Evidence map›Paper›PMID 35618637›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2022

Bioresorbable Nanostructured Chemical Sensor for Monitoring of pH Level In Vivo.

Martina Corsi, Alessandro Paghi, Stefano Mariani, Giulia Golinelli, Aline Debrassi, Gabriella Egri, Giuseppina Leo, Eleonora Vandini, Antonietta Vilella, Lars Dähne and 2 more

Open access · goldAbstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
7.7field-weighted citation impact, top 2% of its field
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

20 citing papers in PubMed, 57 citations in OpenAlex.

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  4. A Review on Biodegradable Materials of Sustainable Soft Robotics and Electronics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
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  11. Design and Development of Transient Sensing Devices for Healthcare Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
    Review
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  14. Review
  15. Review
  16. Dual-Lifetime Referencing (Sensors (Basel, Switzerland) · 2023
    Article
  17. Review
  18. Article
  19. Article
  20. Bioresorbable Nanostructured Chemical Sensor for Monitoring of pH Level In Vivo.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022
    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

12 authors at 3 institutions in 2 countries.

Martina CorsiDipartimento di Ingegneria dell'Informazione, Università di Pisa, via G. Caruso 16, Pisa, 56122, Italy.
Alessandro PaghiDipartimento di Ingegneria dell'Informazione, Università di Pisa, via G. Caruso 16, Pisa, 56122, Italy.
Stefano MarianiDipartimento di Ingegneria dell'Informazione, Università di Pisa, via G. Caruso 16, Pisa, 56122, Italy.
Giulia GolinelliDepartment of Medical and Surgical Sciences for Children & Adults, University-Hospital of Modena and Reggio Emilia, Via del Pozzo 71, Modena, 41124, Italy.
Aline DebrassiSurflay Nanotec GmbH, Max-Planck-Straße 3, 12489, Berlin, Germany.
Gabriella EgriSurflay Nanotec GmbH, Max-Planck-Straße 3, 12489, Berlin, Germany.
Giuseppina LeoDepartment of Biomedical Metabolic and Neural Sciences, University of Modena and Reggio Emilia, via G. Campi 287, Modena, 41125, Italy.
Eleonora VandiniDepartment of Biomedical Metabolic and Neural Sciences, University of Modena and Reggio Emilia, via G. Campi 287, Modena, 41125, Italy.
Antonietta VilellaDepartment of Biomedical Metabolic and Neural Sciences, University of Modena and Reggio Emilia, via G. Campi 287, Modena, 41125, Italy.
Lars DähneSurflay Nanotec GmbH, Max-Planck-Straße 3, 12489, Berlin, Germany.
Daniela GiulianiDepartment of Biomedical Metabolic and Neural Sciences, University of Modena and Reggio Emilia, via G. Campi 287, Modena, 41125, Italy.
Giuseppe BarillaroDipartimento di Ingegneria dell'Informazione, Università di Pisa, via G. Caruso 16, Pisa, 56122, Italy.ORCID 0000-0001-6197-4851
University of Modena and Reggio Emilia · ITUniversity of Pisa · ITSurflay Nanotec (Germany) · DE

Funding

European Union Horizon Europe 101046946Italian Ministry of Education, University and Research
6 · The paper itself

Abstract

Here, the authors report on the manufacturing and in vivo assessment of a bioresorbable nanostructured pH sensor. The sensor consists of a micrometer-thick porous silica membrane conformably coated layer-by-layer with a nanometer-thick multilayer stack of two polyelectrolytes labeled with a pH-insensitive fluorophore. The sensor fluorescence changes linearly with the pH value in the range 4 to 7.5 upon swelling/shrinking of the polymer multilayer and enables performing real-time measurements of the pH level with high stability, reproducibility, and accuracy, over 100 h of continuous operation. In vivo studies carried out implanting the sensor in the subcutis on the back of mice confirm real-time monitoring of the local pH level through skin. Full degradation of the pH sensor occurs in one week from implant in the animal model, and its biocompatibility after 2 months is confirmed by histological and fluorescence analyses. The proposed approach can be extended to the detection of other (bio)markers in vivo by engineering the functionality of one (at least) of the polyelectrolytes with suitable receptors, thus paving the way to implantable bioresorbable chemical sensors.

Indexed as

Absorbable ImplantsNanostructuresAnimalsHydrogen-Ion ConcentrationMicePolyelectrolytesReproducibility of ResultsPolyelectrolytesbioresorbable chemical sensorfluorescence sensorimplanted chemical sensorin vivo pH sensorlayer-by-layer (LbL)porous siliconwireless sensor

Identifiers

PMID35618637
PMCPMC9353472
OpenAlexW4281855877

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.