Innovative Biosystems and Bioengineering <p>The international scientific journal Innovative Biosystems and Bioengineering was founded in 2017. IBB introduces a systems approach to life sciences problems.</p> <p>IBB is a quarterly peer-reviewed Open Access e-journal in which readers, immediately upon online publication, can access articles free of costs and subscription charges.</p> <p>e-ISSN 2616-177X</p> <p>Founder: National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”.</p> <p>Frequency: 4 issues a year.</p> <p>We accept papers in following languages: English, Ukrainian.</p> <p>Cite the title as: Innov Biosyst Bioeng.</p> <p>Readership: Biotechnologists, Biologists, Biophysicists, Bioinformaticists, Biomedical researchers and engineers, Medical, biochemical, and pharmaceutical scientists.</p> <p>Keywords: Applied biology, Biotechnology, Bioengineering, Biophysics, Bioinformatics, Bioorganic chemistry, Biomedicine, Biochemical engineering, Biomaterials, Bioprocess engineering.</p> <p>Indexing: DOAJ; ROAD; HINARI; Chemical Abstracts Service; CNKI Scholar; Norwegian Register for Scientific Journals, Series and Publishers; J-Gate; Public Knowledge Project Index; ICMJE; JournalTOCs; WCOSJ; Vifabio; EZB; Federation of Finnish Learned Societies; Zeitschriftendatenbank; Polska Bibliografia Naukowa; Scilit; Bielefeld Academic Search Engine; OpenAir; WorldCat.</p> Igor Sikorsky Kyiv Polytechnic Institute en-US Innovative Biosystems and Bioengineering 2616-177X <p><span>The ownership of copyright remains with the Authors.</span></p><p>Authors may use their own material in other publications provided that the Journal is acknowledged as the original place of publication and National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” as the Publisher.</p><p>Authors are reminded that it is their responsibility to comply with copyright laws. It is essential to ensure that no part of the text or illustrations have appeared or are due to appear in other publications, without prior permission from the copyright holder.</p>IBB articles are published under Creative Commons licence:<br /><ol type="a"><li>Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under <a href="">CC BY 4.0</a> that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.<br /><br /></li><li>Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.<br /><br /></li><li>Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.</li></ol> An Experimental Study of the Cryopreserved Placenta Extract Effect On the Sodium Diclofenac Anti-Inflammatory Activity <p><strong>Background.</strong> As a means of correcting the ulcerogenic effect of nonsteroidal anti-inflammatory drugs, our attention was attracted by a cryopreserved extract of the human placenta, which has a multivector spectrum of biological activity. To date, there is no information about its effect on the specific activity of this class of medicines (anti-inflammatory, analgesic, etc.) in published sources.</p> <p><strong>Objective. </strong>We are aimed to characterize the effect of cryopreserved placenta extract on the anti-inflammatory activity of diclofenac sodium when administered separately in a model of acute exudative inflammation.</p> <p><strong>Methods.</strong> Experimental studies <em>in vivo</em> were conducted on 28 nonlinear male laboratory rats. The model of acute exudative inflammation was reproduced by subplantar injection of 0.1 ml of 1.0% aqueous solution of λ-karagenin into the right hind limb of rats. Cryopreserved placenta extract was administered intramuscularly at a dose of 0.16 ml/kg 60 minutes before diclofenac sodium (8 mg/kg).</p> <p><strong>Results.</strong> Preventive administration of diclofenac sodium caused an antiexudative effect as early as 30 minutes after administration of λ-karagenin – its anti-inflammatory activity was 11.0%, which is 4.6 times higher than similar indicators at the same time in rats injected with placental cryoextract. At 60 minutes of observation, diclofenac sodium was comparable in anti-inflammatory activity with cryopreserved placenta extract: 28.6% and 22.2%, respectively, but at 120 and 180 minutes, diclofenac sodium exceeded the studied cryoextract in antiphlogistic effect by 1.6 times in both periods of observation. The anti-inflammatory effect of the combined separate administration of placenta cryoextract and diclofenac sodium before λ-karagenin for 30 and 60 minutes was 12.7% and 32.3%, respectively, which is comparable with analogous indicators against the background of diclofenac sodium monotherapy. However, at 120 minutes of observation, the group of combined use of placenta cryoextract and diclofenac sodium showed the greatest anti-inflammatory effect among rats of all the studied groups – 52.6%, which was 2.2 times higher than the indicators of the placenta cryoextract monotherapy group and 1.4 times lower than the indicators of the rats of the diclofenac sodium monotherapy group.</p> <p><strong>Conclusions.</strong> 4 hours after administration, placental cryoextract had a suppressive effect on kinins like diclofenac sodium, and in the prostaglandin period of caragenin-induced inflammation against the background of combined use of the studied cryoextract and diclofenac sodium, the anti-inflammatory activity was 46.4 %. This suggests a suppressive effect on the production of prostaglandins as a possible mechanism of anti-exudative action of cryopreserved placenta extract.</p> Fedir Hladkykh Copyright (c) 2021 The Author(s) 2021-09-15 2021-09-15 5 3 144 152 10.20535/ibb.2021.5.3.237505 Comparative Analysis of Classification Algorithms in the Analysis of Medical Images From Speckle Tracking Echocardiography Video Data <p><strong>Background.</strong> Machine learning allows applying various intelligent algorithms to produce diagnostic and/or prognostic models. Such models can be used to determine the functional state of the heart, which is diagnosed by speckle-tracking echocardiography. To determine the patient's heart condition in detail, a classification approach is used in machine learning. Each of the classification algorithms has a different performance when applied to certain situations. Therefore, the actual task is to determine the most efficient algorithm for solving a specific task of classifying the patient's heart condition when applying the same speckle-tracking echocardiography data set.</p> <p><strong>Objective</strong><strong>.</strong> We are aimed to evaluate the effectiveness of the application of prognostic models of logistic regression, the group method of data handling (GMDH), random forest, and adaptive boosting (AdaBoost) in the construction of algorithms to support medical decision-making on the diagnosis of coronary heart disease.</p> <p><strong>Methods</strong><strong>.</strong> Video data from speckle-tracking echocardiography of 40 patients with coronary heart disease and 16 patients without cardiac pathology were used for the study. Echocardiography was recorded in B-mode in three positions: long axis, 4-chamber, and 2-chamber. Echocardiography frames that reflect the systole and diastole of the heart (308 samples in total) were taken as objects for classification. To obtain informative features of the selected objects, the genetic GMDH approach was applied to identify the best structure of harmonic textural features. We compared the efficiency of the following classification algorithms: logistic regression method, GMDH classifier, random forest method, and AdaBoost method.</p> <p><strong>Results</strong><strong>.</strong> Four classification models were constructed for each of the three B-mode echocardiography positions. For this purpose, the data samples were divided into 3: training sample (60%), validation sample (20%), and test sample (20%). Objective evaluation of the models on the test sample showed that the best classification method was random forest (90.3% accuracy on the 4-chamber echocardiography position, 74.2% on the 2-chamber, and 77.4% on the long axis). This was also confirmed by ROC analysis, wherein in all cases, the random forest was the most effective in classifying cardiac conditions.</p> <p><strong>Conclusions.</strong> The best classification algorithm for cardiac diagnostics by speckle-tracking echocardiography was determined. It turned out to be a random forest, which can be explained by the ensemble approach of begging, which is inherent in this classification method. It will be the mainstay of further research, which is planned to be performed to develop a full-fledged decision support system for cardiac diagnostics.</p> Olena Petrunina Diana Shevaga Vitalii Babenko Volodymyr Pavlov Sergiy Rysin Ievgen Nastenko Copyright (c) 2021 The Author(s) 2021-09-10 2021-09-10 5 3 153 166 10.20535/ibb.2021.5.3.234990 Prospects for the Development of Biomedical Engineering as an Educational and Scientific Field in Ukraine Alexander Galkin Copyright (c) 2021 The autror(s) 2021-08-17 2021-08-17 5 3 138 143 10.20535/ibb.2021.5.3.239021