Evidence map›Paper›PMID 40605535›Full record

ArticleRenal failure2025

Prediction of postoperative infection through early-stage salivary microbiota following kidney transplantation using machine learning techniques.

Xuyu Xiang, Hong Liu, Tianyin Wang, Peng Ding, Yi Zhu, Ke Cheng, Yingzi Ming

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Renal failure, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Xuyu XiangThe Transplantation Center of the Third Xiangya Hospital, Central South University, Changsha, China.
Hong LiuThe Transplantation Center of the Third Xiangya Hospital, Central South University, Changsha, China.
Tianyin WangThe Transplantation Center of the Third Xiangya Hospital, Central South University, Changsha, China.
Peng DingThe Transplantation Center of the Third Xiangya Hospital, Central South University, Changsha, China.
Yi ZhuThe Transplantation Center of the Third Xiangya Hospital, Central South University, Changsha, China.
Ke ChengThe Transplantation Center of the Third Xiangya Hospital, Central South University, Changsha, China.
Yingzi MingThe Transplantation Center of the Third Xiangya Hospital, Central South University, Changsha, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Kidney transplantation (KT) is an effective treatment for end-stage renal disease; however, the lifelong immunosuppressive regimen increases the risk of infection, presenting significant clinical, and economic challenges. Identifying predictive biomarkers for infection onset is critical. In this study, 122 postoperative saliva samples from 39 KT recipients were analyzed using 16S rRNA sequencing, with 16 developing infections within one year. The composition of the salivary microbiota differed significantly between the infection and control groups, with notable variations at the Phylum level. Infected patients exhibited higher alpha diversity and 12 dominant taxa. A random forest model, utilizing five-fold three-times repeated cross-validation and incorporating differential biomarkers, significantly outperformed baseline peripheral blood lymphocyte subpopulation (PBLS) counts in predicting infections (area under the curve, 85.97% ± 10.64% vs. 67.03% ± 15.54%,

Indexed as

Kidney Failure, ChronicKidney TransplantationMachine LearningMicrobiotaPostoperative ComplicationsSalivaAdultBiomarkersFemaleHumansMaleMiddle AgedRNA, Ribosomal, 16SBiomarkersRNA, Ribosomal, 16S16S rRNAinfectionkidney transplantPredictionsalivary microbiota

Identifiers

PMID40605535
PMCPMC12231263

What OpenQuestion holds

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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.