Rat pheochromocytoma PC12 cells undergo neuronal differentiation in response to nerve growth factor. We show here that exposure of PC12 cells to Nardostachys chinensis glycoside induces the outgrowth of neurites, increases the activity of AChE, triggers cell cycle arrest in G1 and enhances the expression of growth associated protein 43 (GAP-43). Both the outgrowth of neurites and the increase in AChE activity are prevented partly by PD98059, a specific inhibitor of MEK1. These results suggest that N. chinensis glycoside induces the characteristics of neuronal differentiation in PC12 cells via the mitogen-activated protein kinase (MAPK)-related signal cascade.
Background Continued use of mHealth apps can achieve better effects in health management. Gamification is an important factor in promoting users’ intention to continue using mHealth apps. Past research has rarely explored the factors underlying the continued use of mobile health (mHealth) apps and gamification’s impact mechanism or path on continued use. Objective This study aimed to explore the factors influencing mHealth app users’ intention to continue using mHealth apps and the impact mechanism and path of users’ feelings induced by gamification on continued mHealth app use. Methods First, based on the expectation confirmation model of information system continuance, we built a theoretical model for continued use of mHealth apps based on users’ feelings toward gamification. We used self-determination theory to analyze gamification’s impact on user perceptions and set the resulting feelings (competence, autonomy, and relatedness) as constructs in the model. Second, we used the survey method to validate the research model, and we used partial least squares to analyze the data. Results A total of 2988 responses were collected from mHealth app users, and 307 responses were included in the structural equation model after passing the acceptance criteria. The intrinsic motivation for using mHealth apps is significantly affected by autonomy (β=.312; P<.001), competence (β=.346; P<.001), and relatedness (β=.165; P=.004) induced by gamification. The intrinsic motivation for using mHealth apps has a significant impact on satisfaction (β=.311, P<.001) and continuance intention (β=.142; P=.045); furthermore, satisfaction impacts continuance intention significantly (β=.415; P<.001). Confirmation has a significant impact on perceived usefulness (β=.859; P<.001) and satisfaction (β=.391; P<.001), and perceived usefulness has a significant impact on satisfaction (β=.269; P<.001) and continuance intention (β=.273; P=.001). The mediating effect analysis showed that in the impact path of the intrinsic motivation for using the mHealth apps on continuance intention, satisfaction plays a partial mediating role (β=.129; P<.001), with a variance accounted for of 0.466. Conclusions This study explored the impact path of users’ feelings induced by gamification on the intention of continued mHealth app use. We confirmed that perceived usefulness, confirmation, and satisfaction in the classical continued use theory for nonmedical information systems positively affect continuance intention. We also found that the path and mechanism of users' feelings regarding autonomy, competence, and relatedness generated during interactions with different gamification elements promote the continued use of mHealth apps.
In this work, the effect of split ratio and the column flow in determination of OCPs in Traditional Chinese medicines are discussed in the name of heptachlor. When the split ratio is 60:1, the measured concentration is close to the actual concentration; on the contrary, they lead the measured concentration lower than the actual concentration. The column flow has not obviously effect on result. The split ratio is considered in OCPs analysis.
Explaining deep learning models operating on time series data is crucial in various applications of interest which require interpretable and transparent insights from time series signals. In this work, we investigate this problem from an information theoretic perspective and show that most existing measures of explainability may suffer from trivial solutions and distributional shift issues. To address these issues, we introduce a simple yet practical objective function for time series explainable learning. The design of the objective function builds upon the principle of information bottleneck (IB), and modifies the IB objective function to avoid trivial solutions and distributional shift issues. We further present TimeX++, a novel explanation framework that leverages a parametric network to produce explanation-embedded instances that are both in-distributed and label-preserving. We evaluate TimeX++ on both synthetic and real-world datasets comparing its performance against leading baselines, and validate its practical efficacy through case studies in a real-world environmental application. Quantitative and qualitative evaluations show that TimeX++ outperforms baselines across all datasets, demonstrating a substantial improvement in explanation quality for time series data. The source code is available at \url{https://github.com/zichuan-liu/TimeXplusplus}.
The activation of the Ras signaling pathway is a crucial process in hepatocarcinogenesis. Till now, no reports have scrutinized the role of dynamic metabolic changes in Ras oncogene-induced transition of the normal and precancerous liver cells to hepatocellular carcinoma in vivo. In the current study, we attempted a comprehensive investigation of Hras12V transgenic mice (Ras-Tg) by concatenating nontargeted metabolomics, transcriptomics analysis, and targeted-metabolomics incorporating [U-13C] glucose. A total of 631 peaks were detected, out of which 555 metabolites were screened. Besides, a total of 122 differently expressed metabolites (DEMs) were identified, and they were categorized and subtyped with the help of variation tendency analysis of the normal (W), precancerous (P), and hepatocellular carcinoma (T) liver tissues. Thus, the positive or negative association between metabolites and the hepatocellular carcinoma and Ras oncogene were identified. The bioinformatics analysis elucidated the hepatocarcinogenesis-associated significant metabolic pathways: glycolysis, mitochondrial citrate-malate shuttle, lipid biosynthesis, pentose phosphate pathway (PPP), cholesterol and bile acid biosynthesis, and glutathione metabolism. The key metabolites and enzymes identified in this analysis were further validated. Moreover, we confirmed the PPP, glycolysis, and conversion of pyruvate to cytosol acetyl-CoA by mitochondrial citrate-malate shuttle, in vivo, by incorporating [U-13C] glucose. In summary, the current study presented the comprehensive bioinformatics analysis, depicting the Ras oncogene-induced dynamic metabolite variations in hepatocarcinogenesis. A significant finding of our study was that the mitochondrial citrate-malate shuttle plays a crucial role in detoxification of lactic acid, maintenance of mitochondrial integrity, and enhancement of lipid biosynthesis, which, in turn, promotes hepatocarcinogenesis.
BACKGROUND Natural language processing (NLP) is an important traditional field in computer science, but its application in medical research has faced many challenges. With the extensive digitalization of medical information globally and increasing importance of understanding and mining big data in the medical field, NLP is becoming more crucial. OBJECTIVE The goal of the research was to perform a systematic review on the use of NLP in medical research with the aim of understanding the global progress on NLP research outcomes, content, methods, and study groups involved. METHODS A systematic review was conducted using the PubMed database as a search platform. All published studies on the application of NLP in medicine (except biomedicine) during the 20 years between 1999 and 2018 were retrieved. The data obtained from these published studies were cleaned and structured. Excel (Microsoft Corp) and VOSviewer (Nees Jan van Eck and Ludo Waltman) were used to perform bibliometric analysis of publication trends, author orders, countries, institutions, collaboration relationships, research hot spots, diseases studied, and research methods. RESULTS A total of 3498 articles were obtained during initial screening, and 2336 articles were found to meet the study criteria after manual screening. The number of publications increased every year, with a significant growth after 2012 (number of publications ranged from 148 to a maximum of 302 annually). The United States has occupied the leading position since the inception of the field, with the largest number of articles published. The United States contributed to 63.01% (1472/2336) of all publications, followed by France (5.44%, 127/2336) and the United Kingdom (3.51%, 82/2336). The author with the largest number of articles published was Hongfang Liu (70), while Stéphane Meystre (17) and Hua Xu (33) published the largest number of articles as the first and corresponding authors. Among the first author’s affiliation institution, Columbia University published the largest number of articles, accounting for 4.54% (106/2336) of the total. Specifically, approximately one-fifth (17.68%, 413/2336) of the articles involved research on specific diseases, and the subject areas primarily focused on mental illness (16.46%, 68/413), breast cancer (5.81%, 24/413), and pneumonia (4.12%, 17/413). CONCLUSIONS NLP is in a period of robust development in the medical field, with an average of approximately 100 publications annually. Electronic medical records were the most used research materials, but social media such as Twitter have become important research materials since 2015. Cancer (24.94%, 103/413) was the most common subject area in NLP-assisted medical research on diseases, with breast cancers (23.30%, 24/103) and lung cancers (14.56%, 15/103) accounting for the highest proportions of studies. Columbia University and the talents trained therein were the most active and prolific research forces on NLP in the medical field.
A catalyst based on molybdenum oxides was prepared and used for the catalytic pyrolysis of PET (poly(ethylene terephthalate)) in a H2 atmosphere to facilitate the direct conversion of PET for producing olefins (ethylene and propylene) and TPA (terephthalic acid). At a reaction temperature of 400 °C, the yields of olefins and aromatic products were the highest, reaching 10.10 and 52.16 wt %, respectively. Meanwhile, the carbon black could be entirely removed. The content of TPA in aromatic products was about 70%, and the content of benzoic acid reached 22.64%. Analyses using in situ gas chromatography (GC) and high-performance liquid chromatography (HPLC) were performed to clarify the possible reaction mechanism involved in the catalytic pyrolysis of PET. Vinyl ester was found to be an important intermediate in the formation of olefins. The molybdenum oxide catalyst promoted the cleavage of the C–O bond in the side chain of PET molecules to form TPA and monovinyl terephthalate. By catalytic hydrogenation, vinyl ester was easily attacked by H to drop off the vinyl end to form olefins, simultaneously converting the ester groups into a carboxyl group and further into a TPA. This is also the main path for the production of olefins. Meanwhile, the source of olefins also involved the hydrogenation of the coordinated quaternary cycle transition state of vinyl esters and the H transfer and cracking of monoethyl terephthalate. In addition, it was found that the presence of H2 was the main reason for the reduction of carbon black products. This work is promising to promote the highly efficient utilization of waste PET and directional adjustment of pyrolysis products.
The bronchial vascular endothelial network plays important roles in pulmonary pathology during respiratory viral infections, including respiratory syncytial virus (RSV), influenza A(H1N1) and importantly SARS-Cov-2. All of these infections can be severe and even lethal in patients with underlying risk factors.A major obstacle in disease prevention is the lack of appropriate efficacious vaccine(s) due to continuous changes in the encoding capacity of the viral genome, exuberant responsiveness of the host immune system and lack of effective antiviral drugs. Current management of these severe respiratory viral infections is limited to supportive clinical care. The primary cause of morbidity and mortality is respiratory failure, partially due to endothelial pulmonary complications, including edema. The latter is induced by the loss of alveolar epithelium integrity and by pathological changes in the endothelial vascular network that regulates blood flow, blood fluidity, exchange of fluids, electrolytes, various macromolecules and responses to signals triggered by oxygenation, and controls trafficking of leukocyte immune cells. This overview outlines the latest understanding of the implications of pulmonary vascular endothelium involvement in respiratory distress syndrome secondary to viral infections. In addition, the roles of infection-induced cytokines, growth factors, and epigenetic reprogramming in endothelial permeability, as well as emerging treatment options to decrease disease burden, are discussed.
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