An <em>In Vitro</em> Model for Measuring Immune Responses to Malaria in the Context of HIV Co-infection
Bibliographic record
Abstract
Malaria and HIV co-infection is a growing health priority. However, most research on malaria or HIV currently focuses on each infection individually. Although understanding the disease dynamics for each of these pathogens independently is vital, it is also important that the interactions between these pathogens are investigated and understood. We have developed a versatile in vitro model of HIV-malaria co-infection to study host immune responses to malaria in the context of HIV infection. Our model allows the study of secreted factors in cellular supernatants, cell surface and intracellular protein markers, as well as RNA expression levels. The experimental design and methods used limit variability and promote data reliability and reproducibility. All pathogens used in this model are natural human pathogens (Plasmodium falciparum and HIV-1), and all infected cells are naturally infected and used fresh. We use human erythrocytes parasitized with P. falciparum and maintained in continuous in vitro culture. We obtain freshly isolated peripheral blood mononuclear cells from chronically HIV-infected volunteers. Every condition used has an appropriate control (P. falciparum parasitized vs. normal erythrocytes), and every HIV-infected donor has an HIV uninfected control, from which cells are harvested on the same day. This model provides a realistic environment to study the interactions between malaria parasites and human immune cells in the context of HIV infection.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".