Decompression Illness: a Comprehensive Overview
Diving and Hyperbaric Medicine Journal(2024)
Department of Anaesthesiology
Abstract
Decompression illness is a collective term for two maladies (decompression sickness [DCS] and arterial gas embolism [AGE]) that may arise during or after surfacing from compressed gas diving. Bubbles are the presumed primary vector of injury in both disorders, but the respective sources of bubbles are distinct. In DCS bubbles form primarily from inert gas that becomes dissolved in tissues over the course of a compressed gas dive. During and after ascent (‘decompression’), if the pressure of this dissolved gas exceeds ambient pressure small bubbles may form in the extravascular space or in tissue blood vessels, thereafter passing into the venous circulation. In AGE, if compressed gas is trapped in the lungs during ascent, pulmonary barotrauma may introduce bubbles directly into the pulmonary veins and thence to the systemic arterial circulation. In both settings, bubbles may provoke ischaemic, inflammatory, and mechanical injury to tissues and their associated microcirculation. While AGE typically presents with stroke-like manifestations referrable to cerebral involvement, DCS can affect many organs including the brain, spinal cord, inner ear, musculoskeletal tissue, cardiopulmonary system and skin, and potential symptoms are protean in both nature and severity. This comprehensive overview addresses the pathophysiology, manifestations, prevention and treatment of both disorders.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined