Malariotherapy: The Old-Renewed Immunotherapeutic Candidate for Systemic Lupus Erythematosus
Bibliographic record
Abstract
Therapeutic options for Autoimmune Diseases (ADS) are very limited with no real curable value. The etiology of this category of diseases is not clear however; environmental factors are well known to participate in the development of ADs. Infectious agents like malaria parasites have historically been positively linked with psychiatric and ADs. Jauregg J Wagner has noticed an obvious amelioration in the neurological abnormalities associated with general paralysis of the insane (GPI) when some of his patients have encounter malaria infection and subsequently the term malariotherapy has been introduced. Many years later, Greenwood has noted a lower prevalence of the autoimmune condition, rheumatoid arthritis (RA) in West Nigerian population and concluded that this low incidence may be a result of immunological modulation resulting from recurrent exposure to Plasmodium sp. He could also report a suppressed spontaneous autoimmune activity in BWF1 lupus mice infected with Plasmodium berghei. Additionally, a lower prevalence of autoimmune allergic diseases has been observed in native populations in Northern Canada compared to other populations. These results augment the immunotherapeutic value of malaria infection in ADs. The current review will focus on this therapeutic value of malarial infection both in human and experimental animal models.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".