Chrysanthemum indicum linnéextracts attenuate the mitogenic effect of epinephrine on human HCC cells

Objective: To investigate the stimulatory effect of epinephrine(Epi) and the antagonistic effect of Chrysanthemum indicum Linné extracts (CILE) on Epi-induced growth of human hepatocellular carcinoma(HCC) cells. Methods: The stimulatory effect of Epi and inhibitory effect of CILE on the growth of HepG2 and MHCC97H cells were investigated using a proliferation assay in correlation with βadrenergic receptor(β 2-AR) blockade, a MEK1/MEK2 inhibitor, and assessment of MAPK/ERK1/2 intracellular activity. Results:...     

Effects of transforming growth factor β-1 infected human bone marrow mesenchymal stem cells on high- and low-metastatic potential hepatocellular carcinoma

Background

This study investigates the effects of human bone marrow-derived mesenchymal stem cell (hMSC) on migration and proliferation ability of hepatocellular carcinoma (HCC) with high- and low-metastatic potential.

Methods

The hMSC and transforming growth factor-β1 (TGFβ-1) gene infected hMSC were co-cultured with hepatoma cells. The ability of cells migration was assessed by Transwell assay. The ability of cells proliferation was detected using CCK-8 assay. The mice were engrafted with hMSC and TGFβ-1 gene infected hMSC, respectively, after hepatoma cells inoculation 15 days, twice a week for 6 weeks successively. The tumor inhibition rate was calculated. TGFβ-1, osteopontin (OPN), and programmed cell death protein 4 (PDCD4) genes expression of hepatoma cells were detected by quantitative real-time polymerase chain reaction (qPCR) before and after co-cultured experiments.

Results

TGFβ-1 infected hMSC or hMSC co-culture with hepatoma cells groups can significantly promote hepatoma cells proliferation (P < 0.05). The migration numbers of hepatoma cells with TGFβ-1 infected hMSC co-culture groups were significantly reduced compared with the other two groups (P < 0.05). The tumors weight inhibition rates of MHCC97-H and MHCC97-L animal models were the highest in the third week by hMSC engraftment. But the highest tumor inhibition rate of MHCC97-H animal models was observed in the fourth week and MHCC97-L animal models in the fifth week after TGFβ-1 infected hMSC engraftment. OPN gene relative quantitative expression of hepatoma cells was significantly down-regulated after co-cultured with hMSC and TGFβ-1 gene infected hMSC groups (P < 0.05). TGFβ-1 gene relative quantitative expression of MHCC97-H and MHCC97-L cells was significantly up-regulated after co-cultured with TGFβ-1 gene infected hMSC groups (P < 0.05). PDCD4 expression had no statistical differences among groups.

Conclusions

hMSC and TGFβ-1 gene infected hMSC can promote hepatoma cells proliferation and inhibit hepatoma cells migration. hMSC and TGFβ-1 gene infected hMSC exhibit anti-tumor activity in a time-dependent manner. TGFβ-1 cytokine may be the main factor in HCC proliferation. OPN makes a significant contribution to the changes of hepatoma cells metastasis.     

Role of CD34, vascular endothelial growth factor, and p53 in neoangiogenesis as correlated with stage of disease in colorectal carcinoma

Background

Angiogenesis is critical for the growth of colorectal carcinoma (CRC). Vascular endothelial growth factor is the most important angiogenic growth factor.

Method

Fifty cases of CRC operated at INHS Asvini were studied by using immunohistochemical labelling of the tumours by using CD34, p53, and vascular endothelial growth factor (VEGF).

Results

Out of the 50 cases, 31 (62%) were positive for p53; of which 80.7% showed high expression. Significant staining (> 20% tumour cells showing positivity) was seen in 33 patients (66%), while 34% were negative. Of the 33 positive cases, 57.58% showed high-grade VEGF positivity.

Conclusion

Vascular endothelial growth factor correlated significantly with the stage and grade of disease. Intra tumours microvessel density as calculated from CD34 expression increased with the stage and grade of the CRC.     

Cross-Topic Learning for Work Prioritization in Systematic Review Creation and Update

Objective

Machine learning systems can be an aid to experts performing systematic reviews (SRs) by automatically ranking journal articles for work-prioritization. This work investigates whether a topic-specific automated document ranking system for SRs can be improved using a hybrid approach, combining topic-specific training data with data from other SR topics.

Design

A test collection was built using annotated reference files from 24 systematic drug class reviews. A support vector machine learning algorithm was evaluated with cross-validation, using seven different fractions of topic-specific training data in combination with samples from the other 23 topics. This approach was compared to both a baseline system, which used only topic-specific training data, and to a system using only the nontopic data sampled from the remaining topics.

Measurements

Mean area under the receiver-operating curve (AUC) was used as the measure of comparison.

Results

On average, the hybrid system improved mean AUC over the baseline system by 20%, when topic-specific training data were scarce. The system performed significantly better than the baseline system at all levels of topic-specific training data. In addition, the system performed better than the nontopic system at all but the two smallest fractions of topic specific training data, and no worse than the nontopic system with these smallest amounts of topic specific training data.

Conclusions

Automated literature prioritization could be helpful in assisting experts to organize their time when performing systematic reviews. Future work will focus on extending the algorithm to use additional sources of topic-specific data, and on embedding the algorithm in an interactive system available to systematic reviewers during the literature review process.     

Troglitazone Induced Apoptosis via PPARγ Activated POX-induced ROS Formation in HT29 Cells

Objective In order to investigate the potential mechanisms in troglitazone-induced apoptosis in HT29 cells,the effects of PPARγ and POX-induced ROS were explored.Methods [3-(4,5)-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide (MTT) assay,Annexin V and PI staining using FACS,plasmid transfection,ROS formation detected by DCFH staining,RNA interference,RT-PCR & RT-QPCR,and Western blotting analyses were employed to investigate the apoptotic effect of troglitazone and the potential role of PPARγ pathway...     

Local proinflammatory effects of repeated skin exposure to warfarin, an anticoagulant rodenticide in rats

Objective: To evaluate the effects of epicutaneous application of anticoagulant warfarin, by examining the presence of tissue injury and immune/inflammatory activity in exposed skin. Methods: Rats were exposed to warfarin by applying 10 μg of warfarin‐sodium to 10‐12 cm 2 skin (range 0.8‐1 μg per 1 cm 2 ) for 3 consecutive days. Tissue injury was evaluated by lipid peroxidation, histomorphological changes and signs of reparative activity in skin. T cell infiltration and selected aspects of epidermal cell ac...     

A Text Mining Approach to the Prediction of Disease Status from Clinical Discharge Summaries

Objective

The authors present a system developed for the Challenge in Natural Language Processing for Clinical Data—the i2b2 obesity challenge, whose aim was to automatically identify the status of obesity and 15 related co-morbidities in patients using their clinical discharge summaries. The challenge consisted of two tasks, textual and intuitive. The textual task was to identify explicit references to the diseases, whereas the intuitive task focused on the prediction of the disease status when the evidence was not explicitly asserted.

Design

The authors assembled a set of resources to lexically and semantically profile the diseases and their associated symptoms, treatments, etc. These features were explored in a hybrid text mining approach, which combined dictionary look-up, rule-based, and machine-learning methods.

Measurements

The methods were applied on a set of 507 previously unseen discharge summaries, and the predictions were evaluated against a manually prepared gold standard. The overall ranking of the participating teams was primarily based on the macro-averaged F-measure.

Results

The implemented method achieved the macro-averaged F-measure of 81% for the textual task (which was the highest achieved in the challenge) and 63% for the intuitive task (ranked 7^th out of 28 teams—the highest was 66%). The micro-averaged F-measure showed an average accuracy of 97% for textual and 96% for intuitive annotations.

Conclusions

The performance achieved was in line with the agreement between human annotators, indicating the potential of text mining for accurate and efficient prediction of disease statuses from clinical discharge summaries.     

Evaluation of the in vitro and in vivo genotoxicity of almond skins

Objective It aims to study potential genotoxicity of almond skins.Methods A bacterial reverse mutation assay was performed on S.typhimurium strains TA97,TA98,TA100,TA102,and TA1535 in the absence or presence of S-9 mixture at a dose range of 312.5 to 5 000 μg/plate.A micronucleus test and a mammalian bone marrow chromosome aberration tests were performed in Swiss Albino (CD-1) mice at doses of 625,1 250,and 2 500 mg/kg bw used.Results Almond skins exerted no mutagenic activity in various bacterial strains o...     

A Globally Optimal k-Anonymity Method for the De-Identification of Health Data

Background

Explicit patient consent requirements in privacy laws can have a negative impact on health research, leading to selection bias and reduced recruitment. Often legislative requirements to obtain consent are waived if the information collected or disclosed is de-identified.

Objective

The authors developed and empirically evaluated a new globally optimal de-identification algorithm that satisfies the k-anonymity criterion and that is suitable for health datasets.

Design

Authors compared OLA (Optimal Lattice Anonymization) empirically to three existing k-anonymity algorithms, Datafly, Samarati, and Incognito, on six public, hospital, and registry datasets for different values of k and suppression limits.

Measurement

Three information loss metrics were used for the comparison: precision, discernability metric, and non-uniform entropy. Each algorithm's performance speed was also evaluated.

Results

The Datafly and Samarati algorithms had higher information loss than OLA and Incognito; OLA was consistently faster than Incognito in finding the globally optimal de-identification solution.

Conclusions

For the de-identification of health datasets, OLA is an improvement on existing k-anonymity algorithms in terms of information loss and performance.     

Effect of Rapamycin on TGF-β1- induced epithelial-mesenchymal transition in LoVo colonic adenocarcinoma cells

Objective:To investigate the effect of Rapamycin on epithelial-mesenchyrnal transition(EMT) of LoVo colonic adenocarcinoma cells in vitro.Methods:Cultured LoVo colonic adenocarcinoma cells were divided into three groups: negative control group,EMT-inducing group(TGF-β1) and EMT-interfering group(TGF-β1 plus Rapamycin).E-cadherin expression in LoVo cells was detected by Western Blot,while the expression of vimentin was evaluated through immunocytochemistry.The Snail mRNA in LoVo cells was examined by RT-PCR.Results:TGF-β1 induced LoVo cell switching from polygonal to spindle-shaped.TGF-β1 enhanced the expression of vimentin,but lowered the level of E-cadherin.In contrast,Rapamycin impaired the transition induced by TGF-β1.Rapamycin dramatically abrogated TGF-β1-induced vimentin expression and restored E-cadherin expression in LoVo cells.Rapamycin significantly repressed the up-regulation of Snail mRNA expression induced by TGF-β1.Conclusion:Rapamycin dramatically abrogated TGF-β1 induced Snail mRNA expression in LoVo cells,hence inhibiting EMT of these cells in vitro.