Path toward a malaria-free future: an overview of current challenges and strategies for malaria vaccine development
Bibliographic record
Abstract
Malaria is one of the most devastating parasitic diseases worldwide. According to the World Health Organization (2014) malaria remains to be a major public health concern, representing a substantial global burden with an approximately 584,000 deaths in 97 countries 1,2,3 . Despite the efforts that have been done via the malaria-related UN millennium development goals with the global initiatives and partnerships of UNICEF, WHO, UNDP, and the World Bank of the RBM initiative, there is still a high mortality and morbidity of the disease. Furthermore, the recent rise of insecticide and malaria drug resistance treatments have caused many countries to undergo national changes to incorporate the newer more efficacious artemisinin-based combination therapies (ACTs) in their protocols. Misuse of prevention technologies and inaccuracy in clinical diagnosis and microscopy techniques are other challenges that need to be addressed. The emergence of drug resistance in addition to the current challenges with prevention strategies calls for a technically feasible and efficacious intervention. It is clear that based on the existing conventional approaches and current research data, to fully eliminate malaria at large-scale national and global levels, further efforts are required. Currently, new vaccine technologies such as novel adjuvants and vectored prime-boost regimes are under investigation with the goal of creating a partially effective vaccine that can target different stages of the parasite’s complicated life cycle 5,6 . Approval of the newly tested RTS,S vaccine, known as MosquirixTM, as the first ever vaccine against a human parasite has been a milestone in malaria elimination strategies 7 . Despite 30% efficacy rate, the hope is to combine this vaccine with other malaria control strategies (insecticides, bed nets, chemotherapy, early diagnosis), and reach the goal of a malaria-free world.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".