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The marine sponge Lendenfeldia sp., collected from the Southern waters of Taiwan, was subjected to chemical composition screening, resulting in the isolation of four new 24-homoscalarane compounds, namely lendenfeldaranes R–U (1–4). The structures and relative stereochemistry of the new metabolites 1–4 were assigned based on NMR studies. The absolute configurations of compounds 1–4 were determined by comparing the calculated and experimental values of specific optical rotation. The antioxidant and anti-inflammatory activities of the isolated compounds were assayed using superoxide anion generation and elastase release assays. These assays are used to determine neutrophilic inflammatory responses of respiratory burst and degranulation. Compounds 2 and 4 inhibited superoxide anion generation by human neutrophils in response to formyl-L-methionyl-L-leucyl-L-phenylalanine/cytochalasin B (fMLP/CB) with IC50: 3.98–4.46 μM. Compounds 2 and 4 inhibited fMLP/CB-induced elastase release, with IC50 values ranging from 4.73 to 5.24 μM. These findings suggested that these new 24-homoscalarane compounds possess unique structures and potential anti-inflammatory activity.

BackgroundTraditional Chinese medicine (TCM) has been shown to be an efficient mode to manage advanced lung cancer, and accurate syndrome differentiation is crucial to treatment. Documented evidence of TCM treatment cases and the progress of artificial intelligence technology are enabling the development of intelligent TCM syndrome differentiation models. This is expected to expand the benefits of TCM to lung cancer patients. ObjectiveThe objective of this work was to establish end-to-end TCM diagnostic models to imitate lung cancer syndrome differentiation. The proposed models used unstructured medical records as inputs to capitalize on data collected for practical TCM treatment cases by lung cancer experts. The resulting models were expected to be more efficient than approaches that leverage structured TCM datasets. MethodsWe approached lung cancer TCM syndrome differentiation as a multilabel text classification problem. First, entity representation was conducted with Bidirectional Encoder Representations from Transformers and conditional random fields models. Then, five deep learning–based text classification models were applied to the construction of a medical record multilabel classifier, during which two data augmentation strategies were adopted to address overfitting issues. Finally, a fusion model approach was used to elevate the performance of the models. ResultsThe F1 score of the recurrent convolutional neural network (RCNN) model with augmentation was 0.8650, a 2.41% improvement over the unaugmented model. The Hamming loss for RCNN with augmentation was 0.0987, which is 1.8% lower than that of the same model without augmentation. Among the models, the text-hierarchical attention network (Text-HAN) model achieved the highest F1 scores of 0.8676 and 0.8751. The mean average precision for the word encoding–based RCNN was 10% higher than that of the character encoding–based representation. A fusion model of the text-convolutional neural network, text-recurrent neural network, and Text-HAN models achieved an F1 score of 0.8884, which showed the best performance among the models. ConclusionsMedical records could be used more productively by constructing end-to-end models to facilitate TCM diagnosis. With the aid of entity-level representation, data augmentation, and model fusion, deep learning–based multilabel classification approaches can better imitate TCM syndrome differentiation in complex cases such as advanced lung cancer.

A known polyoxygenated briarane, briaexcavatolide P (1), was isolated from a Formosan octocoral Briareum stechei. Moreover, the same species B. stechei, collected from Okinawan waters, yielded three chlorine-containing briaranes, including two new compounds, briastecholides B (2) and C (3) as well as a known analogue, briarenol R (4). The structures of 1–4 were established using spectroscopic methods. In addition, briarane 1 demonstrated anti-inflammatory activity in lipo-polysaccharide-induced RAW 264.7 mouse macrophage cells by suppressing the expression of inducible nitric oxide synthase (iNOS) protein.

Sponge-derived scalaranes are remarkable sesterterpenoids previously found to exhibit profound inhibitory effects against neutrophilic inflammation. In our current work, we constructed the metabolomic profile of marine sponge Lendenfeldia sp. for the first time using a tandem mass spectrometry (MS/MS) molecular networking approach. The results highlighted the rich chemical diversity of these scalaranes, motivating us to conduct further research to discover novel scalaranes targeting neutrophilic inflammation. MS- and NMR-assisted isolation and elucidation led to the discovery of seven new homoscalaranes, lendenfeldaranes K–Q (1–7), characterized by methylation at C-24, together with five known derivatives, lendenfeldarane B (8), 25-nor-24-methyl-12,24-dioxoscalar-16-en-22-oic acid (9), 24-methyl-12,24,25-trioxoscalar-16-en-22-oic acid (10), felixin B (11), and 23-hydroxy-20-methyldeoxoscalarin (12). Scalaranes 1–4 and 6–12 were assayed against superoxide anion generation and elastase release, which represented the neutrophilic inflammatory responses of respiratory burst and degranulation, respectively. The results indicated that 1–3 and 6–12 exhibited potential anti-inflammatory activities (IC50 for superoxide anion scavenging: 0.87~6.57 μM; IC50 for elastase release: 1.12~6.97 μM).

In this review, 170 natural substances, including steroid, diterpenoid, sesquiterpenoid, peptide, prostaglandin, base, chlorolipid, bicyclolactone, amide, piperazine, polyketide, glycerol, benzoic acid, glycyrrhetyl amino acid, hexitol, pentanoic acid, aminoethyl ester, octadecanone, alkaloid, and a 53-kD allergenic component from octocorals belonging to genus Dendronephthya, were listed. Some of these compounds displayed potential bioactivities.

Five 8,17-epoxybriaranes, including three new compounds—briarenols I−K (1−3), along with two known analogues, briaexcavatolide P (4) and briaexcavatin P (5), were isolated from the octocoral Briareum excavatum. The structures of briaranes 1−3 were elucidated by spectroscopic methods, including 1D and 2D NMR studies and (+)-HRESIMS. Briarane 4 exerted inhibition effects on inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) release from RAW 264.7.

Two new briarane metabolites—fragilides K (1) and L (2)—along with five known analogues—gemmacolide X, praelolide, juncins P and ZI, and gemmacolide V (3–7)—were extracted and purified from Junceella fragilis, a gorgonian coral. Based on data obtained via spectroscopic techniques, the structures of new briaranes 1 and 2 were determined and the cyclohexane rings in 1 and 2 were found to exist in chair and twist boat conformation, respectively. Additionally, anti-inflammatory analysis showed that briaranes 2, 3, and 6 inhibited pro-inflammatory inducible nitric oxide synthase protein expression and briaranes 3 and 7 suppressed the cyclooxygenase-2 level, in LPS-stimulated murine macrophage-like RAW264.7 cells.

Abstract Background Dimension reduction is a critical issue in the analysis of microarray data, because the high dimensionality of gene expression microarray data set hurts generalization performance of classifiers. It consists of two types of methods, i.e. feature selection and feature extraction. Principle component analysis (PCA) and partial least squares (PLS) are two frequently used feature extraction methods, and in the previous works, the top several components of PCA or PLS are selected for modeling according to the descending order of eigenvalues. While in this paper, we prove that not all the top features are useful, but features should be selected from all the components by feature selection methods. Results We demonstrate a framework for selecting feature subsets from all the newly extracted components, leading to reduced classification error rates on the gene expression microarray data. Here we have considered both an unsupervised method PCA and a supervised method PLS for extracting new components, genetic algorithms for feature selection, and support vector machines and k nearest neighbor for classification. Experimental results illustrate that our proposed framework is effective to select feature subsets and to reduce classification error rates. Conclusion Not only the top features newly extracted by PCA or PLS are important, therefore, feature selection should be performed to select subsets from new features to improve generalization performance of classifiers.

Abstract Background Analysis of gene expression data for tumor classification is an important application of bioinformatics methods. But it is hard to analyse gene expression data from DNA microarray experiments by commonly used classifiers, because there are only a few observations but with thousands of measured genes in the data set. Dimension reduction is often used to handle such a high dimensional problem, but it is obscured by the existence of amounts of redundant features in the microarray data set. Results Dimension reduction is performed by combing feature extraction with redundant gene elimination for tumor classification. A novel metric of redundancy based on DIScriminative Contribution (DISC) is proposed which estimates the feature similarity by explicitly building a linear classifier on each gene. Compared with the standard linear correlation metric, DISC takes the label information into account and directly estimates the redundancy of the discriminative ability of two given features. Based on the DISC metric, a novel algorithm named REDISC (Redundancy Elimination based on Discriminative Contribution) is proposed, which eliminates redundant genes before feature extraction and promotes performance of dimension reduction. Experimental results on two microarray data sets show that the REDISC algorithm is effective and reliable to improve generalization performance of dimension reduction and hence the used classifier. Conclusion Dimension reduction by performing redundant gene elimination before feature extraction is better than that with only feature extraction for tumor classification, and redundant gene elimination in a supervised way is superior to the commonly used unsupervised method like linear correlation coefficients.

Abstract Background Activities of drug molecules can be predicted by QSAR (quantitative structure activity relationship) models, which overcomes the disadvantages of high cost and long cycle by employing the traditional experimental method. With the fact that the number of drug molecules with positive activity is rather fewer than that of negatives, it is important to predict molecular activities considering such an unbalanced situation. Results Here, asymmetric bagging and feature selection are introduced into the problem and asymmetric bagging of support vector machines (asBagging) is proposed on predicting drug activities to treat the unbalanced problem. At the same time, the features extracted from the structures of drug molecules affect prediction accuracy of QSAR models. Therefore, a novel algorithm named PRIFEAB is proposed, which applies an embedded feature selection method to remove redundant and irrelevant features for asBagging. Numerical experimental results on a data set of molecular activities show that asBagging improve the AUC and sensitivity values of molecular activities and PRIFEAB with feature selection further helps to improve the prediction ability. Conclusion Asymmetric bagging can help to improve prediction accuracy of activities of drug molecules, which can be furthermore improved by performing feature selection to select relevant features from the drug molecules data sets.

Abstract Background Since the high dimensionality of gene expression microarray data sets degrades the generalization performance of classifiers, feature selection, which selects relevant features and discards irrelevant and redundant features, has been widely used in the bioinformatics field. Multi-task learning is a novel technique to improve prediction accuracy of tumor classification by using information contained in such discarded redundant features, but which features should be discarded or used as input or output remains an open issue. Results We demonstrate a framework for automatically selecting features to be input, output, and discarded by using a genetic algorithm, and propose two algorithms: GA-MTL (Genetic algorithm based multi-task learning) and e-GA-MTL (an enhanced version of GA-MTL). Experimental results demonstrate that this framework is effective at selecting features for multi-task learning, and that GA-MTL and e-GA-MTL perform better than other heuristic methods. Conclusions Genetic algorithms are a powerful technique to select features for multi-task learning automatically; GA-MTL and e-GA-MTL are shown to to improve generalization performance of classifiers on microarray data sets.

Abstract Background In Traditional Chinese Medicine (TCM), the lip diagnosis is an important diagnostic method which has a long history and is applied widely. The lip color of a person is considered as a symptom to reflect the physical conditions of organs in the body. However, the traditional diagnostic approach is mainly based on observation by doctor’s nude eyes, which is non-quantitative and subjective. The non-quantitative approach largely depends on the doctor’s experience and influences accurate the diagnosis and treatment in TCM. Developing new quantification methods to identify the exact syndrome based on the lip diagnosis of TCM becomes urgent and important. In this paper, we design a computer-assisted classification model to provide an automatic and quantitative approach for the diagnosis of TCM based on the lip images. Methods A computer-assisted classification method is designed and applied for syndrome diagnosis based on the lip images. Our purpose is to classify the lip images into four groups: deep-red, red, purple and pale. The proposed scheme consists of four steps including the lip image preprocessing, image feature extraction, feature selection and classification. The extracted 84 features contain the lip color space component, texture and moment features. Feature subset selection is performed by using SVM-RFE (Support Vector Machine with recursive feature elimination), mRMR (minimum Redundancy Maximum Relevance) and IG (information gain). Classification model is constructed based on the collected lip image features using multi-class SVM and Weighted multi-class SVM (WSVM). In addition, we compare SVM with k-nearest neighbor (kNN) algorithm, Multiple Asymmetric Partial Least Squares Classifier (MAPLSC) and Naïve Bayes for the diagnosis performance comparison. All displayed faces image have obtained consent from the participants. Results A total of 257 lip images are collected for the modeling of lip diagnosis in TCM. The feature selection method SVM-RFE selects 9 important features which are composed of 5 color component features, 3 texture features and 1 moment feature. SVM, MAPLSC, Naïve Bayes, kNN showed better classification results based on the 9 selected features than the results obtained from all the 84 features. The total classification accuracy of the five methods is 84%, 81%, 79% and 81%, 77%, respectively. So SVM achieves the best classification accuracy. The classification accuracy of SVM is 81%, 71%, 89% and 86% on Deep-red, Pale Purple, Red and lip image models, respectively. While with the feature selection algorithm mRMR and IG, the total classification accuracy of WSVM achieves the best classification accuracy. Therefore, the results show that the system can achieve best classification accuracy combined with SVM classifiers and SVM-REF feature selection algorithm. Conclusions A diagnostic system is proposed, which firstly segments the lip from the original facial image based on the Chan-Vese level set model and Otsu method, then extracts three kinds of features (color space features, Haralick co-occurrence features and Zernike moment features) on the lip image. Meanwhile, SVM-REF is adopted to select the optimal features. Finally, SVM is applied to classify the four classes. Besides, we also compare different feature selection algorithms and classifiers to verify our system. So the developed automatic and quantitative diagnosis system of TCM is effective to distinguish four lip image classes: Deep-red, Purple, Red and Pale. This study puts forward a new method and idea for the quantitative examination on lip diagnosis of TCM, as well as provides a template for objective diagnosis in TCM.

Abstract Background There is a long history of coronary heart disease (CHD) diagnosis and treatment in Chinese medicine (CM), but a formalized description of CM knowledge is still unavailable. This study aims to analyze a set of CM clinical data, which is important and urgent. Methods Relative associated density (RAD) was used to analyze the one-way links between the symptoms or syndromes or both. RAD results were further used in symptom selection. Results Analysis of a dataset of clinical CHD diagnosis revealed some significant relationships, not only between syndromes but also between symptoms and syndromes. Using RAD to select symptoms based on different classifiers improved the accuracy of syndrome prediction. Compared with other traditional symptom selection methods, RAD provided a higher interpretability of the CM data. Conclusion The RAD method is effective for CM clinical data analysis, particular for analysis of relationships between symptoms in diagnosis and generation of compact and comprehensible symptom feature subsets.

Abstract Background Coronary heart disease (CHD) is a common cardiovascular disease that is extremely harmful to humans. In Traditional Chinese Medicine (TCM), the diagnosis and treatment of CHD have a long history and ample experience. However, the non-standard inquiry information influences the diagnosis and treatment in TCM to a certain extent. In this paper, we study the standardization of inquiry information in the diagnosis of CHD and design a diagnostic model to provide methodological reference for the construction of quantization diagnosis for syndromes of CHD. In the diagnosis of CHD in TCM, there could be several patterns of syndromes for one patient, while the conventional single label data mining techniques could only build one model at a time. Here a novel multi-label learning (MLL) technique is explored to solve this problem. Methods Standardization scale on inquiry diagnosis for CHD in TCM is designed, and the inquiry diagnostic model is constructed based on collected data by the MLL techniques. In this study, one popular MLL algorithm, ML-kNN, is compared with other two MLL algorithms RankSVM and BPMLL as well as one commonly used single learning algorithm, k-nearest neighbour (kNN) algorithm. Furthermore the influence of symptom selection to the diagnostic model is investigated. After the symptoms are removed by their frequency from low to high; the diagnostic models are constructed on the remained symptom subsets. Results A total of 555 cases are collected for the modelling of inquiry diagnosis of CHD. The patients are diagnosed clinically by fusing inspection, pulse feeling, palpation and the standardized inquiry information. Models of six syndromes are constructed by ML-kNN, RankSVM, BPMLL and kNN, whose mean results of accuracy of diagnosis reach 77%, 71%, 75% and 74% respectively. After removing symptoms of low frequencies, the mean accuracy results of modelling by ML-kNN, RankSVM, BPMLL and kNN reach 78%, 73%, 75% and 76% when 52 symptoms are remained. Conclusions The novel MLL techniques facilitate building standardized inquiry models in CHD diagnosis and show a practical approach to solve the problem of labelling multi-syndromes simultaneously.