This study reports on the successful use of a machine learning approach using attenuated total reflectance Fourier transform infrared (ATR FT-IR) spectroscopy for the classification and prediction of a donor's sex from the fingernails of 63 individuals. A significant advantage of ATR FT-IR is its ability to provide a specific spectral signature for different samples based on their biochemical composition. The infrared spectrum reveals unique vibrational features of a sample based on the different absorption frequencies of the individual functional groups. This technique is fast, simple, non-destructive, and requires only small quantities of measured material with minimal-to-no sample preparation. However, advanced multivariate techniques are needed to elucidate multiplex spectral information and the small differences caused by donor characteristics. We developed an analytical method using ATR FT-IR spectroscopy advanced with machine learning (ML) based on 63 donors' fingernails (37 males, 26 females). The PLS-DA and ANN models were established, and their generalization abilities were compared. Here, the PLS scores from the PLS-DA model were used for an artificial neural network (ANN) to create a classification model. The proposed ANN model showed a greater potential for predictions, and it was validated against an independent dataset, which resulted in 92% correctly classified spectra. The results of the study are quite impressive, with 100% accuracy achieved in correctly classifying donors as either male or female at the donor level. Here, we underscore the potential of ML algorithms to leverage the selectivity of ATR FT-IR spectroscopy and produce predictions along with information about the level of certainty in a scientifically defensible manner. This proof-of-concept study demonstrates the value of ATR FT-IR spectroscopy as a forensic tool to discriminate between male and female donors, which is significant for forensic applications.
Medical cannabis has recently been legalized in many countries, and it is currently prescribed with increasing frequency, particularly for treatment of chronic pain resistant to conventional therapy. The psychoactive substance delta-9-tetrahydro-cannabinol (THC) contained in cannabis may affect driving abilities. Therefore, the aims of this study (open-label, monocentric, nonrandomized) were to evaluate blood and saliva concentrations of THC after oral administration of medical cannabis and to assess the time needed for THC levels to decline below a value ensuring legal driving. The study involved 20 patients with documented chronic pain using long-term medical cannabis therapy. They were divided into two groups and treated with two different doses of cannabis in the form of gelatin capsules (62.5 mg or 125 mg). In all patients, the amount of THC was assessed in saliva and in blood at pre-defined time intervals before and after administration. THC levels in saliva were detected at zero in all subjects following administration of both doses at all-time intervals after administration. Assessment of THC levels in blood, however, showed positive findings in one subject 9 h after administration of the lower dose and in one patient who had been given a higher dose 7 h after administration. Our finding suggested that for an unaffected ability to drive, at least 9-10 h should elapse from the last cannabis use.
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