Evidence map›Paper›PMID 38933861›Full record

ArticleNanoscale advances2024

Side-view optical microscopy-assisted atomic force microscopy for thickness-dependent nanobiomechanics.

Yanqi Yang, Mi Li

Abstract read
In one paragraph

Article in Nanoscale advances, 2024. 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
–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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. TheMaterials today. Bio · 2026
    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

2 authors.

Yanqi YangState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences Shenyang 110016 China limi@sia.cn.
Mi LiState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences Shenyang 110016 China limi@sia.cn.ORCID https://orcid.org/0000-0002-8379-0057

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The mechanical properties of biomaterials play an important role in regulating life processes, and thus accurately delineating the mechanical properties of biomaterials is critical to understand their functionality. Particularly, atomic force microscopy (AFM) has become a powerful and standard tool for characterizing and analyzing the nanomechanical properties of biomaterials, and providing a capability to visualize the thickness of the specimen during AFM-based force spectroscopy experiments benefits the biomedical applications of AFM. Here, we present a study of side-view optical microscopy-assisted AFM based on the integration of AFM and a detachable side-view optical microscopy module, which is able to image in real time the AFM indentation process from the side-view perspective and consequently facilitates the utilization of AFM-based indentation assay to precisely detect the mechanical properties of a specimen by taking its thickness into account. The effectiveness of side-view optical microscopy-assisted AFM was confirmed on four different types of biomaterial systems, including microfabricated structures, hydrogels, living cells, and cell spheroids, and the experimental results significantly show that the mechanical properties of samples at the micro/nanoscale are closely related to their thickness, vividly illustrating side-view optical microscopy-assisted AFM as a promising approach for accurate nanomechanics of biomaterial systems. The study provides additional possibilities for measuring the thickness-dependent nanomechanical properties of biomaterials by AFM, which will enable AFM-based force spectroscopy technology to address more biological issues with enhanced precision and will benefit the field of mechanobiology.

Identifiers

PMID38933861
PMCPMC11197429

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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.