Search results for: Morvarid Lalenoor

Authors: Adel Khorramrouz, Monireh Ahmadi Bani, Ehsan Norouzi, Morvarid Lalenoor

Abstract:

Background: Plantar pressure distribution measurement has been used for a long time to assess foot disorders. Plantar pressure is an important component affecting the foot and ankle function and Changes in plantar pressure distribution could indicate various foot and ankle disorders. Morphologic and mechanical properties of the foot may be important factors affecting the plantar pressure distribution. Accurate and early measurement may help to reduce the prevalence of pes planus. With recent developments in technology, new techniques such as machine learning have been used to assist clinicians in predicting patients with foot disorders. Significance of the study: This study proposes a neural network learning-based flat foot classification methodology using static foot pressure distribution. Methodologies: Data were collected from 895 patients who were referred to a foot clinic due to foot disorders. Patients with pes planus were labeled by an experienced physician based on clinical examination. Then all subjects (with and without pes planus) were evaluated for static plantar pressures distribution. Patients who were diagnosed with the flat foot in both feet were included in the study. In the next step, the leg length was normalized and the network was trained for plantar pressure mapping images. Findings: From a total of 895 image data, 581 were labeled as pes planus. A computational neural network (CNN) ran to evaluate the performance of the proposed model. The prediction accuracy of the basic CNN-based model was performed and the prediction model was derived through the proposed methodology. In the basic CNN model, the training accuracy was 79.14%, and the test accuracy was 72.09%. Conclusion: This model can be easily and simply used by patients with pes planus and doctors to predict the classification of pes planus and prescreen for possible musculoskeletal disorders related to this condition. However, more models need to be considered and compared for higher accuracy.

Keywords: foot disorder, machine learning, neural network, pes planus

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Authors: Monireh Ahmadi Bani, Adel Khorramrouz, Lalenoor Morvarid, Bagheri Mahtab

Abstract:

Background:- Foot disorders are common in musculoskeletal problems. Plantar pressure distribution measurement is one the most important part of foot disorders diagnosis for quantitative analysis. However, the association of plantar pressure and foot disorders is not clear. With the growth of dataset and machine learning methods, the relationship between foot disorders and plantar pressures can be detected. Significance of the study:- The purpose of this study was to predict the probability of common foot disorders based on peak plantar pressure distribution and center of pressure during walking. Methodologies:- 2323 participants were assessed in a foot therapy clinic between 2015 and 2021. Foot disorders were diagnosed by an experienced physician and then they were asked to walk on a force plate scanner. After the data preprocessing, due to the difference in walking time and foot size, we normalized the samples based on time and foot size. Some of force plate variables were selected as input to a deep neural network (DNN), and the probability of any each foot disorder was measured. In next step, we used support vector machine (SVM) and run dataset for each foot disorder (classification of yes or no). We compared DNN and SVM for foot disorders prediction based on plantar pressure distributions and center of pressure. Findings:- The results demonstrated that the accuracy of deep learning architecture is sufficient for most clinical and research applications in the study population. In addition, the SVM approach has more accuracy for predictions, enabling applications for foot disorders diagnosis. The detection accuracy was 71% by the deep learning algorithm and 78% by the SVM algorithm. Moreover, when we worked with peak plantar pressure distribution, it was more accurate than center of pressure dataset. Conclusion:- Both algorithms- deep learning and SVM will help therapist and patients to improve the data pool and enhance foot disorders prediction with less expense and error after removing some restrictions properly.

Keywords: deep neural network, foot disorder, plantar pressure, support vector machine

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Authors: Doostishoar Farzad, Forootanfar Hamid, Hasan-Bikdashti Morvarid, Faramarzi Mohammad Ali, Ameri Atefe

Abstract:

Introduction: Chili peppers and related plants in the family of capsaicum produce a mixture of capsaicins represent anticarcinogenic, antimutagenic, and chemopreventive properties. Vanillylamine, the main product of capsaicin hydrolysis is applied as a precursor for manufacturing of natural vanillin (a famous flavor). It is also used in the production of synthetic capsaicins harboring a wide variety of physiological and biological activities such as antibacterial and anti-inflammatory effects as well as enhancing of adrenal catecholamine secretion, analgesic, and antioxidative activities. The ability of some lipases, such as Novozym 677 BG and Novozym 435 and also some proteases e.g. trypsine and penicillin acylase, in capsaicin hydrolysis and green synthesis of vanillylamine has been investigated. In the present study the optimized culture broth of a newly isolated lipase-producing bacterial strain (Stenotrophomonas maltophilia) applied for the hydrolysis of capsaicin. Materials and methods: In order to compare hydrolytic activity of optimized and basal culture broth through capsaicin 2 mL of each culture broth (as sources of lipase) was introduced to capsaicin solution (500 mg/L) and then the reaction mixture (total volume of 3 mL) was incubated at 40 °C and 120 rpm. Samples were taken every 2 h and analyzed for vanillylamine formation using HPLC. Same reaction mixture containing boiled supernatant (to inactivate lipase) designed as blank and each experiment was done in triplicate. Results: 215 mg/L of vanillylamine was produced after the treatment of capsaicin using the optimized medium for 18 h, while only 61 mg/L of vanillylamine was detected in presence of the basal medium under the same conditions. No capsaicin conversion was observed in the blank sample, in which lipase activity was suppressed by boiling of the sample for 10 min. Conclusion: The application of optimized broth culture for the hydrolysis of capsaicin led to a 43% conversion of that pungent compound to vanillylamine.

Keywords: Capsaicin, green synthesis, lipase, stenotrophomonas maltophilia

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