Relationship between Postmortem Interval and FTIR Spectroscopy Changes of the Rat Skin.

ABSTRACT: Objective To infer postmortem interval （PMI） based on spectral changes of the dorsal skin of rats within 15 days postmortem using Fourier transform infrared （FTIR） spectroscopy. Methods The rats were sacrificed by cervical dislocation after anesthesia, and then placed at 25 ℃ and relative humidity of 50%. The FTIR spectral data collected from the dorsal skin at PMI points were modeled with machine learning technique. Results There was no significant difference of absorption peak location among all the PMI groups but their peak intensities changed as a function of PMIs. The model for PMI estimation was constructed using partial least squares （PLS） regression, reaching a R2 of 0.92 and a root mean square error （RMSE） of 1.30 d. As shown in variable importance for projection （VIP）, four spectral bands including 1 760-1 700 cm-1, 1 660-1 640 cm-1, 1 580-1 540 cm-1 and 1 460-1 420 cm-1 were determined as important contributions to model prediction. Conclusion Application of the FTIR technique to detect postmortem spectral changes of the rat skin provides a novel proposal for PMI estimation.

Application of Artificial Intelligence Automatic Diatom Identification System in Practical Cases.

ABSTRACT: Objective To discuss the application of artificial intelligence automatic diatom identification system in practical cases, to provide reference for quantitative diatom analysis using the system and to validate the deep learning model incorporated into the system. Methods Organs from 10 corpses in water were collected and digested with diatom nitric acid; then the smears were digitally scanned using a digital slide scanner and the diatoms were tested qualitatively and quantitatively by artificial intelligence automatic diatom identification system. Results The area under the curve （AUC） of the receiver operator characteristic （ROC） curve of the deep learning model incorporated into the artificial intelligence automatic diatom identification system, reached 98.22% and the precision of diatom identification reached 92.45%. Conclusion The artificial intelligence automatic diatom identification system is able to automatically identify diatoms, and can be used as an auxiliary tool in diatom testing in practical cases, to provide reference to drowning diagnosis.

Determination of Electrocution Using Fourier Transform Infrared Microspectroscopy and Machine Learning Algorithm.

ABSTRACT: Objective To analyze the differences among electrical damage, burns and abrasions in pig skin using Fourier transform infrared microspectroscopy （FTIR-MSP） combined with machine learning algorithm, to construct three kinds of skin injury determination models and select characteristic markers of electric injuries, in order to provide a new method for skin electric mark identification. Methods Models of electrical damage, burns and abrasions in pig skin were established. Morphological changes of different injuries were examined using traditional HE staining. The FTIR-MSP was used to detect the epidermal cell spectrum. Principal component method and partial least squares method were used to analyze the injury classification. Linear discriminant and support vector machine were used to construct the classification model, and factor loading was used to select the characteristic markers. Results Compared with the control group, the epidermal cells of the electrical damage group, burn group and abrasion group showed polarization, which was more obvious in the electrical damage group and burn group. Different types of damage was distinguished by principal component and partial least squares method. Linear discriminant and support vector machine models could effectively diagnose different damages. The absorption peaks at 2 923 cm-1, 2 854 cm-1, 1 623 cm-1, and 1 535 cm-1 showed significant differences in different injury groups. The peak intensity of electrical injury's 2 923 cm-1 absorption peak was the highest. Conclusion FTIR-MSP combined with machine learning algorithm provides a new technique to diagnose skin electrical damage and identification electrocution.

[Effects of Temperature on FTIR Spectral Characteristics of Renal Tissue in Rats after Death].

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 Spectroscopy and Data Mining Method].

OBJECTIVES: To analyse the relationship between Fourier transform infrared （FTIR） spectrum of rat's spleen tissue and postmortem interval （PMI） for PMI estimation using FTIR spectroscopy combined with 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 （R²ï¼‰ 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. CONCLUSIONS: 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.

OBJECTIVES: 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-1 and 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-1 band was used to identify the different voltages induced skin injuries. The OSC could further optimize the robustness of the 3 600-2 800 cm-1 band model. CONCLUSIONS: It is feasible to identify electrical skin injuries caused by different voltages by using FTIR-MSP technique along with machine learning algorithms.

[Research Progress of MALDI-TOF-IMS in Biomedicine and Its Application Prospect in Forensic Sciences].

Matrix-assisted laser desorption/ionization time-of-flight imaging mass spectrometry （MALDI-TOF-IMS） can analysis unknown compounds in sections and obtain molecule imaging by scanning biological tissue sections, which has become a powerful tool for the research of biomarker, lipid distribution and drug metabolism, etc. This article reviews the application of this technique in protein identification, clinical application, drug discovery, lipid research and brain injury.

[Analysis of Differentially Expressed Proteins Distribution in the Rat Brains with DAI by MALDI-TOF-IMS].

OBJECTIVES: To establish the imaging mass spectrometry for analysis of differentially expressed proteins distribution in the rat brains with diffuse axonal injury （DAI） based on matrix assisted laser desorption/ionization-time of flight imaging mass spectrometry （MALDI-TOF-IMS）. METHODS: MALDI-TOF-IMS scanning were conducted on the brains of DAI group and control group in the m/z range of 1 000 to 20 000 using AutoflexⅢ MALDI-TOF spectrometer. ClinProTool 2.2 software was used for statistical analysis on the data of two groups, and then the differentially expressed proteins were picked out to conduct imaging. The distribution of the proteins with different m/z in the rat brains was observed. RESULTS: Five proteins with different m/z, including 4 963, 5 634, 6 253, 6 714 and 7 532, differentially expressed in the rat brains with DAI. CONCLUSIONS: MALDI-TOF-IMS can be used for studying the differentially expressed proteins in rat brains with DAI and the analysis method is established for exploring the distribution of differentially expressed proteins in the rat brains with DAI using imaging mass spectrometry.

Association between the MDR1 gene variant C3435T and risk of leukaemia: a meta-analysis.

Although a number of genetic studies have attempted to link the multidrug resistance (MDR1) C3435T polymorphism to risk of leukaemia, the results were often inconsistent. The present study aimed at investigating the pooled association using a meta-analysis on the published studies. 1933 cases and 2215 controls of 11 published studies in English before June 2012 were involved in the updated meta-analysis. Furthermore, subgroup analysis was performed in different ethnic and leukaemia subtype groups. This meta-analysis suggests that the MDR1 C3435T polymorphism associate with risk of leukaemia. The effect of the variant on the expression levels and the possible functional role of the variant in leukaemia should be addressed in further studies.