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.     

Identification of the veterinary sedative medetomidine in combination with opioids and xylazine in Maryland

Public health, public safety, and forensic science personnel continue to face the emergence of new compounds into the drug market. While focus is often put on the detection of new analogs of known illicit drugs, monitoring the changes in cutting agents and other compounds can be equally as important. Over the last year, near real-time monitoring of the drug supply in Maryland has been completed through a public health–public safety partnership whereby residue from suspected drug packaging or used paraphernalia is collected and analyzed. Through this project, we have recently detected the presence of the veterinary sedative medetomidine in a small number of samples. The presence of medetomidine has been identified in both public health and law enforcement samples and in the presence of fentanyl and xylazine—another veterinary sedative that has been widely observed over the last year. While the rate at which medetomidine has been detected remains low, it is concerning and worthy of continued monitoring.     

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.     

ForensicNet: Modern convolutional neural network-based image forgery detection network

The advancements in Image editing techniques produce realistic-looking artificial images with ease. These images can easily circumvent the forensic systems making the authentication process more tedious and difficult. To overcome this problem, we introduce a modern convolutional neural network (CNN) named ForensicNet, inspired by the recent developments in computer vision. The three major contributions of our CNNs are inverted bottleneck, separate downsampling layers, and using depth-wise convolutions for mixing information in the spatial dimension. The inverted bottlenecks help improve accuracy and reduce network parameters/FLOPs. The separate downsampling layers help converge the network. The normalization layers also help stabilize training whenever the spatial resolution is changed. The depth-wise convolution is a grouped convolution where the number of groups and channels are the same. The experiments show that ForensicNet outperforms the state-of-the-art methods by a large margin.     

Increased lung weight in fatal intoxications is not unique to opioid drugs

Fatal intoxications with opioids are known to be associated with an increased lung weight, as well as with brain and pulmonary edema and urinary retention. However, there is evidence to suggest that fatal intoxications with non-opioid substances are also associated with increased lung weight; however, the latter aspect has not been comprehensively analyzed. To determine to what extent opioid and non-opioid substances are associated with increased lung and brain weight, we studied these organs in cases where the cause of death was attributed to intoxication with a single agent. Using data from cases autopsied at the National Board of Forensic Medicine (NBFM) in Sweden from 2009 through 2019 where the cause of death was attributed to a single substance, we created models of combined lung weight and brain weight. The models used age and sex as predictors as well as nested varying effects for the specific intoxicant and category of intoxicant. Suicidal hanging with negative toxicology cases served as controls. The population majority was male among both intoxications (68%) and controls (83%). The most common single substance group was opioids. All tested substances were associated with heavier lungs than controls, with the largest effect in the opioid group. Our findings show that several substances are associated with increased lung weight and that among intoxication deaths there is no difference in expected brain weight between substances. Hence, heavy lungs, without a reasonable explanation, should prompt a broad toxicological screening.     

An analysis of data curated from 5 years of identifying human remains

An archive of 5 years of cases involving the identification of human remains was curated, collecting information on: The sample type submitted, the number of STR loci yielding interpretable results, the kinship challenge posed, and the outcome for the case. A total of 129 cases of remains ID were investigated using manual DNA extraction and recovery methods with amplification of STR markers using the Power Plex 21 multiplex STR kit from Promega Corp. In 52 cases, blood spots collected by the ME were provided as sample and in 100% of those cases, probabilities of relatedness to the reference samples was ≥99%. In 77 cases, tissue other than blood was provided as a source of DNA. These other samples were grouped categorically into long bones (femur and tibia; 40 cases), skull bones/teeth (11 cases), other bones (16 cases), and tissue (normally adherent to bone) (10 cases). Reference samples provided for cases included alleged parents or child(ren) of the victim (86 cases), alleged full siblings of the victim (38 cases), or alleged second-order relatives (five cases). The overall success rate in confirming the identity of the source of the remains in these cases was 89.2%. Our results demonstrate that a laboratory can be often successful identifying human remains using methods easily implemented in any DNA typing laboratory.     

Suicide of an adolescent girl with sodium nitrite ordered on the internet

Nitrites are commonly used in the chemical, pharmaceutical, and food industries. Recently, they have been identified in cases of voluntary intoxication. We report the case of a 13-year-old girl who was found lifeless on her bed next to a glass containing a white powder and a bottle containing a white powder with a moistened appearance. External examination and autopsy revealed a nonspecific asphyxia syndrome, which was confirmed by the pathological analysis. Analysis of the samples revealed metoclopramide in the peripheral blood at a concentration of 0.402 mg/L (LC–HRMS). An analysis of the gastric contents was carried out after sodium nitrite was detected in the powders found near the body (Raman spectrometry). Nitrites were found in the gastric fluid at a concentration of 30.9 mg/L. Death occurred secondary to anoxia, following ingestion of nitrites; suicide kits are available on the web and nitrites are relatively easy to source and inexpensive. Nitrites are delivered in powder form to be dissolved in liquid, which may then be consumed with metoclopramide (or an alternative anti-emetic drug) to maximize absorption and reduce emesis. The toxic effect of nitrites lies in their oxidizing power, causing the transformation of hemoglobin into methemoglobin, which, when it accumulates, induces tissue anoxia resulting in death. There has been an alarming increase in the number of cases linked to suicide using nitrites or a nitrite suicide kit. The fact that nitrites are readily available online underscores the importance of establishing effective preventive measures such as limiting the access and use of this chemical.     

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.