AIMC Topic: Metabolic Reprogramming

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Metabolic reprogramming and machine learning-guided cofactor engineering to boost nicotinamide mononucleotide production in Escherichia coli.

Bioresource technology Mar 5, 2025
Nicotinamide mononucleotide (NMN) is a bioactive compound in NAD(P) metabolism, which exhibits diverse pharmaceutical interests. However, enhancing NMN biosynthesis faces the challange of competing with cell growth and disturbing intracellular redox ...
Machine Learning Escherichia coli Metabolic Reprogramming Oxidation-Reduction Fermentation Quorum Sensing Metabolic Engineering NADP Nicotinamide Mononucleotide
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Metabolic reprogramming and macrophage expansion define ACPA-negative rheumatoid arthritis: insights from single-cell RNA sequencing.

Frontiers in immunology Jan 3, 2025
BACKGROUND: Anti-citrullinated peptide antibodies (ACPA)-negative (ACPA-) rheumatoid arthritis (RA) presents significant diagnostic and therapeutic challenges due to the absence of specific biomarkers, underscoring the need to elucidate its distincti...
Humans Macrophages Biomarkers Female Aged Adult Male Middle Aged Leukocytes, Mononuclear Single-Cell Analysis Gene Regulatory Networks Arthritis, Rheumatoid Sequence Analysis, RNA Synovial Membrane Anti-Citrullinated Protein Antibodies Cellular Reprogramming Metabolic Reprogramming
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Integrating automated machine learning and metabolic reprogramming for the identification of microplastic in soil: A case study on soybean.

Journal of hazardous materials Aug 22, 2024
The accumulation of polyethylene microplastic (PE-MPs) in soil can significantly impact plant quality and yield, as well as affect human health and food chain cycles. Therefore, developing rapid and effective detection methods is crucial. In this stu...
Machine Learning Soil Environmental Monitoring Metabolic Reprogramming Soil Pollutants Microplastics Herbicides Polyethylene Glycine max
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Enhancing breast cancer outcomes with machine learning-driven glutamine metabolic reprogramming signature.

Frontiers in immunology May 1, 2024
BACKGROUND: This study aims to identify precise biomarkers for breast cancer to improve patient outcomes, addressing the limitations of traditional staging in predicting treatment responses.
Humans Machine Learning Prognosis Female Breast Neoplasms Biomarkers, Tumor Middle Aged Transcriptome Gene Expression Regulation, Neoplastic Metabolic Reprogramming Glutamine
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