Unraveling Diabetes Mechanisms: A Computational scRNA-Seq Approach to Inflammation and Diabetes.

Katerina Kondaki, Konstantinos Lazaros, Aristidis G Vrahatis

Abstract read

In one paragraph

Article in Advances in experimental medicine and biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

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0citing papers in PubMed

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1 · What the graph read from it

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.

2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Katerina KondakiBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.

Konstantinos LazarosBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.

Aristidis G VrahatisBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diabetes, a widespread metabolic disorder, is characterized by chronic hyperglycemia and is often linked to systemic inflammation and insulin resistance. As the global incidence of diabetes continues to rise, understanding the molecular mechanisms behind the disease is crucial for developing effective treatments. In this study, single-cell RNA sequencing (scRNA-seq) data from a scRNA-seq dataset, GSE161872, were analyzed to explore the relationship between nutrition, inflammation, and diabetes. By employing advanced computational methods, this research aims to identify potential key gene expression profiles and molecular pathways associated with inflammation and its impact on metabolic regulation. The analysis can reveal how different dietary conditions influence immune responses and contribute to the development of diabetes. These findings have the potential to provide new insights into the molecular links between diet, inflammation, and metabolic disorders, offering a foundation for potential therapeutic interventions and personalized dietary strategies.

Indexed as

Diabetes MellitusInflammationRNA-SeqSingle-Cell Gene Expression AnalysisComputational BiologyGene Expression ProfilingGene Expression RegulationHumansInsulin ResistanceTranscriptomeData integrationDiabetesDifferentially expressed genesEnrichment analysisMolecular pathwaysscRNA-seq

Identifiers

PMID41273592

DOI10.1016/j.pupt.2012.07.006

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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.