Evidence map›Paper›PMID 31595214›Full record

ArticleCurrent transplantation reports2019

High Dimensional Renal Profiling: Towards a Better Understanding or Renal Transplant Immune Suppression.

Cyd M Castro-Rojas, Rita R Alloway, E Steve Woodle, David A Hildeman

Abstract read
In one paragraph

Article in Current transplantation reports, 2019. 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

4 authors.

Cyd M Castro-RojasDivision of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH.ORCID 0000-0002-5599-6269
Rita R AllowayDivision of Nephrology, Department of Internal Medicine, University of Cincinnati College of Medicine, Cincinnati, OH.ORCID 0000-0003-4835-9197
E Steve WoodleDivision of Transplantation, Department of Surgery, University of Cincinnati College of Medicine, Cincinnati, OH.ORCID 0000-0003-4280-0842
David A HildemanDivision of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH.ORCID 0000-0002-0421-8483

Funding

Single cell analysis of transplant rejectionR21AI142264 · NIAID · CINCINNATI CHILDRENS HOSP MED CTR · PI HILDEMAN, DAVID A · 2019 to 2020
$452k
NIAID NIH HHS R21 AI142264
6 · The paper itself

Abstract

purpose of reviewThe goal of this review is to discuss new approaches to avoid CNI/CCS toxicities with a focus on new biologics and new methods to understand transplant rejection at the single-cell level. RECENT

findingsRecently developed biologics hold significant promise as the next wave of therapeutics designed to promote CNI/CCS-free long-term allograft acceptance. Indeed, belatacept, soluble CTLA4-Ig, is largely devoid of CNI-like toxicities, although it is accompanied by an increased frequency of acute rejection. Besides belatacept, other biologics hold promise as CNI-free immune suppressive approaches. Finally, powerful new single cell approaches can enable characterization of cellular populations that drive rejection within the rejecting allograft. SUMMARY: We propose that the incorporated single cell profiling into studies investigating new biologics in transplantation, could be tailored to each patient, correlated with potential biomarkers in the blood and urine, and provide a platform where therapeutic targets can be rationally defined, mechanistically-based, and exploited.

Indexed as

belataceptcalcineurin inhibitorhigh dimension profilingimmunosuppressionkidney allograft rejectionmTOR inhibitorsingle cell RNA sequencingtoxicity

Identifiers

PMID31595214
PMCPMC6781878

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.