Evidence map›Paper›PMID 33945042›Full record

ArticleLasers in medical science2022

Evaluating the efficacy of anti-fungal blue light therapies via analyzing tissue section images.

Yun Zhao, Yunchu Zhang, Jianfei Dong

Abstract read
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In one paragraph

Article in Lasers in medical science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.5field-weighted citation impact, top 35% 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

1 citing paper in PubMed, 9 citations in OpenAlex.

  1. 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 2 institutions in 1 country.

Yun ZhaoUniversity of Science and Technology of China, Hefei, China.
Yunchu ZhangUniversity of Science and Technology of China, Hefei, China.
Jianfei DongSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China. jfeidong@hotmail.com.ORCID http://orcid.org/0000-0003-2264-2349
University of Science and Technology of China · CNChinese Academy of Sciences · CN

Funding

Nanjing Institute of Intelligent Health and Innovation Y85K023705Suzhou Institute of Biomedical Engineering and Technology Y85212A705
6 · The paper itself

Abstract

Anti-fungal blue light (ABL) therapies have been studied and applied in treating various diseases caused by fungal infection. The existing work has been mainly devoted to study the effect of various light dosages on the fungal viability and on the induced cytotoxic reactive oxygen species (ROS) in the pathogens. While in vivo experimental studies have also been reported, there is still no work targeted on quantifying the effect of light on prohibiting the pathogens from invading into the deeper sites in the skin of their host. This can be attributed to the lack of methods to analyze the tissue section images, which are the main means of examining infected tissues. This work has been devoted to solve such problems, so as to improve dosimetric analyses of ABL therapies on treating fungal infections. Specifically, the invasion depth of the fungi and their ratios to the tissue in four bins at different depths inside the skin were extracted from the tissue section images. The significance of the treatment with different dosages on inhibiting the fungi was also tested by each of these depth-related metrics. The ABL experiments using 415-nm-wavelength LED light were performed on BALB/c mice, whose skin was infected by Candida albicans (C. albicans). The proposed methods were applied to the tissue sections of the experimental animals. The results clearly verified that the fluence up to 180J/cm

Indexed as

Candida albicansPhototherapyAnimalsMiceMice, Inbred BALB CReactive Oxygen SpeciesSkinReactive Oxygen SpeciesAnti-fungal blue light therapyDosimetric analysisFungal infectionHistological analysisImage processing

Identifiers

PMID33945042
OpenAlexW3159869078

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.