An initial exploration of machine learning for establishing associations between genetic markers and THC levels in Cannabis sativa samples.

Cannabis sativa, a globally commercialized plant used for medicinal, food, fiber production, and recreation, necessitates effective identification to distinguish legal and illegal varieties in forensic contexts. This research utilizes multivariate statistical models and Machine Learning approaches to establish correlations between specific genotypes and tetrahydrocannabinol (Δ9-THC) content (%) in C. sativa samples. 132 cannabis leaves samples were obtained from legal growers in Piedmont, Italy, and illegal drug seizures in Turin. Samples were genetically profiled using a 13-loci STR multiplex and their Δ9-THC content was detected through quantitative GC-MS analysis. This study aims to assess the use of supervised classification modelling on genetic data to distinguish cannabis samples into legal and illegal categories, revealing distinct clusters characterized by unique allele profiles and THC content. t-distributed Stochastic Neighbor Embedding (t-SNE), Random Forest (RF) and Partial Least Squares Regression (PLS-R) were executed for the machine learning modelling. All the tested models resulted effective discriminating between legal samples and illegal. Although further validation is necessary, this study presents a novel forensic investigative approach, potentially aiding law enforcement in significant marijuana seizures or tracking illicit drug trafficking routes.

Development and validation of the UHPLC-MS/MS method for the quantitative determination of 25 PFAS in dried blood spots.

Per- and polyfluoroalkyl substances (PFAS) are anthropogenic fluorine-containing compounds largely used in industrial and consumer applications. They tend to bioaccumulate in the human body after intake from various sources in daily life. Following repeated exposure to PFAS, a broad range of adverse health outcomes has been reported. Consequently, monitoring PFAS levels in human blood is of paramount importance for public health policies. In contrast with traditional venipuncture, dried blood spots (DBS) constitute a reliable, cheap, and less invasive technique to allow microsampling by capillary blood collected on a specific device. This work aimed to develop and validate an innovative analytical method, combining quantitative DBS with UHPLC-MS/MS instrumentation to identify and quantify 25 PFAS. The extraction procedure was developed and optimized within the range 2-100 ng/mL. Specifically, fortified blood was applied on Capitainer®B devices providing 10 µL of blood volume through a microfluidic channel. After 3 h of drying, the extraction was performed by methanol under sonication, followed by centrifugation. Then, the extraction solvent was evaporated; the residue was reconstituted with the mobile phase solution. The validated method evidenced good sensitivity, with limits of detection ranging from 0.4 ng/mL (PFODA, PFOS) to 1.0 ng/mL (PFOA, 3,6-OPFHpA). The ± 20% acceptability criteria established for intra- and inter-day precision and accuracy were fulfilled for all analytes. High recovery-above 80%-was recorded, whereas significant matrix effect resulted in ion enhancement (> 50%) for 13 analytes. In conclusion, the proposed workflow proved to be reliable, fit for purpose, and easily adaptable in the laboratory routine.

Detecting novel psychoactive substances around the world.

PURPOSE OF REVIEW: The worldwide spread of novel psychoactive substances (NPS) in the illicit drug market and their continuous increase in number and type, for the purpose of bypassing controlled substance legislation, represents a continuing challenge for forensic scientists, clinicians and enforcement authorities. We aim to provide information regarding the most urgent harms related to NPS consumption in different world regions and the current state of the art for NPS analysis. RECENT FINDINGS: Unfortunately, the identification of NPS in biological samples is controversial, especially when samples are limited, or the drug is promptly and extensively metabolized. This causes a lack of information on their real diffusion in different parts of the world and in different populations. New technologies and instrumental detection of NPS in alternative samples are offering comprehensive information about NPS use. SUMMARY: The lack of detection and underreporting of NPS in biological samples makes it difficult to obtain complete qualitative and quantitative information about NPS prevalence. The most innovative strategies that have been proposed in the last 2 years to assist NPS analysis and possibly facilitate the understanding of the NPS diffusion around the world are presented.

Enhancing breast cancer screening with urinary biomarkers and Random Forest supervised classification: A comprehensive investigation.

OBJECTIVES: Urinary sex hormones are investigated as potential biomarkers for the early detection of breast cancer, aiming to evaluate their relevance and applicability, in combination with supervised machine-learning data analysis, toward the ultimate goal of extensive screening. METHODS: Sex hormones were determined on urine samples collected from 250 post-menopausal women (65 healthy - 185 with breast cancer, recruited among the clinical patients of Candiolo Cancer Institute FPO-IRCCS (Torino, Italy). Two analytical procedures based on UHPLC-MS/HRMS were developed and comprehensively validated to quantify 20 free and conjugated sex hormones from urine samples. The quantitative data were processed by seven machine learning algorithms. The efficiency of the resulting models was compared. RESULTS: Among the tested models aimed to relate urinary estrogen and androgen levels and the occurrence of breast cancer, Random Forest (RF) proved to underscore all the other supervised classification approaches, including Partial Least Squares - Discriminant Analysis (PLS-DA), in terms of effectiveness and robustness. The final optimized model built on only five biomarkers (testosterone-sulphate, alpha-estradiol, 4-methoxyestradiol, DHEA-sulphate, and epitestosterone-sulphate) achieved an approximate 98% diagnostic accuracy on replicated validation sets. To balance the less-represented population of healthy women, a Synthetic Minority Oversampling TEchnique (SMOTE) data oversampling approach was applied. CONCLUSIONS: By means of tunable hyperparameters optimization, the RF algorithm showed great potential for early breast cancer detection, as it provides clear biomarkers ranking and their relative efficiency, allowing to ground the final diagnostic model on a restricted selection five steroid biomarkers only, as desirable for noninvasive tests with wide screening purposes.

Metabolic profile of N-ethylhexedrone, N-ethylpentedrone, and 4-chloromethcathinone in urine samples by UHPLC-QTOF-HRMS.

Forensic laboratories are constantly required to identify new drugs and their metabolites. N-ethylhexedrone (NEH, HEXEN), N-Ethylpentedrone (NEP), and 4-Chloromethcathinone (4-CMC, clephedrone) are synthetic substances structurally related to natural cathinone, alkaloid present in the leaves of the Catha edulis (Khat) plant. These synthetic cathinones (SC) are members of the heterogenous family of new psychoactive substances (NPS) that raised major concerns in scientific and forensic communities over the past years due to their widespread consumption. In this context, we investigated their metabolic profile using of UHPLC-QTOF-HRMS to elucidate the distribution of the parent drug and its metabolites in urine samples over time. Initially, both male and female volunteers were divided into three groups and eight subjects of each group were administered intranasally or orally with one SC (20-40 mg of NEH or NEP intranasal, 100-150 mg of 4-CMC oral). Urine samples were collected at 0-2 and 2-4 or 2-5 h. Urine (50 µL) was diluted 1:2 with acetonitrile/methanol (95:5) and injected into the UHPLC-QTOF-HRMS. Phase-I and phase-II metabolites were identified on the basis of fragmentation patterns and exact masses. Several phase-I and glucuronide-phase-II metabolites were identified in urine samples. Keto group reduction, hydroxylation and dealkylation were the common metabolic pathways identified for all cathinones and the presence of NEH-glucuronide, NEP-glucuronide and 4-CMC-glucuronide was also relevant. Significant is the slower metabolite formation for 4-CMC, which was detected at high concentrations in its original form even 5 h after administration, due to its long half-life and low intrinsic clearance compared to the other SCs. UHPLC-QTOF-HRMS demonstrated a considerable capability to semi-quantify the three synthetic cathinones and identify the target metabolites with high reliability. The introduction of new target compounds improves the efficiency of toxicological screening analysis on real samples and extends the window of detection of the SCs in biological matrices.

Method development for the quantification of nine nitazene analogs and brorphine in Dried Blood Spots utilizing liquid chromatography - tandem mass spectrometry.

The detection of nitazenes in biological fluids is increasingly needed as they are repeatedly reported in intoxication and overdose cases. A simple method for the quantification of low levels of nine nitazene analogs and brorphine in Dried Blood Spots (DBS) was developed and validated. 10 µL of spiked whole blood is deposited on a Capitainer®B card and allowed to dry. The spot is punched out, and extracted with 500 µL methanol:acetonitrile (3:1 v/v) added with 1.5 µL of fentanyl-D5 as the internal standard. After stirring, sonication, and centrifugation of the vial, the solvent is dried under nitrogen, the extract is reconstituted in 30 µL methanol, and 1 µL is injected into a UHPLC-MS/MS instrument. The method validation showed linear calibration in the 1-50 ng/mL range, LOD values ranging between 0.3 ng/mL (isotonitazene) and 0.5 ng/mL (brorphine), average CV% and bias% within 15 % and 10 % for all compounds, respectively. The matrix effect due to blood and filter paper components was within 85-115 % while recovery was between 15-20 %. Stability tests against time and temperature showed no significant variations for storage periods up to 28 days. Room temperature proved to represent the best samples storage conditions. UHPLC-MS/MS proved capable to reliably identify all target analytes at low concentration even in small specimen volumes, as those obtained from DBS cards, which in turn confirmed to be effective and sustainable micro-sampling devices. This procedure improves the efficiency of toxicological testing and provides an innovative approach for the identification of the nitazene class of illicit compounds.