Evidence map›Paper›PMID 35942315›Full record

ArticleFrontiers in microbiology2022

Bacterial Succession in Microbial Biofilm as a Potential Indicator for Postmortem Submersion Interval Estimation.

Finkelbergs Dmitrijs, Juanjuan Guo, Yecao Huang, Yafei Liu, Xinyue Fang, Kankan Jiang, Lagabaiyila Zha, Jifeng Cai, Xiaoliang Fu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
2.2field-weighted citation impact, top 14% 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. 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

9 authors at 3 institutions in 1 country.

Finkelbergs DmitrijsDepartment of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha, China.
Juanjuan GuoDepartment of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha, China.
Yecao HuangDepartment of Forensic Medicine, School of Basic Medical Sciences and Forensic Medicine, Hangzhou Medical College, Hangzhou, China.
Yafei LiuDepartment of Forensic Medicine, School of Basic Medical Sciences and Forensic Medicine, Hangzhou Medical College, Hangzhou, China.
Xinyue FangDepartment of Forensic Medicine, School of Basic Medical Sciences and Forensic Medicine, Hangzhou Medical College, Hangzhou, China.
Kankan JiangDepartment of Forensic Medicine, School of Basic Medical Sciences and Forensic Medicine, Hangzhou Medical College, Hangzhou, China.
Lagabaiyila ZhaDepartment of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha, China.
Jifeng CaiDepartment of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha, China.
Xiaoliang FuDepartment of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha, China.
Hangzhou Medical College · CNCentral South University · CNShenzhen Institutes of Advanced Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bacteria acts as the main decomposer during the process of biodegradation by microbial communities in the ecosystem. Numerous studies have revealed the bacterial succession patterns during carcass decomposition in the terrestrial setting. The machine learning algorithm-generated models based on such temporal succession patterns have been developed for the postmortem interval (PMI) estimation. However, the bacterial succession that occurs on decomposing carcasses in the aquatic environment is poorly understood. In the forensic practice, the postmortem submersion interval (PMSI), which approximately equals to the PMI in most of the common drowning cases, has long been problematic to determine. In the present study, bacterial successions in the epinecrotic biofilm samples collected from the decomposing swine cadavers submerged in water were analyzed by sequencing the variable region 4 (V4) of 16S rDNA. The succession patterns between the repeated experimental settings were repeatable. Using the machine learning algorithm for establishing random forest (RF) models, the microbial community succession patterns in the epinecrotic biofilm samples taken during the 56-day winter trial and 21-day summer trial were determined to be used as the PMSI predictors with the mean absolute error (MAE) of 17.87 ± 2.48 ADD (≈1.3 day) and 20.59 ± 4.89 ADD (≈0.7 day), respectively. Significant differences were observed between the seasons and between the substrates. The data presented in this research suggested that the influences of the environmental factors and the aquatic bacterioplankton on succession patterns of the biofilm bacteria were of great significance. The related mechanisms of such influence need to be further studied and clarified in depth to consider epinecrotic biofilm as a reliable predictor in the forensic investigations.

Indexed as

bacterial successionmachine learning algorithmmicrobial biofilmpostmortem intervalpostmortem submersion interval

Identifiers

PMID35942315
PMCPMC9356301
OpenAlexW4286669364

What OpenQuestion holds

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LicenceCC BY
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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.