Evidence map›Paper›PMID 39702704›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2025

Non-coding RNA Networks in Infection.

Harshavardhan Janga, Nils Schmerer, Marina Aznaourova, Leon N Schulte

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

4 authors.

Harshavardhan JangaInstitute for Lung Research, Philipps University, Marburg, Germany.
Nils SchmererInstitute for Lung Research, Philipps University, Marburg, Germany.
Marina AznaourovaInstitute for Lung Research, Philipps University, Marburg, Germany.
Leon N SchulteInstitute for Lung Research, Philipps University, Marburg, Germany. leon.schulte@uni-marburg.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the face of global health challenges posed by infectious diseases and the emergence of drug-resistant pathogens, the exploration of cellular non-coding RNA (ncRNA) networks has unveiled new dimensions in infection research. Particularly microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) have emerged as instrumental players in ensuring a balance between protection against hyper-inflammatory conditions and the effective elimination of pathogens. Specifically, ncRNAs, such as the miRNA miR-155 or the lncRNAs MaIL1 (macrophage interferon-regulatory lncRNA 1), and LUCAT1 (lung cancer-associated transcript 1) have been recurrently linked to infectious and inflammatory diseases. Together with other ncRNAs, discussed in this chapter, they form a complex regulatory network shaping the host response to pathogens. Additionally, some pathogens exploit these ncRNAs to establish and sustain infections, underscoring their dual roles in host protection and colonization. Despite the substantial progress made, the vast majority of ncRNA loci remains unexplored, with ongoing research likely to reveal novel ncRNA categories and expand our understanding of their roles in infections. This chapter consolidates current insights into ncRNA-mediated regulatory networks, highlighting their contributions to severe diseases and their potential as targets and biomarkers for innovative therapeutic strategies.

Indexed as

Gene Regulatory NetworksInfectionsMicroRNAsRNA, Long NoncodingAnimalsBiomarkersGene Expression RegulationHost-Pathogen InteractionsHumansBiomarkersMicroRNAsRNA, Long NoncodingBacteriaImmune-responseImmunityInfectionInflammationlncRNAManipulationmiRNANon-coding RNAPathogensViruses

Identifiers

PMID39702704

What OpenQuestion holds

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Registered trials

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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.