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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.

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.

The doping effect of BiFeO3 (BFO) nanoparticles on the growth and properties of Y–Ba–Cu–O (YBCO) superconductors by the powder melt and infiltration growth technique is investigated. The results indicate that nano-BFO doping reduces the growth rate of the Y-123 crystal and finally leads to smaller grain size in the YBCO bulk. Superconducting property measurements prove that improved levitation force and trapped field properties are observed in the 0.2 wt% BFO-doped YBCO sample. Magnetization measurement shows that the YBCO samples exhibit decreased Tc values with the increased BFO amount from 0.1 wt%–0.3 wt%, but the transition width ΔTc shows no evident change. The sample with 0.2 wt% BFO addition presents the largest zero-field Jc value and also the highest secondary peak value, which accounts for the superior performance of the sample. The electron probe micro analysis results combined with the XRD analysis prove the decomposition of the BFO and formation of the YBi-2411 in the final bulk, while Fe element exists in the form of Fe/Cu substitutions. Thus, the simultaneous introduction and cooperation of the two types of flux pinning centers (i.e. non-superconducting inclusions and element substitutions) can be realized through the addition of nano-BFO. [ABSTRACT FROM AUTHOR]

The housing mortgage policy for farmers helps to exert the 'economic attribute of property rights' of farmers' land assets and alleviate the financing difficulties faced by farmers due to the lack of desirable collateral. For farmers who stay in cities, this policy can not only effectively utilize the domicile of the homestead land, but also ease the credit constraints they face in starting businesses. However, the financing effect of this policy on the influence of different entrepreneurial farmers still lacks quantitative analysis currently. Based on the CMDS data in 2017, this paper comprehensively used multiple Logit, Propensity Score Matching (PSM) and Quantile Regression (QR) methods to conduct a deep theoretical analysis and a quantitative study on the impact of homestead mortgage financing on rural households' off-site entrepreneurship. This paper found that: (1) Compared with non-pilot areas, farmers in pilot areas had a higher possibility for choosing entrepreneurship, in which the possibility of opportunistic entrepreneurship was higher than that of subsistence entrepreneurship. (2) The average monthly income of households in the pilot areas was 926.6 yuan higher than that in the non-pilot areas. The average monthly income of rural households with homesteads in the pilot areas was 348.74 yuan higher than that of rural households without homesteads in the pilot areas. (3) With the expansion of homestead areas, the influence of the unit area of homestead on the income of entrepreneurial farmers showed a trend of decreasing first and rising next. (4) From the perspective of geography, the value of homestead per unit area from east to west presented a U-shaped change trend of decreasing first and increasing next. Through further investigation and theoretical deduction, it was found that early credit assessment and credit investigation were important safeguard measures for financial institutions to reduce operational risks. According to the research, homestead mortgage loan could significantly promote the entrepreneurship of farmers. Reasonable evaluation and hedging mechanism could help reduce social risks, and the improvement of insurance and supporting policies for the entrepreneurship of farmers could help reduce the risks. [ABSTRACT FROM AUTHOR]