Deciphering Potential Correlations between New Biomarkers and Pattern Classification in Chinese Medicine by Bioinformatics: Two Examples of Rheumatoid Arthritis / 中国结合医学杂志

Biological complexity and the need for personalized medicine means that biomarker development has become increasingly challenging. Thus, new paradigms for research need to be created that bring together a different classifier of individuals. One potential solution is collaboration between biomarker development and Chinese medicine pattern classification. In this article, two examples of rheumatoid arthritis are discussed, including a new biomarker candidate casein kinase 2 interacting protein 1 (CKIP-1) and a micro RNA 214. The authors obtained a "snapshot" of pattern classification with disease in biomarker identification. Bioinformatics analyses revealed underlying biological functions of two biomarker candidates, in varying degrees, are correlated with Chinese medicine pattern of rheumatoid arthritis. The authors' initial attempt can provide a new window for studying the win-win potential correlation between the biomarkers and pattern classification in Chinese medicine.

Changes of Effective Connectivity in the Alpha Band Characterize Differential Processing of Audiovisual Information in Cross-Modal Selective Attention / 神经科学通报·英文版

Cross-modal selective attention enhances the processing of sensory inputs that are most relevant to the task at hand. Such differential processing could be mediated by a swift network reconfiguration on the macroscopic level, but this remains a poorly understood process. To tackle this issue, we used a behavioral paradigm to introduce a shift of selective attention between the visual and auditory domains, and recorded scalp electroencephalographic signals from eight healthy participants. The changes in effective connectivity caused by the cross-modal attentional shift were delineated by analyzing spectral Granger Causality (GC), a metric of frequency-specific effective connectivity. Using data-driven methods of pattern-classification and feature-analysis, we found that a change in the α band (12 Hz-15 Hz) of GC is a stable feature across different individuals that can be used to decode the attentional shift. Specifically, auditory attention induces more pronounced information flow in the α band, especially from the parietal-occipital areas to the temporal-parietal areas, compared to the case of visual attention, reflecting a reconfiguration of interaction in the macroscopic brain network accompanying different processing. Our results support the role of α oscillation in organizing the information flow across spatially-separated brain areas and, thereby, mediating cross-modal selective attention.

Changes of Effective Connectivity in the Alpha Band Characterize Differential Processing of Audiovisual Information in Cross-Modal Selective Attention / 神经科学通报·英文版

Cross-modal selective attention enhances the processing of sensory inputs that are most relevant to the task at hand. Such differential processing could be mediated by a swift network reconfiguration on the macroscopic level, but this remains a poorly understood process. To tackle this issue, we used a behavioral paradigm to introduce a shift of selective attention between the visual and auditory domains, and recorded scalp electroencephalographic signals from eight healthy participants. The changes in effective connectivity caused by the cross-modal attentional shift were delineated by analyzing spectral Granger Causality (GC), a metric of frequency-specific effective connectivity. Using data-driven methods of pattern-classification and feature-analysis, we found that a change in the α band (12 Hz-15 Hz) of GC is a stable feature across different individuals that can be used to decode the attentional shift. Specifically, auditory attention induces more pronounced information flow in the α band, especially from the parietal-occipital areas to the temporal-parietal areas, compared to the case of visual attention, reflecting a reconfiguration of interaction in the macroscopic brain network accompanying different processing. Our results support the role of α oscillation in organizing the information flow across spatially-separated brain areas and, thereby, mediating cross-modal selective attention.

Pattern Classification of Enterovirus 71-Associated Hand, Foot, and Mouth Disease in Chinese Medicine: A Retrospective Study in 433 Cases / 中国结合医学杂志

<p><b>OBJECTIVE</b>To determine whether patterns of enterovirus 71 (EV71)-associated hand, foot, and mouth disease (HFMD) were classified based on symptoms and signs, and explore whether individual characteristics were correlated with membership in particular pattern.</p><p><b>METHODS</b>Symptom-based latent class analysis (LCA) was used to determine whether patterns of EV71-HFMD existed in a sample of 433 cases from a clinical data warehouse system. Logistic regression was then performed to explore whether demographic, and laboratory data were associated with pattern membership.</p><p><b>RESULTS</b>LCA demonstrated a two-subgroup solution with an optimal fit, deduced according to the Bayesian Information Criterion minima. Hot pattern (59.1% of all patients) was characterized by a very high fever and high endorsement rates for classical HFMD symptoms (i.e., rash on the extremities, blisters, and oral mucosa lesions). Non-hot pattern (40.9% of all patients) was characterized by classical HFMD symptoms. The multiple logistic regression results suggest that white blood cell counts and aspartate transaminase were positively correlated with the hot pattern (adjust odds ratio=1.07, 95% confidence interval: 1.006-1.115; adjust odds ratio=1.051, 95% confidence interval: 1.019-1.084; respectively).</p><p><b>CONCLUSIONS</b>LCA on reported symptoms and signs in a retrospective study allowed different subgroups with meaningful clinical correlates to be defined. These findings provide evidence for targeted prevention and treatment interventions.</p>

Integration of Chinese medicine with Western medicine could lead to future medicine: molecular module medicine / 中国结合医学杂志

The development of an effective classification method for human health conditions is essential for precise diagnosis and delivery of tailored therapy to individuals. Contemporary classification of disease systems has properties that limit its information content and usability. Chinese medicine pattern classification has been incorporated with disease classification, and this integrated classification method became more precise because of the increased understanding of the molecular mechanisms. However, we are still facing the complexity of diseases and patterns in the classification of health conditions. With continuing advances in omics methodologies and instrumentation, we are proposing a new classification approach: molecular module classification, which is applying molecular modules to classifying human health status. The initiative would be precisely defining the health status, providing accurate diagnoses, optimizing the therapeutics and improving new drug discovery strategy. Therefore, there would be no current disease diagnosis, no disease pattern classification, and in the future, a new medicine based on this classification, molecular module medicine, could redefine health statuses and reshape the clinical practice.

Individual identification research of amnestic mild cognitive impairment based on support vector machine / 医学研究生学报

Objective In recent years , multivariate pattern analysis ( MVPA) method was proposed and considered to be a promising tool for automated identification of various neuropsychiatric populations .Support vector machine ( SVM) is one of the most widely used methods of MVPA .Using SVM classifier for MVPA of amnestic mild cognitive impairment (aMCI) and normal control (NC) group, the present study aims to build an individual diagnostic model with significant discriminative power and investigate the gray matter abnor-malities of aMCI patients . Methods Fifty-one aMCI patients and 68 normal controls were scanned on the 3-Tesla magnetic resonance imaging (MRI) for high-resolution T1-weighted images.Gray matter volume map was calculated for each subject and used as features for subsequent discriminative analysis .We first applied feature selection to remove redundant information and reduce feature dimension , and then trained an SVM classifier . Leave-one-out cross validation ( LOOCV) was used to estimate the performance of the classifier , and finally the most discriminative features were identified . Results The proposed classifier achieved a classification accuracy of 83.19%with a sensitivity of 76.47%and a specificity of 88.24%.In ad-dition, the area under the receiver operating characteristic (ROC) curve was 0.8368.Further analysis revealed that the most discrimi-native features for classification included bilateral parahippocampal gyri , bilateral hippocampi , bilateral amygdala , bilateral thalamus , right cingulate , right precuneus , left caudate , left superior temporal gyrus , left middle temporal gyrus , left insula and left orbitofrontal cortex. Conclusion The proposed classification model has achieved significant accuracy for aMCI prediction , and it also displayed the whole brain gray matter atrophy pattern in aMCI patients .It suggests that the proposed method may have important implications for early clinical diagnosis of aMCI patients .

Prognosing acute cerebral infarction using a synthesis of multiple indicators / 中华物理医学与康复杂志

Objective To predict the prognosis after acute cerebral infarction using a combination of indicators. Methods Two hundred and seventeen patients with acute cerebral infarction admitted from October 2005 to December 2008 were studied. Logistic regression analysis of the data from 112 of the patients admitted from October 2005 to March 2007 was used to select 20 indicators for study. The indicators were combined into prognostic indexes using a multi-layer perception (MLP) neural network (NN) model. Data on the subsequent 105 patients were usedto appraise the model. Results The agreement of the prediction results of the NN model with the real recovery observations was rated as "excellent" in 39 cases, "OK" in 27 and "bad" in 32. The sensitivities were 95.1%,87.1% and 96.9% respectively. On average, the differences between the predicted results with the NN model andthe real recovery were not significant. Conclusion The NN model delivered good precision in predicting the outcome of acute cerebral infarction and it is worthy of further investigation.

Automatic classffication of structural mri for diagnosis of neurodegenerative diseaess / Clasificación automática de IRM estructural para el diagnóstico de enfermedades neurodegenerativas

This paper presents an automatic approach which classifies structural Magnetic Resonance images into pathological or healthy controls. A classification model was trained to find the boundaries that allow to separate the study groups. The method uses the deformation values from a set of regions, automatically identified as relevant, in a process that selects the statistically significant regions of a t-test under the restriction that this significance must be spatially coherent within a neighborhood of 5 voxels. The proposed method was assessed to distinguish healthy controls from schizophrenia patients. Classification results showed accuracy between 74% and 89%, depending on the stage of the disease and number of training samples.

Este artículo presenta un método automático para la clasificación de individuos en grupos patológicos o controles sanos haciendo uso de imágenes de resonancia magnética. El método propuesto usa los valores de deformación del sujeto analizado a un cerebro plantilla, para entrenar un modelo de clasificación capaz de identificar las fronteras que separan los grupos de estudio en un espacio de características dado. Con el fin de reducir la dimensionalidad del problema, un conjunto de regiones relevantes es automáticamente extraído en un proceso que selecciona las regiones estadísticamente significativas en una prueba t-student, con la restricción de mantener coherencia en dicha significancia en una vecindad de 5 voxeles. El método propuesto fue evaluado en la clasificación de pacientes con esquizofrenia y sujetos sanos. Los resultados mostraron un desempeño entre el 74 y el 89%, el cual depende principalmente del número de muestras empleadas para el entrenamiento del modelo.

Variable Threshold based Feature Selection using Spatial Distribution of Data / 대한의료정보학회지

OBJECTIVE: In processing high dimensional clinical data, choosing the optimal subset of features is important, not only for reduce the computational complexity but also to improve the value of the model constructed from the given data. This study proposes an efficient feature selection method with a variable threshold. METHODS: In the proposed method, the spatial distribution of labeled data, which has non-redundant attribute values in the overlapping regions, was used to evaluate the degree of intra-class separation, and the weighted average of the redundant attribute values were used to select the cut-off value of each feature. RESULTS: The effectiveness of the proposed method was demonstrated by comparing the experimental results for the dyspnea patients' dataset with 11 features selected from 55 features by clinical experts with those obtained using seven other classification methods. CONCLUSION: The proposed method can work well for clinical data mining and pattern classification applications.