Search results for: cancer classification

Authors: Ella Tyuryumina, Alexey Neznanov

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This study is an attempt to obtain reliable data on the natural history of breast cancer growth. We analyze the opportunities for using classical mathematical models (exponential and logistic tumor growth models, Gompertz and von Bertalanffy tumor growth models) to try to describe growth of the primary tumor and the secondary distant metastases of human breast cancer. The research aim is to improve predicting accuracy of breast cancer progression using an original mathematical model referred to CoMPaS and corresponding software. We are interested in: 1) modelling the whole natural history of the primary tumor and the secondary distant metastases; 2) developing adequate and precise CoMPaS which reflects relations between the primary tumor and the secondary distant metastases; 3) analyzing the CoMPaS scope of application; 4) implementing the model as a software tool. The foundation of the CoMPaS is the exponential tumor growth model, which is described by determinate nonlinear and linear equations. The CoMPaS corresponds to TNM classification. It allows to calculate different growth periods of the primary tumor and the secondary distant metastases: 1) ‘non-visible period’ for the primary tumor; 2) ‘non-visible period’ for the secondary distant metastases; 3) ‘visible period’ for the secondary distant metastases. The CoMPaS is validated on clinical data of 10-years and 15-years survival depending on the tumor stage and diameter of the primary tumor. The new predictive tool: 1) is a solid foundation to develop future studies of breast cancer growth models; 2) does not require any expensive diagnostic tests; 3) is the first predictor which makes forecast using only current patient data, the others are based on the additional statistical data. The CoMPaS model and predictive software: a) fit to clinical trials data; b) detect different growth periods of the primary tumor and the secondary distant metastases; c) make forecast of the period of the secondary distant metastases appearance; d) have higher average prediction accuracy than the other tools; e) can improve forecasts on survival of breast cancer and facilitate optimization of diagnostic tests. The following are calculated by CoMPaS: the number of doublings for ‘non-visible’ and ‘visible’ growth period of the secondary distant metastases; tumor volume doubling time (days) for ‘non-visible’ and ‘visible’ growth period of the secondary distant metastases. The CoMPaS enables, for the first time, to predict ‘whole natural history’ of the primary tumor and the secondary distant metastases growth on each stage (pT1, pT2, pT3, pT4) relying only on the primary tumor sizes. Summarizing: a) CoMPaS describes correctly the primary tumor growth of IA, IIA, IIB, IIIB (T1-4N0M0) stages without metastases in lymph nodes (N0); b) facilitates the understanding of the appearance period and inception of the secondary distant metastases.

Keywords: breast cancer, exponential growth model, mathematical model, metastases in lymph nodes, primary tumor, survival

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Authors: Neda Menbari, Ramin Mehdiabadi

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Background: breast cancer remains a critical global health issue, constituting a leading cause of cancer-related mortality in women. MicroRNAs (miRs) are natural RNA molecules that play an important role in cellular processes and regulate post-transcriptional gene expression. MiR-216b-5p is a miR that acts as a tumor suppressor. The expression levels of FoxM1 and miR-216b-5p in malignant and control cells have been evaluated by quantitative polymerase chain reaction (qPCR) technique and flow cytometry. Results: the results of this study revealed a significant downregulation of miR-216b-5p in cancerous cells compared to the control MCF-10A cells (P=0.0004). Interestingly, the expression of miR-216b-5p exhibited an inverse relationship with key clinical indicators such as tumor size, grade, and lymph node invasion. Conclusion: The study's findings showed the prognostic value of miR-216b-5p levels in breast cancer, and its reduced expression correlates with unfavorable tumor characteristics. This research recommends performing more studies on the role of FoxM1 and miR-216b-5p in breast cancer pathology which potentially paving the way for targeted therapeutic interventions.

Keywords: breast cancer, gene expression, FOXM1, microRNA

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Authors: Omer Ibrahim Abdallh Omer

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Cancer patients often face complex medication regimens, leading to challenges in treatment adherence and clinical outcomes. Pharmacist-led medication therapy management (MTM) has emerged as a potential solution to optimize medication use and improve patient outcomes in oncology settings. In this prospective intervention study, we aimed to evaluate the impact of pharmacist-led MTM on treatment adherence and clinical outcomes among cancer patients. Participants were randomized to receive either pharmacist-led MTM or standard care, with assessments conducted at baseline and follow-up visits. Pharmacist interventions included medication reconciliation, adherence counseling, and personalized care plans. Our findings reveal that pharmacist-led MTM significantly improved medication adherence rates and clinical outcomes compared to standard care. Patients receiving pharmacist interventions reported higher satisfaction levels and perceived value in pharmacist involvement in their cancer care. These results underscore the critical role of pharmacists in optimizing medication therapy and enhancing patient-centered care in oncology settings. Integration of pharmacist-led MTM into routine cancer care pathways holds promise for improving treatment outcomes and quality of life for cancer patients.

Keywords: cancer, medications adherence, medication therapy management, pharmacist

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Authors: Faisal Alshuwaier, Ali Areshey

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Text mining techniques are generally applied for classifying the text, finding fuzzy relations and structures in data sets. This research provides plenty text mining capabilities. One common application is text classification and event extraction, which encompass deducing specific knowledge concerning incidents referred to in texts. The main contribution of this paper is the clarification of a concept graph generation mechanism, which is based on a text classification and optimal fuzzy relationship extraction. Furthermore, the work presented in this paper explains the application of fuzzy relationship extraction and branch and bound method to simplify the texts.

Keywords: extraction, max-prod, fuzzy relations, text mining, memberships, classification, memberships, classification

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Authors: Shaik Asha Begum, S. Joshna Rani, Shaik Abdul Rahaman

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INTRODUCTION: Breast cancer is a disease that creates in breast cells. "KAP" study estimates the Knowledge, Attitude, and Practices of a local area. More than 1.5 million ladies (25% of all ladies with malignancy) are determined to have bosom disease consistently all through the world. Understanding the degrees of Knowledge, Attitude and Practice will empower a more effective cycle of mindfulness creation as it will permit the program to be custom-made all the more properly to the necessities of the local area. OBJECTIVES: The objective of this study is to assess the knowledge on signs and symptoms, risk factors, provide awareness on the practicing of the early detection techniques of breast cancer and provide knowledge on the overall breast cancer including preventive techniques. METHODOLOGY: This is an expressive cross-sectional investigation. This investigation of KAP was done in the Nirmala Educational Institutions from January to April 2021. A total of 300 participants are included from women students in pharmacy graduates & lecturers, and also from graduates other than the pharmacy. The examiners are taken from the BCAM (Breast Cancer Awareness Measure), tool compartment (Version 2). RESULT: According to the findings of the study, the majority of the participants were not well informed about breast cancer. A lump in the breast was the most commonly mentioned sign of breast cancer, followed by pain in the breast or nipple. The percentage of knowledge related to the breast cancer risk factors was also very less. The correct answers for breast cancer risk factors were radiation exposure (58.20 percent), a positive family history (47.6 percent), obesity (46.9 percent), a lack of physical activity (43.6 percent), and smoking (43.2 percent). Breast cancer screening, on the other hand, was uncommon (only 30 and 11.3 percent practiced clinical breast examination and mammography respectively). CONCLUSION: In this study, the knowledge on the signs and symptoms, risk factors of breast cancer - pharmacy graduates have more knowledge than the non-pharmacy graduates but in the preventive techniques and early detective tools of breast cancer -had poor knowledge in the pharmacy and non-pharmacy graduate. After the awareness program, pharmacy and non-pharmacy graduates got supportive knowledge on the preventive techniques and also practiced the early detective techniques of breast cancer.

Keywords: breast cancer, mammography, KAP study, early detection

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Authors: Rizwan Arshad, Alessio Panza, Nimra Inayat, Syeda Mariam Batool Kazmi, Shawana Azmat

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The rapidly expanding use of immunotherapy for a wide range of cancers from late to early stages has, predictably, been accompanied by evidence of inequities in access to these highly effective but costly treatments. In this survey-based case study, we aimed to develop a One-window services model (OWSM) based on Anderson’s behavioral model to enhance competence in accessing cancer medications, particularly immunotherapies, through the analysis of 20 patient surveys conducted in the Armed forces bone marrow transplant center of the district, Rawalpindi from November to December 2022. The purposive sampling technique was used. Cronbach’s alpha coefficient was found to be 0.71. It was analyzed using SPSS version 26 with descriptive analysis, and results showed that the majority of the cancer patients were non-competent to access their prescribed cancer immunotherapy because of individual-level, socioeconomic, and organizational barriers.

Keywords: cancer immunotherapy, one-window services model, accessibility, competence

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Authors: Rajkumar Kolangarakandy

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Feature extraction and selection is the primary part of any mammogram classification algorithms. The choice of feature, attribute or measurements have an important influence in any classification system. Discrete Wavelet Transformation (DWT) coefficients are one of the prominent features for representing images in frequency domain. The features obtained after the decomposition of the mammogram images using wavelet transformations have higher dimension. Even though the features are higher in dimension, they were highly correlated and redundant in nature. The dimensionality reduction techniques play an important role in selecting the optimum number of features from the higher dimension data, which are highly correlated. PCA is a mathematical tool that reduces the dimensionality of the data while retaining most of the variation in the dataset. In this paper, a multilevel classification of mammogram images using reduced discrete wavelet transformation coefficients and lazy classifiers is proposed. The classification is accomplished in two different levels. In the first level, mammogram ROIs extracted from the dataset is classified as normal and abnormal types. In the second level, all the abnormal mammogram ROIs is classified into benign and malignant too. A further classification is also accomplished based on the variation in structure and intensity distribution of the images in the dataset. The Lazy classifiers called Kstar, IBL and LWL are used for classification. The classification results obtained with the reduced feature set is highly promising and the result is also compared with the performance obtained without dimension reduction.

Keywords: PCA, wavelet transformation, lazy classifiers, Kstar, IBL, LWL

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Authors: Sunday Nathaniel Obasi, Chiamak Chinasa Obasi

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Soil characterization and classification provide the basic information necessary to create a functional evaluation and soil classification schemes. Fertility capability classification (FCC) on the other hand is a technical system that groups the soils according to kinds of problems they present for management of soil physical and chemical properties. This research was carried out in Ebonyi state, southeastern Nigeria, which is an agrarian state and a leading rice producing part of southeastern Nigeria. In order to maximize the soil and enhance the productivity of rice in Ebonyi soils, soil classification, and fertility classification information need to be supplied. The state was grouped into three locations according to their agricultural zones namely; Ebonyi north, Ebonyi central and Ebonyi south representing Abakaliki, Ikwo and Ivo locations respectively. Major rice growing areas of the soils were located and two profile pits were sunk in each of the studied zones from which soils were characterized, classified and fertility capability classification (FCC) developed. Soil classification was done using United State Department of Agriculture (USDA) Soil Taxonomy and correlated with World Reference Base for soil resources. Results obtained classified Abakaliki 1 and Abakaliki 2 as Typic Fluvaquents (Ochric Fluvisols). Ikwo 1 was classified as Vertic Eutrudepts (Eutric Vertisols) while Ikwo 2 was classified as Typic Eutrudepts (Eutric Cambisols). Ivo 1 and Ivo 2 were both classified as Aquic Eutrudepts (Gleyic Leptosols). Fertility capability classification (FCC) revealed that all studied soils had mostly loamy topsoils and subsoils except Ikwo 1 with clayey topsoil. Limitations encountered in the studied soils include; dryness (d), low ECEC (e), low nutrient capital reserve (k) and water logging/ anaerobic condition (gley). Thus, FCC classifications were Ldek for Abakaliki 1 and 2, Ckv for Ikwo 1, LCk for Ikwo 2 while Ivo 1 and 2 were Legk and Lgk respectively.

Keywords: soil classification, soil fertility, limitations, modifiers, Southeastern Nigeria

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Authors: Azar Heidarizadi, Mahdieh Salimi, Hossein Mozdarani

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Introduction: As a complex disease, breast cancer results from several genetic and epigenetic changes. Lactoferrin, a member of the transferrin family, is reported to have a number of biological functions, including DNA synthesis, immune responses, iron transport, etc., any of which could play a role in tumor progression. The aim of this study was to investigate the bioinformatics data and experimental assay to find the pattern of promoter methylation and gene expression of LTF in breast cancer in order to study its potential role in cancer management. Material and Methods: In order to evaluate the methylation status of the LTF promoter, we studied the MS-PCR and Real-Time PCR on samples from patients with breast cancer and normal cases. 67 patient samples were conducted for this study, including tumoral, plasma, and normal tissue adjacent samples, as well as 30 plasma from normal cases and 10 tissue breast reduction cases. Subsequently, bioinformatics analyses such as cBioPortal databases, string, and genomatix were conducted to disclose the prognostic value of LTF in breast cancer progression. Results: The analysis of LTF expression showed an inverse relationship between the expression level of LTF and the stages of tissues of breast cancer patients (p<0.01). In fact, stages 1 and 2 had a high expression in LTF, while, in stages 3 and 4, a significant reduction was observable (p < 0.0001). LTF expression frequently alters with a decrease in the expression in ER⁺, PR⁺, and HER2⁺ patients (P < 0.01) and an increase in the expression in the TNBC, LN¯, ER¯, and PR- patients (P < 0.001). Also, LTF expression is significantly associated with metastasis and lymph node involvement factors (P < 0.0001). The sensitivity and specificity of LTF were detected, respectively. A negative correlation was detected between the results of level expression and methylation of the LTF promoter. Conclusions: The altered expression of LTF observed in breast cancer patients could be considered as a promotion in cell proliferation and metastasis even in the early stages of cancer.

Keywords: LTF, expression, methylation, breast cancer

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Authors: Thanh Noi Phan, Martin Kappas, Jan Degener

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The currently launched Sentinel 2 (S2) satellite (June, 2015) bring a great potential and opportunities for land use/cover map applications, due to its fine spatial resolution multispectral as well as high temporal resolutions. So far, there are handful studies using S2 real data for land cover classification. Especially in northern Vietnam, to our best knowledge, there exist no studies using S2 data for land cover map application. The aim of this study is to provide the preliminary result of land cover classification using Sentinel -2 data with a rising state – of – art classifier, Random Forest. A case study with heterogeneous land use/cover in the eastern of Hanoi Capital – Vietnam was chosen for this study. All 10 spectral bands of 10 and 20 m pixel size of S2 images were used, the 10 m bands were resampled to 20 m. Among several classified algorithms, supervised Random Forest classifier (RF) was applied because it was reported as one of the most accuracy methods of satellite image classification. The results showed that the red-edge and shortwave infrared (SWIR) bands play an important role in land cover classified results. A very high overall accuracy above 90% of classification results was achieved.

Keywords: classify algorithm, classification, land cover, random forest, sentinel 2, Vietnam

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Authors: Abigail Tan, Heng Liang Tan, Vanessa Ding, James Hui, Eng Hin Lee, Andre Choo

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Introduction: Comparative oncology investigates the similarities in spontaneous carcinogenesis between humans and animals, in order to identify treatments that can benefit these patients. Companion animals (CA), like canines and felines, are of special interest when it comes to studying human cancers due to their exposure to the same environmental factors and develop tumours with similar features. The purpose of this study is to explore the cross-reactivity of monoclonal antibodies (mAbs) across cancers in humans and CA. Material and Methods: A panel of CA mAbs generated in the lab was screened on multiple human cancer cell lines through flow cytometry to identify for positive binders. Shortlisted candidates were then characterised by biochemical and functional assays e.g., antibody-drug conjugate (ADC) and western blot assays, including glycan studies. Results: Candidate mAb KP52 was generated from whole-cell immunisation using feline mammary carcinoma. KP52 showed strong positive binding to human cancer cells, such as breast cancer and ovarian cancer. Furthermore, KP52 demonstrated strong killing ( > 50%) as an ADC with Saporin as the payload. Western blot results revealed the molecular weight of the antigen targets to be approximately 45kD and 50kD under reduced conditions. Glycan studies suggest that the epitope is glycan in nature, specifically an O-linked glycan. Conclusion: Candidate mAb KP52 has a therapeutic potential as an ADC against feline mammary cancer, human ovarian cancer, human mammary cancer, human pancreatic cancer, and human gastric cancer.

Keywords: ADC, comparative oncology, mAb, therapeutic

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Authors: Dina Fatmawati, Sofia Mubarika, Mae Sri Wahyuningsih

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Chemotherapy for breast cancer is largely ineffective, but innovative combinations of chemotherapeutic agents and natural compounds represent a promising strategy. In our previous study, the combination of Doxorubicin (Dox) and ethanolic extract of Dioscorea esculenta tuber ((EED) was found to have a synergistic effect on T47D human breast cancer cell line. In this study, we investigated the apoptotic effect of the combination on T47D human breast cancer cells and normal fibroblasts cell line and its effects on the expression of Caspase-3 and cleaved poly (ADP-Ribose) Polymerase-1 (cPARP-1) protein. T47D cell lines and fibroblasts cells were treated with the combination of Dox and EED. Apoptotic effect of the combination was determined using flow cytrometry assay. Protein expressions were determined by immunocytochemistry staining. The percentage of apoptotic cells were significantly higher in T47D cell lines (75%) than that of in fibroblast cells (23%). The expression of Caspase 3 (84.53%) and cPARP-1 (83.36%) were significantly higher in the cancer cell lines than those of normal cells. These results indicate that the combination of doxorubicin and Dioscorea esculenta is a promising candidate for the treatment of breast cancer cells.

Keywords: Dioscorea esculenta, Doxorubicin, apoptosis, immunocytochemistry, cancer cells

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Authors: Bryan Crompton, Daniel Giger, Tanay Mehta, Apurva Mody

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The task of classifying radio frequency (RF) signals has seen recent success in employing deep neural network models. In this work, we present a combined signal processing and machine learning approach to signal classification for cochannel anomalous signals. The power spectral density and cyclostationary signal processing features of a captured signal are computed and fed into a neural net to produce a classification decision. Our combined signal preprocessing and machine learning approach allows for simpler neural networks with fast training times and small computational resource requirements for inference with longer preprocessing time.

Keywords: signal processing, machine learning, cyclostationary signal processing, signal classification

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Authors: J. K. Alhassan, S. A. Lawal

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This work is on decision tree-based classification for the disbursement of scholarship. Tree-based data mining classification technique is used in other to determine the generic rule to be used to disburse the scholarship. The system based on the defined rules from the tree is able to determine the class (status) to which an applicant shall belong whether Granted or Not Granted. The applicants that fall to the class of granted denote a successful acquirement of scholarship while those in not granted class are unsuccessful in the scheme. An algorithm that can be used to classify the applicants based on the rules from tree-based classification was also developed. The tree-based classification is adopted because of its efficiency, effectiveness, and easy to comprehend features. The system was tested with the data of National Information Technology Development Agency (NITDA) Abuja, a Parastatal of Federal Ministry of Communication Technology that is mandated to develop and regulate information technology in Nigeria. The system was found working according to the specification. It is therefore recommended for all scholarship disbursement organizations.

Keywords: classification, data mining, decision tree, scholarship

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Authors: Srisopa Ruangnoo, Arunporn Itharat

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The ethanolic and aqueous extracts of Parkia javanica Merr. germinating seeds and Parkia speciosa Hassk. seeds were evaluated for cytotoxic activity against three different types of human cancer cell lines including colon cancer (LS174T), breast cancer (MCF-7) and prostate cancer (PC3) using sulforhodamine B (SRB) assay. The fresh plant parts were divided into 2 parts. The first part was extracted by maceration with 95% ethanol for 3 days and then filtered, and the filtrates were evaporated by rotary evaporator. The other part was squeezed and filtered. Then the filtrates were dried by freeze dryer. The screening found that the aqueous extract of P. javanica Merr. germinating seeds exhibited more than 70% inhibition (at concentration 50 µg/ml) against all types of human cancer cells. The aqueous extract of P. javanica Merr. germinating seeds showed the highest cytotoxic activity against MCF-7 with the IC50 value as 5.63 µg/ml. The aqueous extract of P. javanica Merr. germinating seeds also showed high cytotoxic activity against PC3 and LS174T with the IC50 values as 10.79 and 11.40 µg/ml, respectively. In conclusion, P. javanica Merr. germinating seed is a natural source of anticancer activity and further research to isolate active compounds from this plant should be undertaken.

Keywords: cytotoxic activity, Parkia javanica Merr., Parkia speciosa Hassk., human cancer cell lines

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Authors: Reza Mohammadi, Mahmod R. Sahebi, Mehrnoosh Omati, Milad Vahidi

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Classification of high resolution polarimetric Synthetic Aperture Radar (PolSAR) images plays an important role in land cover and land use management. Recently, classification algorithms based on Bag of Visual Words (BOVW) model have attracted significant interest among scholars and researchers in and out of the field of remote sensing. In this paper, BOVW model with pixel based low-level features has been implemented to classify a subset of San Francisco bay PolSAR image, acquired by RADARSAR 2 in C-band. We have used segment-based decision-making strategy and compared the result with the result of traditional Support Vector Machine (SVM) classifier. 90.95% overall accuracy of the classification with the proposed algorithm has shown that the proposed algorithm is comparable with the state-of-the-art methods. In addition to increase in the classification accuracy, the proposed method has decreased undesirable speckle effect of SAR images.

Keywords: Bag of Visual Words (BOVW), classification, feature extraction, land cover management, Polarimetric Synthetic Aperture Radar (PolSAR)

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Authors: Hui-Hsin Huang, Sheng-Tung Huang, Chi-Ming Lee, Chiao-Han Yen, Chun-Mao Lin

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At initiation stage, there are no symptoms at initiation stage; however, at late stage, patients suffer symptoms as soon as ovarian cancer metastasis. Moreover, ovarian cancer cells are resistant to some anti-ovarian cancer drugs in clinical. Thus, it is very important to find an effective treatment for resistant ovarian cancer. Anthraquinone derivatives are able to induce DNA damage and lead to cell apoptosis, so several derivatives have been used for clinical application. Therefore, to explore more effective anti-ovarian cancer drugs, this study investigates the mechanism of three new anthraquinone compounds bearing different functional groups to camptothecin-resistance ovarian cell line A2780R2000. Cell viability was determined by MTT assay after treating A2780R2000 with the three new anthraquinone compounds. The results indicated that IC50 values are 33.44μM (Compound I), 25.77μM (Compound II) and 24.59μM (Compound III). Next, through cell cycle analysis, the results demonstrated that three new anthraquinone compounds not only induced A2780R2000 cell cycle arrest at early stage but also apoptosis at late stage. Besides, through apoptosis assay, the results indicated new anthraquinone compound induced apoptosis at late stage. Furthermore, the results of western blot show that the three new anthraquinone compounds lead to A2780R2000 apoptosis through intrinsic pathway. Theses results suggested that three new anthraquinone compounds may be potential new drugs for clinical cancer treatment in the future.

Keywords: anthraquinone, camptothecin, resistance, ovarian cancer

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Authors: Wanhyun Cho, Soonja Kang, Sangkyoon Kim, Soonyoung Park

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In this paper, we propose a novel inference algorithm for the multi-class Gaussian process classification model that can be used in the field of human behavior recognition. This algorithm can drive simultaneously both a posterior distribution of a latent function and estimators of hyper-parameters in a Gaussian process classification model with multi-class. Our algorithm is based on the Laplace approximation (LA) technique and variational EM framework. This is performed in two steps: called expectation and maximization steps. First, in the expectation step, using the Bayesian formula and LA technique, we derive approximately the posterior distribution of the latent function indicating the possibility that each observation belongs to a certain class in the Gaussian process classification model. Second, in the maximization step, using a derived posterior distribution of latent function, we compute the maximum likelihood estimator for hyper-parameters of a covariance matrix necessary to define prior distribution for latent function. These two steps iteratively repeat until a convergence condition satisfies. Moreover, we apply the proposed algorithm with human action classification problem using a public database, namely, the KTH human action data set. Experimental results reveal that the proposed algorithm shows good performance on this data set.

Keywords: bayesian rule, gaussian process classification model with multiclass, gaussian process prior, human action classification, laplace approximation, variational EM algorithm

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Authors: Lubomir Vecera, Tomas Gabrhelik, Benjamin Tolmaci, Josef Srovnal, Emil Berta, Petr Prasil, Petr Stourac

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Cancer is the second leading cause of death worldwide and colon cancer is the second most common type of cancer. Currently, there are only a few studies evaluating the effect of postoperative analgesia on the prognosis of patients undergoing radical colon cancer surgery. Postoperative analgesia in patients undergoing colon cancer surgery is usually managed in two ways, either with strong opioids (morphine, piritramide) or epidural analgesia. In our prospective study, we evaluated the effect of postoperative analgesia on the presence of circulating tumor cells or minimal residual disease after colon cancer surgery. A total of 60 patients who underwent radical colon cancer surgery were enrolled in this prospective, randomized, two-center study. Patients were randomized into three groups, namely piritramide, morphine and postoperative epidural analgesia. We evaluated the presence of carcinoembryonic antigen (CEA) and cytokeratin 20 (CK-20) mRNA positive circulating tumor cells in peripheral blood before surgery, immediately after surgery, on postoperative day two and one month after surgery. The presence of circulating tumor cells was assessed by quantitative real-time reverse transcriptase-polymerase chain reaction (qRT-PCR). In the priritramide postoperative analgesia group, the presence of CEA mRNA positive cells was significantly lower on a postoperative day two compared to the other groups (p=0.04). The value of CK-20 mRNA positive cells was the same in all groups on all days. In all groups, both types of circulating tumor cells returned to normal levels one month after surgery. Demographic and baseline clinical characteristics were similar in all groups. Compared with morphine and epidural analgesia, piritramide significantly reduces the amount of CEA mRNA positive circulating tumor cells after radical colon cancer surgery.

Keywords: cancer progression, colon cancer, minimal residual disease, perioperative analgesia.

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Authors: Wenxing Chen, Guangying Chen, Yin Lu, Shile Huang

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Cryptotanshinone (CPT), a fat-soluble tanshinone from Salvia miltiorrhiza Bunge, has been demonstrated to inhibit mTOR pathway, resulting in inhibition of cancer cell proliferation. However, the molecular mechanism how CPT acts on mTOR is unknown. Here, cancer cells expressing rapamycin-resistant mutant mTOR are also sensitive to CPT, while phosphorylation of AMPK and TSC2 was activated, suggesting that CPT inhibition of mTOR maybe due to activating upstream of mTOR, AMPK, but not directly binding to and inhibiting mTOR. Further results indicated that Compound C, inhibitor of AMPK, could partially reversed CPT inhibition effect on cancer cells, and dominant-negative AMPK in cancer cells conferred resistance to CPT inhibition of 4EBP1 and phosphorylation of S6K1, as well as sh-AMPK. Furthermore, compared with MEF cells with AMPK positive, MEF cells with AMPK knock out are less sensitive to CPT by the findings that 4E-BP1 and phosphorylation of S6K1 express comparatively much. Furthermore, downexpression of TSC2 slightly recovered expression of 4EBP1 and phosphorylation of S6K1, while co-immunoprecipitation of TSC2 did not affect expression of TSC1 by CPT. Collectively, the above-mentioned results suggest that CPT inhibited mTOR pathway mostly was due to activation of AMPK-TSC2 pathway rather than specific inhibition of mTOR and then induction of subsequent lethal cellular effect.

Keywords: cryptotanshinone, AMPK, TSC2, mTOR, cancer cells

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Authors: Emad A. Shalaby

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Cancer is a disease that causes abnormal cell proliferation and invades nearby tissues. Lung cancer is the second most frequent cancer worldwide. Natural anti-cancer drugs have been developed with low side effects and toxicity. Citrus peels and extracts have been demonstrated to have significant pharmacological and physiological effects as a result of the high concentration of phenolic compounds found in citrus fruits, particularly peels. Tangerine peels can serve as an effective source of bioactive substances such as phenolics, flavonoids, and catechins, which have antioxidant, antibacterial, anticancer, and anti-inflammatory properties. Consequently, this work aims to determine the anticancer activity of ethanol extract of Tangerine peels against the A549 cell line and identify the phenolic compound profile (19 compounds) by using HPLC. Anticancer and antioxidant potentials of the extract were evaluated by MTT assay and TLC- TLC-bioautography sprayed with DPPH reagent, respectively. The obtained results revealed that tangerine peel extract showed significant activity against the A549 cell line with IC50 of 97.66 μg/mL. HPLC analysis proved that the highest concentration is naringenin 464.05 mg/g. More studies indicate that naringenin has significant anticancer potential on A549 cancer cells. The results showed that naringenin binds t0 EGFR protein in A549 with high binding affinity and thus may reduce lung cancer cell migration and enhance the apoptosis of cancer cells. From the obtained results it could be concluded that tangerine peel extract is an effective anti-cancer agent that may potentially serve as a natural therapeutic option for lung cancer treatment.

Keywords: tangerine peel, A549 cell line, anticancer, naringenin, HPLC analysis, naringenin, TLC bioautography

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Authors: Cecília M. Antão, Paulo Jorge Nogueira

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Breast cancer (BC) is the first cause of cancer mortality among Portuguese women. This retrospective observational study aimed at identifying comorbidities associated with BC female patients admitted to Portuguese public hospitals (2010-2018), investigating the effect of comorbidities on BC mortality rate, and building a predictive model using logistic regression. Results showed that the BC mortality in Portugal decreased in this period and reached 4.37% in 2018. Adjusted odds ratio indicated that secondary malignant neoplasms of liver, of bone and bone marrow, congestive heart failure, and diabetes were associated with an increased chance of dying from breast cancer. Although the Lisbon district (the most populated area) accounted for the largest percentage of BC patients, the logistic regression model showed that, besides patient’s age, being resident in Bragança, Castelo Branco, or Porto districts was directly associated with an increase of the mortality rate.

Keywords: breast cancer, comorbidities, logistic regression, adjusted odds ratio

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Authors: Najoua El Hajjaji El Idrissi, Necip Gokhan Kasapoglu

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Polarimetric Synthetic Aperture Radar-based imaging is a powerful technique used for earth observation and classification of surfaces. Forest evolution has been one of the vital areas of attention for the remote sensing experts. The information about forest areas can be achieved by remote sensing, whether by using active radars or optical instruments. However, due to several weather constraints, such as cloud cover, limited information can be recovered using optical data and for that reason, Polarimetric Synthetic Aperture Radar (PolSAR) is used as a powerful tool for forestry inventory. In this [14paper, we applied support vector machine (SVM) and Mahalanobis distance to the fully polarimetric AIRSAR P, L, C-bands data from the Nezer forest areas, the classification is based in the separation of different tree ages. The classification results were evaluated and the results show that the SVM performs better than the Mahalanobis distance and SVM achieves approximately 75% accuracy. This result proves that SVM classification can be used as a useful method to evaluate fully polarimetric SAR data with sufficient value of accuracy.

Keywords: classification, synthetic aperture radar, SAR polarimetry, support vector machine, mahalanobis distance

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Authors: Amer A. Hasan, Mehri Igci, Ersin Borazan, Rozhgar A. Khailany, Emine Bayraktar, Ahmet Arslan

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Gastric cancer (GC) has high morbidity and fatality rate in various countries and is still one of the most frequent and deadly diseases. Novel mitogenic and motogenic Gastrokine1 (GKN1) and Gastrokine 2 (GKN2) genes that are highly expressed in the normal stomach epithelium and plays an important role in maintaining the integrity and homeostasis of stomach mucosal epithelial cells. Significant loss of copy number and mRNA transcript of GKN1 and GKN2 gene expression were frequently observed in all types of gastric cancer. In this study, 47 paired samples that were grouped according to the types of gastric cancer and the clinical characteristics of the patients, including gender and average of age were investigated with gene expression analysis and mutation screening by monetering RT-PCR, SSCP and nucleotide sequencing techniques. Both GKN1 and GKN2 genes were observed significantly reduced found by (Wilcoxon signed rank test; p<0.05). As a result of gene screening, no mutation (no different genotype) was detected. It is considered that gene mutations are not the cause of inactivation of gastrokines. In conclusion, the mRNA expression level of GKN1 and GKN2 genes statistically was decreased regardless the gender, age or cancer type of patients. Reduced of gastrokine genes seems to occur at the initial steps of cancer development. In order to understand the investigation between gastric cancer and diagnostic biomarker; further analysis is necessary.

Keywords: gastric cancer, diagnostic biomarker, nucleotide sequencing, semi-quantitative RT-PCR

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Authors: Soumya Sajjan

Abstract:

Liver cancer is the most common cancer disease worldwide in men and women, and is one of the few cancers still on the rise. Liver disease is the 4th leading cause of death. According to new NHS (National Health Service) figures, deaths from liver diseases have reached record levels, rising by 25% in less than a decade; heavy drinking, obesity, and hepatitis are believed to be behind the rise. In this study, we focus on Development of Diagnostic Classifier for Ultrasound liver lesion. Ultrasound (US) Sonography is an easy-to-use and widely popular imaging modality because of its ability to visualize many human soft tissues/organs without any harmful effect. This paper will provide an overview of underlying concepts, along with algorithms for processing of liver ultrasound images Naturaly, Ultrasound liver lesion images are having more spackle noise. Developing classifier for ultrasound liver lesion image is a challenging task. We approach fully automatic machine learning system for developing this classifier. First, we segment the liver image by calculating the textural features from co-occurrence matrix and run length method. For classification, Support Vector Machine is used based on the risk bounds of statistical learning theory. The textural features for different features methods are given as input to the SVM individually. Performance analysis train and test datasets carried out separately using SVM Model. Whenever an ultrasonic liver lesion image is given to the SVM classifier system, the features are calculated, classified, as normal and diseased liver lesion. We hope the result will be helpful to the physician to identify the liver cancer in non-invasive method.

Keywords: segmentation, Support Vector Machine, ultrasound liver lesion, co-occurance Matrix

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Authors: Shaik Ayesha Fathima, Shaik Noor Jahan, Duvvada Rajeswara Rao

Abstract:

Earth's environment and its evolution can be seen through satellite images in near real-time. Through satellite imagery, remote sensing data provide crucial information that can be used for a variety of applications, including image fusion, change detection, land cover classification, agriculture, mining, disaster mitigation, and monitoring climate change. The objective of this project is to propose a method for classifying satellite images according to multiple predefined land cover classes. The proposed approach involves collecting data in image format. The data is then pre-processed using data pre-processing techniques. The processed data is fed into the proposed algorithm and the obtained result is analyzed. Some of the algorithms used in satellite imagery classification are U-Net, Random Forest, Deep Labv3, CNN, ANN, Resnet etc. In this project, we are using the DeepLabv3 (Atrous convolution) algorithm for land cover classification. The dataset used is the deep globe land cover classification dataset. DeepLabv3 is a semantic segmentation system that uses atrous convolution to capture multi-scale context by adopting multiple atrous rates in cascade or in parallel to determine the scale of segments.

Keywords: area calculation, atrous convolution, deep globe land cover classification, deepLabv3, land cover classification, resnet 50

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Authors: Gökçe Erdemir, İlhan Yaylım, Serap Erdem-Kuruca, Musa Mutlu Can

Abstract:

It has been determined that the main reason for the death of cancer disease is caused by metastases rather than the primary tumor. The cells that leave the primary tumor and enter the circulation and cause metastasis in the secondary organs are called "circulating tumor cells" (CTCs). The presence and number of circulating tumor cells has been associated with poor prognosis in many major types of cancer, including breast, prostate, and colorectal cancer. It is thought that knowledge of circulating tumor cells, which are seen as the main cause of cancer-related deaths due to metastasis, plays a key role in the diagnosis and treatment of cancer. The fact that tissue biopsies used in cancer diagnosis and follow-up are an invasive method and are insufficient in understanding the risk of metastasis and the progression of the disease have led to new searches. Liquid biopsy tests performed with a small amount of blood sample taken from the patient for the detection of CTCs are easy and reliable, as well as allowing more than one sample to be taken over time to follow the prognosis. However, since these cells are found in very small amounts in the blood, it is very difficult to capture them and specially designed analytical techniques and devices are required. Methods based on the biological and physical properties of the cells are used to capture these cells in the blood. Early diagnosis is very important in following the prognosis of tumors of epithelial origin such as breast, lung, colon and prostate. Molecules such as EpCAM, vimentin, and cytokeratins are expressed on the surface of cells that pass into the circulation from very few primary tumors and reach secondary organs from the circulation, and are used in the diagnosis of cancer in the early stage. For example, increased EpCAM expression in breast and prostate cancer has been associated with prognosis. These molecules can be determined in some blood or body fluids to be taken from patients. However, more sensitive methods are required to be able to determine when they are at a low level according to the course of the disease. The aim is to detect these molecules found in very few cancer cells with the help of sensitive, fast-sensing biosensors, first in breast cancer cells reproduced in vitro and then in blood samples taken from breast cancer patients. In this way, cancer cells can be diagnosed early and easily and effectively treated.

Keywords: electrochemical biosensors, breast cancer, circulating tumor cells, EpCAM, Vimentin, Cytokeratins

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Authors: Dipali Dhawan

Abstract:

Cancerous epithelial cells are confined to a primary site by the continued expression of adhesion molecules and the intact basal lamina. However, as the cancer progresses some cells are believed to undergo an epithelial-mesenchymal transition (EMT) event, leading to increased motility, invasion and, ultimately, metastasis of the cells from the primary tumour to secondary sites within the body. These disseminated cancer cells need the ability to self-renew, as stem cells do, in order to establish and maintain a heterogeneous metastatic tumour mass. Identification of the specific subpopulation of cancer stem cells amenable to the process of metastasis is highly desirable. In this study, we have isolated and characterized cancer stem cells from luminal and basal breast cancer cell lines (MDA-MB-231, MDA-MB-453, MDA-MB-468, MCF7 and T47D) on the basis of cell surface markers CD44 and CD24; as well as Side Populations (SP) using Hoechst 33342 dye efflux. The isolated populations were analysed for epithelial and mesenchymal markers like E-cadherin, N-cadherin, Sfrp1 and Vimentin by Western blotting and Immunocytochemistry. MDA-MB-231 cell lines contain a major population of CD44+CD24- cells whereas MCF7, T47D and MDA-MB-231 cell lines show a side population. We observed higher expression of N-cadherin in MCF-7 SP cells as compared to MCF-7NSP (Non-side population) cells suggesting that the SP cells are mesenchymal like cells and hence express increased N-cadherin with stem cell-like properties. There was an expression of Sfrp1 in the MCF7- NSP cells as compared to no expression in MCF7-SP cells, which suggests that the Wnt pathway is expressed in the MCF7-SP cells. The mesenchymal marker Vimentin was expressed only in MDA-MB-231 cells. Hence, understanding the breast cancer heterogeneity would enable a better understanding of the disease progression and therapeutic targeting.

Keywords: cancer stem cells, epithelial to mesenchymal transition, biomarkers, breast cancer

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Authors: Michelle Sánchez de León Brajkovich, Nuria Martí Audi

Abstract:

The envelope is one of the most important elements when one analyzes the operation of the building in terms of sustainability. Taking this into consideration, this research focuses on setting a classification system of the envelopes opaque systems, crossing the knowledge and parameters of construction systems with requirements in terms of sustainability that they may have, to have a better understanding of how these systems work with respect to their sustainable contribution to the building. Therefore, this paper evaluates the importance of the envelope design on the building sustainability. It analyses the parameters that make the construction systems behave differently in terms of sustainability. At the same time it explains the classification process generated from this analysis that results in a classification where all opaque vertical envelope construction systems enter.

Keywords: sustainable, exterior walls, envelope, facades, construction systems, energy efficiency

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Authors: Atif Zafar Khan, Swarnendra Singh, Imrana Naseem

Abstract:

Breast cancer is one of the most frequent malignancies in women worldwide and a leading cause of cancer-related deaths among women. Therefore, there is a need to identify new chemotherapeutic strategies for cancer treatment. Unlike normal cells, cancer cells contain elevated copper levels which play an integral role in angiogenesis. Copper is an important metal ion associated with the chromatin DNA, particularly with guanine. Thus, targeting copper via copper-specific chelators in cancer cells can serve as effective anticancer strategy. Keeping in view these facts, we evaluated the anticancer activity and copper-dependent cytotoxic effect of coumestrol (phytoestrogen in soybean products) in breast cancer MCF-7 cells. Coumestrol inhibited proliferation and induced apoptosis in MCF-7 cells, which was prevented by copper chelator neocuproine and ROS scavengers. Coumestrol treatment induced ROS generation coupled to DNA fragmentation, up-regulation of p53/p21, cell cycle arrest at G1/S phase, mitochondrial membrane depolarization and caspases 9/3 activation. All these effects were suppressed by ROS scavengers and neocuproine. These results suggest that coumestrol targets elevated copper for redox cycling to generate ROS leading to DNA fragmentation. DNA damage leads to p53 up-regulation which directs the cell cycle arrest at G1/S phase and promotes caspase-dependent apoptosis of MCF-7 cells. In conclusion, coumestrol induces pro-oxidant cell death by chelating cellular copper to produce copper-coumestrol complexes that engages in redox cycling in breast cancer cells. Thus, targeting elevated copper levels might be a potential therapeutic strategy for selective cytotoxic action against malignant cells.

Keywords: apoptosis, breast cancer, copper chelation, coumestrol, reactive oxygens species, redox cycling

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