The measurement of nitrite in adulterated urine samples by high-performance ion chromatography

Presumptive identification of nitrite by Griess reagent test strips-Case reports of fatal poisoning with sodium nitrite

The intentional ingestion of sodium nitrite causes toxicity by inducing methemoglobinemia, which can lead to cyanosis, hypotension and death. The number of reported suicide cases has significantly increased in the past 10 years as sodium nitrite is readily available online. The traditional tests for nitrite and nitrate require specialized detection methods which are not typically available in a postmortem toxicology laboratory. This rise in sodium nitrite overdose cases indicates the need for a simple, quick test for suspected nitrite toxicity. In this study, a common Griess reagent color test (MQuant™ Nitrite Test Strips) was used as a presumptive method in cases where the ingestion of sodium nitrite was suspected. The test results were consistent between specimens in all cases, and vitreous humor was identified as a reliable matrix to be used in the cases of suspected sodium nitrite poisonings. Case reports of five patients who died of suicide by sodium nitrite in a 6-month span are presented.

Applications of ion chromatography in urine analysis: A review

Ion chromatography (IC) plays a crucial role in urine analysis for diverse medical diagnoses. This paper reviews a comprehensive investigation into urine pretreatment techniques, as well as the design and development of IC systems for the measurement of various chemicals. Prior to analysis, urine samples commonly undergo pretreatment procedures such as dilution, filtration, purification, and concentration. These steps effectively eliminate interfering factors and facilitate the accurate and sensitive analysis of ultra-trace components. To separate and quantify different chemical elements or ions present in urine, a range of homemade or commercially available columns coupled with various detectors were employed. This study focuses on the analysis of chemicals such as heavy metals, halogens, pesticides, drugs, and other essential or toxic substances by IC methods.

Interpretable machine learning model to detect chemically adulterated urine samples analyzed by high resolution mass spectrometry

OBJECTIVES

Urine sample manipulation including substitution, dilution, and chemical adulteration is a continuing challenge for workplace drug testing, abstinence control, and doping control laboratories. The simultaneous detection of sample manipulation and prohibited drugs within one single analytical measurement would be highly advantageous. Machine learning algorithms are able to learn from existing datasets and predict outcomes of new data, which are unknown to the model.

METHODS

Authentic human urine samples were treated with pyridinium chlorochromate, potassium nitrite, hydrogen peroxide, iodine, sodium hypochlorite, and water as control. In total, 702 samples, measured with liquid chromatography coupled to quadrupole time-of-flight mass spectrometry, were used. After retention time alignment within Progenesis QI, an artificial neural network was trained with 500 samples, each featuring 33,448 values. The feature importance was analyzed with the local interpretable model-agnostic explanations approach.

RESULTS

Following 10-fold cross-validation, the mean sensitivity, specificity, positive predictive value, and negative predictive value was 88.9, 92.0, 91.9, and 89.2%, respectively. A diverse test set (n=202) containing treated and untreated urine samples could be correctly classified with an accuracy of 95.4%. In addition, 14 important features and four potential biomarkers were extracted.

CONCLUSIONS

With interpretable retention time aligned liquid chromatography high-resolution mass spectrometry data, a reliable machine learning model could be established that rapidly uncovers chemical urine manipulation. The incorporation of our model into routine clinical or forensic analysis allows simultaneous LC-MS analysis and sample integrity testing in one run, thus revolutionizing this field of drug testing.

Systematic Comparison of Bias and Precision Data Obtained with Multiple-Point and One-Point Calibration in Six Validated Multianalyte Assays for Quantification of Drugs in Human Plasma

One-point (linear through zero) calibration is often used as a compromise between necessary calibration, workload, and time. The aim of the present study was to systematically check the applicability of one-point calibration by comparing bias and precision data obtained with full and one-point calibration. Data from validation studies of six mass spectrometry-based multianalyte bioanalytical assays were used for this purpose. Bias and intermediate precision datasets of full multiple-point calibration were compared to six one-point calibration datasets (A-F in rising calibrator concentration order) calculated from the same raw data. The datasets were statistically compared using the Friedman test followed by Dunn's multiple comparison test. The results obtained with full calibration and the different one-point calibrations were found to differ significantly (P < 0.05) in all of the six studied methods. The best one-point calibration results were obtained with calibrator D, with which acceptance criteria for bias and precision were fulfilled for the majority of analytes. However, some extremely high bias and precision data were obtained for some analytes in the low-concentration range. In conclusion, one-point calibration with a calibrator close to the center of the full calibration range can be a feasible alternative to full calibration.

Pitfalls of toxicological analysis

Pitfalls are a permanent risk in all fields of forensic- and clinical-toxicological analysis and are not only concerned with immunoassays, although they are at present mostly discussed with regard to this subject. There are risks inherent in the field of preanalysis (e.g. the sampling, transportation, storage und treatment of the samples immediately prior to analysis). Furthermore, various pitfalls during measurement itself are to be avoided in connection with derivatization, choice of analysis parameters, memory-effects and many other sources of error. Another important section concerns the pharmacokinetic and pharmacodynamic interpretation of analytical results. Many toxicokinetic (e.g. enzyme induction) and toxicodynamic factors (e.g. tolerance effects) must be considered with special regard to plausibility controls (e.g. constellation, longitudinal, transversal and trend monitoring).