The Recent Progress of Oxygen Reduction Electrocatalysts Used at Fuel Cell Level

Proton exchange membrane fuel cells (PEMFCs) are gaining significant interest as an attractive substitute for traditional fuel cells, with higher energy density, lower environmental pollution, and better operation efficiency. However, the cathode reaction, i.e., the oxygen reduction reaction (ORR), is widely proved to be inefficient, and therefore an obstacle to the widespread development of PEMFCs. The requirement for affordable highly-efficient ORR catalysts is extremely urgent to be met, especially at fuel cell level. Unfortunately, most previous reports focus on the ORR performance at rotating disk electrodes (RDE) level instead of membrane electrode assembly (MEA) level, making it harder to evaluate ORR catalysts operating under real vehicle conditions. Obviously, it is extremely necessary to develop an in-depth understanding of the structure-activity relationship of highly-efficient ORR catalysts applied at MEA level. In this work, an overview of the latest advances in ORR catalysts is provided with an emphasis on their performance at MEA level, hoping to cover the novel and systemic insights for innovative and efficient ORR catalyst design and applications in PEMFCs.

MRMPro: a web-based tool to improve the speed of manual calibration for multiple reaction monitoring data analysis by mass spectrometry

BACKGROUND

As a gold-standard quantitative technique based on mass spectrometry, multiple reaction monitoring (MRM) has been widely used in proteomics and metabolomics. In the analysis of MRM data, as no peak picking algorithm can achieve perfect accuracy, manual inspection is necessary to correct the errors. In large cohort analysis scenarios, the time required for manual inspection is often considerable. Apart from the commercial software that comes with mass spectrometers, the open-source and free software Skyline is the most popular software for quantitative omics. However, this software is not optimized for manual inspection of hundreds of samples, the interactive experience also needs to be improved.

RESULTS

Here we introduce MRMPro, a web-based MRM data analysis platform for efficient manual inspection. MRMPro supports data analysis of MRM and schedule MRM data acquired by mass spectrometers of mainstream vendors. With the goal of improving the speed of manual inspection, we implemented a collaborative review system based on cloud architecture, allowing multiple users to review through browsers. To reduce bandwidth usage and improve data retrieval speed, we proposed a MRM data compression algorithm, which reduced data volume by more than 60% and 80% respectively compared to vendor and mzML format. To improve the efficiency of manual inspection, we proposed a retention time drift estimation algorithm based on similarity of chromatograms. The estimated retention time drifts were then used for peak alignment and automatic EIC grouping. Compared with Skyline, MRMPro has higher quantification accuracy and better manual inspection support.

CONCLUSIONS

In this study, we proposed MRMPro to improve the usability of manual calibration for MRM data analysis. MRMPro is free for non-commercial use. Researchers can access MRMPro through http://mrmpro.csibio.com/ . All major mass spectrometry formats (wiff, raw, mzML, etc.) can be analyzed on the platform. The final identification results can be exported to a common.xlsx format for subsequent analysis.

Ion entropy and accurate entropy-based FDR estimation in metabolomics

Accurate metabolite annotation and false discovery rate (FDR) control remain challenging in large-scale metabolomics. Recent progress leveraging proteomics experiences and interdisciplinary inspirations has provided valuable insights. While target-decoy strategies have been introduced, generating reliable decoy libraries is difficult due to metabolite complexity. Moreover, continuous bioinformatics innovation is imperative to improve the utilization of expanding spectral resources while reducing false annotations. Here, we introduce the concept of ion entropy for metabolomics and propose two entropy-based decoy generation approaches. Assessment of public databases validates ion entropy as an effective metric to quantify ion information in massive metabolomics datasets. Our entropy-based decoy strategies outperform current representative methods in metabolomics and achieve superior FDR estimation accuracy. Analysis of 46 public datasets provides instructive recommendations for practical application.

MetaPro: a web-based metabolomics application for LC-MS data batch inspection and library curation

INTRODUCTION

Metabolomics analysis based on liquid chromatography-mass spectrometry (LC-MS) has been a prevalent method in the metabolic field. However, accurately quantifying all the metabolites in large metabolomics sample cohorts is challenging. The analysis efficiency is restricted by the abilities of software in many labs, and the lack of spectra for some metabolites also hinders metabolite identification.

OBJECTIVES

Develop software that performs semi-targeted metabolomics analysis with an optimized workflow to improve quantification accuracy. The software also supports web-based technologies and increases laboratory analysis efficiency. A spectral curation function is provided to promote the prosperity of homemade MS/MS spectral libraries in the metabolomics community.

METHODS

MetaPro is developed based on an industrial-grade web framework and a computation-oriented MS data format to improve analysis efficiency. Algorithms from mainstream metabolomics software are integrated and optimized for more accurate quantification results. A semi-targeted analysis workflow is designed based on the concept of combining artificial judgment and algorithm inference.

RESULTS

MetaPro supports semi-targeted analysis workflow and functions for fast QC inspection and self-made spectral library curation with easy-to-use interfaces. With curated authentic or high-quality spectra, it can improve identification accuracy using different peak identification strategies. It demonstrates practical value in analyzing large amounts of metabolomics samples.

CONCLUSION

We offer MetaPro as a web-based application characterized by fast batch QC inspection and credible spectral curation towards high-throughput metabolomics data. It aims to resolve the analysis difficulty in semi-targeted metabolomics.

Qingchang Mixture Prevents the Intestinal Ischemia-reperfusion Injury through TLR4/NF-kB Pathway

OBJECT

This study aims to determine the protective effect and molecular responses of the traditional Chinese medicine Qingchang mixture on intestinal ischemia-reperfusion (IR) injury.

METHODS

The rat intestinal IR model was prepared. The intestinal ischemic injury was evaluated by HE staining, biochemical assay and western blot. In addition, a human hypoxia-reoxygenation (HR) in vitro model was prepared using intestinal epithelial cells (IEC-6). The viability and apoptosis of IEC-6 cells were measured by CCK8 and apoptosis detection. TAK242 or PDTC was used as a small molecule inhibitor of TLR4 or NF-κB, respectively.

RESULTS

Compared with the IR group, the pretreatment of the Qingchang mixture reduced the morphological damage, oxidative stress, inflammatory response, and barrier function damage of the small intestine tissue. IR significantly increased the expression of TLR4 and NF-κB, while the pretreatment of the Qingchang mixture inhibited the expression of TLR4 and NF-κB. Furthermore, the pretreatment of Qingchang mixture, TAK242, or PDTC effectively improved the viability and hindered apoptosis of the HR-induced IEC-6 cells.

CONCLUSIONS

Traditional Chinese medicine Qingchang mixture prevents intestinal IR injury through TLR4/NF-kB pathway.