Evidence map›Paper›PMID 39800482›Full record

ReviewZhongguo fei ai za zhi = Chinese journal of lung cancer2024

[Application of Nano-drug Delivery Technology in Overcoming Drug Resistance 
in Lung Cancer].

Yingchun Lu, Chunyu Wang, Bin Liu

Abstract readReviewEnglish Abstract
PubMed Full text at PMC Publisher
In one paragraph

Review in Zhongguo fei ai za zhi = Chinese journal of lung cancer, 2024. 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
–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

1 citing paper in PubMed.

  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.

Yingchun LuSchool of Medicine, University of Electronic Science and Technology of China, Chengdu 610054, China.
Chunyu WangSchool of Medical and Life Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu 610032, China.
Bin LiuDepartment of Medical Oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital 
of University of Electronic Science and Technology of China, Chengdu 610042, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung cancer is one of the most malignant tumor, representing a significant threat to human health. In China, its mortality rate is the highest among all malignant tumors. The occurrence of drug resistance has resulted in unfavourable prognosis for patients with lung cancer, and overcoming drug resistance is a significant challenge that needs to be addressed. Nano-drug delivery technology has been an important approach to overcome drug resistance in lung cancer. Targeting to the mechanisms of drug resistance, by enabling the combined delivery of drugs, increasing the efficiency of drug delivery and improving the targeting and safety of drugs, nano-drug delivery technology offers a novel approach to tackling drug resistance in lung cancer. This paper describes the current status of lung cancer treatment, mechanisms of drug resistance, strategies to overcome drug resistance, and the application of nanotechnology in the diagnosis and treatment of lung cancer. In addition, it summarizes the recent research progress on the application of nano-drug delivery technology to overcome drug resistance in lung cancer. Finally, the current prospects and challenges of nano-drug delivery technology are discussed.
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Indexed as

Antineoplastic AgentsDrug Delivery SystemsDrug Resistance, NeoplasmLung NeoplasmsAnimalsHumansNanoparticlesNanotechnologyAntineoplastic AgentsDrug resistanceLung neoplasmsNano-drug delivery technology

Identifiers

PMID39800482
PMCPMC11732387

What OpenQuestion holds

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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.