CLUSTERING MULTIPLE SCLEROSIS SUBGROUPS WITH MULTIFRACTAL METHODS AND SELF-ORGANIZING MAP ALGORITHM

Magnetic resonance imaging (MRI) is the most sensitive method to detect chronic nervous system diseases such as multiple sclerosis (MS). In this paper, Brownian motion Hölder regularity functions (polynomial, periodic (sine), exponential) for 2D image, such as multifractal methods were applied to MR brain images, aiming to easily identify distressed regions, in MS patients. With these regions, we have proposed an MS classification based on the multifractal method by using the Self-Organizing Map (SOM) algorithm. Thus, we obtained a cluster analysis by identifying pixels from distressed regions in MR images through multifractal methods and by diagnosing subgroups of MS patients through artificial neural networks

Development and Assessment of a Coping Scale for Infertile Women in Turkey

Infertile women feel more psychological stress and pressure than their husbands, and the prevalence of anxiety and depression among them are higher. This study aimed to develop a culture-specific measurement tool to identify the strategies of infertile women in dealing with infertility-related problems. This was a scale development study. This study was carried out in three different fertility centers in the three largest cities in Turkey. The data were collected using personal information form and through the application of a Coping Scale for Infertile Women (CSIW) protocol. Ways of Coping with Stress Inventory. Cronbach‘s alpha, Intraclass Correlation Coefficient and Spearman‘s Rank correlation analyses were used to determine the reliability of the scale. The results of explanatory factor analysis and a factor structure of the Coping Scale for Infertile Women, comprising 50 items, were examined, and the data were determined to be suitable to perform factor analysis. The internal consistency of the scale was found to be 0.880. The number of factors in the scale was 10, and the internal consistency of the factors was 0.720. The results showed that the CSIW had good reliability and validity.Keywords: Infertility, Women, Coping, Scale developmen

Angio-seal used as a bailout for incomplete hemostasis after dual perclose ProGlide deployment in transcatheter aortic valve implantation

Background: The failure rate of vascular closure devices remains a significant cause of major vascular complications in contemporary transcatheter aortic valve implantation practice. Methods: This research aimed to evaluate use of the Angio-Seal device in a bailout context in the setting of incomplete hemostasis following use of dual Perclose ProGlide devices in patients undergoing transfemoral transcatheter aortic valve implantation. A total of 185 patients undergoing transfemoral transcatheter aortic valve implantation with either dual Per-close ProGlide (n = 139) or a combination of dual Perclose ProGlide and Angio-Seal (n = 46) were retrospectively analyzed. The baseline, procedural characteristics, and all outcomes (defined according to Valve Academic Research Consortium-2 criteria) were compared. Results: No significant differences were seen between the dual Perclose ProGlide vs dual Perclose ProGlide+Angio-Seal groups with regard to the in-hospital Valve Academic Research Consortium-2 primary end points of major vascular complications (n = 13 [9.4%] vs n = 2 [4.3%]; P =.36), minor vascular complications (n = 13 [9.4%] vs n = 8 [14.7%]; P =.14), major bleeding (n = 16 [11.5%] vs n = 2 [4.3%]; P =.25), and minor bleeding (n = 9 [6.5%] vs n = 5 [10.9%]; P =.34), with higher rates of hematoma in the dual Perclose ProGlide+Angio-Seal group (n = 4 [2.9%] vs n = 5 [10.9%]; P =.044). Conclusion: Finding from the current study suggest that adjunctive Angio-Seal deployment may be feasible and safe, especially in patients with incomplete hemostasis following dual Perclose ProGlide use, and can be an optimal “bailout” procedure. (Tex Heart Inst J. 2022;49(6):e217684)

Artificial intelligence-driven approach to identify and recommend the winner in a tied event in sports surveillance

The proliferation of fractal artificial intelligence (AI)-based decision-making has propelled advances in intelligent computing techniques. Fractal AI-driven decision-making approaches are used to solve a variety of real-world complex problems, especially in uncertain sports surveillance situations. To this end, we present a framework for deciding the winner in a tied sporting event. As a case study, a tied cricket match was investigated, and the issue was addressed with a systematic state-of-the-art approach by considering the team strength in terms of the player score, team score at different intervals, and total team scores (TTSs). The TTSs of teams were compared to recommend the winner. We believe that the proposed idea will help to identify the winner in a tied match, supporting intelligent surveillance systems. In addition, this approach can potentially address many existing issues and future challenges regarding critical decision-making processes in sports. Furthermore, we posit that this work will open new avenues for researchers in fractal AI

Recent Advances in Health Biotechnology During Pandemic

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which emerged in 2019, cut the epoch that will make profound fluctuates in the history of the world in social, economic, and scientific fields. Urgent needs in public health have brought with them innovative approaches, including diagnosis, prevention, and treatment. To exceed the coronavirus disease 2019 (COVID-19) pandemic, various scientific authorities in the world have procreated advances in real time polymerase chain reaction (RT-PCR) based diagnostic tests, rapid diagnostic kits, the development of vaccines for immunization, and the purposing pharmaceuticals for treatment. Diagnosis, treatment, and immunization approaches put for- ward by scientific communities are cross-fed from the accrued knowledge of multidisciplinary sciences in health biotechnology. So much so that the pandemic, urgently prioritized in the world, is not only viral infections but also has been the pulsion in the development of novel approaches in many fields such as diagnosis, treatment, translational medicine, virology, mi- crobiology, immunology, functional nano- and bio-materials, bioinformatics, molecular biol- ogy, genetics, tissue engineering, biomedical devices, and artificial intelligence technologies. In this review, the effects of the COVID-19 pandemic on the development of various scientific areas of health biotechnology are discussed

Multiple skleroz hastalığının ve alt gruplarının belirlenmesi için optimum bir matematiksel modelin oluşturulması

ÖZETMULTİPLE SKLEROZ HASTALIĞININ VE ALT GRUPLARININ BELİRLENMESİ İÇİN OPTİMUM BİR MATEMATİKSEL MODELİN OLUŞTURULMASIMatematiksel modelleme ve tıp etkileşimi konularındaki çalışmalar son yıllarda geniş ilgi uyandırmış ve uygulama alanı bulmuştur. Bu uygulama alanlarından bir tanesi de hastaların Manyetik Rezonans Görüntüleme ile hastalığının teşhisi, seyri ve alt gruplarının arasındaki ilişkiyi kuran sistemlerdir. Hastalığa doğru tanı koyabilmek, seyrini ve alt gruplarını doğru şekilde tanıyabilmek için hastalığın klinik ve laboratuvar yöntemleri ile değerlendirilmesi gerekmektedir. “Multiple Skleroz” tanısında önemli bir adım olan “Manyetik Rezonans Görüntüleme”, hastalığın teşhisi, seyri ve alt gruplarının belirlemesinde önemli bir araçtır. Bu tez çalışmasında Multiple Skleroz hastalığının tanısı ve alt gruplarını tahmin edebilen iki farklı matematik model oluşturulmuştur. Oluşturulan bu modellerden ilki “Doğrusal Model” olup diğeri ise manyetik rezonans görüntülemelerden elde edilen özelliklere ve klinik bulgulara göre oluşturulmuş olan “Saklı Markov Modeli” dir. Bu iki model farklı özellikler kullanılarak çeşitlendirilmiş ve en yüksek başarımı sağlayan parametreler belirlenmiştir. Buna göre doğrusal modelde Multiple Skleroz ayrımında lezyon sayıları, minimum, maksimum, ortalama, varyansların özelliklerinin önemli olduğu belirlenmiştir. Saklı Markov Modelde ise genişletilmiş özürlülük durumu ölçeği ve lezyon sayılarına ait çeşitli gözlem matrisleri kullanılmıştır. En iyi sonuç ise genişletilmiş özürlülük durumu ölçeği ve lezyon sayısı gözlem matrislerinin birleştirilerek elde edilen birleşik gözlem matrisidir. Bu gözlem matrisi ile %86.52’lik bir başarım elde edilmiştir. Bu çalışmada Hacettepe Üniversitesi Tıp Fakültesi Nöroloji Ana Bilim Dalı kontrolündeki 19 birey (MS olmayan) kontrol grubu olarak; ve 120 Multiple Skleroz hastası incelenmiştir. Çalışmada, bu olguların, Hacettepe Üniversitesi Tıp Fakültesi Radyoloji Anabilim Dalı ile Primer Manyetik Rezonans Görüntüleme merkezindeki Manyetik Rezonans Görüntüleri ve Nöroloji Anabilim Dalındaki Genişletilmiş Özürlülük Durumu Ölçeği sonuçları kullanılmıştır.ABSTRACTCONSTITUTING AN OPTIMUM MATHEMATICAL MODEL FOR THE DIAGNOSIS OF MULTIPLE SCLEROSIS In recent years mathematical modelling have found a wide application field in medical sciences. One of these application fields is the systems that sustain the relationships among diagnosis of diseases via magnetic resonance imaging as well as the course of the diseases and sub-groups. In the diagnosis of Multiple Sclerosis and its course and monitoring its phases, “Magnetic Resonance Imaging” constitutes an essential part. In this thesis, two different mathematical models (Linear and Hidden Markov Models) that can diagnose the disease and determine the sub-groups are put forward. These two models have been diversified by applying different properties and the parameters that guarantee the highest success rate have been determined. In accordance with this, in the Linear Model, for differing between the sick and the healthy, it is specified that maksimum, minimum, average and variance of the number of lesions are important indicators. In the Hidden Markov Model various observation matrixes, extracted from Expanded Disability Status Scale and the number of lesions, have been utilized. The best result is the conjoint observation matrix which is obtained through combining Expanded Disability Status Scale and lesion number observation matrix. When observation matrix is applied, the success rate is 86.52%. In this study, the patients’ Magnetic Resonance images were studied at Radiology Department of Hacettepe University Medical Faculty and the Primer MR Imaging Center. The patients were clinically followed-up at the Department of Neurology Hacettepe University Faculty of Medicine and their Expanded Disability Status Scale are rated accordigly during their follow-up

Multiple skleroz hastalığının ve alt gruplarının belirlenmesi için optimum bir matematiksel modelin oluşturulması

MULTİPLE SKLEROZ HASTALIĞININ VE ALT GRUPLARININ BELİRLENMESİ İÇİN OPTİMUM BİR MATEMATİKSEL MODELİN OLUŞTURULMASI Matematiksel modelleme ve tıp etkileşimi konularındaki çalışmalar son yıllarda geniş ilgi uyandırmış ve uygulama alanı bulmuştur. Bu uygulama alanlarından bir tanesi de hastaların Manyetik Rezonans Görüntüleme ile hastalığının teşhisi, seyri ve alt gruplarının arasındaki ilişkiyi kuran sistemlerdir. Hastalığa doğru tanı koyabilmek, seyrini ve alt gruplarını doğru şekilde tanıyabilmek için hastalığın klinik ve laboratuvar yöntemleri ile değerlendirilmesi gerekmektedir. “Multiple Skleroz” tanısında önemli bir adım olan “Manyetik Rezonans Görüntüleme”, hastalığın teşhisi, seyri ve alt gruplarının belirlemesinde önemli bir araçtır. Bu tez çalışmasında Multiple Skleroz hastalığının tanısı ve alt gruplarını tahmin edebilen iki farklı matematik model oluşturulmuştur. Oluşturulan bu modellerden ilki “Doğrusal Model” olup diğeri ise manyetik rezonans görüntülemelerden elde edilen özelliklere ve klinik bulgulara göre oluşturulmuş olan “Saklı Markov Modeli” dir. Bu iki model farklı özellikler kullanılarak çeşitlendirilmiş ve en yüksek başarımı sağlayan parametreler belirlenmiştir. Buna göre doğrusal modelde Multiple Skleroz ayrımında lezyon sayıları, minimum, maksimum, ortalama, varyansların özelliklerinin önemli olduğu belirlenmiştir. Saklı Markov Modelde ise genişletilmiş özürlülük durumu ölçeği ve lezyon sayılarına ait çeşitli gözlem matrisleri kullanılmıştır. En iyi sonuç ise genişletilmiş özürlülük durumu ölçeği ve lezyon sayısı gözlem matrislerinin birleştirilerek elde edilen birleşik gözlem matrisidir. Bu gözlem matrisi ile %86.52’lik bir başarım elde edilmiştir. Bu çalışmada Hacettepe Üniversitesi Tıp Fakültesi Nöroloji Ana Bilim Dalı kontrolündeki 19 birey (MS olmayan) kontrol grubu olarak; ve 120 Multiple Skleroz hastası incelenmiştir. Çalışmada, bu olguların, Hacettepe Üniversitesi Tıp Fakültesi Radyoloji Anabilim Dalı ile Primer Manyetik Rezonans Görüntüleme merkezindeki Manyetik Rezonans Görüntüleri ve Nöroloji Anabilim Dalındaki Genişletilmiş Özürlülük Durumu Ölçeği sonuçları kullanılmıştır. ABSTRACT CONSTITUTING AN OPTIMUM MATHEMATICAL MODEL FOR THE DIAGNOSIS OF MULTIPLE SCLEROSIS In recent years mathematical modelling have found a wide application field in medical sciences. One of these application fields is the systems that sustain the relationships among diagnosis of diseases via magnetic resonance imaging as well as the course of the diseases and sub-groups. In the diagnosis of Multiple Sclerosis and its course and monitoring its phases, “Magnetic Resonance Imaging” constitutes an essential part. In this thesis, two different mathematical models (Linear and Hidden Markov Models) that can diagnose the disease and determine the sub-groups are put forward. These two models have been diversified by applying different properties and the parameters that guarantee the highest success rate have been determined. In accordance with this, in the Linear Model, for differing between the sick and the healthy, it is specified that maksimum, minimum, average and variance of the number of lesions are important indicators. In the Hidden Markov Model various observation matrixes, extracted from Expanded Disability Status Scale and the number of lesions, have been utilized. The best result is the conjoint observation matrix which is obtained through combining Expanded Disability Status Scale and lesion number observation matrix. When observation matrix is applied, the success rate is 86.52%. In this study, the patients’ Magnetic Resonance images were studied at Radiology Department of Hacettepe University Medical Faculty and the Primer MR Imaging Center. The patients were clinically followed-up at the Department of Neurology Hacettepe University Faculty of Medicine and their Expanded Disability Status Scale are rated accordigly during their follow-up

Classification of Erythematous - Squamous Skin Diseases Through SVM Kernels and Identification of Features with 1-D Continuous Wavelet Coefficient

Feature extraction is a kind of dimensionality reduction which refers to the differentiating features of a dataset. In this study, we have worked on ESD_Data Set (33 attributes), composed of clinical and histopathological attributes of erythematous-squamous skin diseases (ESDs) (psoriasis, seborrheic dermatitis, lichen planus, pityriasis rosea, chronic dermatitis, pityriasis rubra pilaris). It's aimed to obtain distinguishing significant attributes in ESD_Data Set for a successful classification of ESDs. We have focused on three areas: (a) By applying 1-D continuous wavelet coefficient analysis, Principle Component Analysis and Linear Discriminant Analysis to ESD_Data Set; w_ESD Data Set, p_ESD Data Set and LESD Data Set were formed. (b) By applying Support Vector Machine kernel algorithms (Linear, Quadratic, Cubic, Gaussian) to these datasets, accuracy rates were obtained. (c) w_ESD Data Set had the highest accuracy. This study seeks to identify deficiencies in literature to determine the distinguishing significant attributes in ESD_Data Set to classify ESDs

Stroke Subtype Clustering by Multifractal Bayesian Denoising with Fuzzy C Means and K-Means Algorithms

Multifractal denoising techniques capture interest in biomedicine, economy, and signal and image processing. Regarding stroke data there are subtle details not easily detectable by eye physicians. For the stroke subtypes diagnosis, details are important due to including hidden information concerning the possible existence of medical history, laboratory results, and treatment details. Recently, K-means and fuzzy C means (FCM) algorithms have been applied in literature with many datasets. We present efficient clustering algorithms to eliminate irregularities for a given set of stroke dataset using 2D multifractal denoising techniques (Bayesian (mBd), Nonlinear (mNold), and Pumping (mPumpD)). Contrary to previous methods, our method embraces the following assets: (a) not applying the reduction of the stroke datasets’ attributes, leading to an efficient clustering comparison of stroke subtypes with the resulting attributes; (b) detecting attributes that eliminate “insignificant” irregularities while keeping “meaningful” singularities; (c) yielding successful clustering accuracy performance for enhancing stroke data qualities. Therefore, our study is a comprehensive comparative study with stroke datasets obtained from 2D multifractal denoised techniques applied for K-means and FCM clustering algorithms. Having been done for the first time in literature, 2D mBd technique, as revealed by results, is the most successful feature descriptor in each stroke subtype dataset regarding the mentioned algorithms’ accuracy rates