Lab-on-the-Needles: A Microneedle Patch-Based Mobile Unit for Highly Sensitive Ex Vivo and in Vivo Detection of Protein Biomarkers
ACS NANO（2025）
Natl Sun Yat Sen Univ
Abstract
Detection of biomarkers associated with physiological conditions provides critical insights into healthcare and disease management. However, challenges in sampling and analysis complicate the detection and quantification of protein biomarkers within the epidermal layer of the skin and in viscous liquid biopsy samples. Here, we present the "Lab-on-the-Needles" concept, utilizing a microneedle patch-based sensing box (MNP-based SenBox) for mobile healthcare applications. This system facilitates the rapid capture of protein biomarkers directly from the in situ epidermal layer of skin or liquid biopsies, followed by on-needle analysis for immediate assessment. The integration of horseradish peroxidase-incorporated zeolitic imidazolate framework-8 (HRP@ZIF-8) as a sensitive and stable signal probe, the detection limit for anti-SARS-CoV-2 NP IgA antibodies and various SARS-CoV-2 S1P mutant strains improves by at least 1,000-fold compared to FDA-approved commercial saliva lateral flow immune rapid tests. Additionally, the MNP-based SenBox demonstrated minimally invasive monitoring and rapid quantification of inflammatory cytokine levels (TNF-α and IL-1β) in rats within 30 min using a portable ColorReader. This study highlights the potential of the MNP-based SenBox for the minimally invasive collection and analysis of protein biomarkers directly from in situ epidermal layers of skin or liquid biopsies that might facilitate mobile healthcare diagnostics and longitudinal monitoring.
MoreTranslated text
Key words
microneedle patch (MNP),zeolitic imidazolate framework-8(ZIF-8),liquid biopsy,transdermal detection,mobile healthcare testing (MHCT)
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined