Evidence map›Paper›PMID 40111434›Full record

ArticleNano letters2025

Deep Learning-Assisted SERS for Therapeutic Drug Monitoring of Clozapine in Serum on Plasmonic Metasurfaces.

Peng Zheng, Steve Semancik, Ishan Barman

Abstract read
In one paragraph

Article in Nano letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

3 authors.

Peng ZhengDepartment of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States.ORCID 0000-0001-5907-8505
Steve SemancikBiomolecular Measurement Division, Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States.ORCID 0000-0002-3930-7726
Ishan BarmanDepartment of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States.ORCID 0000-0003-0800-0825

Funding

Next-generation optical nanoprobes: From quantum biosensing to cellular monitoringR35GM149272 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI ISHAN BARMAN · 2023 to 2026
$1.6M
NIGMS NIH HHS R35 GM149272
6 · The paper itself

Abstract

Clozapine is widely regarded as one of the most effective therapeutics for treatment-resistant schizophrenia. Despite its proven efficacy, the therapeutic use of clozapine is complicated by its narrow therapeutic index, which necessitates rapid and precise therapeutic drug monitoring (TDM) to optimize patient outcomes and minimize adverse effects. However, conventional techniques, such as high-performance liquid chromatography, are limited by their high costs, complex instrumentation, and long turnaround times. Herein, we propose a novel approach that integrates artificial neural networks (ANNs) with surface-enhanced Raman spectroscopy (SERS) on a plasmonic metasurface for rapid TDM of clozapine and its two primary metabolites, norclozapine and clozapine-N-oxide, in human serum. The ANN-SERS strategy enables accurate classification and robust concentration prediction of the three analytes. We envision that the integrated ANN-SERS framework could deliver a scalable biomedical diagnostic and therapeutic tool for studying a wide variety of chemical and biological molecules in clinical settings.

Indexed as

Antipsychotic AgentsClozapineDeep LearningDrug MonitoringSpectrum Analysis, RamanHumansNeural Networks, ComputerSchizophreniaAntipsychotic AgentsClozapineclozapine N-oxidenorclozapineClozapineDeep LearningPlasmonic MetasurfaceRaman SpectroscopySERS

Identifiers

PMID40111434
PMCPMC13001891

What OpenQuestion holds

Textmetadata
LicenceTDM
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.