AIMC Topic: Illicit Drugs

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Support Vector Machine Classification of Adulterated Illicit Opioids Using Paper-Spray Mass Spectrometry.

Analytical chemistry
The complexity and potency of the illicit opioid supply in North America has become increasingly concerning for people who use drugs. Drug checking efforts aim to keep up with the evolving psychoactive components present in the illicit drug supply. W...

From Lab to Body: Advanced Electrochemical Biosensors for Illicit Drug Detection via Nanomaterials, AI, and Wearable Tech.

ACS sensors
Illicit drug detection is entering a transformative era, driven by the convergence of electrochemical sensing, nanomaterials engineering, and artificial intelligence. Traditional analytical approaches, despite their precision, are increasingly misali...

Electrochemical "Super-Fingerprinting" in Combination with Machine Learning for the On-Site Detection of Illicit Drugs.

ACS sensors
On-site multidrug sensing remains challenging due to the complexity of real samples and the differing detection requirements of individual substances. In the current study, we present successful electrochemical multidrug detection that overcomes thes...

Explainable illicit drug abuse prediction using hematological differences.

Scientific reports
This study aimed to develop a reliable and explainable predictive model for illicit drug use (IDU). The model uses a machine learning (ML) algorithm to predict IDU using hematological differences between illicit drug users (IDUr) and non-users (n-IDU...

Optimized machine learning approaches to combine surface-enhanced Raman scattering and infrared data for trace detection of xylazine in illicit opioids.

The Analyst
Infrared absorption spectroscopy and surface-enhanced Raman spectroscopy were integrated into three data fusion strategies-hybrid (concatenated spectra), mid-level (extracted features from both datasets) and high-level (fusion of predictions from bot...

Machine learning to detect recent recreational drug use in intensive cardiac care units.

Archives of cardiovascular diseases
BACKGROUND: Although recreational drug use is a strong risk factor for acute cardiovascular events, systematic testing is currently not performed in patients admitted to intensive cardiac care units, with a risk of underdetection. To address this iss...

X-ray absorption spectroscopy combined with deep learning for auto and rapid illicit drug detection.

The American journal of drug and alcohol abuse
X-ray absorption spectroscopy (XAS) is a widely used substance analysis technique. It bases on the different absorption coefficients at different energy level to achieve material identification. Additionally, the combination of spectral technology a...

3D-printed portable device for illicit drug identification based on smartphone-imaging and artificial neural networks.

Talanta
In this manuscript, a 3D-printed analytical device has been successfully developed to classify illicit drugs using smartphone-based colorimetry. Representative compounds of different families, including cocaine, 3,4-methylenedioxy-methamphetamine (MD...

PSMS: A Deep Learning-Based Prediction System for Identifying New Psychoactive Substances Using Mass Spectrometry.

Analytical chemistry
The rapid proliferation of new psychoactive substances (NPS) poses significant challenges to conventional mass-spectrometry-based identification methods due to the absence of reference spectra for these emerging substances. This paper introduces PSMS...

Deep Learning-Enabled MS/MS Spectrum Prediction Facilitates Automated Identification Of Novel Psychoactive Substances.

Analytical chemistry
The market for illicit drugs has been reshaped by the emergence of more than 1100 new psychoactive substances (NPS) over the past decade, posing a major challenge to the forensic and toxicological laboratories tasked with detecting and identifying th...