Current Research and Prospects on Postmortem Interval Estimation / 法医学杂志

The researches on postmortem interval （PMI） estimation are very important and meaningful in forensic science. PMI estimation is also an important issue that must be solved in practice of forensic pathology. There are many defects existing in traditional methods for PMI estimation, so it is imperative to introduce new pathways. With the emergence of various new technologies, the researches on PMI estimation have a tendency from simple to complex with a growth of data. The present review firstly summarizes a series of methods used for PMI estimation, and then gives an outlook for the application of artificial intelligence algorithms in this field.

Effects of Temperature on FTIR Spectral Characteristics of Renal Tissue in Rats after Death / 法医学杂志

OBJECTIVES@#To analyse the Fourier transform infrared （FTIR） spectral data of renal tissue at different temperatures in rats after death, and to explore the effects of temperature on the FTIR spectral characteristics of renal tissue.@*METHODS@#The rats were sacrificed by cervical dislocation and placed at 4 ℃, 20 ℃ and 30 ℃. The FTIR spectral data of renal tissue were collected at different time points and analysed by data mining method.@*RESULTS@#The principal component analysis （PCA） results showed that there were significant trends of clustering in the samples of partial time point at 4 ℃, 20 ℃ and 30 ℃. Partial least square （PLS） regression models were established with the spectral data at three temperature groups. The performance of PLS regression models in 20 ℃ and 30 ℃ groups were more superior than that in 4 ℃ group, and the stability of the model in 20 ℃ group was better than that in 30 ℃ group.@*CONCLUSIONS@#There are differences in the FTIR spectral characteristics of renal tissue of rats after death at different temperatures. Temperature has a major impact on the performance of FTIR spectral PLS regression model. Therefore, in order to improve the accuracy of postmortem interval estimation, the effects of temperature on the model should be considered in the related study by spectral method.

Establishment of the Mathematical Model for PMI Estimation Using FTIR Spec-troscopy and Data Mining Method / 法医学杂志

Objective To analyse the relationship between Fourier transform infrared （FTIR） spectrum ofrat's spleen tissue and postmortem interval （PMI） for PMI estimation using FTIR spectroscopy combinedwith data mining method. Methods Rats were sacrificed by cervical dislocation, and the cadavers were placed at 20 ℃. The FTIR spectrum data of rats' spleen tissues were taken and measured at different time points. After pretreatment, the data was analysed by data mining method. Results The absorption peak intensity of rat's spleen tissue spectrum changed with the PMI, while the absorption peak position was unchanged. The results of principal component analysis （PCA） showed that the cumulative contribution rate of the first three principal components was 96%. There was an obvious clustering tendency for the spectrum sample at each time point. The methods of partial least squares discriminant analysis （PLS- DA） and support vector machine classification （SVMC） effectively divided the spectrum samples with different PMI into four categories （0-24 h, 48-72 h, 96-120 h and 144-168 h）. The determination coefficient （R2） of the PMI estimation model established by PLS regression analysis was 0.96, and the root mean square error of calibration （RMSEC） and root mean square error of cross validation （RMSECV） were 9.90 h and 11.39 h respectively. In prediction set, the R2 was 0.97, and the root mean square error of prediction （RMSEP） was 10.49 h. Conclusion The FTIR spectrum of the rat's spleen tissue can be effectively analyzed qualitatively and quantitatively by the combination of FTIR spectroscopy and data mining method, and the classification and PLS regression models can be established for PMI estimation.

Infrared Spectral Characteristics of Electrical Injuries on Swine Skin Caused by Different Voltages Based on Machine Learning Algorithms / 法医学杂志

Objective To explore infrared spectrum characteristics of different voltages induced electrical injuries on swine skin by using Fourier transform infrared-microspectroscopy (FTIR-MSP) combined with machine learning algorithms, thus to provide a reference to the identification of electrical skin injuries caused by different voltages.Methods Electrical skin injury model was established on swines.The skin was exposed to 110 V, 220 V and 380 V electric shock for 30 s and then samples were took, with normal skin tissues around the injuries as the control.Combined with the results of continuous section HE staining, the FTIR-MSP spectral data of the corresponding skin tissues were acquired.With the combination of machine learning algorithms such as principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), different spectral bands were selected (full band 4 000-1 000 cm-1and sub-bands 4 000-3 600 cm-1, 3 600-2 800 cm-1, 2 800-1 800 cm-1, and 1 800-1 000 cm-1), and various pretreatment methods were used such as orthogonal signal correction (OSC), standard normal variables (SNV), multivariate scatter correction (MSC), normalization, and smoothing.Thus, the model was optimized, and the classification effects were compared.Results Compared with simple spectrum analysis, PCA seemed to be better at distinguishing electrical shock groups from the control, but was not able to distinguish different voltages induced groups.PLS-DA based on the 3 600-2 800 cm-1band was used to identify the different voltages induced skin injuries.The OSC could further optimize the robustness of the 3 600-2 800 cm-1band model.Conclusion It is feasible to identify electrical skin injuries caused by different voltages by using FTIR-MSP technique along with machine learning algorithms.

Relationship between PMI and ATR-FTIR Spectral Changes in Swine Costal Cartilages and Ribs / 法医学杂志

OBJECTIVE@#To analyze postmortem chemical changes in Landrace costal cartilages and ribs using attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy, and to provide a novel technique for estimation of postmortem interval (PMI).@*METHODS@#The swines were sacrificed by hemorrhage and their costal cartilages and ribs were kept in 20 degrees C. The chemical analysis of the costal cartilages and ribs were performed using ATR-FTIR every 72 h. The correlation between the certain spectral parameters and PMI was also analyzed. The time-dependent changes of costal cartilages were more significant than ribs.@*RESULTS@#There were no obvious changes for the main absorbance bands position, and some absorbance band ratios showed time-dependent changes and significant correlations with the PMI.@*CONCLUSION@#ATR-FTIR has the ability to analyze postmortem chemical changes of the swine costal cartilages and ribs, and it can be a new method to estimate PMI based on spectroscopy.

Prevalence of diabetes in Shenzhen between 1997 and 2009 / 中华预防医学杂志

<p><b>OBJECTIVE</b>To analyze the trend of prevalence and major risk factors for diabetes between 1997 and 2009 in Shenzhen.</p><p><b>METHODS</b>A total sample of 7956 and 8487 residents aged from 20 to 69 years were selected using multi-stage cluster sampling from two cross-sectional chronic diseases survey in Shenzhen in 1997 and 2009 respectively. The variation of prevalence between different age groups (divided into a 5-year age group) were compared. The effect of risk factors was evaluated by population attributable risk percentage (PARP), and time trend of PARP for risk factors were analyzed.</p><p><b>RESULTS</b>Compared with that in 1997, the aged-adjusted prevalence of diabetes has increased from 3.85% (306/7956) to 5.21% (441/8487) in 2009 (χ(2) = 17.54, P < 0.001). The prevalence in age group 50 - 54, 60 - 64, and 65 - 69 years increased significantly from 7.88% (35/444), 8.55% (36/421) and 11.34 (33/291) to 12.09% (59/488), 14.63% (55/376) and 20.71% (70/338) respectively (χ(2) = 4.54, 7.25 and 10.01, P < 0.05). After controlling for age, the rates of abdominal obesity, family history of diabetes, dyslipidemia and hypertension increased from 21.56% (1715/7956), 4.39% (349/7956), 26.34% (2096/7956) and 11.53% (917/7956) to 31.88% (2706/8487), 12.84% (1090/8487), 41.38%(3512/8487) and 14.61% (1240/8487) respectively (χ(2) = 5.52, 14.83, 18.38 and 20.22, P < 0.01). For diabetes, the PARP of aging, dyslipidemia and abdominal obesity were 41.55%, 29.25% and 25.10% respectively.</p><p><b>CONCLUSION</b>The prevalence of diabetes in Shenzhen increases rapidly and more patients are found in younger age. The rise of diabetes was mainly attributed to aging and increasing exposures to risk factors.</p>