Evidence map›Paper›PMID 38184620›Full record

ArticleJournal of nanobiotechnology2024

"All in one" nanoprobe Au-TTF-1 for target FL/CT bioimaging, machine learning technology and imaging-guided photothermal therapy against lung adenocarcinoma.

Zhe Yang, Yujia Zhang, Lu Tang, Xiao Yang, Lei Song, Chun Shen, Andrei V Zvyagin, Yang Li, Bai Yang, Quan Lin

Open access · goldAbstract read
In one paragraph

Article in Journal of nanobiotechnology, 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
2.5field-weighted citation impact, top 9% 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

4 citing papers in PubMed, 9 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. 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

10 authors at 7 institutions in 2 countries.

Zhe Yang *State Key Laboratory of Supramolecular Structure and Material, College of Chemistry, Jilin University, Changchun, 130012, China.
Yujia Zhang *Department of Respiratory Medicine, The First Hospital of Jilin University, Changchun, 130021, China.
Lu TangDepartment of Breast, China-Japan Union Hospital of Jilin University, Changchun, 130031, China.
Xiao YangCollege of Computer Science and Technology Jilin University, Changchun, 130012, China.
Lei SongDepartment of Breast, China-Japan Union Hospital of Jilin University, Changchun, 130031, China.
Chun ShenCollege of Computer Science and Technology Jilin University, Changchun, 130012, China.
Andrei V ZvyaginAustralian Research Council Centre of Excellence for Nanoscale Biophotonics, Macquarie University, Sydney, NSW, 2109, Australia.
Yang LiDepartment of Respiratory Medicine, The First Hospital of Jilin University, Changchun, 130021, China. li_yang99@jlu.edu.cn.
Bai YangState Key Laboratory of Supramolecular Structure and Material, College of Chemistry, Jilin University, Changchun, 130012, China.
Quan LinState Key Laboratory of Supramolecular Structure and Material, College of Chemistry, Jilin University, Changchun, 130012, China. linquan@jlu.edu.cn.
Jilin Province Science and Technology Department · CNJilin University · CNState Key Laboratory of Supramolecular Structure and MaterialsAustralian Research Council · AUFirst Hospital of Jilin University · CNUnion Hospital · CNUnion Hospital · CN

Funding

Interdisciplinary innovation project of the first hospital of Jilin University JDYYJCHX2020005key research and development project of Jilin Provincial Science and Technology Department 20210204142YYNatural Science Foundation of Jilin Province ***202302002the Science and Technology Research Project of Jilin Provincial Department of Education JJKH20211203KJthe scientific research foundation of the first hospital of Jilin University JDYY11202015the WU JIEPING MEDICAL FOUNDATION 320.6750.19089.80
6 · The paper itself

Abstract

The accurate preoperative diagnosis and tracking of lung adenocarcinoma is hindered by non-targeting and diffusion of dyes used for marking tumors. Hence, there is an urgent need to develop a practical nanoprobe for tracing lung adenocarcinoma precisely even treating them noninvasively. Herein, Gold nanoclusters (AuNCs) conjugate with thyroid transcription factor-1 (TTF-1) antibody, then multifunctional nanoprobe Au-TTF-1 is designed and synthesized, which underscores the paramount importance of advancing the machine learning diagnosis and bioimaging-guided treatment of lung adenocarcinoma. Bright fluorescence (FL) and strong CT signal of Au-TTF-1 set the stage for tracking. Furthermore, the high specificity of TTF-1 antibody facilitates selective targeting of lung adenocarcinoma cells as compared to common lung epithelial cells, so machine learning software Lung adenocarcinoma auxiliary detection system was designed, which combined with Au-TTF-1 to assist the intelligent recognition of lung adenocarcinoma jointly. Besides, Au-TTF-1 not only contributes to intuitive and targeted visualization, but also guides the following noninvasive photothermal treatment. The boundaries of tumor are light up by Au-TTF-1 for navigation, it penetrates into tumor and implements noninvasive photothermal treatment, resulting in ablating tumors in vivo locally. Above all, Au-TTF-1 serves as a key platform for target bio-imaging navigation, machine learning diagnosis and synergistic PTT as a single nanoprobe, which demonstrates attractive performance on lung adenocarcinoma.

Indexed as

Adenocarcinoma of LungLung NeoplasmsAmpicillinAntibodiesFluorescenceHumansPhotothermal TherapyTomography, X-Ray ComputedAmpicillinAntibodiesN-(6,7-difluoroquinolonyl)ampicillinBioimaging-guided therapyDual-mode bioimagingLung adenocarcinomaMachine learningNanoprobe

Identifiers

PMID38184620
PMCPMC10770976
OpenAlexW4390638498

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.