SynthesisFrontiers in immunology2022
Carbohydrates Metabolic Signatures in Immune Cells: Response to Infection.
Synthesis in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 15 citations in OpenAlex.
- Trained Immunity in Host Defense: Mechanisms, Pathogen Interactions, and Therapeutic Potential.Pathogens (Basel, Switzerland) · 2026Review
- Review
- Essential redundancies fuel Mycobacterium tuberculosis adaptation to the host.PLoS pathogens · 2025Review
- High Glucose Reduces Influenza and Parainfluenza Virus Productivity by Altering Glycolytic Pattern in A549 Cells.International journal of molecular sciences · 2025Article
- Low temperature increases adenovirus replication via intracellular alkalization.Frontiers in cellular and infection microbiology · 2025Article
- High Glucose Increases Lactate and Induces the Transforming Growth Factor Beta-Smad 1/5 Atherogenic Pathway in Primary Human Macrophages.Biomedicines · 2024Article
- Review
- Role of Glycolysis and Fatty Acid Synthesis in the Activation and T Cell-Modulating Potential of Dendritic Cells Stimulated with a TLR5-Ligand Allergen Fusion Protein.International journal of molecular sciences · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Metabolic reprogramming in immune cells is diverse and distinctive in terms of complexity and flexibility in response to heterogeneous pathogenic stimuli. We studied the carbohydrate metabolic changes in immune cells in different types of infectious diseases. This could help build reasonable strategies when understanding the diagnostics, prognostics, and biological relevance of immune cells under alternative metabolic burdens. Methods: Search and analysis were conducted on published peer-reviewed papers on immune cell metabolism of a single pathogen infection from the four known types (bacteria, fungi, parasites, and viruses). Out of the 131 selected papers based on the PIC algorithm (pathogen type/immune cell/carbohydrate metabolism), 30 explored immune cell metabolic changes in well-studied bacterial infections, 17 were on fungal infections of known medical importance, and 12 and 57 were on parasitic and viral infections, respectively. Results and Discussion: While carbohydrate metabolism in immune cells is signaled by glycolytic shift during a bacterial or viral infection, it is widely evident that effector surface proteins are expressed on the surface of parasites and fungi to modulate metabolism in these cells. Conclusions: Carbohydrate metabolism in immune cells can be categorized according to the pathogen or the disease type. Accordingly, this classification can be used to adopt new strategies in disease diagnosis and treatment.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.