Enhanced Pressure Response of Edge-Deposited Graphene Nanomechanical Resonators.

Nanomechanical resonators made of suspended graphene exhibit high sensitivity to pressure changes. Nevertheless, the graphene resonator pressure performance is affected owing to the gas permeation problem between the graphene film and the substrate. Therefore, we prepared edge-deposited graphene resonators by focused ion beam (FIB) deposition of SiO2, and their gas leakage velocities and pressure-sensing ability were demonstrated. In this paper, we characterize the pressure-sensing response and gas leakage velocities of graphene membranes using an all-optical actuation system. The gas leakage velocities of graphene resonators with diameters of 10, 20, and 40 µm are reduced by 5.0 × 106, 2.0 × 107, and 8.1 × 107 atoms/s, respectively, which demonstrates that the edge deposition structure can reduce the gas leakage of the resonator. Furthermore, the pressure-sensing performance of three graphene resonators with different diameters was evaluated, and their average pressure sensitivities were calculated to be 3.4, 2.4, and 1.9 kHz/kPa, with the largest full-range hysteresis errors of 0.6, 0.7, and 1.0%, respectively. The temperature stabilities of the three sizes of resonators in the temperature range of 300-400 K are 0.016, 0.015, and 0.016%/K, and the maximum resonance frequency drift over 1 h is 0.0058, 0.0048, and 0.0112%, respectively. This work has great significance for the improvement of gas leakage velocity characterization of graphene membrane and graphene resonant pressure sensor performance optimization.

The Discovery, Molecular Cloning, and Characterization of Dextransucrase LmDexA and Its Active Truncated Mutant from Leuconostoc mesenteroides NN710.

Dextransucrases play a crucial role in the production of dextran from economical sucrose; therefore, there is a pressing demand to explore novel dextransucrases with better performance. This study characterized a dextransucrase enzyme, LmDexA, which was identified from the Leuconostoc mesenteroides NN710. This bacterium was isolated from the soil of growing dragon fruit in Guangxi province, China. We successfully constructed six different N-terminal truncated variants through sequential analysis. Additionally, a truncated variant, ΔN190LmDexA, was constructed by removing the 190 amino acids fragment from the N-terminal. This truncated variant was then successfully expressed heterologously in Escherichia coli and purified. The purified ΔN190LmDexA demonstrated optimal hydrolysis activity at a pH of 5.6 and a temperature of 30 °C. Its maximum specific activity was measured to be 126.13 U/mg, with a Km of 13.7 mM. Results demonstrated a significant improvement in the heterologous expression level and total enzyme activity of ΔN190LmDexA. ΔN190LmDexA exhibited both hydrolytic and transsaccharolytic enzymatic activities. When sucrose was used as the substrate, it primarily produced high-molecular-weight dextran (>400 kDa). However, upon the addition of maltose as a receptor, it resulted in the production of a significant amount of oligosaccharides. Our results can provide valuable information for enhancing the characteristics of recombinant dextransucrase and potentially converting sucrose into high-value-added dextran and oligosaccharides.

Green analytical assay for the viability assessment of single maize seeds using double-threshold strategy for catalase activity and malondialdehyde content.

The development of nondestructive technology for the detection of seed viability is challenging. In this study, to establish a green and effective method for the viability assessment of single maize seeds, a two-stage seed viability detection method was proposed. The catalase (CAT) activity and malondialdehyde (MDA) content were selected as the most key biochemical components affecting maize seed viability, and regression prediction models were developed based on their hyperspectral information and a data fusion strategy. Qualitative discrimination models for seed viability evaluation were constructed based on the predicted response values of the selected key biochemical components. The results showed that the double components thresholds strategy achieved the highest discrimination accuracy (92.9%), providing a crucial approach for the rapid and environmentally friendly detection of seed viability.

New advances in protein engineering for industrial applications: Key takeaways.

Recent advancements in protein/enzyme engineering have enabled the production of a diverse array of high-value compounds in microbial systems with the potential for industrial applications. The goal of this review is to articulate some of the most recent protein engineering advances in bacteria, yeast, and other microbial systems to produce valuable substances. These high-value substances include α-farnesene, vitamin B12, fumaric acid, linalool, glucaric acid, carminic acid, mycosporine-like amino acids, patchoulol, orcinol glucoside, d-lactic acid, keratinase, α-glucanotransferases, ß-glucosidase, seleno-methylselenocysteine, fatty acids, high-efficiency ß-glucosidase enzymes, cellulase, ß-carotene, physcion, and glucoamylase. Additionally, recent advances in enzyme engineering for enhancing thermostability will be discussed. These findings have the potential to revolutionize various industries, including biotechnology, food, pharmaceuticals, and biofuels.

Current status and emerging frontiers in enzyme engineering: An industrial perspective.

Protein engineering mechanisms can be an efficient approach to enhance the biochemical properties of various biocatalysts. Immobilization of biocatalysts and the introduction of new-to-nature chemical reactivities are also possible through the same mechanism. Discovering new protocols that enhance the catalytic active protein that possesses novelty in terms of being stable, active, and, stereoselectivity with functions could be identified as essential areas in terms of concurrent bioorganic chemistry (synergistic relationship between organic chemistry and biochemistry in the context of enzyme engineering). However, with our current level of knowledge about protein folding and its correlation with protein conformation and activities, it is almost impossible to design proteins with specific biological and physical properties. Hence, contemporary protein engineering typically involves reprogramming existing enzymes by mutagenesis to generate new phenotypes with desired properties. These processes ensure that limitations of naturally occurring enzymes are not encountered. For example, researchers have engineered cellulases and hemicellulases to withstand harsh conditions encountered during biomass pretreatment, such as high temperatures and acidic environments. By enhancing the activity and robustness of these enzymes, biofuel production becomes more economically viable and environmentally sustainable. Recent trends in enzyme engineering have enabled the development of tailored biocatalysts for pharmaceutical applications. For instance, researchers have engineered enzymes such as cytochrome P450s and amine oxidases to catalyze challenging reactions involved in drug synthesis. In addition to conventional methods, there has been an increasing application of machine learning techniques to identify patterns in data. These patterns are then used to predict protein structures, enhance enzyme solubility, stability, and function, forecast substrate specificity, and assist in rational protein design. In this review, we discussed recent trends in enzyme engineering to optimize the biochemical properties of various biocatalysts. Using examples relevant to biotechnology in engineering enzymes, we try to expatiate the significance of enzyme engineering with how these methods could be applied to optimize the biochemical properties of a naturally occurring enzyme.

Association between sleep duration and serum neurofilament light chain levels among adults in the United States.

Background: Neurofilaments are neuron specific skeleton proteins maintaining axon transduction speed, leaked into cerebrospinal fluid and serum after axonal injury or neuron death. Sleep duration change has long related to many health issues but lack laboratory examination. Methods: This study enrolled total 10,175 participants from 2013 to 2014 National Health and Nutrition Examination Survey and used a multi-variable linear model to analyze the relationship between sleep duration and serum neurofilament light chain (sNfL) level. Results: There was a fixed relationship between sleep duration and sNfL level (ß = 0.65, p = 0.0280). After adjusted for covariates, this relationship still (ß = 0.82, p = 0.0052). Segmented regression showed that the turning point of sleep duration was 7 h 1 h decrease in sleep duration was significantly associated with -1.26 higher sNfL level (95 % CI: 2.25, -0.28; p = 0.0115) when sleep duration <7 h; however, 1 h increase in sleep duration was significantly associated with 3.20 higher sNfL level (95 % CI: 2.13, 4.27; p < 0.0001) when sleep duration >7 h. Furthermore, the stratified analysis indicated that the associations between sleep duration and sNfL level were stronger among those normal body mass index and trouble sleeping (p-interaction <0.0001 and 0.0003). Conclusion: In summary, there was a J-shaped relationship between sleep duration and sNfL level in the United States of America representative group, these may suggest that extreme sleep duration can be deleterious judged by sNfL level. And still need large cohort study to determine the accurate relationship, and cluster analysis to infer the nervous disease connected with extreme sleep duration.

Optogenetic manipulation of lysosomal physiology and autophagy-dependent clearance of amyloid beta.

Lysosomes are degradation centers of cells and intracellular hubs of signal transduction, nutrient sensing, and autophagy regulation. Dysfunction of lysosomes contributes to a variety of diseases, such as lysosomal storage diseases (LSDs) and neurodegeneration, but the mechanisms are not well understood. Altering lysosomal activity and examining its impact on the occurrence and development of disease is an important strategy for studying lysosome-related diseases. However, methods to dynamically regulate lysosomal function in living cells or animals are still lacking. Here, we constructed lysosome-localized optogenetic actuators, named lyso-NpHR3.0, lyso-ArchT, and lyso-ChR2, to achieve optogenetic manipulation of lysosomes. These new actuators enable light-dependent control of lysosomal membrane potential, pH, hydrolase activity, degradation, and Ca2+ dynamics in living cells. Notably, lyso-ChR2 activation induces autophagy through the mTOR pathway, promotes Aß clearance in an autophagy-dependent manner in cellular models, and alleviates Aß-induced paralysis in the Caenorhabditis elegans model of Alzheimer's disease. Our lysosomal optogenetic actuators supplement the optogenetic toolbox and provide a method to dynamically regulate lysosomal physiology and function in living cells and animals.

Life cycle assessment of n-caproic acid production via chain elongation from food waste: Comparison of shunting and staged technology.

n-Caproic acid is a widely used biochemical that can be produced from organic waste through chain elongation technology. This study aims to evaluate the environmental impacts of n-caproic acid production through chain elongation by two processes (i.e., shunting and staged technology). The Open-life cycle assessment (LCA) model was used to calculate the environmental impacts of both technologies based on experimental data. Results showed that the shunting technology had higher environmental impacts than the staged technology. Water and electricity made bigger contribution to the environmental impacts of both technologies. Reusing chain elongation effluent substituting for water and using electricity produced by wind power could reduce the environmental impacts of water and electricity effectively. Using ethanol from food waste had higher global warming potential than fossil ethanol, which suggested that a cradle-to-grave LCA is needed to be carried out for specific raw materials and chain elongation products in the future.

Determination of soluble solids content of multiple varieties of tomatoes by full transmission visible-near infrared spectroscopy.

Introduction: Soluble solids content (SSC) is a pivotal parameter for assessing tomato quality. Traditional measurement methods are both destructive and time-consuming. Methods: To enhance accuracy and efficiency in SSC assessment, this study employs full transmission visible and near-infrared (Vis-NIR) spectroscopy and multi-point spectral data collection techniques to quantitatively analyze SSC in two tomato varieties ('Provence' and 'Jingcai No.8' tomatoes). Preprocessing of the multi-point spectra is carried out using a weighted averaging approach, aimed at noise reduction, signal-to-noise ratio improvement, and overall data quality enhancement. Taking into account the potential influence of various detection orientations and preprocessing methods on model outcomes, we investigate the combination of partial least squares regression (PLSR) with two orientations (O1 and O2) and two preprocessing techniques (Savitzky-Golay smoothing (SG) and Standard Normal Variate transformation (SNV)) in the development of SSC prediction models. Results: The model achieved the best results in the O2 orientation and SNV pretreatment as follows: 'Provence' tomato (Rp = 0.81, RMSEP = 0.69°Brix) and 'Jingcai No.8' tomatoes (Rp = 0.84, RMSEP = 0.64°Brix). To further optimize the model, characteristic wavelength selection is introduced through Least Angle Regression (LARS) with L1 and L2 regularization. Notably, when λ=0.004, LARS-L1 produces superior results ('Provence' tomato: Rp = 0.95, RMSEP = 0.35°Brix; 'Jingcai No.8' tomato: Rp = 0.96, RMSEP = 0.33°Brix). Discussion: This study underscores the effectiveness of full transmission Vis-NIR spectroscopy in predicting SSC in different tomato varieties, offering a viable method for accurate and swift SSC assessment in tomatoes.

Joint Classification of Hyperspectral Images and LiDAR Data Based on Dual-Branch Transformer.

In the face of complex scenarios, the information insufficiency of classification tasks dominated by a single modality has led to a bottleneck in classification performance. The joint application of multimodal remote sensing data for surface observation tasks has garnered widespread attention. However, issues such as sample differences between modalities and the lack of correlation in physical features have limited the performance of classification tasks. Establishing effective interaction between multimodal data has become another significant challenge. To fully integrate heterogeneous information from multiple modalities and enhance classification performance, this paper proposes a dual-branch cross-Transformer feature fusion network aimed at joint land cover classification of hyperspectral imagery (HSI) and Light Detection and Ranging (LiDAR) data. The core idea is to leverage the potential of convolutional operators to represent spatial features, combined with the advantages of the Transformer architecture in learning remote dependencies. The framework employs an improved self-attention mechanism to aggregate features within each modality, highlighting the spectral information of HSI and the spatial (elevation) information of LiDAR. The feature fusion module based on cross-attention integrates deep features from two modalities, achieving complementary information through cross-modal attention. The classification task is performed using jointly obtained spectral and spatial features. Experiments were conducted on three multi-source remote sensing classification datasets, demonstrating the effectiveness of the proposed model compared to existing methods.

A novel pectate lyase with high specific activity from Bacillus sp. B58-2: Gene cloning, heterologous expression and use in ramie degumming.

Pectinase plays a crucial role in ramie degumming. A gene encoding a putative pectate lyase from Bacillus sp. strain B58-2 was cloned and heterologously expressed in Escherichia coli. The amplified gene BvelPL1 encoded a mature protein of 400 amino acids. BvelPL1 shared the highest amino acid sequence identity (78.75%) with the enzymatically characterized pectate lyase Pel from Bacillus subtilis strain RCK (GenBank: AFH66771.1). The purified recombinant enzyme rBvelPL1-Ec exhibited a maximum specific activity of 2433.26 U/mg at pH 8.5 and 50 °C towards polygalacturonic acid. This specific activity was higher than that of most reported pectate lyases. Remarkably, the enzymatic activity of rBvelPL1-Ec increased by 23.28 times in the presence of 0.4 mM calcium ion. The effect of calcium ion on promoting the enzymatic activity of rBvelPL1-Ec was greater than that for all reported pectate lyases. After degumming with rBvelPL1-Ec, a weight loss of 21.27 ± 1.17% of circled ramie fibers was obtained, and the surfaces of the ramie fibers became smoother. Moreover, a weight loss of 30.47 ± 0.46% was obtained through enzymatic treated and subsequent NaOH treated circled ramie fibers. The excellent performance in degumming suggests that rBvelPL1-Ec may serve as a promising biocatalyst in the textile industry.

Lightweight Transmission Line Fault Detection Method Based on Leaner YOLOv7-Tiny.

Aiming to address the issues of parameter complexity and high computational load in existing fault detection algorithms for transmission lines, which hinder their deployment on devices like drones, this study proposes a novel lightweight model called Leaner YOLOv7-Tiny. The primary goal is to swiftly and accurately detect typical faults in transmission lines from aerial images. This algorithm inherits the ELAN structure from YOLOv7-Tiny network and replaces its backbone with depthwise separable convolutions to reduce model parameters. By integrating the SP attention mechanism, it fuses multi-scale information, capturing features across various scales to enhance small target recognition. Finally, an improved FCIoU Loss function is introduced to balance the contribution of high-quality and low-quality samples to the loss function, expediting model convergence and boosting detection accuracy. Experimental results demonstrate a 20% reduction in model size compared to the original YOLOv7-Tiny algorithm. Detection accuracy for small targets surpasses that of current mainstream lightweight object detection algorithms. This approach holds practical significance for transmission line fault detection.

'There is always good fortune in misfortune to encourage us in coping with difficulties'-The lived experiences of family caregivers of people with dementia during the COVID-19 outbreak in China: A phenomenological study.

AIM: To describe the lived experiences of family caregivers of individuals with dementia during the coronavirus disease (COVID-19) outbreak in China. DESIGN: This study used a descriptive phenomenological research method. METHODS: Between May and September 2021, semi-structured interviews were conducted with 22 family caregivers of people with dementia. Colaizzi's method was used for manual analysis. RESULTS: Qualitative data revealed an overarching experience of finding 'There is always good fortune in misfortune to encourage us in coping with difficulties'. Three themes emerged: family reactions to the COVID-19 outbreak, feeling supported by multiple resources performing respective functions and resilient adaptation to new situations. CONCLUSION: During the COVID-19 outbreak, family caregivers of people living with dementia in China looked for positive aspects among difficulties and experienced corresponding reactions, social support resources and resilient adapted coping styles. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Nurses in China and other countries facing similar pandemic characteristics, cultures or economic development levels, can guide family caregivers to look at family hardships from a positive perspective, develop interventions to rapidly respond to families' reactions after a disaster and help them identify social support resources and form adapted coping styles. IMPACT: We identified the resilience and the positive experiences of Chinese family caregivers of individuals with dementia during the COVID-19 outbreak. The results can inform countries with similar cultures and economic levels, offering measures to support their adaptation to pandemics. REPORTING METHOD: This study followed the COREQ guidelines. PATIENT OR PUBLIC CONTRIBUTION: Family caregivers of people with dementia who met the inclusion criteria and who were interested in sharing their understanding of their experiences, participated in the study.

Cross-domain fault diagnosis method for rolling bearings based on contrastive universal domain adaptation.

To address the unknown spatial relationship between source and target domain labels, which leads to poor fault diagnosis accuracy, a contrastive universal domain adaptation model and rolling bearing fault diagnosis approach are proposed. The approach introduces bootstrap your own latent network to mine the data-specific structure of the target domain and proposes rejecting unknown class samples using an entropy separation strategy. Simultaneously, a source class weighting mechanism is designed to improve the transferable semantics augmentation method by assigning various class-level weights to source categories, which improves the alignment of the feature distributions in the shared label space to further construct fault diagnosis models. Experimental validation on two rolling bearing datasets confirmed the superior fault diagnosis accuracy of the proposed method under diverse working conditions.

A novel bi-functional cold-adaptive chitinase from Chitinilyticum aquatile CSC-1 for efficient synthesis of N-acetyl-D-glucosaminidase.

In order to better utilize chitinolytic enzymes to produce high-value N-acetyl-D-glucosamine (GlcNAc) from chitinous waste, there is an urgent need to explore bi-functional chitinases with exceptional properties of temperature, pH and metal tolerance. In this study, we cloned and characterized a novel bi-functional cold-adaptive chitinase called CaChi18A from a newly isolated strain, Chitinilyticum aquatile CSC-1, in Bama longevity village of Guangxi Province, China. The activity of CaChi18A at 50 °C was 4.07 U/mg. However, it exhibited significant catalytic activity even at 5 °C. Its truncated variant CaChi18A_ΔChBDs, containing only catalytic domain, demonstrated significant activity levels, exceeding 40 %, over a temperature range of 5-60 °C and a pH range of 3 to 10. It was noteworthy that it displayed tolerance towards most metal ions at a final concentration of 0.1 mM, including Fe3+ and Cu2+ ions, retaining 122.52 ± 0.17 % and 116.42 ± 1.52 % activity, respectively. Additionally, it exhibited favorable tolerance towards organic solvents with the exception of formic acid. Interestedly, CaChi18A and CaChi18A_ΔChBDs had a low Km value towards colloidal chitin (CC), 0.94 mg mL-1 and 2.13 mg mL-1, respectively. Both enzymes exhibited chitobiosidase and N-acetyl-D-glucosaminidase activities, producing GlcNAc as the primary product when hydrolyzing CC. The high activities across a broader temperature and pH range, strong environmental adaptability, and hydrolytic properties of CaChi18A_ΔChBDs suggested that it could be a promising candidate for GlcNAc production.

Pseudorabies virus causes splenic injury via inducing oxidative stress and apoptosis related factors in mice.

Pseudorabies virus (PRV) is an immunosuppressive virus that causes significant damage to the pig industry. This study aimed to investigate the effects of PRV on oxidative stress and apoptotic related in the spleen of mice to provide basis knowledge for further research on the pathogenesis of PRV in mice model. 36 mice were randomly two groups, the control group which only received 200 µL PBS and infection group which was subcutaneously infected with 200 µL of 1 × 103 TCID50/100 µL PRV, respectively. Spleen tissues in each group were collected for further experiments at 48, 72, and 96 h post-infection (hpi). Pathological observation was performed by hematoxylin and eosin Y staining. Biochemical and Flow cytometry methods were used to determine the reactive oxygen species profile and apoptosis of the spleen post-infection and apoptosis detection. In addition, q-PCR and Western blot were adopted to measure the apoptotic conditions of the spleen infected with PRV. The results indicated that the reactive oxygen species (ROS) level in the PRV infection group was remarkedly increased (p < 0.01) at a time-dependent pattern. Furthermore, the Malondialdehyde levels in the spleen of mice in the infection group increased (p < 0.01) in a time-dependent mode. However, the activity of Catalase, Superoxide dismutase, and glutathione peroxidase and the content of Glutathione in the infection group were decreased with the control group (p < 0.01) at a time-dependent manner. In addition, the ratio of splenocyte apoptosis in the infection group significantly increased (p < 0.01) in a time-dependent manner. In conclusion, PRV infection causes apoptosis of the spleen via oxidative stress in mice.

Non-destructive detection of single-seed viability in maize using hyperspectral imaging technology and multi-scale 3D convolutional neural network.

The viability of Zea mays seed plays a critical role in determining the yield of corn. Therefore, developing a fast and non-destructive method is essential for rapid and large-scale seed viability detection and is of great significance for agriculture, breeding, and germplasm preservation. In this study, hyperspectral imaging (HSI) technology was used to obtain images and spectral information of maize seeds with different aging stages. To reduce data input and improve model detection speed while obtaining more stable prediction results, successive projections algorithm (SPA) was used to extract key wavelengths that characterize seed viability, then key wavelength images of maize seed were divided into small blocks with 5 pixels ×5 pixels and fed into a multi-scale 3D convolutional neural network (3DCNN) for further optimizing the discrimination possibility of single-seed viability. The final discriminant result of single-seed viability was determined by comprehensively evaluating the result of all small blocks belonging to the same seed with the voting algorithm. The results showed that the multi-scale 3DCNN model achieved an accuracy of 90.67% for the discrimination of single-seed viability on the test set. Furthermore, an effort to reduce labor and avoid the misclassification caused by human subjective factors, a YOLOv7 model and a Mask R-CNN model were constructed respectively for germination judgment and bud length detection in this study, the result showed that mean average precision (mAP) of YOLOv7 model could reach 99.7%, and the determination coefficient of Mask R-CNN model was 0.98. Overall, this study provided a feasible solution for detecting maize seed viability using HSI technology and multi-scale 3DCNN, which was crucial for large-scale screening of viable seeds. This study provided theoretical support for improving planting quality and crop yield.

In vivo self-assembly and delivery of VEGFR2 siRNA-encapsulated small extracellular vesicles for lung metastatic osteosarcoma therapy.

The prognosis of lung metastatic osteosarcoma (OS) remains disappointing. siRNA-based gene silencing of VEGFR2 is a promising treatment strategy for lung metastatic OS, but there is a lack of safe and efficient delivery systems to encapsulate siRNAs for in vivo administration. This study presented a synthetic biological strategy that remolds the host liver with synthesized genetic circuits for efficient in vivo VEGFR2 siRNA delivery. After being taken-up by hepatocytes, the genetic circuit (in the form of a DNA plasmid) reprogrammed the liver to drive the autonomous intrahepatic assembly and encapsulation of VEGFR2 siRNAs into secretory small extracellular vesicles (sEVs), thus allowing for the transport of self-assembled VEGFR2 siRNAs towards the lung. The results showed that our strategy was superior to the positive medicine (Apatinib) for OS lung metastasis in terms of therapeutic efficacy and toxic adverse effects and may provide a feasible and viable therapeutic solution for lung metastatic OS.

Advances in understanding entire process of medium chain carboxylic acid production from organic wastes via chain elongation.

Chain elongation is an environmentally friendly biological technology capable of converting organic wastes into medium chain carboxylic acids (MCCAs). This review aims to offer a comprehensive analysis of MCCA production from organic wastes via chain elongation. Seven kinds of organic wastes are introduced and classified as easily degradable and hardly degradable. Among them, food waste, fruit and vegetable waste are the most potential organic wastes for MCCA production. Combined pretreatment technologies should be encouraged for the pretreatment of hardly degradable organic wastes. Furthermore, the mechanisms during MCCA production are analyzed, and the key influencing factors are evaluated, which affect the MCCA production and chain elongation efficiency indirectly. Extracting MCCA simultaneously is the most important way to improve MCCA production efficiency, and technologies for sequentially extracting different kinds of MCCAs are recommended. Finally, some perspectives for future chain elongation researches are proposed to promote the large-scale application of chain elongation.

The NMR studies of CMP inhibition of polysialylation.

The overexpression of polysialic acid (polySia) on neural cell adhesion molecules (NCAM) promotes hypersialylation, and thus benefits cancer cell migration and invasion. It has been proposed that the binding between the polysialyltransferase domain (PSTD) and CMP-Sia needs to be inhibited in order to block the effects of hypersialylation. In this study, CMP was confirmed to be a competitive inhibitor of polysialyltransferases (polySTs) in the presence of CMP-Sia and triSia (oligosialic acid trimer) based on the interactional features between molecules. The further NMR analysis suggested that polysialylation could be partially inhibited when CMP-Sia and polySia co-exist in solution. In addition, an unexpecting finding is that CMP-Sia plays a role in reducing the gathering extent of polySia chains on the PSTD, and may benefit for the inhibition of polysialylation. The findings in this study may provide new insight into the optimal design of the drug and inhibitor for cancer treatment.