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.

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.

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.

[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 （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.

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.

Pathologic features of initial adenomas as predictors for metachronous adenomas of the rectum

BACKGROUND

Colorectal cancer is the third most common cancer in the world, arising mostly from pre-existing adenomatous polyps (adenomas) of the large bowel. Patients with colorectal adenomas are at increased risk of colorectal cancer because of a high recurrence rate for adenomas. We followed a cohort of 1490 patients with rectal adenomas to determine whether recurrence might be related to pathologic characteristics of the initial adenomas.

METHODS

The patients were identified in Haining County, China, from 1977 through 1978 by means of examination with a 15-cm rigid sigmoidoscope. They were followed by endoscopic examination at years 2, 4, 6, 11, and 16 after their initial polypectomy. New adenomas in the rectum were identified in 280 patients in these follow-up examinations.

RESULTS

Statistically significant twofold to threefold elevated risks of metachronous (recurrent) adenomas were observed for patients who had more than two initial adenomas or whose most advanced initial adenoma was more than 1.0 cm in size, was of villous/tubulovillous type, or showed moderate to severe dysplasia. Much stronger associations were observed for advanced metachronous neoplasms, which are defined as cancers or adenomas with severe dysplasia, with multivariate adjusted relative risks (95% confidence interval) of 4.2 (1.8-9.9) for a large initial adenoma (>1.0 cm), 8.1 (4.2-15.6) for villous/tubulovillous architecture, and 14.4 (5.0-41.3) for severe dysplasia. In particular, patients who had a large (>1.0 cm) adenoma with severe dysplasia at baseline had a relative risk of 37 (7.8-174.7) of developing advanced metachronous neoplasms compared with patients who had small adenoma(s) with mild dysplasia.

CONCLUSIONS

The risk of metachronous adenomas is closely related to the pathology of initial adenomas, thus allowing identification of a high-risk group of adenoma patients for close surveillance after their initial polypectomy.

[10 year-follow up of 2815 patients with resected recto-anal adenomas or polyps]

Ten year-follow up of 2815 patients with resected rectoanal adenomas or polyps revealed recurrence in 225 patients with a rate of 7.99%. The recurrence rate of villotubular adenoma and villous adenoma was 18.26% and 15.79% respectively, the more the volume of adenomas and polyps is, the higher the recurrence, in which the recurrence rate of more than 2 cm in diameter is 19.23% and the recurrence rate of multiple adenoma and polyps had a higher recurrence rate than single ones (17.39% vs 6.88%). Canceration in 10 years was found in 16 patients with a rate of 0.57%.