Evidence map›Paper›PMID 39713028›Full record

ReviewACS measurement science au2024

From Insulin Measurement to Partial Exocytosis Model: Advances in Single Pancreatic Beta Cell Amperometry over Four Decades.

Amir Hatamie, Xiulan He, Andrew Ewing, Patrik Rorsman

Abstract readReview
In one paragraph

Review in ACS measurement science au, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

4 authors.

Amir HatamieDepartment of Physiology, Sahlgrenska Academy, University of Gothenburg, Medicinaregatan 11-13, 41390 Gothenburg, Sweden.ORCID https://orcid.org/0000-0002-7085-893X
Xiulan HeCollege of Chemistry, Beijing Normal University, Beijing 100875, China.
Andrew EwingDepartment of Physiology, Sahlgrenska Academy, University of Gothenburg, Medicinaregatan 11-13, 41390 Gothenburg, Sweden.ORCID https://orcid.org/0000-0002-2084-0133
Patrik RorsmanDepartment of Physiology, Sahlgrenska Academy, University of Gothenburg, Medicinaregatan 11-13, 41390 Gothenburg, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Single cell Amperometry (SCA) is a powerful, sensitive, high temporal resolution electrochemical technique used to quantify secreted molecular messengers from individual cells and vesicles. This technique has been extensively applied to study the process of exocytosis, and it has also been applied, albeit less frequently, to investigate insulin exocytosis from single pancreatic beta cells. Insufficient insulin release can lead to diabetes, a chronic lifestyle disorder that affects millions of people worldwide. This review aims to summarize and highlight electrochemical measurements of insulin via monitoring its secretion from beta cells by SCA with micro- and nanoelectrodes since the 1990s and to explain how and why serotonin is used as a proxy for monitoring insulin during exocytosis from single beta cells. Finally, we describe how the combination of SCA measurements with the intracellular vesicle impact electrochemical cytometry (IVIEC) technique has led to important findings regarding fractional release types in beta cells. These findings, reported recently, have opened a new window in the study of pore formation, exocytosis from single vesicles, and the mechanisms of insulin secretion. This sensitive cellular electroanalysis approach should help in the development of novel therapeutic strategies targeting diabetes in the future.

Identifiers

PMID39713028
PMCPMC11659994

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.