Evidence map›Paper›PMID 31315232›Full record

ReviewInternational journal of molecular sciences2019

Seeing Better and Going Deeper in Cancer Nanotheranostics.

Maharajan Sivasubramanian, Yao Chen Chuang, Nai-Tzu Chen, Leu-Wei Lo

Open access · goldAbstract readReview
In one paragraph

Review in International journal of molecular sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.1field-weighted citation impact, top 24% 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

7 citing papers in PubMed, 20 citations in OpenAlex.

  1. Nanotheranostics: A Cutting-edge Technology for Cancer Management.Current pharmaceutical biotechnology · 2025
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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

4 authors at 2 institutions in 1 country.

Maharajan SivasubramanianInstitute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Zhunan 350, Taiwan.
Yao Chen ChuangInstitute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Zhunan 350, Taiwan.
Nai-Tzu ChenDepartment of Cosmeceutics, China Medical University, Taichung 40402, Taiwan. ohnonancy@mail.cmu.edu.tw.ORCID 0000-0001-6660-7065
Leu-Wei LoInstitute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Zhunan 350, Taiwan. lwlo@nhri.org.tw.
National Health Research Institutes · TWChina Medical University · TW

Funding

Ministry of Science and Technology, Taiwan MOST 105-2113-M-400-007-MY3Ministry of Science and Technology, Taiwan MOST-107-2113-M-039-003-MY2National Health Research Institutes of Taiwan BN-107-PP-04
6 · The paper itself

Abstract

Biomedical imaging modalities in clinical practice have revolutionized oncology for several decades. State-of-the-art biomedical techniques allow visualizing both normal physiological and pathological architectures of the human body. The use of nanoparticles (NP) as contrast agents enabled visualization of refined contrast images with superior resolution, which assists clinicians in more accurate diagnoses and in planning appropriate therapy. These desirable features are due to the ability of NPs to carry high payloads (contrast agents or drugs), increased in vivo half-life, and disease-specific accumulation. We review the various NP-based interventions for treatments of deep-seated tumors, involving "seeing better" to precisely visualize early diagnosis and "going deeper" to activate selective therapeutics in situ.

Indexed as

AnimalsHumansNeoplasmsTheranostic Nanomedicinecancer imaging and therapyclinical diagnosisimage-guided therapynanoparticles

Identifiers

PMID31315232
PMCPMC6678689
OpenAlexW2959343076

What OpenQuestion holds

Textmetadata
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.