New Inflammation-Related Biomarkers during Malaria Infection
Bibliographic record
Abstract
Malaria is one of the most prevalent infectious diseases worldwide with more than 250 million cases and one million deaths each year. One of the well-characterized malarial-related molecules is hemozoin (HZ), which is a dark-brown crystal formed by the parasite and released into the host during the burst of infected red blood cells. HZ has a stimulatory effect on the host immune system such as its ability to induce pro-inflammatory mediators responsible for some of the malaria related clinical symptoms such as fever. However, the host serum proteins interacting with malarial HZ as well as how this interaction modifies its recognition by phagocytes remained elusive. In the actual study, using proteomic liquid chromatographic mass spectrometry (LC-MS/MS) and immunochemical approaches, we compared the serum protein profiles of malaria patients and healthy individuals. Particularly, we utilized the malarial HZ itself to capture serum proteins capable to bind to HZ, enabling us to identify several proteins such as apolipoprotein E (ApoE), serum amyloid A (SAA), gelsolin, complement factor H and fibrinogen that were found to differ among healthy and malaria individual. Of particular interest is LPS binding protein (LBP), which is reported herein for the first time in the context of malaria. LBP is usually produced during innate inflammatory response to gram-negative bacterial infections. The exact role of these biomarkers and acute phase responses in malaria in general and HZ in particular remains to be investigated. The identification of these inflammation-related biomarkers in malaria paves the way to potentially utilize them as diagnostic and therapeutic targets.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".