Evidence map›Paper›PMID 25405797›Full record

ReviewNanomedicine (London, England)2014

Future nanomedicine for the diagnosis and treatment of osteoarthritis.

Lauren R Eichaker, Hongsik Cho, Craig L Duvall, Thomas A Werfel, Karen A Hasty

Abstract readReview
In one paragraph

Review in Nanomedicine (London, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
0.5field-weighted citation impact, top 33% 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

14 citing papers in PubMed, 28 citations in OpenAlex.

  1. Review
  2. How Selective are Nanomaterials to Treat Osteoarthritis.International journal of nanomedicine · 2026
    Review
  3. Article
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  7. Review
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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

5 authors at 3 institutions in 1 country.

Lauren R EichakerDepartment of Biomedical Engineering & Orthopaedic Surgery/Campbell Clinic, University of Tennessee Health Science Center, Memphis, TN 38163, USA.
Hongsik Cho
Craig L Duvall
Thomas A Werfel
Karen A Hasty
Memphis VA Medical Center · USVanderbilt University · USUniversity of Tennessee Health Science Center · US

Funding

Early Detection and Quantification of OAR21AR060408 · NIAMS · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · PI HASTY, KAREN A. · 2012 to 2013
$372k
Targeted therapy for osteoarthritisI01BX002392 · VA · MEMPHIS VA MEDICAL CENTER · PI HASTY, KAREN A. · 2014 to 2017
–
BLRD VA I01 BX002392NIAMS NIH HHS R21 AR060408NIAMS NIH HHS R21AR060408
6 · The paper itself

Abstract

Current treatments for osteoarthritis (OA) are largely palliative until the joints become totally dysfunctional and prosthetic replacement becomes necessary. Effective methods are needed for diagnosing OA and monitoring its progression during its early stages, when the effects of therapeutic drugs or biological agents are most likely to be effective. Theranostic nanosomes and nanoparticles have the potential to noninvasively detect, track and treat the early stages of OA. As articular cartilage does not regenerate once it is degraded, cell-based treatments aided by superparamagnetic iron oxide nanoparticle tracking are attractive future treatment modalities for the later stages of OA progression, when significant cartilage replacement is needed. This article will describe the current and future translational approaches for the detection and noninvasive treatment of degenerative OA.

Indexed as

NanomedicineHumansLiposomesMagneticsOsteoarthritisLiposomesliposomenanosomenoninvasive imagingosteoarthritisquantum dotsiRNA carriersmart nanoparticleSPIOtheranostic technology

Identifiers

PMID25405797
PMCPMC4962602
OpenAlexW1980432870

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.