.

The urgent development of effective electrocatalysts for hydrogen evolution and hydrogen oxidation reaction (HER/HOR) is needed due to the sluggish alkaline hydrogen electrocatalysis. Here, an unusual face-centered cubic (fcc) Ru nanocrystal with favorable HER/HOR performance is offered. Guided by the lower calculated surface energy of fcc Ru than that of hcp Ru in NH3 , the carbon-supported fcc Ru electrocatalyst is facilely synthesized in the NH3 reducing atmosphere. The specific HOR kinetic current density of fcc Ru can reach 23.4 mA cmPGM -2 , which is around 20 and 21 times greater than that of hexagonal close-packed (hcp) Ru and Pt/C, respectively. Additionally, the HER specific activity is enhanced more than six times in fcc Ru electrocatalyst when compared to Pt/C. Experimental and theoretical analysis indicate that the phase transition from hcp Ru to fcc Ru can negatively shift the d band center, weaken the interaction between catalysts and key intermediates and therefore enhances the HER/HOR kinetics.

ASMT determines gut microbiota and increases neurobehavioral adaptability to exercise in female mice.

N-acetylserotonin O-methyltransferase (ASMT) is responsible for melatonin biosynthesis. The Asmt gene is located on the X chromosome, and its genetic polymorphism is associated with depression in humans. However, the underlying mechanism remains unclear. Here, we use CRISPR/Cas9 to delete 20 bp of exon 2 of Asmt, and construct C57BL/6J mouse strain with Asmt frameshift mutation (Asmtft/ft). We show that female Asmtft/ft mice exhibit anxiety- and depression-like behaviors, accompanied by an obvious structural remodeling of gut microbiota. These behavioral abnormalities are not observed in male. Moreover, female Asmtft/ft mice show a lower neurobehavioral adaptability to exercise, while wild-type shows a "higher resilience". Cross-sectional and longitudinal analysis indicates that the structure of gut microbiota in Asmtft/ft mice is less affected by exercise. These results suggests that Asmt maintains the plasticity of gut microbiota in female, thereby enhancing the neurobehavioral adaptability to exercise.