Determination of time since deposition of bloodstains through NIR and UV-Vis spectroscopy - A critical comparison.

Time elapsed since bloodstain deposition is a crucial aspect in forensic investigations, where non-destructive spectroscopic methods play a pivotal role. While extensive research has been conducted by UV-Vis spectroscopy, showcasing its utility in specific cases, there is still a paucity of studies based on NIR spectroscopy, which has the potential to overcome the limitations of the UV-Vis-based methods. To compensate for this disequilibrium, the present study aimed to evaluate the NIR applicability for estimating the age of forensic bloodstains and develop a performance comparison with UV-Vis spectroscopy methods. Capillary blood was sampled and subjected to a 16-day aging, during which it was repeatedly analyzed using both spectroscopic methods. Subsequently, chemometric analysis was applied to process the spectral data and independently assess the methods' performance. Classical preprocessing transforms (i.e., Savitzky-Golay derivatives and SNV transform) were used together with more targeted strategies, such as class centering, whose benefit was highlighted by PCA. Lastly, PLS regression models were computed to evaluate the effectiveness of both spectroscopic methods in estimating the time elapsed since blood trace deposition. Comparable root mean square errors in prediction (RMSEP) - 40 and 55 h for UV-Vis and NIR spectroscopy, respectively - were observed for both techniques, featuring an improvement with respect to the existing literature for NIR spectroscopy. Data fusion strategies for a multi-instrumental platform were also explored, evaluating advantages and disadvantages of low-level and mid-level approaches. The results indicated that NIR spectroscopy integrated with adequate chemometric strategies deserves increased appreciation in forensic bloodstain dating.

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.

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.

A multivariable analysis delineating hair color, hair dyeing, and hat wearing as predictors of level of cocaine and MDMA detection in human hair samples.

Research suggests that hair color, hair dyeing, and perspiration can bias hair test results regarding drug exposure, but research is needed to examine such associations in a multivariable manner. In this epidemiology study, adults were surveyed entering nightclubs and dance festivals in New York City, and 328 provided hair samples, which were analyzed using ultra-high-performance liquid chromatography-tandem mass spectrometry to determine the level of detection of cocaine and 3,4-methylenedioxymethamphetamine (MDMA). Reporting use was not an inclusion criterion for analysis. We used two-part multivariable models to delineate associations of hair color, past-year hair dyeing, and frequency of past-month hat wearing (which may increase perspiration) in relation to any vs. no detection of cocaine and MDMA as well as level of detection, controlling for hair length, self-reported past-year cocaine/ecstasy/MDMA use, and age, sex, and race/ethnicity. Those reporting having dyed their hair were at increased odds of having any level of cocaine detected (adjusted odds ratio [aOR] = 3.75, 95% CI confidence interval [CI]: 1.85-6.70), and compared to those with brown hair, those with blond(e) hair on average had lower levels of cocaine (ng/mg) detected (beta = -7.97, p = 0.025). Those reporting having dyed their hair were at increased odds of having any level of MDMA detected (aOR = 3.05, 95% CI: 1.44-6.48), and compared to those who reported never wearing a hat, those who reported wearing a hat daily or almost daily on average had lower levels of MDMA (ng/mg) detected (beta = -6.61, p = 0.025). This study demonstrates the importance of using multivariable models to delineate predictors of drug detection.

Detection of fentanyl, synthetic opioids, and ketamine in hair specimens from purposive samples of American and Italian populations.

With the current crisis related to the diffusion of fentanyl and other novel opioids in several countries and populations, new and effective approaches are needed to better elucidate the phenomenon. In this context, hair testing offers a unique perspective in the investigation of drug consumption, producing useful information in terms of exposure to psychoactive substances. In this research, we applied targeted ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) analytical methods to detect novel synthetic and prescription opioids and other common controlled psychoactive drugs in the keratin matrix. A total of 120 hair samples were analyzed from the United States (US) and Italy, segmented when longer than 6 cm, and then analyzed. In the 60 samples (83 segments in total) analyzed from a purposive sample of data collected in the US, fentanyl was detected in 14 cases (16.9%), with no detection of nitazens or brorphine. We also detected fentanyl metabolites, despropionyl-p-fluorofentanyl, and prescription opioids. In the 60 samples collected in Italy (91 segments in total), ketamine was the most prevalent compound detected (in 41 cases; 45.1%), with ketamine demonstrating a strong correlation with detection of amphetamines and MDMA, likely due to co-use of these substances in recreational contexts. Several common drugs were also detected but no exposure to fentanyl or its analogs were detected. Results of this retrospective exploration of drug use add to increasing evidence that hair testing can serve as a useful adjunct to epidemiology studies that seek to determine biologically confirmed use and exposure in high-risk populations.

Breaking with trends in forensic dating: A likelihood ratio-based comparison approach.

Further steps toward understanding the time-related information contained within bloodstains found at the crime scene are rightly considered a top priority in forensic science. Contrary to widely held assumptions, the reason for the delayed exploitation of bloodstains dating methods in practice is not the lack of suitable analytical techniques for monitoring degradation processes. The problem lies in the variability of the environmental and circumstantial conditions, playing a vital role in the degradation kinetics of blood deposits. The present article demonstrates the possibility of breaking with current approaches based on absolute age estimations to finally answer time-centered questions in real forensic scenarios. The proposed novel framework for situating forensic traces in time is based on the likelihood ratio assessment of the (dis)similarity between the evidence decomposition and sets of reference materials obtained through supervised aging. In such a strategy, every dating procedure is constructed on a case-by-case basis to fit examined blood traces, thereby limiting the adverse influence of external factors on the validity of age estimations and providing a way for future crime scene implementation.

Wastewater surveillance of 105 pharmaceutical drugs and metabolites by means of ultra-high-performance liquid-chromatography-tandem high resolution mass spectrometry.

Water pollution from pharmaceutical drugs is becoming an environmental issue of increasing concern, making water quality monitoring a crucial priority to safeguard public health. In particular, the presence of antidepressants, benzodiazepines, antiepileptics, and antipsychotics require specific attention as they are known to be harmful to aquatic biota. In this study, a multi-class comprehensive method for the detection of 105 pharmaceutical residues in small (30 mL) water samples was developed according to fit-for-purpose criteria and then applied to provide wide screening of samples obtained from four Wastewater Treatment Plants (WWTPs) in northern Italy. The filtered samples (0.22 µm filters) were extracted by SPE, and then eluted. 5 µL of the concentrated samples were analyzed by a UHPLC-QTOF-HRMS method validated for screening purposes. Adequate sensitivity was recorded for all target analytes, with limits of detection below 5 ng/L for 76 out of 105 analytes. A total of 23 out of the 105 targeted pharmaceutical drugs was detected in all samples. Several further compounds were detected over wide concentration intervals, ranging from ng/L to µg/L. In addition, the retrospective analysis of full-scan QTOF-HRMS data was exploited to carry out an untargeted screening of some drugs' metabolites. As a proof of concept, it was investigated the presence of the carbamazepine metabolites, which is among the most frequently detected contaminants of emerging concern in wastewater. Thanks to this approach, 10,11-dihydro-10-hydroxycarbamazepine, 10,11-dihydro-10,11-dihydroxycarbamazepine and carbamazepine-10,11-epoxide were identified, the latter requiring particular attention, since it exhibits antiepileptic properties similar to carbamazepine and potential neurotoxic effects in living organism.

Metabolic study of new psychoactive substance methoxpropamine in mice by UHPLC-QTOF-HRMS.

Methoxpropamine (MXPr) is an arylcyclohexylamine dissociative drug structurally similar to 3-methoxyeticyclidine, ketamine, and deschloroketamine, recently appeared in the European illegal market, and was classified within the new psychoactive substances (NPS). Our study investigated the metabolism of MXPr to elucidate the distribution of the parent drug and its metabolites in body fluids and fur of 16 mice. After the intraperitoneal administration of MXPr (1, 3, and 10 mg/kg), urine samples from eight male and eight female mice were collected every hour for six consecutive hours and then at 12- to 24-h intervals. Additionally, plasma samples were collected 24 h after MXPr (1 and 3 mg/kg) administration. Urine and plasma were diluted 1:3 with acetonitrile/methanol (95:5) and directly injected into the UHPLC-QTOF-HRMS system. The phase-I and phase-II metabolites were preliminarily identified by means of the fragmentation patterns and the exact masses of both their precursor and fragment ions. Lastly, the mice fur was analyzed following an extraction procedure specific for the keratin matrix. Desmethyl-MXPr-glucoronide was identified in urine as the main metabolite, detected up to 24 h after administration. The presence of norMXPr in urine, plasma, and fur was also relevant, following a N-dealkylation process of the parent drug. Other metabolites that were identified in fur and plasma included desmethyl-MXPr and dihydro-MXPr. Knowledge of the MXPr metabolites evolution is likely to support their introduction as target compounds in NPS toxicological screening analysis on real samples, both to confirm intake and extend the detection window of the dissociative drug MXPr in the biological matrices.

Recent advances in the detection of drugs of abuse by dried blood spots.

The purpose of this review study was to sum up the main recent advances reported in the scientific literature about the detection of common drugs of abuse in biological samples upon their collection by dried blood spot devices. The most recent, innovative and fully validated methods for the qualitative and/or quantitative detection of common drugs of abuse are reported, including alprazolam, clonazepam, diazepam, 3,4-methylenedioxymethamphetamine, 3,4-methylenedioxyamphetamine, 3,4-methyl-enedioxyethylamphetamine, amphetamine, methamphetamine, cocaine, tetrahydrocannabinol, 6-monoacetylmorphine, morphine, codeine, hydromorphone, hydrocodone, oxycodone, noroxycodone, tramadol, methadone, buprenorphine, fentanyl, ketamine and their respective metabolites and γ-hydroxybutyric acid. Dried blood spot proved to be extremely promising for routine analysis of forensic cases, although large-scale experiments on real samples need to be performed to confirm the emerging advantages of the technique and remove the potential limitations still affecting its widespread application.

GC-MS Identification and Quantification of the Synthetic Cannabinoid MDMB-4en- PINACA in Cannabis-derived Material Seized in the Turin Metropolitan Area (Italy).

BACKGROUND: The presence of the synthetic cannabinoid receptor agonist MDMB-4en-PINACA in adulterated low-THC cannabis products was recently highlighted in several reports. Moreover, numerous intoxication cases involving MDMB-4en-PINACA have been described. OBJECTIVE: In order to monitor the diffusion of cannabis products containing MDMB-4en-PINACA in our territory, a total of 358 cannabis-derived samples (213 vegetal material and 145 resins) seized in the period November 2020 - February 2021 in the western Piedmont Area (Italy) was analyzed. METHODS: General screening analyses for traditional and synthetic cannabinoids were performed by a GC-MS device operating in full scan mode (40-600 amu). The MDMB-4en-PINACA was quantified by means of a specific GC-SIM-MS protocol purposely developed and validated, while the quantification of THC, CBD, and CBN was carried out by a GC-SIM-MS method routinely employed in our laboratory. RESULTS: MDMB-4en-PINACA was detected in 12 out of 358 samples (3.4% of the total). Among these, the molecule was found in 11 vegetal materials and in one resin sample. Considering solely the analysis of the 213 herb products, a positive rate of 5.2% was found for the presence of MDMB-4en-PINACA in these samples. MDMB-4en-PINACA was found in the seized materials at concentration levels ranging from 0.4 up to 6.3 mg/g (mean 2.5 mg/g; median 1.7 mg/g). Concerning the traditional cannabinoids, the THC concentration was in the interval 3-43 mg/g (mean 12 mg/g; median 7 mg/g), while CBD was found at higher concentrations in all specimens, specifically in the range 47-140 mg/g (mean 87 mg/g; median 80 mg/g). CONCLUSION: The adulteration of low-THC cannabis products with synthetic cannabinoid receptor agonists is widespread today. Since these substances are potentially more toxic than THC, their consumption poses a high risk of overdose for unaware users and a health-threatening situation. This study confirmed the sporadic presence on the market of CBD-prevalent cannabis products adulterated with MDMB-4en-PINACA.

Non-destructive age estimation of biological fluid stains: An integrated analytical strategy based on near-infrared hyperspectral imaging and multivariate regression.

From a criminalistic point of view, the accurate dating of biological traces found at the crime scene, together with its compatibility with the estimated crime perpetration timeframe, enables to limit the number of suspects by assessing their alibis and clarifying the sequence of events. The present study delineates, for the first time, the possibility of dating biological fluids such as semen and urine, as well as blood traces, by using a novel non-destructive analytical strategy based on hyperspectral imaging in the near infared region (HSI-NIR), coupled with multivariate regression methods. Investigated aspects of the present study include not only the progressive degradation of the biological trace itself, but also the effects of its interactions with the support on which it is absorbed, in particular the hydrophilic vs. hydrophobic character of fabric tissues. Results are critically discussed, highlighting potential and limitations of the proposed approach for a practical implementation.

Development and Validation of a GC-EI-MS/MS Method for Ethyl Glucuronide Quantification in Human Hair.

Ethyl glucuronide (EtG) is a minor, non-oxidative ethanol metabolite detectable in several matrices for specific periods of time. In recent years, quantification of EtG in hair has been established as the most reliable biomarker for long-term alcohol consumption, with the Society of Hair Testing offering cut-off values for assessment of both abstinence and heavy drinking. Instrumental constrains and wide inter- and intra-laboratory variability represent the ultimate barriers to widespread acceptance of hair EtG determination in the forensic context. In this study, a new analytical method for hair EtG based on gas chromatographic (GC) separation, electron impact (EI) ionization, and tandem mass spectrometry (MS/MS) detection was developed and validated. At the same time, several parameters for sample pretreatment and instrumental analysis were optimized using real hair samples obtained from different drinking subjects. A full-factorial design-of-experiment approach included procedures for hair washing, pulverization, and extraction. Rigorous multi-step washing proved not to reduce the EtG content extracted in the subsequent sample incubation. Hair pulverization with a ball mill significantly improved the EtG extraction from the keratin matrix and allowed us to reduce the time needed for the subsequent extraction step, without affecting the extraction recovery. The hair extract was derivatized with N-methyl-N-(trimethylsilyl)-trifluoroacetamide. Upon electron impact ionization of the EtG-TMS derivative, triple quadrupole mass analyzers were operated in the selected reaction monitoring (SRM) mode using the fragment m/z 405 as the precursor ion (m/z 410 for the EtG-D5 internal standard), the transitions m/z 405 → 359 and m/z 410 → 359 for quantitation, and m/z 405 → 331 and m/z 405 → 287 for qualification/confirmation, all at 10 V collision energy. The final method was fully validated and then applied to 25 real hair samples. The calibration curve proved linear between 6 and 60 pg/mg. The limit of detection (LOD) was 4 pg/mg. Intra- and inter-assay precision and accuracy tests showed a variability and bias close to 15% or lower over the entire calibration range. The new method is routinely applied in the Italian State Police's toxicology laboratory for hair analyses addressed to exclude excessive alcohol drinking and verify the psycho-physical requirements of the personnel.

Comprehensive biomarker profiles and chemometric filtering of urinary metabolomics for effective discrimination of prostate carcinoma from benign hyperplasia.

Prostate cancer (PCa) is the most commonly diagnosed cancer in male individuals, principally affecting men over 50 years old, and is the leading cause of cancer-related deaths. Actually, the measurement of prostate-specific antigen level in blood is affected by limited sensitivity and specificity and cannot discriminate PCa from benign prostatic hyperplasia patients (BPH). In the present paper, 20 urine samples from BPH patients and 20 from PCa patients were investigated to develop a metabolomics strategy useful to distinguish malignancy from benign hyperplasia. A UHPLC-HRMS untargeted approach was carried out to generate two large sets of candidate biomarkers. After mass spectrometric analysis, an innovative chemometric data treatment was employed involving PLS-DA classification with repeated double cross-validation and permutation test to provide a rigorously validated PLS-DA model. Simultaneously, this chemometric approach filtered out the most effective biomarkers and optimized their relative weights to yield the highest classification efficiency. An unprecedented portfolio of prostate carcinoma biomarkers was tentatively identified including 22 and 47 alleged candidates from positive and negative ion electrospray (ESI+ and ESI-) datasets. The PLS-DA model based on the 22 ESI+ biomarkers provided a sensitivity of 95 ± 1% and a specificity of 83 ± 3%, while that from the 47 ESI- biomarkers yielded an 88 ± 3% sensitivity and a 91 ± 2% specificity. Many alleged biomarkers were annotated, belonging to the classes of carnitine and glutamine metabolites, C21 steroids, amino acids, acetylcholine, carboxyethyl-hydroxychroman, and dihydro(iso)ferulic acid.

Limited Role of Hair Cortisol and Cortisone Measurement for Detecting Cortisol Autonomy in Patients With Adrenal Incidentalomas.

Several studies demonstrated the diagnostic accuracy of hair glucocorticoid measurement in patients with overt Cushing syndrome, but few data are available for patients with adrenal incidentaloma (AI) and cortisol autonomy. The aim of our study was to assess whether measurement of 5 corticosteroid hormones with the ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method in the keratin matrix is useful to stratify patients with AI by the presence of autonomous cortisol secretion [ACS] (defined as serum cortisol after 1 mg dexamethasone suppression test (DST) > 138 nmol/l) or possible ACS [PACS] (defined as serum cortisol after 1 mg DST > 50 nmol/l but ≤138 nmol/l). We analysed data of 67 AI patients (32 with cortisol autonomy) and 81 healthy subjects. We did not find any significant statistical difference comparing hair cortisol, cortisone, and 20ß-dihydrocortisol concentrations between healthy controls and AI patients, while 6ß-hydroxycortisol and 11-deoxycortisol were undetectable. Moreover, no significant difference was found in hair cortisol, cortisone, and 20ß-dihydrocortisol levels of AI patients with or without cortisol autonomy. Finally, we did not find any correlation in patients with AI between hormonal concentrations in the keratin matrix and serum, salivary, and urinary cortisol levels, or with body mass index. In conclusion, our findings suggest that hair glucocorticoid measurement is not suitable as a diagnostic test for cortisol autonomy (ACS and PACS).

Development and validation of a UHPLC-HRMS-QTOF method for the detection of 132 New Psychoactive Substances and synthetic opioids, including fentanyl, in Dried Blood Spots.

Dried Blood Spots (DBS) represents a promising micro-sampling technique in the field of forensic toxicology to carry out minimally invasive blood sample collection. In DBS, cheap, fast and easy sampling is combined with effortless store and transport. These properties aimed us to develop and validate a quick and easy procedure for the detection of a large and diverse range of emerging and alarming New Psychoactive Substances (NPS). A drop of whole blood sample was collected on a DBS card and dried for 3 h, from which a total of 132 analytes (including NPS, synthetic opioids NSO and metabolites) plus 13 deuterated internal standards could be extracted using 500 µL of a methanol/acetonitrile mixture (3:1, v/v) and subsequently separated and identified by means of ultra-high-performance liquid-chromatography (UHPLC) coupled to high resolution mass spectrometry (HRMS). The extraction efficiency proved to be reproducible with yields ranging from 30% to 100% depending on the different classes of drugs. Trueness, repeatability, and intermediate precision fulfilled acceptance criteria for almost all synthetic opioids, cathinones and hallucinogens (bias and CV% below ±20%); in particular, the aggregate inter-day trueness data showed extremely limited deviation from the expected concentrations (-10% < bias% < +10%) for 114 target analytes out of 132. The calculated limits of detection ranged from 1.3 to 6.3 ng/mL, consistently exceeding the values experimentally tested. Moderate ion suppression was observed for most analytes, partly caused by blood fortification itself. Good stability of the target analytes at -20 °C, 4 °C, and 35 °C on DBS cards after drying was observed, even for long periods of time. Optimal storage condition appeared to be at 4 °C resulting in virtually no drugs degradation for up to 40 days. The novel analytical method based on DBS sampling, verified on venous whole blood real samples previously tested positive with our routine procedure, conveys remarkable potential in analytical toxicology, clinical analysis, and doping control.

Multitarget Antioxidant NO-Donor Organic Nitrates: A Novel Approach to Overcome Nitrates Tolerance, an Ex Vivo Study.

Chronic use of glyceryl trinitrate (GTN) is limited by serious side effects, such as tolerance and endothelial dysfunction of coronary and resistance arteries. Although GTN is used as a drug since more than 130 years, the mechanisms of the vasodilatory effects and of tolerance development to organic nitrates are still incompletely elucidated. New synthesized organic nitrates with and without antioxidant properties were characterized for their ex vivo tolerance profile, in order to investigate the oxidative stress hypothesis of nitrate tolerance. The organic nitrates studied showed different vasodilation and tolerance profiles, probably due to the ability or inability of the compounds to interact with the aldehyde dehydrogenase-2 enzyme (ALDH-2) involved in bioactivation. Furthermore, nitrooxy derivatives endowed with antioxidant properties did not determine the onset of tolerance, even if bioactivated by ALDH-2. The results of this study could be further evidence of the involvement of ALDH-2 in the development of nitrate tolerance. Moreover, the behavior of organic nitrates with antioxidant properties supports the hypothesis of the involvement of ROS in inactivating ALDH-2.

The "DOLPHINS" Project: A Low-Cost Real-Time Multivariate Process Control From Large Sensor Arrays Providing Sparse Binary Data.

The "DOLPHINS" project started in 2018 under a collaboration between three partners: CNH Industrial Iveco (CHNi), RADA (an informatics company), and the Chemistry Department of the University of Turin. The project's main aim was to establish a predictive maintenance method in real-time at a pilot plant (CNHi Iveco, Brescia, Italy). This project currently allows maintenance technicians to intervene on machinery preventively, avoiding breakdowns or stops in the production process. For this purpose, several predictive maintenance models were tested starting from databases on programmable logic controllers (PLCs) already available, thus taking advantage of Machine Learning techniques without investing additional resources in purchasing or installing new sensors. The instrumentation and PLCs related to the truck sides' paneling phase were considered at the beginning of the project. The instrumentation under evaluation was equipped with sensors already connected to PLCs (only on/off switches, i.e., neither analog sensors nor continuous measurements are available, and the data are in sparse binary format) so that the data provided by PLCs were acquired in a binary way before being processed by multivariate data analysis (MDA) models. Several MDA approaches were tested (e.g., PCA, PLS-DA, SVM, XGBoost, and SIMCA) and validated in the plant (in terms of repeated double cross-validation strategies). The optimal approach currently used involves combining PCA and SIMCA models, whose performances are continuously monitored, and the various models are updated and tested weekly. Tuning the time range predictions enabled the shop floor and the maintenance operators to achieve sensitivity and specificity values higher than 90%, but the performance results are constantly improved since new data are collected daily. Furthermore, the information on where to carry out intervention is provided to the maintenance technicians between 30 min and 3 h before the breakdown.

Untargeted Metabolomics in Forensic Toxicology: A New Approach for the Detection of Fentanyl Intake in Urine Samples.

The misuse of fentanyl, and novel synthetic opioids (NSO) in general, has become a public health emergency, especially in the United States. The detection of NSO is often challenged by the limited diagnostic time frame allowed by urine sampling and the wide range of chemically modified analogues, continuously introduced to the recreational drug market. In this study, an untargeted metabolomics approach was developed to obtain a comprehensive "fingerprint" of any anomalous and specific metabolic pattern potentially related to fentanyl exposure. In recent years, in vitro models of drug metabolism have emerged as important tools to overcome the limited access to positive urine samples and uncertainties related to the substances actually taken, the possible combined drug intake, and the ingested dose. In this study, an in vivo experiment was designed by incubating HepG2 cell lines with either fentanyl or common drugs of abuse, creating a cohort of 96 samples. These samples, together with 81 urine samples including negative controls and positive samples obtained from recent users of either fentanyl or "traditional" drugs, were subjected to untargeted analysis using both UHPLC reverse phase and HILIC chromatography combined with QTOF mass spectrometry. Data independent acquisition was performed by SWATH in order to obtain a comprehensive profile of the urinary metabolome. After extensive processing, the resulting datasets were initially subjected to unsupervised exploration by principal component analysis (PCA), yielding clear separation of the fentanyl positive samples with respect to both controls and samples positive to other drugs. The urine datasets were then systematically investigated by supervised classification models based on soft independent modeling by class analogy (SIMCA) algorithms, with the end goal of identifying fentanyl users. A final single-class SIMCA model based on an RP dataset and five PCs yielded 96% sensitivity and 74% specificity. The distinguishable metabolic patterns produced by fentanyl in comparison to other opioids opens up new perspectives in the interpretation of the biological activity of fentanyl.