法医学杂志最新文献

Objectives: To explore the method of postmortem submersion interval (PMSI) estimation for bodies from the Jingzhou reach of the Yangtze River, utilizing an aquatic decomposition score.

Methods: A total of 105 cases of known PMSI bodies recovered from the Jingzhou reach of the Yangtze River were collected from the cases handled by the Jingzhou Branch of Yangtze River Shipping Public Security Bureau from 2018 to 2022. Considering the average monthly temperature, these cases were categorized into summer and winter groups, with the threshold set at 20 ℃. For each case, the total aquatic decomposition score (TADS) was assessed using the aquatic decomposition score table. The relationship between TADS and PMSI was explored using statistical approach, and two regression equations were established respectively through the natural logarithmic transformation of PMSI. Six bodies recovered in 2023 from the Jingzhou reach of the Yangtze River were selected to verify the equations.

Results: In both summer and winter groups, PMSI was positively correlated with TADS (R²>0.70). The regression equation of summer group was TADS=5.117+4.825×ln(PMSI), and for the winter group, it was TADS=3.191+3.967×ln(PMSI).

Conclusions: The decomposition degree of the bodies can be used to estimate PMSI combined with the feature of water temperature of the Jingzhou reach of the Yangtze River.

Objectives: To optimize and establish GC-MS, LC-MS, and infrared spectroscopy (IR) methods for analyzing 1-phenyl-2-propanone (P-2-P) and 3,4-methylenedioxyphenyl-2-propanone (MDP-2-P) precursors.

Methods: Eleven precursor substances of P-2-P and MDP-2-P were analyzed by GC-MS, LC-MS and IR methods, some key analytical parameters, such as solvent and injection temperature, were optimized.

Results: Substances such as 3-oxo-2-phenylbutyl methyl ester (MAPA) contain ester bonds in their structures were prone to ester exchange reactions. Therefore, alcohol solvent should be avoided to prevent the esterification. Instead, non-alcohol solvents, such as acetonitrile, were recommended. Substances such as MAPA that may undergo decomposition at the gas phase injection port temperature exceeded 170 ℃. It was recommended to lower the temperature of the injection port to 170 ℃. For substances that did not show peaks during GC-MS analysis, such as 2-methyl-3-phenylglycidic acid sodium salt (BMK sodium glycidylate) and 2-methyl-3-[3,4-(methylenedioxy)phenyl]glycidic acid sodium salt(PMK sodium glycidylate), it was recommended for IR detection.

Conclusions: This study established GC-MS, LC-MS and IR methods that can accurately qualitatively analyze eleven P-2-P and MDP-2-P precursors, which can provide technical support for the detection of such substances in related cases.

Objectives: To explore the possible mechanisms of changes in neural metabolic cells in the white matter injury sites of the brain in mice with carbon monoxide (CO) poisoning and the abnormal absorption of iron ions in the body caused by intestinal flora and intestinal flora disorders.

Methods: C57BL/6 mice were placed in a closed jar and given CO to establish a mouse model of CO poisoning. The control group was given the same treatment but not given CO. Samples were taken from the white matter to observe the morphological changes of white matter, the expression of transferrin (TRF) and glutathione peroxidase 4 (GPX4) was analyzed by immunohistochemistry and Western blotting. Meanwhile, feces of mice were collected at 3rd and 7th day after CO exposure, and 16S rRNA sequencing was performed to analyze the changes of intestinal flora in mice caused by CO poisoning.

Results: Electron microscopy showed that CO poisoning caused obvious demyelination, with increased expression of TRF and decreased expression of GPX4 in the white matter (P<0.05). CO poisoning led to abnormal distribution of intestinal flora in mice, resulting in abnormal absorption of iron in intestinal tissue.

Conclusions: CO poisoning can cause damage to white matter nerve cells in the brain, leading to demyelination. It can also cause abnormal intestinal flora distribution and iron absorption in mice.

Objectives: To analyze the factors contributing to inconsistent opinions on assessments of mental disability degrees caused by traumatic brain injury (TBI).

Methods: A retrospective analysis was conducted on 50 cases of re-assessment of mental disability caused by TBI at Forensic Medicine Center of Sun Yat-sen University from 2018 to 2019. General demographic information of the assessed individuals, TBI conditions, and initial and re-assessment opinions were collected. Descriptive statistics, Spearman correlation analysis and generalized estimating equations were used to analyze the differences in mental disorder diagnosis and disability degrees between initial and re-assessment. The reasons for inconsistent opinions were analyzed.

Results: The inconsistency rate for two mental disability assessment opinions was 70.0% (including only mental disorder diagnosis were inconsistent, only disability degrees were inconsistent and both inconsistent). The responses to questioning, memory, intelligence, emotional activities, volitional behavior activities, and self-awareness during the assessment were correlated with the location of the cerebral malacia foci caused by TBI. There were significant differences between the two assessments in the degree of impairment to some mental symptoms and the living ability.

Conclusions: The reasons for the inconsistent opinions on the two assessments may be: (1) depending on different brain imaging information (including changes in brain imaging information in the recent three months, and the location of cerebral malacia foci); (2) examiners have different understandings of the degree of damage caused by mental disorders; (3) examiner's assessment of the degree of impairment in living ability varies.

Objectives: To use uncertainty as an indicator to evaluate the main factors affecting data quality in the quantitative analysis of 12 volatile components in blood, including ethanol and toluene, and to assess the impact of different quality parameters, such as different hardware platforms on analytical results.

Methods: Two established headspace gas chromatography platforms were used following the method specified in Examination Methods for Ethanol, Methanol, n-Propanol, Acetone, Isopropanol and n-Butanol in Blood and Urine (GB/T 42430-2023) for analysis. According to the requirements of Guidance on Quantifying Uncertainty in Chemical Analysis (CNAS-GL006：2019) and Evaluation and Expression of Uncertainty in Measurement (JJF 1059.1-2012), the uncertainty of the whole process of 12 volatile components quantitative analysis such as ethanol and toluene in blood was calculated. The differences of individual uncertainty components and the same uncertainty components across different hardware platforms were compared sequentially, and the results were verified by quantitative analysis of actual samples.

Results: There was no significant difference in the uncertainty components of quantitative analysis of 12 volatile components, whether it was a hardware platform composed of domestic or imported instruments. Among them, the relative standard uncertainty of type A introduced by repeatability tests and analysts ranged from 2.81×10^-3 to 9.28×10^-3; the type B relative combined standard uncertainties introduced by the standard solution and internal standard solution were 5.65×10^-3 to 1.15×10^-2, 4.85×10^-3, respectively, the type B relative standard uncertainties introduced by the calibration curve and equipment were 1.45×10^-2 to 2.47×10^-2 and 5.00×10^-3, respectively. The overall relative combined standard uncertainty of each component ranged from 1.74×10^-2 to 3.07×10^-2.

Conclusions: In the analysis of 12 volatile components in blood, including ethanol and toluene, calibration curve fitting is the dominant source of uncertainty. Reasonable parallel operation can effectively control the uncertainty. The selection of different hardware platforms and other quality parameters does not significantly affect the quantitative results of 12 volatile components in blood.