NF-κB targeting by way of IKK inhibition sensitizes lung cancer cells to adenovirus delivery of TRAIL

<p>Abstract</p> <p>Background</p> <p>Lung cancer causes the highest rate of cancer-related deaths both in men and women. As many current treatment modalities are inadequate in increasing patient survival, new therapeutic strategies are required. TNF-related apoptosis-inducing ligand (TRAIL) selectively induces apoptosis in tumor cells but not in normal cells, prompting its current evaluation in a number of clinical trials. The successful therapeutic employment of TRAIL is restricted by the fact that many tumor cells are resistant to TRAIL. The goal of the present study was to test a novel combinatorial gene therapy modality involving adenoviral delivery of TRAIL (Ad5hTRAIL) and IKK inhibition (AdIKKβKA) to overcome TRAIL resistance in lung cancer cells.</p> <p>Methods</p> <p>Fluorescent microscopy and flow cytometry were used to detect optimum doses of adenovirus vectors to transduce lung cancer cells. Cell viability was assessed via a live/dead cell viability assay. Luciferase assays were employed to monitor cellular NF-κB activity. Apoptosis was confirmed using Annexin V binding.</p> <p>Results</p> <p>Neither Ad5hTRAIL nor AdIKKβKA infection alone induced apoptosis in A549 lung cancer cells, but the combined use of Ad5hTRAIL and AdIKKβKA significantly increased the amount of A549 apoptosis. Luciferase assays demonstrated that both endogenous and TRAIL-induced NF-κB activity was down-regulated by AdIKKβKA expression.</p> <p>Conclusions</p> <p>Combination treatment with Ad5hTRAIL and AdIKKβKA induced significant apoptosis of TRAIL-resistant A549 cells, suggesting that dual gene therapy strategy involving exogenous TRAIL gene expression with concurrent IKK inhibition may be a promising novel gene therapy modality to treat lung cancer.</p

TRAIL Death Receptor-4, Decoy Receptor-1 and Decoy Receptor-2 Expression on CD8+ T Cells Correlate with the Disease Severity in Patients with Rheumatoid Arthritis

BACKGROUND: Rheumatoid Arthritis (RA) is a chronic autoimmune inflammatory disorder. Although the pathogenesis of disease is unclear, it is well known that T cells play a major role in both development and perpetuation of RA through activating macrophages and B cells. Since the lack of TNF-Related Apoptosis Inducing Ligand (TRAIL) expression resulted in defective thymocyte apoptosis leading to an autoimmune disease, we explored evidence for alterations in TRAIL/TRAIL receptor expression on peripheral T lymphocytes in the molecular mechanism of RA development. METHODS: The expression of TRAIL/TRAIL receptors on T cells in 20 RA patients and 12 control individuals were analyzed using flow cytometry. The correlation of TRAIL and its receptor expression profile was compared with clinical RA parameters (RA activity scored as per DAS28) using Spearman Rho Analysis. RESULTS: While no change was detected in the ratio of CD4+ to CD8+ T cells between controls and RA patient groups, upregulation of TRAIL and its receptors (both death and decoy) was detected on both CD4+ and CD8+ T cells in RA patients compared to control individuals. Death Receptor-4 (DR4) and the decoy receptors DcR1 and DcR2 on CD8+ T cells, but not on CD4+ T cells, were positively correlated with patients' DAS scores. CONCLUSIONS: Our data suggest that TRAIL/TRAIL receptor expression profiles on T cells might be important in revelation of RA pathogenesis

Comparison of artificial neural network and K-means for clustering dairy cattle

Artificial neural network models (ANN's) are machine-learning systems, a type of artificial intelligence. They have been inspired by and developed along the working principles of the human brain and its nerve cells. ANN's are especially used in the modelling of nonlinear systems. With the information learned through repeated experience, similar to human learning, ANN's can provide classification, pattern recognition, optimisation and the realisation of forward-looking forecasts. Artificial neural network studies have been performed in animal husbandry in recent years. They have been used for the prediction of yield characteristics and classification, animal breeding, quality assessment, and disease diagnosis. In this study, classification of dairy cattle using artificial neural networks and cluster analysis are compared. Artificial neural networks models were determined to be more successful than cluster analysis. © 2016 Inderscience Enterprises Ltd

Fuzzy logic approach in the evaluation of raw milk quality [Çig süt kalite degerlendirmesinde bulani{dotless}k manti{dotless}k yaklaşi{dotless}mi{dotless}]

The problems that faced with in real life and perspective of the events change with developing structure of society. The people in the face of problem use a variety of methods with their verbal and numerical data to find solution. Mathematical methods that including precision are sufficient in the analyses of numerical data while the modeling of verbal data may be insufficient in case of uncertainty. In recent years, fuzzy logic is one of the artificial intelligence methods that used in solution of the problems which are rosed from quality evaluation situations that consists of uncertainty cases. The fuzzy logic theory that has more flexible structure than the theory of classical logic, describe the events with degree of accuracy which is between "0" and "1" appointed to object. Fuzzy logic-based decision support system offers to people a more realistic and objective perspective in decision making. In this study, fuzzy logic base decision support system which aims to classify raw milk samples in quality has been developed. System inputs are; bacteria count for milk samples, somatic cell count and values for measured protein amounts. Designed fuzzy logic output is consist of raw milk quality value measurement; in order to calculate the success of the analysis, results have been compared to specialist's decisions and due to the comparison, it noticed that the system has 80% success rate. Modeling of the system has been made via Matlab (version R2010b) programme

A Cutaneous False Positive in Radioiodine Scintigraphy for Metastatic Thyroid Cancer

Contamination of external sites with secretions or excretory products can mimic metastases and yield false positives in radioiodine whole-body scintigraphy for thyroid cancer. We present a case of a 26-year-old woman with differentiated papillary thyroid carcinoma who received radioiodine 131 (I-131) for treatment of persistent upper mediastinal metastasis. Her post-treatment whole-body scintigraphy revealed an unexpected focus of increased uptake near the scalp in addition to the mediastinal lesion. Although the scalp is the most common site of cutaneous thyroid cancer metastasis, differentiated thyroid cancers rarely manifest with cutaneous thyroid cancer metastasis and thus it is prudent to consider etiologies of false positive I-131 uptake in such cases. Contamination of our patient’s hair from salivary secretions was confirmed on history and with coiffure repositioning during whole-body scintigraphy

Potential Effects of the Eu's Carbon Border Adjustment Mechanism on the Turkish Economy

In December 2019, the EU announced the European Green Deal (EGD) to create a climate-neutral continent by 2050. Accordingly, the EU Emission Trading System (ETS) will be revised to maintain economic growth against possible losses in competitiveness, leading to carbon leakage. Carbon border adjustment (CBA) is one of the mechanisms proposed to tackle the carbon leakage problem. In this paper, we provide a first-order estimate of the potential impacts of a possible CBA across production sectors and build a dynamic, multi-sectoral applied general equilibrium (AGE) model to study the overall macroeconomic impact of the EGD on the Turkish economy. Then, we extend our analysis to document the potential benefits of a more active climate policy. The model is in the Walrasian tradition wherein aggregate supply and demand actions are simulated with the interplay of relative prices to bring equilibrium in the markets for goods, for labor, and for foreign exchange. Constructed around 24 production sectors, the model accommodates flexible, multi-level functional forms to link production activities with gaseous emissions, a government entity to maintain taxation, and public expenditures, as well as administration of environmental policy instruments, all within an open-economy macroeconomic environment. Our results suggest that the potential adverse impact of the CBA on the Turkish economy would range between 2.7 and 3.6% loss of the GDP by 2030 over the business-as-(un)usual base path. We also document that under an alternative scenario through which Turkey is modeled as an active agent in the international climate policy arena embedding decarbonization into her official macroeconomic policy agenda, she can achieve a superior pathway for national income and a reduced carbon burden