Evidence map›Paper›PMID 40725768›Full record

ArticleJournal of clinical medicine2025

The New Horizon: A Viewpoint of Novel Drugs, Biomarkers, Artificial Intelligence, and Self-Management in Improving Kidney Transplant Outcomes.

Artur Quintiliano, Andrew J Bentall

Abstract read
In one paragraph

Article in Journal of clinical medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Artur QuintilianoDivision of Nephrology and Hypertension, Mayo Clinic, Rochester, MN 55905, USA.ORCID 0000-0001-9262-6403
Andrew J BentallDivision of Nephrology and Hypertension, Mayo Clinic, Rochester, MN 55905, USA.ORCID 0000-0002-8495-9782

Funding

National Council for Scientific and Technological Development 444016/2024-4
6 · The paper itself

Abstract

The increasing prevalence of chronic kidney disease (CKD) and end-stage kidney disease (ESKD) has led to a growing demand for kidney transplantation (KTx). Identifying risk factors that enable improved allograft survival through novel therapeutic agents, advanced biomarkers, and artificial intelligence (AI)-driven data integration are critical to addressing this challenge. Drugs, such as SGLT2 inhibitors and finerenone, have demonstrated improved outcomes in patients but lack comprehensive long-term evidence in KTx patients. The use of biomarkers, including circulating cytokines and transcriptomics, coupled with AI, could enhance early detection and personalized treatment strategies. Addressing patient self-management and addressing health access disparities may be more achievable using technologies used at home rather than traditional models of healthcare and thus lead to increased transplant success, both in terms of transplantation rates and allograft longevity.

Indexed as

artificial intelligencebiomarkerschronic kidney diseasedrugkidney transplantationnovel therapeuticspatient self-management

Identifiers

PMID40725768
PMCPMC12295147

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.