Evidence map›Paper›PMID 38818126›Full record

ReviewBioengineering & translational medicine2024

Sensor-integrated brain-on-a-chip platforms: Improving the predictive validity in neurodegenerative research.

Sarah Spitz, Silvia Schobesberger, Konstanze Brandauer, Peter Ertl

Abstract readReview
In one paragraph

Review in Bioengineering & translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 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

4 authors.

Sarah SpitzFaculty of Technical Chemistry Vienna University of Technology Vienna Austria.ORCID https://orcid.org/0000-0001-7980-7955
Silvia SchobesbergerFaculty of Technical Chemistry Vienna University of Technology Vienna Austria.
Konstanze BrandauerFaculty of Technical Chemistry Vienna University of Technology Vienna Austria.
Peter ErtlFaculty of Technical Chemistry Vienna University of Technology Vienna Austria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Affecting millions of individuals worldwide, neurodegenerative diseases (NDDs) pose a significant and growing health concern in people over the age of 60 years. Contributing to this trend are the steady increase in the aging population coupled with a persistent lack of disease-altering treatment strategies targeting NDDs. The absence of efficient therapeutics can be attributed to high failure rates in clinical trials and the ineptness of animal models in preceding preclinical studies. To that end, in recent years, significant research effort has been dedicated to the development of human cell-based preclinical disease models characterized by a higher degree of predictive validity. However, a key requirement of any in vitro model constitutes the precise knowledge and replication of the target tissues' (patho-)physiological microenvironment. Herein, microphysiological systems have demonstrated superiority over conventional static 2D/3D in vitro cell culture systems, as they allow for the emulation and continuous monitoring of the onset, progression, and remission of disease-associated phenotypes. This review provides an overview of recent advances in the field of NDD research using organ-on-a-chip platforms. Specific focus is directed toward non-invasive sensing strategies encompassing electrical, electrochemical, and optical sensors. Additionally, promising on- and integrable off-chip sensing strategies targeting key analytes in NDDs will be presented and discussed in detail.

Indexed as

microfluidicsmicrophysiological systemsneurodegenerative diseasesnon‐invasive monitoringorgan‐on‐a‐chip technologysensors

Identifiers

PMID38818126
PMCPMC11135156

What OpenQuestion holds

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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 OpenQuestion graph.