Key biomarkers for the diagnosis of intervertebral disc degeneration associated with oxidative stress:identification based on bioinformatics and machine learning / 中国组织工程研究

BACKGROUND:Oxidative stress is closely associated with the occurrence and progression of intervertebral disc degeneration,but its underlying mechanisms and effective treatment methods remain unclear. OBJECTIVE:To identify key genes associated with intervertebral disc degeneration accompanied by oxidative stress based on bioinformatics and three machine learning algorithms,as well as to conduct an immune infiltration analysis,followed by experimental validation. METHODS:Gene expression profiles related to intervertebral disc degeneration were obtained from the GEO database and oxidative stress-related genes obtained from the GeneCards database.Differential analysis and weighted gene co-expression networks analysis were performed on the intervertebral disc degeneration dataset.The intersection of the two analyses and the intersection with the oxidative stress-related genes were taken to obtain candidate hub genes.Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses on the candidate hub genes were performed.Machine learning algorithms(LASSO regression,SVM-RFE,and random forest)were used to select the optimal feature genes and perform the receiver operator characteristic curve validation.Simultaneously,immune infiltration analysis was conducted.Nucleus pulposus samples from patients with cervical spondylosis who were treated at the Second Hospital of Shanxi Medical University from July to November 2023 were enrolled as the intervertebral disc degeneration group and nucleus pulposus samples from patients with cervical spinal cord injury as the control group.The relative expression of feature genes in the degenerated intervertebral disc was validated using qPCR method. RESULTS AND CONCLUSION:After differential gene analysis,424 differentially expressed genes were obtained.Weighted gene co-expression networks analysis yielded 5 087 genes,and 1 399 oxidative stress genes were identified,leading to the identification of 23 candidate hub genes.Gene ontology analysis revealed that these candidate hub genes are primarily involved in bacterial defense response,molecular response to bacteria,and other biological processes.In terms of cellular component,they are associated with secretion granule lumen and cytoplasmic vesicle lumen,among others.As for molecular function,they are related to endopeptidase activity and compound binding,including sulfur compounds.Kyoto Encyclopedia of Genes and Genomes analysis demonstrated that these candidate hub genes are associated with neutrophil extracellular trap formation and the renin-angiotensin system pathway,among other signaling pathways.By applying three machine learning algorithms and conducting the receiver operator characteristic curve validation,two key genes,HSPA6 and PKD1,were determined.Immune infiltration analysis revealed a strong correlation between HSPA6 and activated dendritic cells(r=0.88,P＜0.001)as well as activated CD4+ T cells(r=-0.72,P＜0.01).Similarly,PKD1 showed close associations with effector memory CD8+ T cells(r=0.55,P＜0.05)and activated dendritic cells(r=-0.56,P＜0.05).qPCR experimental results indicated that the expression level of HSPA6 was lower in the intervertebral disc degeneration group compared with the control group(P＜0.000 1),while the expression level of PKD1 was higher in the intervertebral disc degeneration group(P＜0.000 1).These findings suggest that HSPA6 and PKD1 can serve as biomarkers for intervertebral disc degeneration accompanied by oxidative stress.Interventions targeting HSPA6 and PKD1 may hold promise for improving intervertebral disc degeneration.

Daily Sediment Yield Prediction Using Hybrid Machine Learning Approach

In this study, four different soft computing AI techniques were tested for the prediction of sediment yield based on hydro-meteorological variables at Jondhara station, Seonath stream in Rajnandgaon district, India. In order to fulfill this purpose, the models namely, multilayer perceptron (MLP), support vector machine (SVM), multilayer perceptron coupled with genetic algorithm (MLP-GA), and support vector machine coupled with genetic algorithm (SVM-GA) models were employed. To select the optimal input variables, a statistical method such as the Gamma test was considered among several methods. Based on the results of the analysis, all models were evaluated by using the following statistical indices: Coefficient of Correlation (CC), room mean square error (RMSE) and percent bias (PBAIS). Overall, the performance of the studied models indicates that all of them are capable of simulation sediments yield at Jondhara station, Seonath river basin in a satisfactory manner. Comparison of results showed that the MLP-GA with CC = 0.988, RMSE = 0.006 and PBIAS = 0.000 in training period and CC= 0.990, RMSE = 0.007 and PBIAS = 0.000 in testing period for S-6 model and CC = 0.986, RMSE = 0.025 and PBIAS = -0.001 in training period and CC = 0.988, RMSE = 0.029 and PBIAS = -0.001 in testing period for S-13 model were able to yield better results than the other models considered. Furthermore, an SVM model is also observed to have some advantages over MLP models and SVM-GA models since it can represent the output data in a continuous manner by fitting a linear regression function to the output data, which has the advantage of making the model more precise than MLP and SVM-GA models.

Development and validation of a mobile application based on a machine learning model to aid in predicting dosage of vitamin K antagonists among Indian patients post mechanical heart valve replacement

Patients who undergo heart valve replacements with mechanical valves need to take Vitamin K Antagonists (VKA) drugs (Warfarin, Nicoumalone) which has got a very narrow therapeutic range and needs very close monitoring using PT-INR. Accessibility to physicians to titrate drugs doses is a major problem in low-middle income countries (LMIC) like India. Our work was aimed at predicting the maintenance dosage of these drugs, using the de-identified medical data collected from patients attending an INR Clinic in South India. We used artificial intelligence (AI) - machine learning to develop the algorithm. A Support Vector Machine (SVM) regression model was built to predict the maintenance dosage of warfarin, who have stable INR values between 2.0 and 4.0. We developed a simple user friendly android mobile application for patients to use the algorithm to predict the doses. The algorithm generated drug doses in 1100 patients were compared to cardiologist prescribed doses and found to have an excellent correlation.

Children's pneumonia diagnosis system based on Mach-Zehnder optical fiber sensing technology / 国际生物医学工程杂志

Objective:To explore a fast and accurate method to diagnose children's pneumonia according to respiratory signals, so as to avoid the cancer induction caused by traditional X-ray examination.Methods:A Mach Zehnder optical fiber sensor was used to build a respiratory signals(RSPs) detection system, and the RSPs of the monitored children were extracted according to the vibration signal generated by the children's lung rales. Preprocessing methods such as the discrete cosine transform(DCT) were used to compress and denoise the RSPs. Multi-feature extraction of RSPs was conducted through signal processing methods such as the Hilbert transform and autoregressive (AR) model spectrum estimation. A support vector machine (SVM) classification model was constructed to classify the collected RSPs.Results:The accuracy rate of the proposed RSP classification of children with or without pneumonia was 94.41%, which was higher than the previous methods.Conclusions:The children's pneumonia diagnosis system based on an optical fiber sensor has a higher detection accuracy, and is expected to be widely used in clinical practice.

The evaluation of students' physical health based on the integration of family and school physical education / Avaliação da saúde física dos estudantes com base na integração da educação física familiar e escolar / Evaluación de la salud física de los estudiantes basada en la integración del deporte familiar y escolar

ABSTRACT In recent years, China has paid more and more attention to students' physical health, but it is difficult for schools to provide scientific guarantee for students' physical health evaluation. How to use scientific algorithm for accurate guidance has become the current hotspot. Based on this, this paper studies the evaluation model of students' physical health based on the integration of home and school sports. Firstly, this paper analyzes the research status of physical health evaluation at home and outside, then optimizes and improves the deficiencies in the integration of home and school sports in the current research hotspot, then applies SVM algorithm to the physical health evaluation model. Finally, the experimental results show that the SVM algorithm can objectively evaluate the integration of home and school sports, and can optimize the evaluation strategy according to the differences of students in the process of physical exercise, and the accuracy of physical health evaluation can reach more than 97%.

RESUMO Nos últimos anos, a China tem prestado cada vez mais atenção à saúde física dos estudantes, mas é difícil para as escolas fornecer garantias científicas para o processo de avaliação da saúde física dos estudantes. Como usar o algoritmo científico para orientação precisa tornou-se um ponto crucial. Com base nisso, este documento estuda o modelo de avaliação da saúde física dos estudantes com base na integração dos esportes domésticos e escolares. Em primeiro lugar, este artigo analisa o estado de investigação da avaliação da saúde física em casa e fora de casa, e, em seguida, otimiza e melhora as deficiências na integração dos esportes domésticos e escolares no atual foco de pesquisa, e, em seguida, aplica o algoritmo SVM ao modelo de avaliação da saúde física. Finalmente, os resultados experimentais mostram que o algoritmo SVM pode realizar a avaliação objetiva do processo de integração de esportes domésticos e escolares, e pode otimizar a estratégia de avaliação de acordo com as diferenças dos estudantes no processo de exercício físico, e a precisão da avaliação de saúde física pode atingir mais de 97%.

RESUMEN En los últimos años, China ha prestado cada vez más atención a la salud física de los estudiantes, pero es difícil para las escuelas brindar garantías científicas para la evaluación de la salud física de los estudiantes. Cómo utilizar el algoritmo científico para una guía precisa se ha convertido en el punto de acceso actual. Con base en esto, este trabajo estudia el modelo de evaluación de la salud física de los estudiantes basado en la integración de los deportes domésticos y escolares. En primer lugar, este artículo analiza el estado de la investigación de la evaluación de la salud física en el hogar y en el exterior, luego optimiza y mejora las deficiencias en la integración de los deportes en el hogar y la escuela en el punto de acceso de investigación actual. Luego aplica el algoritmo SVM al modelo de evaluación de la salud física. Finalmente, los resultados experimentales muestran que el algoritmo SVM puede evaluar objetivamente la integración de los deportes en el hogar y la escuela, y puede optimizar la estrategia de evaluación de acuerdo con las diferencias de los estudiantes en el proceso de ejercicio físico, y la precisión de la evaluación de la salud física puede alcanzar más del 97%.

Classification Model of Corneal Opacity Based on Digital Image Features / 中国医疗器械杂志

OBJECTIVE@#According to the digital image features of corneal opacity, a multi classification model of support vector machine (SVM) was established to explore the objective quantification method of corneal opacity.@*METHODS@#The cornea digital images of dead pigs were collected, part of the color features and texture features were extracted according to the previous experience, and the SVM multi classification model was established. The test results of the model were evaluated by precision, sensitivity and @*RESULTS@#In the classification of corneal opacity, the highest @*CONCLUSIONS@#The SVM multi classification model can classify the degree of corneal opacity.

Classification of glioma and solitary brain metastasis using magnetic resonance imaging radiomics feature / 解剖学报

Objective To analyze the difference of radiomics features between solitary brain metastasis and glioma using routine 3T TI, T2 and fluid attenuation inversion recovery (FLAIR) magnetic resonance imaging, to explore the significance of texture features constructed in different directions and angles in tumor regions in distinguishing the two kinds of tumors, and to explore a feasible method for high-precision classification of solitary brain metastases and gliomas. Methods Given the multimodal images of 43 patients with glioma and 45 age- and sex- matched patients with solitary brain metastasis, the gray level co-occurrence matrices of different angles of each slice were constructed from the transverse, coronal and sagittal directions of the tumor regions of these images, and the texture spatial relationship features (including contrast, correlation, energy and homogeneity) were calculated. Wilcoxon rank sum test was used to eliminate redundant features and select features with strong distinguishing ability. Finally, SVM linear kernel classifier was used to classify the selected features to achieve the identification of the two kinds of tumors. Results When classifying glioma and solitary brain metastasis, the precision, recall, Fl score and accuracy of multimodal and multidirectional combination features were 0.8857, 0.9114, 0.8944 and 0.8922, respectively. The area under the receiver operating characteristic curve obtained by linear kernel SVM classifier was 0. 9602. Totally 40 of the 45 patients with solitary brain metastases were correctly classified, and 39 of the 43 gliomas were correctly classified. Conclusion The multimodal and multi-directional combination features of tumor areas can be classified by linear kernel SVM classifier to distinguish gliomas from solitary brain metastases, which can be used as a second opinion to effectively assist doctors in making diagnosis.

Establishment of spectrum-antibacterial effect correlation quality evaluation system of Yinhuang Granules and its raw medicinal materials, extracts and preparation / 中草药

Objective: To establish the quality evaluation system based on spectrum-antibacterial effect correlation of Yinhuang Granules in order to detect its raw medicinal materials, extracts, and preparation simultaneously. Methods: Firstly, the spectrum- antibacterial effect correlation quality evaluation system of Yinhuang Granules was established by using Least Squares Support Vector Machine (LS-SVM). Then, by using supervised partial least square-discriminant analysis models (PLS-DA), the antibacterial effect evaluation was judged based on the spectrum-antibacterial effect data of Scutellariae Radix, Lonicerae Japonicae Flos, Scutellaria extract, Lonicerae japonica extract, and Yinhuang Granules. Results: The mathematical model of Yinhuang Granules based on spectrum-antibacterial effect correlation was established; The average relative error of the prediction results was less than 5%, and the antibacterial rate of Scutellariae Radix, Scutellaria Radix extract, Lonicerae Japonicae Flos, Lonicerae japonica extract was greater than 43%, 5.5%, 11%, 37% calculated by their mathematical model, which can ensure the antibacterial effect was greater than 11% (correct rate was 87%). Conclusion: The quality evaluation system can realize the quality control of the key link of the production of traditional Chinese medicine, the evaluation results are more scientific, comprehensive, and accurate.

Study of extracting natural resources of Chinese medicinal materials planted area in Luoning of Henan province based on UAV of low altitude remote sensing technology and remote sensing image of satellite / 中国中药杂志

The study is aimed to effectively obtain the planting area of traditional Chinese medicine resources. The herbs used as the material for traditional Chinese medicine are mostly planted in natural environment suitable mountainous areas. The UAV low altitude remote sensing data were used as the samples and the GF-2 remote sensing images were applied for the data source to extract the planting area of Salvia miltiorrhiza and Artemisia argyi in Luoning county combined with field investigation. Remote sensing satellite data of standard processing obtain specific remote sensing data coverage. The UAV data were pre-processed to visually interpret the species and distribution of traditional Chinese medicine resources in the sample quadrat. Support vector machine( SVM) was used to classify and estimate the area of traditional Chinese medicine resources in Luoning county,confusion matrix was used to determine the accuracy of spatial distribution of traditional Chinese medicine resources. The result showed that the application of UAV of low altitude remote sensing technology and remote sensing image of satellite in the extraction of S. miltiorrhiza and other varieties planting area was feasible,it also provides a scientific reference for poverty alleviation policies of the traditional Chinese medicine Industry in local areas.Meanwhile,research on remote sensing classification of Chinese medicinal materials based on multi-source and multi-phase high-resolution remote sensing images is actively carried out to explore more effective methods for information extraction of Chinese medicinal materials.

Study on pharmacodynamic material basis of Naomaitong to protect neuronal cells based on PK-PD model / 中国中药杂志

The PK-PD correlation models by using pharmacodynamics and pharmacokinetics were applied to study the material basis of Naomaitong,a clinical empirical prescription for the treatment of cerebral apoplexy,in inhibiting the death of PC12 nerve cells induced by Na_2S_2O_4 and Glu. In this experiment,PC12 cell death models induced by Na_2S_2O_4 and Glu were established respectively.With LDH lateral leakage and NO content as pharmacodynamic indexes,PK-PD model was established by SVM algorithm to evaluate the effective components of Naomaitong in inhibiting neural cell death. The results showed that the positive correlation of emodin methyl ether-8-O-β-D-glucopyranoside,aloe emodin,chrysophanol,rhein,emodin,ginsenoside Rg1,ginsenoside Rc,3'-methoxypuerarin and ligustilide was significant,obviously improving the LDH release and NO content. The results indicated that the contribution of Radix Puerariae Lobatae Radix and Rhei Radix et Rhizoma in Naomaitong could protect the nerve cell death induced by Na_2S_2O_4 and Glu respectively. PK-PD model was used to screen the neuroprotective components in Naomaitong,revealing the possible pharmacodynamic material basis of Naomaitong in the treatment of cerebral ischemia injury.

A Novel Run-length based wavelet features for Screening Thyroid Nodule Malignancy

Abstract: Thyroid nodules are cell growths in the thyroid which might be for in one of two categories benign or malignant. Nodular thyroid disease is common and because of the associated risk of malignancy and hyper-function; these nodules have to be examined thoroughly. Hence diagnosing thyroid nodule malignancy in the early stage can mitigate the possibility of death. This paper presents an intelligent thyroid nodules malignancy diagnosis using texture information in run-length matrix derived from 2- level 2D wavelet transform bands (approximation and details). In this work, ANOVA test has been used to for feature selection to reduce for feature selection about 45 run-length features with and without wavelet generated, before feeding those features which clinical importance to the Support Vector Machine(SVM) and Decision Tree (DT) classifier to perform the automated diagnosis. The validation of this work is activated using 100-thyroid nodule images spliced equally between the two categories (50 Benign and 50 Malignant). The proposed system can detect thyroid nodules malignancy with an average accuracy of about 97% using SVM classifier for the run- length matrix, features derived from spatial domain while the average accuracy is increased to 98% in case of hybrid feature derived from spatial domain and 2-level wavelet decomposition. For the other proposed classifier (DT), the average accuracy in case of spatial domain based features is 93% whereas the average accuracy of the hybrid features system is 97%. Hence the proposed system can be used for the screening of thyroid nodules.

Classification of Imaginary Motor Task from Electroencephalographic Signals: A Comparison of Feature Selection Methods and Classification Algorithms / Clasificación de una Motricidad Imaginaria por medio de Señales Electroencefalográficas: Una Comparación de Métodos de Selección de Características y Algoritmos de Clasificación

Abstract: In this work, a Brain Computer interface able to decode imagery motor task from EEG is presented. The method uses time-frequency representation of the brain signal recorded in different regions of the brain to extract important features. Principal Component Analysis and Sequential Forward Selection methods are compared in their ability to represent the feature set in a compact form, removing at the same time unnecessary information. Finally, two method based on machine learning are implemented for the task of classification. Results show that it is possible to decode the mental activity of the subjects with accuracy above 80%. Furthermore, visualization of the main components extracted from the brain signal allow for physiological insights on the activity that take place in the sensorimotor cortex during execution of imaginary movement of different parts of the body.

Resumen: En este trabajo es presentada una Interfaz Cerebro Computadora que tiene la capacidad de decodificar actividades motrices. El método utiliza representación en el dominio de la frecuencia y el tiempo de las señales del cerebro grabadas en distintas regiones de este mismo, con el fin de extraer características importantes. Los métodos: Análisis de Componentes Principales y Selección Secuencial, son comparados en términos de su capacidad para representar características de la señal de una forma compacta, removiendo de esta forma, información innecesaria. Finalmente, dos métodos basados en aprendizaje de máquinas fueron implementados para la clasificación de actividades motrices utilizando solo las señales cerebrales. Los resultados muestran que es posible decodificar la actividad mental en los sujetos con una precisión superior al 80%. Además, la visualización de las componentes principales extraídas de las señales del cerebro permite un analísis de la actividad que toma lugar en la corteza cerebral sensorimotora durante la ejecución de la imaginación de movimientos de distintas partes del cuerpo.

Gastrointestinal polyp detection in endoscopic images using an improved feature extraction method

Gastrointestinal polyps are treated as the precursors of cancer development. So, possibility of cancers can be reduced at a great extent by early detection and removal of polyps. The most used diagnostic modality for gastrointestinal polyps is video endoscopy. But, as an operator dependant procedure, several human factors can lead to miss detection of polyps. In this peper, an improved computer aided polyp detection method has been proposed. Proposed improved method can reduce polyp miss detection rate and assists doctors in finding the most important regions to pay attention. Color wavelet features and convolutional neural network features are extracted from endoscopic images, which are used for training a support vector machine. Then a target endoscopic image will be given to the classifier as input in order to find whether it contains any polyp or not. If polyp is found, it will be marked automatically. Experiment shows that, color wavelet features and convolutional neural network features together construct a highly representative of endoscopic polyp images. Evaluations on standard public databases show that, proposed system outperforms state-of-the-art methods, gaining accuracy of 98.34%, sensitivity of 98.67% and specificity of 98.23%. In this paper, the strength of color wavelet features and power of convolutional neural network features are combined. Fusion of these two methodology and use of support vector machine results in an improved method for gastrointestinal polyp detection. An analysis of ROC reveals that, proposed method can be used for polyp detection purposes with greater accuracy than state-of-the-art methods.

Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy

The high-pace rise in advanced computing and imaging systems has given rise to a new research dimension called computer-aided diagnosis (CAD) system for various biomedical purposes. CAD-based diabetic retinopathy (DR) can be of paramount significance to enable early disease detection and diagnosis decision. Considering the robustness of deep neural networks (DNNs) to solve highly intricate classification problems, in this paper, AlexNet DNN, which functions on the basis of convolutional neural network (CNN), has been applied to enable an optimal DR CAD solution. The DR model applies a multilevel optimization measure that incorporates pre-processing, adaptive-learning-based Gaussian mixture model (GMM)-based concept region segmentation, connected component-analysis-based region of interest (ROI) localization, AlexNet DNN-based highly dimensional feature extraction, principle component analysis (PCA)- and linear discriminant analysis (LDA)-based feature selection, and support-vector-machine-based classification to ensure optimal five-class DR classification. The simulation results with standard KAGGLE fundus datasets reveal that the proposed AlexNet DNN-based DR exhibits a better performance with LDA feature selection, where it exhibits a DR classification accuracy of 97.93% with FC7 features, whereas with PCA, it shows 95.26% accuracy. Comparative analysis with spatial invariant feature transform (SIFT) technique (accuracy—94.40%) based DR feature extraction also confirms that AlexNet DNN-based DR outperforms SIFT-based DR.

Synergistic mechanism of traditional Chinese medicine based on target combination of PepT1 and PPARα / 中国中药杂志

Synergistic effect is main pharmacological mechanism of traditional Chinese medicine(TCM). The research method based on the key targets combination is an important method to explore the synergistic effect of TCM. Peptide transporter 1 (PepT1) is an essential target for drug uptake into the bloodstream, accounting for about 50% of the total transporter protein content from the small intestine. Peroxisome proliferator-activated receptor α(PPARα) is the lipid-lowering target of fibrates, which have a good hypolipidemic effect by activating PPARα. It has been reported that PPARα could activate the gene expression of PepT1s, and PPARα agonists can promote the uptake of PepT1 substrates, indicating their synergistic effect. In this paper, PepT1 substrates and PPARα agonists from TCM were discovered, and their synergistic mechanism was also been discussed based on the target combination of PepT1 and PPARα. The support vector machine(SVM) model of PepT1 substrates was first constructed and utilized to predict potential TCM components. Meanwhile, merged pharmacophore and docking model of PPARα agonists was used to screen the potential active ingredients from TCM. According to the analysis results of two groups, the TCM combination of Panax notoginseng and Ganoderma lucidum, as well as TCM combination of P. notoginseng and Salvia miltiorrhiza were identified to have the synergistic mechanism based on target combination of PepT1 and PPARα. In this study, synergistic mechanism of TCM was analyzed for absorption and hypolipidemic effect based on target combination, which provides a new way to explore the synergetic mechanism of TCM related to pharmacokinetics.

Classification of Diabetes Diet Problems Oriented by Deep Automatic Question Answering / 医学信息学杂志

Taking the diet problem of diabetic patients as an example,the paper puts forward the problems classification system based on functions in the view of users,classifies the problems put forward by patients through the Support Vector Machine (SVM) algorithm,and provides important support for the construction of the deep automatic Question Answering (QA) system.

Herramienta alternativa para la clasificación de células cervicales utilizando solo rasgos del núcleo / Alternative tool for classification of cervical cells using only features of the nucleus

El cáncer de cérvix uterino representa una de las mayores amenazas de muerte por cáncer entre las mujeres. Con el avance continuo en la medicina y la tecnología, las muertes por esta enfermedad han disminuido significativamente. Las investigaciones referentes a este tema han podido determinar síntomas claves que permiten detectar a tiempo esta enfermedad para darle un tratamiento oportuno. La citología convencional es una de las técnicas más utilizadas, siendo ampliamente aceptada, de bajo costo, y con mecanismos de control. Con el objetivo de aliviar la carga de trabajo a los especialistas, algunos investigadores han propuesto el desarrollo de herramientas de visión computacional para detectar y clasificar las transformaciones en las células de la región del cuello uterino. La presente investigación tiene como objetivo proveer a los investigadores de una herramienta de clasificación automática, aplicable a las condiciones existentes en los centros médicos y de investigación del país. Esta herramienta debe ser capaz de clasificar las células del cuello del útero, basándose solamente en las características extraídas de la región del núcleo y sin utilizar las características del citoplasma, de manera que se reduzca la tasa de falsos negativos en la prueba de Papanicolaou. A partir del estudio realizado, se obtuvo una herramienta haciendo uso de la técnica k-vecinos más cercanos con la distancia manhattan, el cual mostró un alto desempeño manteniendo valores de AUC superiores al 91 por ciento y llegando hasta un 97.1 por ciento con respecto a los clasificadores SVM y RBF Network, los que también fueron analizados(AU)

Cervix cancer is one of the biggest threats of cancer death among women. With continued advances in medicine and technology, deaths from the disease have fallen significantly. The investigations concerning this issue have determined key symptoms to detect the disease in time to give timely treatment. Conventional cytology is one of the most widely used techniques, being widely accepted, inexpensive, and with control mechanisms. In order to alleviate the workload of specialists, some researchers have proposed the development of computer vision tools to detect and classify the changes in the cells of the cervical region. This research aims to provide a tool for automatic classification, applicable to medical conditions and research centers of the country. This tool should be able to classify the cells of the cervix, based solely on the features extracted from the core region without using the characteristics of the cytoplasm, so that the rate of false negative Pap test is reduced. From the study, a tool is obtained using the k nearest-neighbors manhattan distance technique, which showed a high performance maintaining AUC values greater than 91 percent and reaching 97.1 percent over classifiers SVM and RBF Network, which were also analyzed(AU)

Lukasiewicz logic based Fuzzy similarity classifier for Denver group chromosomal classification / Utilizando lógica de Lukasiewicz baseada em similaridade Fuzzy na classificação de cromossomos do tipo Denver group

This paper proposes a novel P1-weighted Lukasiewicz Logic based Fuzzy Similarity Classifier for classifying Denver Group of chromosomes and compares its performance with the other classifiers under study. A chromosome is classified to one of the seven groups from A to G, based on the Denver System of classification of chromosomes. Chromosomes within a particular Denver Group are difficult to identify, possessing almost identical characteristics for the extracted features. This work evaluates the performance of supervised classifiers including Naive Bayes, Support Vector Machine with Gaussian Kernel (SVM), Multilayer perceptron (MLP) and a novel, unsupervised, P1-weighted Lukasiewicz Logic based Fuzzy Similarity Classifier, in classifying the Denver Group of chromosomes. A fundamental review on fuzzy similarity based classification is presented. Experimental results clearly demonstrates that the proposed P1-weighted Lukasiewicz Logic based Fuzzy Similarity Classifier using the generalized Minkowski mean metric, produces the best classification results, almost identical to the Ground Truth values. One-way Analysis of Variance (ANOVA) at 95% and 99% level of confidence and Tukey's post-hoc analysis is performed to validate the selection of the classifier. The proposed P1-weighted Lukasiewicz Logic based Fuzzy Similarity Classifier gives the most promising classification results and can be applied to any large scale biomedical data and other applications.

Este trabalho propõe uma nova lógica P1pondera de Lukasiewicz de acordo com o classificador de similarida fuzzy para classificar cromossomas do Grupo Denver e compara o seu desempenho com os outros classificadores em estudo. Um cromossoma é classificado com um dos sete grupos de A a G, com base no Sistema de Denver de classificação de cromossomos. Cromossomos dentro de um grupo de Denver particular são difíceis de identificar, com características quase idênticas para os recursos extraídos. Este trabalho avalia o desempenho de classificadores supervisionados, incluindo Naive Bayes, Support Vector Machine com Gaussian Kernel (SVM), perceptron multicamadas (MLP) e um novo classificador sem supervisão, P1-weighted, lógica de Lukasiewicz de acordo com o classificador de similaridade Fuzzy para a classificação do Grupo Denver de cromossomos . Apresenta-se ma revisão fundamentada na classificação de acordo com similaridade difusa. Resultados experimentais demonstram claramente que Classificador Similaridade Fuzzy proposto de acordo com a lógica de Lukasiewicz P1-weighted usando a médica métrica de Minkowski para produz melhores resultados de classificação. Estes valores foram muito similares aos valores de Ground Truth . Análise de variancia (ANOVA) com 95% de grau de confiança e análise post-hoc de Tukey 99% foram realizadas para validar a seleção do classificador. Este classificador P1-weighted de lógica de Lukasiewicz está de acordo com o classificador de similaridade difusa oferecendo resultados declassificação mais promissoras. Portanto, podendo ser aplicado a dados biomédicos em larga escala além de outras aplicações.

Remifentanil blood concentration forecast model based on support vector machine with particle swarm optimization / 中国药学杂志

OBJECTIVE: To develop a SVM model which is constructed by using particle swarm optimization to a predict the plasma concentration of remifentail. METHODS: This research establishes a PSO-SVM model which is constructed by using particle swarm optimization to a predict the plasma concentration of remifentanil. The model was capable of capturing the nonlinear relationship among plasma concentration, time, and the patient's signs exactly. RESULTS: The average error of PSO-SVM is -1.07%, while that of NONMEM is -2.24%. The absolute average error of PSO-SVM is 9.09%, while that of NONMEM is 19.92%. CONCLUSION: Experimental results indicate that PSO-SVM model could predict the plasma concentration of remifentanil rapidly and stably, with high accuracy and low error. For the characteristic of simple principle and fast computing speed, this method is suitable to data analysis of short-acting anesthesia drug population pharmacokinetics and pharmacodynamics.

Purification and characterization of highly thermostable amylopullulanase from a thermophilic, anaerobic bacterium Clostridium thermosulfurogenes SVM17

A highly thermostable amylopullulanase was purified to homogeneity from the culture filtrate of the Clostridium thermosulfurogenes SVM17. On SDS-PAGE, the purified fraction having both amylase and pullulanase activities were observed as a single band. The molecular weight of the purified amylopullulanase on SDS-PAGE was 97 kDa. The optimum temperature for both amylase and pullulanase was 70 °C. The enzyme was completely stable at 70 °C for 2 h. The presence of 5% starch increased the thermal stability of the enzyme at 100 °C up to 2 h. Both amylase and pullulanase activities were optimum at pH 5.5 to 6.0 and were stable over a pH range of 4.0 to 6.5. The TLC analysis of the reaction products on starch showed that maltose was the main product along with trace amounts of glucose. The analysis of hydrolysis product of pullulan showed that maltotriose was the main product. At 5 mM concentration, Mn2+ and Ag+ strongly stimulated both amylase and pullulanase activities, where as Mg2+, Ca2+, Cu2+, Fe3+, Zn2+, Hg2+, EDTA, Cd2+ and Li2+ inhibited both amylase and pullulanase activities. When the concentration of metal ions was increased from 5 to10 mM, a further increase in amylase activity was observed in the presence of Ni2+, Mn2+ and Co2+. Where as substantial decrease was observed at 10 mM concentration of Ag+, Pb2+ and Ca2+.