The Paradigm Shift of Using Natural Molecules Extracted from Northern Canada to Combat Malaria
Bibliographic record
Abstract
Parasitic diseases, such as malaria, are an immense burden to many low- and middle-income countries. In 2022, 249 million cases and 608,000 deaths were reported by the World Health Organization for malaria alone. Climate change, conflict, humanitarian crises, resource constraints and diverse biological challenges threaten progress in the elimination of malaria. Undeniably, the lack of a commercialized vaccine and the spread of drug-resistant parasites beg the need for novel approaches to treat this infectious disease. Most approaches for the development of antimalarials to date take inspiration from tropical or sub-tropical environments; however, it is necessary to expand our search. In this review, we highlight the origin of antimalarial treatments and propose new insights in the search for developing novel antiparasitic treatments. Plants and microorganisms living in harsh and cold environments, such as those found in the largely unexploited Northern Canadian boreal forest, often demonstrate interesting properties that are not found in other environments. Most prominently, the essential oil of Rhododendron tomentosum spp. Subarcticum from Nunavik and mortiamides isolated from Mortierella species found in Nunavut have shown promising activity against Plasmodium falciparum.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".