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Your search keyword '"Gao Wenqing"' showing total 379 results
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379 results on '"Gao Wenqing"'

29. Determination of Ethanol and Aromatics in Blood by Headspace Portable Gas Chromatography-Mass Spectrometry (HS-PGC-MS).

Author

Geng, Yigong, Zhang, Shun, Lin, Jing, Zhu, Xuchu, Gao, Wenqing, Li, Junhui, Wang, Chenlu, Wu, Yong, Han, Renlu, Tang, Keqi, and Yu, Jiancheng

Subjects
GAS chromatography/Mass spectrometry (GC-MS), BLOOD alcohol, MASS spectrometry, BLOOD volume, GAS analysis
Abstract

Ethanol abuse is increasingly becoming a global issue, adversely affecting both society and families. In blood alcohol concentration (BAC) detection, traditional techniques have long experimental cycles and harsh experimental conditions and often fail to produce timely results. Consequently, achieving swift and precise on-site detection of ethanol poisoning in humans poses a formidable challenge in analytical science. We designed a novel assay for swift on-site analysis of ethanol and BTEX (benzene, toluene, ethylbenzene, and xylene) poisoning in humans using a custom portable headspace injector (portable HS) and portable gas chromatography-mass spectrometry (portable GC-MS). This setup, known for its lightweight design and speed, allows on-site sampling and delivers rapid results. We established a calibration curve (R2 > 0.99) for ethanol in blood using portable gas chromatography-mass spectrometry (portable GC-MS). This on-site blood alcohol concentration (BAC) assay proves simpler than traditional methods, enabling rapid analysis of each sample within a short timeframe and requiring only a small blood volume (0.1 ml). In addition to our investigation on ethanol, we developed a portable method for detecting BTEX in blood. This method provides a favorable coefficient of determination (R2 > 0.99) for various substances, thereby broadening the utility of portable mass spectrometry in areas such as traffic inspection and forensic identification. [ABSTRACT FROM AUTHOR]

Published
2025
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32. Association between stress hyperglycemia ratio and all-cause mortality in critically ill patients with sepsis: results from the MIMIC-IV database.

Author

Zhang, Shijie, Shen, Hechen, Wang, Yuchao, Ning, Meng, Zhou, Jianghui, Liang, Xiaoyu, Chang, Yun, Gao, Wenqing, and Li, Tong

Subjects
HOSPITAL mortality, MORTALITY, INTENSIVE care units, PUBLIC health, MEDICAL databases
Abstract

Background: This study aimed to explore the association between the stress hyperglycemia ratio (SHR) and short- and long-term outcomes in critically ill patients with sepsis. Methods: This retrospective observational cohort study was conducted using the Medical Information Mart for Intensive Care-IV (MIMIC-IV v2.2) database. Patients were categorized into 4 SHR quartiles. The main focus was on in-hospital mortality and 1-year all-cause mortality as primary endpoints, while intensive care unit and hospital stays were considered as secondary outcomes. Regression and subgroup analyses were used to assess the correlation between SHR and the primary and secondary outcomes. Restricted cubic spline analysis was utilized to explore the nonlinear relationships between SHR and in-hospital and 1-year all-cause mortality. Results: This study included two groups of patients, comprising 7456 and 6564 individuals. The in-hospital and 1-year mortality was 11.96% and 17.96% in Cohort 1 and 2, respectively. SHR was associated with an elevated risk of in-hospital mortality (OR: 2.08, 95%CI 1.66–2.61) and 1-year mortality (HR: 1.70, 95% CI 1.42–2.04). Patients in SHR quartile 4 had a higher risk of in-hospital (OR: 1.86, 95% CI 1.51–2.30) and 1-year (HR: 1.44, 95% CI 1.23–1.69) mortality than those in quartile 2. Restricted cubic spline analysis showed a "J-shaped" relationship between SHR and all-cause mortality in both cohorts. The relationship between high SHR and mortality remained consistent across almost all predefined subgroups. Conclusions: Our study suggests that high SHR is associated with increased in-hospital and 1-year mortality in critically ill sepsis patients. Further investigations are needed to validate these results. [ABSTRACT FROM AUTHOR]

Published
2025
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39. Discontinuous Atmospheric Pressure Interface in-Trap Single Photon Ionization Ion Trap Mass Spectrometry (DAPI in-Trap SPI ITMS).

Author

Zhang, Tianhan, Xie, Chengyi, Wu, Yong, Gao, Wenqing, Wang, Chenlu, Tang, Keqi, and Yu, Jiancheng

Subjects
TANDEM mass spectrometry, VOLATILE organic compounds, ATOMIC mass, ION traps, ATMOSPHERIC pressure
Abstract

A compact in-trap single photon ionization (SPI) system, termed discontinuous atmospheric pressure interface (DAPI) ion trap mass spectrometry (ITMS), for the rapid determination of trace volatile organic compounds (VOCs). DAPI enables the maintenance of high analyte pressure in the SPI source while preserving the vacuum. Utilizing vacuum ultraviolet (VUV) lamp as the ion source, we ionize the analyte within an enclosed miniature 3D ion trap for efficient analysis. Experimental findings demonstrate that the system achieves a mass resolution of less than 0.5 atomic mass units for full width at half maxima (FWHM), providing unit resolution. For the response for benzene, the system achieves almost instantaneous response, with rise and fall times of only 2 s, which is a significant advantage compared to the membrane inlet (MI) approach necessary for on-site detection. Additionally, tandem mass spectrometry (MS/MS) with unit m/z isolation window was achieved and resolved dimethyl disulfide and phenol with similar molecular weights. Our results demonstrate that the system provides excellent performance and we anticipate its application in rapidly determining VOCs. [ABSTRACT FROM AUTHOR]

Published
2025
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44. An improved cancer diagnosis algorithm for protein mass spectrometry based on PCA and a one-dimensional neural network combining ResNet and SENet.

Author

Ma, Liang, Gao, Wenqing, Hu, Xiangyang, Zhou, Dongdong, Wang, Chenlu, Yu, Jiancheng, and Tang, Keqi

Subjects
*CONVOLUTIONAL neural networks, *EARLY detection of cancer, *PRINCIPAL components analysis, *MACHINE learning, *DIAGNOSTIC errors
Abstract

Cancer is one of the most serious health problems worldwide. Because cancer has no specific symptoms in its early stages, it is often not diagnosed until it is in advanced stages, reducing the likelihood of successful treatment. Therefore, early diagnosis of cancer is a formidable challenge. Mass spectrometry-based proteomics offers a robust technical foundation for cancer diagnosis. However, mass spectrometry data are characterized by high dimensionality, large data volume, and noise interference, which can lead to diagnostic errors in clinical applications. To address this challenge, an improved algorithm combining principal component analysis (PCA) with a convolutional neural network (CNN) algorithm (denoted as PCA-1DSE-ResCNN) was proposed to assist in analyzing high-dimensional mass spectral data. The algorithm initially reduced the dimensionality of the data through the PCA technique. Subsequently, the convolutional neural network algorithm (1DSE-ResCNN) integrating residual blocks and squeeze-and-excitation blocks was used as a classifier. This approach can not only alleviate the issues of overfitting and gradient vanishing caused by deep network layers but also reduce redundant information, enabling the algorithm to effectively learn high-dimensional data features and deal with nonlinear relationships. To validate the effectiveness of the algorithm, the high-dimensional ovarian cancer mass spectrometry dataset was selected as an example to examine its application performance in early diagnosis of ovarian cancer. The experimental results demonstrated that the PCA-1DSE-ResCNN algorithm outperforms other methods in terms of accuracy, specificity, and sensitivity on three high-dimensional ovarian cancer datasets. This study will contribute to the rapid diagnosis and early detection of cancer. [ABSTRACT FROM AUTHOR]

Published
2024
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