Evidence map›Paper›PMID 40034257›Full record

ArticleBiochemistry and biophysics reports2025

Identification and assessment of hub genes and miRNAs coregulatory associated with immune infiltrations and drug interactions in latent tuberculosis based on MicroarrayData analysis, molecular docking, and dynamic simulation.

PhongSon Dinh, ChauMyThanh Tran, ThiPhuongHoai Dinh, Hai-Anh Ha, Aigul Utegenova, Awais Ali, Abdulaziz Alamri

Abstract read
In one paragraph

Article in Biochemistry and biophysics reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
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

7 authors.

PhongSon DinhCollege of Medicine and Pharmacy, Duy Tan University, Danang, 550000, Viet Nam.
ChauMyThanh TranCollege of Medicine and Pharmacy, Duy Tan University, Danang, 550000, Viet Nam.
ThiPhuongHoai DinhDepartment of Neurosurgery, Hue University Hospital, Hue University of Medicine and Pharmacy, Hue University, Hue, 530000, Viet Nam.
Hai-Anh HaCollege of Medicine and Pharmacy, Duy Tan University, Danang, 550000, Viet Nam.
Aigul UtegenovaDepartment of Microbiology and Virology, Astana Medical University, Astana, 010000, Kazakhstan.
Awais AliDepartment of Biochemistry, Abdul Wali Khan University Mardan, Mardan, 2300, Pakistan.
Abdulaziz AlamriDepartment of Biochemistry, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tuberculosis (TB) remains a major global health concern, with the transition from latent to active TB still poorly understood. Therefore, enhancing clinical management and prevention strategies for TB is essential. High-throughput sequencing data of genes and miRNAs from individuals at different TB stages were obtained from NCBI. Differential expression analysis was performed using the R package limma, alongside GO and KEGG analyses. The central regulatory network of miRNAs was visualized with Cytoscape, and relevant genes were validated using ROC analysis. The predicted key genes involved in the transition from latent to active TB, including PLEKHG1, CLPB, DOK4, IL1β, and TLR3, are primarily associated with multicellular organism processes, stimulus-response, GPCR ligand binding, and immune functions. Finally, we screened Celastrol and Cefaclor Anhydrous targeting IL1β as potent anti-inflammatory drug to reduce the inflammation due to TB. These findings were further validated with Molecular dynamic simulation MM-GBSA and PCA analysis. Our study advances the understanding of latent tuberculosis and identifies genes and microRNAs as potential biomarkers for diagnosis, monitoring, and treatment, with broader implications for complex disease research.

Indexed as

BioinformaticsBiomarkersLatent tuberculosisMM-GBSAMolecular dynamic simulation

Identifiers

PMID40034257
PMCPMC11875834

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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.