Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 234

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Insulin-Like Growth Factor Receptors

Nissley, Peter, Lopaczynski, Wlodzimierz·1991·Growth Factors

Abstract

Competitive binding experiments from a number of laboratories showed that IGF receptors are distinct from insulin receptors, and that there are two types of IGF receptors based on their relative affinities for IGF-I and IGF-II and whether or not they bind insulin (reviewed in Rechler and Nissley, 1985). Later, affinity crosslinking experiments provided a physical basis for the two IGF receptor subtypes. The receptor which preferred IGF-II over IGF-I and did not bind insulin, was a large 250 kDa species with no apparent subunit structure (Fig. 1). The receptor which prefered IGF-I over IGF-II and bound insulin with low affinity had a binding sub-unit of 130 kDa after reduction of disulfide bonds. Biosynthetic labeling experiments demonstrated a 95 kDa beta subunit addition to the 130 kDa alpha subunit (Fig. 1). The heterotetrameric IGF-I receptor (a,& was therefore very similar to the insulin receptor, and the case for similarity was increased when the beta subunit was found to be auto-phosphorylated in response to IGF-I. Molecular cloning of the IGF-I receptor confirmed that the IGF-I receptor and the insulin receptor areclosely related structures (Ullrich et al., 1986).

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3109/08977199109000269

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 1 references

Metadata

Authors
Nissley, P., & Lopaczynski, W.
Venue
Growth Factors
Published
1991-01
Volume
5
Issue
1
Pages
29-43
ISSN
  • 0897-7194 (ISSN-L)
  • 1029-2292
OpenAlex
W2117324144
Cited by
234
References
1
FWCI
13.27
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Insulin-Like Growth Factor Receptors. https://app.cochat.ai/papers/10.3109/08977199109000269
The paper
Nissley, P., & Lopaczynski, W. (1991). Insulin-Like Growth Factor Receptors. Growth Factors. https://doi.org/10.3109/08977199109000269

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.