Search results for: lung cancer

Authors: Frank Emmert Streib, Matthias Dehmer, Jing Liu, Max Mühlhauser

Abstract:

In this paper we present a method for gene ranking from DNA microarray data. More precisely, we calculate the correlation networks, which are unweighted and undirected graphs, from 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 progression of the tumor. 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 and, hence, indicates that our method captures essential information from the underlying DNA microarray data of cervical cancer.

Keywords: Graph similarity, DNA microarray data, cancer.

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Authors: Hung-Ming Lai, Andreas Albrecht, Kathleen Steinhöfel

Abstract:

Cancers could normally be marked by a number of differentially expressed genes which show enormous potential as biomarkers for a certain disease. Recent years, cancer classification based on the investigation of gene expression profiles derived by high-throughput microarrays has widely been used. The selection of discriminative genes is, therefore, an essential preprocess step in carcinogenesis studies. In this paper, we have proposed a novel gene selector using information-theoretic measures for biological discovery. This multivariate filter is a four-stage framework through the analyses of feature relevance, feature interdependence, feature redundancy-dependence and subset rankings, and having been examined on the colon cancer data set. Our experimental result show that the proposed method outperformed other information theorem based filters in all aspect of classification errors and classification performance.

Keywords: Colon cancer, feature interdependence, feature subset selection, gene selection, microarray data analysis.

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Authors: Seid Ali Asghar Terohid, Vahid Fayaz

Abstract:

Thyroid cancer-s overall contribution to the worldwide cancer burden is relatively small, but incidence rates have increased over the last three decades throughout the world. This trend has been hypothesised to reflect a combination of technological advances enabling increased detection, but also changes in environmental factors, including population exposure to ionising radiation from fallout, diagnostic tests and treatment for benign and malignant conditions. The Thyroid dose received apparently shielded by cerrobend blocks was about 8cGy in 100cGy Expose

Keywords: Absorbed Dose, Thyroid, Radiotherapy

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Authors: Liansheng Ren, Xihua Yang, Guoping Wang, Hong Zhang, Lili Zhao, Zhenguo Mi

Abstract:

The inhibition effect of brazilin to human bladder tumor cell line T24 in vitro and in vivo was studied. The results of the in vitro experiments showed that brazilin has strong inhibition activity on the target cells. The inhibition ratio of 100 μg/mL brazilin and 100 μg/mL mitomycin to the target cells was 90.90 % and 63.24 % respectively, which showed that brazilin has higher inhibition activity than mitomycin under the same concentration. Brazilin could induce cell apoptosis in T24 cells. Significant antitumor activity of brazilin was also showed in the animals experiments. The life extention rate of 200 mg/mL, 300 mg/kg, and 400 mg/kg brazilin intraperitoneally injected into Balb/c-nu-nu nude mice that with human bladder cancer were 51.50 %, 56.90 %, and 58.42 %（P<0.05）. Our study showed that brazilin has significant inhibitory effect on human bladder tumor cell.

Keywords: bladder cancer, brazilin, inhibition, T24 cell line

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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: 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

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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: 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: Thusitha Mabotuwana, Christopher S. Hall

Abstract:

Radiologists routinely make follow-up imaging recommendations, usually based on established clinical practice guidelines, such as the Fleischner Society guidelines for managing lung nodules. In order to ensure optimal care, it is important to make guideline-compliant recommendations, and also for patients to follow-up on these imaging recommendations in a timely manner. However, determining such compliance rates after a specific finding has been observed usually requires many time-consuming manual steps. To address some of these limitations with current approaches, in this paper we discuss a methodology to automatically detect finding-specific follow-up recommendations from radiology reports and create a visualization for relevant subsequent exams showing the modality transitions. Nearly 5% of patients who had a lung related follow-up recommendation continued to have at least eight subsequent outpatient CT exams during a seven year period following the recommendation. Radiologist and section chiefs can use the proposed tool to better understand how a specific patient population is being managed, identify possible deviations from established guideline recommendations and have a patient-specific graphical representation of the imaging pathways for an abstract view of the overall treatment path thus far.

Keywords: Follow-up recommendations, care pathways, imaging pathway visualization, follow-up tracking.

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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: 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: Diky Mudhakir, Satrialdi, Sukmadjaja Asyarie, Yeyet C. Sumirtapura

Abstract:

Today, cancer remains one of the major diseases that lead to death. The main obstacle in chemotherapy as a main cancer treatment is the toxicity to normal cells due to Multidrug Resistance (MDR) after the use of anticancer drugs. Proposed solution to overcome this problem is the use of MDR efflux inhibitor of cinchona alkaloids which is delivered together with anticancer drugs encapsulated in the form of polymeric nanoparticles. The particles were prepared by the hydration method. The characterization of nanoparticles was particle size, zeta potential, entrapment efficiency and in vitro drug release. Combination nanoparticle size ranged 29-45 nm with a neutral surface charge. Entrapment efficiency was above 87% for the use quinine, quinidine or cinchonidine in combination with etoposide. The release test results exhibited that the cinchona alkaloids release released faster than that of etoposide. Collectively, cinchona alkaloids can be packaged along with etoposide in nanomicelles for better cancer therapy.

Keywords: Cinchona alkaloids, etoposide, MDR efflux inhitor, polymeric nanomicelles.

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Authors: T. Valdbjørn Rasmussen

Abstract:

Design criteria for achieving an acceptable indoor radon concentration are presented in this paper. The paper suggests three design criteria. These criteria have to be considered at the early stage of the building design phase to meet the latest recommendations from the World Health Organization in most countries. The three design criteria are; first, establishing a radon barrier facing the ground; second, lowering the air pressure in the lower zone of the slab on ground facing downwards; third, diluting the indoor air with outdoor air. The first two criteria can prevent radon from infiltrating from the ground, and the third criteria can dilute the indoor air. By combining these three criteria, the indoor radon concentration can be lowered achieving an acceptable level. In addition, a cheap and reliable method for measuring the radon concentration in the indoor air is described. The provision on radon in the Danish Building Regulations complies with the latest recommendations from the World Health Organization. Radon can cause lung cancer and it is not known whether there is a lower limit for when it is not harmful to human beings. Therefore, it is important to reduce the radon concentration as much as possible in buildings. Airtightness is an important factor when dealing with buildings. It is important to avoid air leakages in the building envelope both facing the atmosphere, e.g. in compliance with energy requirements, but also facing the ground, to meet the requirements to ensure and control the indoor environment. Infiltration of air from the ground underneath a building is the main providing source of radon to the indoor air.

Keywords: Radon, natural radiation, barrier, pressure lowering, ventilation.

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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: 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

Abstract:

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: C. Gunavathi, K. Premalatha

Abstract:

Microarray technology is universally used in the study of disease diagnosis using gene expression levels. The main shortcoming of gene expression data is that it includes thousands of genes and a small number of samples. Abundant methods and techniques have been proposed for tumor classification using microarray gene expression data. Feature or gene selection methods can be used to mine the genes that directly involve in the classification and to eliminate irrelevant genes. In this paper statistical measures like T-Statistics, Signal-to-Noise Ratio (SNR) and F-Statistics are used to rank the genes. The ranked genes are used for further classification. Particle Swarm Optimization (PSO) algorithm and Shuffled Frog Leaping (SFL) algorithm are used to find the significant genes from the top-m ranked genes. The Naïve Bayes Classifier (NBC) is used to classify the samples based on the significant genes. The proposed work is applied on Lung and Ovarian datasets. The experimental results show that the proposed method achieves 100% accuracy in all the three datasets and the results are compared with previous works.

Keywords: Microarray, T-Statistics, Signal-to-Noise Ratio, FStatistics, Particle Swarm Optimization, Shuffled Frog Leaping, Naïve Bayes Classifier.

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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: David J. Russell SC

Abstract:

Widespread use of asbestos over the last century has left a terrible legacy of lung disease. Doctors knew of the health risks long ago, but almost nothing was done to protect workers and the public. Some aspects of nanotechnology may have risks similar to asbestos.

Keywords: Asbestos, causation, nanotechnology.

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Authors: Maha Salah Abdullah Ismail, Mahmoud M. Alsagheir, Mohammed Salah Abd Allah

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Acute respiratory distress syndrome (ARDS) is characterized by permeability pulmonary edema and refractory hypoxemia. Lung-protective ventilation is still the key of better outcome in ARDS. Prone position reduces the trans-pulmonary pressure gradient, recruiting collapsed regions of the lung without increasing airway pressure or hyperinflation. Prone ventilation showed improved oxygenation and improved outcomes in severe hypoxemic patients with ARDS. This study evaluates the effect of prone positioning on mechanically ventilated patients with ARDS. A quasi-experimental design was carried out at Critical Care Units, on 60 patients. Two tools were utilized to collect data; Socio demographic, medical and clinical outcomes data sheet. Results of the present study indicated that prone position improves oxygenation in patients with severe respiratory distress syndrome. The study recommended that use prone position in patients with severe ARDS, as early as possible and for long sessions. Also, replication of this study on larger probability sample at the different geographical location is highly recommended.

Keywords: Acute respiratory distress syndrome, Critical care, Mechanical ventilation and Prone position.

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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

Abstract:

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: N. V. Enwuru, B. Nkeki, E. A. Adekoya, O. A. Adebesin, B. O. Ojo, R. F. Peters, V. A. Aikhomu, U. E. Mendie, O. Akinloye

Abstract:

Awareness about probiotic health benefits is increasing tremendously. However, cell viability is often low due to harsh conditions exposed during processing, handling, storage, and gastrointestinal transit. Thus, encapsulation is a promising technique that increases cell viability. The study aims to encapsulate the probiotic, evaluate its viability and cytotoxic activity of its postbiotic on the Michigan Cancer Foundation (MCF)-7 breast cancer cell line. Human and animal raw milk was sampled for lactic acid bacteria. Isolated bacteria were identified using conventional and VITEK 2 systems. The identified bacteria were encapsulated using the spray-drying method. The free and encapsulated probiotic cells were exposed to simulated gastric intestinal (SGI) fluid conditions and different storage conditions for their viability. The properties of the formed probiotic granules, their disintegration time, and the weight uniformity of the microcapsules were tested. Furthermore, the postbiotic of the free cells was extracted, and its cytotoxic effect on the MCF-7 breast cancer cell line was tested through [3-(4,5-dimethylthiazolyl-2)-2,5 diphenyltetrazolium bromide] (MTT) assay. The bacteria isolated were identified as Lactobacillus plantarum. The size of the formed probiotic granules ranges within 0.71-1.00 mm in diameter, and disintegration time ranges from 2.14 ± 0.045 to 2.91 ± 0.293 minutes, while the average weight is 502.1 mg. The viability of encapsulated cells stored at refrigerated condition (4oC) was higher than that of cells stored at room temperature (25 oC). The encapsulated probiotic cells exhibited better viability after exposure to SGI solution at different pH levels than free cells. The Postbiotic Metabolites (PM) of L. plantarum produced a cytotoxic effect that shows significant activity similar to 5FU, a standard antineoplastic agent. The inhibition concentration of 50% growth (IC50) of postbiotic metabolite was consistent with the IC50 of the positive control (Cisplatin). Lactobacillus plantarum postbiotic exhibited a cytotoxic effect on the MCF-7 breast cancer cell line and could be used as combined adjuvant therapy in breast cancer management. The microencapsulation technique protects the probiotics and maintains their viability.

Keywords: Cytotoxicity effect, encapsulation, postbiotic, probiotic.

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