Evidence map›Paper›PMID 41436928›Full record

ArticleBMC microbiology2025

Super donor assessment tool for oral microbiome transplantation.

Sonia Nath, Murthy Mittinty, Peter Zilm, Pedro Henrique Ribeiro Santiago, Don Kevin Hashan Ketagoda, Lisa Jamieson, Laura Weyrich

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PubMed Full text at PMC Publisher
In one paragraph

Article in BMC microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

7 authors.

Sonia NathAdelaide Dental School, The University of Adelaide, Adelaide, SA, 5000, Australia. sonia.nath@adelaide.edu.au.
Murthy MittintyCollege of Medicine and Public Health, Flinders University, Adelaide, SA, Australia.
Peter ZilmAdelaide Dental School, The University of Adelaide, Adelaide, SA, 5000, Australia.
Pedro Henrique Ribeiro SantiagoAdelaide Dental School, The University of Adelaide, Adelaide, SA, 5000, Australia.
Don Kevin Hashan KetagodaAdelaide Dental School, The University of Adelaide, Adelaide, SA, 5000, Australia.
Lisa JamiesonAdelaide Dental School, The University of Adelaide, Adelaide, SA, 5000, Australia.
Laura WeyrichSchool of Biological Sciences, University of Adelaide, Adelaide, SA, Australia.

Funding

National Health and Medical Research Council 2019/GNT1187737
6 · The paper itself

Abstract

aimsOral microbiome transplantation (OMT) involves transferring microbiota from donor to recipient. However, selecting suitable donors remains challenging due to a lack of standardised guidelines. This study developed a novel super donor assessment tool (SDAT) combining a multi-criteria decision-making (MCDM) process and an analytical hierarchical process (AHP) to identify OMT "super donors" for dental caries prevention.

methodsThis cross-sectional study used four sequential screening phases with data from 93 healthy participants, capturing socio-demographics, lifestyle, dietary and oral health behaviours. The SDAT employed MCDM, AHP, combining criteria with normalised and weighted ranks to establish the top 10 donors for three models: "Optimal donor" (Model 1), "Ideal donor" (Model 2), and "Sub-optimal donor" (Model 3). Donor plaque samples underwent 16S ribosomal RNA amplicon sequencing for microbial profiling, examining alpha and betadiversity, differential abundance, and network analysis.

resultsAlpha diversity analysis showed significant differences among groups (Kruskal-Wallis p < 0.001), with Model 1 showing the lowest diversity and Model 3 the highest. Beta diversity analysis using Permutational Multivariate Analysis of Variance revealed significant differences in microbial community composition (R² = 0.19, p = 0.001). Differential abundance analysis (False Discovery Rate < 0.05, controlling for age and sex) identified health-associated genera (Neisseria, Lautropia, Streptococcus, Veillonella) in Model 1, whereas Model 3 showed higher levels of disease-associated taxa (Treponema, Capnocytophaga). Network analysis revealed that Model 1 was organised around Actinomyces and Prevotella, Model 2 around Rothia and Haemophilus, and Model 3 was dominated by pathogenic taxa.

conclusionSDAT provides a systematic, transparent framework for super-donor selection, ensuring precision and reproducibility in donor rankings. The scoring system standardises the donor selection process, the effectiveness of donor screening, and reduces the risk of adverse events for OMT.

Indexed as

BacteriaDonor SelectionMicrobiotaMouthAdultCross-Sectional StudiesDental CariesDental PlaqueFemaleHumansMaleMiddle AgedRNA, Ribosomal, 16STissue DonorsYoung AdultRNA, Ribosomal, 16SAnalytical hierarchy processDecision makingDecision support modelMicrobiomeMicrobiotaOral health

Identifiers

PMID41436928
PMCPMC12836760

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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.