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.

Trends in reported and biologically confirmed drug use among people who use ecstasy in the nightclub/festival-attending population, 2016-2022.

Background: Nightclub/festival attendees are a population with high rates of party drug use, but research is needed to determine whether there have been shifts in unintended drug exposure in this population (e.g., via adulterants) to inform prevention and harm reduction efforts. Methods: Adults entering nightclubs and festivals in New York City were asked about past-year drug use in 2016 through 2022, with a subset providing a hair sample for testing. We focused on the 1943 who reported ecstasy use (of which 247 had a hair sample analyzed) and compared trends in self-reported drug use, drug positivity, and adjusted prevalence (adjusting for unreported use). Results: MDMA positivity decreased from 74.4 % to 42.3 %, and decreases occurred regarding detection of synthetic cathinones ("bath salts"; a 100.0 % decrease), MDA (a 76.9 % decrease), amphetamine (an 81.3 % decrease), methamphetamine (a 64.2 % decrease), and ketamine (a 33.4 % decrease) (ps < .05). Although prevalence of MDA and synthetic cathinone use was comparable between self-report and adjusted report in 2022, gaps in prevalence were wider in 2016 (ps < .01). Adjusted prevalence of synthetic cathinone use decreased more across time than prevalence based on self-report (a 79.4 % vs. 69.1 % decrease) and adjusted report for MDA use decreased more than prevalence based on self-report (a 50.6 % vs. 38.9 % decrease). Conclusions: Combining self-report and toxicology tests helped us determine that decreases in drug use/exposure were steeper regarding adjusted prevalence. Underreported drug exposure-possibly due to exposure to adulterants-appears to have had less of an effect on prevalence in 2022 than it did in 2016.

Oxidative Stress in a Mother Consuming Alcohol during Pregnancy and in Her Newborn: A Case Report.

Fetal alcohol spectrum disorder (FASD) is a set of conditions resulting from prenatal alcohol exposure (PAE). FASD is estimated to affect between 2% and 5% of people in the United States and Western Europe. The exact teratogenic mechanism of alcohol on fetal development is still unclear. Ethanol (EtOH) contributes to the malfunctioning of the neurological system in children exposed in utero by decreasing glutathione peroxidase action, with an increase in the production of reactive oxygen species (ROS), which causes oxidative stress. We report a case of a mother with declared alcohol abuse and cigarette smoking during pregnancy. By analyzing the ethyl glucuronide (EtG, a metabolite of alcohol) and the nicotine/cotinine in the mother's hair and meconium, we confirmed the alcohol and smoking abuse magnitude. We also found that the mother during pregnancy was a cocaine abuser. As a result, her newborn was diagnosed with fetal alcohol syndrome (FAS). At the time of the delivery, the mother, but not the newborn, had an elevation in oxidative stress. However, the infant, a few days later, displayed marked potentiation in oxidative stress. The clinical complexity of the events involving the infant was presented and discussed, underlining also the importance that for cases of FASD, it is crucial to have more intensive hospital monitoring and controls during the initial days.

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.

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.

Prospective evaluation of urinary steroids and prostate carcinoma-induced deviation: preliminary results.

BACKGROUND: The serum prostate-specific antigen is the most widespread biomarker for prostate disease. Its low specificity for prostatic malignancies is a matter of concern and the reason why new biomarkers for screening purposes are needed. The correlation between altered production of the main steroids and prostate carcinoma (PCa) occurrence is historically known. The purpose of this study is to evaluate the modifications of a comprehensive urinary endogenous steroidal profile (USP) induced by PCa, by multivariate statistical methods. METHODS: A total of 283 Italian subjects were included in the study, 139 controls and 144 PCa-affected patients. The USP, including 17 steroids and five urinary steroidal ratios, was quantitatively evaluated using gas chromatography coupled with single quadrupole mass spectrometry (GC-MS). The data were interpreted using a chemometric, multivariate approach (intrinsically more sensible to alterations with respect to traditional statistics) and a model for the discrimination of cancer-affected profiles was built. RESULTS: Two multivariate classification models were calculated, the former including three steroids with the highest statistical significance (e.g. testosterone, etiocholanolone and 7ß-OH-DHEA) and PSA values, the latter considering the three steroids' levels only. Both models yielded high sensitivity and specificity scores near to 70%, resulting significantly higher than PSA alone. CONCLUSIONS: Three USP steroids resulted significantly altered in our PCa population. These preliminary results, combined with the simplicity and low-cost of the analysis, open to further investigation of the potential role of this restricted USP in PCa diagnosis.

Optimization and validation of a GC-MS quantitative method for the determination of an extended estrogenic profile in human urine: Variability intervals in a population of healthy women.

An analytical method based on GC-MS was developed for the determination of a wide panel of urinary estrogens, together with their principal metabolites. Because of the low concentration of estrogens in urine, an efficient sample pre-treatment was optimized by a design of experiment (DoE) procedure to achieve satisfactory sensitivity. A second DoE was built for the optimization of the chromatographic run, with the purpose of reaching the most efficient separation of analytes with potentially interfering ions and similar chromatographic properties. The method was fully validated using a rigorous calibration strategy: from several replicate analyses of blank urine samples spiked with the analytes, calibration models were built with particular attention to the study of heteroscedasticity and quadraticity. Other validation parameters, including the limit of detection, intra-assay precision and accuracy, repeatability, selectivity, specificity, and carry-over, were obtained using the same set of data. Further experiments were performed to evaluate matrix effect and extraction recovery. Then the urinary estrogen profiles of 138 post-menopausal healthy women were determined. These profiles provide a representation of physiological concentration ranges, which, in forthcoming studies, will be matched on the base of multivariate statistics with the urinary estrogenic profile of women with breast or ovarian cancer.

Determination of cannabinoids in urine, oral fluid and hair samples after repeated intake of CBD-rich cannabis by smoking.

Cannabidiol prevalent (CBD-rich) cannabis derivatives are increasingly popular and widely available on the market as replacement of THC, tobacco substitutes or therapeutics for various health conditions. In this paper, we evaluate the impact of a repeated CBD-rich cannabis intake on levels of cannabinoids in biological samples. Urine, oral fluid and hair (pubic and head) samples were obtained from a naive user during a 26-day smoking period of one 250-mg CBD-rich cannabis joint/day containing 6.0% cannabidiol (CBD; 15mg) and 0.2% delta-9-tetrahydrocannabinol (THC; 0.5mg). In total, 35 urine, 8 oral fluid and 4hair sample were collected. Cannabinoids concentrations were quantified by a UHPLC/MSn technique. The results suggested that the repeated exposure to CBD-rich cannabis (containing small amounts of THC) can generate positive results in biological samples. Urinary concentrations of 11-nor-9-carboxy-delta-9-tetrahydrocannabinol (THC-COOH) were quantitatively detected after 8 days from the smoking start and exceeded the 15ng/mL cut-off limit on day-15 even in the urine sample collected 12h after the last intake. In the oral fluid collected on day-26, no cannabinoids were found before the cannabis intake, thus excluding accumulation, while THC was detectable up to 3h after the cannabis intake, at concentrations progressively decreasing from about 18 to 6ng/mL. Hair samples collected one week after the end of the study turned out negative for THC and THC-COOH, suggesting that this matrix is suitable to discriminate the chronic consumption of CBD-rich cannabis from THC-prevalent products. The obtained findings are relevant for the interpretations of cannabinoids levels in biological fluids, also in light of the legal implications of a positive result.

Untargeted Metabolomic Profile for the Detection of Prostate Carcinoma-Preliminary Results from PARAFAC2 and PLS-DA Models.

Prostate-specific antigen (PSA) is the main biomarker for the screening of prostate cancer (PCa), which has a high sensibility (higher than 80%) that is negatively offset by its poor specificity (only 30%, with the European cut-off of 4 ng/mL). This generates a large number of useless biopsies, involving both risks for the patients and costs for the national healthcare systems. Consequently, efforts were recently made to discover new biomarkers useful for PCa screening, including our proposal of interpreting a multi-parametric urinary steroidal profile with multivariate statistics. This approach has been expanded to investigate new alleged biomarkers by the application of untargeted urinary metabolomics. Urine samples from 91 patients (43 affected by PCa; 48 by benign hyperplasia) were deconjugated, extracted in both basic and acidic conditions, derivatized with different reagents, and analyzed with different gas chromatographic columns. Three-dimensional data were obtained from full-scan electron impact mass spectra. The PARADISe software, coupled with NIST libraries, was employed for the computation of PARAFAC2 models, the extraction of the significative components (alleged biomarkers), and the generation of a semiquantitative dataset. After variables selection, a partial least squares-discriminant analysis classification model was built, yielding promising performances. The selected biomarkers need further validation, possibly involving, yet again, a targeted approach.

Individual and cyclic estrogenic profile in women: Structure and variability of the data.

The concentration of estrogens in the body fluids of women is highly variable, due to the menstrual cycle, circadian oscillations, and other physiological and pathological causes. To date, only the cyclic fluctuations of the principal estrogens (estradiol and estrone) have been studied, with limited outcome of general significance. Aim of the present study was to examine in detail the cyclic variability of a wide estrogens' panel and to interpret it by multivariate statistics. Four estrogens (17α-estradiol, 17ß-estradiol, estrone, estriol) and eleven of their metabolites (4-methoxyestrone, 2-methoxyestrone, 16α-hydroxyestrone, 4-hydroxyestrone, 2-hydroxyestrone, 4-methoxyestradiol, 2-methoxyestradiol, 4-hydroxyestradiol, 2-hydroxyestradiol, estriol, 16-epiestriol, and 17-epiestriol) were determined in urine by a gas chromatography - mass spectrometry method, which was developed by design of experiments and fully validated according to ISO 17025 requirements. Then, urine samples collected every morning for a complete menstrual cycle from 9 female volunteers aged 24-35 years (1 parous) were analysed. The resulting three-dimensional data (subjects × days × estrogens) were interpreted using several statistical tools. Parallel Factor Analysis compared the estrogen profiles in order to explore the cyclic and inter-individual variability of each analyte. Principal Component Analysis (PCA) provided clear separation of the sampling days along the cycle, allowing discrimination among the luteal, ovulation, and follicular phases. The scores obtained from PCA were used to build a Linear Discriminant Analysis classification model which enhanced the recognition of the three cycle's phases, yielding an overall classification non-error rate equal to 90%. These statistical models may find prospective application in fertility studies and the investigation of endocrinology disorders and other hormone-dependent diseases.

Determination of several synthetic cathinones and an amphetamine-like compound in urine by gas chromatography with mass spectrometry. Method validation and application to real cases.

Most routine practices for drugs-of-abuse testing do not include screening procedures for new psychoactive substances, despite their increasing diffusion, preventing clear knowledge of the real consumption of these drugs in the populations. To make up for this shortcoming, a gas chromatography with mass spectrometry method was developed for the simultaneous determination of 18 synthetic cathinones and one amphetamine-like compound in human urine. The sample preparation was based on liquid-liquid extraction under alkaline condition followed by derivatization with trifluoroacetic anhydride. The separation of the 19 analytes was achieved in less than 10 min. The whole methodology was validated according to national and international guidelines. Selectivity, linearity range, limit of detection and limit of quantitation, precision and accuracy were evaluated. For all the analytes, the calibration curve was linear in the 100-1000 ng/mL concentration range. The limits of detection ranged from 10 to 30 ng/mL and limits of quantitation from 30 to 100 ng/mL. Precisions were in the ranges 0.1-10.4%, and 1.0-12.1% for low (100 ng/mL) and high (1000 ng/mL) concentration, respectively. The accuracy, expressed as bias% was within ±20% for all the analytes. The present method was successfully applied to urine samples originating from autopsies, drug abuse/withdrawal controls, clinical investigations, roadside controls, driving re-licensing, and workplace testing.

Testing hair for fentanyl exposure: a method to inform harm reduction behavior among individuals who use heroin.

BACKGROUND: Deaths from fentanyl exposure continue to increase in the US. Fentanyl test strips are now available to test urine for presence of fentanyl, but additional testing methods are needed to determine past exposure and to determine exposure to specific analogs. OBJECTIVES: To investigate exposure to such analogs through hair testing. METHODS: Forty individuals in inpatient detoxification (7.5% female) reporting past-month heroin use were surveyed and provided a hair sample to be tested at a later date. While results could not be provided to patients, they were asked how they would respond if informed that their hair tested positive for fentanyl. UHPLC-MS/MS was used to test for past exposure to fentanyl, six other novel synthetic opioids, and fentanyl biomarkers/metabolites. RESULTS: 27.5% reported known fentanyl use in the past year and 67.5% reported suspected exposure. 97.5% (39 of 40) tested positive for fentanyl, 90.0% tested positive for 4-ANPP (a biomarker) and norfentanyl (a metabolite); 82.5% tested positive for acetyl-fentanyl, 47.5% tested positive for furanyl-fentanyl, and 7.5% tested positive for U-47700. Most participants (82.5%) reported they would warn others about fentanyl if they learned their hair tested positive; 75.0% reported they would try to stop using heroin, and 65.0% reported they would ensure that someone nearby has naloxone to reverse a potential overdose. CONCLUSIONS: Hair testing is useful in detecting past exposure to fentanyl, its analogs, and other novel synthetic opioids. Further research is needed to determine whether individuals who use heroin learning about exposure affects drug-taking and treatment-seeking behavior.

Correlation between chronological and physiological age of males from their multivariate urinary endogenous steroid profile and prostatic carcinoma-induced deviation.

The biosynthesis of endogenous androgenic anabolic steroids (EAAS) in males varies with age. Knowledge of the general urinary EAAS profile's dependence from aging - not reported up to now - may represents a prerequisite for its exploitation in the screening and diagnostic support for several pathologies. Extended urinary EAAS profiles were obtained from healthy and pathological individuals, using a GC-MS method which was fully validated by a stepwise, analyst-independent scheme. Seventeen EAAS and five of their concentration ratios were determined and investigated using multivariate statistical methods. A regression model based on Kernel partial least squares algorithm was built to correlate the chronological age of healthy male individuals with their "physiological age" as determined from their urinary EAAS profile. Strong correlation (R2 = 0.75; slope = 0.747) and good prediction ability of the real chronological age was inferred from EAAS data. In contrast, patients with recent diagnosis (not pharmacologically treated) of prostatic carcinoma (PCa) exhibited a comprehensive EAAS profile with strong negative deviation from the model, corresponding a younger predicted age. This result is possibly related to the activation of anomalous steroid biosynthesis induced from PCa. Over a restricted 60-80 years-old population, PLS-discriminant analysis (DA) was used to distinguish healthy subjects from patients with untreated PCa. PLS-DA yielded excellent discrimination (sensitivity and specificity >90%) between healthy and pathological individuals. This proof-of-concept study provides a preliminary evaluation of multivariate DA on wide EAAS profiles as a screening method to distinguish PCa from non-pathological conditions, overcoming the potentially interfering effect of ageing.

High-speed gas chromatography in doping control: fast-GC and fast-GC/MS determination of beta-adrenoceptor ligands and diuretics.

In official doping controls, about 300 drugs and metabolites have to be screened for each sample. Moreover, the number of determinations to be routinely processed increases continuously as the number of both samples and potential illicit drugs keeps growing. As a consequence, increasingly specific, sensitive, and, above all, fast methods for doping controls are needed. The present study presents an efficient fast-GC/MS approach to the routine screening of two different classes of doping agents, namely beta-adrenoceptor ligands and diuretics (belonging to the S3, P2, and S5 groups of the WADA list of prohibited substances). Narrow bore columns (100 mm id) of different lengths and coated with apolar stationary phases were successfully used to separate the derivatized analytes; preliminary experiments (results not shown) showed better performances with OV-1701 for the separation of beta-adrenoceptor ligands. On the same stationary phase some diuretics required too high a temperature or a long isothermal time for elution, in which case a DB1-MS column was preferred. Two methods of sample preparation, derivatization, and analysis were used on aqueous standard mixtures of, respectively, (i) eight beta-adrenoceptor ligands, including five beta-antagonists (acebutolol, alprenolol, atenolol, metoprolol, pindolol) and three beta2-agonists (salbutamol, clenbuterol, terbutaline) and (ii) seventeen diuretic drugs (acetazolamide, althiazide, bendroflumethiazide, bumethanide, canrenone, chlorothiazide, chlortalidone, clopamide, ethacrinic acid, furosemide, hydrochlorothiazide, hydroflumethiazide, indapamide, indomethacine, spironolactone, triamterene, trichloromethiazide) and one masking agent (probenecid). The mixture of beta-adrenoceptor ligand derivatives was efficiently separated in about 5.6 min, while the one of 18 diuretics and masking agents required less than 5 min for analysis. Limits of detection were from 1 microg/L for pindolol, ethacrinic acid, furosemide, indomethacine, and trichloromethiazide, to 20 microg/L for terbutaline, salbutamol, and metoprolol, and 50 microg/L for clopamide; the instrumental repeatability proved to be excellent (area RSD% <2 for almost all analytes). For this work a quadrupole MS with inert ion source has been used, demonstrating that the quadrupole technology is perfectly adequate to provide precise integration of 400 ms-wide GC peaks.