|Year : 2016 | Volume
| Issue : 1 | Page : 8-11
Estimation of the Postmortem Interval by Measuring Blood Oxidation-reduction Potential Values
Zhuqing Jiang, Meng You, Xu Wang, Di Lu, Haidong Zhang, Shengli Di, Fengqin Zhang, Zhaoming Guo, Xiaofei E, Lin Chang, Jian Xiang, Rufeng Bai, Tiantong Yang
Center of Cooperative Innovation for Judicial Civilization, China University of Political Science and Law & Jilin University & Wuhan University; China University of Political Science and Law, Key Laboratory of Evidence Science, Ministry of Education, Beijing 100040, China
|Date of Web Publication||3-Feb-2016|
Collaborative Innovation Center of Judicial Civilization, Key Laboratory of Evidence Science, China University of Political Science and Law, Ministry of Education, 26 Houtun North Road, Qinghe, Haidian District, Beijing, PRC, 100192
Source of Support: None, Conflict of Interest: None
Accurate estimation of the postmortem interval (PMI) is an important task in forensic practice. In the last half-century, the use of postmortem biochemistry has become an important ancillary method in determining the time of death. The present study was carried out to determine the correlation between blood oxidation-reduction potential (ORP) values and PMIs, and to develop a three-dimensional surface equation to estimate the PMI under various temperature conditions. A total of 48 rabbits were placed into six groups and sacrificed by air embolism. Blood was obtained from the right ventricle of each rabbit, and specimens were stored at 10°C, 15°C, 20°C, 25°C, 30°C, and 35°C. At different PMIs (once every 4 h), the blood ORP values were measured using a PB-21 electrochemical analyzer. Statistical analysis and curve fitting of the data yielded cubic polynomial regression equations and a surface equation at different temperatures. Result: The results showed that there was a strong positive correlation between the blood ORP values at different temperatures and the PMI. This study provides another example of using a three-dimensional surface equation as a tool to estimate the PMI at various temperature conditions.
Keywords: Forensic science, interpolation function, oxidation-reduction potential, postmortem interval, three-dimensional surface equation
|How to cite this article:|
Jiang Z, You M, Wang X, Lu D, Zhang H, Di S, Zhang F, Guo Z, Xiaofei, Chang L, Xiang J, Bai R, Yang T. Estimation of the Postmortem Interval by Measuring Blood Oxidation-reduction Potential Values. J Forensic Sci Med 2016;2:8-11
|How to cite this URL:|
Jiang Z, You M, Wang X, Lu D, Zhang H, Di S, Zhang F, Guo Z, Xiaofei, Chang L, Xiang J, Bai R, Yang T. Estimation of the Postmortem Interval by Measuring Blood Oxidation-reduction Potential Values. J Forensic Sci Med [serial online] 2016 [cited 2020 Jan 17];2:8-11. Available from: http://www.jfsmonline.com/text.asp?2016/2/1/8/155727
| Introduction|| |
Postmortem interval (PMI) determination is one of the most important procedures in forensic practice. Scientific investigations on the estimation of PMIs can be traced back to the 19 th century, and numerous studies on PMI estimation have been carried out using postmortem physical and morphological changes, entomological evidence, and biochemical studies. ,,, As of now, there is still no generally accepted objective method available that allows for reliable PMI estimation. Many factors affect the onset and the course of the postmortem changes. Studies have shown that the ambient temperature, acidity/alkalinity, decomposition process, bacterial fermentation, and even oxygen pressure will influence the estimation of the PMI. ,,, Of all the factors, ambient temperature is considered the most influential factor in a PMI estimation. ,
There have been increasing efforts in forensic practice to establish an accurate and objective method that can be applied in PMI estimation at ambient temperature. The majority of researchers believe that as the cadaver is at a continuously variable ambient temperature, postmortem evaluation of a single substance at a single temperature is not suitable for PMI estimation. , Multivariate approaches, such as generalized additive models (GAMs)  or support vector machines (SVMs)  allow various indicator substances to be incorporated into the model, thus improving the prediction of PMIs. The aims of our study are to analyze the correlation between blood oxidation-reduction potential (ORP) values and PMIs, and to develop a three-dimensional surface equation to estimate the PMI under various temperature conditions by interpolation function.
| Materials and Methods|| |
This research met the standards and principles of animal care and use as outlined by the Chinese Association for Laboratory Animal Science. All animal experiments were approved by the Committee on Animal Care and Use of the Key Laboratory of Evidence Science (China University of Political Science and Law, Ministry of Education).
Animal samples and apparatus
Forty-eight New Zealand White rabbits (male and female) weighing 3000 ± 20 g were obtained by the Beijing Laboratory Animal Research Center (Beijing, China). All rabbits were bred and housed at a constant temperature (23 ± 2°C), with a 12-h light/dark cycle. Each animal was given an individual number, and fed with standard feed and given water ad libitum.
A PB-21 type electrochemical analyzer was provided by Sartorius Scientific Instrument Limited Company (Munich, Germany). The ORP detection range (in millivotes, i.e. mV) was -1500.0 ~+1800.0, the ORP resolution (mV) was ± 0.1, the ORP accuracy (mV) was ± 0.2, the temperature range was -5.0-105.0°C, the temperature resolution was ± 0.1°C, and the temperature accuracy was ± 0.2°C. A low-temperature sink (SDC-6), provided by Chen Bio-Technology (Nanjing, China), had a temperature range of -5 ~ 100°C and temperature control precision of ± 0.05°C.
Sample preparation and detection
Forty-eight rabbits were sacrificed by air embolism and randomly divided into 6 groups (8 rabbits in each group). Five milliliters (5 mL) of blood from the right ventricle of each rabbit was sampled immediately after death and then placed in a 5 mL sterile blood tube. The blood specimens from each group were stored in water baths with constant temperatures of 10 ± 0.05°C, 15 ± 0.05°C, 20 ± 0.05°C, 25 ± 0.05°C, 30 ± 0.05°C, and 35 ± 0.05°C, respectively, for further analysis. The blood ORP from each specimen was measured every 4 h until 132 h after death. The electrochemical analyzer was used to determine the ORP values of blood samples. The ORP was measured in millivolts (mV).
All values are reported as Mean ± SEM. A one-way analysis of variance (ANOVA) test was performed for the comparisons of more than two groups. A value of P < 0.05 was considered significant. The statistical analysis of the data was analyzed with SPSS software (version 17.0). Interpolation fitting and curve image plotting were performed with MATLAB software (version 10.0, Natick, Massachusetts).
| Results|| |
[Table 1] shows the ORP values of rabbit blood within 0 ~ 132 h postmortem at temperatures of 10 ± 0.05°C, 15 ± 0.05°C, 20 ± 0.05°C, 25 ± 0.05°C, 30 ± 0.05°C and 35 ± 0.05°C. The blood ORP values increased much more rapidly at higher temperatures with an increased PMI compared to the values at lower temperatures.
A regression analysis was performed on the ORP data. The PMIs and various temperature values were used as independent variables, where x represented PMI, and y represented ORP. The optimal regression function results showed that the R2 value of the cubic curve-fitting equation ranged 0.974-0.986, indicating that there was a strong positive correlation between the blood ORP values at different temperatures and the PMIs. [Table 2] shows the cubic regression equations at different temperatures. [Figure 1] shows the correlation between the blood ORP values and PMIs at different temperatures. At higher temperatures (35 ± 0.05°C), the blood ORP values increased rapidly, reaching a maximum value of more than 60 mV within a 28-h PMI. At lower temperatures (10 ± 0.05°C), however, the ORP values increased slowly over an 80-h PMI and reached a maximum value at a 120-h PMI. This result indicates that variations in ambient temperature after death need to be considered when estimating the PMI based on biochemical measurements.
|Figure 1: The correlation between blood ORP value and PMI at different temperatures|
Click here to view
|Table 2: The optimal regression function results between PMI and blood ORP value |
Click here to view
The interpolation function was fitted for regression equations obtained with MATLAB software.
The equation given above represents a three-variable fifth-degree equation, and also represents a three-dimensional surface, in which z represents the ORP value, and the variables x and y are PMI and temperature. [Figure 2] shows the three-dimensional surface.
| Discussion|| |
Over the last few decades, extensive work has been carried out to determine the PMI from changes in the biochemical constituents of various body fluids, such as blood, cerebrospinal fluid, and vitreous humor, immediately or shortly after death.  Postmortem levels of urea, creatinine, glucose, iron, potassium, sodium, calcium, chloride, and hypoxanthine in the vitreous humor have been widely studied as biochemical markers to determine the PMI. , Other biochemical markers include insulin in pancreatic β-cells, strontium-90, and RNA and DNA degradation in body tissues. ,,, New lines of research, such as those on temperature-dependent postmortem changes of calcineurin A, calmodulin-dependent kinase II, myristoylated alanine-rich C-kinase substrate, and protein phosphatase 2A,  and the postmortem observation of alterations in brain metabolites by means of proton magnetic resonance spectroscopy (1H-MRS) at different temperatures and PMIs,  may yield more precise methods for PMI determination.
The main goal of this study was to develop a reliable, alternative approach for PMI estimation at various ambient temperatures. In order to study the effect of temperature, changes in the ORP were determined from the blood of rabbits at six different temperatures between 10°C and 35°C, with measurements repeated up to 132 h postmortem. The ORP represents all chemical reactions in which the oxidation states of atoms are changed. ORP is a measure of the tendency of a chemical species to acquire electrons and thereby be reduced. Just as the transfer of hydrogen ions between chemical species determines the pH of an aqueous solution, the transfer of electrons between chemical species determines the reduction potential of an aqueous solution. ORP measurements have been used to monitor water treatment processes, and ORP values are known to be temperature-dependent. , To our knowledge, this is the first time that ORP has been used to estimate the PMI at different ambient temperatures. The study demonstrated a strong positive correlation between the blood ORP values and ambient temperatures. The blood ORP values increased much more rapidly at higher temperatures with increased PMIs when compared to the values at lower temperatures. The obtained surface equation in this study was composed of ambient temperatures, ORP values, and PMIs. The results of this study indicated that under the tested temperature conditions, postmortem variations in the ORP and PMI had an optimal linear relationship. The three-dimensional surface equation allows the effect of ambient temperature to be reliably included in a PMI determination based on biochemical measurements.
| Conclusion|| |
We applied the curve equations obtained at six different temperatures to develop a surface equation for PMI estimation within a certain temperature range, and found that the variation in ORP with PMI was correlated with ambient temperature variation. This study provides a reliable alternative approach for the forensic practice of estimating the PMI at various ambient temperatures. Using the three-dimensional surface equation, blood ORP values determined by a simple and rapid test can be used, together with known ambient temperatures, to calculate the PMI of a cadaver.
| Acknowledgment|| |
This study was supported by the Key Projects in the National Science and Technology Pillar Program during the Eleventh Five-year Plan Period (2012BAK16B02), the Scientific Research Foundation for the Returned Overseas Chinese Scholars, the State Education Ministry [(2013) 1792], the Training Programmers Foundation for the Beijing Talents (2013D002023000002), the Beijing Planning Project of Philosophy and Social Science (13FXC032), and the Project of Young Teachers' Academic Innovation Team by China University of Political Science and Law (2014CXTD04).
| References|| |
Aydin B, Colak B, Balci Y, Demirüstü C. Consistency of postmortem interval estimations of physicians using only postmortem changes of putrefied dead bodies. Am J Forensic Med Pathol 2010;31:243-6.
Kovarik C, Stewart D, Cockerell C. Gross and histologic postmortem changes of the skin. Am J Forensic Med Pathol 2005;26:305-8.
Saigusa K, Takamiya M, Matsumasa M, Aoki Y. The forensic availability of a simple and time-saving method for the identification of dipteran species to estimate postmortem interval using entomological evidence. Leg Med (Tokyo) 2006;8:253-4.
Jashnani KD, Kale SA, Rupani AB. Vitreous humor: Biochemical constituents in estimation of postmortem interval. J Forensic Sci 2010;55:1523-7.
Vass AA. The elusive universal post-mortem interval formula. Forensic Sci Int 2011;204:34-40.
Forbes SL, Stuart BH, Dadour IR. Dent BB. A preliminary investigation of the stages of adipocere formation. J Forensic Sci 2004;49:566-74.
Forbes SL, Stuart BH, Dent BB. The effect of the burial environment on adipocere formation. Forensic Sci Int 2005;154:24-34.
Megyesi MS, Nawrocki SP, Haskell NH. Using accumulated degree-days to estimate the postmortem interval from decomposed human remains. J Forensic Sci 2005;50:618-26.
Ith M, Scheurer E, Kreis R, Thali M, Dirnhofer R, Boesch C. Estimation of the postmortem interval by means of 1
H-MRS of decomposing brain tissue: Influence of ambient temperature. NMR Biomed 2011;24:791-8.
Poloz YO, O'Day DH. Determining time of death: Temperature-dependent postmortem changes in calcineurin A, MARCKS, CaMKII, and protein phosphatase 2A in mouse. Int J Legal Med 2009;123:305-14.
Kimura A, Ishida Y, Hayashi T, Nosaka M, Kondo T. Estimating time of death based on the biological clock. Int J Legal Med 2011;125:385-91.
Matoba K, Terazawa K. Estimation of the time of death of decomposed or skeletonized bodies found outdoors in cold season in Sapporo city, located in the northern district of Japan. Leg Med (Tokyo) 2008;10:78-82.
Muñoz Barús JI, Febrero-Bande M, Cadarso-Suárez C. Flexible regression models for estimating postmortem interval (PMI) in forensic medicine. Stat Med 2008;27:5026-38.
Roca-Pardiñas J, Cadarso-Suárez C, Nácher V, Acuña C. Bootstrap-based methods for testing factor-by-curve interactions in generalized additive models: Assessing prefrontal cortex neural activity related to decision making. Stat Med 2006;25:2483-501.
Madea B, Musshoff F. Postmortem biochemistry. Forensic Sci Int 2007;165:165-71.
Rognum TO, Hauge S, Oyasaeter S, Saugstad OD. A new biochemical method for estimation of postmortem time. Forensic Sci Int 1991;51:139-46.
Wehner F, Wehner HD, Schieffer MC, Subke J. Delimitation of the time of death by immunohistochemical detection of insulin in pancreatic beta-cells. Forensic Sci Int 1999;105:161-9.
Neis P, Hille R, Paschke M, Pilwat G, Schnabel A, Neiss C, et al
. Strontium90 for determination of time since death. Forensic Sci Int 1999;99:47-51.
Bauer M, Gramlich I, Polzin S, Patzelt D. Quantification of mRNA degradation as possible indicator of postmortem interval-a pilot study. Leg Med (Tokyo) 2003;5:220-7.
Yi SH, Zhao XH, Liu L. Selection of parameters to infer postmortem interval by detecting DNA degradation using comet assay. Chinese J Forensic Med 2008;23:1-4.
Ith M, Scheurer E, Kreis R, Thali M, Dirnhofer R, Boesch C. Estimation of the postmortem interval by means of ¹H-MRS of decomposing brain tissue: Influence of ambient temperature. NMR Biomed 2011;24:791-8.
Khanal SK, Shang C, Huang JC. Use of ORP (oxidation-reduction potential) to control oxygen dosing for online sulfide oxidation in anaerobic treatment of high sulfate wastewater. Water Sci Technol 2003;47:183-9.
Gao D, Wang S, Peng Y, Liang H. Temperature effects on DO and ORP in the wastewater treatment. Huan Jing Ke Xue 2003;24:63-9.
[Figure 1], [Figure 2]
[Table 1], [Table 2]