Evidence map›Paper›PMID 39035559›Full record

ReviewThe Saudi dental journal2024

Histogical changes in oral mucosa (gingiva) as a method for estimating post-mortem interval: A literature review.

Roben Suhadi Pasaribu, Elza Ibrahim Auerkari, Antonius Winoto Suhartono

Abstract readReview
In one paragraph

Review in The Saudi dental journal, 2024. 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
–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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. RedefiningFrontiers in microbiology · 2025
    Review
  5. 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

3 authors.

Roben Suhadi PasaribuDivision of Forensic Odontology, Department of Oral Biology, Faculty of Dentistry, University of Indonesia, Jakarta, Indonesia.
Elza Ibrahim AuerkariDivision of Forensic Odontology, Department of Oral Biology, Faculty of Dentistry, University of Indonesia, Jakarta, Indonesia.
Antonius Winoto SuhartonoDivision of Forensic Odontology, Department of Oral Biology, Faculty of Dentistry, University of Indonesia, Jakarta, Indonesia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Estimating the post-mortem interval (PMI) is an essential step in forensic investigations, particularly those involving homicides and unwitnessed deaths. However, traditional methods occasionally yield inconsistent estimates. Histological and molecular techniques are considered crucial in forensic pathology and are frequently employed to estimate the time interval of death. The gingiva is an oral mucosal tissue used to estimate PMI. This review aimed to examine the potential of histological methods to determine PMI using oral mucosal tissue, namely the gingiva, and to investigate changes that occur in oral mucosal tissue at different time intervals when compared with those in normal tissues. The oral mucosa comprises layers of stratified squamous epithelium and connective tissue. Similar to other body tissues, changes are known to occur in the gingiva after death, and these cellular and tissue changes should also be considered. Alterations in the gingiva include homogenisation, karyorrhexis, pyknosis, karyolysis, chromatin clumping, eosinophilia, collagen fibre degradation, and the loss of tissue architecture. Reviews collating the results of original trials have consistently reported how the oral mucosa is altered by autolysis and how such changes can be observed in histological tissue morphology after death. Histology is an acceptably accurate technique for estimating PMIs.

Indexed as

ForensicGingivaHistologyOral mucosaPost-mortem interval

Identifiers

PMID39035559
PMCPMC11255919

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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.