Evidence map›Paper›PMID 40790084›Full record

ArticleScientific reports2025

Selective deletion or preservation of tissue components via enzymatic digestion monitored by scanning acoustic microscopy.

Katsutoshi Miura, Toshihide Iwashita

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Katsutoshi MiuraDepartment of Regenerative and Infectious Pathology, Hamamatsu University School of Medicine, 1-20-1 Handa-yama, Chuoku, Hamamatsu, Shizuoka, 431-3192, Japan. kmiura.hama.med@gmail.com.
Toshihide IwashitaDepartment of Regenerative and Infectious Pathology, Hamamatsu University School of Medicine, 1-20-1 Handa-yama, Chuoku, Hamamatsu, Shizuoka, 431-3192, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Detecting specific tissue components is valuable in histology. Scanning acoustic microscopy (SAM) measures the attenuation-of-sound (AOS) through tissue sections, enabling the generation of histological images without staining. AOS values decrease as tissues degrade. In this study, we enzymatically digested target components and monitored the process using AOS imaging over time. Additionally, we applied specific dyes and antibodies to inhibit enzyme activity and preserve target component. Collagenase digested the bone to clearly visualise the internal structure. The target component showed a distinct decline in AOS values. Actinase digested the artery except for amyloid deposits, which were detected by Congo red staining. Actinase-digested lymphoid cells remained positive for horseradish peroxidase (HRP) staining. Amylase digested some corpora amylacea (CA) in the brain, which became negative for periodic acid-Schiff (PAS) staining and diminished in size under electron microscopy. DNase digested and deleted cell nuclei, except for those stained with HRP. Residual nuclear images of AOS matched those of light microscopy. Enzyme-specific inhibition of enzymes preserved the target cells and materials. Our method offers a practical method for intentionally deleting or retaining target components in a section. Furthermore, it provides a means to adjust and compare the degree of degradation using AOS values.

Indexed as

Microscopy, AcousticAnimalsCollagenasesCollagenasesAcoustic microscopyAmylaseAttenuation of soundCollagenaseCorpora amylaceaDNase

Identifiers

PMID40790084
PMCPMC12340083

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.