[Chemical constituents from Alangium chinense subsp. pauciflorum].

Chemical constituents of 70% ethanol extract of Alangium chinense subsp. pauciflorum were investigated. The 70% ethanol extract of A. chinense subsp. pauciflorum was isolated and purified by D-101 macroporous resins, silica gel, Sephadex LH-20 and other methods. As a result, nineteen compounds were isolated and identified as 4-cyclohexene-1α,2α,3α-triol-1-O-ß-D-glucoside(1), 1ß,4α,6α,13-tetrahydroxy-eudesm-11(12)-ene(2), sucrose(3), 1'-O-benzyl-α-L-rhamnopyranosyl-(1″→6')-ß-D-glucopyranoside(4), bis(2-ethylhexyl)benzene-1,2-dicarboxylate(5),(Z)-10-heneicosenoic acid(6), di-O-methylcrenati(7), methyl-α-D-fructofuranoside(8), ß-daucosterol(9), syringic acid(10), vanillicacid(11), octacosanol(12), isoarborinol(13), 2,7-dihydroxy-6-methyl-4-(1-methylethyl)-1-naphthalenecarboxylate(14),vanillin(15), coniferyl aldehyde(16), 9(11)-dehydroergosterolperoxide(17), 5α,8α-epidioxy-(22E,24R)-ergosta-6,22-dien-3ß-ol(18), ß-sitosterol(19), respectively. Compounds 1 and 2 were new compounds, compounds 5-11, 13, 15-18 were isolated from Alangium for the first time.The anti-inflammatory activity of compourd 1 was determinded by the LPS-induced RAW264.7 macrophage inflammation model. The results showed that the new compound 1 has a certain inhibitory effect on LPS-induced NO production of RAW264.7 cells, and the inhibitory rate was 54.57%.

Novel ratio-expressions of genes enables estimation of wound age in contused skeletal muscle.

Given that combination with multiple biomarkers may well raise the predictive value of wound age, it appears critically essential to identify new features under the limited cost. For this purpose, the present study explored whether the gene expression ratios provide unique time information as an additional indicator for wound age estimation not requiring the detection of new biomarkers and allowing full use of the available data. The expression levels of four wound-healing genes (Arid5a, Ier3, Stom, and Lcp1) were detected by real-time polymerase chain reaction, and a total of six expression ratios were calculated among these four genes. The results showed that the expression levels of four genes and six ratios of expression changed time-dependent during wound repair. The six expression ratios provided additional temporal information, distinct from the four genes analyzed separately by principal component analysis. The overall performance metrics for cross-validation and external validation of four typical prediction models were improved when six ratios of expression were added as additional input variables. Overall, expression ratios among genes provide temporal information and have excellent potential as predictive markers for wound age estimation. Combining the expression levels of genes with ratio-expression of genes may allow for more accurate estimates of the time of injury.

Postmortem Interval Estimation Using Protein Chip Technology Combined with Multivariate Analysis Methods.

OBJECTIVES: To estimate postmortem interval (PMI) by analyzing the protein changes in skeletal muscle tissues with the protein chip technology combined with multivariate analysis methods. METHODS: Rats were sacrificed for cervical dislocation and placed at 16 ℃. Water-soluble proteins in skeletal muscles were extracted at 10 time points (0 d, 1 d, 2 d, 3 d, 4 d, 5 d, 6 d, 7 d, 8 d and 9 d) after death. Protein expression profile data with relative molecular mass of 14 000-230 000 were obtained. Principal component analysis (PCA) and orthogonal partial least squares (OPLS) were used for data analysis. Fisher discriminant model and back propagation (BP) neural network model were constructed to classify and preliminarily estimate the PMI. In addition, the protein expression profiles data of human skeletal muscles at different time points after death were collected, and the relationship between them and PMI was analyzed by heat map and cluster analysis. RESULTS: The protein peak of rat skeletal muscle changed with PMI. The result of PCA combined with OPLS discriminant analysis showed statistical significance in groups with different time points (P<0.05) except 6 d, 7 d and 8 d after death. By Fisher discriminant analysis, the accuracy of internal cross-validation was 71.4% and the accuracy of external validation was 66.7%. The BP neural network model classification and preliminary estimation results showed the accuracy of internal cross-validation was 98.2%, and the accuracy of external validation was 95.8%. There was a significant difference in protein expression between 4 d and 25 h after death by the cluster analysis of the human skeletal muscle samples. CONCLUSIONS: The protein chip technology can quickly, accurately and repeatedly obtain water-soluble protein expression profiles in rats' and human skeletal muscles with the relative molecular mass of 14 000-230 000 at different time points postmortem. The establishment of multiple PMI estimation models based on multivariate analysis can provide a new idea and method for PMI estimation.

Exploring postmortem succession of rat intestinal microbiome for PMI based on machine learning algorithms and potential use for humans.

The microbial communities may undergo a meaningful successional change during the progress of decay and decomposition that could aid in determining the post-mortem interval (PMI). However, there are still challenges to applying microbiome-based evidence in law enforcement practice. In this study, we attempted to investigate the principles governing microbial community succession during decomposition of rat and human corpse, and explore their potential use for PMI of human cadavers. A controlled experiment was conducted to characterize temporal changes in microbial communities associated with rat corpses as they decomposed for 30 days. Obvious differences of microbial community structures were observed among different stages of decomposition, especially between decomposition of 0-7d and 9-30d. Thus, a two-layer model for PMI prediction was developed based on the succession of bacteria by combining classification and regression models using machine learning algorithms. Our results achieved 90.48% accuracy for discriminating groups of PMI 0-7d and 9-30d, and yielded a mean absolute error of 0.580d within 7d decomposition and 3.165d within 9-30d decomposition. Furthermore, samples from human cadavers were collected to gain the common succession of microbial community between rats and humans. Based on the 44 shared genera of rats and humans, a two-layer model of PMI was rebuilt to be applied for PMI prediction of human cadavers. Accurate estimates indicated a reproducible succession of gut microbes across rats and humans. Together these results suggest that microbial succession was predictable and can be developed into a forensic tool for estimating PMI.

Combined metabolomics and machine learning algorithms to explore metabolic biomarkers for diagnosis of acute myocardial ischemia.

Acute myocardial ischemia (AMI) remains the leading cause of death worldwide, and the post-mortem diagnosis of AMI represents a current challenge for both clinical and forensic pathologists. In the present study, the untargeted metabolomics based on ultra-performance liquid chromatography combined with high-resolution mass spectrometry was applied to analyze serum metabolic signatures from AMI in a rat model (n = 10 per group). A total of 28 endogenous metabolites in serum were significantly altered in AMI group relative to control and sham groups. A set of machine learning algorithms, namely gradient tree boosting (GTB), support vector machine (SVM), random forest (RF), logistic regression (LR), and multilayer perceptron (MLP) models, was used to screen the more valuable metabolites from 28 metabolites to optimize the biomarker panel. The results showed that classification accuracy and performance of MLP model were better than other algorithms when the metabolites consisting of L-threonic acid, N-acetyl-L-cysteine, CMPF, glycocholic acid, L-tyrosine, cholic acid, and glycoursodeoxycholic acid. Finally, 17 blood samples from autopsy cases were applied to validate the classification model's value in human samples. The MLP model constructed based on rat dataset achieved accuracy of 88.23%, and ROC of 0.89 for predicting AMI type II in autopsy cases of sudden cardiac death. The results demonstrated that MLP model based on 7 molecular biomarkers had a good diagnostic performance for both AMI rats and autopsy-based blood samples. Thus, the combination of metabolomics and machine learning algorithms provides a novel strategy for AMI diagnosis.

A novel method for determining postmortem interval based on the metabolomics of multiple organs combined with ensemble learning techniques.

Determining postmortem interval (PMI) is one of the most challenging and essential endeavors in forensic science. Developments in PMI estimation can take advantage of machine learning techniques. Currently, applying an algorithm to obtain information on multiple organs and conducting joint analysis to accurately estimate PMI are still in the early stages. This study aimed to establish a multi-organ stacking model that estimates PMI by analyzing differential compounds of four organs in rats. In a total of 140 rats, skeletal muscle, liver, lung, and kidney tissue samples were collected at each time point after death. Ultra-performance liquid chromatography coupled with high-resolution mass spectrometry was used to determine the compound profiles of the samples. The original data were preprocessed using multivariate statistical analysis to determine discriminant compounds. In addition, three interrelated and increasingly complex patterns (single organ optimal model, single organ stacking model, multi-organ stacking model) were established to estimate PMI. The accuracy and generalized area under the receiver operating characteristic curve of the multi-organ stacking model were the highest at 93% and 0.96, respectively. Only 1 of the 14 external validation samples was misclassified by the multi-organ stacking model. The results demonstrate that the application of the multi-organ combination to the stacking algorithm is a potential forensic tool for the accurate estimation of PMI.

DA5-CH and Semaglutide Protect against Neurodegeneration and Reduce α-Synuclein Levels in the 6-OHDA Parkinson's Disease Rat Model.

Insulin desensitization has been observed in the brains of patients with Parkinson's disease (PD), which is a progressive neurodegenerative disorder for which there is no cure. Semaglutide is a novel long-actingglucagon-likepeptide-1 (GLP-1) receptor agonist that is on the market as a treatment for type 2 diabetes. It is in a phase II clinical trial in patients with PD. Two previous phase II trials in PD patients showed good effects with the older GLP-1 receptor agonists, exendin-4 and liraglutide. We have developed a dual GLP-1/GIP receptor agonist (DA5-CH) that can cross the blood-brain barrier (BBB) at a higher rate than semaglutide. We tested semaglutide and DA5-CH in the 6-OHDA-lesion rat model of PD. Treatment was semaglutide or DA5-CH (25 nmol/kg, i.p.) daily for 30 days postlesion. Both drugs reduced the apomorphine-induced rotational behavior and alleviated dopamine depletion and the inflammation response in the lesioned striatum as shown in reduced IL-1ß and TNF-α levels, with DA5-CH being more effective. In addition, both drugs protected dopaminergic neurons and increased TH expression in the substantia nigra. Furthermore, the level of monomer and aggregated α-synuclein was reduced by the drugs, and insulin resistance as shown in reduced pIRS-1ser312 phosphorylation was also attenuated after drug treatment, with DA5-CH being more effective. Therefore, while semaglutide showed good effects in this PD model, DA5-CH was superior and may be a better therapeutic drug for neurodegenerative disorders such as PD than GLP-1 receptor agonists that do not easily cross the BBB.

Application of Linear Regression Model of Gpnmb Gene in Rat Injury Time Estimation.

OBJECTIVES: To investigate the effects of injury time, postmortem interval (PMI) and postmortem storage temperature on mRNA expression of glycoprotein non-metastatic melanoma protein B (Gpnmb), and to establish a linear regression model between Gpnmb mRNA expression and injury time, to provide aimed at providing potential indexes for injury time estimation. METHODS: Test group SD rats were anesthetized and subjected to blunt contusion and randomly divided into 0 h, 4 h, 8 h, 12 h, 16 h, 20 h and 24 h groups after injury, with 18 rats in each group. After cervical dislocation, 6 rats in each group were collected and stored at 0 ℃, 16 ℃ and 26 ℃, respectively. The muscle tissue samples of quadriceps femoris injury were collected at 0 h, 12 h and 24 h postmortem at the same temperature. The grouping method and treatment method of the rats in the validation group were the same as above. The expression of Gpnmb mRNA in rat skeletal muscle was detected by RT-qPCR. The Pearson correlation coefficient was used to evaluate the correlation between Gpnmb mRNA expression and injury time, PMI, and postmortem storage temperature. SPSS 25.0 software was used to construct a linear regression model, and the validation group data was used for the back-substitution test. RESULTS: The expression of Gpnmb mRNA continued to increase with the prolongation of injury time, and the expression level was highly correlated with injury time (P<0.05), but had little correlation with PMI and postmortem storage temperature (P>0.05). The linear regression equation between injury time (y) and Gpnmb mRNA relative expression (x) was y=0.611 x+4.489. The back-substitution test proved that the prediction of the model was accurate. CONCLUSIONS: The expression of Gpnmb mRNA is almost not affected by the PMI and postmortem storage temperature, but is mainly related to the time of injury. Therefore, a linear regression model can be established to infer the time of injury.

Combining with lab-on-chip technology and multi-organ fusion strategy to estimate post-mortem interval of rat.

Background: The estimation of post-mortem interval (PMI) is one of the most important problems in forensic pathology all the time. Although many classical methods can be used to estimate time since death, accurate and rapid estimation of PMI is still a difficult task in forensic practice, so the estimation of PMI requires a faster, more accurate, and more convenient method. Materials and methods: In this study, an experimental method, lab-on-chip, is used to analyze the characterizations of polypeptide fragments of the lung, liver, kidney, and skeletal muscle of rats at defined time points after death (0, 1, 2, 3, 5, 7, 9, 12, 15, 18, 21, 24, 27, and 30 days). Then, machine learning algorithms (base model: LR, SVM, RF, GBDT, and MLPC; ensemble model: stacking, soft voting, and soft-weighted voting) are applied to predict PMI with single organ. Multi-organ fusion strategy is designed to predict PMI based on multiple organs. Then, the ensemble pruning algorithm determines the best combination of multi-organ. Results: The kidney is the best single organ for predicting the time of death, and its internal and external accuracy is 0.808 and 0.714, respectively. Multi-organ fusion strategy dramatically improves the performance of PMI estimation, and its internal and external accuracy is 0.962 and 0.893, respectively. Finally, the best organ combination determined by the ensemble pruning algorithm is all organs, such as lung, liver, kidney, and skeletal muscle. Conclusion: Lab-on-chip is feasible to detect polypeptide fragments and multi-organ fusion is more accurate than single organ for PMI estimation.

Insight into molecular profile changes after skeletal muscle contusion using microarray and bioinformatics analyses.

Muscle trauma frequently occurs in daily life. However, the molecular mechanisms of muscle healing, which partly depend on the extent of the damage, are not well understood. The present study aimed to investigate gene expression profiles following mild and severe muscle contusion, and to provide more information about the molecular mechanisms underlying the repair process. A total of 33 rats were divided randomly into control (n=3), mild contusion (n=15), and severe contusion (n=15) groups; the contusion groups were further divided into five subgroups (1, 3, 24, 48, and 168 h post-injury; n=3 per subgroup). A total of 2844 and 2298 differentially expressed genes (DEGs) were identified using microarray analyses in the mild and severe contusions, respectively. From the analysis of the 1620 coexpressed genes in mildly and severely contused muscle, we discovered that the gene profiles in functional modules and temporal clusters were similar between the mild and severe contusion groups; moreover, the genes showed time-dependent patterns of expression, which allowed us to identify useful markers of wound age. The functional analyses of genes in the functional modules and temporal clusters were performed, and the hub genes in each module-cluster pair were identified. Interestingly, we found that genes down-regulated at 24-48 h were largely associated with metabolic processes, especially of the oxidative phosphorylation (OXPHOS), which has been rarely reported. These results improve our understanding of the molecular mechanisms underlying muscle repair, and provide a basis for further studies of wound age estimation.

Glucagon-like peptide-1/glucose-dependent insulinotropic polypeptide dual receptor agonist DA-CH5 is superior to exendin-4 in protecting neurons in the 6-hydroxydopamine rat Parkinson model.

Patients with Parkinson's disease (PD) have impaired insulin signaling in the brain. Incretin hormones, including glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP), can re-sensitize insulin signaling. In a recent phase II clinical trial, the first GLP-1 mimic, exendin-4, has shown reliable curative effect in patients with PD. DA-CH5 is a novel GLP-1/GIP receptor unimolecular co-agonist with a novel peptide sequence added to cross the blood-brain barrier. Here we showed that both exendin-4 and DA-CH5 protected against 6-hydroxydopamine (6-OHDA) cytotoxicity, inhibited apoptosis, improved mitogenesis and induced autophagy flux in SH-SY5Y cells via activation of the insulin receptor substrate-1 (IRS-1)/alpha serine/threonine-protein kinase (Akt)/cAMP response element-binding protein (CREB) pathway. We also found that DA-CH5 (10 nmol/kg) daily intraperitoneal administration for 30 days post-lesion alleviated motor dysfunction in rats and prevented stereotactic unilateral administration of 6-OHDA induced dopaminergic neurons loss in the substantia nigra pars compacta. However, DA-CH5 showed curative effects in reducing the levels of α-synuclein and the levels of pro-inflammatory cytokines (tumor necrosis factor-α, interleukin-1ß). It was also more effective than exendin-4 in inhibiting apoptotic process and protecting mitochondrial functions. In addition, insulin resistance was largely alleviated and the expression of autophagy-related proteins was up-regulated in PD model rats after DA-CH5 treatment. These results in this study indicate DA-CH5 plays a therapeutic role in the 6-OHDA-unilaterally lesioned PD rat model and is superior to GLP-1 analogue exendin-4. The study was approved by the Animal Ethics Committee of Shanxi Medical University of China.

Identifying biomarkers for evaluating wound extent and age in the contused muscle of rats using microarray analysis: a pilot study.

Wound age estimation is still one of the most important and significant challenges in forensic practice. The extent of wound damage greatly affects the accuracy and reliability of wound age estimation, so it is important to find effective biomarkers to help diagnose wound degree and wound age. In the present study, the gene expression profiles of both mild and severe injuries in 33 rats were assayed at 0, 1, 3, 24, 48, and 168 hours using the Affymetrix microarray system to provide biomarkers for the evaluation of wound age and the extent of the wound. After obtaining thousands of differentially expressed genes, a principal component analysis, the least absolute shrinkage and selection operator, and a time-series analysis were used to select the most predictive prognostic genes. Finally, 15 genes were screened for evaluating the extent of wound damage, and the top 60 genes were also screened for wound age estimation in mild and severe injury. Selected indicators showed good diagnostic performance for identifying the extent of the wound and wound age in a Fisher discriminant analysis. A function analysis showed that the candidate genes were mainly related to cell proliferation and the inflammatory response, primarily IL-17 and the Hematopoietic cell lineage signalling pathway. The results revealed that these genes play an essential role in wound-healing and yield helpful and valuable potential biomarkers for further targeted studies.

Temporal changes in Egr-1 and c-fos expression in rat models of myocardial ischemia.

BACKGROUND: The pathological diagnosis of sudden cardiac death caused by myocardial ischemia is a difficult problem. Relevant evidence shows that the expression of Egr-1 and c-fos undergo changes in the early stage of myocardial ischemia, but the detailed temporal variation of them is not clear. Therefore, the aim of this study was to observe the temporal changes in mRNA and protein expression of Egr-1 and c-fos in ischemic myocardium in rats. METHODS: Sixty-six Sprague-Dawley rats were divided into the control group, the early myocardial ischemia (EMI) group, the sham operated group and the allergy group. The EMI rats were further divided into eight subgroups according to the different time points (30 min and 1, 2, 4, 8, 12, 24, and 48 h) after modeling. The mRNA and protein of Egr-1 and c-fos of each group were detected by real-time quantitative polymerase chain reaction and immunohistochemistry, respectively. RESULTS: In the EMI group, Egr-1 mRNA in ischemic myocardium rose 30 min after ischemia and peaked at 2 h; the plateau was maintained up to 8 h after ischemia, and then returned to the baseline level at 12 h. The c-fos mRNA in ischemic myocardium demonstrated a consistent changing curve with that of Egr-1. The mRNA of Egr-1 and c-fos showed no significant changes in the control group, the sham operated group and the allergy group. Immunohistochemistry showed that Egr-1 protein in the myocardial ischemic area was slightly positive 30 min after ischemia, and then strongly positive at 4 and 8 h, decreased at 12 h, and was negative at 24 h. The changing trends of c-fos protein were almost the same as that of Egr-1. Immunohistochemistry of Egr-1 and c-fos protein were all negative in the control group, the sham operated group and the allergy group. CONCLUSIONS: The mRNA and protein expression of Egr-1 and c-fos presented rapid and temporal changes after myocardial ischemia, and this may be helpful in distinguishing sudden death induced by myocardial ischemia from that of allergy.

Novel insights into wound age estimation: combined with "up, no change, or down" system and cosine similarity in python environment.

Wound age estimation is a complex, multifactorial issue. It is considered to have great practical significance that combining multi-biomarkers and multi-methods for injury time estimation. We optimized our earlier "up, no change, or down" model by adding data on the expression levels of mRNAs encoding ABHD2, MAD2L2, and ARID5A, and we converted the relative quantitative expression levels of seven genes into a vector rather than a color model. We used Python to derive the cosine similarity (CS) between a test set and the vector matrix; the highest similarity most accurately reflected the injury time. For the optimized model, the internal and external verifications were approximately 0.71 and 0.66, respectively. The good double-blinded results indicated that the model was stable and reliable. In summary, we used a vector matrix and cosine similarities derived by Python to mine the levels of genes expressed in contused skeletal muscle. We are the first to combine several biomarkers and methods for wound age estimation.

Recent Development on Controlled Synthesis of Metal Sulfides Hollow Nanostructures via Hard Template Engaged Strategy: A Mini-Review.

In this mini-review, we highlighted the recent progresses in the controlled synthesis of metal sulfides hollow nanostructures via hard template technique. After a brief introduction about the formation mechanism of the inorganic hollow nanostructures via hard template technique, the discussions primarily focused on the emerging development of metal sulfides hollow nanostructures. Various synthetic strategies were summarized concerning the use of the hard template engaged strategies to fabricate various metal sulfides hollow nanostructures, such as hydrothermal method, solvothermal method, ion-exchange, sulfidation or calcination etc. Finally, the perspectives and summaries have been presented to demonstrate that a facile synthetic technique would be widely used to fabricate metal sulfides hollow nanostructures with multi-shells and components.

Temporal expression of wound healing-related genes inform wound age estimation in rats after a skeletal muscle contusion: a multivariate statistical model analysis.

Although many time-dependent parameters involved in wound healing have been exhaustively investigated, establishing an objective and reliable means for estimating wound age remains a challenge. In this study, 78 Sprague-Dawley rats were divided randomly into a control group and contusion groups at 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, and 48 h post-injury (n = 6 per group). The expression of 35 wound healing-related genes was explored in contused skeletal muscle by real-time polymerase chain reaction. Differences between the groups were assessed by partial least squares discriminant analysis (PLS-DA). The results show that the samples were classified into three groups by wound age (4-12, 16-24, and 28-48 h). A Fisher discriminant analysis model of 14 selected genes was constructed, and 94.9% cross-validated grouped cases were correctly classified. A PLS regression analysis using 14 genes showed reasonable internal predictive validity, with a root mean squared error of cross-validation of approximately 8 h. To examine whether the prediction models were capable of analyzing new (ungrouped) cases, an external validation was carried out using the expression data from an additional 30 rats. Approximately 76.7% of ungrouped cases were correctly classified, which was a lower proportion than that for cross-validation. Similarly, the prediction results of the PLS model showed lower relatively external predictive validity (root mean squared error of prediction = 11 h) than internal predictive validity. Although the prediction results were less accurate than expected, the gene expression modeling and multivariate analyses showed great potential for estimating injury time. These multivariate methods may be valuable when devising future wound time estimation strategies.

lncRNA CASC2/miR­18a­5p axis regulates the malignant potential of nasopharyngeal carcinoma by targeting RBBP8.

Nasopharyngeal carcinoma (NPC) is a prevalent head and neck tumor which has a high mortality rate in Southeast Asia, especially in Southern China. Cancer susceptibility candidate 2 (CASC2) is a newly identified long non­coding RNA (lncRNA) that has been found to play a suppressive role in several types of tumors. However, the expression and functional role of CASC2 in NPC are still unclear. In the present study, using NPC tissues, cells and transplanted mice, we investigated the mechanism of CASC2­mediated regulation of NPC. We showed that the CASC2 level is reduced in NPC tissues and cells. CASC2 downregulation promoted proliferation and inhibited apoptotic cell death in NPC cells. In contrast, CASC2 upregulation inhibited proliferation and increased apoptosis. There were putative binding sites of microRNA (miR)­18a­5p in the promoter of CASC2. The level of miR­18a­5p was upregulated in NPC tissues and cells. We further confirmed that CASC2 could directly bind with miR­18a­5p and inhibit miR­18a­5p expression, using reporter gene and RNA immunoprecipitation assays. miR­18a­5p suppressed CASC2 upregulation­mediated decrease in proliferation and increase in apoptotic cell death. Bioinformatics predicted the putative binding site of miR­18a­5p in the 3' untranslated region of C­terminal binding protein interacting protein (CtIP)/RBBP8. It was further confirmed that miR­18a­5p could directly bind with RBBP8 and inhibit RBBP8 expression. Downregulation of RBBP8 inhibited the anti­miR­18a­5p­mediated increase in apoptosis and decrease in proliferation. Downregulation of CASC2 increased tumor growth, increased the level of miR­18a­5p and decreased RBBP8 expression in vivo. In summary, CASC2 regulates NPC malignancy through modulation of RBBP8 via sponging miR­18a­5p. Our findings highlight the CASC2/miR­18a­5p/RBBP8 axis in NPC pathogenesis and provide new biomarkers and potential targets for the therapy of NPC.

Comparison of the serum metabolic signatures based on 1H NMR between patients and a rat model of deep vein thrombosis.

Deep vein thrombosis (DVT) and pulmonary embolism (PE) have high morbidity, reduce quality of life, and can cause death. Biomarkers or genetic risk factors have not been identified in patients with DVT. In present study, serum of 61 patients suffering from DVT and a rat DVT model (n = 10) were assayed by a proton nuclear magnetic resonance (1H NMR) metabolomics technique combing with multivariate statistical analysis to identify the metabolites. The MetPA platform was used to identify differences in the metabolic pathways between the rat model and patients. The metabolomics results discovered that 11 different metabolites in rats and 20 different metabolites in DVT patients. Seven metabolites both altered in the rats and patients. Moreover, we observed changes in the metabolic pathways, including carbohydrate metabolism, lipid metabolism, and amino acid metabolism that were induced immediately by the thrombosis. Pathway of aminoacyl-tRNA biosynthesis perturbed only in the patients which was associated with the genetic risk factor of DVT. The study demonstrated that serum 1H NMR metabolomics can be used to diagnose DVT in the clinic. The altered pathways related to thrombosis and genetics will provide a foundation and new strategies for understanding the pathological mechanism and pharmacological targets of DVT.

Integrating microRNA and messenger RNA expression profiles in a rat model of deep vein thrombosis.

Deep vein thrombosis (DVT) is a disease involving multiple genes and systems. MicroRNAs (miRNAs) represent a class of non-coding small RNAs that post-transcriptionally suppress their target genes. The expression patterns of miRNA and messenger RNA (mRNA) in DVT remain poorly characterized. The aim of the present study was to evaluate miRNA and mRNA expression profiles in a stasis-induced DVT rat model. Male SD rats were randomly divided into three groups as follows: DVT, sham and control. The inferior vena cava (IVC) of rats was ligated to construct stasis-induced DVT models. Rats were sacrificed three days after ligation, and morphological changes in the vein tissues were observed by hematoxylin and eosin and Masson staining. The miRNA and mRNA expression profiles were evaluated by microarrays, followed by bioinformatics analysis. The microarray analysis identified 22 miRNAs and 487 mRNAs that were significantly differentially expressed between the experimental and control groups, and between the experimental and sham groups, but not between the control and sham groups (P≤0.05; ≥2.0­fold change). By subsequent bioinformatics analysis, a 19 miRNA-98 mRNAs network was constructed in the stasis-induced DVT rat model. Notably, the majority of these miRNAs and mRNAs are reported to be expressed by endothelial cells (ECs) and are associated with the function of ECs. The results provide evidence indicating that the regulatory association of miRNA and mRNA points to key roles played by ECs in thrombosis. These findings advance our understanding of the molecular regulatory mechanisms underlying the pathophysiology of DVT.

Mast cell tryptase and carboxypeptidase A expression in body fluid and gastrointestinal tract associated with drug-related fatal anaphylaxis.

AIM: To investigate the expression of mast cell tryptase and carboxypeptidase A in drug-related fatal anaphylaxis. METHODS: The expression of mast cell tryptase and carboxypeptidase A in 15 autopsy cases of drug-related fatal anaphylaxis and 20 normal autopsy cases were detected. First, the expression of mast cell tryptase was determined in stomach, jejunum, lung, heart, and larynx by immunofluorescence. Different tissues were removed and fixed in paraformaldehyde solution, then paraffin sections were prepared for immunofluorescence. Using specific mast cell tryptase and carboxypeptidase A antibodies, the expression of tryptase and carboxypeptidase A in gastroenterology tract and other tissues were observed using fluorescent microscopy. The postmortem serum and pericardial fluid were collected from drug-related fatal anaphylaxis and normal autopsy cases. The level of mast cell tryptase and carboxypeptidase A in postmortem serum and pericardial fluid were measured using fluor enzyme linked immunosorbent assay (FEIA) and enzyme linked immunosorbent assay (ELISA) assay. The expression of mast cell tryptase and carboxypeptidase A was analyzed in drug-related fatal anaphylaxis cases and compared to normal autopsy cases. RESULTS: The expression of carboxypeptidase A was less in the gastroenterology tract and other tissues from anaphylaxis-related death cadavers than normal controls. Immunofluorescence revealed that tryptase expression was significantly increased in multiple organs, especially the gastrointestinal tract, from anaphylaxis-related death cadavers compared to normal autopsy cases (46.67 ± 11.11 vs 4.88 ± 1.56 in stomach, 48.89 ± 11.02 vs 5.21 ± 1.34 in jejunum, 33.72 ± 5.76 vs 1.30 ± 1.02 in lung, 40.08 ± 7.56 vs 1.67 ± 1.03 in larynx, 7.11 ± 5.67 vs 1.10 ± 0.77 in heart, P < 0.05). Tryptase levels, as measured with FEIA, were significantly increased in both sera (43.50 ± 0.48 µg/L vs 5.40 ± 0.36 µg/L, P < 0.05) and pericardial fluid (28.64 ± 0.32 µg/L vs 4.60 ± 0.48 µg/L, P < 0.05) from the anaphylaxis group in comparison with the control group. As measured by ELISA, the concentration of carboxypeptidase A was also increased more than 2-fold in the anaphylaxis group compared to control (8.99 ± 3.91 ng/mL vs 3.25 ± 2.30 ng/mL in serum, 4.34 ± 2.41 ng/mL vs 1.43 ± 0.58 ng/mL in pericardial fluid, P < 0.05). CONCLUSION: Detection of both mast cell tryptase and carboxypeptidase A could improve the forensic identification of drug-related fatal anaphylaxis.