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Patent documents in the patent database (PatDB) are crucial for research, development, and innovation as they contain valuable technical information. However, PatDB presents a multifaceted challenge compared to publicly available preprocessed databases due to the intricate nature of the patent text and the inherent sparsity within the patent citation network. Although patent text analysis and citation analysis bring new opportunities to explore patent data mining, no existing work exploits the complementation of them. To this end, we propose a joint semantic-topological evolutionary graph learning approach (PatSTEG) to model the formation dynamics of patent citation networks. More specifically, we first create a real-world dataset of Chinese patents named CNPat and leverage its patent texts and citations to construct a patent citation network. Then, PatSTEG is modeled to study the evolutionary dynamics of patent citation formation by considering the semantic and topological information jointly. Extensive experiments are conducted on CNPat and public datasets to prove the superiority of PatSTEG over other state-of-the-art methods. All the results provide valuable references for patent literature research and technical exploration.

Non alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease, which can be predicted accurately to prevent advanced fibrosis and cirrhosis. While, a liver biopsy, the gold standard for NAFLD diagnosis, is invasive, expensive, and prone to sampling errors. Therefore, non-invasive studies are extremely promising, yet they are still in their infancy due to the lack of comprehensive research data and intelligent methods for multi-modal data. This paper proposes a NAFLD diagnosis system (DeepFLDDiag) combining a comprehensive clinical dataset (FLDData) and a multi-modal learning based NAFLD prediction method (DeepFLD). The dataset includes over 6000 participants physical examinations, laboratory and imaging studies, extensive questionnaires, and facial images of partial participants, which is comprehensive and valuable for clinical studies. From the dataset, we quantitatively analyze and select clinical metadata that most contribute to NAFLD prediction. Furthermore, the proposed DeepFLD, a deep neural network model designed to predict NAFLD using multi-modal input, including metadata and facial images, outperforms the approach that only uses metadata. Satisfactory performance is also verified on other unseen datasets. Inspiringly, DeepFLD can achieve competitive results using only facial images as input rather than metadata, paving the way for a more robust and simpler non-invasive NAFLD diagnosis.

We propose an end-to-end model to predict drug-drug interactions (DDIs) by employing graph-augmented convolutional networks. And this is implemented by combining graph CNN with an attentive pooling network to extract structural relations between drug pairs and make DDI predictions. The experiment results suggest a desirable performance achieving ROC at 0.988, F1-score at 0.956, and AUPR at 0.986. Besides, the model can tell how the two DDI drugs interact structurally by varying colored atoms. And this may be helpful for drug design during drug discovery.

AIM: Blocking of lysophosphatidic acid (LPA) receptor (LPAR) 1 may be a novel therapeutic option for bronchopulmonary dysplasia (BPD) by preventing the LPAR1-mediated adverse effects of its ligand (LPA), consisting of lung inflammation, pulmonary arterial hypertension (PAH) and fibrosis.METHODS: In Wistar rats with experimental BPD, induced by continuous exposure to 100% oxygen for 10 days, we determined the beneficial effects of LPAR1 deficiency in neonatal rats with a missense mutation in cytoplasmic helix 8 of LPAR1 and of LPAR1 and -3 blocking with Ki16425. Parameters investigated included survival, lung and heart histopathology, fibrin and collagen deposition, vascular leakage, and differential mRNA expression in the lungs of key genes involved in LPA signalling and BPD pathogenesis.RESULTS: LPAR1 mutant rats were protected against experimental BPD and mortality with reduced alveolar septal thickness, lung inflammation (reduced influx of macrophages and neutrophils, and CINC1 expression), and collagen III deposition. However, LPAR1 mutant rats were not protected against alveolar enlargement, increased medial wall thickness of small arterioles, fibrin deposition, and vascular alveolar leakage. Treatment of experimental BPD with Ki16425 confirmed the data observed in LPAR1 mutant rats, but did not reduce the pulmonary influx of neutrophils, CINC1 expression, and mortality in rats with experimental BPD. In addition, Ki16425 treatment protected against PAH and right ventricular hypertrophy.CONCLUSION: LPAR1 deficiency attenuates pulmonary injury by reducing pulmonary inflammation and fibrosis, thereby reducing mortality, but does not affect alveolar and vascular development and, unlike Ki16425 treatment, does not prevent PAH in neonatal rats with experimental BPD. This article is protected by copyright. All rights reserved.

AIM: Blocking of lysophosphatidic acid (LPA) receptor (LPAR) 1 may be a novel therapeutic option for bronchopulmonary dysplasia (BPD) by preventing the LPAR1-mediated adverse effects of its ligand (LPA), consisting of lung inflammation, pulmonary arterial hypertension (PAH) and fibrosis.METHODS: In Wistar rats with experimental BPD, induced by continuous exposure to 100% oxygen for 10 days, we determined the beneficial effects of LPAR1 deficiency in neonatal rats with a missense mutation in cytoplasmic helix 8 of LPAR1 and of LPAR1 and -3 blocking with Ki16425. Parameters investigated included survival, lung and heart histopathology, fibrin and collagen deposition, vascular leakage, and differential mRNA expression in the lungs of key genes involved in LPA signalling and BPD pathogenesis.RESULTS: LPAR1 mutant rats were protected against experimental BPD and mortality with reduced alveolar septal thickness, lung inflammation (reduced influx of macrophages and neutrophils, and CINC1 expression), and collagen III deposition. However, LPAR1 mutant rats were not protected against alveolar enlargement, increased medial wall thickness of small arterioles, fibrin deposition, and vascular alveolar leakage. Treatment of experimental BPD with Ki16425 confirmed the data observed in LPAR1 mutant rats, but did not reduce the pulmonary influx of neutrophils, CINC1 expression, and mortality in rats with experimental BPD. In addition, Ki16425 treatment protected against PAH and right ventricular hypertrophy.CONCLUSION: LPAR1 deficiency attenuates pulmonary injury by reducing pulmonary inflammation and fibrosis, thereby reducing mortality, but does not affect alveolar and vascular development and, unlike Ki16425 treatment, does not prevent PAH in neonatal rats with experimental BPD. This article is protected by copyright. All rights reserved.