Ning Wu Source Confirmed

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

High Impact

Researcher

University of Arkansas at Little Rock

faculty

23 h-index 100 pubs 2,916 cited

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Biography and Research Information

OverviewAI-generated summary

Ning Wu's research group investigates the intersection of metabolism, inflammation, and physiological responses. Recent work has examined the effects of dietary carbohydrate intake on mitochondrial integrity, specifically within brown adipose tissue. This research also explores the roles of key proteins like TXNIP and GLUT1 in cellular processes, including their dependence on PI(4,5)P2. Further studies have delved into the response of brown adipose tissue to cold stress, focusing on the involvement of PPARγ and C/EBPα.

Beyond metabolic studies, Wu's group also applies computational methods to health-related data. This includes the use of AI-powered topic modeling, such as LDA and BERTopic, to analyze large datasets for identifying patterns related to health risks. Additionally, their research has explored the relationship between anthropometric indicators associated with obesity and cognitive function in older adults. Another area of investigation involves the potential therapeutic effects of compounds like cannabidiol in preclinical models, examining its impact on behaviors and memory associated with conditions such as post-traumatic stress disorder, and its mechanisms related to neuroinflammation and specific receptor actions.

Wu has an h-index of 23 and has authored over 100 publications, with total citations exceeding 2,900. This body of work has led to designations as a highly cited researcher. Key collaborators include researchers from the National Center for Toxicological Research, including Paul Rogers, Wen Zou, Weida Tong, and Weigong Ge, with whom Wu has co-authored publications.

Metrics

  • h-index: 23
  • Publications: 100
  • Citations: 2,916

Selected Publications

  • Echo Chamber Dynamics in LLMs: Mitigating Bias and Model Drift (2026) DOI
  • AI-powered topic modeling: comparing LDA and BERTopic in analyzing opioid-related cardiovascular risks in women (2025) DOI

Collaborators

Researchers in the database who share publications

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