Evidence map›Paper›PMID 35283706›Full record

ArticleSaudi journal of medicine & medical sciences

NGAL/MMP-9 as a Biomarker for Epithelial Ovarian Cancer: A Case-Control Diagnostic Accuracy Study.

Rohit Kumar Gupta, Sagar Dholariya, Madhuri Radadiya, Pragati Agarwal

Open access · diamondAbstract read
In one paragraph

Article in Saudi journal of medicine & medical sciences. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.5field-weighted citation impact, top 45% 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

5 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Bioinformatic analysis of theFrontiers in cell and developmental biology · 2022
    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 at 2 institutions in 1 country.

Rohit Kumar GuptaDepartment of Biochemistry, G.S. Medical College, Pikhuwa, India.
Sagar DholariyaDepartment of Biochemistry, All India Institutes of Medical Sciences, Rajkot, Gujarat, India.
Madhuri RadadiyaDepartment of Radiology, Pandit Dindayal Upadhyay Medical College, Rajkot, Gujarat, India.
Pragati AgarwalDepartment of Electronics and Communication, IFTM University, Moradabad, Uttar Pradesh, India.
All India Institute of Medical Sciences · INIFTM University · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Epithelial ovarian cancer (EOC) are often diagnosed late due to lack of specific symptoms and efficient tumor markers. Neutrophil gelatinase-associated lipocalin/matrix metallopeptidase-9 (NGAL/MMP-9) complex are involved in the development and progression of various cancers and have potential as a biomarker for diagnosing ovarian cancer. Objectives: To compare the serum NGAL/MMP-9 complex levels in patients with EOC, benign ovarian tumor, and healthy controls, and determine the potential cut-off values of NGAL/MMP-9 complex for diagnosing EOC. Materials and Methods: The study included 50 patients each with EOC and benign ovarian tumor, along with 50 age-matched healthy controls ( Results: A significant difference was found in the median values of the NGAL/MMP-9 complex (malignant EOC: 67.5 ng/ml, benign ovarian tumor: 53.7 ng/ml, controls: 29.2 ng/ml; Conclusion: The NGAL/MMP-9 complex may be a promising biomarker for determining the progression of EOC as well as in detecting advanced-stage ovarian cancer.

Indexed as

Biomarkerdiagnosisepithelial ovarian cancerFIGO stagingmatrix metalloproteinaseneutrophil gelatinase-associated lipocalin

Identifiers

PMID35283706
PMCPMC8869267
OpenAlexW4206525649

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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.