Two complementary methods for the identification and production of novel biomarkers of Plasmodium falciparum
Bibliographic record
Abstract
Ribosome profiling (RP) is a novel technique that exploits RNA sequencing and ribosome immobilization to quantify transcription and translation at different cell growth stages. Therefore, RP provides invaluable information for expression dynamics studies. Quantitative –omics studies are of crucial importance for identification of potential biomarkers of infection. An ideal parasite detection system should definitely establish the presence or absence of infection; determine the species involved; be detectable even in low concentrations; be proportional to parasite density; and determine the presence of antibiotic resistance. Here, we propose a simple workflow that attempts to identify a set of biomarkers that fulfill some of the above criteria for the ideal detection system. RP expression profiles were ranked for abundance, crosschecked with PlasmoDB for homogeneity along infection cycles and probed for availability of structural stability. The latter is of fundamental importance for the development of molecular biosensors to be give birth to rapid diagnostic kits. In addition, a simple biochemistry workflow was developed for easy production of the selected biomarkers in E. coli. Altogether, the present work provides two complementary and novel workflows that shall aid researchers to rapidly produce molecular biomarkers and develop biosensors based on antibodies or aptamers.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".