Evidence map›Paper›PMID 27547698›Full record

ReviewCurrent transplantation reports

Autologous Cells for Kidney Bioengineering.

Bettina Wilm, Riccardo Tamburrini, Giuseppe Orlando, Patricia Murray

Abstract readReview
In one paragraph

Review in Current transplantation reports. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Regenerative medicine: the red planet for clinicians.Internal and emergency medicine · 2019
    Review
  5. Urine-derived cells for human cell therapy.Stem cell research & therapy · 2018
    Review
  6. Review
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.

Bettina WilmInstitute of Translational Medicine, Centre for Preclinical Imaging, University of Liverpool, Crown Street, Liverpool, L69 3BX UK.
Riccardo TamburriniDepartment of Surgery, Section of Transplantation, Wake Forest School of Medicine,Wake Forest Baptist Hospital, Medical Center Blvd, Winston Salem, NC 27157 USA.
Giuseppe OrlandoDepartment of Surgery, Section of Transplantation, Wake Forest School of Medicine,Wake Forest Baptist Hospital, Medical Center Blvd, Winston Salem, NC 27157 USA.
Patricia MurrayInstitute of Translational Medicine, Centre for Preclinical Imaging, University of Liverpool, Crown Street, Liverpool, L69 3BX UK.

Funding

Medical Research Council MR/K026739/1
6 · The paper itself

Abstract

Worldwide, increasing numbers of patients are developing end-stage renal disease, and at present, the only treatment options are dialysis or kidney transplantation. Dialysis is associated with increased morbidity and mortality, poor life quality and high economic costs. Transplantation is by far the better option, but there are insufficient numbers of donor kidneys available. Therefore, there is an urgent need to explore alternative approaches. In this review, we discuss how this problem could potentially be addressed by using autologous cells and appropriate scaffolds to develop 'bioengineered' kidneys for transplantation. In particular, we will highlight recent breakthroughs in pluripotent stem cell biology that have led to the development of autologous renal progenitor cells capable of differentiating to all renal cell types and will discuss how these cells could be combined with appropriate scaffolds to develop a bioengineered kidney.

Indexed as

BioprintingDecellularisationInduced pluripotent stem cellsKidney organoidsKidney scaffoldsRenal progenitor cells

Identifiers

PMID27547698
PMCPMC4980402

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.