Evidence map›Paper›PMID 36338135›Full record

ArticleFrontiers in bioengineering and biotechnology2022

The targeting imaging and treatment capacity of gelsolin-targeted and paclitaxel-loaded PLGA nanoparticles

Jiamei Ji, Haocheng Qin, Yan Yang, Jun Wu, Juan Wu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.4field-weighted citation impact, top 46% 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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Tumor-Targeted Delivery Therapy Based on PLGA Nanoparticles.Journal of functional biomaterials · 2026
    Review
  2. Article
  3. 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

5 authors at 3 institutions in 1 country.

Jiamei JiDepartment of Ultrasound, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
Haocheng QinDepartment of Ultrasound, Lianyungang First People's Hospital, Lianyungang, Jiangsu, China.
Yan YangDepartment of Ultrasound, Huainan First People's Hospital, Huainan, Anhui, China.
Jun WuDepartment of Ultrasound, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
Juan WuDepartment of Ultrasound, Lanzhou University Second Hospital, Lanzhou, Gansu, China.
Dalian Medical University · CNHuainan Normal University · CNThe First People’s Hospital of Lianyungang · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As a vital sign of carcinomas, lymph node metastasis is closely related to poor prognosis due to a lack of identification and effective treatment in the early stage. Nanoscale contrast agents targeting specific tumor antigens are expected to identify tumor metastasis in the early stage and achieve precise treatment. As a biomarker in the early stage of tumor invasion and metastasis, gelsolin (GSN) might be a promising molecular target to identify and screen tumor metastasis through the lymphatic system. Therefore, GSN-targeted paclitaxel-loaded poly(lactic-co-glycolic acid) nanoparticles (GSN-PTX-PLGA NPs) were prepared, and their physicochemical properties, encapsulation efficiency, drug loading, and dissolution were determined. Besides, uptake experiments and the fluorescent imaging system were used to evaluate their targeting capability. The targeting imaging and treatment capacity were also assessed by experiments

Indexed as

gelsolinlymphatic metastasispaclitaxeltargeted therapyultrasound imaging

Identifiers

PMID36338135
PMCPMC9632342
OpenAlexW4306875483

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.