Evidence map›Paper›PMID 31693014›Full record

ArticleTrends in applied spectroscopy2013

Fourier-transform infrared spectroscopic imaging of articular cartilage and biomaterials: A review.

Nagarajan Ramakrishnan, Yang Xia

Abstract read
In one paragraph

Article in Trends in applied spectroscopy, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

2 authors.

Nagarajan RamakrishnanInfrared Group, Defence Laboratory, Jodhpur-342 011, India.
Yang XiaDepartment of Physics and Center for Biomedical Research, Oakland University, Rochester, MI 48309, USA.

Funding

Quantitative molecular imaging in articular cartilageR01AR045172 · NIAMS · OAKLAND UNIVERSITY · PI XIA, YANG · 1999 to 2009
$2.6M
Adaptability of Articular Cartilage to External Loading by Microscopic ImagingR01AR052353 · NIAMS · OAKLAND UNIVERSITY · PI XIA, YANG · 2008 to 2013
$2.1M
NIAMS NIH HHS R01 AR045172NIAMS NIH HHS R01 AR052353
6 · The paper itself

Abstract

Fourier transform infrared spectroscopy (FTIR) has the potential to mark up the chemical changes of the materials, as almost all the materials contain their signatures in infrared region. Spectroscopy combined with spatial resolution enables the possibility of characterizing samples up to microscopic level. The emerging development of instrumentation to provide spatial information for infrared (IR) spectroscopy, termed as IR microscopy, provides an opening for newer applications in terms of image analysis, novel data processing tools, etc. Characterization of biomaterials using IR spectroscopy has a trace back to 1950s. The advent of FTIR with imaging capability made characterization possible in cartilage tissue and other biological systems. Extensive analysis of chemical constituents of cartilage and tendon, collagen orientation and polarization property of cartilage using FTIR imaging (FTIRI) has been actively explored during the last two decades. Also, studies using specialized instrumentations like synchrotron FTIR imaging have been attempted to understand the characteristics of biological samples like cartilage. This review covers most of those investigations on cartilage with FTIRI to characterize the same in terms of component characteristics and quantification, collagen orientation, zonal boundary determination, influence of mechanical compression on tissue nature and its correlation to other techniques in last 20 years.

Indexed as

anisotropycartilagechemometric techniquescollagendichroismFTIR imaginginfrared spectroscopypolarizationproteoglycan

Identifiers

PMID31693014
PMCPMC6830739

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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.