Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
3'-Sialyllactose is a major sialylated human milk oligosaccharide that plays an important role in promoting infant development and supporting various physiological functions critical for early life. In this study, -acetylneuraminic acid and 3'-sialyllactose were successfully synthesized utilizing four genes across three modules through a whole-cell catalysis. Among these, Module III was employed to facilitate the conversion of cytidine-5'-monophosphate to cytidine 5'-triphosphate utilizing the intrinsic enzyme system of . This study presents the inaugural report demonstrating the synergistic synthesis of 3'-sialyllactose through three modules derived from and , utilizing pyruvate, -acetylglucosamine, lactose, and cytidine-5'-monophosphate as substrates. The induction and the whole-cell catalytic substrate dosage of the engineered JM109(DE3) were optimized, leading to a 3'-sialyllactose titer of 71.62 g/L. Ultimately, the strain underwent culture and whole-cell catalysis in a 5 L bioreactor, achieving a maximum titer of 78.03 g/L for 3'-sialyllactose.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1021/acs.jafc.5c04764 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
ResDeepGS: A deep learning-based method for crop phenotype prediction.
Methods
September 2025
School of Computer and Information Engineering, Henan University, Kaifeng, Henan, China; Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, Henan, China. Electronic address:
Genomic selection (GS) is a breeding technique that utilizes genomic markers to predict the genetic potential of crops and animals. This approach holds significant promise for accelerating the improvement of agronomic traits and addressing food security challenges. While traditional breeding methods based on statistical or machine learning techniques have been useful in predicting traits for some crops, they often fail to capture the complex interactions between genotypes and phenotypes.
View Article and Find Full Text PDFA 3D multi-task network for the automatic segmentation of CT images featuring hip osteoarthritis.
Biomed Phys Eng Express
September 2025
Southwest Jiaotong University School of Mechanical Engineering, No. 111, North Section 1, Second Ring Road, Jinniu District, Chengdu, Chengdu, Sichuan, 610031, CHINA.
Total hip arthroplasty (THA) is the standard surgical treatment for end-stage hip osteoarthritis, with its success dependent on precise preoperative planning, which, in turn, relies on accurate three-dimensional segmentation and reconstruction of the periarticular bone of the hip joint. However, patients with hip osteoarthritis often exhibit pathological characteristics, such as joint space narrowing, femoroacetabular impingement, osteophyte formation, and joint deformity. These changes present significant challenges for traditional manual or semi-automatic segmentation methods.
View Article and Find Full Text PDFThe WRF-NAQPMS (Weather Research and Forecasting- Nested Air Quality Prediction Modeling System) model are used to simulate radioactive fallout of nuclear explosions from the stabilized nuclear cloud. The distribution of particle radioactivity and the radioactive distribution are described by modeling the stabilized nuclear cloud. In addition, a three-dimensional radiation calculation module and a radionuclide calculation module are constructed based on the vertical diffusion theory of radioactive materials and the radionuclide fractionation model.
View Article and Find Full Text PDF3D-CNN Enhanced Multiscale Progressive Vision Transformer for AD Diagnosis.
IEEE J Biomed Health Inform
September 2025
Vision Transformer (ViT) applied to structural magnetic resonance images has demonstrated success in the diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, three key challenges have yet to be well addressed: 1) ViT requires a large labeled dataset to mitigate overfitting while most of the current AD-related sMRI data fall short in the sample sizes. 2) ViT neglects the within-patch feature learning, e.
View Article and Find Full Text PDFscGraphDap: Integrating Functional State Pseudo-labels and Graph Structure Learning for Robust Cell Type Annotation in Tumor Microenvironments.
IEEE J Biomed Health Inform
September 2025
The tumor microenvironment is a dynamic eco system where cellular interactions drive cancer progression. However, inferring cell-cell communication from non-spatial scRNA-seq data remains challenging due to incomplete li gand-receptor databases and noisy cell type annotations. H ere, we propose scGraphDap, a graph neural network frame work that integrates functional state pseudo-labels and graph structure learning to improve both cell type annotation an d CCC inference.
View Article and Find Full Text PDF