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BackgroundUniversal goniometer (UG) is commonly used as a standard method to evaluate range of motion (ROM) as part of joint motions. It has some restrictions, such as involvement of both hands of the physician, leads to instability of hands and error. Nowadays smartphones usage has been increasing due to its easy application.ObjectivesThe study was designed to compare the smartphone inclinometer-based app and UG in evaluation of ROM of elbow.Materials and MethodsThe maximum ROM of elbow in position of flexion and pronation and supination of forearm were examined in 60 healthy volunteers with UG and smartphone. Data were analyzed using SPSS (ver. 16) software and appropriate statistical tests were applied, such as paired t-test, ICC and Bland Altman curves.ResultsThe results of this study showed high reliability and validity of smartphone in regarding UG with ICC > 0.95. The highest reliability for both methods was in elbow supination and the lowest was in the elbow flexion (0.84).ConclusionsSmartphones due to ease of access and usage for the physician and the patient, may be good alternatives for UG.

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Nonsuicidal self-injury, the deliberate-direct destruction of body tissue without suicidal intent is a relatively common event in forensic referrals. It is very important to distinguish between this and other types from forensic point of view. Forensic practitioners must be expert and trained for this purpose.

The aim of this study was to compare system efficiency and analysis duration regarding the solvent consumption and system maintenance in high-pressure liquid chromatography (HPLC) and ultra high-pressure liquid chromatography (UHPLC).In a case–control study, standard solutions of 7 benzodiazepines (BZs) and 73 biological samples such as urine, tissue, stomach content, and bile that screened positive for BZs were analyzed by HPLC and UHPLC in laboratory of forensic toxicology during 2012 to 2013. HPLC analysis was performed using a Knauer by 100-5 C-18 column (250 mm × 4.6 mm) and Knauer photodiode array detector (PAD). UHPLC analysis was performed using Knauer PAD detector with cooling autosampler and Eurospher II 100-3 C-18 column (100 mm × 3 mm) and also 2 pumps. The mean retention time, standard deviation, flow rate, and repeatability of analytical results were compared by using 2 methods.Routine runtimes in HPLC and UHPLC took 40 and 15 minutes, respectively. Changes in mobile phase composition of the 2 methods were not required. Flow rate and solvent consumption in UHPLC decreased. Diazepam and flurazepam were detected more frequently in biological samples.In UHPLC, small particle size and short length of column cause effective separation of BZs in a very short time. Reduced flow rate, solvent consumption, and injection volume cause more efficiency and less analysis costs. Thus, in the detection of BZs, UHPLC is an accurate, sensitive, and fast method with less cost of analysis.

Objective:Benzodiazepines are frequently screened drugs in emergency toxicology, drugs of abuse testing, and in forensic cases. As the variations of benzodiazepines concentrations in biological samples during bleeding, postmortem changes, and redistribution could be biasing forensic medicine examinations, hence selecting a suitable sample and a validated accurate method is essential for the quantitative analysis of these main drug categories. The aim of this study was to develop a valid method for the determination of four benzodiazepines (flurazepam, lorazepam, alprazolam, and diazepam) in vitreous humor using liquid–liquid extraction and high-performance liquid chromatography.Methods:Sample preparation was carried out using liquid–liquid extraction with n-hexane: ethyl acetate and subsequent detection by high-performance liquid chromatography method coupled to diode array detector. This method was applied to quantify benzodiazepines in 21 authentic vitreous humor samples. Linear curve for each drug was obtained within the range of 30–3000 ng/mL with coefficient of correlation higher than 0.99.Results:The limit of detection and quantitation were 30 and 100 ng/mL respectively for four drugs. The method showed an appropriate intra- and inter-day precision (coefficient of variation < 10%). Benzodiazepines recoveries were estimated to be over 80%. The method showed high selectivity; no additional peak due to interfering substances in samples was observed.Conclusion:The present method was selective, sensitive, accurate, and precise for the quantitative analysis of benzodiazepines in vitreous humor samples in forensic toxicology laboratory.

Introduction: This study was designed to develop and evaluate machine learning algorithms for predicting seizure due to acute tramadol poisoning, identifying high-risk patients and facilitating appropriate clinical decision-making. Methods: Several characteristics of acute tramadol poisoning cases were collected in the Emergency Department (ED) (2013–2019). After selecting important variables in random forest method, prediction models were developed using the Support Vector Machine (SVM), Naïve Bayes (NB), Artificial Neural Network (ANN) and K-Nearest Neighbor (K-NN) algorithms. Area Under the Curve (AUC) and other diagnostic criteria were used to assess performance of models. Results: In 909 patients, 544 (59.8%) experienced seizures. The important predictors of seizure were sex, pulse rate, arterial blood oxygen pressure, blood bicarbonate level and pH. SVM (AUC = 0.68), NB (AUC = 0.71) and ANN (AUC = 0.70) models outperformed k-NN model (AUC = 0.58). NB model had a higher sensitivity and negative predictive value and k-NN model had higher specificity and positive predictive values than other models. Conclusion: A perfect prediction model may help improve clinicians’ decision-making and clinical care at EDs in hospitals and medical settings. SVM, ANN and NB models had no significant differences in the performance and accuracy; however, validated logistic regression (LR) was the superior model for predicting seizure due to acute tramadol poisoning.