Evidence map›Paper›PMID 39866795›Full record

ArticleMaterials today. Bio2025

Functionalized biomimetic nanoparticles loaded with salvianolic acid B for synergistic targeted triple-negative breast cancer treatment.

Nuo Cheng, Qianqian Zhou, Zongfang Jia, Yang Mu, Sheng Zhang, Lei Wang, Yunna Chen

Abstract read
In one paragraph

Article in Materials today. Bio, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Review
  2. Ultrasound-activated CuJournal of nanobiotechnology · 2026
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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

7 authors.

Nuo ChengAnhui University of Chinese Medicine, Hefei, 230012, China.
Qianqian ZhouAnhui University of Chinese Medicine, Hefei, 230012, China.
Zongfang JiaAnhui University of Chinese Medicine, Hefei, 230012, China.
Yang MuAnhui University of Chinese Medicine, Hefei, 230012, China.
Sheng ZhangAnhui University of Chinese Medicine, Hefei, 230012, China.
Lei WangAnhui University of Chinese Medicine, Hefei, 230012, China.
Yunna ChenAnhui University of Chinese Medicine, Hefei, 230012, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The therapeutic effect of immune checkpoint inhibitors (ICIs) in triple-negative breast cancer (TNBC) is unsatisfactory. The immune "cold" microenvironment caused by tumor-associated fibroblasts (TAFs) has an adverse effect on the antitumor response. Therefore, in this study, mixed cell membrane-coated porous magnetic nanoparticles (PMNPs) were constructed to deliver salvianolic acid B (SAB) to induce an antitumor immune response, facilitating the transition from a "cold" to a "hot" tumor and ultimately enhancing the therapeutic efficacy of immune checkpoint inhibitors. PMNP-SAB, which is based on a mixed coating of red blood cell membrane and TAF membrane (named PMNP-SAB@RTM), can simultaneously achieve the dual effects of "immune escape" and "homologous targeting". Under the influence of an external magnetic field (MF), SAB can be targeted and concentrated at the tumor site. The SAB released in tumors can effectively inhibit the production of extracellular matrix (ECM) by TAFs, promote T-cell infiltration, and induce antitumor immune responses. Ultimately, the combination of PMNP-SAB@RTM and BMS-1 (PD-1/PD-L1 inhibitor 1) effectively inhibited tumor growth. Finally, this study presents a precise and effective new strategy for TNBC immunotherapy on the basis of the differentiation of "cold" and "hot" microenvironments.

Indexed as

Immune microenvironmentsSalvianolic acid BTriple negative breast cancerTumor-associated fibroblasts

Identifiers

PMID39866795
PMCPMC11762562

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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.