Evidence map›Paper›PMID 37247067›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2023

Hepatic Stellate Cell Targeting Using Peptide-Modified Biologicals.

Ruchi Bansal, Klaas Poelstra

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Article in Methods in molecular biology (Clifton, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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.

Ruchi BansalTranslational Liver Research, Department of Medical Cell BioPhysics, Technical Medical Centre, Faculty of Science and Technology, University of Twente, Enschede, The Netherlands. r.bansal@utwente.nl.
Klaas PoelstraDepartment of Nanomedicine and Drug Targeting, Groningen Research Institute of Pharmacy, University of Groningen, Groningen, The Netherlands. k.poelstra@rug.nl.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Liver diseases are a leading cause of death worldwide and are rising exponentially due to increasing prevalence of metabolic disorders. Hepatic stellate cells (HSCs) are recognized as a key therapeutic target in liver diseases as these cells, upon activation during liver damage and ongoing liver inflammation, secrete excessive amounts of extracellular matrix that leads to liver tissue scarring (fibrosis) responsible for liver dysfunction (end-stage liver disease) and desmoplasia in hepatocellular carcinoma. Targeting of HSCs to reverse fibrosis progression has been realized by several experts in the field, including us. We have developed strategies to target activated HSCs by utilizing the receptors overexpressed on the surface of activated HSCs. One well-known receptor is platelet derived growth factor receptor-beta (PDGFR-β). Using PDGFR-β recognizing peptides (cyclic PPB or bicyclic PPB), we can deliver biologicals, e.g., interferon gamma (IFNγ) or IFNγ activity domain (mimetic IFNγ), to the activated HSCs that can inhibit their activation and reverse liver fibrosis. In this chapter, we provide the detailed methods and the principles involved in the synthesis of these targeted (mimetic) IFNγ constructs. These methods can be adapted for synthesizing constructs for targeted/cell-specific delivery of peptides/proteins, drugs, and imaging agents useful for various applications including diagnosis and treatment of inflammatory and fibrotic diseases and cancer.

Indexed as

Biological ProductsLiver DiseasesFibrosisHepatic Stellate CellsHumansPeptidesBiological ProductsPeptidesChemical conjugationHepatic stellate cellsHSCs targeting strategiesPDGFR-βReceptor targeting peptidesTargeted biologicals

Identifiers

What OpenQuestion holds

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Registered trials

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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.