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As a rapid-acting dissociative anesthetic, ketamine has been used in drug-facilitated crimes. The aim of this study is to investigate the disposition of ketamine and its main metabolite norketamine in hair after a single dose of ketamine. Four healthy volunteers were recruited into the study. Hair was collected 1, 2, 3, 4, 8, 12 and 16 weeks after a single oral dose of ketamine solution (10 mg) and analyzed by liquid chromatography/electrospray ionization tandem mass spectrometry. The wet cotton swab wiped the scalp of the subjects at 1 h, 24 h, 48 h and 1 week after administration. Maximum hair concentrations (C (max)) for ketamine and norketamine were 19.0 ± 6.5 and 18.7 ± 13.3 pg/mg, respectively. Except for the first week, the ratio of ketamine to norketamine in most of segments (87.5%) was greater than 1. All the cotton swab samples collected at 24 and 48 h were positive. The results from cotton swabs and the concentrations of ketamine and norketamine in hair segments collected at different times showed that some of ketamine and norketamine incorporated into hair originated from sweat and sebum on the scalp of the subjects.

Many studies have proven the usefulness of biofluid-based infrared spectroscopy in the clinical domain for diagnosis and monitoring the progression of diseases. Here we present a state-of-the-art study in the forensic field that employed Fourier transform infrared microspectroscopy for postmortem diagnosis of sudden cardiac death (SCD) by in situ biochemical investigation of alveolar edema fluid in lung tissue sections. The results of amide-related spectral absorbance analysis demonstrated that the pulmonary edema fluid of the SCD group was richer in protein components than that of the neurologic catastrophe (NC) and lethal multiple injuries (LMI) groups. The complementary results of unsupervised principle component analysis (PCA) and genetic algorithm-guided partial least-squares discriminant analysis (GA-PLS-DA) further indicated different global spectral band patterns of pulmonary edema fluids between these three groups. Ultimately, a random forest (RF) classification model for postmortem diagnosis of SCD was built and achieved good sensitivity and specificity scores of 97.3% and 95.5%, respectively. Classification predictions of unknown pulmonary edema fluid collected from 16 cases were also performed by the model, resulting in 100% correct discrimination. This pilot study demonstrates that FTIR microspectroscopy in combination with chemometrics has the potential to be an effective aid for postmortem diagnosis of SCD.

Anaphylaxis is a rapid allergic reaction that may cause sudden death. Currently, postmortem diagnosis of anaphylactic shock is sometimes difficult and often achieved through exclusion. The aim of our study was to investigate whether Fourier transform infrared (FTIR) microspectroscopy combined with pattern recognition methods would be complementary to traditional methods and provide a more accurate postmortem diagnosis of fatal anaphylactic shock. First, the results of spectral peak area analysis showed that the pulmonary edema fluid of the fatal anaphylactic shock group was richer in protein components than the control group, which included mechanical asphyxia, brain injury, and acute cardiac death. Subsequently, principle component analysis (PCA) was performed and showed that the anaphylactic shock group contained more turn and α-helix protein structures as well as less tyrosine-rich proteins than the control group. Ultimately, a partial least-square discriminant analysis (PLS-DA) model combined with a variables selection method called the genetic algorithm (GA) was built and demonstrated good separation between these two groups. This pilot study demonstrates that FTIR microspectroscopy has the potential to be an effective aid for postmortem diagnosis of fatal anaphylactic shock.

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A sensitive liquid chromatography-tandem mass spectrometry method is presented for determination of triazolam and alpha-hydroxytriazolam in guinea pig hair after a single dose of triazolam. Eighteen guinea pigs were divided into three dosage groups (10, 100, and 500 microg/kg) and administrated a single dose of triazolam intragastrically. Before administration, drug-free hair was shaved from their back. Newly grown hair in shaved area was collected every seven days after administration. About 20 mg of decontaminated hair was cut into small segments and incubated in 2 mL of phosphate buffer (pH 8.4) at 45 degrees C overnight. Triazolam-d(4) and alpha-hydroxytriazolam-d(4) were used as internal standards, and liquid-liquid extraction was performed with 3 mL of ethyl ether. The sample was separated on an Allure propyl PFP column with a mobile phase of acetonitrile/20 mM ammonium acetate (7:3, v/v). Detection was implemented with multiple reaction monitoring mode by an API4000 triple-quadrupole tandem mass spectrometer. Limits of detection for triazolam and alpha-hydroxytriazolam were 1 and 5 pg/mg, respectively. Triazolam and alpha-hydroxytriazolam could only be detected in the first week, and 100 microg/kg was the minimal dosage detectable in guinea pig hair. The concentration of triazolam in hair was related with administration dosage and hair color. alpha-Hydroxytriazolam has a higher concentration than triazolam in guinea pig hair.

This study investigated whether infrared spectroscopy combined with a deep learning algorithm could be a useful tool for determining causes of death by analyzing pulmonary edema fluid from forensic autopsies. A newly designed convolutional neural network-based deep learning framework, named DeepIR and eight popular machine learning algorithms, were used to construct classifiers. The prediction performances of these classifiers demonstrated that DeepIR outperformed the machine learning algorithms in establishing classifiers to determine the causes of death. Moreover, DeepIR wasgenerally less dependent on preprocessing procedures than were the machine learning algorithms; it provided the validation accuracy with a narrow range from 0.9661 to 0.9856 and the test accuracy ranging from 0.8774 to 0.9167 on the raw pulmonary edema fluid spectral dataset and the nine preprocessing protocolbased datasets in our study. In conclusion, this study demonstrates that the deep learning-equipped Fourier transform infrared spectroscopy technique has the potential to be an effective aid for determining causes of death. K E Y W O R D S chemometrics, deep learning, forensic science, infrared spectroscopy, pulmonary edema fluid

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