Prevalence and blood concentrations of benzodiazepines and opioids in opioid-positive death investigations in Ontario,Canada, from 2017 to 2021

The aim of this study was to investigate the incidence of benzodiazepines in opioid-positive death investigations, including trends in frequency and combination of drugs, as well as demographic data and blood concentrations, where available. Additionally, naloxone concentrations in polysubstance compared to opioid-only cases were analyzed. This was a retrospective study that consisted of all post-mortem toxicology cases in Ontario, Canada, from January 01, 2017, to December 31, 2021, with an opioid finding in any analyzed autopsy specimen. There were 11,033 death investigations identified. The overall rate of benzodiazepine co-involvement was 54.5%. Males accounted for the majority of cases (71%), and the most affected age group was 30- to 39-year-olds. The most frequently detected opioid was fentanyl and the most frequently detected benzodiazepine was etizolam, which was also the most frequently observed opioid/benzodiazepine combination. Findings related to differences in concentrations of opioids when naloxone was also present were mostly non-significant, except for methadone. The rate of benzodiazepine detection with opioids grew faster than opioid detections overall, potentially due to the increasingly toxic drug supply. Detection of novel psychoactive drugs fluctuated more unpredictably than opioids and benzodiazepines associated with clinical use. These findings can help inform policy decisions by public health agencies in exploring harm reduction efforts, for example, education and drug-checking services.     

Effects of language mismatch in automatic forensic voice comparison using deep learning embeddings

In forensic voice comparison, deep learning has become widely popular recently. It is mainly used to learn speaker representations, called embeddings or embedding vectors. Speaker embeddings are often trained using corpora mostly containing widely spoken languages. Thus, language dependency is an important factor in automatic forensic voice comparison, especially when the target language is linguistically very different from that the model is trained on. In the case of a low-resource language, developing a corpus for forensic purposes containing enough speakers to train deep learning models is costly. This study aims to investigate whether a model pre-trained on multilingual (mostly English) corpus can be used on a target low-resource language (here, Hungarian), not represented by the model. Often multiple samples are not available from the offender (unknown speaker). Samples are therefore compared pairwise with and without speaker enrollment for suspect (known) speakers. Two corpora are used that were developed especially for forensic purposes and a third that is meant for traditional speaker verification. Speaker embedding vectors are extracted by the x-vector and ECAPA-TDNN techniques. Speaker verification was evaluated in the likelihood-ratio framework. A comparison is made between the language combinations (modeling, LR calibration, and evaluation). The results were evaluated by Cllr_min and EER metrics. It was found that the model pre-trained on a different language but on a corpus with a significant number of speakers can be used on samples with language mismatch. Sample duration and speaking style also seem to affect the performance.     

Lead isotope measurement of primer gunshot residues and likelihood ratio predictions for forensic cartridge discrimination and individualization in China

Gunshot residues (GSR), cartridge projectiles, and casings are frequently encountered evidence in gun-related forensic investigations. However, in circumstances where the investigation of striation marks is impossible, such as unrecovered or deformed projectiles and cartridge casings, GSR deposited on the hands or clothes of the shooter and victim-related items can provide information to establish a link between the suspect, the firearms used, and the victim. Since the formula of primers used by all cartridge manufacturers in China is identical, links based on the conventional morphological and compositional analysis of GSR are difficult to establish. However, the abundance of lead isotopes in primer components of lead styphnate varies significantly, and a fundamental understanding of these differences may facilitate the validation of primer (p)GSR evidence in forensic investigations. Here, 44 pGSR samples were characterized by Pb isotope ratios of ²⁰⁶Pb/²⁰⁴Pb, ²⁰⁷Pb/²⁰⁴Pb, and ²⁰⁸Pb/²⁰⁴Pb using laser ablation multicollector inductively coupled plasma mass spectrometry. There was no obvious mass fractionation of the lead isotope ratios of the primers from individual cartridges analyzed before and after the shooting process, thereby establishing a basis for the comparison of pGSR and unfired cartridges. Evaluation of the results using univariate likelihood ratio (LR) computations revealed low rates of misleading evidence (<0.53%) The results demonstrated that lead isotope ratio analysis of pGSR and LR predictions can provide a practicable method for forensic cartridge discrimination and individualization.     

Identification of document paper using hybrid feature extraction

Document forgery is a significant issue in Korea, with around ten thousand cases reported every year. Analyzing paper plays a crucial role in examining questionable documents such as marketable securities and contracts, which can aid in solving criminal cases of document forgery. Paper analysis can also provide essential insights in other types of criminal cases, serving as an important clue for solving cases such as the source of a blackmail letter. The papermaking process generates distinct forming fabric marks and formations, which are critical features for paper classification. These characteristics are observable under transmitted light and are created by the forming fabric pattern and the distribution of pulp fibers, respectively. In this study, we propose a novel approach for paper identification based on hybrid features. This method combines texture features extracted from images converted using the gray-level co-occurrence matrix (GLCM) approach and a convolutional neural network (CNN), with another set of features extracted by the CNN using the same images as input. We applied the proposed method to classification tasks for seven major paper brands available in the Korean market, achieving an accuracy of 97.66%. The results confirm the applicability of this method for visually inspecting paper products and demonstrate its potential for assisting in solving criminal cases involving document forgery.     

Rapid and non-destructive approach for characterization and differentiation of sealing wax using ATR-FTIR spectroscopy

Sealing wax is used for maintaining the integrity and authenticity of a document or physical evidence. Any tampering with the seal calls into question the overall integrity and authenticity of the tangible evidence or document. In these circumstances, determining the authenticity of the sealing material (physical and chemical) becomes imperative. In this study, ATR-FTIR spectroscopy supported by chemometrics has been used to differentiate sealing wax samples belonging to 12 different brands available across India. All the samples were first melted, cooled, and then analyzed using ATR-FTIR spectroscopy in the mid-infrared region (4000–600 cm⁻¹). The obtained spectra were first examined visually for the presence of different functional groups. Principal component analysis (PCA) and principal component analysis-linear discriminant analysis (PCA-LDA) were employed to analyze the sample clustering patterns and to categorize them into their respective groups, respectively. For classification, a PCA-LDA training model was applied, and it demonstrated 95.83% accuracy. The validation test resulted in an accuracy of 83.33%. PCA-LDA model offered 100% accurate prediction for samples on various substrates, including cloth, cardboard, and paper. A blind study was also performed using five unknown samples, which were accurately classified into their respective groups. PCA-LDA model will be helpful in providing investigative leads by linking a questioned sealing wax sample with its respective group.     

Copycatting the smell of death: Deciphering the role of cadaveric scent components used by detection dogs to locate human remains

Human remains detection dogs (HRDD) are commonly used by law enforcement agencies to search for cadavers. Biological material is typically used as a training stimulus, also called aids, to train dogs to recognize the smell of cadavers. While HRDD training approaches have received extensive attention, information remains limited on the olfactory cues used to train them. Here, we aimed to decipher the chemical basis of detection dog olfaction. Five specific objectives were explored to precise whether the composition or the concentration of the training aids drives the HRDDs responses. We recorded the behavioral responses of four HRDDs exposed to different cadaveric-like smells. We found that HRDDs recognized a simplified synthetic aid composed of cadaveric compounds. The lowest concentration at which HRDDs continued to perceive the cadaveric smell was determined. HRDDs were not impacted by slight modifications to the chemical composition of a blend of odors that they have been trained with. HRDDs associated sulfur and nitrogen compounds as human cadaver. Our findings highlight a lack of specificity of HRDDs to cadaveric compounds, which could lead to error of detection. Moreover, all dogs did not positively respond to the same blends, despite being trained with the same aids and procedure. However, we confirmed that dogs could be trained with a simplified blend of molecules. The chemical composition of a training aid has, therefore, high consequences on the performance of the trained animal, and this conclusion opens additional questions regarding olfaction-based detection animals.     

Subnuclear renal tubular vacuoles in alcohol use disorder

Subnuclear vacuoles in the proximal renal tubules have been reported as a histologic sign of ketoacidosis. Originally described in diabetic ketoacidosis, renal vacuoles can be found in other ketogenic states such as alcoholic ketoacidosis (AKA), starvation, and hypothermia, underpinned by deranged fatty acid metabolism. A retrospective analysis of 133 deaths associated with alcohol use disorder (AUD) examined at autopsy between 2017 and 2020 was undertaken. This study aimed to determine the prevalence of subnuclear vacuoles in deaths of those with AUD and their specificity for deaths from AKA, and to elucidate what demographic, biochemical, and pathologic findings are associated with subnuclear vacuoles. In each case, vitreous humor biochemistry including electrolytes, glucose, and beta-hydroxybutyrate (BHB) was analyzed alongside postmortem hemoglobin A1c and renal and liver histology. Renal histology was graded for the presence of vacuoles as absent (0), scanty (1), or easily identifiable (2). Liver histology was graded for steatosis and for fibrosis if Masson trichrome staining was available. Vacuoles were commonly seen in the deaths of those with AUD. They were seen in deaths due to AKA but were not specific to that cause of death. With vacuoles present, lower vitreous sodium (139 vs. 142 mmol/L; p = 0.005), higher vitreous BHB (1.50 vs. 1.39 mmol/L; p = 0.04), severe hepatic steatosis, and severe hepatic fibrosis were seen, compared with those without renal vacuoles.     

Phenibut screening and quantification with liquid chromatography–tandem mass spectrometry and its application to driving cases

An analytical strategy for identification by an LC–MS/MS multitarget screening method and a suitable LC–MS/MS based quantification were developed for the psychotropic drug phenibut. The samples analyzed were collected during traffic control and were associated with driving under the influence of drugs. A positive sample for phenibut was identified in a single case of driving under the influence. The quantification revealed a drug concentration of 1.9 μg/mL. An interaction with blood alcohol (BAC = 0.10%) was discussed as the explanation of the way of driving and deficit manifestations observed (swaying, nystagmus, quivering of the eyelid, and reddened eyes). According to the available information, the quantified phenibut concentration could be explained by an intake of four tablets (self-reported) during the day containing 250 mg of the drug. Chromatography was performed with a Luna 5 μm C18 (2) 100 A, 150 mm × 2 mm analytical column, and a buffer system consisted of 10 mM ammonium acetate and 0.1% acetic acid (v/v) included in mobile phases marked as A (H₂O/methanol = 95/5, v/v) and B (H₂O/methanol = 3/97, v/v). An effective limit of detection (LOD = 0.002 μg/mL) could be achieved for the multitarget screening method. The quantification of phenibut was performed on a second LC–MS/MS system with LOD/LOQ values of 0.22/0.40 μg/mL. Since phenibut quantification data are rare, the presented information can be used with caution for evaluation of positive cases in the future.     

Radiographic age estimation based on degenerative changes of vertebrae

Age estimation is an important component of decedent identification. When assessing adult remains, anthropologists frequently use gross examination of skeletal elements, such as clavicles, ribs, and pubic symphyses. For fleshed bodies, this requires the removal of these elements and maceration prior to analysis. A new method was developed using radiographic imaging to estimate age from degenerative changes of the lower thoracic and upper lumbar vertebrae. This technique will complement anthropological age estimation methods in young and middle-aged adults and may serve as a stand-alone method for older individuals. Digital radiographs from 240 medical examiner cases were evaluated. The sample included 120 females and 120 males between the ages of 18 and 101 years. A 3-phased scoring system was used for the target vertebrae. Transition analysis was conducted on binned average scores and a Bayesian approach was used to assign age intervals. At the 90% credible interval, individuals in Bin 1 were under 36 years of age while those in Bin 3 were over 47 years of age. Individuals in Bin 2 showed too much age variation to be informative. No significant differences were found between males and females. These findings will be especially useful in the age estimation of older adults and may eliminate the need for skeletal sampling in medicolegal cases where advanced degenerative changes are radiographically observed in the lower thoracic and/or upper lumbar vertebrae. This method was developed for use on fleshed individuals but may also be applicable to skeletonized remains.     

IoT forensics: Exploiting log records from the DAHUA technology CCTV systems

CCTV surveillance systems are ubiquitous IoT appliances. Their forensic examination has proven critical for investigating crimes. DAHUA Technology is a well-known manufacturer of such products. Despite its global market share, research regarding digital forensics of DAHUA Technology CCTV systems is scarce and currently limited to extracting their video footage, overlooking the potential presence of valuable artifacts within their log records. These pieces of evidence remain unexploited by major commercial forensic software, yet they can hide vital information for an investigation. For instance, these log records document user actions, such as formatting the CCTV system's hard drive or disabling camera recording. This information can assist in attributing nefarious actions to specific users and hence can be invaluable for understanding the sequence of events related to incidents. Therefore, in this paper, several DAHUA Technology CCTV systems are thoroughly analyzed for these unexplored pieces of evidence, and their forensic value is presented.