ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
Fully Automated Deep Learning Enabled Miniature Mass Spectrometry System for Psychoactive Therapeutic Drug Monitoring.
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 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed.
- Interpol review of forensic drug chemistry, 2022-2025.Forensic science international. Synergy · 2026Review
- Lab-on-a-Chip and Microfluidics Technologies for Nano Drug Delivery.Bioengineering (Basel, Switzerland) · 2026Review
- Fully Automated Deep Learning Enabled Miniature Mass Spectrometry System for Psychoactive Therapeutic Drug Monitoring.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Advancing precision medicine requires efficient small molecule biomarker detection in biofluids, yet existing methods encounter challenges in complexity, portability, and throughput. This study presents an integrated miniature blood processing and mass spectrometry (MS) analysis system, which incorporates automated magnetic solid-phase extraction, self-aspiration sampling miniature mass spectrometer, and deep learning algorithms for automated quantitative analysis. It achieves full automation from sample preparation to detection, demonstrating the capability to analyze serum psychoactive drugs with a 15-second/MS acquisition and 8-sample parallel processing within 30 minutes (including pretreatment). This has significantly increased detection throughput and facilitated the establishment of the standard curve. The novel dual-target ion parallel tandem MS analysis technique, combined with a U-net peak area recognition algorithm, achieved over 98% identification accuracy with less than 0.2% area prediction deviation. Quantitative analysis showed high correlation coefficients >0.99 in medically relevant ranges, supported by relative standard deviation < 10% and average back-calculated accuracy deviation < 3.5%. Clinical validation revealed strong concordance with LC-MS/MS. The system's integration of automated sample processing, miniature MS hardware, and AI-driven data analysis establishes a paradigm for high-throughput clinical detection. The advantages of accuracy, automation, intelligence, miniaturization, and high throughput suggest significant potential for this system in clinical detection and personalized medicine.
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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.