CMBEBIH 2017

Name: CMBEBIH 2017 | Proceedings of the International Conference on Medical and Biological Engineering 2017
Brand: Springer
Price: 309.23 EUR
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Proceedings of the International Conference on Medical and Biological Engineering 2017

Almir Badnjevic(Herausgeber*in)

Springer (Verlag)

Erschienen am 14. März 2017

XIX, 806 Seiten

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1 - Preface [Seite 6]
2 - Organization [Seite 8]
3 - Contents [Seite 12]
4 - Plenary Lectures I - Session I:BIOMEDICAL SIGNAL PROCESSING 1 [Seite 21]
5 - 1Using machine learning tool in classification of breast cancer [Seite 22]
5.1 - Abstract. [Seite 22]
5.2 - Keyword [Seite 22]
5.3 - 1 Introduction [Seite 22]
5.4 - 2 Methods [Seite 23]
5.4.1 - 2.1 Wisconsin Breast Cancer Database (WBCD [Seite 23]
5.4.2 - 2.2 Design of Artificial Neural Network for BreastCancer Classification [Seite 23]
5.5 - 3 Results and discussion [Seite 24]
5.5.1 - 3.1 Predictive ability of neural network architecturewith different number of hidden layers [Seite 24]
5.6 - 4 conclusion [Seite 25]
6 - 2MULTISAB project: a web platform based on specialized frameworks for heterogeneous biomedical time series analysis - an architectural overview [Seite 28]
6.1 - Abstract. [Seite 28]
6.2 - Keywords: [Seite 28]
6.3 - 1 Introduction [Seite 28]
6.4 - 2 Processing frameworks [Seite 29]
6.4.1 - 2.1 Common signal features framework [Seite 29]
6.4.2 - 2.2 Data handling framework [Seite 30]
6.4.3 - 2.3 Specialized biomedical time series analysisframeworks [Seite 30]
6.5 - 4 Signal visualization [Seite 31]
6.6 - 3 Database [Seite 31]
6.7 - 5 Discussion and conclusion [Seite 33]
6.8 - Conflict of Interest [Seite 33]
6.9 - Acknowledgements [Seite 33]
6.10 - References [Seite 34]
7 - 3Short-term variations of parameters of heart rate variability in subjects with mild hypertension and normotensive subjects during preoperative period [Seite 35]
7.1 - Abstract [Seite 35]
7.2 - Introduction [Seite 35]
7.3 - Methods and subjects [Seite 35]
7.4 - Keywords [Seite 35]
7.5 - Results [Seite 36]
7.6 - Discussion [Seite 37]
7.7 - Conclusion [Seite 37]
8 - 4Cardiac pulse waves modeling and analysis in laser Doppler perfusion signals of the skin microcirculation [Seite 39]
8.1 - Abstract. [Seite 39]
8.2 - 1 Introduction [Seite 39]
8.3 - 2 Materials and methods [Seite 39]
8.3.1 - 2.1 Dataset [Seite 39]
8.3.2 - 2.2 Experimental setting [Seite 39]
8.3.3 - 2.3 Gaussian-based cardiac pulse waves modeling [Seite 40]
8.4 - 3 Results [Seite 42]
8.5 - 4 Conclusion [Seite 43]
8.6 - Acknowledgement [Seite 44]
8.7 - References [Seite 44]
9 - 5Discrimination of Psychotic Symptoms from Controls Through Data Mining Methods Based on Emotional Principle Components [Seite 45]
9.1 - Abstract. [Seite 45]
9.2 - Keywords: [Seite 45]
9.3 - 1 Introduction [Seite 45]
9.4 - 2 Methods [Seite 46]
9.4.1 - 2.1 Data Collection [Seite 46]
9.4.2 - 2.2 Feature Extraction [Seite 46]
9.4.3 - 2.3 Data Mining Methods for Emotion Recognition [Seite 46]
9.4.4 - 2.4 Performance Criteria [Seite 47]
9.5 - 3 Results [Seite 47]
9.6 - 4 Discussion and Conclusion [Seite 48]
9.7 - Acknowledgements [Seite 48]
9.8 - References [Seite 48]
10 - 6Differences in temporal gait parameters between multiple sclerosis and healthy people [Seite 50]
10.1 - Abstract. [Seite 50]
10.2 - Keywords: [Seite 50]
10.3 - 1 Introduction [Seite 50]
10.4 - 2 Methods [Seite 51]
10.5 - 3 Results [Seite 52]
10.6 - 4 Summary and conclusions [Seite 53]
10.7 - References [Seite 54]
11 - 7An Adaptive Scheme for X-ray Medical Image Denoising using Artificial Neural Networks and Additive White Gaussian Noise Level Estimation in SVD Domain [Seite 55]
11.1 - Abstract. [Seite 55]
11.2 - Keywords: [Seite 55]
11.3 - 1 Introduction [Seite 55]
11.4 - 2 Singular r Value Dec composition n and NoiseLevel E stimation [Seite 56]
11.5 - 3 O verview of M Multilayer P Perceptron [Seite 56]
11.6 - 4 Adaptive Image Denoising [Seite 57]
11.7 - 5 Results and Discussion [Seite 57]
11.8 - 6 Conclusion [Seite 59]
11.9 - References [Seite 59]
12 - 8Using Neural Networks and Ensemble Techniques based on Decision Trees for Skin Permeability Prediction [Seite 60]
12.1 - Abstract. [Seite 60]
12.2 - Keywords: [Seite 60]
12.3 - 1 Introduction [Seite 60]
12.4 - 2 Methods [Seite 62]
12.5 - 2.1 Identification of Parametes for Prediction of drug permeability [Seite 62]
12.6 - 2.2 Artifical Neural Network for prediction of drug permeability [Seite 62]
12.7 - 2.3 Ensemble techniques based on Decision Trees forprediction of skin permeability [Seite 63]
12.8 - 2.4 Dataset Distribution [Seite 64]
12.9 - 3 Result [Seite 64]
12.10 - 3.1 Predictive ability of nerual network arhitecturs with different number of neurous in hidden layer [Seite 64]
12.11 - 3.2 Predictive ability of nerual network arhitecturs with different Distribution of Dataset [Seite 65]
12.12 - 3.3 Predictive ability of nerual network for prediction of drug permeability [Seite 65]
12.13 - 3.4 Predictive ability of REPTree, Bagging, andRandom SubSpace [Seite 66]
12.14 - 3.5 Comparison of ANN and Ensemble Techniques [Seite 66]
12.15 - 4 Conclusion [Seite 66]
12.16 - References [Seite 67]
13 - Plenary Lectures I - Session II:BIOMEDICAL IMAGING AND IMAGE PROCESSING [Seite 70]
14 - 9Fully Automated Brain Tumor Segmentation and Volume Estimation Based on Symmetry Analysis in MR Images [Seite 71]
14.1 - Abstract. [Seite 71]
14.2 - Keywords: [Seite 71]
14.3 - 1 INTRODUCTION [Seite 71]
14.4 - 2 MATERIAL AND METHODS [Seite 72]
14.5 - 3 RESULTS [Seite 77]
14.6 - 4 CONCLUSION [Seite 78]
14.7 - REFERENCES [Seite 78]
15 - 10Multi-Regional Adaptive Image Compression (AIC) for Hip Fractures in Pelvis Radiography [Seite 79]
15.1 - Abstract. [Seite 79]
15.2 - Keywords: [Seite 79]
15.3 - INTRODUCTION [Seite 79]
15.4 - METHODOLOGY [Seite 80]
15.5 - 1 Specifying Regions of Interest [Seite 80]
15.6 - 2 Lossless Compression [Seite 81]
15.7 - 3 Lossy Compression [Seite 82]
15.8 - EXPERIMENTAL RESULTS [Seite 83]
15.9 - CONCLUSION [Seite 84]
15.10 - REFERENCES [Seite 85]
16 - 11Endovascular treatment of intracranial arteriovenous malformations using ONYX [Seite 86]
16.1 - 1 Abstract [Seite 86]
16.2 - Keywords: [Seite 86]
16.3 - 2 Materials and methods [Seite 87]
16.4 - 3 Results [Seite 88]
16.5 - 4 Discussion [Seite 88]
16.6 - 5 Conclusions [Seite 90]
16.7 - References [Seite 91]
17 - 12Evaluation of spatial distribution of skin blood flow using optical imaging [Seite 92]
17.1 - Abstract. [Seite 92]
17.2 - 1 Introduction [Seite 92]
17.3 - 2 Materials and methods [Seite 93]
17.3.1 - 2.1 Data acquisition [Seite 93]
17.3.2 - 2.2 Reference signal [Seite 94]
17.3.3 - 2.3 Delay estimation [Seite 95]
17.3.4 - 2.4 Image analysis [Seite 95]
17.4 - 3 Results [Seite 96]
17.5 - 4 Conclusions [Seite 97]
17.6 - References [Seite 97]
18 - 13Computer-assisted diagnosis of osteoartrithis on hip radiographs [Seite 99]
18.1 - Abstract. [Seite 99]
18.2 - Keywords: [Seite 99]
18.3 - 1 Introduction [Seite 99]
18.4 - 2 Material a and Methods [Seite 100]
18.4.1 - 1.1Data sets [Seite 100]
18.4.1.1 - 1.2 Method1 (Traditional manual radiographical angle and ratio measurements to detect OA) [Seite 100]
18.4.1.2 - 1.3 Method2 (he Th proposed Computer assisted angle and ratio ment-measurement system [Seite 100]
18.5 - 3 Experiments [Seite 103]
18.6 - 4 Conclusion [Seite 104]
18.7 - References [Seite 104]
19 - 14DETERMINATION OF SEX BY DISCRIMINANT FUNCTION ANALYSIS OF LINEAR DIAMETERS IN BOSNIAN HUMAN SKULLS [Seite 106]
19.1 - Abstract. [Seite 106]
19.2 - Keywords: [Seite 106]
19.3 - 1 INTRODUCTION [Seite 106]
19.4 - 2 METHODS AND MATERIAL [Seite 106]
19.4.1 - 2.1 Statistic methods [Seite 108]
19.5 - 3 RESEARCH RESULTS [Seite 108]
19.6 - 4 DISCUSSION [Seite 111]
19.7 - 5 CONCLUSION [Seite 112]
19.8 - REFERENCES [Seite 112]
20 - Plenary Lectures I - Session III:BIOSENSORS AND BIOINSTRUMENTATION [Seite 113]
21 - 15Eustachian Tube Dysfunction Assessment Through Tympanic Cavity Air Exchange Sensor [Seite 114]
21.1 - Abstract. [Seite 114]
21.2 - Keywords: [Seite 114]
21.3 - 1 Introduction [Seite 114]
21.4 - 2 Principles of ET air exchange sensor and measurment method [Seite 115]
21.4.1 - 2.1 Using Boyle's Law for Finding Exchange Volume [Seite 117]
21.5 - 3 Classification of ETD cases [Seite 119]
21.5.1 - 3.1 Observations about ET muscular signals [Seite 119]
21.6 - 4 CONCLUSIONS [Seite 119]
21.7 - REFERENCES [Seite 119]
22 - 16Design, Simulation and Implementation of a Selective Recording System from Peripheral nervous system [Seite 121]
22.1 - Abstract. [Seite 121]
22.2 - Keywords: [Seite 121]
22.3 - 1 Introduction [Seite 121]
22.4 - 2 Materials and methodes [Seite 122]
22.4.1 - 2.1 Simulations [Seite 122]
22.4.2 - 2.2 Implementation [Seite 123]
22.5 - 3 RESULTS [Seite 123]
22.6 - 4 DISCUSSION [Seite 125]
22.7 - References [Seite 125]
23 - 17Fabrication and testing of a multi-electrode spiral nerve cuff [Seite 127]
23.1 - Abstract. [Seite 127]
23.2 - Keywords: [Seite 127]
23.3 - 1 Introduction [Seite 127]
23.4 - 2 Materials and methods [Seite 127]
23.5 - 3 Results [Seite 128]
23.6 - 4 Discussion [Seite 129]
23.7 - References [Seite 129]
24 - 18Personal electromyographic biofeedback system "MyMyo" [Seite 131]
24.1 - Abstract. [Seite 131]
24.2 - Keywords: [Seite 131]
24.3 - 1 Introduction [Seite 131]
24.4 - 2 Existing solutions to the problem [Seite 131]
24.5 - 3 Concept and requirements for the device [Seite 132]
24.6 - 4 Device development [Seite 132]
24.6.1 - 4.1 Power supply [Seite 133]
24.6.2 - 4.2 Analog processing [Seite 133]
24.6.3 - 4.3 Microcontroller and BLE [Seite 134]
24.6.4 - 4.4 Data coding [Seite 134]
24.6.5 - 4.5 Mobile phone application [Seite 135]
24.6.6 - 4.6 Device design [Seite 135]
24.7 - 5 Device testing [Seite 136]
24.7.1 - 5.1 Transfer function [Seite 136]
24.7.2 - 5.2 Data throughput problem [Seite 137]
24.8 - 6 Conclusion [Seite 137]
24.9 - Acknowledgement [Seite 137]
24.10 - Conflicts of Interest [Seite 137]
24.11 - Literature [Seite 137]
25 - 19A NOVEL ACTIVE DEVICE FABRICATION METHOD FOR INTERVENTIONAL MRI PROCEDURES [Seite 139]
25.1 - Abstract. [Seite 139]
25.2 - Keywords: [Seite 139]
25.3 - Introduction [Seite 139]
25.4 - Conclusion [Seite 144]
25.5 - References [Seite 144]
26 - 20Experimental verification of EOG signal measurement using the modified digital stochastic measurement method [Seite 146]
26.1 - Abstract. [Seite 146]
26.2 - Keywords: [Seite 146]
26.3 - 1 Introduction [Seite 146]
26.4 - 2 Proposed method and simulation model [Seite 146]
26.5 - 3 Experimental hardware solution [Seite 148]
26.6 - 4 Results and discusion [Seite 150]
26.7 - 5 Conclusion [Seite 151]
26.8 - Acknowledgment [Seite 151]
26.9 - References [Seite 151]
27 - Plenary Lectures I - Session IV:BIO-MICRO/NANO TECHNOLOGIES [Seite 152]
28 - 21Application of Artificial Neural Network in modelling of photo-degradation suspension of manganese doped zinc oxide nanoparticles under visible-light irradiation [Seite 153]
28.1 - Abstract. [Seite 153]
28.2 - Keywords: [Seite 153]
28.3 - 1 Introduction [Seite 153]
28.4 - 2 Methods [Seite 154]
28.5 - 3 Results and discussion [Seite 155]
28.6 - 5. Conclusion [Seite 156]
28.7 - References [Seite 157]
29 - 22Quantification of protein concentration adsorbed on gold nanoparticles using Artificial Neural Network [Seite 158]
29.1 - Abstract. [Seite 158]
29.2 - Keywords: [Seite 158]
29.3 - 1 Introduction [Seite 158]
29.4 - 2 Methods [Seite 158]
29.4.1 - 2.1 Sample collection [Seite 158]
29.4.2 - 2.2 Development of Artificial Neural Network for quantification of protein concentration bind on surface of gold nanoparticles NPs [Seite 159]
29.4.3 - 2.3 Testing of ANN [Seite 159]
29.5 - 3 Results [Seite 159]
29.5.1 - 3.1 ANN training results [Seite 159]
29.6 - 4 Conclusion [Seite 161]
29.7 - Conflict of interest [Seite 162]
29.8 - References [Seite 162]
30 - 23 Design and Fabrication of a PDMS Microfluidic Devicefor Titration of Biological Solutions [Seite 163]
30.1 - Keywords: [Seite 163]
30.2 - 1. Introduction [Seite 163]
30.3 - 2. Chip design [Seite 163]
30.4 - 3. Material and Methods [Seite 164]
30.5 - 3. Results and discussion [Seite 165]
30.6 - 4. Conclusion and future work [Seite 167]
30.7 - Acknowledgments [Seite 167]
30.8 - References [Seite 167]
31 - 24BEAUTY OF FINE DOTS [Seite 169]
31.1 - Abstract. [Seite 169]
31.2 - Keywords: [Seite 169]
31.3 - 1 Introduction [Seite 169]
31.4 - 2 Why are quantum dots so unique? [Seite 170]
31.5 - 3 Application of DHLA-coated CdSe/ZnSquantum dots [Seite 170]
31.6 - 4 Nanotoxicology [Seite 171]
31.7 - 5 Conclusion [Seite 172]
31.8 - References [Seite 172]
32 - 25EFFECT OF CHEMICALLY-SYNTHESIZED SILVER NANOPARTICLES (AG-NP) ON GLYCEMIC AND LIPIDEMIC STATUS IN RAT MODEL [Seite 174]
32.1 - Abstract. [Seite 174]
32.2 - Keywords: [Seite 174]
32.3 - Introduction [Seite 174]
32.4 - RESULTS [Seite 176]
32.5 - CONCLUSION [Seite 178]
32.6 - REFERENCES [Seite 178]
33 - 26Towards green nanotechnology: maximizing benefits and minimizing harm [Seite 180]
33.1 - Abstract. [Seite 180]
33.2 - Keywords: [Seite 180]
33.3 - 1 Introduction [Seite 180]
33.4 - 2 Nanotechnology and its potential negative impact on human health and environment [Seite 181]
33.5 - 3 Green nanotechnology - a way to changethe future [Seite 182]
33.6 - 4 Conclusion [Seite 185]
33.7 - References [Seite 185]
34 - 27Development of the method for quantification of amino acid adsorbed on nanoparticle surface [Seite 187]
34.1 - Abstract. [Seite 187]
34.2 - Keywords: [Seite 187]
34.3 - 1 Introduction [Seite 187]
34.3.1 - 1.1 Nanoparticles [Seite 187]
34.3.2 - 1.2 Gold nanoparticles (Au NPs) [Seite 187]
34.3.3 - 1.3 Silicon nanoparticles (Si NPs). [Seite 188]
34.3.4 - 1.4 Interaction bewtween nanoparticles-biologicalsystems [Seite 188]
34.4 - 2 Materials and Methods [Seite 188]
34.4.1 - 2.1 Materials [Seite 188]
34.4.2 - 2.2 Methods [Seite 188]
34.5 - 3 Results and Discussion [Seite 189]
34.5.1 - 3.1 Adsorption of amino acids on gold nanoparticles [Seite 189]
34.5.2 - 3.2 Adsorption of amino acids on SiO2 nanoparticles [Seite 190]
34.6 - 4 Conclusion [Seite 191]
34.7 - 5 References [Seite 191]
35 - 28Application of biological surface adsorption index approach (BSAI) in characterization of interactions between gold nanoparticles and biomolecules [Seite 192]
35.1 - Abstract. [Seite 192]
35.2 - Keywords: [Seite 192]
35.3 - 1. Introduction [Seite 192]
35.4 - 2. Materials and Methods [Seite 193]
35.4.1 - 2.1 Materials and reagents [Seite 193]
35.4.2 - 2.2. Synthesis of AuNPs [Seite 194]
35.4.3 - 2.2. Preparation of probe compounds solutions [Seite 194]
35.4.4 - 2.3. Sample preparation [Seite 194]
35.4.5 - 2.4. SPME/GC-MS instrumentation and analyticalparameters [Seite 194]
35.4.6 - 2.5. Quantification of the adsorption of probe compoundsby NPs [Seite 195]
35.4.7 - 2.6. Adsorption coefficient calculation [Seite 195]
35.5 - 3. Results [Seite 195]
35.6 - 3. Conclusion [Seite 197]
35.7 - References [Seite 197]
36 - Plenary Lectures I - Session V: BIOMECHANICS, ROBOTICS AND MINIMALLYINVASIVE SURGERY [Seite 199]
37 - 29MECHANICAL TESTING STRATEGIES FOR DENTAL IMPLANTS [Seite 200]
37.1 - Abstract. [Seite 200]
37.2 - Keywords: [Seite 200]
37.3 - 1 INTRODUCTION [Seite 200]
37.4 - 2 STRESS AND STRAINMEASUREMENTS [Seite 201]
37.5 - 3 RESONANCE FREQUENCYANALYSIS [Seite 204]
37.6 - 4 FRACTURE RESISTANCE [Seite 205]
37.7 - REFERENCES [Seite 205]
38 - 30CONTACT FORCE PROBLEM IN THE REHABILITATION ROBOT CONTROL DESIGN [Seite 208]
38.1 - Abstract. [Seite 208]
38.2 - Keyword: [Seite 208]
38.3 - 1 INTRODUCTION [Seite 208]
38.4 - 2 REVIEW OF CURRENT REHABILITATIONROBOTS [Seite 209]
38.4.1 - 2.1 Target population and rehabilitation robots [Seite 209]
38.4.2 - 2.2 Trends of rehabilitation robots [Seite 209]
38.5 - 3 THE ROBOT - ENVIRONMENTINTERACTION PROBLEM [Seite 210]
38.5.1 - 3.1 Control of contact tasks [Seite 211]
38.5.2 - 3.2 Cooperative manipulation [Seite 212]
38.6 - 4 MANIPULATOR AND CONTACTDYNAMICS MODEL [Seite 214]
38.6.1 - 4.1 Manipulator kinematics and dynamics [Seite 214]
38.6.2 - 4.2 Environment model [Seite 215]
38.6.3 - 4.3 Impedance controller for cooperative manipulation [Seite 215]
38.7 - 5 DISCUSSION AND CONCLUSION [Seite 216]
38.8 - 6 FUTURE RECOMMENDATIONS [Seite 216]
38.9 - REFERENCES [Seite 217]
39 - 31 Implementation and Validation of Human Kinematics measured using IMUs for Musculoskeletal Simulations by the Evaluation of Joint Reaction Forces [Seite 220]
39.1 - Abstract: [Seite 220]
39.2 - Keywords: [Seite 220]
39.3 - I. INTRODUCTION [Seite 220]
39.4 - II. MATERIALS AND METHODS [Seite 221]
39.5 - III. RESULTS [Seite 222]
39.6 - IV. DISCUSSION A ND LIMITATIONS [Seite 225]
39.7 - V. CONCLUSION [Seite 226]
39.8 - REFERENCES [Seite 226]
40 - 32FEA of the transiliacal internal fixator as an osteosynthesis of pelvic ring fractures [Seite 227]
40.1 - Abstract: [Seite 227]
40.2 - Keywords: [Seite 227]
40.3 - I. INTRODUCTION [Seite 227]
40.4 - II. MATERIALS AND METHODS [Seite 228]
40.5 - III. RESULTS [Seite 229]
40.6 - IV. DISCUSSION AND LIMITATIONS [Seite 230]
40.7 - V. CONCLUSION [Seite 231]
40.8 - VI. CONFLICT OF INTEREST DECLARATION [Seite 231]
40.9 - VII. REFERENCES [Seite 232]
41 - 33Overview of the Development of Hydraulic Above Knee Prosthesis [Seite 233]
41.1 - Abstract. [Seite 233]
41.2 - Keywords: [Seite 233]
41.3 - 1 INTRODUCTION [Seite 233]
41.4 - 2 CURRENT ACCOMPLISHMENTS [Seite 233]
41.5 - 3 DEVELOPMENT OF HAKP [Seite 233]
41.6 - 4 CURRENT R&D OF HAKP [Seite 235]
41.6.1 - 4.1 Prosthetic foot development [Seite 235]
41.6.2 - 4.2 Design of HAKP prototype with knee andankle actuators [Seite 235]
41.6.3 - 4.3 Development of motion recognition equipment for HAKP [Seite 236]
41.7 - 5 CONCLUSION AND FUTURE WORK [Seite 237]
41.8 - REFERENCES [Seite 237]
42 - 34Non-invasive estimation of respiratory depression profiles during robot-assisted laparoscopic surgery using a model-based approach [Seite 238]
42.1 - Abstract. [Seite 238]
42.2 - Keywords: [Seite 238]
42.3 - 1 Introduction [Seite 238]
42.4 - 2 Methods [Seite 239]
42.5 - 3 Results [Seite 241]
42.6 - 4 Discussion [Seite 244]
42.7 - References [Seite 245]
43 - Plenary Lectures I - Session VI: CARDIOVASCULAR, RESPIRATORY AND ENDOCRINESYSTEMS ENGINEERING [Seite 247]
44 - 35Subclinical inflammation: The link between increased cardiovascular risk and subclinical hypothyroidism in postmenopausal women [Seite 248]
44.1 - ABSTRACT. [Seite 248]
44.2 - KEYWORDS: [Seite 248]
44.3 - INTRODUCTION [Seite 248]
44.4 - MATERIALS AND METHODS [Seite 248]
44.5 - RESULTS [Seite 249]
44.6 - DISCUSSION [Seite 251]
44.7 - CONCLUSION [Seite 252]
44.8 - REFERENCES [Seite 252]
45 - 36Computational Vascular Surgery Planning and Predicting for Abdominal Aortic Aneurysm [Seite 254]
45.1 - Abstract. [Seite 254]
45.2 - 1 Introduction [Seite 254]
45.3 - 2 Methods [Seite 255]
45.3.1 - 2.1 Experimental determination of tissue deformation [Seite 255]
45.3.2 - 2.2 Computer models [Seite 256]
45.3.3 - 2.3 Drag force calculation [Seite 256]
45.4 - 3 Results [Seite 256]
45.5 - 4 Discussion and conclusion [Seite 258]
45.6 - Acknowledgments [Seite 258]
45.7 - References [Seite 258]
46 - 37Determination of Probabilistic Neural Network's Accuracy in Context of Cardiac Stress Test [Seite 259]
46.1 - Abstract. [Seite 259]
46.2 - Keywords: [Seite 259]
46.3 - 1 Introduction [Seite 259]
46.4 - 2 Cardiac Stress Test Basics [Seite 260]
46.5 - 3 PNN Basics [Seite 262]
46.6 - 4 Experiment Setup and Results [Seite 262]
46.7 - 5 Conclusion [Seite 264]
46.8 - References [Seite 264]
47 - 38Effects of electrical stimulation as a new method of treating diabetes on animal models: Review [Seite 266]
47.1 - Abstract. [Seite 266]
47.2 - Keywords: [Seite 266]
47.3 - 1 Introduction [Seite 266]
47.4 - 2 Effects of electrical stimulation of dorsalmotornucleus of vagus nerve [Seite 267]
47.5 - 3 Electrical field stimulation (EFS) on thesecretion of insulin and glucagon [Seite 267]
47.6 - 4 Effects of non-invasive peripheralstimulation in normal and insulinresistant rats [Seite 267]
47.7 - 5 Effect of hepatic electrical stimulation ondiabatetic rats [Seite 268]
47.8 - 6 Effects of mild electrical stimulation andheat shock on different models of mice [Seite 269]
47.9 - 7 Conclusion [Seite 270]
47.10 - Conflict of interest declaration [Seite 270]
47.11 - References [Seite 270]
48 - 39Numerical Simulation of Blood Flow Through the Aortic Arch [Seite 272]
48.1 - Abstract. [Seite 272]
48.2 - Keywords: [Seite 272]
48.3 - 1 Background [Seite 272]
48.4 - 2 Methodology [Seite 272]
48.4.1 - 2.1. Mathematical model [Seite 272]
48.4.2 - 2.2 Discretization [Seite 273]
48.5 - 3 Physiology of blood flow [Seite 273]
48.5.1 - 3.1 Basic presumptions of fluid mechanics of bloodflow through the aorta [Seite 274]
48.5.2 - 3.2 Entrance region into aorta [Seite 274]
48.6 - 4 Problem description [Seite 274]
48.6.1 - 4.1 Geometry and mesh [Seite 276]
48.6.2 - 4.2 Initial and boundary conditions [Seite 276]
48.7 - 5 Result and discussion [Seite 277]
48.8 - 6. Conclusion [Seite 280]
48.9 - References [Seite 281]
49 - 40Computational modeling of plaque development in the coronary arteries [Seite 282]
49.1 - Abstract. [Seite 282]
49.2 - 1 Introduction [Seite 282]
49.3 - 2 Methods [Seite 283]
49.4 - 3 Results [Seite 284]
49.5 - 4 Discussion and conclusion [Seite 286]
49.6 - Acknowledgments [Seite 286]
49.7 - References [Seite 286]
50 - Plenary Lectures II: Poster Session [Seite 288]
51 - 41People identification using Kinect sensor [Seite 289]
51.1 - Abstract. [Seite 289]
51.2 - Keywords: [Seite 289]
51.3 - 1 Introduction [Seite 289]
51.4 - 2 Proposed solution [Seite 290]
51.4.1 - 2.1 Sensors used, preprocessing performed [Seite 290]
51.4.2 - 2.2 Features used [Seite 290]
51.4.3 - 2.3 Training and testing phase [Seite 290]
51.4.4 - 2.4 Experimental results [Seite 292]
51.5 - 3 Conclusion and future work [Seite 294]
51.6 - References [Seite 294]
52 - 42Artificial Neural Network: Gas recognition [Seite 295]
52.1 - Abstract. [Seite 295]
52.2 - Keywords: [Seite 295]
52.3 - 1 Introduction [Seite 295]
52.4 - 2 Methods [Seite 295]
52.5 - 3 Results and discussion [Seite 297]
52.6 - 4 Conclusion [Seite 298]
52.7 - References [Seite 299]
53 - 43A Fuzzy Model to Predict Risk of Urinary Tract Infection [Seite 301]
53.1 - Abstract. [Seite 301]
53.2 - Keywords: [Seite 301]
53.3 - 1. Introduction [Seite 301]
53.4 - 2. Methods [Seite 302]
53.4.1 - 2.1 Fuzzification and Membership Functions [Seite 302]
53.4.2 - 2.2 Fuzzy Inference System [Seite 303]
53.4.3 - 2.3 Set of Rules [Seite 303]
53.4.4 - 2.4 Defuzzification [Seite 304]
53.5 - 3. Results [Seite 304]
53.6 - 4. Discussion [Seite 305]
53.7 - Acknowledgements [Seite 305]
53.8 - References [Seite 305]
54 - 44Conceptual image of heart - path to patient benefit [Seite 306]
54.1 - Abstract: [Seite 306]
54.2 - Keywords: [Seite 306]
54.3 - 1. Introduction [Seite 306]
54.4 - 2. Conceptual design [Seite 307]
54.5 - 3. Aim [Seite 308]
54.6 - 4. Material and methods [Seite 308]
54.7 - 5. Results [Seite 308]
54.8 - 6. The use and future directions [Seite 310]
54.9 - 7. Conclusion [Seite 310]
54.10 - References: [Seite 311]
55 - 45A Novel Gait Detection Algorithm Based on Wireless Inertial Sensors [Seite 312]
55.1 - Abstract. [Seite 312]
55.2 - Keywords: [Seite 312]
55.3 - 1 Introduction [Seite 312]
55.4 - 2 Method [Seite 313]
55.4.1 - 2.1 Hardware [Seite 313]
55.4.2 - 2.2 Algorithm design. [Seite 313]
55.4.3 - 2.3 Experiment [Seite 314]
55.4.4 - 2.4 The accuracy and latency [Seite 315]
55.5 - 3 Results [Seite 315]
55.6 - 4 Conclusion [Seite 316]
55.7 - Acknowledgement [Seite 316]
55.8 - References [Seite 316]
56 - 46Single-Chip Intrabody Communication Node [Seite 317]
56.1 - Abstract. [Seite 317]
56.2 - Keywords: [Seite 317]
56.3 - 1 Introduction [Seite 317]
56.4 - 2 The IBC system based on PSoC [Seite 318]
56.4.1 - 2.1 Transmitter [Seite 318]
56.4.2 - 2.2 Recevier [Seite 319]
56.5 - 3 Measurement results [Seite 321]
56.6 - 4 Conclusion [Seite 321]
56.7 - Acknowledgement [Seite 322]
56.8 - Conflicts of Interest [Seite 322]
56.9 - References [Seite 322]
57 - 47Microneedle-assisted delivery of NSAIDs [Seite 323]
57.1 - Abstract [Seite 323]
57.2 - Keyword [Seite 323]
57.3 - I Introduction [Seite 323]
57.4 - II MICRONEEDLES-MODERN SYSTEM OF DRUG DELIVERY [Seite 323]
57.5 - III MICRONEEDLE-ASSISTED DELIVERY OF NSAIDS [Seite 325]
57.6 - IV CONCLUSION [Seite 326]
57.7 - REFERENCE [Seite 327]
58 - 48PREPARATION OF NANOEMULSIONS BY HIGH-ENERGY AND LOW-ENERGY EMULSIFICATION METHODS [Seite 329]
58.1 - Abstract. [Seite 329]
58.2 - Keywords: [Seite 329]
58.3 - 1 Introduction [Seite 329]
58.4 - 2 Methods of preparation of nanoemulsions [Seite 330]
58.4.1 - 2.1. High-energy methods for preparation of nanoemulsions [Seite 330]
58.4.2 - 2.2. Low-energy methods for preparation of nanoemulsions [Seite 331]
58.5 - 3 Conclusions [Seite 333]
58.6 - References [Seite 333]
59 - 49Characterisation of NiTi orthodontic archwires characteristic functional properties [Seite 335]
59.1 - Abstract [Seite 335]
59.2 - Key word [Seite 335]
59.3 - Abbreviations [Seite 335]
59.4 - 1 Introduction [Seite 335]
59.5 - 2 Materials and Methods [Seite 337]
59.6 - 3 Results and Discussion [Seite 338]
59.7 - 4 Conclusions [Seite 344]
59.8 - References [Seite 344]
60 - 50LONG-LATENCY INTRACORTICAL INHIBITION DURING UNILATERAL MUSCLE ACTIVITY [Seite 345]
60.1 - Abstract. [Seite 345]
60.2 - Keywords: [Seite 345]
60.3 - Introduction [Seite 345]
60.4 - Methods [Seite 346]
60.5 - Results [Seite 347]
60.6 - Discussion [Seite 349]
60.7 - Conclusions [Seite 349]
60.8 - Acknowledgement [Seite 349]
60.9 - References [Seite 349]
61 - 51Simulation of kinematic behaviour of prosthetic devices [Seite 351]
61.1 - Abstract. [Seite 351]
61.2 - Keywords: [Seite 351]
61.3 - 1 Introduction [Seite 351]
61.4 - 2 Methods [Seite 352]
61.5 - 3 Results [Seite 353]
61.6 - 4 Conclusion [Seite 355]
61.7 - References [Seite 355]
62 - 52Thyroid pathology and platelet functional activity [Seite 356]
62.1 - Abstract. [Seite 356]
62.2 - Keywords: [Seite 356]
62.3 - INTRODUCTION. [Seite 356]
62.4 - MATERIALS AND METHODS. [Seite 357]
62.5 - OWN RESEARCH. [Seite 358]
62.6 - CONCLUSION. [Seite 360]
62.7 - LITERATURE [Seite 361]
63 - 53TRENDS AMONG NEONATOLOGISTS IN DECISION TO VENTILATE PRETERM INFANTS WITH PERMISSIVE HYPERCAPNIA [Seite 362]
63.1 - ABSTRACT: [Seite 362]
63.2 - Aim [Seite 362]
63.3 - Methods [Seite 362]
63.4 - Results [Seite 362]
63.5 - Conclusion [Seite 362]
63.6 - Keywords [Seite 362]
63.7 - INTRODUCTION: [Seite 362]
63.8 - METHODS [Seite 363]
63.9 - RESULTS [Seite 363]
63.10 - DISCUSSION [Seite 364]
63.11 - Literature [Seite 365]
64 - 54A mathematical model of the effect of metabolic control on joint mobility in young type 1 diabetic subjects [Seite 367]
64.1 - Abstract. [Seite 367]
64.2 - 1 Introduction [Seite 367]
64.3 - 2 Materials and methods [Seite 368]
64.3.1 - 2.1 Determination of ankle joint mobility [Seite 368]
64.3.2 - 2.2 Mathematical model [Seite 368]
64.3.3 - 2.3 Fitting [Seite 369]
64.4 - 3 Results [Seite 369]
64.5 - 4 Discussion [Seite 370]
64.6 - References [Seite 370]
65 - 55Basics of mathematical modeling of pulmonary ventilation mechanics and gas exchange [Seite 372]
65.1 - Abstract. [Seite 372]
65.2 - Keywords: [Seite 372]
65.3 - 1 Introduction [Seite 372]
65.4 - 2 Breathing process [Seite 373]
65.4.1 - 2.1 Air movement into and out of the lungs, and processes which cause it [Seite 373]
65.5 - 3 Mathematical modeling of human respiratorysystem [Seite 375]
65.5.1 - 3.1 Generalized system properties [Seite 375]
65.5.2 - 3.2 A simplified linear model of pulmonary ventilation mechanics [Seite 375]
65.5.3 - 3.3 Pulmonary gases exchange [Seite 376]
65.6 - 4 Conclusions [Seite 378]
65.7 - References [Seite 378]
66 - 56TESTING OF THE INFLUENCE OF MEDIA'S pH VALUE ON THE SOLUBILITY AND PARTITION COEFFICIENT OF THE ACETYLSALICYLIC ACID [Seite 380]
66.1 - Abstract. [Seite 380]
66.2 - Keywords: [Seite 380]
66.3 - Introduction* [Seite 380]
66.4 - Material and methods [Seite 380]
66.5 - Results and discussion [Seite 381]
66.6 - Conclusion [Seite 383]
66.7 - References [Seite 383]
67 - 57The use of ELM and MnM servers for the prediction of RANK function in osteoclast formation [Seite 384]
67.1 - Abstract. [Seite 384]
67.2 - Keywords: [Seite 384]
67.3 - 1 Introduction [Seite 384]
67.4 - 1.1. ELM server [Seite 385]
67.5 - 1.2. MiniMotif Miner [Seite 386]
67.6 - 2. Aim and research goals [Seite 386]
67.7 - 3. Material and methods [Seite 387]
67.7.1 - 3.2.The results of RANK analysis with the MnM web-tool [Seite 387]
67.8 - 4.Results [Seite 387]
67.8.1 - 4.1. The results of RANK analysis with the ELM server [Seite 387]
67.9 - 4.Discussion [Seite 387]
67.10 - 5.Conclusion [Seite 389]
67.11 - References [Seite 390]
68 - 58QSAR modeling and structure based virtual screening of new PI3K/mTOR inhibitors as potential anticancer agents [Seite 391]
68.1 - Abstract. [Seite 391]
68.2 - Keywords: [Seite 391]
68.3 - Introduction [Seite 391]
68.4 - Materials and methods [Seite 392]
68.5 - Results and discussion [Seite 394]
68.6 - Conclusion [Seite 395]
68.7 - Conflict of interest [Seite 395]
68.8 - Acknowledgement [Seite 395]
68.9 - References [Seite 395]
69 - 59Career development in Green Biotechnology in B&H: roadblocks and prospects [Seite 396]
69.1 - Abstract. [Seite 396]
69.2 - Keywords: [Seite 396]
69.3 - 1 Introduction [Seite 396]
69.4 - 2 Green biotechnology/Plant biotechnology [Seite 397]
69.5 - 3 Career development [Seite 398]
69.6 - Conflict of interest [Seite 399]
69.7 - References [Seite 399]
70 - 60E-health in Bosnia and Herzegovina: exploring the challenges of widespread adoption [Seite 400]
70.1 - Abstract. [Seite 400]
70.2 - Keywords: [Seite 400]
70.3 - 1 Introduction [Seite 400]
70.4 - 2 Benefits and challenges of e-health [Seite 400]
70.5 - 3 Analysis of the challenges affecting e-health adoption in Bosnia and Herzegovina [Seite 402]
70.5.1 - 3.1 Political issues [Seite 402]
70.5.2 - 3.2 Economic concerns and factors [Seite 402]
70.5.3 - 3.3 Technological challenges [Seite 404]
70.6 - 4 E-health implementation in Bosnia and Herzegovina: SWOT analysis [Seite 405]
70.7 - 5 Conclusion [Seite 406]
70.8 - References [Seite 406]
71 - 615-HIAA and HVA in the Coma Cerebri, Hydrocephalus and Tumor Cerebri [Seite 408]
71.1 - Abstract. [Seite 408]
71.2 - Keywords: [Seite 408]
71.3 - 1 INTRODUCTION [Seite 408]
71.4 - 2 MATERIALS AND METHODS [Seite 410]
71.5 - 3 RESULTS [Seite 411]
71.6 - 4 DISCUSSION [Seite 412]
71.7 - 5 CONCLUSION [Seite 412]
71.8 - REFERENCES [Seite 412]
72 - 62Estimation of lipophilicity data for derivatives of alkandiamine-N,N'-di-2-(3-cyclohexyl) propanoic acid with potential antineoplastic activity, by UHPLC-MS method [Seite 414]
72.1 - ABSTRACT: [Seite 414]
72.2 - Keywords: [Seite 414]
72.3 - 1 INTRODUCTION [Seite 414]
72.4 - 2 EXPERIMENTAL [Seite 415]
72.4.1 - 2.1 Chemicals [Seite 415]
72.4.2 - 2.3 Lipophilicity determination by UHPLC/MS method [Seite 418]
72.5 - 3 RESULTS AND DISCUSSION [Seite 420]
72.6 - CONCLUSION [Seite 421]
72.7 - REFERENCES [Seite 421]
73 - 63Detremination of kinetic effect of Metoprolol and Ranitidine on HRP- modified GC electrode biosensor [Seite 422]
73.1 - Abstract. [Seite 422]
73.2 - Introduction [Seite 422]
73.3 - Materials and methods [Seite 423]
73.4 - Results and discussion [Seite 423]
73.5 - Conclusion [Seite 426]
73.6 - Reference: [Seite 426]
74 - 64Monitoring of bisoprolol fumarate stability under different stress conditions [Seite 427]
74.1 - Abstract. [Seite 427]
74.2 - Keywords: [Seite 427]
74.3 - Introduction [Seite 427]
74.4 - Materials and Methods [Seite 427]
74.5 - Results and Discussion [Seite 428]
74.6 - Conclusion [Seite 435]
74.7 - REFERENCES: [Seite 436]
75 - 65Measuring the feeling: correlations of sensorial to instrumental analyses of cosmetic products [Seite 437]
75.1 - Abstract. [Seite 437]
75.2 - Keywords: [Seite 437]
75.3 - 1 Introduction [Seite 437]
75.4 - 2 Sensorial analysis [Seite 437]
75.5 - 3 Instrumental analyses [Seite 438]
75.6 - 4 Making connections and correlations [Seite 438]
75.7 - 5 Conclusion [Seite 440]
75.8 - References [Seite 440]
76 - 66Hydrophilic antioxidant scores against hydroxyl and peroxyl radicals in honey samples from Bosnia and Herzegovina [Seite 441]
76.1 - Abstract. [Seite 441]
76.2 - Keywords: [Seite 441]
76.3 - 1 Introduction [Seite 441]
76.4 - 2 Methods [Seite 442]
76.4.1 - 2.1 Samples [Seite 442]
76.4.2 - 2.2 Procedure [Seite 442]
76.4.3 - 2.3 Instrumentation [Seite 443]
76.4.4 - 2.4 Statistics [Seite 443]
76.5 - 3 Results and discussion [Seite 443]
76.6 - 4 Conclusion [Seite 444]
76.7 - Acknowledgements [Seite 444]
76.8 - Abbreviations [Seite 444]
76.9 - References [Seite 444]
77 - 67Polymorphisms of 1691G>A and 4070A>G FV in Bosnian women with pregnancy loss [Seite 447]
77.1 - Abstract. [Seite 447]
77.2 - Keywords: [Seite 447]
77.3 - Background [Seite 447]
77.4 - Aim [Seite 448]
77.5 - Statistical analysis [Seite 448]
77.6 - Results [Seite 448]
77.7 - Discussion [Seite 449]
77.8 - Conclusion [Seite 450]
77.9 - Declaration of interest [Seite 450]
77.10 - References [Seite 450]
78 - 68 Lack of association between I/D ACE and -675 ID 4G / 5G PAI-1 polymorphisms and predicting risk of pregnancy loss (PROPALO) in Bosnian women [Seite 452]
78.1 - Abstract. [Seite 452]
78.2 - Keywords: [Seite 452]
78.3 - Introduction [Seite 452]
78.4 - Material and Methods [Seite 453]
78.5 - Statistical analysis [Seite 454]
78.6 - Results [Seite 454]
78.7 - Disscusion [Seite 454]
78.8 - Conclusion [Seite 455]
78.9 - References [Seite 455]
79 - 69Gene clustering using Gene expression data and Self-Organizing Map (SOM) [Seite 457]
79.1 - Abstract. [Seite 457]
79.2 - Keywords: [Seite 457]
79.3 - 1 Introduction [Seite 457]
79.4 - 2 Methods [Seite 458]
79.4.1 - 2.1 Principle of Self-organizing maps (SOM) [Seite 458]
79.4.2 - 2.2 The architecture of the SOM network [Seite 458]
79.4.3 - 2.3 The learning algorithm of SOM [Seite 458]
79.4.4 - 2.4 Establishment of a SOM [Seite 459]
79.5 - 3 Results [Seite 460]
79.6 - 4 Conclusion [Seite 461]
79.7 - References [Seite 462]
80 - 70Public opinion toward GMOs and biotechnology in Bosnia and Herzegovina [Seite 464]
80.1 - Abstract. [Seite 464]
80.2 - Keywords: [Seite 464]
80.3 - 1 Introduction [Seite 464]
80.4 - 2 Methods [Seite 465]
80.5 - 3 Results [Seite 465]
80.6 - 4 Discussion [Seite 467]
80.7 - 5 Conclusion [Seite 468]
80.8 - References [Seite 469]
81 - 71Future trends and possibilities of using induced pluripotent stem cells (iPSC) in regenerative medicine [Seite 471]
81.1 - Abstract: [Seite 471]
81.2 - Keywords. [Seite 471]
81.3 - I. INTRODUCTION [Seite 471]
81.4 - II. ROLE, CLASSIFICATION AND FUNCTION OF STEM CELLS IN THE BODY [Seite 471]
81.5 - III. CONCLUSION [Seite 475]
81.6 - REFERENCES [Seite 476]
82 - Plenary Lectures II - Session VII:BIOMEDICAL SIGNAL PROCESSING 2 [Seite 477]
83 - 72ECG S Signal Cla n assification Using Ar rtificial N eural Netw works: Co omparison n f oft Different Feature T Types [Seite 478]
83.1 - Abstract. [Seite 478]
83.2 - Keywords: [Seite 478]
83.3 - 1 Introduction [Seite 478]
83.4 - 1.1 Related work [Seite 479]
83.5 - 2 Artificial Neural Networks [Seite 479]
83.5.1 - 2.1 Proposed method [Seite 479]
83.6 - 3 Feature extraction [Seite 480]
83.6.1 - 3.1 Time-domain features [Seite 480]
83.6.2 - 3.2 Morphological features [Seite 481]
83.6.3 - 3.3 Statistical features [Seite 482]
83.7 - 4 Results [Seite 482]
83.8 - 5 Conclusion [Seite 484]
83.9 - References [Seite 484]
84 - 73Surface EMG Signal Classification by Using WPD and Ensemble Tree Classifiers [Seite 486]
84.1 - Abstract. [Seite 486]
84.2 - Keywords: [Seite 486]
84.3 - 1 Introduction [Seite 486]
84.4 - 2 Material and Methods [Seite 487]
84.4.1 - 2.1 EMG Signal Data [Seite 487]
84.4.2 - 2.2 Multi-Scale Principal Component Analysis(MSPCA) for De-Noising [Seite 487]
84.4.3 - 2.3 Feature Extraction and Dimension Reduction [Seite 487]
84.4.4 - 2.4 Decision Tree Classifiers [Seite 488]
84.4.5 - 2.5 Ensemble Tree Classifiers [Seite 488]
84.5 - 3 Results and Discussion [Seite 488]
84.5.1 - 3.1 Performance Evaluation Criteria [Seite 488]
84.5.2 - 3.2 Experimental Results [Seite 488]
84.5.3 - 3.3 Discussion [Seite 490]
84.6 - 4 Conclusion [Seite 490]
84.7 - References [Seite 491]
85 - 74Tool for Comparative Case Studies of Heart Rate and Heart Rate Variability [Seite 493]
85.1 - Abstract. [Seite 493]
85.2 - Keywords: [Seite 493]
85.3 - 1 Introduction [Seite 493]
85.4 - 2 Heart Rate and Heart Rate VariabilityAnalyses [Seite 493]
85.5 - 3 HR-HRV Analysis Tool [Seite 494]
85.6 - 4 Case Study: Stress level [Seite 495]
85.7 - 5 Conclusion [Seite 496]
85.8 - References [Seite 496]
86 - 75 A n novel appr roach for p parameter r estimatio on of Fricke -Morse model using D al Differential Impedance Analysis [Seite 498]
86.1 - Abstract . [Seite 498]
86.2 - Keywords [Seite 498]
86.3 - 1 Introduction [Seite 498]
86.4 - 2 A new approach for parameter estimation of Fricke-Morse model using Differential Impedance Analysis [Seite 499]
86.5 - 3 Evaluation of the proposed method [Seite 500]
86.6 - 4 Conclusion [Seite 505]
86.7 - Acknowledgement [Seite 505]
86.8 - References [Seite 505]
87 - 76Wavelet and Teager Energy Operator (TEO) for Heart Sound Processing and Identification [Seite 506]
87.1 - Abstract [Seite 506]
87.2 - Keywords: [Seite 506]
87.3 - 1 Introduction [Seite 506]
87.4 - 2 Materials and Methods [Seite 507]
87.4.1 - 2.1 Teager Energy Operator [Seite 508]
87.4.2 - 2.2 Wavelet Transform [Seite 508]
87.5 - 3 Results [Seite 508]
87.5.1 - 3.1 Graphical User Interface (GUI) [Seite 509]
87.5.2 - 3.2 Examples of TEO without/with Wavelets [Seite 510]
87.6 - 4 Discussion [Seite 511]
87.7 - 5 Conclusion [Seite 513]
87.8 - Conflict of interest [Seite 513]
87.9 - References [Seite 513]
88 - 77Ovary Cancer Detection using Decision Tree Classifiers based on Historical Data of Ovary Cancer Patients [Seite 514]
88.1 - Abstract. [Seite 514]
88.2 - Keywords: [Seite 514]
88.3 - 1 Introduction [Seite 514]
88.4 - 2 Methodology [Seite 515]
88.4.1 - 2.1 Dataset [Seite 515]
88.4.2 - 2.2 Algorithms [Seite 516]
88.5 - 3 Experiments and results [Seite 517]
88.5.1 - 3.1 Feature Selection [Seite 519]
88.6 - 4 Discussion and Conclusion [Seite 519]
88.7 - Conflict of interest [Seite 520]
88.8 - References [Seite 520]
89 - 78Mental workload vs. stress differentiation using single-channel EEG [Seite 522]
89.1 - Abstract. [Seite 522]
89.2 - 1 Introduction [Seite 522]
89.3 - 2 Methods [Seite 522]
89.3.1 - 2.1 Experimental procedure [Seite 522]
89.3.2 - 2.2 Feature extraction [Seite 523]
89.3.3 - 2.3 ECG and EDA features [Seite 524]
89.3.4 - 2.4 Feature selection [Seite 524]
89.4 - 3 Results [Seite 525]
89.5 - 4 Discussion [Seite 525]
89.6 - 5 Conclusion [Seite 526]
89.7 - References [Seite 526]
90 - 79Human-machine interface via EMG signals derived from EEG measurement device [Seite 527]
90.1 - Abstract. [Seite 527]
90.2 - Keywords: [Seite 527]
90.3 - 1 Introduction [Seite 527]
90.4 - 2 Measurement method [Seite 529]
90.5 - 2 Experiment Results [Seite 530]
90.5.1 - 2.1 Discussion [Seite 530]
90.5.2 - 2.2 Conclusion [Seite 530]
90.6 - References [Seite 531]
91 - Plenary Lectures II - Session VIII:BIOMEDICAL IMAGING AND IMAGE PROCESSING 2 [Seite 532]
92 - 80A Novel Feature Extraction Approach with VBM 3D ROI Masks on MRI [Seite 533]
92.1 - Abstract. [Seite 533]
92.2 - Keywords: [Seite 533]
92.3 - 1 Introduction [Seite 533]
92.4 - 2 Material [Seite 534]
92.4.1 - 2.1 OASIS Database [Seite 534]
92.4.2 - 2.2 Preprocessing [Seite 534]
92.5 - 3 Methods [Seite 534]
92.5.1 - 3.1 Voxel Based Morphometry [Seite 534]
92.5.2 - 3.2 Masking of 3D ROI [Seite 535]
92.5.3 - 3.3 Histogram Based First Order Statistics [Seite 536]
92.6 - 4 Experimentals [Seite 538]
92.6.1 - 4.1 Statistical Analysis of FOS [Seite 538]
92.7 - 5 Conclusion [Seite 538]
92.8 - Acknowledgement [Seite 539]
92.9 - References [Seite 539]
93 - 81A modified fuzzy C means algorithm for shading correction in craniofacial CBCT images [Seite 541]
93.1 - Abstract. [Seite 541]
93.2 - Keywords: [Seite 541]
93.3 - 1 Introduction [Seite 541]
93.4 - 2 Methods [Seite 542]
93.4.1 - 2.1 Bias-field estimation [Seite 542]
93.4.2 - 2.2 Data Generation [Seite 544]
93.4.3 - 2.3 Evaluation [Seite 544]
93.5 - 3 Results [Seite 544]
93.5.1 - 3.1 Application on craniofacial CBCT images [Seite 544]
93.6 - 4 Conclusion [Seite 546]
93.7 - References [Seite 547]
94 - 82Detection and Segmentation of Nodules in Chest Radiographs Based on Lifetime Approach [Seite 549]
94.1 - Abstract. [Seite 549]
94.2 - Keywords: [Seite 549]
94.3 - 1 INTRODUCTION [Seite 549]
94.4 - 2 PROPOSED METHOD [Seite 550]
94.5 - 1.1 The Image Database [Seite 551]
94.6 - 1.2 Preprocessing [Seite 551]
94.7 - 1.3 Detection of Candidate Nodules [Seite 551]
94.8 - 1.4 Segmentation of Candidate Nodules [Seite 554]
94.9 - 1.5 Texture Based Feature Extraction [Seite 554]
94.10 - 3 EXPER?MENTAL RESULTS [Seite 555]
94.11 - 4 DISCUSSION and CONCLUSIONS [Seite 556]
94.12 - REFERENCES [Seite 556]
95 - 83Automated Colony Counting Based on Histogram Modeling Using Gaussian Mixture Models [Seite 558]
95.1 - Abstract. [Seite 558]
95.2 - Keywords: [Seite 558]
95.3 - 1 Introduction [Seite 558]
95.4 - 2 Image Processing and Colony Counting [Seite 558]
95.4.1 - 2.1 Image Processing [Seite 559]
95.4.2 - 2.2 Rating and Feature Extraction [Seite 561]
95.5 - 3 Results [Seite 561]
95.6 - 4 Conclusions [Seite 562]
95.7 - Bibliography [Seite 562]
96 - Plenary Lectures II - Session IX: CLINICAL ENGINEERING AND HEALTHTECHNOLOGY ASSESSMENT [Seite 564]
97 - 84The electrode setup for vibratory evoked potentials [Seite 565]
97.1 - Abstract. [Seite 565]
97.2 - Keywords: [Seite 565]
97.3 - 1 Introduction [Seite 565]
97.4 - 2 Materials and methods [Seite 566]
97.5 - 3 Results and discussion [Seite 567]
97.6 - 4 Conclusion [Seite 570]
97.7 - References [Seite 570]
98 - 85Quality control of angular tube current modulation [Seite 571]
98.1 - Abstract: [Seite 571]
98.2 - Keywords: [Seite 571]
98.3 - I. INTRODUCTION [Seite 571]
98.4 - II. MATERIALS AND METHODS [Seite 571]
98.5 - III. RESULTS [Seite 573]
98.6 - IV. DISCUSSION [Seite 573]
98.7 - V. CONCLUSION [Seite 574]
98.8 - ACKNOWLEDGEMENTS [Seite 574]
98.9 - DISCLOSURE OF POTENTIAL CONFLICTS OFINTEREST [Seite 574]
98.10 - REFERENCES [Seite 574]
99 - 86A testbed evaluation of MAC layer protocols for smart home remote monitoring of the elderly mobility pattern [Seite 576]
99.1 - Abstract. [Seite 576]
99.2 - Keywords: [Seite 576]
99.3 - 1 Introduction [Seite 576]
99.4 - 2 Related work [Seite 577]
99.5 - 3 Smart Home Testbed Design [Seite 578]
99.6 - 4 Simulation and testbed analysis [Seite 578]
99.6.1 - 4.1 Observed Event Evaluations [Seite 579]
99.6.2 - 4.2 MAC layer evaluation using our tested [Seite 579]
99.6.3 - 4.3 MAC Layer Evaluation Comparison of CASAS and Conducted Simulation [Seite 580]
99.7 - 5 Conclusion and future works [Seite 581]
99.8 - Acknowledgment [Seite 582]
99.9 - References [Seite 582]
100 - 87Proposal of integrated software system for simulation and GIS visualization of accidents caused by emission of hazardous gases [Seite 584]
100.1 - Abstract. [Seite 584]
100.2 - Keywords: [Seite 584]
100.3 - 1 Introduction [Seite 584]
100.4 - 2 Methods [Seite 585]
100.5 - 3 Results and discussion [Seite 586]
100.5.1 - 3.1 Integrated software system [Seite 586]
100.5.2 - 3.2 "XY plume" model [Seite 588]
100.5.3 - 3.3 "XYZ plume" model [Seite 588]
100.6 - 4 Conclusion [Seite 589]
100.7 - References [Seite 590]
101 - 88LEGAL METROLOGY: MEDICAL DEVICES [Seite 591]
101.1 - Abstract. [Seite 591]
101.2 - Keywords: [Seite 591]
101.3 - 1 Introduction [Seite 591]
101.4 - 2 Directives for conformity assessment of medical devices [Seite 592]
101.5 - 3 Medical devices with measuring function in legal metrology in Bosin and Herzegovina [Seite 593]
101.6 - 4 Conclusion [Seite 595]
101.7 - References [Seite 595]
102 - 89Diagnosis of Chronic Kidney Disease by Using Random Forest [Seite 597]
102.1 - Abstract. [Seite 597]
102.2 - Keywords: [Seite 597]
102.3 - Introduction [Seite 597]
102.4 - Materials and Methods [Seite 598]
102.5 - 2.1 Chronic kidney disease dataset [Seite 598]
102.6 - 2.2 Artificial Neural Network (ANN) [Seite 598]
102.7 - 2.3 Support Vector Machine (SVM) [Seite 599]
102.8 - 2.4 C4.5 Decision Tree [Seite 599]
102.9 - 2.5 Random Forest [Seite 600]
102.10 - 2.6 Experimental Setup and Performance Evaluation [Seite 600]
102.11 - Results and Discussion [Seite 601]
102.12 - Conclusions [Seite 601]
102.13 - Acknowledgments [Seite 601]
102.14 - References [Seite 601]
103 - 90Global Survey on Biomedical Engineering Professionals in Health Technology Assessment [Seite 603]
103.1 - Abstract. [Seite 603]
103.2 - Keywords: [Seite 603]
103.3 - 1 Introduction [Seite 603]
103.4 - 2 Materials and Methods [Seite 604]
103.5 - 3 Results and Discussion [Seite 604]
103.6 - 4 Conclusions [Seite 605]
103.7 - References [Seite 605]
104 - Plenary Lectures II - Session X:BIOINFORMATICS AND COMPUTATIONAL BIOLOGY [Seite 607]
105 - 91CLASSIFICATION OF METABOLIC SYNDROME PATIENTS USING IMPLEMENTED EXPERT SYSTEM [Seite 608]
105.1 - Abstract. [Seite 608]
105.2 - Keywords: [Seite 608]
105.3 - Introduction [Seite 608]
105.4 - Methods [Seite 609]
105.5 - Results [Seite 610]
105.6 - Conclusion [Seite 612]
105.7 - Conflict of interest declaration [Seite 612]
105.8 - References [Seite 612]
106 - 92Pre-classification process symptom questionnaire based on fuzzy logic for pulmonary function test cost reduction [Seite 615]
106.1 - Abstract. [Seite 615]
106.2 - Keywords: [Seite 615]
106.3 - 1 Introduction [Seite 615]
106.4 - 2 The probability of the presence of Asthma or COPD in a patient [Seite 616]
106.4.1 - 2.1 Fuzzy logic algorithm for pre-classification of COPD and asthma [Seite 616]
106.4.2 - 2.2 COPD/Asthma Symptom questionnaire [Seite 617]
106.4.3 - 2.3 Pre-classification system validation [Seite 618]
106.5 - 3 Result and discussion [Seite 619]
106.6 - 4 Conclusion [Seite 622]
106.7 - References [Seite 622]
107 - 93Artificial Neural Network and Docking Study in Design and Synthesis of Xanthenes as Antimicrobial Agents [Seite 624]
107.1 - Abstract. [Seite 624]
107.2 - Keywords: [Seite 624]
107.3 - 1 INTRODUCTION [Seite 624]
107.4 - 2 EXPERIMENTAL [Seite 625]
107.4.1 - 2.1. Instrumentation [Seite 625]
107.4.2 - 2.2. General procedure for the synthesis of arylsubstituted xanthenes [Seite 625]
107.4.3 - 2.3. Antimicrobial activity [Seite 625]
107.4.4 - 2.4. Calculating lipophilicity parameters [Seite 625]
107.4.5 - 2.5. Docking study and physicochemical properties calculations [Seite 625]
107.4.6 - 2.6 Artificial Neural Network for prediction thecompounds activity against Escherichia coli and Candida albicans strains [Seite 626]
107.5 - 3 RESULTS AND DISSCUSION [Seite 626]
108 - 94Mathematical and Computational Models of Cell Cycle in Higher Eukaryotes [Seite 634]
108.1 - Abstract. [Seite 634]
108.2 - Keywords: [Seite 634]
108.3 - 1 INTRODUCTION [Seite 634]
108.4 - 2 MATHEMATICAL ANDCOMPUTATIONAL MODELING [Seite 634]
108.4.1 - 2.1 MODELING WITH DIFFERENTIALEQUATIONS [Seite 635]
108.4.2 - 2.2 MODELING WITH PETRI NETS [Seite 635]
108.4.3 - 2.3 LAW OF MASS ACTION [Seite 635]
108.4.4 - 2.4 FIRST-ORDER OR UNIMOLECULAR REACTIONS [Seite 636]
108.4.5 - 2.5 SECOND-ORDER OR BIMOLECULARREACTIONS [Seite 636]
108.4.6 - 2.6 REVERSIBILE MASS ACTION ORREVERSIBLE REACTION [Seite 636]
108.4.7 - 2.7 STEADY STATES [Seite 637]
108.4.8 - 2.8 STABILITY ANALYSIS [Seite 639]
108.5 - 3 CONCLUSION [Seite 639]
108.6 - APPENDIX [Seite 640]
108.7 - REFERENCES [Seite 640]
109 - Plenary Lectures II - Session XI: HEALTH INFORMATICS, E-HEALTH AND TELEMEDICINE [Seite 642]
110 - 95Using Information and Communications Technology as an Enabler for Designing an Efficient National Level Vaccination Planning and Dispensing System [Seite 643]
110.1 - Abstract: [Seite 643]
110.2 - Keywords: [Seite 643]
110.3 - I. INTRODUCTION AND MOTIVATION [Seite 643]
110.4 - II. LITERATURE REVIEW [Seite 644]
110.5 - III. PROPOSED METHODOLOGY [Seite 644]
110.6 - ACKNOWLEDGMENT [Seite 648]
110.7 - CONFLICT OF INTEREST [Seite 648]
110.8 - REFERENCES [Seite 648]
110.9 - V. CONCLUSION [Seite 648]
111 - 96Stroke Center Heart Rate Data Acquisition [Seite 650]
111.1 - Abstract. [Seite 650]
111.2 - Keywords: [Seite 650]
111.3 - 1 Introduction [Seite 650]
111.4 - 2 Stroke Center Description [Seite 651]
111.5 - 3 Heart Rate Monitor Implementation [Seite 651]
111.6 - 3.1 Hardware Implementation [Seite 652]
111.7 - 4 Discussion and Conclusion [Seite 653]
111.8 - References [Seite 653]
112 - 97Health service quality measurement from patient reviews in Turkish by opinion mining [Seite 655]
112.1 - Abstract. [Seite 655]
112.2 - Keywords: [Seite 655]
112.3 - 1 Introduction [Seite 655]
112.4 - 2 Experiment Setup [Seite 656]
112.4.1 - 2.1 Data Collection [Seite 656]
112.4.2 - 2.2 Data Pre-Processing [Seite 656]
112.4.3 - 2.3 Classification [Seite 658]
112.4.4 - 2.4 Interpretation of Results [Seite 658]
112.5 - 3 Conclusions [Seite 659]
112.6 - References [Seite 659]
113 - 98e-Medical Test Recommendation System Based on the Analysis of Patients' Symptoms and Anamneses [Seite 660]
113.1 - Abstract. [Seite 660]
113.2 - Keywords: [Seite 660]
113.3 - 1 Introduction [Seite 660]
113.4 - 2 Related Work [Seite 660]
113.5 - 3 Methodology [Seite 661]
113.5.1 - 3.1 Data [Seite 661]
113.5.2 - 3.2 Methods [Seite 661]
113.6 - 4 Results [Seite 663]
113.7 - 5 Conclusion [Seite 663]
113.8 - References [Seite 664]
114 - 99Antihypertensive therapy dosage calculator [Seite 666]
114.1 - Keywords: [Seite 666]
114.2 - 1. Introduction [Seite 666]
114.3 - 2. Aim [Seite 668]
114.4 - 3. Material and methods [Seite 668]
114.5 - 4. Software [Seite 670]
114.6 - 5. Conclusion [Seite 671]
114.7 - References [Seite 671]
115 - 100Wireless Body Area Network Studies for Telemedicine Applications Using IEEE 802.15.6 Standard [Seite 672]
115.1 - Abstract. [Seite 672]
115.2 - 1 Introduction [Seite 672]
115.3 - 1.1 Related Work [Seite 673]
115.4 - 2 Design [Seite 674]
115.4.1 - 2.1 Hardware [Seite 674]
115.4.2 - 2.2 Firmware [Seite 674]
115.5 - 2.3 Service Layer [Seite 674]
115.6 - 3 Conclusions [Seite 676]
115.7 - 4 Acknowledgments [Seite 676]
115.8 - References [Seite 676]
116 - 101Real-Time Monitoring of ST Change for Telemedicine [Seite 677]
116.1 - Abstract. [Seite 677]
116.2 - Keywords: [Seite 677]
116.3 - 1 Introduction [Seite 677]
116.4 - 2 Related Studies [Seite 678]
116.5 - 3 Proposed Method [Seite 678]
116.5.1 - 3.1 Database [Seite 679]
116.5.2 - 3.2 Feature Extraction [Seite 679]
116.5.3 - 3.3 Classification [Seite 680]
116.5.4 - 3.4 Results [Seite 680]
116.6 - 4 Discussion and Conclusions [Seite 682]
116.7 - Acknowledgment [Seite 682]
116.8 - References [Seite 682]
117 - Plenary Lectures II - Session XII:BIOMEDICAL SIGNAL PROCESSING 3 [Seite 684]
118 - 102Micro cell culture analog Apparatus (uCCA) output prediction using microcontroller system based on a Artificial Neural Network [Seite 685]
118.1 - Abstract [Seite 685]
118.2 - Keywords [Seite 685]
118.3 - I Introduction [Seite 685]
118.4 - II Theoretical Considerations [Seite 686]
118.5 - III System Description [Seite 686]
118.6 - IV The Implementation [Seite 687]
118.7 - V Results and Discussion [Seite 687]
118.8 - VI Conclusion [Seite 688]
118.9 - References [Seite 688]
119 - 103CLASSIFICATION OF PREDIABETES AND TYPE 2 DIABETES USING ARTIFICIAL NEURAL NETWORK [Seite 689]
119.1 - Abstract. [Seite 689]
119.2 - Keywords: [Seite 689]
119.3 - 1 Introduction [Seite 689]
119.4 - 2 Methods [Seite 690]
119.5 - 2.1 Dataset for development of Artificial NeuralNetwork for classification of prediabetes anddiabetes type 2 [Seite 690]
119.6 - 2.2 Artificial Neural Network for classification of prediabetes and diabetes type 2 [Seite 690]
119.7 - 3 Results [Seite 691]
119.7.1 - 3.1 Training performance of developed ANN [Seite 691]
119.7.2 - 3.2 Testing performance of developed ANN [Seite 692]
119.8 - 4 Conclusion [Seite 693]
119.9 - References [Seite 693]
120 - 104Multi-biophysical event detection using blind source separated audio signals [Seite 694]
120.1 - Abstract. [Seite 694]
120.2 - 1 Introduction [Seite 694]
120.3 - 2 Background [Seite 694]
120.4 - 3 Materials and Methods [Seite 695]
120.5 - 4 Results [Seite 696]
120.6 - 5 Discussion [Seite 697]
120.7 - 6 Conclusion [Seite 700]
120.8 - References [Seite 700]
121 - 105Diversity performance of microstrip patch antennas placed on human body at ISM and MBAN frequencies. [Seite 702]
121.1 - Abstract. [Seite 702]
121.2 - Keywords: [Seite 702]
121.3 - 1 Introduction [Seite 702]
121.4 - 2 Design and simulation of the microstripantenna placed on a body [Seite 702]
121.5 - 3 Diversity measurements in the indoor environment for static and dynamic cases [Seite 703]
121.6 - 4 Results and Conclusion [Seite 708]
121.7 - References [Seite 708]
122 - 106Flexible system for HRV analysis using PPG signal [Seite 709]
122.1 - Abstract. [Seite 709]
122.2 - Keywords: [Seite 709]
122.3 - 1 Introduction [Seite 709]
122.4 - 2 Processing approach [Seite 710]
122.5 - 3 Verification and comparison procedure [Seite 711]
122.6 - 4 Experimental results [Seite 711]
122.7 - 5 Conclusion and future work [Seite 714]
122.8 - 6 Statements [Seite 714]
122.9 - References [Seite 716]
122.10 - Appendix [Seite 716]
123 - 107AN APPLICATION OF KINECT DEPTH SENSOR FOR SCOLIOSIS AND KYPHOSIS SCREENING [Seite 717]
123.1 - Abstract. [Seite 717]
123.2 - Keywords: [Seite 717]
123.3 - 1 Introduction [Seite 717]
123.4 - 2 Material and Method [Seite 717]
123.5 - 3 RESULTS AND DISCUSSION [Seite 718]
123.6 - 3.1 Scoliosis evaluation [Seite 718]
123.7 - 3.2 Kyphosis evaluation [Seite 720]
123.8 - 4 CONCLUSIONS [Seite 720]
123.9 - References [Seite 721]
124 - 108Development of a muscle activated switch for the severelydebilitated [Seite 722]
124.1 - Abstract. [Seite 722]
124.2 - Keywords: [Seite 722]
124.3 - 1 INTRODUCTION [Seite 722]
124.4 - 2 METHODOLOGY [Seite 722]
124.4.1 - 2.1 The Existing System and Setup [Seite 723]
124.4.2 - 2.2 Investigating EMG Signal Sensitivity [Seite 723]
124.4.3 - 2.3 Statistical Measures of Performance [Seite 724]
124.4.4 - 2.4 Detecting EMG Signals of Differing Magnitudes [Seite 724]
124.5 - 3 RESULTS & DISCUSSION [Seite 724]
124.5.1 - 3.2 Findings for detecting EMG Signals with differingmagnitudes and velocity [Seite 725]
124.6 - 4 Conclusion [Seite 725]
124.7 - References [Seite 726]
125 - 109A Dynamic Stopping Algorithm for P300 Based Brain Computer Interface Systems [Seite 727]
125.1 - Abstract. [Seite 727]
125.2 - Keywords: [Seite 727]
125.3 - 1 INTRODUCTION [Seite 727]
125.4 - 2 Mater?al and Method [Seite 728]
125.4.1 - 2.1 Dataset I: BCI Competition III Dataset II [Seite 728]
125.4.2 - 2.2 Dataset II: Region Based Paradigm [Seite 728]
125.4.3 - 2.3 Signal Processing and Classification [Seite 729]
125.5 - 3 Dynamic Stopping Algorithm [Seite 729]
125.6 - 4 Results and Discussion [Seite 730]
125.7 - 5 Conclusion [Seite 732]
125.8 - CONFLICT OF INTEREST [Seite 732]
125.9 - REFERENCES [Seite 732]
126 - Plenary Lectures II - Session XIII:PHARMACEUTICAL ENGINEERING [Seite 733]
127 - 110Effects of various metal and drug agents on excretion of enzyme aspartyl proteinasein Candida albicans and its role in human physiological processes [Seite 734]
127.1 - Abstract. [Seite 734]
127.2 - Keywords [Seite 734]
127.3 - 1 Introduction [Seite 734]
127.4 - 2 Materials and Methods [Seite 735]
127.4.1 - 2.1 Reagents, strains and growth conditions [Seite 735]
127.4.2 - 2.2 Metal and drug supplements [Seite 735]
127.4.3 - 2.3 Sample preparation and aspartyl proteinase assay [Seite 735]
127.4.4 - 2.4 Determination of total protein amount (aspartyl proteinase) [Seite 735]
127.5 - 3 Results and Discussion [Seite 735]
127.5.1 - 3.1 Addition of iron supplements promotesC. albicans proliferation in vitro [Seite 735]
127.5.2 - 3.2 Iron uptake by C. albicans induces itsaspartyl proteinase excretion [Seite 735]
127.5.3 - 3.3 Analgesics are potential promoters of fungalvirulence and pathogenicity [Seite 736]
127.5.4 - 3.4 Subinhibitory concentrations of antibiotics stimulate excretion of aspartyl proteinasein C. albicans [Seite 736]
127.6 - 4 Conclusion [Seite 737]
127.7 - 5 Declaration of Conflict Interests [Seite 737]
127.8 - References [Seite 737]
128 - 111 practical Transport Optimization Method and Concept in Pharmaceutical Industry [Seite 739]
128.1 - Abstract. [Seite 739]
128.2 - Keyword: [Seite 739]
128.3 - 1 Introduction [Seite 739]
128.4 - 2 Case Study [Seite 740]
128.5 - 3 Results Discussion [Seite 743]
128.6 - 4 Conclusion and Future Research [Seite 745]
128.7 - References [Seite 745]
129 - 112Antiproliferative Evaluation and Docking Study of Synthesized Biscoumarin Derivatives [Seite 747]
129.1 - Abstract. [Seite 747]
129.2 - Keywords: [Seite 747]
129.3 - 1 INTRODUCTION [Seite 747]
129.4 - 2 MATERIALS AND METHODS [Seite 747]
129.5 - Cell culturing [Seite 748]
129.6 - Proliferation assays [Seite 748]
129.7 - Docking study and physicochemical properties calculations [Seite 749]
129.8 - 3 RESULTS AND DISCUSSION [Seite 749]
129.9 - 4 CONCLUSIONS [Seite 757]
129.10 - REFERENCES [Seite 757]
130 - 113Passive absorption prediction of transdermal drug application with Artificial Neural Network [Seite 759]
130.1 - Abstract [Seite 759]
130.2 - Keywords: [Seite 759]
130.3 - INTRODUCTION [Seite 759]
130.4 - Materials and methods [Seite 761]
130.5 - Results and discussion [Seite 762]
130.6 - CONCLUSION [Seite 763]
130.7 - References [Seite 763]
131 - 114The role of population pharmacokinetic analysis in rational antibiotic therapy in neonates [Seite 765]
131.1 - ABSTRACT. [Seite 765]
131.2 - Keywords: [Seite 766]
131.3 - INTRODUCTION [Seite 766]
131.4 - METHODS [Seite 766]
131.5 - RESULTS [Seite 766]
131.6 - DISCUSSION AND CONCLUSION [Seite 769]
131.7 - CONFLICT OF INTEREST STATEMENT [Seite 770]
131.8 - REFERENCES [Seite 770]
132 - 115The ratio of hematological parameters and markers of inflammation in patients with iron deficiency and pernicious anemia [Seite 772]
132.1 - ABSTRACT [Seite 772]
132.2 - Keywords [Seite 772]
132.3 - AIM: [Seite 772]
132.4 - MATERIALS AND METHODS: [Seite 772]
132.5 - RESULTS: [Seite 772]
132.6 - Introduction: [Seite 772]
132.7 - Metodology: [Seite 773]
132.8 - Equipment: [Seite 773]
132.9 - Results and discusion: [Seite 773]
132.10 - References: [Seite 776]
133 - 116BLOOD GROUP, HYPERTENSION, AND OBESITY IN THE STUDENT POPULATION OF NORTHEAST BOSNIA AND HERZEGOVINA [Seite 777]
133.1 - Abstract [Seite 777]
133.2 - Keywords: [Seite 777]
133.3 - INTRODUCTION [Seite 777]
133.4 - MATERIALS AND METHODS [Seite 777]
133.5 - RESULTS [Seite 778]
133.6 - DISCUSSION [Seite 779]
133.7 - CONCLUSION [Seite 779]
133.8 - ACKNOWLEDGEMENTS [Seite 779]
133.9 - Conflict of interest [Seite 780]
133.10 - LITERATURE [Seite 780]
134 - Plenary Lectures II - Session XIV:GENETIC ENGINEERING [Seite 781]
135 - 117Free fatty acid profile in Type 2 diabetic subjects with different control of glycemia [Seite 782]
135.1 - Abstract. [Seite 782]
135.2 - Keywords: [Seite 782]
135.3 - INTRODUCTION [Seite 782]
135.4 - MATERIALS AND METHODS [Seite 783]
135.5 - RESULTS [Seite 783]
135.6 - DISCUSSION [Seite 785]
135.7 - CONCLUSION [Seite 786]
135.8 - REFERENCES [Seite 786]
136 - 118Clonal selection of autochthonous grape variety Vranac in Montenegro [Seite 788]
136.1 - Abstract. [Seite 788]
136.2 - Keywords [Seite 788]
136.3 - 1 Introduction [Seite 788]
136.4 - 2 Matherial and methods [Seite 789]
136.5 - 3 Results [Seite 789]
136.6 - 4 Discussion and conclusions [Seite 791]
136.7 - 5 References [Seite 791]
137 - 119A Dissimilar Approach to Associating Angiotensin Converting Enzyme Polymorphisms [Seite 792]
137.1 - Abstract. [Seite 792]
137.2 - Keywords: [Seite 792]
137.3 - 1 Introduction [Seite 792]
137.4 - 2 Methods [Seite 793]
137.4.1 - 2.1 Genomic DNA isolation [Seite 793]
137.4.2 - 2.2 Spectrophotometry [Seite 793]
137.4.3 - 2.3 Quantification with Gel Electrophoresis [Seite 793]
137.4.4 - 2.4 ACE Gene Amplification [Seite 793]
137.4.5 - 2.5 Confirmatory Polymerase Chain Reaction [Seite 794]
137.4.6 - 2.6 Polymerase Chain Reaction Product GelElectrophoresis [Seite 794]
137.4.7 - 2.7 Genotyping [Seite 794]
137.4.8 - 2.8 Artificial Neural Network Development [Seite 794]
137.5 - 3 Results [Seite 794]
137.6 - 4 Discussion [Seite 795]
137.7 - Conflict of Interest Declaration [Seite 795]
137.8 - Ethical Statement [Seite 795]
137.9 - References [Seite 795]
138 - 120Successful collection of stem cells in one day in the process of autologous stem cell transplantation [Seite 797]
138.1 - Abstract. [Seite 797]
138.2 - Keywords: [Seite 797]
138.3 - 1 Introduction [Seite 797]
138.4 - 2 Material and methods [Seite 798]
138.5 - 3 Results [Seite 798]
138.6 - 4 Discussion: [Seite 800]
138.7 - Conflict of intrest [Seite 801]
138.8 - References [Seite 801]
139 - Author Index [Seite 803]

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