New developments in malaria diagnostics
Bibliographic record
Abstract
Currently available rapid diagnostic tests (RDTs) for malaria show large variation in sensitivity and specificity, and there are concerns about their stability under field conditions. To improve current RDTs, monoclonal antibodies (mAbs) for novel malaria antigens have been developed and screened for their possible use in new diagnostic tests. Three antigens, glutamate rich protein (GLURP), dihydrofolate reductase-thymidylate synthase (DHFR-TS) and heme detoxification protein (HDP), were selected based on literature searches. Recombinant antigens were produced and used to immunize mice. Antibody-producing cell lines were subsequently selected and the resulting antibodies were screened for specificity against Plasmodium falciparum and Plasmodium vivax. The most optimal antibody couples were selected based on antibody affinity (expressed as dissociation constants, KD) and detection limit of crude antigen extract from P. falciparum 3D7 culture. The highest affinity antibodies have KD values of 0.10 nM ± 0.014 (D5) and 0.068 ± 0.015 nM (D6) for DHFR-TS mAbs, 0.10 ± 0.022 nM (H16) and 0.21 ± 0.022 nM (H18) for HDP mAbs and 0.11 ± 0.028 nM (G23) and 0.33 ± 0.093 nM (G22) for GLURP mAbs. The newly developed antibodies performed at least as well as commercially available histidine rich protein antibodies (KD of 0.16 ± 0.13 nM for PTL3 and 1.0 ± 0.049 nM for C1-13), making them promising reagents for further test development.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".