ArticleSTAR protocols2023
Protocol to fabricate wearable stretchable microneedle-based sensors.
Article in STAR protocols, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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.
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Who cites it
2 citing papers in PubMed, 2 citations in OpenAlex.
- Wearable Electronics for Precision Diagnosis Through Advanced Manufacturing and Integration.Nano-micro letters · 2026Review
- Real-time prognostic biomarkers for predicting in-hospital mortality and cardiac complications in COVID-19 patients.PLOS global public health · 2024Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Creating highly stretchable and robust electrodes while retaining conductivity and stability is challenging. Furthermore, combining these elastic parts with rigid ones brings its own problems due to the discrepancy in firmness between the flexible patches and rigid constructions. Here, we present a protocol to create a stable, conductive, and flexible microneedle sensor patch. We describe steps for using polystyrene-block-polyisoprene-block-polystyrene with silver nanowires, besides fabricating rigid microneedles and combining them together using a thickness-gradient strategy. For complete details on the use and execution of this protocol, please refer to Zheng et al. (2022).
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Registered trials
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.