Volume 1, Issue 2

Volume 1, Issue 2

September 2023

Pages: 48-100

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Volume 1, Issue 2

Review Article
Open Access
Roles of post-translational modifications of C-type lectin receptor-induced signaling cascades in innate immune responses against Candida albicans
Ping Li
Ping Li
Graduate School, Wannan Medical College, Anhui 241000, China.
,
Lindong Cheng
Lindong Cheng
Graduate School, Hebei North University, Hebei 075000, China.
,
Chunhua Liao
Chunhua Liao
School of Anesthesiology, Naval Medical University, Shanghai 200433, China.
,
Jianhua Xia
Jianhua Xia
Department of Anesthesiology, Shanghai Pudong New District People's Hospital, Shanghai 200433, China.
,
Li Tan
Li Tan
tanlihh@163.com
Department of Anesthesiology, Chongqing University Cancer Hospital, Chongqing 400030, China.
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Candida albicans (C. albicans), a conditional pathogenic fungus, is widespread in nature and can live in symbiosis with organisms in small quantities. When the normal microflora is imbalanced, the epithelial barrier is disrupted or the immune system becomes dysfunctional, C. albicans can change from commensal to pathogenic pathogen, causing both superficial and life-threatening systemic infections with no effective treatment. The morbidity and mortality of invasive Candida infections in perioperative patients are high due to underlying chronic diseases, immune deficiencies, and pathophysiological disorders. C-type lectin receptors (CLRs) are the main pattern-recognition receptors for fungal activation of innate immunity and host defense. Upon binding to ligands, CLRs induce multiple signal transduction cascades followed by activation of nuclear factor kappa B through spleen tyrosine kinase - and caspase recruitment domain containing protein 9-dependent pathways. Analyzing the effects of regulatory CLR-induced signaling cascades on host immune cells is critical for understanding the molecular mechanism in regulating antifungal immunity. As one of the core factors in host innate immune regulation, protein post-translational modifications regulate the strength of immune effects by modulating protein conformation, stability, affinity, subcellular localization, etc. This makes the post-translational modification sites promising as potential targets for modulating antifungal immunity. This review primarily described the study progress of post-translational modifications in controlling CLR-induced signaling cascades throughout the process of innate immunity against C. albicans. We aim to provide better understanding of these mechanisms and aid in the identification and development of biomarkers and drug targets for invasive candidiasis.

Review Article
Open Access
Research progress of frontier image processing in medical endoscopes
Jinjing Wu
Jinjing Wu
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
,
Yang Yuan
Yang Yuan
School of Computer Science and Artificial Intelligence, Changzhou University, Jiangsu 213164, China.
,
Long Liu
Long Liu
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
,
Haipo Cui
Haipo Cui
h_b_cui@163.com
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
,
Tianying Xu
Tianying Xu
School of Anesthesiology, Second Military Medical University/Naval Medical University, Shanghai 200433, China.
,
Miao Zhou
Miao Zhou
Department of Anesthesiology, the Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University, Nanjing 210009, Jiangsu, China.
,
Zhanheng Chen
Zhanheng Chen
chenzhanheng17@mails.ucas.ac.cn
School of Anesthesiology, Second Military Medical University/Naval Medical University, Shanghai 200433, China.
,
Bing Xu
Bing Xu
mzxubing1992@163.com
School of Anesthesiology, Second Military Medical University/Naval Medical University, Shanghai 200433, China.
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In the modern medical diagnosis, digital medical images can provide physicians with a more accurate, visualized, and three-dimensional view of various tissues. These images assist in predicting, diagnosing, and treating diseases. However, medical images are highly susceptible to noise contamination from the influence of imaging equipment and the capture process, which poses a significant challenge in the analysis of medical images. This review summarizes the image processing technologies applied in endoscopy, such as image denoising, image deblurring, image enhancement, and image segmentation, involving traditional computational models and deep learning algorithms used in these technologies. Additionally, the clinical applications of these techniques are also discussed.
Review Article
Open Access
Medical image processing using graph convolutional networks: A review
Long Liu
Long Liu
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
,
Xiaobo Zhu
Xiaobo Zhu
College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China.
,
Jinjing Wu
Jinjing Wu
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
,
Qianyuan Hu
Qianyuan Hu
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
,
Haipo Cui
Haipo Cui
h_b_cui@163.com
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
,
Zhanheng Chen
Zhanheng Chen
chenzhanheng17@mails.ucas.ac.cn
School of Anesthesiology, Second Military Medical University/Naval Medical University, Shanghai 200433, China.
,
Tianying Xu
Tianying Xu
xty7910@163.com
School of Anesthesiology, Second Military Medical University/Naval Medical University, Shanghai 200433, China.
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Deep learning, especially graph convolutional networks (GCNs), has been widely applied in various scenarios. Particularly in the field of medical image processing, the research on GCNs have continued to make breakthroughs and has been successfully applied to various tasks, such as medical image segmentation, as well as disease detection, localization, classification and diagnosis. GCNs have demonstrated the capacity to autonomously learn latent disease features from vast medical image datasets. Their potential value and enhanced capabilities in prediction, analysis, and decision-making in perioperative medical imaging have become evident. In recent years, GCNs have rapidly emerged as a research focus in the realm of medical image analysis. First, this review provides a concise overview of the development from convolutional neural networks to GCNs, delineating their algorithmic foundations and network structures. Subsequently, the diverse applications of GCNs in perioperative medical image processing are extensively reviewed, including medical image segmentation, image reconstruction, disease prediction, lesion detection and localization, disease classification and diagnosis, and surgical intervention. Finally, this review discusses the prevailing challenges and offers insights into future research directions for the utilization of GCN methods in the medical field.
Research Article
Open Access
Impact of central venous pressure measurement on the prognosis of patients with septic shock: A retrospective analysis of the MIMIC-IV database
Yanchen Lin
Yanchen Lin
Graduate School, Hebei North University, Zhangjiakou 075000, Hebei, China.
,
Jing Huang
Jing Huang
Graduate School, Wannan Medical College, Wuhu 241002, Anhui, China.
,
Ying Zhang
Ying Zhang
Graduate School, Hebei North University, Zhangjiakou 075000, Hebei, China.
,
Houfeng Li
Houfeng Li
Graduate School, Hebei North University, Zhangjiakou 075000, Hebei, China.
,
Huixiu Hu
Huixiu Hu
Graduate School, Hebei North University, Zhangjiakou 075000, Hebei, China.
,
Li Tan
Li Tan
tanlihh@163.com
Department of Anesthesiology, Chongqing University Cancer Hospital, Chongqing 400030, China.
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Objective: To assess the impact of measuring central venous pressure (CVP) on the prognosis of patients with septic shock. Methods: Septic shock patients with and without CVP measurements were identified in the Medical Information Mart for Intensive Care IV database. The primary outcome was 28-day mortality, and a multivariate logistic regression model was used to analyze the association between CVP measurement and 28-day mortality in patients with septic shock. The results were validated using logistic regression after propensity score matching. Secondary outcomes were in-hospital mortality, 1-year mortality, incidence of acute kidney injury within the first 7 days in the intensive care unit (ICU), and length of stay in the ICU. After propensity score matching, logistic regression analysis was conducted to analyze the correlation between CVP measurements and secondary outcomes in patients with septic shock. Results: A total of 2966 patients were included, including 1219 patients whose CVP was measured within 24h after admission to the ICU. CVP measurement was found to be not correlated with 28-day mortality (odds ratio=0.978, 95% Confidence Interval 0.798-1.200, P=0.835). Analyzing the cohort after propensity score matching, CVP measurement was found to be associated with prolonged ICU stay (4.9 vs. 3.2 days; P<0.001). No statistical differences were found in the primary outcome and other secondary outcomes between those with CVP measurement and those not. Conclusion: CVP measurement is associated with prolonged ICU stay in patients with septic shock but not associated with mortality and incidence of acute kidney injury within 7 days. 

Perioperative Precision Medicine
ISSN: 2957-5443
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