Malaria - Epidemiology, Treatment, and Prevention.
New England Journal of Medicine(2023)
From the Harvard T.H. Chan School of Public Health | Harvard TH Chan Sch Publ Hlth | Res Inst Hlth Sci | Med Malaria Venture | Shoklo Malaria Res unit | Monash Univ | Imperial Coll London | Mahidol Oxford Trop Med Res Unit
Abstract
In this documentary video from the New England Journal of Medicine, physicians and scientists from across the world discuss the epidemiology of malaria and outline key strategies for prevention and treatment of the disease. The narrative takes a deep dive into prevention of the disease, including strategies to control mosquito vectors, new vaccines, and monoclonal antibodies. The video anticipates the challenges of eliminating malaria, given the emergence of drug-resistant strains, and looks to promising new therapies on the horizon. For further reading, the following articles, referenced in this video, are available at the Journal's website: A Malaria Vaccine for Africa — An Important Step in a Century-Long Quest , in the March 17, 2022, issue; Safety and Efficacy of a Monoclonal Antibody against Malaria in Mali in the November 17, 2022, issue; and Low-Dose Subcutaneous or Intravenous Monoclonal Antibody to Prevent Malaria, in the November 17, 2022, issue. This video includes footage and photos provided by the Global Fund to Fight AIDS, Tuberculosis, and Malaria; MMV Medicines for Malaria Venture; Caz Tanner; Suphak Nosten for Shoklo Malaria Research Unit; and Saw HTEE K Paung for Shoklo Malaria Research Unit.
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