Evidence map›Paper›PMID 40710075›Full record

ArticleBiosensors2025

Labeled Bovine Serum Albumin as a Fluorescent Biosensor to Monitor the Stability of Lipid-Based Formulations.

Stefania Bova, Serena Faggiano, Omar De Bei, Marialaura Marchetti, Stefano Bruno, Barbara Campanini, Stefano Bettati, Luca Ronda

Abstract read
In one paragraph

Article in Biosensors, 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

8 authors.

Stefania BovaSITEIA.PARMA, University of Parma, 43124 Parma, Italy.ORCID 0000-0001-9786-2413
Serena FaggianoDepartment of Food and Drug, University of Parma, 43124 Parma, Italy.ORCID 0000-0002-7447-9945
Omar De BeiDepartment of Medicine and Surgery, University of Parma, 43125 Parma, Italy.ORCID 0000-0003-3916-8858
Marialaura MarchettiDepartment of Medicine and Surgery, University of Parma, 43125 Parma, Italy.ORCID 0000-0002-4615-6786
Stefano BrunoDepartment of Food and Drug, University of Parma, 43124 Parma, Italy.ORCID 0000-0002-7890-2472
Barbara CampaniniDepartment of Food and Drug, University of Parma, 43124 Parma, Italy.ORCID 0000-0003-0359-0627
Stefano BettatiSITEIA.PARMA, University of Parma, 43124 Parma, Italy.ORCID 0000-0001-6787-0594
Luca RondaInstitute of Biophysics, National Research Council, 56124 Pisa, Italy.ORCID 0000-0002-5389-2669

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the pharmaceutical field, lipid-based nanoparticles are extensively used for drug or vaccine delivery, particularly for treating respiratory disorders. However, their physico-chemical instability, particularly associated with lipid degradation through hydrolysis or oxidation, can affect their encapsulation properties. To monitor the stability of lipid-based formulations over time, we prepared acrylodan-labeled bovine serum albumin (here called albuminodan), and showed it is a fluorescent biosensor capable of concomitantly detect phospholipids as well as their degradation products, i.e., fatty acids and lysophospholipids. We demonstrated that this tool can be used to follow the distribution of lipids in an aqueous phase and hence could be suitable to characterize the hydrolysis of phospholipids in a lipid-based formulation to monitor the stability of nanoparticles.

Indexed as

Biosensing TechniquesLipidsSerum Albumin, BovineAnimalsCattleNanoparticlesPhospholipidsLipidsPhospholipidsSerum Albumin, BovineacrylodanalbuminfluorescenceFRETlipid-based nanoparticlesphospholipid hydrolysisprotein biosensor

Identifiers

PMID40710075
PMCPMC12293067

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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.