Evidence map›Paper›PMID 42028167›Full record

ArticleBiophotonics discovery2024

Predictive model for laser-induced tissue necrosis with immunohistochemistry validation.

J Junior Arroyo, Arunima Sharma, Jiaxin Zhang, Muyinatu A Lediju Bell

Abstract read
In one paragraph

Article in Biophotonics discovery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Journal of biomedical optics · 2026
    Article
  3. Article
  4. 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

4 authors.

J Junior ArroyoJohns Hopkins University, Department of Biomedical Engineering, Baltimore, Maryland, United States.ORCID https://orcid.org/0000-0002-1752-4754
Arunima SharmaJohns Hopkins University, Department of Electrical and Computer Engineering, Baltimore, Maryland, United States.
Jiaxin ZhangJohns Hopkins University, Department of Electrical and Computer Engineering, Baltimore, Maryland, United States.ORCID https://orcid.org/0009-0005-4514-925X
Muyinatu A Lediju BellJohns Hopkins University, Department of Biomedical Engineering, Baltimore, Maryland, United States.ORCID https://orcid.org/0000-0002-8394-4482

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Significance: Photoacoustic imaging holds promise to provide critical guidance in surgical interventions, but its widespread use is challenged by the absence of applicable safety guidelines across diverse target tissues. The biosafety of this technology is primarily associated with the risk of necrosis generation, which is an irreversible thermal effect that can result from prolonged, high-energy laser applications. Aim: We introduce the first known numerical simulation approaches to assess laser-induced necrosis in liver tissue and present a novel microscopy analysis framework to validate performance. Approach: Our simulation methods integrate Monte Carlo simulations of laser-tissue interaction with the COMSOL interface, model local tissue heating, and predict associated tissue damage to quantify the percentage of tissue necrosis resulting from laser application. Our initial predictions are based on 30 and 73 mJ mean laser energies, laser irradiation times of 1, 10, and 20 min, and a 750 nm laser wavelength. Empirical validations with Results: Negligible tissue damage was observed with necrosis predictions Conclusions: We successfully demonstrated an

Indexed as

laser safetymicroscopynecrosisnumerical simulationphotoacoustic imagingsingle-cell segmentation

Identifiers

PMID42028167
PMCPMC13098639

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.