Acquisition Time for Resting-State HbO/Hb Coupling Measured by Functional Near-Infrared Spectroscopy in Assessing Autism.

Functional near-infrared spectroscopy was used to record spontaneous hemodynamic fluctuations form the bilateral temporal lobes in 25 children with autism spectrum disorder (ASD) and 22 typically developing (TD) children. The coupling between oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (Hb) was calculated by Pearson correlation coefficient, showing significant difference between ASD and TD, thus the coupling could be a characteristic feature for ASD. To evaluate the discrimination ability of the feature obtained in different acquisition times, the receiver operating characteristic curve (ROC) was constructed and the area under curve (AUC) was calculated. The results showed AUC > 0.8 when the time duration was longer than 1.5 min, but longer than 4 min, AUC value (~0.87) hardly varied, implying the maximal discrimination ability reached. This study demonstrated the coupling could be one of characteristic features for ASD even acquired in a short measurement time.

Exact Probability Distribution for the ROC Area under Curve.

The Receiver Operating Characteristic (ROC) is a de facto standard for determining the accuracy of in vitro diagnostic (IVD) medical devices, and thus the exactness in its probability distribution is crucial toward accurate statistical inference. We show the exact probability distribution of the ROC AUC-value, hence exact critical values and p-values are readily obtained. Because the exact calculations are computationally intense, we demonstrate a method of geometric interpolation, which is exact in a special case but generally an approximation, vastly increasing computational speeds. The method is illustrated through open access data, demonstrating superiority of 26 composite biomarkers relative to a predicate device. Especially under correction for testing of multiple hypotheses, traditional asymptotic approximations are encumbered by considerable imprecision, adversely affecting IVD device development. The ability to obtain exact p-values will allow more efficient IVD device development.

Has_Circ_0002490 Circular RNA: A Potential Novel Biomarker for Lung Cancer.

Background: Lung cancer (LC) is ranked as a leading cause of cancer-related death worldwide. However, there are still few reliable screening biomarkers for daily clinical practice in LC. Circular RNAs (circRNAs) have been suggested as valuable diagnostic biomarkers in various cancers. In this study, the expression and diagnostic potential of several circRNAs for LC were explored. Methods: Seventy-two pairs of LC tissues and adjacent normal lung tissues were collected to measure the relative expression level of circRNAs using quantitative reverse transcription-polymerase chain reaction. In addition, the relationships between circRNAs and the clinicopathological features of LC patients were analyzed. Furthermore, the sensitivities and specificities of the circRNAs were evaluated by receiver operating characteristic (ROC) analysis. Results: The expression levels of has_circ_0002490, has_circ_0087357, has_circ_0004891, has_circ_0074368, and has_circ_0000896 were downregulated in LC tissues compared with adjacent normal lung tissues. The lower levels of has_circ_0002490, has_circ_0087357, has_circ_0004891, and has_circ_0000896 were significantly correlated with advanced disease stages. The area under the ROC curves of has_circ_0002490, has_circ_0087357, has_circ_0074368, has_circ_0004891, and has_circ_0000896 were 0.833, 0.793, 0.773, 0.730, and 0.645, respectively. Conclusions: Has_circ_0002490, has_circ_0087357, has_circ_0074368, has_circ_0004891, and has_circ_0000896 are capable of distinguishing LC tissues from normal lung tissues. Besides, the biggest area under the ROC curve value of has_circ_000249 suggests it appears to be a better diagnosis marker for LC patients.

Incorporating Distance-Based Top-n-gram and Random Forest To Identify Electron Transport Proteins.

Cellular respiration provides direct energy substances for living organisms. Electron storage and transportation should be completed through electron transport chains during the cellular respiration process. Thus, identifying electron transport proteins is an important research task. In protein identification, selection of the feature extraction method and classification algorithm has a direct bearing on classification. The distance-based Top-n-gram method, which was proposed based on the frequency profile and considered evolutionary information, was used in this study for feature extraction. The Max-Relevance-Max-Distance algorithm was adopted for feature selection. The first 4D features that greatly influenced the classification result were selected to form the feature data set. Finally, the random forest algorithm was used to identify electron transport proteins. Under the 10-fold cross-validation of the model constructed in this study, sensitivity, specificity, and accuracy rates surpassed 85%, 80%, and 82%, respectively. In the testing set, F-measure, AUC value, and accuracy exceeded 74%, 95%, and 86%, respectively. These experimental results indicated that the classification model built in this study is an effective tool in identifying electron transport proteins.