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INTRODUCTION: Computer vision models have been used to diagnose some disorders using computer tomography (CT) and magnetic resonance (MR) images. In this work, our objective is to detect large and small brain vessel occlusion using a deep feature engineering model in acute of ischemic stroke. METHODS: We use our dataset. which contains 324 patient's CT images with two classes; these classes are large and small brain vessel occlusion. We divided the collected image into horizontal and vertical patches. Then, pretrained AlexNet was utilized to extract deep features. Here, fc6 and fc7 (sixth and seventh fully connected layers) layers have been used to extract deep features from the created patches. The generated features from patches have been concatenated/merged to generate the final feature vector. In order to select the best combination from the generated final feature vector, an iterative selector (iterative neighborhood component analysis-INCA) has been used, and this selector has chosen 43 features. These 43 features have been used for classification. In the last phase, we used a kNN classifier with tenfold cross-validation. RESULTS: By using 43 features and a kNN classifier, our AlexNet-based deep feature engineering model surprisingly attained 100% classification accuracy. CONCLUSION: The obtained perfect classification performance clearly demonstrated that our proposal could separate large and small brain vessel occlusion detection in non-contrast CT images. In this aspect, this model can assist neurology experts with the early recanalization chance.
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AVC Isquêmico , Tomografia Computadorizada por Raios X , Humanos , Tomografia Computadorizada por Raios X/métodos , AVC Isquêmico/diagnóstico por imagem , Masculino , Aprendizado Profundo , Feminino , Encéfalo/diagnóstico por imagemRESUMO
We synthesized herein 16 compounds (SUT1-SUT16) as potential carbonic anhydrase (CA) inhibitors utilizing the tail-approach design. Based on this strategy, we connected benzenesulfonamide, the zinc-binding scaffold, to different urea moieties with the 1,3,4-thiadiazole ring as a linker. We obtained the target compounds by the reaction of 4-(5-amino-1,3,4-thiadiazol-2-yl)benzenesulfonamide with aryl isocyanates. Upon confirmation of their structures, the compounds were screened for their ability to inhibit the tumor-related human (h) isoforms human carbonic anhydrase (hCA) IX and XII, as well as the physiologically dominant hCA I and II. Most of the molecules demonstrated Ki values ≤ 10 nM with different selectivity profiles. The binding modes of SUT9, SUT10, and SUT5, the most effective inhibitors of hCA II, IX, and XII, respectively, were predicted by molecular docking. SUT16 (4-{5-[3-(naphthalen-1-yl)ureido]-1,3,4-thiadiazol-2-yl}benzenesulfonamide) was found to be the most selective inhibitor of the cancer-associated isoforms hCA IX and XII over the off-target isoforms, hCAI and II. The interaction dynamics and stability of SUT16 within hCA IX and XII were investigated by molecular dynamics simulations as well as dynophore analysis. Based on computational data, increased hydrophobic contacts and hydrogen bonds in the tail part of these molecules within hCA IX and XII were found as favorable interactions leading to effective inhibitors of cancer-related isoforms.
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In this article, we report the synthesis and cytotoxicity evaluation of novel indole-carrying semicarbazide derivatives (IS1-IS15). The target molecules were obtained by the reaction of aryl/alkyl isocyanates with 1H-indole-2-carbohydrazide that was in-house synthesized from 1H-indole-2-carboxylic acid. Following structural characterization by 1 H-NMR, 13 C-NMR, and HR-MS, IS1-IS15 were investigated for their cytotoxic activity against human breast cancer cell lines, MCF-7 and MDA-MB-231. According to the data obtained from the MTT assay, phenyl ring with a lipophilic group at its para-position and alkyl moiety were preferential substituents on the indole-semicarbazide scaffold for antiproliferative activity. The effect of IS12 (N-(4-chloro-3-(trifluoromethyl)phenyl)-2-(1H-indole-2-carbonyl)hydrazine-1-carboxamide), the compound that demonstrated remarkable antiproliferative activity on both cell lines, was also evaluated on the apoptotic pathway. Moreover, the calculation of critical descriptors constituting drug-likeness confirmed the position of the selected compounds in the anticancer drug development process. Finally, molecular docking studies suggested the inhibition of tubulin polymerization as the potential activity mechanism of this class of molecules.
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Antineoplásicos , Neoplasias da Mama , Humanos , Feminino , Relação Estrutura-Atividade , Simulação de Acoplamento Molecular , Neoplasias da Mama/tratamento farmacológico , Ensaios de Seleção de Medicamentos Antitumorais , Proliferação de Células , Antineoplásicos/química , Linhagem Celular , Indóis/química , Semicarbazidas/farmacologia , Estrutura Molecular , Linhagem Celular TumoralRESUMO
This is the first report on the separation and biological assessment of all metabolites derived from Pulicaria armena (Asteraceae) which is an endemic species narrowly distributed in the eastern part of Turkey. The phytochemical analysis of P. armena resulted in the identification of one simple phenolic glucoside together with eight flavon and flavonol derivatives whose chemical structures were elucidated by NMR experiments and by the comparison of the spectral data with the relevant literature. The screening of all molecules for their antimicrobial, anti-quorum sensing, and cytotoxic activities revealed the biological potential of some of the isolated compounds. Additionally, quorum sensing inhibitory activity of quercetagetin 5,7,3' trimethyl ether was supported by molecular docking studies in the active site of LasR which is the primary regulator of this cell-to-cell communication system in bacteria. Lastly, the critical molecular properties indicating drug-likeness of the compounds isolated from P. armena were predicted. As microbial infections can be a serious problem for cancer patients with compromised immune systems, this comprehensive phytochemical research on P. armena with its anti-quorum sensing and cytotoxic compounds can provide a new approach to the treatment.
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Anti-Infecciosos , Asteraceae , Flavonoides , Pulicaria , Percepção de Quorum , Humanos , Antibacterianos/química , Antibacterianos/farmacologia , Anti-Infecciosos/química , Anti-Infecciosos/farmacologia , Asteraceae/química , Flavonoides/química , Flavonoides/farmacologia , Simulação de Acoplamento Molecular , Compostos Fitoquímicos/química , Compostos Fitoquímicos/metabolismo , Compostos Fitoquímicos/farmacologia , Pulicaria/química , Percepção de Quorum/efeitos dos fármacosRESUMO
Detecting neurological abnormalities such as brain tumors and Alzheimer's disease (AD) using magnetic resonance imaging (MRI) images is an important research topic in the literature. Numerous machine learning models have been used to detect brain abnormalities accurately. This study addresses the problem of detecting neurological abnormalities in MRI. The motivation behind this problem lies in the need for accurate and efficient methods to assist neurologists in the diagnosis of these disorders. In addition, many deep learning techniques have been applied to MRI to develop accurate brain abnormality detection models, but these networks have high time complexity. Hence, a novel hand-modeled feature-based learning network is presented to reduce the time complexity and obtain high classification performance. The model proposed in this work uses a new feature generation architecture named pyramid and fixed-size patch (PFP). The main aim of the proposed PFP structure is to attain high classification performance using essential feature extractors with both multilevel and local features. Furthermore, the PFP feature extractor generates low- and high-level features using a handcrafted extractor. To obtain the high discriminative feature extraction ability of the PFP, we have used histogram-oriented gradients (HOG); hence, it is named PFP-HOG. Furthermore, the iterative Chi2 (IChi2) is utilized to choose the clinically significant features. Finally, the k-nearest neighbors (kNN) with tenfold cross-validation is used for automated classification. Four MRI neurological databases (AD dataset, brain tumor dataset 1, brain tumor dataset 2, and merged dataset) have been utilized to develop our model. PFP-HOG and IChi2-based models attained 100%, 94.98%, 98.19%, and 97.80% using the AD dataset, brain tumor dataset1, brain tumor dataset 2, and merged brain MRI dataset, respectively. These findings not only provide an accurate and robust classification of various neurological disorders using MRI but also hold the potential to assist neurologists in validating manual MRI brain abnormality screening.
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Doença de Alzheimer , Neoplasias Encefálicas , Humanos , Imageamento por Ressonância Magnética/métodos , Neuroimagem , Encéfalo/diagnóstico por imagem , Neoplasias Encefálicas/diagnóstico por imagem , Neoplasias Encefálicas/patologia , Aprendizado de Máquina , Doença de Alzheimer/diagnóstico por imagemRESUMO
Microscopic examination of urinary sediments is a common laboratory procedure. Automated image-based classification of urinary sediments can reduce analysis time and costs. Inspired by cryptographic mixing protocols and computer vision, we developed an image classification model that combines a novel Arnold Cat Map (ACM)- and fixed-size patch-based mixer algorithm with transfer learning for deep feature extraction. Our study dataset comprised 6,687 urinary sediment images belonging to seven classes: Cast, Crystal, Epithelia, Epithelial nuclei, Erythrocyte, Leukocyte, and Mycete. The developed model consists of four layers: (1) an ACM-based mixer to generate mixed images from resized 224 × 224 input images using fixed-size 16 × 16 patches; (2) DenseNet201 pre-trained on ImageNet1K to extract 1,920 features from each raw input image, and its six corresponding mixed images were concatenated to form a final feature vector of length 13,440; (3) iterative neighborhood component analysis to select the most discriminative feature vector of optimal length 342, determined using a k-nearest neighbor (kNN)-based loss function calculator; and (4) shallow kNN-based classification with ten-fold cross-validation. Our model achieved 98.52% overall accuracy for seven-class classification, outperforming published models for urinary cell and sediment analysis. We demonstrated the feasibility and accuracy of deep feature engineering using an ACM-based mixer algorithm for image preprocessing combined with pre-trained DenseNet201 for feature extraction. The classification model was both demonstrably accurate and computationally lightweight, making it ready for implementation in real-world image-based urine sediment analysis applications.
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Algoritmos , Processamento de Imagem Assistida por Computador , Processamento de Imagem Assistida por Computador/métodos , MicroscopiaRESUMO
Incidental adrenal masses are seen in 5% of abdominal computed tomography (CT) examinations. Accurate discrimination of the possible differential diagnoses has important therapeutic and prognostic significance. A new handcrafted machine learning method has been developed for the automated and accurate classification of adrenal gland CT images. A new dataset comprising 759 adrenal gland CT image slices from 96 subjects were analyzed. Experts had labeled the collected images into four classes: normal, pheochromocytoma, lipid-poor adenoma, and metastasis. The images were preprocessed, resized, and the image features were extracted using the center symmetric local binary pattern (CS-LBP) method. CT images were next divided into 16 × 16 fixed-size patches, and further feature extraction using CS-LBP was performed on these patches. Next, extracted features were selected using neighborhood component analysis (NCA) to obtain the most meaningful ones for downstream classification. Finally, the selected features were classified using k-nearest neighbor (kNN), support vector machine (SVM), and neural network (NN) classifiers to obtain the optimum performing model. Our proposed method obtained an accuracy of 99.87%, 99.21%, and 98.81% with kNN, SVM, and NN classifiers, respectively. Hence, the kNN classifier yielded the highest classification results with no pathological image misclassified as normal. Our developed fixed patch CS-LBP-based automatic classification of adrenal gland pathologies on CT images is highly accurate and has low time complexity [Formula: see text]. It has the potential to be used for screening of adrenal gland disease classes with CT images.
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Adenoma , Doenças das Glândulas Suprarrenais , Humanos , Tomografia Computadorizada por Raios X/métodos , Redes Neurais de Computação , Aprendizado de MáquinaRESUMO
Modern computer vision algorithms are based on convolutional neural networks (CNNs), and both end-to-end learning and transfer learning modes have been used with CNN for image classification. Thus, automated brain tumor classification models have been proposed by deploying CNNs to help medical professionals. Our primary objective is to increase the classification performance using CNN. Therefore, a patch-based deep feature engineering model has been proposed in this work. Nowadays, patch division techniques have been used to attain high classification performance, and variable-sized patches have achieved good results. In this work, we have used three types of patches of different sizes (32 × 32, 56 × 56, 112 × 112). Six feature vectors have been obtained using these patches and two layers of the pretrained ResNet50 (global average pooling and fully connected layers). In the feature selection phase, three selectors-neighborhood component analysis (NCA), Chi2, and ReliefF-have been used, and 18 final feature vectors have been obtained. By deploying k nearest neighbors (kNN), 18 results have been calculated. Iterative hard majority voting (IHMV) has been applied to compute the general classification accuracy of this framework. This model uses different patches, feature extractors (two layers of the ResNet50 have been utilized as feature extractors), and selectors, making this a framework that we have named PatchResNet. A public brain image dataset containing four classes (glioblastoma multiforme (GBM), meningioma, pituitary tumor, healthy) has been used to develop the proposed PatchResNet model. Our proposed PatchResNet attained 98.10% classification accuracy using the public brain tumor image dataset. The developed PatchResNet model obtained high classification accuracy and has the advantage of being a self-organized framework. Therefore, the proposed method can choose the best result validation prediction vectors and achieve high image classification performance.
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Neoplasias Encefálicas , Redes Neurais de Computação , Humanos , Algoritmos , Neoplasias Encefálicas/diagnóstico por imagem , Imageamento por Ressonância Magnética , EncéfaloRESUMO
Background: The acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease seriously affected worldwide health. It remains an important worldwide concern as the number of patients infected with this virus and the death rate is increasing rapidly. Early diagnosis is very important to hinder the spread of the coronavirus. Therefore, this article is intended to facilitate radiologists automatically determine COVID-19 early on X-ray images. Iterative Neighborhood Component Analysis (INCA) and Iterative ReliefF (IRF) feature selection methods are applied to increase the accuracy of the performance criteria of trained deep Convolutional Neural Networks (CNN). Materials and methods: The COVID-19 dataset consists of a total of 15153 X-ray images for 4961 patient cases. The work includes thirteen different deep CNN model architectures. Normalized data of lung X-ray image for each deep CNN mesh model are analyzed to classify disease status in the category of Normal, Viral Pneumonia and COVID-19. The performance criteria are improved by applying the INCA and IRF feature selection methods to the trained CNN in order to improve the analysis, forecasting results, make a faster and more accurate decision. Results: Thirteen different deep CNN experiments and evaluations are successfully performed based on 80-20% of lung X-ray images for training and testing, respectively. The highest predictive values are seen in the analysis using INCA feature selection in the VGG16 network. The means of performance criteria obtained using the accuracy, sensitivity, F-score, precision, MCC, dice, Jaccard, and specificity are 99.14%, 97.98%, 99.58%, 98.80%, 97.81%, 98.83%, 97.68%, and 99.56%, respectively. This proposed study is indicated the useful application of deep CNN models to classify COVID-19 in X-ray images.
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In the present work, cytotoxic potential of Jurinea macrocephala DC. (Asteraceae) was evaluated on A549 lung cancer and MCF-7 breast cancer cell lines. Isolation studies were carried out using various and repetitive chromatographic methods in order to determine the phytochemical profile of the extracts. These studies led to the identification of twelve compounds; four triterpenes (1-4) and eight flavonoids (5-12). Spectroscopic examination (1D and 2D NMR, ESI-MS) and comparison with relevant literature data were used to deduce the structures of all isolated molecules. To rationalize the obtained cytotoxicity data against breast cancer cell lines, the isolated compounds were docked into the binding site of aromatase, an important target enzyme for the treatment of breast cancer. Molecular docking studies revealed that flavonoids without sugar moieties (5-8) showed the best binding affinities. Overall, these mentioned compounds turned out to be also the most appropriate oral drug candidates after the calculation of their Lipinski parameters.
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Antineoplásicos Fitogênicos , Asteraceae , Neoplasias da Mama , Antineoplásicos Fitogênicos/química , Asteraceae/química , Neoplasias da Mama/tratamento farmacológico , Linhagem Celular Tumoral , Feminino , Flavonoides/química , Humanos , Simulação de Acoplamento Molecular , Estrutura Molecular , Extratos Vegetais/químicaRESUMO
Recently, deep models have been very popular because they achieve excellent performance with many classification problems. Deep networks have high computational complexities and require specific hardware. To overcome this problem (without decreasing classification ability), a hand-modeled feature selection method is proposed in this paper. A new shape-based local feature extractor is presented which uses the geometric shape of the frustum. By using a frustum pattern, textural features are generated. Moreover, statistical features have been extracted in this model. Textures and statistics features are fused, and a hybrid feature extraction phase is obtained; these features are low-level. To generate high level features, tunable Q factor wavelet transform (TQWT) is used. The presented hybrid feature generator creates 154 feature vectors; hence, it is named Frustum154. In the multilevel feature creation phase, this model can select the appropriate feature vectors automatically and create the final feature vector by merging the appropriate feature vectors. Iterative neighborhood component analysis (INCA) chooses the best feature vector, and shallow classifiers are then used. Frustum154 has been tested on three basic hand-movement sEMG datasets. Hand-movement sEMG datasets are commonly used in biomedical engineering, but there are some problems in this area. The presented models generally required one dataset to achieve high classification ability. In this work, three sEMG datasets have been used to test the performance of Frustum154. The presented model is self-organized and selects the most informative subbands and features automatically. It achieved 98.89%, 94.94%, and 95.30% classification accuracies using shallow classifiers, indicating that Frustum154 can improve classification accuracy.
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Algoritmos , Análise de Ondaletas , Mãos , Força da Mão , MovimentoRESUMO
The recent emergence of drug-resistant strains of Mycobacterium tuberculosis (Mtb) has complicated and significantly slowed efforts to eradicate and/or reduce the worldwide incidence of life-threatening acute and chronic cases of tuberculosis. To overcome this setback, researchers have increased the intensity of their work to identify new small-molecule compounds that are expected to remain efficacious antimicrobials against Mtb. Here, we describe our effort to apply the principles of molecular hybridization to synthesize 16 compounds carrying thiophene and thiazole rings beside the core urea functionality (TTU1-TTU16). Following extensive structural characterization, the obtained compounds were initially evaluated for their antimycobacterial activity against Mtb H37Rv. Subsequently, three derivatives standing out with their anti-Mtb activity profiles and low cytotoxicity (TTU5, TTU6, and TTU12) were tested on isoniazid-resistant clinical isolates carrying katG and inhA mutations. Additionally, due to their pharmacophore similarities to the well-known InhA inhibitors, the molecules were screened for their enoyl acyl carrier protein reductase (InhA) inhibitory potentials. Molecular docking studies were performed to support the experimental enzyme inhibition data. Finally, drug-likeness of the selected compounds was established by theoretical calculations of physicochemical descriptors.
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Proteínas de Bactérias , Ureia , Antituberculosos/química , Testes de Sensibilidade Microbiana , Simulação de Acoplamento Molecular , Ureia/farmacologiaRESUMO
Magnetic resonance (MR) is one of the special imaging techniques used to diagnose orthopedics and traumatology. In this study, a new method has been proposed to detect highly accurate automatic meniscal tear and anterior cruciate ligament (ACL) injuries. In this study, images in three different slices were collected. These are the sagittal, coronal, and axial slices, respectively. Images taken from each slice were categorized in 3 different ways: sagittal database (sDB), coronal database (cDB), and axial database (aDB). The proposed model in the study uses deep feature extraction. In this context, deep features have been obtained by using fully-connected layers of AlexNet architecture. In the second stage of the study, the most significant features were selected using the iterative RelifF (IRF) algorithm. In the last step of the application, the features are classified by using the k-nearest neighbor (kNN) method. Three datasets were used in the study. These datasets, sDB, and cDB, have four classes and consist of 442 and 457 images, respectively. The aDB used in the study has two class labels and consists of 190 images. The model proposed within the scope of the study was applied in 3 datasets. In this context, 98.42%, 100%, and 100% accuracy values were obtained for sDB, cDB, and aDB datasets, respectively. The study results showed that the proposed method detected meniscal tear and anterior cruciate ligament (ACL) injuries with high accuracy.
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Lesões do Ligamento Cruzado Anterior , Traumatismos do Joelho , Ortopedia , Ligamento Cruzado Anterior/diagnóstico por imagem , Lesões do Ligamento Cruzado Anterior/diagnóstico por imagem , Humanos , Traumatismos do Joelho/diagnóstico por imagem , Traumatismos do Joelho/patologia , Imageamento por Ressonância Magnética/métodos , Estudos RetrospectivosRESUMO
Nowadays, Covid-19 is the most important disease that affects daily life globally. Therefore, many methods are offered to fight against Covid-19. In this paper, a novel fuzzy tree classification approach was introduced for Covid-19 detection. Since Covid-19 disease is similar to pneumonia, three classes of data sets such as Covid-19, pneumonia, and normal chest x-ray images were employed in this study. A novel machine learning model, which is called the exemplar model, is presented by using this dataset. Firstly, fuzzy tree transformation is applied to each used chest image, and 15 images (3-level F-tree is constructed in this work) are obtained from a chest image. Then exemplar division is applied to these images. A multi-kernel local binary pattern (MKLBP) is applied to each exemplar and image to generate features. Most valuable features are selected using the iterative neighborhood component (INCA) feature selector. INCA selects the most distinctive 616 features, and these features are forwarded to 16 conventional classifiers in five groups. These groups are decision tree (DT), linear discriminant (LD), support vector machine (SVM), ensemble, and k-nearest neighbor (k-NN). The best-resulted classifier is Cubic SVM, and it achieved 97.01% classification accuracy for this dataset.
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Optical coherence tomography (OCT) images coupled with many learning techniques have been developed to diagnose retinal disorders. This work aims to develop a novel framework for extracting deep features from 18 pre-trained convolutional neural networks (CNN) and to attain high performance using OCT images. In this work, we have developed a new framework for automated detection of retinal disorders using transfer learning. This model consists of three phases: deep fused and multilevel feature extraction, using 18 pre-trained networks and tent maximal pooling, feature selection with ReliefF, and classification using the optimized classifier. The novelty of this proposed framework is the feature generation using widely used CNNs and to select the most suitable features for classification. The extracted features using our proposed intelligent feature extractor are fed to iterative ReliefF (IRF) to automatically select the best feature vector. The quadratic support vector machine (QSVM) is utilized as a classifier in this work. We have developed our model using two public OCT image datasets, and they are named database 1 (DB1) and database 2 (DB2). The proposed framework can attain 97.40% and 100% classification accuracies using the two OCT datasets, DB1 and DB2, respectively. These results illustrate the success of our model.
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The development of new antimicrobial compounds is in high demand to overcome the emerging drug resistance against infectious microbial pathogens. In the present study, we carried out the extensive antimicrobial screening of disubstituted urea derivatives. In addition to the classical synthesis of urea compounds by the reaction of amines and isocyanates, we also applied a new route including bromination, oxidation and azidination reactions, respectively, to convert 2-amino-3-methylpyridine to 1,3-disubstituted urea derivatives using various amines. The evaluation of antimicrobial activities against various bacterial strains, Candida albicans as well as Mycobacterium tuberculosis resulted in the discovery of new active molecules. Among them, two compounds, which have the lowest MIC values on Pseudomonas aeruginosa, were further evaluated for their inhibition capacities of biofilm formation. In order to evaluate their potential mechanism of biofilm inhibition, these two compounds were docked into the active site of LasR, which is the transcriptional regulator of bacterial signaling mechanism known as quorum sensing. Finally, the theoretical parameters of the bioactive molecules were calculated to establish their drug-likeness properties.
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Antibacterianos/farmacologia , Candida albicans/efeitos dos fármacos , Simulação de Acoplamento Molecular , Mycobacterium tuberculosis/efeitos dos fármacos , Ureia/farmacologia , Antibacterianos/síntese química , Antibacterianos/química , Biofilmes/efeitos dos fármacos , Relação Dose-Resposta a Droga , Testes de Sensibilidade Microbiana , Estrutura Molecular , Relação Estrutura-Atividade , Ureia/análogos & derivados , Ureia/químicaRESUMO
Coronavirus is normally transmitted from animal to person, but nowadays it is transmitted from person to person by changing its form. Covid-19 appeared as a very dangerous virus and unfortunately caused a worldwide pandemic disease. Radiology doctors use X-ray or CT images for the diagnosis of Covid-19. It has become crucial to help diagnose such images using image processing methods. Therefore, a novel intelligent computer vision method to automatically detect the Covid-19 virus was proposed. The proposed automatic Covid-19 detection method consists of preprocessing, feature extraction, and feature selection stages. Image resizing and grayscale conversion are used in the preprocessing phase. The proposed feature generation method is called Residual Exemplar Local Binary Pattern (ResExLBP). In the feature selection phase, a novel iterative ReliefF (IRF) based feature selection is used. Decision tree (DT), linear discriminant (LD), support vector machine (SVM), k nearest neighborhood (kNN), and subspace discriminant (SD) methods are chosen as classifiers in the classification phase. Leave one out cross-validation (LOOCV), 10-fold cross-validation, and holdout validation are used for training and testing. In this work, SVM classifier achieved 100.0% classification accuracy by using 10-fold cross-validation. This result clearly has shown that the perfect classification rate by using X-ray image for Covid-19 detection. The proposed ResExLBP and IRF based method is also cognitive, lightweight, and highly accurate.
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The discovery of new antimicrobial agents is extremely needed to overcome multidrug-resistant bacterial and tuberculosis infections. In the present study, eight novel substituted urea derivatives (10a-10h) containing disulfide bond were designed, synthesized and screened for their inâ vitro antimicrobial activities on standard strains of Gram-positive and Gram-negative bacteria as well as on Mycobacterium tuberculosis. According to the obtained results, antibacterial effects of the compounds were found to be considerably better than their antimycobacterial activities along with their weak cytotoxic effects. Molecular docking studies were performed to gain insights into the antibacterial activity mechanism of the synthesized compounds. The interactions and the orientation of compound 10a (1,1'-((disulfanediylbis(methylene))bis(2,1-phenylene))bis(3-phenylurea)) were found to be highly similar to the original ligand within the binding pocket E. faecalis ß-ketoacyl acyl carrier protein synthase III (FabH). Finally, a theoretical study was established to predict the physicochemical properties of the compounds.
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Antibacterianos/síntese química , Dissulfetos/química , Ureia/análogos & derivados , 3-Oxoacil-(Proteína de Transporte de Acila) Sintase/química , 3-Oxoacil-(Proteína de Transporte de Acila) Sintase/metabolismo , Animais , Antibacterianos/farmacologia , Antituberculosos/síntese química , Antituberculosos/farmacologia , Sítios de Ligação , Sobrevivência Celular/efeitos dos fármacos , Enterococcus faecalis/enzimologia , Bactérias Gram-Negativas/efeitos dos fármacos , Bactérias Gram-Positivas/efeitos dos fármacos , Camundongos , Testes de Sensibilidade Microbiana , Simulação de Acoplamento Molecular , Estrutura Terciária de Proteína , Células RAW 264.7 , Relação Estrutura-Atividade , Ureia/farmacologiaRESUMO
Neuropsychiatric disorders are one of the leading causes of disability. Mental health problems can occur due to various biological and environmental factors. The absence of definitive confirmatory diagnostic tests for psychiatric disorders complicates the diagnosis. It's critical to distinguish between bipolar disorder, depression, and schizophrenia since their symptoms and treatments differ. Because of brain-heart autonomic connections, electrocardiography (ECG) signals can be changed in behavioral disorders. In this research, we have automatically classified bipolar, depression, and schizophrenia from ECG signals. In this work, a new hand-crafted feature engineering model has been proposed to detect psychiatric disorders automatically. The main objective of this model is to accurately detect psychiatric disorders using ECG beats with linear time complexity. Therefore, we collected a new ECG signal dataset containing 3,570 ECG beats with four categories. The used categories are bipolar, depression, schizophrenia, and control. Furthermore, a new ternary pattern-based signal classification model has been proposed to classify these four categories. Our proposal contains four essential phases, and these phases are (i) multileveled feature extraction using multilevel discrete wavelet transform and ternary pattern, (ii) the best features selection applying iterative Chi2 selector, (iii) classification with artificial neural network (ANN) to calculate lead wise results and (iv) calculation the voted/general classification accuracy using iterative majority voting (IMV) algorithm. tenfold cross-validation is one of the most used validation techniques in the literature, and this validation model gives robust classification results. Using ANN with tenfold cross-validation, lead-by-lead and voted results have been calculated. The lead-by-lead accuracy range of the proposed model using the ANN classifier is from 73.67 to 89.19%. By deploying the IMV method, the general classification performance of our ternary pattern-based ECG classification model is increased from 89.19 to 96.25%. The findings and the calculated classification accuracies (single lead and voted) clearly demonstrated the success of the proposed ternary pattern-based advanced signal processing model. By using this model, a new wearable device can be proposed.
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BACKGROUND: Timely detection of neurodevelopmental and neurological conditions is crucial for early intervention. Specific Language Impairment (SLI) in children and Parkinson's disease (PD) manifests in speech disturbances that may be exploited for diagnostic screening using recorded speech signals. We were motivated to develop an accurate yet computationally lightweight model for speech-based detection of SLI and PD, employing novel feature engineering techniques to mimic the adaptable dynamic weight assignment network capability of deep learning architectures. MATERIALS AND METHODS: In this research, we have introduced an advanced feature engineering model incorporating a novel feature extraction function, the Factor Lattice Pattern (FLP), which is a quantum-inspired method and uses a superposition-like mechanism, making it dynamic in nature. The FLP encompasses eight distinct patterns, from which the most appropriate pattern was discerned based on the data structure. Through the implementation of the FLP, we automatically extracted signal-specific textural features. Additionally, we developed a new feature engineering model to assess the efficacy of the FLP. This model is self-organizing, producing nine potential results and subsequently choosing the optimal one. Our speech classification framework consists of (1) feature extraction using the proposed FLP and a statistical feature extractor; (2) feature selection employing iterative neighborhood component analysis and an intersection-based feature selector; (3) classification via support vector machine and k-nearest neighbors; and (4) outcome determination using combinational majority voting to select the most favorable results. RESULTS: To validate the classification capabilities of our proposed feature engineering model, designed to automatically detect PD and SLI, we employed three speech datasets of PD and SLI patients. Our presented FLP-centric model achieved classification accuracy of more than 95% and 99.79% for all PD and SLI datasets, respectively. CONCLUSIONS: Our results indicate that the proposed model is an accurate alternative to deep learning models in classifying neurological conditions using speech signals.