Search bioRxivSearch

Biology subjects

Nakanishi, Y.

Publications and source records attributed to Nakanishi, Y..

2 recordsLinked to original sources

Neutrophil elastase inhibitor sivelestat ameliorates gefitinib-naphthalene-induced acute pneumonitis in mice

Background and objectiveGefitinib, an epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI), is an effective therapeutic agent for non-small cell lung cancer with EGFR mutations. It can cause severe acute pneumonitis in some patients. We previously demonstrated that mice with naphthalene-induced airway epithelial injury developed severe gefitinib-induced pneumonitis and that neutrophils played important roles in the development of the disease. This study aimed to investigate the effects of the neutrophil elastase inhibitor sivelestat on gefitinib-induced pneumonitis in mice.\n\nMethodsC57BL/6J mice received naphthalene (200 mg/kg) intraperitoneally on day 0. Gefitinib (250 or 300 mg/kg) was orally administered to mice from day -1 until day 13. Sivelestat (150 mg/kg) was administered intraperitoneally from day 1 until day 13. Bronchoalveolar lavage fluid (BALF) and lung tissues were sampled on day 14.\n\nResultsSivelestat treatment significantly reduced the protein level, neutrophil count, neutrophil elastase activity in BALF, and severity of histopathologic findings on day 14 for mice administered with 250 mg/kg of gefitinib. Moreover, sivelestat treatment significantly improved the survival of mice administered with 300 mg/kg of gefitinib. Conclusions: These results indicate that sivelestat is a promising therapeutic agent for severe acute pneumonitis caused by gefitinib.\n\nSummary statementNeutrophil elastase inhibitor sivelestat is a promising therapeutic agent for severe acute pneumonitis caused by gefitinib.

pharmacology and toxicology

Liberation from equations: An equation-free method reveals the ecological interaction networks within complex microbial ecosystems

O_LIMapping the network of ecological interactions is key to understanding the composition, stability, function and dynamics of microbial communities. In recent years various approaches have been used to reveal microbial interaction networks from metagenomic sequencing data, such as time-series analysis, machine learning and statistical techniques. Despite these efforts it is still not possible to capture details of the ecological interactions behind complex microbial dynamics.\nC_LIO_LIWe developed the sparse S-map method (SSM), which generates a sparse interaction network from a multivariate ecological time-series without presuming any mathematical formulation for the underlying microbial processes. The advantage of the SSM over alternative methodologies is that it fully utilizes the observed data using a framework of empirical dynamic modelling. This makes the SSM robust to non-equilibrium dynamics and underlying complexity (nonlinearity) in microbial processes.\nC_LIO_LIWe showed that an increase in dataset size or a decrease in observational error improved the accuracy of SSM whereas, the accuracy of a comparative equation-based method was almost unchanged for both cases and equivalent to the SSM at best. Hence, the SSM outperformed a comparative equation-based method when datasets were large and the magnitude of observational errors were small. The results were robust to the magnitude of process noise and the functional forms of inter-specific interactions that we tested. We applied the method to a microbiome data of six mice and found that there were different microbial interaction regimes between young to middle age (4-40 week-old) and middle to old age (36-72 week-old) mice.\nC_LIO_LIThe complexity of microbial relationships impedes detailed equation-based modeling. Our method provides a powerful alternative framework to infer ecological interaction networks of microbial communities in various environments and will be improved by further developments in metagenomics sequencing technologies leading to increased dataset size and improved accuracy and precision.\nC_LI

ecology