Review on cerebral malaria—Pathogenesis and role of EphA2 receptor in maintaining blood brain barrier integrity
Bibliographic record
Abstract
Plasmodium species causes cerebral malaria (CM), a dangerous parasitic illness. The World Health Organization estimates that there were roughly 228 million cases of malaria in 2018, which resulted in 405,000 tragic deaths. There was an approximate 20% mortality rate among children diagnosed with CM who were admitted to the hospital. 67% of the victims were children less than five years old. Patients who survive CM may experience lifetime post-CM complications and a higher risk of childhood neurodisability. The present review discuss about cellular mechanisms and immunological responses causing cerebral malaria. The breakdown of tight junctions and adherence junctions between endothelial cells (ECs) is the cause of an increase in the permeability of the blood brain barrier (BBB). It is obvious that this barrier can be breached by two different mechanisms: a) the apoptosis of ECs, and b) a loosening of the tight junctions between cells. T cells have been shown to have an important role in the development of cerebral malaria in an experimental mouse model (ECM). EphA2 receptors has been identified as contributing to many neurological illnesses and plays an important role in CM, associated with an impairment of the BBB. EphA2 is a key target protein that promotes EC apoptosis by targeting ephrin A ligand-expressing CD8+ T cell adhesion. Prominent role of the EphA2 receptors in CM has never been highlighted so far. Despite an intensive research, there is still a lack of successful malaria vaccines. The most advanced vaccines created so far, RTS,S/AS01 and R21/Matrix-M, focus on combating the early stages of the P. falciparum parasite. This review also discuss about the progress made for developing an effective vaccine for cerebral malaria.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".