Home | Help Center

Endless possibilities in academia

ISSN: 2957-5443
Indexed in: Europub, Crossref, Dimensions, Google Scholar
Editor-in-Chief: Lize XIONG
Submit Review
Volume 1, Issue 2
Roles of post-translational modifications of C-type lectin receptor-induced signaling cascades in innate immune responses against Candida albicans

Ping Li1, Lindong Cheng2, Chunhua Liao3, Jianhua Xia4, Li Tan5


1Graduate School, Wannan Medical College, Anhui 241000, China. 2Graduate School, Hebei North University, Hebei 075000, China. 3School of Anesthesiology, Naval Medical University, Shanghai 200433, China. 4Department of Anesthesiology, Shanghai Pudong New District People's Hospital, Shanghai 200433, China. 5Department of Anesthesiology, Chongqing University Cancer Hospital, Chongqing 400030, China.


Address correspondence to: Li Tan, Department of Anesthesiology, Chongqing University Cancer Hospital, No.181 Hanyu Road, Chongqing 400030, China. E-mail: tanlihh@163.com.


Received July 18, 2023; Accepted September 11, 2023; Published September 30, 2023


DOI: https://doi.org/10.61189/550782gbbqxs


Highlights

Risk of invasive candida infection and its related mortality are increasing significantly in perioperative patients.

C-type lectin receptors are the primary pattern-recognition receptors for fungi-induced host defense and innate immune activation.

Protein post-translational modifications are one of the core factors in host innate immune regulation.

Post-translational modifications sites on proteins are anticipated to serve as potential targets for modulating anti-fungal immunity.


Review Article |Published on: 30 September 2023

[Perioperative Precision Medicine] 2023; 1 (2): 48-61.

DOI: https://doi.org/10.61189/550782gbbqxs
PDF
CITE
Research progress of frontier image processing in medical endoscopes

Jinjing Wu1,*, Yang Yuan2,*, Long Liu1, Haipo Cui1, Tianying Xu3, Miao Zhou4, Zhanheng Chen3, Bing Xu3


1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China. 2School of Computer Science and Artificial Intelligence, Changzhou University, Jiangsu 213164, China. 3School of Anesthesiology, Second Military Medical University/Naval Medical University, Shanghai 200433, China. 4Department 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. 

*The authors contribute equally.


Address correspondence to: Haipo Cui, School of Health Science and Engineering, University of Shanghai for Science and Technology, NO.516, Jungong Road, Shanghai 200093, China. E-mail: h_b_cui@163.com, Tel: +86-21-55271290; Bing Xu, School of Anesthesiology, Second Military Medical University/Naval Medical University, 800 Xiangyin Road, Shanghai 200433, China. E-mail: mzxubing1992@163.com, Tel: +86-21-81872030; Zhanheng Chen, School of Anesthesiology, Second Military Medical University/Naval Medical University, 800 Xiangyin Road, Shanghai 200433, China. E-mail: chenzhanheng17@mails.ucas.ac.cn, Tel: +86 21 81872034. 


Received July 19, 2023; Accepted September 6, 2023; Published September 30, 2023


DOI: https://doi.org/10.61189/663074tcakcn


Highlights

Medical endoscopic images can provide doctors with more accurate, visualized, and three-dimensional views of various internal tissues.

Image processing techniques such as image denoising, image deblurring, image enhancement, and image segmentation can improve the imaging quality of endoscopes.


Review Article |Published on: 30 September 2023

[Perioperative Precision Medicine] 2023; 1 (2): 62-77.

DOI: https://doi.org/10.61189/663074tcakcn
PDF
CITE
Medical image processing using graph convolutional networks: A review

Long Liu1, Xiaobo Zhu3, Jinjing Wu1, Qianyuan Hu1, Haipo Cui1, Zhanheng Chen2, Tianying Xu2


1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China. 2School of Anesthesiology, Second Military Medical University/Naval Medical University, Shanghai 200433, China. 3College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China.


Address correspondence to: Haipo Cui, School of Health Science and Engineering, University of Shanghai for Science and Technology, NO.516, Jungong Road, Shanghai 200093, China. Tel: +86-21-55271290, E-mail: h_b_cui@163.com; Zhanheng Chen, School of Anesthesiology, Second Military Medical University/Naval Medical University, 800 Xiangyin Road, Shanghai 200433, China. Tel: +86 21 81872034, E-mail: chenzhanheng17@mails.ucas.ac.cn; Tianying Xu, School of Anesthesiology, Second Military Medical University/Naval Medical University, 800 Xiangyin Road, Shanghai 200433, China. Tel: +86 21 81872029, E-mail: xty7910@163.com.


Received July 19, 2023; Accepted September 7, 2023; Published September 30, 2023


DOI: https://doi.org/10.61189/803479emewvv


Highlights

The development history of convolutional neural networks and the transition to graph convolutional networks are introduced, as well as the evolution of network layers.

Graph convolutional networks have been widely demonstrated to be applicable in various perioperative medical image processing scenarios.

This is the first comprehensive review of the applications of graph convolutional networks in image segmentation, image reconstruction, disease prediction, lesion detection and localization, disease classification and diagnosis, and surgical interventions.

Review Article |Published on: 30 September 2023

[Perioperative Precision Medicine] 2023; 1 (2): 79-92.

DOI: https://doi.org/10.61189/803479emewvv
PDF
CITE
Impact of central venous pressure measurement on the prognosis of patients with septic shock: A retrospective analysis of the MIMIC- IV database

Yanchen Lin1, Jing Huang2, Ying Zhang1, Houfeng Li1, Huixiu Hu1, Li Tan3


1Graduate School, Hebei North University, Zhangjiakou 075000, Hebei, China. 2Graduate School, Wannan Medical College, Wuhu 241002, Anhui, China. 3Department of Anesthesiology, Chongqing University Cancer Hospital, Chongqing 400030, China.


Address correspondence to: Li Tan, Department of Anesthesiology, Chongqing University Cancer Hospital, No.181 Hanyu Road, Chongqing 400030, China. E-mail: tanlihh@163.com.


Received August 24, 2023; Accepted September 7, 2023; Published September 30, 2023


DOI: https://doi.org/10.61189/377184mkfywu


Highlights

The measurement of central venous pressure in patients diagnosed with septic shock does not yield prognostic improvements.

Central venous pressure measurement in patients with septic shock is associated with prolonged ICU stay. 

Central venous pressure measurement is not advised for patients diagnosed with septic shock.  

Research Article |Published on: 30 September 2023

[Perioperative Precision Medicine] 2023; 1 (2): 92-100.

DOI: https://doi.org/10.61189/377184mkfywu
PDF
CITE