Evidence map›Paper›PMID 39943290›Full record

ArticleSensors (Basel, Switzerland)2025

Repackaging and Performance Analysis of Implantable Pressure Sensor.

Liu Cui, Shuangkui Wang, Kai Zhao, Zhisen Si

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 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

4 authors.

Liu CuiDepartment of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai 201418, China.ORCID 0000-0002-3705-5115
Shuangkui WangDepartment of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai 201418, China.
Kai ZhaoSchool of Electronics, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Zhisen SiDepartment of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai 201418, China.

Funding

National Natural Science Foundation of China 61973307, 61903256,62472272Shanghai Science and Technology Innovation Action Plan 22S31903900
6 · The paper itself

Abstract

In recent years, repackaging technology has been widely used in miniaturized implantable pressure sensors. However, the current packaging structure still has significant problems regarding biocompatibility, environmental adaptability, and measurement accuracy, which greatly limits its application in vivo measurement systems. In this paper, we report a method for implantable pressure sensor repackaging based on silicone oil, polydimethylsiloxane (PDMS) film, and polymer (parylene) coating. A systematic investigation using finite element analysis is conducted to assess the impact of packaging components on sensor performance, providing a solid theoretical foundation for packaging optimization. Experimental results demonstrate that when the parylene coating thickness is below 30 µm, the sensors exhibit superior linearity, repeatability, and reliability, along with exceptional stability and dynamic response across clinically relevant pressure ranges. This research provides valuable insights into the packaging design of implantable pressure sensors, facilitating the development of more stable, reliable, and cost-effective sensors for in vivo measurement systems.

Indexed as

Biosensing TechniquesProduct PackagingProstheses and ImplantsDimethylpolysiloxanesEquipment DesignFinite Element AnalysisHumansPolymersPressureXylenesbaysilonDimethylpolysiloxanesparylenePolymersXyleneselectronic capsule systemimplantable pressure sensorparylene coatingPDMSsensor repackaging

Identifiers

PMID39943290
PMCPMC11820466

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.