Izmir Kâtip Çelebi University

Organ-on-a-chip systems are miniaturized microfluidic 3D human tissue and organ models designed to recapitulate the important biological and physiological parameters of their in vivo counterparts. They have recently emerged as a viable platform for personalized medicine and drug screening. These in vitro models, featuring biomimetic compositions, architectures, and functions, are expected to replace the conventional planar, static cell cultures and bridge the gap between the currently used preclinical animal models and the human body. Multiple organoid models may be further connected together through the microfluidics in a similar manner in which they are arranged in vivo, providing the capability to analyze multiorgan interactions. Although a wide variety of human organ-on-a-chip models have been created, there are limited efforts on the integration of multisensor systems. However, in situ continual measuring is critical in precise assessment of the microenvironment parameters and the dynamic responses of the organs to pharmaceutical compounds over extended periods of time. In addition, automated and noninvasive capability is strongly desired for long-term monitoring. Here, we report a fully integrated modular physical, biochemical, and optical sensing platform through a fluidics-routing breadboard, which operates organ-on-a-chip units in a continual, dynamic, and automated manner. We believe that this platform technology has paved a potential avenue to promote the performance of current organ-on-a-chip models in drug screening by integrating a multitude of real-time sensors to achieve automated in situ monitoring of biophysical and biochemical parameters.

Extensive research on nature-inspired cellular metamaterials has globally inspired innovations using single material and limited multifunctionality. Additive manufacturing (AM) of intricate geometries using multi-materials provides additional functionality, environmental adaptation, and improved mechanical properties. Recently, several studies have been conducted on multi-material additive manufacturing (MMAM) technologies, including multi-materials, methodologies, design, and optimization. However, in the past six years, very few or no systematic and complete reviews have been conducted in this research domain. This review intends to comprehensively summarize MMAM systems and the working principles of its fundamental processes. Herein, the Multi-material combinations and their design, modeling, and analysis strategies have been reviewed systematically. In particular, the focus is on applications and opportunities for using MMAM for several industries and postprocessing MMAM fabricated parts. Furthermore, this review identified the limitations and challenges of existing software packages, MMAM processes, materials, and joining mechanisms, especially at the multi-material interfaces. Finally, we discuss the possible strategies to overcome the aforementioned technological challenges and state the future directions, which will provide insights to researchers and engineers designing and manufacturing complex nature-inspired objects.

A strengths, weaknesses, opportunities, and threats (SWOT) analysis has become a key tool used by businesses for strategic planning. Scholars have conducted SWOT research for over six decades. However, a collective understanding of SWOT analysis remains vague. This study accessed, analyzed, and synthesized the SWOT literature, allowing for new theoretical perspectives and frameworks to emerge. Using an integrative literature review, this study reviewed SWOT studies historically, providing a greater understanding of the SWOT analysis in different sectors and the different approaches used in SWOT studies. Furthermore, it fills the knowledge gap in the strategic planning context and indicates meaningful implications for managers that could help improve their strategic decisions.

Colorectal cancer is the third most common cancer worldwide with a high mortality rate at the advanced stages. However, colorectal cancer is not a single type of tumor; its pathogenesis depends on the anatomical location of the tumor and differs between right side and left side of the colon. Tumors in the proximal colon (right side) and distal colon (left side) exhibit different molecular characteristics and histology. In the right-sided tumors, mutations in the DNA mismatch repair pathway are commonly observed; and these tumors generally have a flat histology. In the left-sided tumors, chromosomal instability pathway-related mutations, such as KRAS, APC, PIK3CA, p53 mutations are observed and these tumors demonstrate polypoid-like morphology. Therapy responses are totally different between these tumor entities. Left-sided colorectal cancer (LCRC) patients benefit more from adjuvant chemotherapies such as 5-fluorouracil (5-FU)-based regimes, and targeted therapies such as anti- epidermal growth factor receptor (EGFR) therapy, and have a better prognosis. Right-sided colorectal cancer (RCRC) patients do not respond well to conventional chemotherapies, but demonstrate more promising results with immunotherapies because these tumors have high antigenic load. For the development of effective therapy regimes and better treatment options, it is essential to evaluate right-sided and left-sided tumors as separate entities, and design the therapy regime considering the differences between these tumors. Gastroenterol Res. 2018;11(4):264-273 doi: https://doi.org/10.14740/gr1062w

Introduction: Intravenous(IV) immunoglobulin(Ig) treatment is known to alleviate behavioral deficits in the experimentally induced model of sepsis. To delineate the mechanisms by which IVIg treatment prevents neuronal dysfunction, an array of immunological and apoptosis markers was investigated. Methods: Sepsis was induced by cecal ligation perforation(CLP) in rats. The animals were divided into five groups; sham, control, CLP + saline, CLP + immunoglobulin G IgG(250 mg/kg,iv), and CLP + immunoglobulins enriched with immunoglobulin M-IgGAM(250 mg/kg,iv). Blood and brain samples were taken in two sets of experiments after CLP to see the early(24 hrs) and late(10 days) effects of treatment. Total complement activity, complement 3(C3) and soluble complement C5b-9 levels were measured in sera of rats using ELISA-based methods. Cerebral complement content was analyzed by Western Blot. Immune cell infiltration and gliosis were examined by immunohistochemistry using cluster of differentiation 3, CD4, CD8, CD11b, CD19 and glial fibrillary acidic protein antibodies. Apoptotic neuronal death was investigated by TUNEL staining and Western Blot-based semi-quantitative evaluation of brain homogenates by bax and bcl-2 antibodies. Results: IV IgG and IgGAM administration significantly reduced systemic complement activity but increased serum C3 and soluble C5b-9 levels. Likewise, Western Blot data showed slightly increased C5b-9 expression and significantly reduced C1q expression in brain samples of IgGAM-treated but not IgG-treated septic rats especially in the first day of administration. No cerebral cellular infiltrates were observed in treated and non-treated septic rats. By contrast, IV IgG and IgGAM treatment induced considerable amelioration in glial cell proliferation which was increased in non-treated rats. IgG and IgGAM treated rats exhibited significantly reduced numbers of apoptotic neurons and cerebral expression levels of bax and bcl-2 as compared to nontreated rats. Conclusions: We suggest that IV IgG and IgGAM administration ameliorates neuronal dysfunction and behavioral deficits by reducing apoptotic cell death and glial cell proliferation. IgGAM treatment might be suppressing classical complement pathway by reducing C1q expression.

The purpose of this study is to reveal the extent to which different leadership models in education are studied, including the change in the trends of research on each model over time, the most prominent scholars working on each model, and the countries in which the articles are based. The analysis of the related literature was conducted by first employing a bibliometric analysis of the research and review papers indexed in the Web of Science database between 1980 and 2014. Then, a more in-depth analysis of selected papers was done using the content analysis method. The results showed that there has been increasing interest in leadership models in educational research over time. Distributed leadership, instructional leadership, teacher leadership, and transformational leadership are the most studied leadership models in educational research. It was also found that related research increasingly focuses on the effects of leaders on organizational behaviors/conditions and on student achievement. Accordingly, usage of quantitative methodology has significantly increased during the last decade. Possible reasons for these changes, implications, and recommendations for future research are also discussed.

Summary Sentiment analysis is one of the major tasks of natural language processing, in which attitudes, thoughts, opinions, or judgments toward a particular subject has been extracted. Web is an unstructured and rich source of information containing many text documents with opinions and reviews. The recognition of sentiment can be helpful for individual decision makers, business organizations, and governments. In this article, we present a deep learning‐based approach to sentiment analysis on product reviews obtained from Twitter. The presented architecture combines TF‐IDF weighted Glove word embedding with CNN‐LSTM architecture. The CNN‐LSTM architecture consists of five layers, that is, weighted embedding layer, convolution layer (where, 1‐g, 2‐g, and 3‐g convolutions have been employed), max‐pooling layer, followed by LSTM, and dense layer. In the empirical analysis, the predictive performance of different word embedding schemes (ie, word2vec, fastText, GloVe, LDA2vec, and DOC2vec) with several weighting functions (ie, inverse document frequency, TF‐IDF, and smoothed inverse document frequency function) have been evaluated in conjunction with conventional deep neural network architectures. The empirical results indicate that the proposed deep learning architecture outperforms the conventional deep learning methods.

Abstract Massive open online courses (MOOCs) are recent innovative approaches in distance education, which provide learning content to participants without age‐, gender‐, race‐, or geography‐related barriers. The purpose of our research is to present an efficient sentiment classification scheme with high predictive performance in MOOC reviews, by pursuing the paradigms of ensemble learning and deep learning. In this contribution, we seek to answer several research questions on sentiment analysis on educational data. First, the predictive performance of conventional supervised learning methods, ensemble learning methods and deep learning methods has been evaluated. Besides, the efficiency of text representation schemes and word‐embedding schemes has been evaluated for sentiment analysis on MOOC evaluations. For the evaluation task, we have analyzed a corpus containing 66,000 MOOC reviews, with the use of machine learning, ensemble learning, and deep learning methods. The empirical analysis indicate that deep learning‐based architectures outperform ensemble learning methods and supervised learning methods for the task of sentiment analysis on educational data mining. For all the compared configurations, the highest predictive performance has been achieved by long short‐term memory networks in conjunction with GloVe word‐embedding scheme‐based representation, with a classification accuracy of 95.80%.

Abstract The transition to the circular economy (CE) creates value through the closed‐loop systems, reverse logistics, product life cycle management, and clean production in terms of corporate environmental management. During this transition process, the organization faces many barriers such as financial, organizational, technology‐based, social, policy‐related, market‐based, and logistics‐based barriers. The objectives of this study are to propose a framework highlighting policy‐related barriers for a supply chain in the transition to CE and finally discuss potential implications on enhancing corporate environmental performance of a business. Further, this study evaluates the causal relationships between the policy‐related barriers using fuzzy Decision‐Making Trial and Evaluation Laboratory (DEMATEL) method. The application was conducted in an apparel firm in Turkey. From findings, lack of legislation for efficient CE (C4), lack of mandatory requirements and responsibilities for manufacturers/suppliers for the CE (C17), and lack of government support for environmentally friendly policies (C2) are revealed as the most important barriers, respectively. It is found that lack of attitude and awareness about CE in government institutions (C19) is the most influencing factor, whereas lack of effective recycling policies to achieve quality in waste management (C8) is the most influenced factor. The recommendations were developed for enhancing the corporate environmental performance of businesses through incentives and unique rewards, improving communication among stakeholders, the government's perception of CE and current linear economy, cooperation with nongovernmental organization (NGOs) and civil actions, the vision of government towards circular principles, the circular public procurement, the local governments in circular policymaking, and awareness of bureaucracy and government officials.

Sarcasm identification on text documents is one of the most challenging tasks in natural language processing (NLP), has become an essential research direction, due to its prevalence on social media data. The purpose of our research is to present an effective sarcasm identification framework on social media data by pursuing the paradigms of neural language models and deep neural networks. To represent text documents, we introduce inverse gravity moment based term weighted word embedding model with trigrams. In this way, critical words/terms have higher values by keeping the word-ordering information. In our model, we present a three-layer stacked bidirectional long short-term memory architecture to identify sarcastic text documents. For the evaluation task, the presented framework has been evaluated on three-sarcasm identification corpus. In the empirical analysis, three neural language models (i.e., word2vec, fastText and GloVe), two unsupervised term weighting functions (i.e., term-frequency, and TF-IDF) and eight supervised term weighting functions (i.e., odds ratio, relevance frequency, balanced distributional concentration, inverse question frequency-question frequency-inverse category frequency, short text weighting, inverse gravity moment, regularized entropy and inverse false negative-true positive-inverse category frequency) have been evaluated. For sarcasm identification task, the presented model yields promising results with a classification accuracy of 95.30%.

Climate-induced migration is one of the most hotly debated topics in the current discourse on global warming and its consequences. There is a burgeoning field in economics and other social sciences linking climatic factors or climate-related natural disasters to migration. Existent empirical studies use different measures to quantify migration flows and climatic factors and apply a variety of methodologies to disparate data sets and samples of countries. Our review article aims to provide a unifying perspective over this complex field by structuring the literature and summarizing the empirical findings.

Wound healing is an unmet therapeutic challenge among medical society since wound assessment and management is a complex procedure including several factors playing major role in healing process. Wounds can mainly be categorized as acute or chronic. It is well referred that the acute wound displays normal wound physiology while healing, in most cases, is seemed to progress through the normal phases of wound healing. On the other hand, a chronic wound is physiologically impaired. The main problem in wound management is that the majority of wounds are colonized with microbes, whereas this does not mean that all wounds will be infected. In this review, we address the problems that clinicians face to manage while treat acute and chronic wounds. Moreover, we demonstrate the pathophysiology, etiology, prognosis and microbiology of wounds. We further introduce the state of art in pharmaceutical technology field as part of wound management aiming to assist health professionals to overcome the current implications on wound assessment. In addition, authors review researches which included the use of gels and dermal films as wound healing agents. It can be said that natural and synthetic drugs or carriers provide promising solutions in order to meet the wound management standards. However, are the current strategies as desirable as medical society wish?

Abstract High blood cholesterol is typically considered a feature of wealthy western countries 1,2 . However, dietary and behavioural determinants of blood cholesterol are changing rapidly throughout the world 3 and countries are using lipid-lowering medications at varying rates. These changes can have distinct effects on the levels of high-density lipoprotein (HDL) cholesterol and non-HDL cholesterol, which have different effects on human health 4,5 . However, the trends of HDL and non-HDL cholesterol levels over time have not been previously reported in a global analysis. Here we pooled 1,127 population-based studies that measured blood lipids in 102.6 million individuals aged 18 years and older to estimate trends from 1980 to 2018 in mean total, non-HDL and HDL cholesterol levels for 200 countries. Globally, there was little change in total or non-HDL cholesterol from 1980 to 2018. This was a net effect of increases in low- and middle-income countries, especially in east and southeast Asia, and decreases in high-income western countries, especially those in northwestern Europe, and in central and eastern Europe. As a result, countries with the highest level of non-HDL cholesterol—which is a marker of cardiovascular risk—changed from those in western Europe such as Belgium, Finland, Greenland, Iceland, Norway, Sweden, Switzerland and Malta in 1980 to those in Asia and the Pacific, such as Tokelau, Malaysia, The Philippines and Thailand. In 2017, high non-HDL cholesterol was responsible for an estimated 3.9 million (95% credible interval 3.7 million–4.2 million) worldwide deaths, half of which occurred in east, southeast and south Asia. The global repositioning of lipid-related risk, with non-optimal cholesterol shifting from a distinct feature of high-income countries in northwestern Europe, north America and Australasia to one that affects countries in east and southeast Asia and Oceania should motivate the use of population-based policies and personal interventions to improve nutrition and enhance access to treatment throughout the world.

Sentiment analysis has been a well-studied research direction in computational linguistics. Deep neural network models, including convolutional neural networks (CNN) and recurrent neural networks (RNN), yield promising results on text classification tasks. RNN-based architectures, such as, long short-term memory (LSTM) and gated recurrent unit (GRU) can process sequences of any length. However, using them in the feature extraction layer of a deep neural network architecture increases the dimensionality of the feature space. In addition, such models value different features equally. To solve these issues, we propose a bidirectional convolutional recurrent neural network architecture, which utilizes two separate bidirectional LSTM and GRU layers, to derive both past and future contexts by connecting two hidden layers of opposite directions to the same context. The group-wise enhancement mechanism has been employed on the features extracted by bidirectional layers, which divides features into multiple classes, enhancing important features in each group while weakening the less important ones. The presented scheme employs convolution and pooling layers to extract high level features and to reduce the dimensionality of the feature space. The experimental results indicate that the presented bidirectional convolutional recurrent neural network architecture with group-wise enhancement mechanism can outperform the state-of-the-art results for sentiment analysis.

Purpose The purpose of this paper is to investigate the impact of customer satisfaction, service quality, the perceived value of services, corporate image and corporate reputation on customer loyalty and their relationship in the Turkish banking industry. Mediation effects of the perceived value and corporate image and reputation are also studied. Understanding the relationships between the determinants of customer loyalty toward the bank helps management to use corporate image and reputation more effectively in its strategy, thus enhancing the institution’s position in the minds of consumers. Design/methodology/approach A model is proposed to explore the relationships of service quality and customer satisfaction with a perceived value and their effect on transforming the corporate image and corporate reputation into the form of customer loyalty toward the bank. A survey is designed within this framework and SEM analysis is conducted in order to study the nature of relationships between variables of interest hypothesized to affect customer behavior and customer loyalty. Mediation tests for perceived value and corporate image and reputation are also conducted. Findings The findings of the survey indicate that corporate image and corporate reputation can be used as a common marketing benchmark to measure a bank’s performance. The results demonstrated that customers perceive quality and satisfaction effects loyalty through perceived value, image and reputation. Research limitations/implications The study was conducted in Izmir, the third biggest city of Turkey. The sample is composed of regular customers, and the sample size is enough for the study but more studies are needed to generalize the results. Practical implications The results provide information to bank managers to effectively assist them to offer appropriate customer service levels sustaining satisfaction, quality and value to the customers within the transactions. Originality/value The paper studies the determinants of customer loyalty in the Turkish banking industry and considers the effects of corporate image and corporate reputation as measured by customer satisfaction, service quality and perceived value, on customer loyalty toward banks in Turkey. This model is not studied in bank marketing in Turkey and also in the banking literature.

This study empirically tests and compares the influence of friends’ recommendations on social media and anonymous reviews on shopping websites in the context of online purchase intention. For this purpose, we analyse the impacts of these two platforms based on the components of information adoption model (IAM) which are borrowed as information quality, information credibility, information usefulness and information adoption. We conduct a survey and find anonymous reviews as more influential on consumer’ online purchase intentions than friends’ recommendations on social media. However, as this result was contrary to that expected, we conduct another study through in-depth interviews in order to enlighten our results found in the first study. In Study 2, we find the reasons why consumers prefer anonymous reviews rather than friends’ recommendations. Information quantity, information readiness, detailed information and dedicated information are factors which make shopping websites superior than social media in terms of the impact of electronic word of mouth (eWOM). Academic and managerial implications are discussed.

Abstract Student evaluations of teaching (SET) provides potentially essential source of information to achieve educational quality objectives of higher educational institutions. The findings can be utilized as a measure of teaching effectiveness and they may aid the administrative decision‐making process. The purpose of our research is to establish an efficient sentiment classification scheme on instructor evaluation reviews by pursuing the paradigm of deep learning. Deep learning is a recent research direction of machine learning, which seeks to identify a classification scheme with higher predictive performance based on multiple layers of nonlinear information processing. In this study, we present a recurrent neural network (RNN) based model for opinion mining on instructor evaluation reviews. We analyze a corpus containing 154,000 such reviews, with the use of conventional machine learning algorithms, ensemble learning methods, and deep learning architectures. In the empirical analysis, three conventional text representation schemes (namely, term‐presence, term‐frequency [TF], and TF‐inverse document frequency schemes) and four word embedding schemes (namely, word2vec, global vector [GloVe], fastText, and LDA2Vec) have been taken into consideration. The predictive performance of supervised machine learning methods (such as, Naïve Bayes, support vector machines, logistic regression, K‐nearest neighbor, and random forest) and three ensemble learning methods have been examined on word embedding schemes. The extensive empirical analysis indicates that deep learning‐based architectures outperform the conventional machine learning classifiers for the task of sentiment classification on instructor reviews. For the RNN with attention mechanism in conjunction with GloVe word embedding scheme‐based representation a classification accuracy of 98.29% has been obtained.

This ECCO topical review of the European Crohn's and Colitis Organisation [ECCO] focused on prediction, diagnosis, and management of fibrostenosing Crohn's disease [CD]. The objective was to achieve evidence-supported, expert consensus that provides guidance for clinical practice.

BACKGROUND: This study evaluated the prevalence of the signs and symptoms of temporomandibular joint disorder (TMD) among patients with TMD symptoms. METHODS: Between September 2011 and December 2011, 243 consecutive patients (171 females, 72 males, mean age 41 years) who were referred to the Department of Prosthodontics, Faculty of Dentistry, Karadeniz Technical University, Trabzon were examined physically and completed a questionnaire regarding age, gender, social status, general health, antidepressant drug usage, dental status, limited mouth opening, temporomandibular joint (TMJ) sounds, and parafunctions (bruxism, clenching). The data were analyzed using the chi-square test and binary logistic regression model (alpha = 0.05). RESULTS: With a frequency of 92%, pain in the temporal muscle was the most common symptom, followed by pain during mouth opening (89%) in both genders. TMJ pain at rest, pain in the masseter muscle, clicking, grinding, and anti-depressant use were significantly more frequent in females than males. Age (p=0.006; odds ratio 0.954; 95% CI 0.922-0.987) and missing teeth (p=0.003; odds ratio 3.753; 95% CI 1.589-8.863) had significant effects on the prevalence of TMD. CONCLUSION: Females had TMD signs and symptoms more frequently than males in the study population. The most common problem in both genders was pain.

Topic extraction is an essential task in bibliometric data analysis, data mining and knowledge discovery, which seeks to identify significant topics from text collections. The conventional topic extraction schemes require human intervention and involve also comprehensive pre-processing tasks to represent text collections in an appropriate way. In this paper, we present a two-stage framework for topic extraction from scientific literature. The presented scheme employs a two-staged procedure, where word embedding schemes have been utilized in conjunction with cluster analysis. To extract significant topics from text collections, we propose an improved word embedding scheme, which incorporates word vectors obtained by word2vec, POS2vec, word-position2vec and LDA2vec schemes. In the clustering phase, an improved clustering ensemble framework, which incorporates conventional clustering methods (i.e., k-means, k-modes, k-means++, self-organizing maps and DIANA algorithm) by means of the iterative voting consensus, has been presented. In the empirical analysis, we analyze a corpus containing 160,424 abstracts of articles from various disciplines, including agricultural engineering, economics, engineering and computer science. In the experimental analysis, performance of the proposed scheme has been compared to conventional baseline clustering methods (such as, k-means, k-modes, and k-means++), LDA-based topic modelling and conventional word embedding schemes. The empirical analysis reveals that ensemble word embedding scheme yields better predictive performance compared to the baseline word vectors for topic extraction. Ensemble clustering framework outperforms the baseline clustering methods. The results obtained by the proposed framework show an improvement in Jaccard coefficient, Folkes & Mallows measure and F1 score.