Cocaine True, Cocaine Blue

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Other more dangerous adulterants, such as the stimulant amphetamine or synthetic opioids, including fentanyl, may also be used to cut the drug. Cutting cocaine with other illicit drugs can be especially harmful as the user is not aware of the added drug and an accidental overdose or death can occur. This is the first study to demonstrate cocaine detection using NIR spectral data covering a limited‐wavelength range and using a low‐cost handheld device. Although the first results show impressive performance, optimization of several aspects might even further increase the performance of the model and reduce the risk of erroneous results. In this study limited attention is given to the threshold and cutoff values that determine the preferred routes within the model. For example, stricter kNN thresholds will force more spectra to be predicted by the ANN model, and the optimal number of NNs could be different for more uncommon spectra. For practical implementation, the reference database must be expanded with a large variety of cocaine samples to create a more robust model. One limitation of the machine learning model is that it does not exclusively focus on spectral features originating from the illicit compound itself (ie, cocaine in this case). This is contrary to traditional IR, Raman, or mass spectral library search approaches where database spectra originate only from (and could even be theoretically explained as belonging to) the reference compound. Cocaine samples can vary widely in composition, National Institute on Drug Abuse (NIDA). NIH. Research Report Series. Cocaine: Abuse and Addiction. August 26, 2020 at https://www.ehd.org/pdf/RRCocain.pdf ANNs are another machine learning approach that has successfully been applied on spectral data for classification purposes.

Figure S8 k‐Nearest Neighbors distances plots of levamisole, lidocaine, acetaminophen and ketamine on NIR spectra after SNV‐first derivative pre‐processing Determining the ‘lethal dose’ of cocaine is difficult, due to the high degree of variability associated with the manner in which users react to cocaine intake and metabolize it, due to the variations in the metabolic rate of individuals, potential drug interactions and genetic polymorphisms of metabolizing enzymes [ 96]. Furthermore, there seems to be no relation between blood concentrations and toxicity, with plasma cocaine concentrations of 0.029 mg/L found in a patient who died, and few symptoms occurring in an individual with 3.9 mg/L (a concentration previously considered nearly fatal) [ 155]. Comparatively, in Swiss-Webster mice, cocaine’s LD50 was estimated at 93 mg/Kg [ 156].Of particular relevance, the co-consumption of cocaine and alcohol leads to the formation of CE, a transesterification product of both drugs. Interestingly, it is only produced in vivo via catalysis by hCE 1 [ 65]. Harris et al. carried out a study where 10 human subjects received intravenous administrations of cocaine (at 0.3, 0.6 and 1.2 mg/Kg) and a single oral dose of ethanol (at 1 g/Kg), 1 h prior to cocaine intake [ 66]. They demonstrated that 17% of the intravenous cocaine dose was converted into CE and that ethanol ingestion decreased urinary levels of BE. Another study, resorting to 10 experienced cocaine users, demonstrated that the percentage of CE originated through oral administration of cocaine was larger than that produced by intravenous administration and inhalation (34 ± 20% vs. 24 ± 11% and 18 ± 11%, respectively) [ 67]. CE is an active metabolite that displays pharmacological activity, with a longer average half-life (148 ± 15 min) than cocaine [ 24]. Some studies report that it may be more lethal and induce graver acute toxic reactions, also producing a greater increase in heart rate, compared to cocaine [ 67, 68]. CE is used as a biomarker for concomitant use of alcohol and cocaine, which can be detected either in urine to determine recent use, or in hair for chronic exposure [ 69, 70]. Cocaine Habit Blues at Grateful Dead Lyric & Song Finder". clearlight.com. Archived from the original on September 29, 2011 . Retrieved November 20, 2011. The 76 external validation samples were case materials seized by the Amsterdam police between October 2019 and March 2020. The 33 validation standards were inositol, levamisole, and caffeine, each mixed with cocaine HCl from 0% to100% in 10% intervals. A well‐known limitation of these cobalt(II)thiocyanate‐based tests is their false‐positive (FP) response to several common adulterants such as levamisole and lidocaine. As a first demonstration for the applicability of the developed model, 54 seized off‐white case samples were randomly selected by the Amsterdam Police Laboratory staff between October 2019 and March 2020. In addition, 22 seized case samples known to contain different drug types, mainly NPSs, were selected. Furthermore, external validation samples were prepared by diluting a pure cocaine HCl sample with levamisole, inositol, and caffeine from 0% to 100% with 10% intervals. All these samples were scanned fivefold, and spectral data were processed and predicted using the developed model. The original laboratory results from GC–MS, FT‐IR, and Raman analyses were used for comparison. The results on the individual scan level, presented in Table 1C, are consistent with the model performance from the cross‐validation. Of the 54 case samples, 34 were reported as “cocaine containing” by the laboratory (based on GC–MS results). Within these, 31 were correctly predicted as cocaine positive using the NIR‐based model. The three FN samples contained an unusual low level of cocaine (estimated between 5% and 17%) below the threshold as applied in the model. A possible explanation of this relatively high number of low level cocaine samples might be some selection bias from the forensic experts selecting more “out‐of‐the‐ordinary” case samples for this study. The other 20 case samples were found to contain no illicit compound or contain another drug from prior laboratory data. Within this group, only one sample produced an FP result for cocaine on the NIR model. The GC–MS data revealed that this specific specimen contained amphetamine adulterated with caffeine. As the first kNN submodel was inconclusive for this sample, the FP result originated from the ANN submodel. The NPS and other drug‐containing case samples produced no FP results for cocaine, and both cocaine HCl and cocaine base samples were correctly identified. In most cases, no identity was predicted by the kNN submodel, which was correct as these novel substances were not included in the database. Euclidean distances and Pearson correlations fell outside the threshold limits for all database spectra as shown for a synthetic drug (2C‐B) containing sample in Figure S10. When the model did predict an identity, it was in most cases consistent with the GC–MS results. Some exceptions were two ketamine‐containing samples misidentified as a levamisole:phenacetin mixture, and a 3‐methylmethcathinone‐containing sample was misidentified as containing 4‐methylmethcathinone (mephedrone). The complete results for all case samples are shown in Tables S2 and S3 (supporting information).

For every individual sample within Set A and Set B, 65 replicate scans collected using 13 different scanners were available. In general, the five scans collected as replicates on a single scanner were visually similar; however, major intensity differences were observed among different scanners. The spectra marked as “raw” in Figure 2A show the unprocessed raw data from multiple scanners for an 86.6% cocaine HCl case sample. Each colored line indicates five replicate spectra from a single scanner, and each color indicates a single scanner. Additive baseline shifts could be clearly observed, with most scanners providing a near‐similar intensity, whereas three individual scanners returned a notably less‐intense signal. From observations of different samples, it was evident that this additive effect could not be attributed to individual poorly performing sensors, as sensors producing a low‐intensity signal for one sample did produce a high‐intensity signal for other samples. Two possible explanations for these additive effects are variation in sample vial positioning and signal scatter. As the glass vial containing the sample needs to be placed on top of the sensor before scanning (Figure 1), the variation in signal intensity might be due to the alignment of the sample. Operators were instructed to simply put the sample vial on top of the sensor such that both the NIR light source and the detector cell were covered by the vial. No special attention was given to the perfect alignment of the samples as this will also not be the case in the actual on‐scene analysis by police officers. However, with a vial diameter of 15 mm and a NIR light source and detector diameter 13 mm wide, there is limited tolerance. It is therefore possible that a vial placed more toward the detector surface will lose more light emitted from the sensor through the glass wall of the vial, thereby reflecting less signal. The other explanation given for the signal variation is the scattering effect of the material. Sample vials were necessarily touched and moved between analyses, and the powder in the vials was consequently shaken and redistributed. As the particle size of the powders might not be constant in actual case samples, more or less scattering resulting in varying signal intensities can occur. Sample scattering and the consequent signal variation is a regular phenomenon in diffuse reflectance NIR spectroscopy. Monitoring the Future Study: 2021 Overview Key Findings on Adolescent Drug Use. National Institute on Drug Abuse (NIDA). Accessed June 20, 2021 at http://www.monitoringthefuture.org//pubs/monographs/mtf-overview2021.pdf Overall cocaine use in these groups declined in 2021 with cocaine showing a relative decline in annual prevalence for the three grades combined of 57% (p<.001). This results in an overall annual prevalence at 0.7%, down from 1.4% in 2020. As NIR spectra, in general, and these small‐range spectra, in particular, are relatively information poor, a subsequent derivative preprocessing step is often suggested to put emphasis on the spectral differences. Consistent with an earlier NIR study on narcotics from Liu et al, Prolonged cocaine use—in any form—can adversely affect the neurological and cardiovascular systems and damage the liver, kidneys, and other organs. 5,8 Specific longer-term effects may vary by person and their method of use.

Is cocaine dangerous?

Pennings EJM, Leccese AP, Wolff FA. Effects of concurrent use of alcohol and cocaine. Addict Abingdon Engl. 2002;97(7):773-783. August 26, 2020.

Figure S10 kNN distances and correlations plots for a non‐matching unknown “2C‐B” sample projected on all database spectra a b "Tell It To Me at Grateful Dead Lyric & Song Finder". clearlight.com. Archived from the original on September 29, 2011 . Retrieved November 20, 2011. Cocaine hydrochloride (HCL) is water soluble due to the HCL salt and can be injected; it is also snorted in powder form. Mixing cocaine with heroin and injecting is called a "speedball" and can be especially lethal.Long-term, cocaine use can lead to an increased risk for becoming malnourished due to loss of appetite. The occurrence of Parkinson's disease has been reported as well. Is cocaine addictive? A 2020 review of a group of research studies (a meta-analysis) found that combined cognitive behavioral therapy and medication treatment (pharmacotherapy) for cocaine addiction treatment was associated with an increased benefit compared with usual care and pharmacotherapy. In addition to test solutions prepared in the forensic laboratory, a large variety of commercial test kits based on this complex are manufactured in the form of pouches, ampules, swabs, and wipes. In the short term, even small amounts of cocaine in either form can produce euphoria, pleasure, and alertness. 5 Larger doses may intensify these effects and also lead some to experience bizarre, erratic, even violent behavior. 5 Other short-term effects of use may include: 5