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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.

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.

Abstract Significant interest exists in establishing synergistic research in bioinformatics, systems biology and intelligent computing. Supported by the United States National Science Foundation (NSF), International Society of Intelligent Biological Medicine (http://www.ISIBM.org), International Journal of Computational Biology and Drug Design (IJCBDD) and International Journal of Functional Informatics and Personalized Medicine, the ISIBM International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing (ISIBM IJCBS 2009) attracted more than 300 papers and 400 researchers and medical doctors world-wide. It was the only inter/multidisciplinary conference aimed to promote synergistic research and education in bioinformatics, systems biology and intelligent computing. The conference committee was very grateful for the valuable advice and suggestions from honorary chairs, steering committee members and scientific leaders including Dr. Michael S. Waterman (USC, Member of United States National Academy of Sciences), Dr. Chih-Ming Ho (UCLA, Member of United States National Academy of Engineering and Academician of Academia Sinica), Dr. Wing H. Wong (Stanford, Member of United States National Academy of Sciences), Dr. Ruzena Bajcsy (UC Berkeley, Member of United States National Academy of Engineering and Member of United States Institute of Medicine of the National Academies), Dr. Mary Qu Yang (United States National Institutes of Health and Oak Ridge, DOE), Dr. Andrzej Niemierko (Harvard), Dr. A. Keith Dunker (Indiana), Dr. Brian D. Athey (Michigan), Dr. Weida Tong (FDA, United States Department of Health and Human Services), Dr. Cathy H. Wu (Georgetown), Dr. Dong Xu (Missouri), Drs. Arif Ghafoor and Okan K Ersoy (Purdue), Dr. Mark Borodovsky (Georgia Tech, President of ISIBM), Dr. Hamid R. Arabnia (UGA, Vice-President of ISIBM), and other scientific leaders. The committee presented the 2009 ISIBM Outstanding Achievement Awards to Dr. Joydeep Ghosh (UT Austin), Dr. Aidong Zhang (Buffalo) and Dr. Zhi-Hua Zhou (Nanjing) for their significant contributions to the field of intelligent biological medicine.

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).

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.