Ten-year functional and oncological outcomes of a prospective randomized controlled trial comparing laparoscopic versus robot-assisted radical prostatectomy.

BACKGROUND: Among prostate cancer (PCa) treatment options, mini-invasive surgical approaches have gained a wide diffusion in the last decades. The aim of this study was to present oncological, functional, and quality of life data after 10 years of follow-up of a prospective randomized controlled trial (RCT) (ISRCTN11552140) comparing robot-assisted radical prostatectomy (RARP) versus laparoscopic radical prostatectomy (LRP) for the treatment of PCa. METHODS: Patients with localized PCa were randomized to undergo LRP or RARP between January 2010 and January 2011. Functional (continence and potency) and oncological (prostate-specific antigen, biochemical recurrence [BCR] and BCR-free survival [BCRFS]) variables were evaluated. BCRFS curves were estimated by the Kaplan-Meier method and compared using the log-rank test. Machine learning partial least square-discriminant analysis (PLS-DA) was used to identify the variables characterizing more the patients who underwent RARP or LRP. RESULTS: Seventy-five of the originally enrolled 120 patients remained on follow-up for 10 years; 40 (53%) underwent RARP and 35 (47%) LRP. Continence and potency recovery rates did not show significant differences (p = 0.068 and p = 0.56, respectively), despite a Δ12% for continence and Δ8% for potency in favor of the robotic approach. However, the quality of continence (in terms of International Consultation on Incontinence Questionnaire-Short Form [ICIQ-SF] score) and erection (in terms of International Index of Erectile Function-5 [IIEF-5] score) was significantly better after 10 years in the robotic group (p = 0.02 and p < 0.001). PLS-DA revealed that LRP was characterized by the worst functional-related outcomes analyzing the entire follow-up period. Four (10%) and six (17%) patients experienced BCR in RARP and LRP groups, respectively (p = 0.36), with an overall 10-year BCR-free survival of 88% and 78% (p = 0.16). CONCLUSIONS: Comparable continence and potency rates were observed between RARP and LRP after a 10-year follow-up. However, the RARP group exhibited superior totally dry rate and erection quality. No difference in terms of oncological outcomes was found.

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.

Development of Machine Learning Algorithm to Predict the Risk of Incontinence After Robot-Assisted Radical Prostatectomy.

Introduction: Predicting postoperative incontinence beforehand is crucial for intensified and personalized rehabilitation after robot-assisted radical prostatectomy. Although nomograms exist, their retrospective limitations highlight artificial intelligence (AI)'s potential. This study seeks to develop a machine learning algorithm using robot-assisted radical prostatectomy (RARP) data to predict postoperative incontinence, advancing personalized care. Materials and Methods: In this propsective observational study, patients with localized prostate cancer undergoing RARP between April 2022 and January 2023 were assessed. Preoperative variables included age, body mass index, prostate-specific antigen (PSA) levels, digital rectal examination (DRE) results, Gleason score, International Society of Urological Pathology grade, and continence and potency questionnaires responses. Intraoperative factors, postoperative outcomes, and pathological variables were recorded. Urinary continence was evaluated using the Expanded Prostate cancer Index Composite questionnaire, and machine learning models (XGBoost, Random Forest, Logistic Regression) were explored to predict incontinence risk. The chosen model's SHAP values elucidated variables impacting predictions. Results: A dataset of 227 patients undergoing RARP was considered for the study. Post-RARP complications were predominantly low grade, and urinary continence rates were 74.2%, 80.7%, and 91.4% at 7, 13, and 90 days after catheter removal, respectively. Employing machine learning, XGBoost proved the most effective in predicting postoperative incontinence risk. Significant variables identified by the algorithm included nerve-sparing approach, age, DRE, and total PSA. The model's threshold of 0.67 categorized patients into high or low risk, offering personalized predictions about the risk of incontinence after surgery. Conclusions: Predicting postoperative incontinence is crucial for tailoring rehabilitation after RARP. Machine learning algorithm, particularly XGBoost, can effectively identify those variables more heavily, impacting the outcome of postoperative continence, allowing to build an AI-driven model addressing the current challenges in post-RARP rehabilitation.

Development of Bleeding Artificial Intelligence Detector (BLAIR) System for Robotic Radical Prostatectomy.

BACKGROUND: Addressing intraoperative bleeding remains a significant challenge in the field of robotic surgery. This research endeavors to pioneer a groundbreaking solution utilizing convolutional neural networks (CNNs). The objective is to establish a system capable of forecasting instances of intraoperative bleeding during robot-assisted radical prostatectomy (RARP) and promptly notify the surgeon about bleeding risks. METHODS: To achieve this, a multi-task learning (MTL) CNN was introduced, leveraging a modified version of the U-Net architecture. The aim was to categorize video input as either "absence of blood accumulation" (0) or "presence of blood accumulation" (1). To facilitate seamless interaction with the neural networks, the Bleeding Artificial Intelligence-based Detector (BLAIR) software was created using the Python Keras API and built upon the PyQT framework. A subsequent clinical assessment of BLAIR's efficacy was performed, comparing its bleeding identification performance against that of a urologist. Various perioperative variables were also gathered. For optimal MTL-CNN training parameterization, a multi-task loss function was adopted to enhance the accuracy of event detection by taking advantage of surgical tools' semantic segmentation. Additionally, the Multiple Correspondence Analysis (MCA) approach was employed to assess software performance. RESULTS: The MTL-CNN demonstrated a remarkable event recognition accuracy of 90.63%. When evaluating BLAIR's predictive ability and its capacity to pre-warn surgeons of potential bleeding incidents, the density plot highlighted a striking similarity between BLAIR and human assessments. In fact, BLAIR exhibited a faster response. Notably, the MCA analysis revealed no discernible distinction between the software and human performance in accurately identifying instances of bleeding. CONCLUSION: The BLAIR software proved its competence by achieving over 90% accuracy in predicting bleeding events during RARP. This accomplishment underscores the potential of AI to assist surgeons during interventions. This study exemplifies the positive impact AI applications can have on surgical procedures.

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.

A prospective randomized controlled trial comparing target prostate biopsy alone approach vs. target plus standard in naïve patients with positive mpMRI.

BACKGROUND: In the era of mpMRI guided target fusion biopsy (FB), the role of concomitant standard biopsy (SB) in naïve patients still remains under scrutiny. The aim of this study was to compare the detection rate (DR) of clinically significant prostate cancer (csPCa) in biopsy naïve patients with positive mpMRI who underwent FB alone (Arm A) vs FB+SB (Arm B). Secondary objectives were to compare the incidence of complications, the overall PCa DR and the biopsy results with final pathological findings after robotic prostatectomy (RARP). METHODS: This is a single center prospective non-inferiority parallel two arms (1:1) randomized control trial (ISRCTN registry number ISRCTN60263108) which took place at San Luigi Gonzaga University Hospital, Orbassano (Turin, Italy) from 4/2019 to 10/2021. Eligible participants were all adults aged<75 years old, biopsy naïve, with serum PSA<15 ng/mL and positive mpMRI (Pi-Rads V.2>3). FB was performed under ultrasound guidance using the BioJet fusion system; four to six target samples were obtained for each index lesion. SB was performed in accordance with the protocol by Rodríguez-Covarrubias. RARP with total anatomical reconstruction was carried out when indicated. DR of PCa and csPCA (Gleason Score >7) were evaluated. Post-biopsy complications according to Clavien-Dindo were recorded. Concordance between biopsy and RARP pathological findings was evaluated. Fisher's Exact test and Mann-Whitney test were applied; furthermore, Logistic Principal Component Analysis (LogPCA) and Pearson's correlation method, in terms of correlation funnel plots, were performed to explore data in a multivariate way. RESULTS: 201 and 193 patients were enrolled in Arm A and B, respectively. csPCa DR was 60.2% vs. 60.6% in Arm A and B respectively (Δ 0.4%; P=0.93); whilst overall PCa DR was 63.7% vs. 71.0% (Δ 7.3%; P=0.12). However, in a target only setting, the addition of SB homolaterally to the index lesion reaching a non-inferior performance compared to the combined sampling (Δ PCa DR 3%). Although the differences of 7.3% in PCa DR, during RARP were registered similar nerve sparing rate (P=0.89), positive surgical margins (P=0.67) and rate of significant upgrading (P=0.12). LogPCA model showed no distinction between the two cohorts; and Pearson's correlation values turned to be between -0.5 and +0.5. In Arm B, the lesion diameter <10 mm is the only predictive variable of positive SB only for PCa (P=0.04), with an additional value +3% for PCa DR. CONCLUSIONS: In biopsy naïve patients, FB alone is not inferior to FB+SB in detecting csPCa (Δ csPCa DR 0.4%). Δ 7.3% in overall PCa DR was registered between the two Arms, however the addition of further standard samples homolaterally to mp-MRI index lesion improved the overall PCa DR of FB only sampling (Δ PCa DR 3%). The omission of SB did not influence the post-surgical outcomes in terms of NS approach, PSMr and upgrading/downgrading.

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.

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.

Novel "Matrix-Corrected Calibration" study for the detection of polyunsaturated fatty acids (PUFAs) in plasma and erythrocytes by means of a gas chromatography-mass spectrometry approach optimized to follow up long-term parental patients.

An accurate and specific gas chromatography-mass spectrometry (GC-MS) method was optimized to quantify specific polyunsaturated fatty acids (PUFAs) in plasma and in erythrocyte membranes for clinical purposes. The developed and fully-validated method showed optimal linearity in addition to adequate results in terms of accuracy, intra-day and inter-day precision. By adopting the Matrix-Corrected Calibration approach on all the biological matrices tested, both the constant and the proportional errors of the developed analytical methodology were considered to assure that the method was not affected by matrix bias. Moreover, a pilot study involving patients in parental nutrition with two different compositions of the administered fat emulsion was performed. The comparison of results obtained in these patients with a group of healthy subjects (i.e. control population) showed significant differences in the collected values of PUFAs in both plasma and erythrocyte membranes, thus providing evidence that the described GC-MS method could be employed as a simple tool for fast and accurate PUFAs analysis aimed at optimizing parenteral nutrition protocols.

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.

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.

"One-pot" ethyl chloroformate derivatization and liquid-liquid extraction of reduced glutathione in erythrocyte and its quantitative GC-MS analysis.

A simple "one-pot" derivatization and liquid-liquid extraction (LLE) procedure was developed for GC-MS analysis of reduced glutathione (GSH) analysis in erythrocytes. The metabolite was extracted by 5% (w/v) TCA, the supernatant treated with ECF and ethanol-pyridine media, the derivative separated and detected by gas chromatography-mass spectrometry using a short non-polar capillary GC column at a high column-head pressure. Total analysis time was 11min. The process was optimized by a Design of Experiment. The method was validated showing a good linearity over the 25.4-813.4µM concentration range, providing satisfactory results in terms of intra-day and inter-day precision as well as an optimal accuracy. The new method was evaluated in a pilot study involving patients with severe protein malnutrition. Comparison of this group with a group of healthy subjects revealed significantly lower GSH concentrations in erythrocytes in the former, thus proving that the described GC-MS method could be employed for fast and simple GSH analysis in clinical studies.

Development and validation of a GC-MS method for nicotine detection in Calliphora vomitoria (L.) (Diptera: Calliphoridae).

Entomotoxicology is the application of toxicological methods and analytical procedures on necrophagous insects feeding on decomposing tissues to detect drugs and other chemical components, and their mechanisms affecting insect development and morphology and modifying the methodology for estimation of minimum time since death. Nicotine is a readily available potent poison. Because of its criminal use, a gas chromatography-mass spectrometry (GC-MS) method for the detection of nicotine in Calliphora vomitoria L. (Diptera: Calliphoridae) was developed and validated. Furthermore, the effect of nicotine on the development, growth rate, and survival of this blowfly was studied. Larvae were reared on liver substrates homogeneously spiked with measured amounts of nicotine (2, 4, and 6 ng/mg) based on concentrations that are lethal to humans. The results demonstrated that (a) the GC-MS method can detect both nicotine and its metabolite cotinine in immature C. vomitoria; (b) the presence of nicotine in the aforementioned three concentrations in food substrates did not modify the developmental time of C. vomitoria; (c) during the pupation period, larvae exposed to nicotine died depending on the concentration of nicotine in the substrate; and (d) the resultant lengths of larvae and pupae exposed to 4 and 6 ng/mg concentrations of nicotine were significantly shorter than those of the control.