Evidence map›Paper›PMID 28663857›Full record

ArticleBiomedical optics express2017

Interstitial photoacoustic spectral analysis: instrumentation and validation.

Haonan Zhang, Wan-Yu Chao, Qian Cheng, Shengsong Huang, Xueding Wang, Denglong Wu, Guan Xu

Abstract read
In one paragraph

Article in Biomedical optics express, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Photoacoustic clinical imaging.Photoacoustics · 2019
    Review
  7. Article
  8. Article
  9. Article
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

7 authors.

Haonan ZhangDepartment of Biomedical Engineering, University of Michigan Medical School, 2200 Bonisteel Boulevard, Ann Arbor, MI 48019, USA.
Wan-Yu ChaoFaculty of Science, University of Western Ontario, 1151 Richmond St, London, ON N6A 3K7, Canada.
Qian ChengInstitute of Acoustics, School of Physics Science and Engineering, Tongji University, Shanghai 200092, China.
Shengsong HuangDepartment of Urology, Tongji Hospital, Tongji University School of Medicine, Shanghai 200065, China.
Xueding WangDepartment of Biomedical Engineering, University of Michigan Medical School, 2200 Bonisteel Boulevard, Ann Arbor, MI 48019, USA.
Denglong WuDepartment of Urology, Tongji Hospital, Tongji University School of Medicine, Shanghai 200065, China.
Guan XuDepartment of Radiology, University of Michigan Medical School, 1301 Catherine St, Ann Arbor, MI 48019, USA.

Funding

Physiology of Inflammatory Arthritis in High ResolutionR01AR060350 · NIAMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI WANG, XUEDING · 2011 to 2021
$6.0M
NIAMS NIH HHS R01 AR060350
6 · The paper itself

Abstract

Photoacoustic (PA) spectral analysis (PASA) is a recently developed approach for quantifying molecular components and microscopic architectures in tissue. The PASA relies on signals with sufficient temporal length and narrow dynamic range for statistics based analysis. However, the optical and acoustic attenuation within the biological tissue make it difficult to acquire desirable signals from deep locations in biological tissue for PASA. This study proposes an interstitial PASA approach. By combining a fiber optics diffuser and a small aperture needle hydrophone, a fine needle PA probe facilitates PASA in deep tissue. A prototype probe has been fabricated and tested in quantifying the prostate cancer cell concentrations

Indexed as

(110.5125) Photoacoustics(170.6510) Spectroscopy, tissue diagnostics

Identifiers

PMID28663857
PMCPMC5480572

What OpenQuestion holds

Textmetadata
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.