Search results for: cancer drugs

Authors: S. Yodyingyong, C. Chaichana, C. Nuchsuk, S. Roytrakul, N. P. T-Thienprasert, S. Ratanapo

Abstract:

Protein hydrolysates prepared from a number of medicinal plants are promising sources of various bioactive peptides. In this work, proteins from dried whole plant of Euphorbia hirta Linn. were extracted and digested with pepsin for 12h. The hydrolysates of lesser than 3 KDa were fractionated by a cut-off membrane. The peptide hydrolysate was then purified by an anion-exchange chromatography on DEAE-Sephacel™ column and reverse-phase chromatography on Sep-pak C₁₈ column, respectively. The cytotoxic effect of each peptide fraction against a gastric carcinoma cell line (KATO-III, ATCC No. HTB103) was investigated using colorimetric MTT viability assay. A human liver cell line (Chang Liver, CLS No. 300139) was used as a control normal cell line. Two purified peptide peaks, peak l and peak ll at 100µg peptides mL^-1 affected cell viability of the gastric cancer cell lines to 63.85±4.94 and 66.92±6.46%, respectively. Our result showed for the first time that the peptide fractions derived from protein hydrolysate of Euphorbia hirta Linn. have anti-gastric cancer activity, which offers a potential novel and natural anti-gastric cancer remedy.

Keywords: Cytotoxic, peptides, Euphorbia hirta Linn., gastric carcinoma.

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Authors: Benjamin Y. M. Kwan, Hon Keung Kwan

Abstract:

Early detection of lung cancer through chest radiography is a widely used method due to its relatively affordable cost. In this paper, an approach to improve lung nodule visualization on chest radiographs is presented. The approach makes use of linear phase high-frequency emphasis filter for digital filtering and histogram equalization for contrast enhancement to achieve improvements. Results obtained indicate that a filtered image can reveal sharper edges and provide more details. Also, contrast enhancement offers a way to further enhance the global (or local) visualization by equalizing the histogram of the pixel values within the whole image (or a region of interest). The work aims to improve lung nodule visualization of chest radiographs to aid detection of lung cancer which is currently the leading cause of cancer deaths worldwide.

Keywords: Chest radiographs, Contrast enhancement, Digital filtering, Lung nodule detection

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Authors: Kuniyasu Soda

Abstract:

Surgical trauma seems to facilitate metastatic spread, although the underlying mechanisms are not known. Increased concentrations of polyamines (spermine and spermidine) in the blood seem to have associated with the enhanced malignant potential of cancer cells and decrease in anti-tumor immunity of cancer patients. In addition to de novo synthesis in rapidly growing cells such as normal regenerating cells and cancer cells, cells can take up polyamines from extra-cellular sources. We have shown that increased polyamine concentration results in decreases in cytokine production and expression of adhesion molecules involved in anti-tumor immunity, such as CD11a. And, immune cells in an environment with increased polyamine levels lose anti-tumor immune functions, such as lymphokine activated killer cell (LAK) activities. Because blood polyamine levels are increased in post-surgical patients, polyamine seems to have roles on post-traumatic tumor spread.

Keywords: Immune function, LAK, Polyamine, Surgical trauma.

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Authors: N. A. Ibrahim, A. Kudus, I. Daud, M. R. Abu Bakar

Abstract:

Competing risks survival data that comprises of more than one type of event has been used in many applications, and one of these is in clinical study (e.g. in breast cancer study). The decision tree method can be extended to competing risks survival data by modifying the split function so as to accommodate two or more risks which might be dependent on each other. Recently, researchers have constructed some decision trees for recurrent survival time data using frailty and marginal modelling. We further extended the method for the case of competing risks. In this paper, we developed the decision tree method for competing risks survival time data based on proportional hazards for subdistribution of competing risks. In particular, we grow a tree by using deviance statistic. The application of breast cancer data is presented. Finally, to investigate the performance of the proposed method, simulation studies on identification of true group of observations were executed.

Keywords: Competing risks, Decision tree, Simulation, Subdistribution Proportional Hazard.

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Authors: Emma R. Arakelova, Stepan G. Grigoryan, Ashot M. Khachatryan, Karapet E. Avjyan, Lilia M. Savchenko, Flora G. Arsenyan

Abstract:

The paper presents a new drugs delivery system, based on the thin film technology. As a model antitumor drug, highly toxic doxorubicin is chosen. The system is based on the technology of obtaining zinc oxide composite of doxorubicin by deposition of nanosize ZnO films on the surface of doxorubicin coating on glass substrate using DC magnetron sputtering of zinc targets in Ar:O₂ medium at room temperature. For doxorubicin zinc oxide compositions in the form of coatings and gels with 180-200nm thick ZnO films, higher (by a factor 2) in vivo (ascitic Ehrlich's carcinoma) antitumor activity is observed at low doses of doxorubicin in comparison with that of the initial preparation at therapeutic doses. The vector character of the doxorubicin zinc oxide composite transport to tumor tissues ensures the increase in antitumor activity as well as decrease of toxicity in comparison with the initial drug.

Keywords: Antitumor activity, doxorubicin, DC magnetron sputtering, zinc oxide.

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Authors: Rezvan Najafi, Korosh Heydari, Massoud Saidijam

Abstract:

5-FU is a chemotherapeutic agent that has been used in colorectal cancer (CRC) treatment. However, it is usually associated with the acquired resistance, which decreases the therapeutic effects of 5-FU. miR-200c is involved in chemotherapeutic drug resistance, but its mechanism is not fully understood. In this study, the effect of inhibition of miR-200c in sensitivity of HCT-116 CRC cells to 5-FU was evaluated. HCT-116 cells were transfected with LNA-anti- miR-200c for 48 h. mRNA expression of miR-200c was evaluated using quantitative real- time PCR. The protein expression of phosphatase and tensin homolog (PTEN) and E-cadherin were analyzed by western blotting. Annexin V and propidium iodide staining assay were applied for apoptosis detection. The caspase-3 activation was evaluated by an enzymatic assay. The results showed LNA-anti-miR-200c inhibited the expression of PTEN and E-cadherin protein, apoptosis and activation of caspase 3 compared with control cells. In conclusion, these results suggest that miR-200c as a prognostic marker can overcome to 5-FU chemoresistance in CRC.

Keywords: Colorectal cancer, miR-200c, 5-FU resistance, E-cadherin, PTEN.

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Authors: Nebi Gedik, Ayten Atasoy

Abstract:

This paper investigates successful sub-bands of wave atom transform via classification of mammograms, when the coefficients of sub-bands are used as features. A computer-aided diagnosis system is constructed by using wave atom transform, support vector machine and k-nearest neighbor classifiers. Two-class classification is studied in detail using two data sets, separately. The successful sub-bands are determined according to the accuracy rates, coefficient numbers, and sensitivity rates.

Keywords: Breast cancer, wave atom transform, SVM, k-NN.

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Authors: Guo Xiuhua, Sun Tao, Wu Haifeng, He Wen, Liang Zhigang, Zhang Mengxia, Guo Aimin, Wang Wei

Abstract:

Purpose: To explore the use of Curvelet transform to extract texture features of pulmonary nodules in CT image and support vector machine to establish prediction model of small solitary pulmonary nodules in order to promote the ratio of detection and diagnosis of early-stage lung cancer. Methods: 2461 benign or malignant small solitary pulmonary nodules in CT image from 129 patients were collected. Fourteen Curvelet transform textural features were as parameters to establish support vector machine prediction model. Results: Compared with other methods, using 252 texture features as parameters to establish prediction model is more proper. And the classification consistency, sensitivity and specificity for the model are 81.5%, 93.8% and 38.0% respectively. Conclusion: Based on texture features extracted from Curvelet transform, support vector machine prediction model is sensitive to lung cancer, which can promote the rate of diagnosis for early-stage lung cancer to some extent.

Keywords: CT image, Curvelet transform, Small pulmonary nodules, Support vector machines, Texture extraction.

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Authors: Pranee Suecharoen, Jaturat Kanpittaya

Abstract:

Background: The incidence of adverse reactions to iodinated contrast media has risen. The dearth of reports on reactions to the administration of iso- and low-osmolar contrast media should be addressed. We, therefore, studied the profile of adverse reactions to iodinated contrast media; viz., (a) the body systems affected (b) causality, (c) severity, and (d) preventability. Objective: To study adverse reactions (causes and severity) to iodinated contrast media at Srinagarind Hospital. Method: Between March and July, 2015, 1,101 patients from the Department of Radiology were observed and interviewed for the occurrence of adverse reactions. The patients were classified per Naranjo’s algorithm and through use of an adverse reactions questionnaire. Results: A total of 105 cases (9.5%) reported adverse reactions (57% male; 43% female); among whom 2% were iso-osmolar vs. 98% low-osmolar. Diagnoses included hepatoma and cholangiocarcinoma (24.8%), colorectal cancer (9.5%), breast cancer (5.7%), cervical cancer (3.8%), lung cancer (2.9%), bone cancer (1.9%), and others (51.5%). Underlying diseases included hypertension and diabetes mellitus type 2. Mild, moderate, and severe adverse reactions accounted for 92, 5 and 3%, respectively. The respective groups of escalating symptoms included (a) mild urticaria, itching, rash, nausea, vomiting, dizziness, and headache; (b) moderate hypertension, hypotension, dyspnea, tachycardia and bronchospasm; and (c) severe laryngeal edema, profound hypotension, and convulsions. All reactions could be anticipated per Naranjo’s algorithm. Conclusion: Mild to moderate adverse reactions to low-osmolar contrast media were most common and these occurred immediately after administration. For patient safety and better outcomes, improving the identification of patients likely to have an adverse reaction is essential.

Keywords: Adverse reactions, contrast media, computed tomography, iodinated contrast agents.

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Authors: Efendi Zaenudin, Chien-Hung Huang, Ka-Lok Ng

Abstract:

Essential genes play an important role in the survival of an organism. It has been shown that cancer-associated essential genes are genes necessary for cancer cell proliferation, where these genes are potential therapeutic targets. Also, it was demonstrated that mutations of the cancer-associated essential genes give rise to the resistance of immunotherapy for patients with tumors. In the present study, we focus on studying the biological effects of the essential genes from a network perspective. We hypothesize that one can analyze a biological molecular network by decomposing it into both three-node and four-node digraphs (subgraphs). These network subgraphs encode the regulatory interaction information among the network’s genetic elements. In this study, the frequency of occurrence of the subgraph-associated essential genes in a molecular network was quantified by using the statistical parameter, odds ratio. Biological effects of subgraph-associated essential genes are discussed. In summary, the subgraph approach provides a systematic method for analyzing molecular networks and it can capture useful biological information for biomedical research.

Keywords: Biological molecular networks, essential genes, graph theory, network subgraphs.

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Authors: M. Anidha, K. Premalatha

Abstract:

Feature Selection is significant in order to perform constructive classification in the area of cancer diagnosis. However, a large number of features compared to the number of samples makes the task of classification computationally very hard and prone to errors in microarray gene expression datasets. In this paper, we present an innovative method for selecting highly informative gene subsets of gene expression data that effectively classifies the cancer data into tumorous and non-tumorous. The hybrid gene selection technique comprises of combined Mutual Information and Fisher score to select informative genes. The gene selection is validated by classification using Support Vector Machine (SVM) which is a supervised learning algorithm capable of solving complex classification problems. The results obtained from improved Mutual Information and F-Score with SVM as a classifier has produced efficient results.

Keywords: Gene selection, mutual information, Fisher score, classification, SVM.

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Authors: A. Sindhuja, V. Sadasivam

Abstract:

Breast Cancer is the most common malignancy in women and the second leading cause of death for women all over the world. Earlier the detection of cancer, better the treatment. The diagnosis and treatment of the cancer rely on segmentation of Sonoelastographic images. Texture features has not considered for Sonoelastographic segmentation. Sonoelastographic images of 15 patients containing both benign and malignant tumorsare considered for experimentation.The images are enhanced to remove noise in order to improve contrast and emphasize tumor boundary. It is then decomposed into sub-bands using single level Daubechies wavelets varying from single co-efficient to six coefficients. The Grey Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) features are extracted and then selected by ranking it using Sequential Floating Forward Selection (SFFS) technique from each sub-band. The resultant images undergo K-Means clustering and then few post-processing steps to remove the false spots. The tumor boundary is detected from the segmented image. It is proposed that Local Binary Pattern (LBP) from the vertical coefficients of Daubechies wavelet with two coefficients is best suited for segmentation of Sonoelastographic breast images among the wavelet members using one to six coefficients for decomposition. The results are also quantified with the help of an expert radiologist. The proposed work can be used for further diagnostic process to decide if the segmented tumor is benign or malignant.

Keywords: Breast Cancer, Segmentation, Sonoelastography, Tumor Detection.

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Authors: Wittawat Wasusathien, Samran Santalunai, Thanaset Thosdeekoraphat, Chanchai Thongsopa

Abstract:

This paper presents breast cancer detection by observing the specific absorption rate (SAR) intensity for identification tumor location, the tumor is identified in coordinates (x,y,z) system. We examined the frequency between 4-8 GHz to look for the most appropriate frequency. Results are simulated in frequency 4-8 GHz, the model overview include normal breast with 50 mm radian, 5 mm diameter of tumor, and ultra wideband (UWB) bowtie antenna. The models are created and simulated in CST Microwave Studio. For this simulation, we changed antenna to 5 location around the breast, the tumor can be detected when an antenna is close to the tumor location, which the coordinate of maximum SAR is approximated the tumor location. For reliable, we experiment by random tumor location to 3 position in the same size of tumor and simulation the result again by varying the antenna position in 5 position again, and it also detectable the tumor position from the antenna that nearby tumor position by maximum value of SAR, which it can be detected the tumor with precision in all frequency between 4-8 GHz.

Keywords: Specific absorption rate (SAR), ultra wideband (UWB), coordinates and cancer detection

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Authors: Mohamed Al-Badrashiny, Abdelghani Bellaachia

Abstract:

This paper presents a classifier ensemble approach for predicting the survivability of the breast cancer patients using the latest database version of the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute. The system consists of two main components; features selection and classifier ensemble components. The features selection component divides the features in SEER database into four groups. After that it tries to find the most important features among the four groups that maximizes the weighted average F-score of a certain classification algorithm. The ensemble component uses three different classifiers, each of which models different set of features from SEER through the features selection module. On top of them, another classifier is used to give the final decision based on the output decisions and confidence scores from each of the underlying classifiers. Different classification algorithms have been examined; the best setup found is by using the decision tree, Bayesian network, and Na¨ıve Bayes algorithms for the underlying classifiers and Na¨ıve Bayes for the classifier ensemble step. The system outperforms all published systems to date when evaluated against the exact same data of SEER (period of 1973-2002). It gives 87.39% weighted average F-score compared to 85.82% and 81.34% of the other published systems. By increasing the data size to cover the whole database (period of 1973-2014), the overall weighted average F-score jumps to 92.4% on the held out unseen test set.

Keywords: Classifier ensemble, breast cancer survivability, data mining, SEER.

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Authors: S. Zolghadri, M. Mousavi-Daramoroudi, H. Yousefnia, F. Abbasi-Davani

Abstract:

In this study, the absorbed dose of human organs after injection of ¹⁷⁷Lu-DOTATOC was studied based on the biodistribution of the complex in adenocarcinoma breast cancer bearing mice. For this purpose, the biodistribution of the radiolabelled complex was studied and compartmental modeling was applied to calculate the absorbed dose with high precision. As expected, ¹⁷⁷Lu-DOTATOC illustrated a notable specific uptake in tumor and pancreas, organs with high level of somatostatin receptor on their surface and the effectiveness of the radio-conjugate for targeting of the breast adenocarcinoma tumors was indicated. The elicited results of modeling were the exponential equations, and those are utilized for obtaining the cumulated activity data by taking their integral. The results also exemplified that non-target absorbed-doses such as the liver, spleen and pancreas were approximately 0.008, 0.004, and 0.039, respectively. While these values were so much lower than target (tumor) absorbed-dose, it seems due to this low toxicity, this complex is a good agent for therapy.

Keywords: Breast cancer, compartmental modeling, 177Lu, dosimetry.

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Authors: Patcharawalai Jaisamut, Kamonthip Wiwattanawongsa, Ruedeekorn Wiwattanapatapee

Abstract:

Supersaturation of drugs in the gastrointestinal tract is one approach to increase the absorption of poorly water-soluble drugs. The stabilization of a supersaturated state was achieved by adding precipitation inhibitors that may act through a variety of mechanisms. In this study, the effect of the natural gums, acacia, gelatin, pectin and tragacanth on curcumin supersaturation in simulated gastric fluid (SGF) (pH 1.2), fasted state simulated gastric fluid (FaSSGF) (pH 1.6), and simulated intestinal fluid (SIF) (pH 6.8) was investigated. The results indicated that all natural gums significantly increased the curcumin solubility (about 1.2-6-fold) when compared to the absence of gum, and assisted in maintaining the supersaturated drug solution. Among the tested gums, pectin at 3% w/w was the best precipitation inhibitor with a significant increase in the degree of supersaturation about 3-fold in SGF, 2.4-fold in FaSSGF and 2-fold in SIF.

Keywords: Curcumin, Solubility, Supersaturation, Precipitation inhibitor.

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Authors: Frank Emmert-Streib, Matthias Dehmer, Jing Liu, Max Muhlhauser

Abstract:

The major objective of this paper is to introduce a new method to select genes from DNA microarray data. As criterion to select genes we suggest to measure the local changes in the correlation graph of each gene and to select those genes whose local changes are largest. More precisely, we calculate the correlation networks from DNA microarray data of cervical cancer whereas each network represents a tissue of a certain tumor stage and each node in the network represents a gene. From these networks we extract one tree for each gene by a local decomposition of the correlation network. The interpretation of a tree is that it represents the n-nearest neighbor genes on the n-th level of a tree, measured by the Dijkstra distance, and, hence, gives the local embedding of a gene within the correlation network. For the obtained trees we measure the pairwise similarity between trees rooted by the same gene from normal to cancerous tissues. This evaluates the modification of the tree topology due to tumor progression. Finally, we rank the obtained similarity values from all tissue comparisons and select the top ranked genes. For these genes the local neighborhood in the correlation networks changes most between normal and cancerous tissues. As a result we find that the top ranked genes are candidates suspected to be involved in tumor growth. This indicates that our method captures essential information from the underlying DNA microarray data of cervical cancer.

Keywords: Graph similarity, generalized trees, graph alignment, DNA microarray data, cervical cancer.

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Authors: S. Sudhakar, Geetha Manjunath, Siva Teja Kakileti, Himanshu Madhu

Abstract:

Diagnosis of breast cancer at early stages has seen better clinical and survival outcomes. Survival rates in developing countries like India are very low due to accessibility and affordability issues of screening tests such as Mammography. In addition, Mammography is not much effective in younger women with dense breasts. This leaves a gap in current screening methods. Thermalytix is a new technique for detecting breast abnormality in a non-contact, non-invasive way. It is an AI-enabled computer-aided diagnosis solution that automates interpretation of high resolution thermal images and identifies potential malignant lesions. The solution is low cost, easy to use, portable and is effective in all age groups.  This paper presents the results of a retrospective comparative analysis of Thermalytix over Mammography and Clinical Breast Examination for breast cancer screening. Thermalytix was found to have better sensitivity than both the tests, with good specificity as well. In addition, Thermalytix identified all malignant patients without palpable lumps.

Keywords: Breast Cancer Screening, Radiology, Thermalytix, Artificial Intelligence, Thermography.

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Authors: Nevine M. Labib, Michael N. Malek

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Data Mining aims at discovering knowledge out of data and presenting it in a form that is easily comprehensible to humans. One of the useful applications in Egypt is the Cancer management, especially the management of Acute Lymphoblastic Leukemia or ALL, which is the most common type of cancer in children. This paper discusses the process of designing a prototype that can help in the management of childhood ALL, which has a great significance in the health care field. Besides, it has a social impact on decreasing the rate of infection in children in Egypt. It also provides valubale information about the distribution and segmentation of ALL in Egypt, which may be linked to the possible risk factors. Undirected Knowledge Discovery is used since, in the case of this research project, there is no target field as the data provided is mainly subjective. This is done in order to quantify the subjective variables. Therefore, the computer will be asked to identify significant patterns in the provided medical data about ALL. This may be achieved through collecting the data necessary for the system, determimng the data mining technique to be used for the system, and choosing the most suitable implementation tool for the domain. The research makes use of a data mining tool, Clementine, so as to apply Decision Trees technique. We feed it with data extracted from real-life cases taken from specialized Cancer Institutes. Relevant medical cases details such as patient medical history and diagnosis are analyzed, classified, and clustered in order to improve the disease management.

Keywords: Data Mining, Decision Trees, Knowledge Discovery, Leukemia.

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Authors: Laura Elena De León Prado, Dora Alicia Cortés Hernández, Javier Sánchez

Abstract:

Among the different cancer treatments that are currently used, hyperthermia has a promising potential due to the multiple benefits that are obtained by this technique. In general terms, hyperthermia is a method that takes advantage of the sensitivity of cancer cells to heat, in order to damage or destroy them. Within the different ways of supplying heat to cancer cells and achieve their destruction or damage, the use of magnetic nanoparticles has attracted attention due to the capability of these particles to generate heat under the influence of an external magnetic field. In addition, these nanoparticles have a high surface area and sizes similar or even lower than biological entities, which allow their approaching and interaction with a specific region of interest. The most used magnetic nanoparticles for hyperthermia treatment are those based on iron oxides, mainly magnetite and maghemite, due to their biocompatibility, good magnetic properties and chemical stability. However, in order to fulfill more efficiently the requirements that demand the treatment of magnetic hyperthermia, there have been investigations using ferrites that incorporate different metallic ions, such as Mg, Mn, Co, Ca, Ni, Cu, Li, Gd, etc., in their structure. This paper reports the synthesis of nanosized Mg_xMn_1-xFe₂O₄ (x = 0.3 and 0.4) ferrites by sol-gel method and their evaluation in terms of heating capability and in vitro hemolysis to determine the potential use of these nanoparticles as thermoseeds for the treatment of cancer by magnetic hyperthermia. It was possible to obtain ferrites with nanometric sizes, a single crystalline phase with an inverse spinel structure and a behavior near to that of superparamagnetic materials. Additionally, at concentrations of 10 mg of magnetic material per mL of water, it was possible to reach a temperature of approximately 45°C, which is within the range of temperatures used for the treatment of hyperthermia. The results of the in vitro hemolysis assay showed that, at the concentrations tested, these nanoparticles are non-hemolytic, as their percentage of hemolysis is close to zero. Therefore, these materials can be used as thermoseeds for the treatment of cancer by magnetic hyperthermia.

Keywords: Ferrites, heating capability, hemolysis, nanoparticles, sol-gel.

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Authors: M. Kumpugdee-Vollrath, T. G. D. Phu, M. Helmis

Abstract:

A suitable model membrane to study the pharmacological effect of pharmaceutical products is human stratum corneum because this layer of human skin is the outermost layer and it is an important barrier to be passed through. Other model membranes which were also used are for example skins from pig, mouse, reptile or fish. We are interested in fish skins in this project. The advantages of the fish skins are, that they can be obtained from the supermarket or fish shop. However, the fish skins should be freshly prepared and used directly without storage. In order to understand the effect of different model drugs e.g. lidocaine HCl, resveratrol, paracetamol, ibuprofen, acetyl salicylic acid on the properties of the model membrane from various types of fishes e.g. trout, salmon, cod, plaice permeation tests were performed and differential scanning calorimetry was applied.

Keywords: Fish skin, model membrane, permeation, DSC, lidocaine HCl, resveratrol, paracetamol, ibuprofen, acetyl salicylic acid.

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Authors: Simone C. F. Neves, Lúcio F. A. Campos, Ewaldo Santana, Ginalber L. O. Serra, Allan K. Barros

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We propose a method for discrimination and classification of ovarian with benign, malignant and normal tissue using independent component analysis and neural networks. The method was tested for a proteomic patters set from A database, and radial basis functions neural networks. The best performance was obtained with probabilistic neural networks, resulting I 99% success rate, with 98% of specificity e 100% of sensitivity.

Keywords: Cancer ovarian, Proteomic patterns in serum, independent component analysis and neural networks.

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Authors: C. T. S. Ching, P. W. C hen, T. P. Sun, H. L. Shieh

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The biomarker for colorectal cancer (CRC) is CEACAM-6 antigen (C6AG). Therefore, this study aims to develop a novel, simple and low-cost CEACAM-6 antigen immumosensor (C6AG-IMS), based on electrical impedance measurement, for precise determination of C6AG. A low-cost screen-printed graphite electrode was constructed and used as the sensor, with CEACAM-6 antibody (C6AB) immobilized on it. The procedures of sensor fabrication and antibody immobilization are simple and low-cost. Measurement of the electrical impedance at a definite frequency ranges (0.43 – 1.26 MHz) showed that the C6AG-IMS has an excellent linear (r2>0.9) response range (8.125 – 65 pg/mL), covering the normal physiological and pathological ranges of blood C6AG levels. Also, the C6AG-IMS has excellent reliability and validity, with the intraclass correlation coefficient being 0.97. In conclusion, a novel, simple, low-cost and reliable C6AG-IMS was designed and developed, being able to accurately determine blood C6AG levels in the range of pathological and normal physiological regions. The C6AG-IMS can provide a point-of-care and immediate screening results to the user at home.

Keywords: Colorectal Cancer, Immunosensor, Electrical Impedance, CEACAM-6, Measurement, Point-of-Care

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Authors: Sajjad Seifi Mofarah, S. K. Sadrnezhaad, Shokooh Moghadam, Javad Tavakoli

Abstract:

In recent years a new method of combination treatment for cancer has been developed and studied that has led to significant advancements in the field of cancer therapy. Hyperthermia is a traditional therapy that, along with a creation of a medically approved level of heat with the help of an alternating magnetic AC current, results in the destruction of cancer cells by heat. This paper gives details regarding the production of the spherical nanocomposite PVA/γ-Fe2O3 in order to be used for medical purposes such as tumor treatment by hyperthermia. To reach a suitable and evenly distributed temperature, the nanocomposite with core-shell morphology and spherical form within a 100 to 200 nanometer size was created using phase separation emulsion, in which the magnetic nano-particles γ- Fe2O3 with an average particle size of 20 nano-meters and with different percentages of 0.2, 0.4, 0.5 and 0.6 were covered by polyvinyl alcohol. The main concern in hyperthermia and heat treatment is achieving desirable specific absorption rate (SAR) and one of the most critical factors in SAR is particle size. In this project all attempts has been done to reach minimal size and consequently maximum SAR. The morphological analysis of the spherical structure of the nanocomposite PVA/γ-Fe2O3 was achieved by SEM analyses and the study of the chemical bonds created was made possible by FTIR analysis. To investigate the manner of magnetic nanocomposite particle size distribution a DLS experiment was conducted. Moreover, to determine the magnetic behavior of the γ- Fe2O3 particle and the nanocomposite PVA/γ-Fe2O3 in different concentrations a VSM test was conducted. To sum up, creating magnetic nanocomposites with a spherical morphology that would be employed for drug loading opens doors to new approaches in developing nanocomposites that provide efficient heat and a controlled release of drug simultaneously inside the magnetic field, which are among their positive characteristics that could significantly improve the recovery process in patients.

Keywords: Nanocomposite, hyperthermia, cancer therapy, drug release.

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Authors: Florin Gorunescu

Abstract:

Diagnosis can be achieved by building a model of a certain organ under surveillance and comparing it with the real time physiological measurements taken from the patient. This paper deals with the presentation of the benefits of using Data Mining techniques in the computer-aided diagnosis (CAD), focusing on the cancer detection, in order to help doctors to make optimal decisions quickly and accurately. In the field of the noninvasive diagnosis techniques, the endoscopic ultrasound elastography (EUSE) is a recent elasticity imaging technique, allowing characterizing the difference between malignant and benign tumors. Digitalizing and summarizing the main EUSE sample movies features in a vector form concern with the use of the exploratory data analysis (EDA). Neural networks are then trained on the corresponding EUSE sample movies vector input in such a way that these intelligent systems are able to offer a very precise and objective diagnosis, discriminating between benign and malignant tumors. A concrete application of these Data Mining techniques illustrates the suitability and the reliability of this methodology in CAD.

Keywords: Endoscopic ultrasound elastography, exploratorydata analysis, neural networks, non-invasive cancer detection.

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Authors: D. Bhaskar, N. R. Wadehra, M. Gulati, A. Narula, R. Vishnu, G. Katyal

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With drug resistance becoming widespread in Plasmodium falciparum infections, the development of the alternative drugs is the desired strategy for prevention and cure of malaria. Three drug targets were selected to screen promising drug molecules from the GSK library of 13469 molecules. Using an in silico structure-based drug designing approach, the differences in binding energies of the substrate and inhibitor were exploited between target sites of parasite and human to design a drug molecule against Plasmodium. The docking studies have shown several promising molecules from GSK library with more effective binding as compared to the already known inhibitors for the drug targets. Though stronger interaction has been shown by several molecules as compared to the reference, few molecules have shown the potential as drug candidates though in vitro studies are required to validate the results. In case of thymidylate synthase-dihydrofolatereductase (TS-DHFR), three compounds have shown promise for future studies as potential drugs.

Keywords: Drug resistance, Drug targets, In silico studies, Plasmodium falciparum.

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Authors: R. Sullivan, T. Holden, G. Tremberger, Jr, E. Cheung, C. Branch, J. Burrero, G. Surpris, S. Quintana, A. Rameau, N. Gadura, H. Yao, R. Subramaniam, P. Schneider, S. A. Rotenberg, P. Marchese, A. Flamhlolz, D. Lieberman, T. Cheung

Abstract:

Migration in breast cancer cell wound healing assay had been studied using image fractal dimension analysis. The migration of MDA-MB-231 cells (highly motile) in a wound healing assay was captured using time-lapse phase contrast video microscopy and compared to MDA-MB-468 cell migration (moderately motile). The Higuchi fractal method was used to compute the fractal dimension of the image intensity fluctuation along a single pixel width region parallel to the wound. The near-wound region fractal dimension was found to decrease three times faster in the MDA-MB- 231 cells initially as compared to the less cancerous MDA-MB-468 cells. The inner region fractal dimension was found to be fairly constant for both cell types in time and suggests a wound influence range of about 15 cell layer. The box-counting fractal dimension method was also used to study region of interest (ROI). The MDAMB- 468 ROI area fractal dimension was found to decrease continuously up to 7 hours. The MDA-MB-231 ROI area fractal dimension was found to increase and is consistent with the behavior of a HGF-treated MDA-MB-231 wound healing assay posted in the public domain. A fractal dimension based capacity index has been formulated to quantify the invasiveness of the MDA-MB-231 cells in the perpendicular-to-wound direction. Our results suggest that image intensity fluctuation fractal dimension analysis can be used as a tool to quantify cell migration in terms of cancer severity and treatment responses.

Keywords: Higuchi fractal dimension, box-counting fractal dimension, cancer cell migration, wound healing.

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Authors: P. Bountris, E. Farantatos, N. Apostolou

Abstract:

Autofluorescence (AF) bronchoscopy is an established method to detect dysplasia and carcinoma in situ (CIS). For this reason the “Sotiria" Hospital uses the Karl Storz D-light system. However, in early tumor stages the visualization is not that obvious. With the help of a PC, we analyzed the color images we captured by developing certain tools in Matlab®. We used statistical methods based on texture analysis, signal processing methods based on Gabor models and conversion algorithms between devicedependent color spaces. Our belief is that we reduced the error made by the naked eye. The tools we implemented improve the quality of patients' life.

Keywords: Bronchoscopy, digital image processing, lung cancer, texture analysis.

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Authors: Loredana G. Marcu, David Marcu, Sanda M. Filip

Abstract:

Advanced head and neck cancers are aggressive tumours, which require aggressive treatment. Treatment efficiency is often hindered by cancer cell repopulation during radiotherapy, which is due to various mechanisms triggered by the loss of tumour cells and involves both stem and differentiated cells. The aim of the current paper is to present in silico simulations of radiotherapy schedules on a virtual head and neck tumour grown with biologically realistic kinetic parameters. Using the linear quadratic formalism of cell survival after radiotherapy, altered fractionation schedules employing various treatment breaks for normal tissue recovery are simulated and repopulation mechanism implemented in order to evaluate the impact of various cancer cell contribution on tumour behaviour during irradiation. The model has shown that the timing of treatment breaks is an important factor influencing tumour control in rapidly proliferating tissues such as squamous cell carcinomas of the head and neck. Furthermore, not only stem cells but also differentiated cells, via the mechanism of abortive division, can contribute to malignant cell repopulation during treatment.

Keywords: Radiation, tumour repopulation, squamous cell carcinoma, stem cell.

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Authors: U. Umadevi, T. Umakanthan

Abstract:

Globally, mycotic dermatitis is very common but there is no single proven specific allopathic treatment regimen. In this study, domestic animals with skin diseases of different age and breed from geographically varied regions of Tamil Nadu state, India were employed. Most of them have had previous treatment with native and allopathic medicines without success. Clinically, the skin lesions were found to be mild to severe. The trial animals were treated with poly herbal formulation (ointment) prepared using the indigenous medicinal plants – viz Andrographis paniculata, Lawsonia inermis and Madhuca longifolia. Allopathic antifungal drugs and ointments, povidone iodine and curabless (Terbinafine HCl, Ofloxacin, Ornidazole, Clobetasol propionate) were used in control. Comparatively, trial animals were found to have lesser course of treatment time and higher recovery rate than control. In Ethnoveterinary, this combination was tried for the first time. This herbal formulation is economical and an alternative for skin diseases.

Keywords: Allopathic drugs, dermatitis, domestic animals, poly herbal formulation.

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