Evidence map›Paper›PMID 38572418›Full record

ReviewFrontiers in genetics2024

Gene variants polymorphisms and uterine leiomyoma: an updated review.

Sonal Upadhyay, Pawan K Dubey

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
8.7field-weighted citation impact, top 2% of its field
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

8 citing papers in PubMed, 15 citations in OpenAlex.

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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

2 authors at 1 institution in 1 country.

Sonal UpadhyayCentre for Genetic Disorders, Institute of Science, Banaras Hindu University, Varanasi, Uttar Pradesh, India.
Pawan K DubeyCentre for Genetic Disorders, Institute of Science, Banaras Hindu University, Varanasi, Uttar Pradesh, India.
Banaras Hindu University · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Uterine leiomyoma, commonly referred to as fibroids, is a benign tumor that develops in the muscular wall of the uterus. These growths are non-cancerous and can vary in size, ranging from tiny nodules to larger masses. Uterine leiomyomas often occur during a woman's reproductive years and can lead to symptoms such as heavy menstrual bleeding, pelvic pain, and pressure on nearby organs. While the exact cause is not fully understood, hormonal factors, particularly estrogen and progesterone, are believed to play a role in their development. The exploration of connections between genetic variants and uterine leiomyoma has captivated scientific attention for numerous years. The results from investigations remain a subject of intrigue within the scientific community. To date, the findings regarding the relationships between single nucleotide polymorphisms (SNPs) and uterine leiomyoma have exhibited some inconsistencies. However, amidst these inconsistencies, several promising outcomes have emerged that hold the potential to shape future research endeavors. These promising leads could pave the way for the development of innovative targeted therapies and novel prognostic biomarkers. This review specifically centers on accentuating the existing literature data concerning genetic variants that have been explored for their potential connections to uterine leiomyoma. Additionally, it underscores the prospects of employing genetic variations as diagnostic and prognostic biomarkers for individuals diagnosed with uterine leiomyoma.

Indexed as

biomarkerfibroidgenetic variantssingle nucleotide polymorphismUterine leiomyoma

Identifiers

PMID38572418
PMCPMC10987786
OpenAlexW4393012919

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.