Evidence map›Paper›PMID 42867828›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2026

Measuring Skeletal Muscle Mitochondrial Architecture in 3D Using Focused Ion Beam Scanning Electron Microscopy (FIB-SEM).

Hailey A Parry, Brian Glancy

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2026. 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.

Hailey A ParryNational Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
Brian GlancyNational Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA. Brian.glancy@nih.gov.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent advances in volume electron microscopy have permitted unprecedented visualizations and in-depth quantitative analyses of mitochondrial network structure in skeletal muscle. Nanoscale, 3D analyses of skeletal muscle mitochondria have provided novel insights into how mitochondria interact among themselves and with other skeletal muscle cell organelles, such as the sarcoplasmic reticulum and the sarcomere. The identification of mitochondria within volume electron microscopy datasets and the downstream analyses of segmented structures remain two major bottlenecks in the analysis of 3D mitochondrial network structure at the nanoscale. Here, we provide a step-by-step guide on how to use machine learning programs to segment skeletal muscle mitochondria from volume electron microscopy datasets, followed by instructions on several types of 3D analyses of individual mitochondria and mitochondrial network structure that can be performed using ImageJ. We highlight key quality-control steps to ensure precise and accurate segmentation and analyses and to enhance reproducibility across groups.

Indexed as

Imaging, Three-DimensionalMicroscopy, Electron, ScanningMitochondria, MuscleMuscle, SkeletalAnimalsImage Processing, Computer-AssistedMachine LearningVolume Electron MicroscopyFocus Ion Beam Scanning Electron MicroscopyMitochondriaSkeletal muscleVolume electron microscopy

Identifiers

PMID42867828

What OpenQuestion holds

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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.