Evidence map›Paper›PMID 40411388›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025

A Wearable In-Pad Diagnostic for the Detection of Disease Biomarkers in Menstruation Blood.

Lucas Dosnon, Thomas Rduch, Charlotte Meyer, Inge K Herrmann

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Review
  10. Review
  11. A Wearable In-Pad Diagnostic for the Detection of Disease Biomarkers in Menstruation Blood.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  12. 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

4 authors.

Lucas DosnonNanoparticle Systems Engineering Laboratory, Institute of Energy and Process Engineering (IEPE), Department of Mechanical and Process Engineering (D-MAVT), ETH Zurich, Sonneggstrasse 3, Zurich, 8092, Switzerland.
Thomas RduchParticles Biology Interactions Laboratory, Department of Materials Meet Life, Swiss Federal Laboratories for Materials Science and Technology (Empa), Lerchenfeldstrasse 5, St. Gallen, 9014, Switzerland.
Charlotte MeyerNanoparticle Systems Engineering Laboratory, Institute of Energy and Process Engineering (IEPE), Department of Mechanical and Process Engineering (D-MAVT), ETH Zurich, Sonneggstrasse 3, Zurich, 8092, Switzerland.
Inge K HerrmannNanoparticle Systems Engineering Laboratory, Institute of Energy and Process Engineering (IEPE), Department of Mechanical and Process Engineering (D-MAVT), ETH Zurich, Sonneggstrasse 3, Zurich, 8092, Switzerland.ORCID https://orcid.org/0000-0002-3018-6796

Funding

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 181290Vontobel-Stiftung
6 · The paper itself

Abstract

The pain-free monitoring of blood-based biomarkers is essential for early detection of diseases like cancers, infections, and metabolic disorders such as diabetes. While research often focuses on venous blood analysis, menstruation blood is an overlooked but promising source. Evidence shows a strong correlation between biomarker levels in menstruation and venous blood for many clinical analytes. A wearable, microfluidic monitoring platform integrated into hygiene pads is presented for electronic-free, naked-eye detection of disease biomarkers in menstruation blood (MenstruAI). Semi-quantitative detection of C-reactive protein (CRP), cancer biomarkers carcinoembryonic antigen (CEA) and cancer antigen 125 (CA-125), and endometriosis biomarker CA-125 is demonstrated. The biomarker-induced color changes can be read by the naked eye or a smartphone app with a machine-learning algorithm for semi-quantitative analysis. MenstruAI can revolutionize women's health by offering a non-invasive, affordable, and accessible health monitoring method, democratizing healthcare, and enhancing service availability and equity.

Indexed as

BiomarkersMenstruationWearable Electronic DevicesCA-125 AntigenCarcinoembryonic AntigenC-Reactive ProteinEndometriosisFemaleHumansBiomarkersCA-125 AntigenCarcinoembryonic AntigenC-Reactive Proteinbiomarkersbloodmenstruationnon‐invasivewearablewomen's health

Identifiers

PMID40411388
PMCPMC12407263

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.