Evidence map›Paper›PMID 37344853›Full record

ReviewBiomaterials research2023

Polymeric biomaterial-inspired cell surface modulation for the development of novel anticancer therapeutics.

Ashok Kumar Jangid, Sungjun Kim, Kyobum Kim

Open access · goldAbstract readReview
In one paragraph

Review in Biomaterials research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
4.6field-weighted citation impact, top 5% 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

12 citing papers in PubMed, 30 citations in OpenAlex.

  1. Review
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  8. A tailored 4GRSC advances · 2025
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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

3 authors at 1 institution in 1 country.

Ashok Kumar JangidDepartment of Chemical and Biochemical Engineering, College of Engineering, Dongguk University, Seoul, South Korea.
Sungjun KimDepartment of Chemical and Biochemical Engineering, College of Engineering, Dongguk University, Seoul, South Korea.
Kyobum KimDepartment of Chemical and Biochemical Engineering, College of Engineering, Dongguk University, Seoul, South Korea. kyobum.kim@dongguk.edu.ORCID http://orcid.org/0000-0003-3678-2078
Dongguk University · KR

Funding

National Research Foundation of Korea 2021R1A4A3024237the Korean Fund for Regenerative Medicine (KFRM) grant RS-2022-00070304
6 · The paper itself

Abstract

Immune cell-based therapies are a rapidly emerging class of new medicines that directly treat and prevent targeted cancer. However multiple biological barriers impede the activity of live immune cells, and therefore necessitate the use of surface-modified immune cells for cancer prevention. Synthetic and/or natural biomaterials represent the leading approach for immune cell surface modulation. Different types of biomaterials can be applied to cell surface membranes through hydrophobic insertion, layer-by-layer attachment, and covalent conjugations to acquire surface modification in mammalian cells. These biomaterials generate reciprocity to enable cell-cell interactions. In this review, we highlight the different biomaterials (lipidic and polymeric)-based advanced applications for cell-surface modulation, a few cell recognition moieties, and how their interplay in cell-cell interaction. We discuss the cancer-killing efficacy of NK cells, followed by their surface engineering for cancer treatment. Ultimately, this review connects biomaterials and biologically active NK cells that play key roles in cancer immunotherapy applications.

Indexed as

Cancer immunotherapyCancer recognitionNK cell surface modulationPolymeric biomaterialsPolymers for cell surface modification

Identifiers

PMID37344853
PMCPMC10283342
OpenAlexW4381597899

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.