Evidence map›Paper›PMID 24361391›Full record

ReviewAdvanced drug delivery reviews2014

Tissue-engineered kidney disease models.

Teresa M Desrochers, Erica Palma, David L Kaplan

Abstract readReview
In one paragraph

Review in Advanced drug delivery reviews, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed
7.6field-weighted citation impact, top 2% of its field
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

41 citing papers in PubMed, 93 citations in OpenAlex.

  1. CaAntibodies (Basel, Switzerland) · 2026
    Article
  2. Review
  3. Review
  4. Key parameters for designing robust 2D and 3D spheroid models forBioengineering & translational medicine · 2025
    Review
  5. Article
  6. Review
  7. Review
  8. Article
  9. Article
  10. Review
  11. Article
  12. Review
  13. Review
  14. Article
  15. Article
  16. Review
  17. Review
  18. Review
  19. Article
  20. 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

3 authors at 1 institution in 1 country.

Teresa M DesrochersDepartment of Biomedical Engineering, Tufts University, Medford, MA, USA.
Erica PalmaDepartment of Biomedical Engineering, Tufts University, Medford, MA, USA.
David L KaplanDepartment of Biomedical Engineering, Tufts University, Medford, MA, USA. Electronic address: david.kaplan@tufts.edu.
Tufts University · US

Funding

Translational Bioreactors - Engineering Complex TissuesP41EB002520 · NIBIB · TUFTS UNIVERSITY MEDFORD · PI KAPLAN, DAVID L. · 2004 to 2018
$16.7M
NIBIB NIH HHS P41 EB002520
6 · The paper itself

Abstract

Renal disease represents a major health problem that often results in end-stage renal failure necessitating dialysis and eventually transplantation. Historically these diseases have been studied with patient observation and screening, animal models, and two-dimensional cell culture. In this review, we focus on recent advances in tissue engineered kidney disease models that have the capacity to compensate for the limitations of traditional modalities. The cells and materials utilized to develop these models are discussed and tissue engineered models of polycystic kidney disease, drug-induced nephrotoxicity, and the glomerulus are examined in detail. The application of these models has the potential to direct future disease treatments and preclinical drug development.

Indexed as

Kidney DiseasesModels, BiologicalAnimalsCell Culture TechniquesCell Line, TransformedDrug Evaluation, PreclinicalHumansRenal AgentsTissue EngineeringRenal Agents3D tissuesAcute kidney injuryChronic kidney diseaseDrug-induced nephrotoxicityGlomerulusMicrofluidicsPolycystic kidney diseaseRenalRenal cell carcinomaRenal fibrosis

Identifiers

PMID24361391
PMCPMC4019700
OpenAlexW2099638616

What OpenQuestion holds

Textmetadata
LicenceTDM
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.