Pub Date : 2023-08-25DOI: 10.4015/s1016237223500187
Alka Yadav, Yoggender Aggarwal, K. Mukherjee
Breast cancer (BC) is a critical health issue that affects countless women, and it is the second leading reason of death worldwide. The phosphatidylinositol 3 kinases (PI3Ks) constitute a group of lipid kinases that play a role in tumorigenesis, development, migration, infiltration, programmed cell death, glycogen synthesis, DNA correction and viability by the PI3K/Akt cascade. The PI3K pathway has been linked to a variety of malignancies and increases the activation rate of cancer. Here, focus was given to the study of PI3K pathway involved in BC and emphasis was given on a particular nSH2 domain that resides in the regulatory subunit of PI3K to find a potent inhibitor. A detailed pathway and interaction study was performed from KEGG pathway database and from the cystoscope. A total list of 60 compounds, comprises phytochemicals, and herbal compounds were screened based on structural similarity and eight FDA-approved drugs were considered. The docking analysis was carried over through the AutoDock software and Ligplot analysis was performed to investigate the interaction between the nSH2 domain and the potent inhibitors. To ensure the complex stability, 20 ns of simulation run was also performed on the best complexes using GROMACS. From this study, it can be concluded that Evodia fruit has the maximum stability in the catalytic region among all the listed inhibitors against the target proteins and can act as a potent inhibitor among the others.
{"title":"IDENTIFICATION AND SCREENING OF PLANT-BASED POTENT INHIBITORS AGAINST NSH2 DOMAIN OF PI3K OF BREAST CANCER USING DOCKING AND SIMULATION STUDIES","authors":"Alka Yadav, Yoggender Aggarwal, K. Mukherjee","doi":"10.4015/s1016237223500187","DOIUrl":"https://doi.org/10.4015/s1016237223500187","url":null,"abstract":"Breast cancer (BC) is a critical health issue that affects countless women, and it is the second leading reason of death worldwide. The phosphatidylinositol 3 kinases (PI3Ks) constitute a group of lipid kinases that play a role in tumorigenesis, development, migration, infiltration, programmed cell death, glycogen synthesis, DNA correction and viability by the PI3K/Akt cascade. The PI3K pathway has been linked to a variety of malignancies and increases the activation rate of cancer. Here, focus was given to the study of PI3K pathway involved in BC and emphasis was given on a particular nSH2 domain that resides in the regulatory subunit of PI3K to find a potent inhibitor. A detailed pathway and interaction study was performed from KEGG pathway database and from the cystoscope. A total list of 60 compounds, comprises phytochemicals, and herbal compounds were screened based on structural similarity and eight FDA-approved drugs were considered. The docking analysis was carried over through the AutoDock software and Ligplot analysis was performed to investigate the interaction between the nSH2 domain and the potent inhibitors. To ensure the complex stability, 20 ns of simulation run was also performed on the best complexes using GROMACS. From this study, it can be concluded that Evodia fruit has the maximum stability in the catalytic region among all the listed inhibitors against the target proteins and can act as a potent inhibitor among the others.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"199 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75975212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-24DOI: 10.4015/s1016237223500205
Saeideh Fouladlou, Mehdi Rajabioun, Darya Bahojb Hashemian
Cancer is a major health concern that affects a significant number of people worldwide and can often result in fatalities. Therefore, there is a growing need to develop effective approaches for early diagnosis and classification of different types of cancer. Early detection of cancer is crucial for prompt and accurate treatment. Thus, researchers have been working to identify non-invasive and precise methods for the early diagnosis, monitoring, and control of cancer. Leukemia and prostate cancer are two of the most common types of cancer globally. Microarray data analysis has become a valuable tool for diagnosing and classifying different types of cancerous tissues. To improve the accuracy of diagnosis, hybrid algorithms and neural networks are being employed. This paper provides a review of different biomarkers for leukemia and prostate cancer and proposes a novel method for distinguishing between the two cancers. The proposed method includes appropriate gene selection, a new hybrid model, and differential analysis of microarray data to create a diagnostic tool. The results indicate that the proposed algorithm is highly accurate and efficient in selecting a small set of valuable genes to improve classification accuracy. In conclusion, the accurate diagnosis and classification of cancer are essential for timely and effective treatment. The proposed method can contribute to the development of a reliable diagnostic tool for leukemia and prostate cancer, and the application of microarray data and hybrid algorithms can be useful for diagnosing other types of cancer as well.
{"title":"IDENTIFICATION OF EFFECTIVE GENES OF MULTIPLE CANCERS USING NEURAL NETWORK","authors":"Saeideh Fouladlou, Mehdi Rajabioun, Darya Bahojb Hashemian","doi":"10.4015/s1016237223500205","DOIUrl":"https://doi.org/10.4015/s1016237223500205","url":null,"abstract":"Cancer is a major health concern that affects a significant number of people worldwide and can often result in fatalities. Therefore, there is a growing need to develop effective approaches for early diagnosis and classification of different types of cancer. Early detection of cancer is crucial for prompt and accurate treatment. Thus, researchers have been working to identify non-invasive and precise methods for the early diagnosis, monitoring, and control of cancer. Leukemia and prostate cancer are two of the most common types of cancer globally. Microarray data analysis has become a valuable tool for diagnosing and classifying different types of cancerous tissues. To improve the accuracy of diagnosis, hybrid algorithms and neural networks are being employed. This paper provides a review of different biomarkers for leukemia and prostate cancer and proposes a novel method for distinguishing between the two cancers. The proposed method includes appropriate gene selection, a new hybrid model, and differential analysis of microarray data to create a diagnostic tool. The results indicate that the proposed algorithm is highly accurate and efficient in selecting a small set of valuable genes to improve classification accuracy. In conclusion, the accurate diagnosis and classification of cancer are essential for timely and effective treatment. The proposed method can contribute to the development of a reliable diagnostic tool for leukemia and prostate cancer, and the application of microarray data and hybrid algorithms can be useful for diagnosing other types of cancer as well.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"80 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88172444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-24DOI: 10.4015/s1016237223500217
Bipasha Patnaik, H. Palo, Santanu Sahoo
Cardiac Arrhythmia is an abnormal heart rhythm that develops when the electrical impulses control the heart’s contraction which does not function properly. The heart can beat too fast (tachycardia), too slow (bradycardia), or in an irregular pattern. Observing ECG signal peaks and channels freehand is difficult due to their ingenious modification. Automated detection of cardiovascular abnormalities is preferred for the early diagnosis of cardiac disorders. This paper used machine learning approaches for detecting ECG abnormality utilizing a Support Vector Machine (SVM) and Cost-Sensitive Decision-Tree (CS-DT) classifier. The Empirical Mode Decomposition approach was utilized to examine the properties of R-peaks and QRS complexes in ECG signs. Various morphological characteristics are analyzed from the signal penetrated by the classifier to diagnose the irregular beats. A set of twenty-two clinically feasible features comprising temporal, morphological, and statistical were extracted from the processed ECG signals and applied to the classifier to categorize cardiovascular irregularities like Normal (N), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), Atrial Premature Beats (APB), and Premature Ventricular Contraction (PVC). The Beth Israel Hospital at Massachusetts Institute of Technology (MIT-BIH) dataset has been used for this work, where feature datasets are split into training and evaluation subsets. The training set is used to train machine learning models on the extracted features, while the evaluation set is used to assess the performance of the trained models. The evaluation metrics such as Accuracy (Acc), Sensitivity (Se), Specificity (Sp), and Positive Predictivity (Pp), are frequently used to evaluate the model’s performance in Arrhythmia detection along with classification. The simulation has been conducted using SVM and CS-DT classifier with performance for all individual class labels at a Confidence Factor (CF) of 0.5. The performance of the time and frequency domain features is merged resulting in higher classification of Sensitivity, Specificity, Positive Predictivity, and Accuracy of 89.5%, 98.11%, 87.76%, and 96.8% in SVM, 97.71%, 99.58%, 97.66%, 99.32% in CS-DT classifier in identifying the irregular heartbeats.
{"title":"MACHINE LEARNING APPROACH TO DETECT ECG ABNORMALITIES USING COST-SENSITIVE DECISION TREE CLASSIFIER","authors":"Bipasha Patnaik, H. Palo, Santanu Sahoo","doi":"10.4015/s1016237223500217","DOIUrl":"https://doi.org/10.4015/s1016237223500217","url":null,"abstract":"Cardiac Arrhythmia is an abnormal heart rhythm that develops when the electrical impulses control the heart’s contraction which does not function properly. The heart can beat too fast (tachycardia), too slow (bradycardia), or in an irregular pattern. Observing ECG signal peaks and channels freehand is difficult due to their ingenious modification. Automated detection of cardiovascular abnormalities is preferred for the early diagnosis of cardiac disorders. This paper used machine learning approaches for detecting ECG abnormality utilizing a Support Vector Machine (SVM) and Cost-Sensitive Decision-Tree (CS-DT) classifier. The Empirical Mode Decomposition approach was utilized to examine the properties of R-peaks and QRS complexes in ECG signs. Various morphological characteristics are analyzed from the signal penetrated by the classifier to diagnose the irregular beats. A set of twenty-two clinically feasible features comprising temporal, morphological, and statistical were extracted from the processed ECG signals and applied to the classifier to categorize cardiovascular irregularities like Normal (N), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), Atrial Premature Beats (APB), and Premature Ventricular Contraction (PVC). The Beth Israel Hospital at Massachusetts Institute of Technology (MIT-BIH) dataset has been used for this work, where feature datasets are split into training and evaluation subsets. The training set is used to train machine learning models on the extracted features, while the evaluation set is used to assess the performance of the trained models. The evaluation metrics such as Accuracy (Acc), Sensitivity (Se), Specificity (Sp), and Positive Predictivity (Pp), are frequently used to evaluate the model’s performance in Arrhythmia detection along with classification. The simulation has been conducted using SVM and CS-DT classifier with performance for all individual class labels at a Confidence Factor (CF) of 0.5. The performance of the time and frequency domain features is merged resulting in higher classification of Sensitivity, Specificity, Positive Predictivity, and Accuracy of 89.5%, 98.11%, 87.76%, and 96.8% in SVM, 97.71%, 99.58%, 97.66%, 99.32% in CS-DT classifier in identifying the irregular heartbeats.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"26 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90933466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-02DOI: 10.4015/s101623722350014x
R. Jeevan, B. M. Murari
Tissue valve in combination with a mechanical valve is predominantly used in stented valvular prostheses. Porcine pericardium (PP) is a promising xenograft in addition to the predominately used porcine aortic valve (PAV) and bovine pericardium (BP) in heart valve replacement. Tissue valves are structurally similar to the valve cusps, upon fixation they function as structural and functional units to restore the failing heart valves. In this paper, the characterization, design and performance of PP based prosthetic mitral leaflets are analyzed. Uniaxial tensile test was performed to characterize glutaraldehyde (GA)-treated PP and evaluate its mechanical properties. Finite element methods were instrumental to design and analyze the performance of PP leaflets. Different geometric parameters were analyzed to obtain ideal valve performance. Since geometrical parameters influence valve performance, two leaflet models of trileaflet and quadrileaflet configuration were studied. BP and PAV leaflet models were designed and analyzed as controls to compare the performance of PP. The stress distribution, bending momentum and coaptation pattern from the finite element determine the performance of the geometrical models. PP exhibited anisotropy, promising tensile strength and pliability. A thinner porcine pericardium with promising tensile strength and pliability is ideal for the development of low-profile prosthetic valves. The quadrileaflet model exhibited.
{"title":"IN SILICO MODELING OF PORCINE PERICARDIAL TISSUE LEAFLETS FOR TRANSCATHETER MITRAL VALVE REPLACEMENT","authors":"R. Jeevan, B. M. Murari","doi":"10.4015/s101623722350014x","DOIUrl":"https://doi.org/10.4015/s101623722350014x","url":null,"abstract":"Tissue valve in combination with a mechanical valve is predominantly used in stented valvular prostheses. Porcine pericardium (PP) is a promising xenograft in addition to the predominately used porcine aortic valve (PAV) and bovine pericardium (BP) in heart valve replacement. Tissue valves are structurally similar to the valve cusps, upon fixation they function as structural and functional units to restore the failing heart valves. In this paper, the characterization, design and performance of PP based prosthetic mitral leaflets are analyzed. Uniaxial tensile test was performed to characterize glutaraldehyde (GA)-treated PP and evaluate its mechanical properties. Finite element methods were instrumental to design and analyze the performance of PP leaflets. Different geometric parameters were analyzed to obtain ideal valve performance. Since geometrical parameters influence valve performance, two leaflet models of trileaflet and quadrileaflet configuration were studied. BP and PAV leaflet models were designed and analyzed as controls to compare the performance of PP. The stress distribution, bending momentum and coaptation pattern from the finite element determine the performance of the geometrical models. PP exhibited anisotropy, promising tensile strength and pliability. A thinner porcine pericardium with promising tensile strength and pliability is ideal for the development of low-profile prosthetic valves. The quadrileaflet model exhibited.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"81 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81666605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-27DOI: 10.4015/s1016237223500151
R. K. Sinha, Joyani Das, P. Mazumder, Yogender Aggarwal
Diabetes mellitus (DM) is a multifaceted disease that leads to higher cardiovascular events with neuronal damage, inflammation, and oxidative stress in subjects. It also causes an autonomic imbalance with the onset of the disease which disturbs the cardiac dynamics. This work demonstrates the rutin in treating the inflammation caused by hyperglycemia through nonlinear heart rate variability features in predicting diabetes using a support vector machine (SVM). The lead-I electrocardiogram was acquired from the control, experimental, and treated group of the male Wister rats ([Formula: see text] gm and age 10–12 weeks). A dataset of 669 samples was obtained from the recorded ECG signal and taken as input vectors to the SVM. The observed results presented an accuracy of 92.9% in classifying the control and experimental group. Further, the same model with the treated group dataset showed an accuracy of 7.7% (samples nearer to the experimental group) while 92.3% of samples were close to the control group. The findings suggested the efficacy of rutin drugs in restoring the blood sugar level and the sympathovagal balance. The usefulness of the non-invasive technique in the prognosis of the disease gives direction in the design and development of the computer-aided cost-effective wearable system. However, the need for expert clinicians cannot be ignored.
{"title":"NONLINEAR HEART RATE VARIABILITY FEATURES IN DEPICTING THE EFFICACY OF RUTIN UNDER STREPTOZOTOCIN-INDUCED DIABETES MODEL WITH SUPPORT VECTOR MACHINE","authors":"R. K. Sinha, Joyani Das, P. Mazumder, Yogender Aggarwal","doi":"10.4015/s1016237223500151","DOIUrl":"https://doi.org/10.4015/s1016237223500151","url":null,"abstract":"Diabetes mellitus (DM) is a multifaceted disease that leads to higher cardiovascular events with neuronal damage, inflammation, and oxidative stress in subjects. It also causes an autonomic imbalance with the onset of the disease which disturbs the cardiac dynamics. This work demonstrates the rutin in treating the inflammation caused by hyperglycemia through nonlinear heart rate variability features in predicting diabetes using a support vector machine (SVM). The lead-I electrocardiogram was acquired from the control, experimental, and treated group of the male Wister rats ([Formula: see text] gm and age 10–12 weeks). A dataset of 669 samples was obtained from the recorded ECG signal and taken as input vectors to the SVM. The observed results presented an accuracy of 92.9% in classifying the control and experimental group. Further, the same model with the treated group dataset showed an accuracy of 7.7% (samples nearer to the experimental group) while 92.3% of samples were close to the control group. The findings suggested the efficacy of rutin drugs in restoring the blood sugar level and the sympathovagal balance. The usefulness of the non-invasive technique in the prognosis of the disease gives direction in the design and development of the computer-aided cost-effective wearable system. However, the need for expert clinicians cannot be ignored.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"96 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76717560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-27DOI: 10.4015/s1016237223500138
Lyna Henaa Hasnaoui, A. Benabdallah, A. Djebbari
The neuroscience field provides extensive knowledge regarding cerebral activity principles. Therefore, it enables congregating consumer information and anticipating its preferences. Unlike classical marketing techniques, for instance, interviews with consumers, in which they usually do not communicate their real preferences, biomedical methodologies provide more powerful tools such as electroencephalogram signals and brain imaging, to explore the activity within the brain and examine its miscellaneous responses, which contribute efficiently to understanding human behavior related to its purchasing decision-making. Aiming to highlight the impact of neuroscience on marketing advancement, we first present in this paper a thoughtful background based on state-of-the-art studies to investigate the rate of several neurology techniques’ contribution to the advancement of the marketing field and their effect on purchasing decision-making. Second, we propose a predictive modeling framework based on the analysis of EEG signals recorded during decision-making in terms of “like” or “dislike” of specific consumer products. The discrete wavelet transform (DWT) and kNN classifier were combined to develop such an automated model. For evaluation purposes, the developed model was performed on a well-known and public EEG dataset collected for marketing studies. Achieving promising results confirms that the developed framework can be used as a reliable tool for market strategy development.
{"title":"INVESTMENT OF BIOMEDICAL APPLICATIONS IN MARKETING: ELECTROENCEPHALOGRAM-BASED CONSUMER DECISION PREDICTION","authors":"Lyna Henaa Hasnaoui, A. Benabdallah, A. Djebbari","doi":"10.4015/s1016237223500138","DOIUrl":"https://doi.org/10.4015/s1016237223500138","url":null,"abstract":"The neuroscience field provides extensive knowledge regarding cerebral activity principles. Therefore, it enables congregating consumer information and anticipating its preferences. Unlike classical marketing techniques, for instance, interviews with consumers, in which they usually do not communicate their real preferences, biomedical methodologies provide more powerful tools such as electroencephalogram signals and brain imaging, to explore the activity within the brain and examine its miscellaneous responses, which contribute efficiently to understanding human behavior related to its purchasing decision-making. Aiming to highlight the impact of neuroscience on marketing advancement, we first present in this paper a thoughtful background based on state-of-the-art studies to investigate the rate of several neurology techniques’ contribution to the advancement of the marketing field and their effect on purchasing decision-making. Second, we propose a predictive modeling framework based on the analysis of EEG signals recorded during decision-making in terms of “like” or “dislike” of specific consumer products. The discrete wavelet transform (DWT) and kNN classifier were combined to develop such an automated model. For evaluation purposes, the developed model was performed on a well-known and public EEG dataset collected for marketing studies. Achieving promising results confirms that the developed framework can be used as a reliable tool for market strategy development.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"41 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87001005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-27DOI: 10.4015/s1016237223500163
Mengmeng Liu, Chi Yu, Ying-feng Su, Shuai Li, Guoqian Yang
With the continuous innovation and development of materials science and tissue engineering technology, biomaterials are gradually being used to replace autologous bone and allogeneic bone grafts in clinical practice for the treatment and repair of bone defects. In this paper, L-arginine-modified chitosan/hydroxyapatite composites were prepared by solution blending of L-arginine-modified chitosan and hydroxyapatite. A hormone drug prednisone acetate was loaded into the composites by supercritical impregnation technique. The results of in vitro release showed that under the loading condition of 12 MPa and 318 K, the cumulative release amount of the drug was 74.6% in 72 h, which had an excellent sustained release effect. In addition, a numerical model of the nasal bone was developed and numerical calculations were performed to analyze and compare the stresses of the healthy nasal bone and the nasal bone repaired with CA/HA composite when subjected to a force of 100 N, in different directions. The total deformation difference at the material was 0.002-0.004 mm/mm, and the stress difference was 0.004–1.373 MPa for the nasal bone in both states, with the sagittal plane under 0–90 degrees of force. The above results indicate that the CA/HA composite has good biological and mechanical properties and can be used to repair nasal bone defects. This material and numerical calculation method can also be applied to other related bone tissue engineering and biomedical materials, which have broad application prospects.
{"title":"PREPARATION OF DRUG-LOADED CHITOSAN/HYDROXYAPATITE COMPOSITE MATERIAL AND ITS NUMERICAL SIMULATION IN NASAL DEFECT REPAIR","authors":"Mengmeng Liu, Chi Yu, Ying-feng Su, Shuai Li, Guoqian Yang","doi":"10.4015/s1016237223500163","DOIUrl":"https://doi.org/10.4015/s1016237223500163","url":null,"abstract":"With the continuous innovation and development of materials science and tissue engineering technology, biomaterials are gradually being used to replace autologous bone and allogeneic bone grafts in clinical practice for the treatment and repair of bone defects. In this paper, L-arginine-modified chitosan/hydroxyapatite composites were prepared by solution blending of L-arginine-modified chitosan and hydroxyapatite. A hormone drug prednisone acetate was loaded into the composites by supercritical impregnation technique. The results of in vitro release showed that under the loading condition of 12 MPa and 318 K, the cumulative release amount of the drug was 74.6% in 72 h, which had an excellent sustained release effect. In addition, a numerical model of the nasal bone was developed and numerical calculations were performed to analyze and compare the stresses of the healthy nasal bone and the nasal bone repaired with CA/HA composite when subjected to a force of 100 N, in different directions. The total deformation difference at the material was 0.002-0.004 mm/mm, and the stress difference was 0.004–1.373 MPa for the nasal bone in both states, with the sagittal plane under 0–90 degrees of force. The above results indicate that the CA/HA composite has good biological and mechanical properties and can be used to repair nasal bone defects. This material and numerical calculation method can also be applied to other related bone tissue engineering and biomedical materials, which have broad application prospects.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"19 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82518761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-27DOI: 10.4015/s1016237223500084
Vidya Sagvekar, Manjusha S. Joshi
The most significant issue with diabetes is diabetic retinopathy (DR), which is the primary cause of blindness. DR typically develops no symptoms at the beginning of the disease, thus numerous physical examinations, including pupil dilation and a visual activity test, are necessary for DR identification. Due to the differences and challenges of DR, it is more challenging to identify it during the manual assessment. For DR patients, visual loss is prevented thanks to early detection and accurate therapy. Therefore, it is even more necessary to classify the severity levels of DR in order to provide a successful course of treatment. This study develops a deep learning method based on chronological rider sea lion optimization (CRSLO) for the classification of DR. The segmentation process divides the image into multiple subgroups, which is necessary for the appropriate detection and classification procedure. For the efficient identification of DR and classification of DR severity, the deep learning approach is used. Additionally, the CRSLO scheme is used to train the deep learning technique to achieve higher performance. With respect to testing accuracy, sensitivity, and specificity of 0.9218, 0.9304 and 0.9154, the newly introduced CRSLO-based deep learning approach outperformed other existing DR classification techniques like convolutional neural networks (CNNs), deep convolutional neural network (DCNN), synergic deep learning (SDL), HPTI-V4 and DR[Formula: see text]GRADUATE. The Speech Enhancement Generative Adversarial Network (SEGAN) model in use also produced increased segmentation accuracy of 0.90300.
{"title":"SEGAN-BASED LESION SEGMENTATION AND OPTIMIZED RideNN FOR DIABETIC RETINOPATHY CLASSIFICATION","authors":"Vidya Sagvekar, Manjusha S. Joshi","doi":"10.4015/s1016237223500084","DOIUrl":"https://doi.org/10.4015/s1016237223500084","url":null,"abstract":"The most significant issue with diabetes is diabetic retinopathy (DR), which is the primary cause of blindness. DR typically develops no symptoms at the beginning of the disease, thus numerous physical examinations, including pupil dilation and a visual activity test, are necessary for DR identification. Due to the differences and challenges of DR, it is more challenging to identify it during the manual assessment. For DR patients, visual loss is prevented thanks to early detection and accurate therapy. Therefore, it is even more necessary to classify the severity levels of DR in order to provide a successful course of treatment. This study develops a deep learning method based on chronological rider sea lion optimization (CRSLO) for the classification of DR. The segmentation process divides the image into multiple subgroups, which is necessary for the appropriate detection and classification procedure. For the efficient identification of DR and classification of DR severity, the deep learning approach is used. Additionally, the CRSLO scheme is used to train the deep learning technique to achieve higher performance. With respect to testing accuracy, sensitivity, and specificity of 0.9218, 0.9304 and 0.9154, the newly introduced CRSLO-based deep learning approach outperformed other existing DR classification techniques like convolutional neural networks (CNNs), deep convolutional neural network (DCNN), synergic deep learning (SDL), HPTI-V4 and DR[Formula: see text]GRADUATE. The Speech Enhancement Generative Adversarial Network (SEGAN) model in use also produced increased segmentation accuracy of 0.90300.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"204 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76054791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-22DOI: 10.4015/s1016237223500175
U. Snekhalatha, Raja Dhason, T. Rajalakshmi
This study aims to develop a patient-specific hip implant for osteoarthritis conditions and to compare with intact and conventional implant. The femoral bone with head and shaft region was segmented from the pelvic griddle and converted into 3D model. The parameters such as femoral ball diameter, shaft length, acetabular cup diameter, and neck angle were measured from the segmented 3D model. In this study, designed part of hip implant was assembled together to form a customized hip implant. The von Mises stress was measured by means of Finite element analysis (FEA) method by applying various forces applied at the distal end of hip implant. The forces applied at hip implant were based on the assumption of 500 N force for standing, 2000 N force for walking, and 3000 N force for jogging condition. The minimum stress attained at the femur bone of custom-model is 1.32 MPa for 500 N loading condition, 5.3 MPa for 2000 N and 7.96 MPa for the maximum load of 3000 N. Thus the customized model experienced better stress distribution compared to conventional model under the maximum load of 3000 N. In pelvic region, the custom model attained a lower stress of 23% compared to conventional model. Thus, the study recommends the customized hip implants for the osteoarthritis conditions to avoid revision surgery.
{"title":"DESIGN OF PATIENT SPECIFIC HIP PROSTHESIS BASED ON FINITE ELEMENT ANALYSIS: A COMPARATIVE STUDY","authors":"U. Snekhalatha, Raja Dhason, T. Rajalakshmi","doi":"10.4015/s1016237223500175","DOIUrl":"https://doi.org/10.4015/s1016237223500175","url":null,"abstract":"This study aims to develop a patient-specific hip implant for osteoarthritis conditions and to compare with intact and conventional implant. The femoral bone with head and shaft region was segmented from the pelvic griddle and converted into 3D model. The parameters such as femoral ball diameter, shaft length, acetabular cup diameter, and neck angle were measured from the segmented 3D model. In this study, designed part of hip implant was assembled together to form a customized hip implant. The von Mises stress was measured by means of Finite element analysis (FEA) method by applying various forces applied at the distal end of hip implant. The forces applied at hip implant were based on the assumption of 500 N force for standing, 2000 N force for walking, and 3000 N force for jogging condition. The minimum stress attained at the femur bone of custom-model is 1.32 MPa for 500 N loading condition, 5.3 MPa for 2000 N and 7.96 MPa for the maximum load of 3000 N. Thus the customized model experienced better stress distribution compared to conventional model under the maximum load of 3000 N. In pelvic region, the custom model attained a lower stress of 23% compared to conventional model. Thus, the study recommends the customized hip implants for the osteoarthritis conditions to avoid revision surgery.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"88 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72972302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.4015/s1016237223500072
Fábio Rodrigo Mandello Rodrigues, M. Ferreira, S. Ignácio, M. Luersen, P. Borges
Objective: To perform an experimental-numerical analysis to study the influence of the interbracket distance (IBD) on the spring’s mechanical behavior and on the resulting force system during space closure in the segmented arch technique (SAT). Material and Methods: Twenty delta springs (DSs) made of beta-titanium alloy, [Formula: see text] inch, were tested on a platform transducer. A Young’s modulus ([Formula: see text] of 69 GPa ([Formula: see text] psi) and Yield’s strength ([Formula: see text] of 1240 MPa ([Formula: see text] psi) were used. The springs were activated considering different IBDs. The spring was modeled in autodesk Inventor software and its behavior was simulated using the finite element (FE) code Ansys Workbench. Results: The ANOVA showed a significant difference in the studied variables with a reliability of over 95% (only for the activation variable there was an effect upon the horizontal forces (Fx). The Tukey HSD and the Games–Howell post hoc multiple comparisons tests were applied to identify differences between the treatments for heterogeneous variances. Conclusions: The IBDs do not significantly affect the force system during space closure, even though there was an increase in the Mz/Fx ratio as spring deactivates. Activation can cause a statistically significant effect on the force system even though the force showed safe levels. At 4[Formula: see text]mm activation (19[Formula: see text]mm IBD), the spring wire starts yielding, i.e. plastic deformation occurs near the anterior attachment due to the shorter IBD.
{"title":"SPACE CLOSURE EFFECT ON FORCE SYSTEM IN THE SEGMENTED ARCH: AN EXPERIMENTAL-NUMERICAL STUDY","authors":"Fábio Rodrigo Mandello Rodrigues, M. Ferreira, S. Ignácio, M. Luersen, P. Borges","doi":"10.4015/s1016237223500072","DOIUrl":"https://doi.org/10.4015/s1016237223500072","url":null,"abstract":"Objective: To perform an experimental-numerical analysis to study the influence of the interbracket distance (IBD) on the spring’s mechanical behavior and on the resulting force system during space closure in the segmented arch technique (SAT). Material and Methods: Twenty delta springs (DSs) made of beta-titanium alloy, [Formula: see text] inch, were tested on a platform transducer. A Young’s modulus ([Formula: see text] of 69 GPa ([Formula: see text] psi) and Yield’s strength ([Formula: see text] of 1240 MPa ([Formula: see text] psi) were used. The springs were activated considering different IBDs. The spring was modeled in autodesk Inventor software and its behavior was simulated using the finite element (FE) code Ansys Workbench. Results: The ANOVA showed a significant difference in the studied variables with a reliability of over 95% (only for the activation variable there was an effect upon the horizontal forces (Fx). The Tukey HSD and the Games–Howell post hoc multiple comparisons tests were applied to identify differences between the treatments for heterogeneous variances. Conclusions: The IBDs do not significantly affect the force system during space closure, even though there was an increase in the Mz/Fx ratio as spring deactivates. Activation can cause a statistically significant effect on the force system even though the force showed safe levels. At 4[Formula: see text]mm activation (19[Formula: see text]mm IBD), the spring wire starts yielding, i.e. plastic deformation occurs near the anterior attachment due to the shorter IBD.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"10 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82859383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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