Inflammation and Elevated Osteopontin in Plasma and CSF in Cerebral Malaria Compared to Plasmodium-Negative Neurological Infections
Bibliographic record
Abstract
Cerebral malaria in young African children is associated with high mortality, and persisting neurological deficits often remain in survivors. Sequestered Plasmodium-infected red blood cells lead to cerebrovascular inflammation and subsequent neuroinflammation. Brain inflammation can play a role in the pathogenesis of neurologic sequelae. Therefore, we assessed a select set of proinflammatory analytes (IP10, IL23, MIP3α, GRO, MCP-1, and osteopontin in both the plasma and cerebrospinal fluid(CSF) of Zambian children with cerebral malaria and compared this with children with neurological symptoms that were negative for Plasmodium falciparum (non-cerebral malaria). Several similarities in plasma and CSF levels were found, as were some striking differences. We confirmed that IP10 levels were higher in the plasma of cerebral malaria patients, but this was not found in CSF. Levels of osteopontin were elevated in both the plasma and CSF of CM patients compared to the non-CM patients. These results show again a highly inflammatory environment in both groups but a different profile for CM when compared to non-cerebral malaria. Osteopontin may play an important role in neurological inflammation in CM and the resulting sequelae. Therefore, osteopontin could be a valid target for further biomarker research and potentially for therapeutic interventions in neuroinflammatory infections.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".