Evidence map›Paper›PMID 35401780›Full record

ArticleComputational and mathematical methods in medicine2022

Bioinformatic Characterization of Whole Blood Neutrophils in Pelvic Inflammatory Disease: A Potential Prognostic Indicator for Transumbilical Single-Port Laparoscopic Pelvic Abscess Surgery.

Haining Li, Yanling Hu, Dan Liu, Juanjuan Wang, Panpan Han, Nan Zhang, Yan Li

RetractedOpen access · hybridAbstract readRetracted Publication
In one paragraph

Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 44% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Haining LiGeneral Hospital of Ningxia Medical University, China.
Yanling HuNingxia Medical University, China.
Dan LiuGeneral Hospital of Ningxia Medical University, China.
Juanjuan WangNingxia Medical University, China.
Panpan HanNingxia Medical University, China.
Nan ZhangNingxia Medical University, China.
Yan LiGeneral Hospital of Ningxia Medical University, China.ORCID https://orcid.org/0000-0002-7737-0718
Ningxia Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The purpose of this research is to determine the prognosis of patients treated with transumbilical single-port laparoscopic surgery for acute pelvic inflammatory illness. Postoperative data on 129 patients treated with laparoscopic surgery for acute pelvic inflammatory illness were obtained retrospectively. It was observed that the shorter the time required for postoperative leukocyte recovery to normal, the shorter the time required for postoperative pain and diet recovery, as well as hospital stay, in such individuals. CIBERSORT was used to examine patient data from GEO. The most significant difference between the normal and pelvic inflammatory groups was in neutrophil content. Association study found a substantial positive correlation between the quantity of neutrophils infiltrating the immune system and the abundance of monocyte M0 infiltrating the immune system. Neutrophil immune infiltration was strongly inversely linked with plasma cells, activated CD8+ Tm cells, and active CD4+ Tm cells. Four mRNAs linked with pelvic inflammatory illness were revealed to be strongly associated with neutrophil immune infiltration, notably

Indexed as

LaparoscopyPelvic Inflammatory DiseaseAbscessComputational BiologyHumansLength of StayNeutrophilsPrognosisRetrospective Studies

Identifiers

PMID35401780
PMCPMC8993565
OpenAlexW4220810502

What Socratic 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 Socratic graph.