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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.
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Animals , Rats , Autopsy , Postmortem Changes , Spectroscopy, Fourier Transform InfraredABSTRACT
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.
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Animals , Algorithms , Fourier Analysis , Least-Squares Analysis , Machine Learning , SwineABSTRACT
MSC transplantation has been explored as a new clinical approach to stem cell-based therapies for bone diseases in regenerative medicine due to their osteogenic capability. However, only a small population of implanted MSC could successfully reach the injured areas. Therefore, enhancing MSC migration could be a beneficial strategy to improve the therapeutic potential of cell transplantation. Catharmus tinctorius volatile oil (CTVO) was found to facilitate MSC migration. Further exploration of the underlying molecular mechanism participating in the pro-migratory ability may provide a novel strategy to improve MSC transplantation efficacy. This study indicated that CTVO promotes MSC migration through enhancing ROCK2 mRNA and protein expressions. MSC migration induced by CTVO was blunted by ROCK2 inhibitor, which also decreased myosin light chain (MLC) phosphorylation. Meanwhile, the siRNA for ROCK2 inhibited the effect of CTVO on MSC migration ability and attenuated MLC phosphorylation, suggesting that CTVO may promote BMSC migration via the ROCK2/MLC signaling. Taken together, this study indicates that C. tinctorius volatile oil could enhance MSC migration via ROCK2/MLC signaling in vitro. C. tinctorius volatile oil-targeted therapy could be a beneficial strategy to improve the therapeutic potential of cell transplantation for bone diseases in regenerative medicine.
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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.
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BACKGROUND:Bushen Huoxue Decoction(BSHXD)can promote osteogenesis of bone marrow mesenchymal stem cells(BMSCs)in vitro.Exploring the molecular mechanisms involved is of clinical benefits. OBJECTIVE: To discuss the changes in the expression of SP7/Osterix and alkaline phosphatase (ALP) in BMSCs with Cbfal/RUNX2 gene silencing regulated by the water extracts from BSHXD. METHODS: BMSCs were isolated and cultured by the bone marrow adherent method, and BMSCs at passage 3 were used in the assay. BMSCs were transfected with nothing (blank control group), Cbfal/RUNX2 gene silencing lentivirus (silencing group), and negative viral vector (negative control group), respectively. Then, the cells were cultured in 100 mg/L BSHXD water extract, and 3 days later, the protein and mRNA expression of RUNX2 and Osterix was detected by western blot and qPCR, respectively. Activity of ALP in the BMSCs was also detected in each group. RESULTS AND CONCLUSION: The transfection efficiency of Cbfal/RUNX2 gene silencing lentivirus was about 90%. The protein and mRNA expressions of RUNX2 and Osterix were significantly decreased in the BMSCs transfected with Cbfal/RUNX2 gene silencing lentivirus as compared with the other two groups, and so was the ALP activity (P < 0.01). After treated with the water extracts from BSHXD, the expression of RUNX2 and Osterix as well as the ALP activity in the BMSCs transfected with Cbfal/RUNX2 gene silencing lentivirus increased significantly (P < 0.01). To conclude, the water extract from the BXHXD can up-regulate the expression of RUNX2 and Osterix and the activity of ALP, thus promoting BMSCs osteogenic differentiation.
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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.
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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.
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Animals , Rats , Autopsy , Death , Postmortem Changes , Spectroscopy, Fourier Transform Infrared/methods , TemperatureABSTRACT
The rapidly evolving aging society in China is associated with increased incidences of osteoporosis and fractures, which have become common health problems that threaten the quality of life of the elderly. Gut microbiota colonizing in the human intestinal tract form a mutual symbiotic relationship with the host and play an important role in the metabolism and immune regulation of the host. In recent years increasing studies have demonstrated that gut microbiota not only affect the digestive system but also contribute to the disease conditions involving the immune system, and have a close relationship with the occurrence and progression of osteoporosis. This review summarizes the progress and hotspots in recent researches of the associations among gut microbiota, the immune system, osteoporosis.