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Multiple analytical techniques for the screening of fentanyl‐related compounds exist. High discriminatory methods such as GC–MS and LC–MS are expensive, time‐consuming, and less amenable to onsite analysis. Raman spectroscopy provides a rapid, inexpensive alternative. Raman variants such as electrochemical surface‐enhanced Raman scattering (EC‐SERS) can provide signal enhancements with 1010 magnitudes, allowing for the detection of low‐concentration analytes, otherwise undetected using conventional Raman. Library search algorithms embedded in instruments utilizing SERS may suffer from accuracy when multicomponent mixtures involving fentanyl derivatives are analyzed. The complexing of machine learning techniques to Raman spectra demonstrates an improvement in the discrimination of drugs even when present in multicomponent mixtures of various ratios. Additionally, these algorithms are capable of identifying spectral features difficult to detect by manual comparisons. Therefore, the goal of this study was to evaluate fentanyl‐related compounds and other drugs of abuse using EC‐SERS and to process the acquired data using machine learning—convolutional neural networks (CNN). The CNN was created using Keras v 2.4.0 with Tensorflow v 2.9.1 backend. In‐house binary mixtures and authentic adjudicated case samples were used to evaluate the created machine‐learning models. The overall accuracy of the model was 98.4 ± 0.1% after 10‐fold cross‐validation. The correct identification for the in‐house binary mixtures was 92%, while the authentic case samples were 85%. The high accuracies achieved in this study demonstrate the advantage of using machine learning to process spectral data when screening seized drug materials comprised of multiple components. [ABSTRACT FROM AUTHOR]

Applications and advances in surface-enhanced Raman spectroscopy (SERS) have grown over the last several years, demonstrating improvements in sensitivity and selectivity. Applications for SERS-related techniques have been explored in many analytical disciplines including forensic applications. Drug chemistry and toxicology may benefit from the growth of SERS since many methods have demonstrated high sensitivity, potential for quantitative analysis, and portable instrumentation for onsite testing. SERS techniques have been developed using many substrates including nanoparticles and colloids; microfluidic devices; paper-based substrates; electrodes; and wearable/flexible devices. These methods take advantage of the SERS phenomenon, providing more potential applications for Raman analysis and overcoming the traditional challenges of the technique. This is desirable for forensic drug chemistry applications which require screening and confirmatory approaches and analysis of mixtures with low weight percent contributions of analytes, as well as forensic toxicology, where matrix interferences, sensitivity requirements, and a growing introduction of novel drug entities pose challenges. Several chemometric approaches have been applied to Raman data to improve identification, classification, and quantification. These advances position SERS for incorporation into forensic chemistry laboratories. While some SERS applications are available commercially, widespread use of SERS in practicing forensic laboratories remains limited. Due to the requirements for use of analytical techniques in the courtroom, future studies for SERS should revolve around the assessment of validation parameters common in other analytical methods. SERS techniques that are rapid, simple, and inexpensive may be more quickly adapted for forensic testing, where use as advanced screening techniques is a clear avenue of research. This article is categorized under: Forensic Chemistry and Trace Evidence > Controlled and Emerging Drug Compounds Toxicology > New Psychoactive Substances Forensic Chemistry and Trace Evidence > Emerging Technologies and Methods. [ABSTRACT FROM AUTHOR]

Analysis of gunshot residue currently lacks effective screening methods that can be implemented in real time at the crime scene. Historically, SEM‐EDS has been the standard for analysis; however, advances in technology have brought portable instrumentation to the forefront of forensic science disciplines, including the screening of GSR. This study proposes electrochemical methods with disposable screen‐printed carbon electrodes for GSR screening at the laboratory and points of care due to their rapid, cost‐efficient, and compact platform. GSR residues were extracted from typical aluminum/carbon adhesive collection stubs and analyzed via square‐wave anodic stripping voltammetry. Benchtop and portable electrochemical instruments were compared for the assessment and classification of authentic shooter samples by monitoring a panel of inorganic and organic GSR elements and compounds including lead, antimony, copper, 2,4‐dinitrotoluene, diphenylamine, nitroglycerin, and ethyl centralite. The evaluation included the assessment of figures of merit and performance measures from quality controls, nonshooter, and shooter data sets. Samples collected from the hands of 200 background individuals (nonshooters), and shooters who fired leaded ammunition (100) and lead‐free ammunition (50) were analyzed by the benchtop and portable systems with accuracies of 95.7% and 96.5%, respectively. The findings indicate that electrochemical methods are fast, sensitive, and specific for the identification of inorganic and organic gunshot residues. The portable potentiostat provided results comparable with the benchtop system, serving as a proof‐of‐concept to transition this methodology to crime scenes for a practical and inexpensive GSR screening that could reduce backlogs, improve investigative leads, and increase the impact of gunshot residues in forensic science. [ABSTRACT FROM AUTHOR]

Progressive vision loss and neurocognitive impairment are early and frequent presentations in CLN3 disease. This highlights neurodevelopmental functioning as critical to the disease, but limits the neuropsychological test repertoire. We evaluated the convergent validity of the Vineland Adaptive Behavior Scales as a potential outcome measure. In a prospective observational study of 22 individuals (female:male 11:11; 6–20 years‐old) with a molecular diagnosis of CLN3, we used generalized linear models and Spearman correlations to quantify the relationship of the adaptive behavior composite (ABC) standard score with established outcomes of verbal IQ (VIQ) and disease severity (Unified Batten Disease Rating Scale, UBDRS) scores. We analyzed ABC changes in 1‐year follow‐up data in a subset of the same cohort (n = 17). The ABC and VIQ, both standard scores, exhibited a strong positive correlation in cross‐sectional data (r = 0.81). ABC and UBDRS scores were strongly and positively correlated in cross‐sectional data (rrange = 0.87–0.93). Participants' ABC scores decreased slightly over the 1‐year follow‐up period (mean change, 95% CI: −5.23, −2.16). The convergent validity of the Vineland‐3 for use in CLN3 is supported by its relationships with the established outcomes of VIQ and UBDRS. Future longitudinal research, including replication in other cohorts and evaluation of sensitivity to change, will be important to establish utility of the Vineland‐3 for monitoring change in CLN3. [ABSTRACT FROM AUTHOR]

The increasing demand for rapid methods to identify both inorganic and organic gunshot residues (IGSR and OGSR) makes electrochemical methods, an attractive screening tool to modernize current practice. Our research group has previously demonstrated that electrochemical screening of GSR samples delivers a simple, inexpensive, and sensitive analytical solution that is capable of detecting IGSR and OGSR in less than 10 min per sample. In this study, we expand our previous work by increasing the number of GSR markers and applying machine learning classifiers to the interpretation of a larger population data set. Utilizing bare screen‐printed carbon electrodes, the detection and resolution of seven markers (IGSR; lead, antimony, and copper, and OGSR; nitroglycerin, 2,4‐dinitrotoluene, diphenylamine, and ethyl centralite) was achieved with limits of detection (LODs) below 1 µg/mL. A large population data set was obtained from 395 authentic shooter samples and 350 background samples. Various statistical methods and machine learning algorithms, including critical thresholds (CT), naïve Bayes (NB), logistic regression (LR), and neural networks (NN), were utilized to calculate the performance and error rates. Neural networks proved to be the best predictor when assessing the dichotomous question of detection of GSR on the hands of shooter versus nonshooter groups. Accuracies for the studied population were 81.8 % (CT), 88.1% (NB), 94.7% (LR), and 95.4% (NN), respectively. The ability to detect both IGSR and OGSR simultaneously provides a selective testing platform for gunshot residues that can provide a powerful field‐testing technique and assist with decisions in case management. [ABSTRACT FROM AUTHOR]

The increasing demand for rapid methods to identify both inorganic and organic gunshot residues (IGSR and OGSR) makes electrochemical methods, an attractive screening tool to modernize current practice. Our research group has previously demonstrated that electrochemical screening of GSR samples delivers a simple, inexpensive, and sensitive analytical solution that is capable of detecting IGSR and OGSR in less than 10 min per sample. In this study, we expand our previous work by increasing the number of GSR markers and applying machine learning classifiers to the interpretation of a larger population data set. Utilizing bare screen‐printed carbon electrodes, the detection and resolution of seven markers (IGSR; lead, antimony, and copper, and OGSR; nitroglycerin, 2,4‐dinitrotoluene, diphenylamine, and ethyl centralite) was achieved with limits of detection (LODs) below 1 µg/mL. A large population data set was obtained from 395 authentic shooter samples and 350 background samples. Various statistical methods and machine learning algorithms, including critical thresholds (CT), naïve Bayes (NB), logistic regression (LR), and neural networks (NN), were utilized to calculate the performance and error rates. Neural networks proved to be the best predictor when assessing the dichotomous question of detection of GSR on the hands of shooter versus nonshooter groups. Accuracies for the studied population were 81.8 % (CT), 88.1% (NB), 94.7% (LR), and 95.4% (NN), respectively. The ability to detect both IGSR and OGSR simultaneously provides a selective testing platform for gunshot residues that can provide a powerful field‐testing technique and assist with decisions in case management. [ABSTRACT FROM AUTHOR]

Problem: The tissues of the human female reproductive tract (Fallopian tube, uterus, cervix, and vagina) may play different roles in the provision of mucosal immunity. The purpose of this study was to develop a uniform method suitable for quantitative comparison of the leukocytes from all these tissues., Method of Study: Tissues, typically 0.5-1.0 g, were dispersed by enzyme treatment. A flow cytometric gating procedure based on CD45-positivity and low far-red autofluorescence permitted unfractionated, freshly dispersed cells to be phenotyped with respect to T lymphocytes, B lymphocytes, macrophages, and granulocytes., Results: Reproductive tract tissues contain leukocytes that represent approximately 6-20% of the total number of cells, with the Fallopian tubes and uterus containing a higher proportion of leukocytes than the cervix and vagina. The uterine endometrium from post-menopausal women has fewer leukocytes than does uterine endometrium from pre-menopausal women. T lymphocytes are a major constituent (30-60%) of leukocytes from all tissues. The Fallopian tube contains granulocytes as another major constituent; granulocytes are significantly less numerous in the other tissues. All tissues contain B lymphocytes and macrophages as clearly detectable but minor components., Conclusions: Three-color flow cytometry is an appropriate method for quantitative comparison of leukocytes from the different tissues of the female reproductive tract, during all phases of the menstrual cycle and within post-menopausal samples. Results indicate that the tissues differ from each other, particularly with respect to the large number of granulocytes in the Fallopian tubes.

Problem: The goal of this study was to measure anti-sperm antibodies in serum following intraluminal uterine immunization and to compare this with the formation of antibodies following intraperitoneal (i.p.) or anal immunization., Method: Mice were immunized with human sperm, and antibody was measured using an indirect immunobead assay., Results: High levels of anti-sperm antibodies could be measured following intrauterine immunization and i.p. immunization. Anal immunization resulted in lower levels of antibody formation. Antibody formation after intrauterine immunization was less consistent following transcervical insertion than following needle insertion of sperm., Conclusion: The data suggest that intrauterine immunization can result in anti-sperm antibody formation under some conditions.