A study of a self diagnostic platform for the detection of A2 biomarker for Leishmania donovani
Bibliographic record
Abstract
Visceral leishmaniasis (L.donovani) is a protozoan infection that attacks mononuclear phagocytes and causes the liver and spleen damage that can cause death. The investigation presented is a proof of concept development applying a plasmonic diagnostic platform with simple microfluidic sample delivery and optical readout. An immune-assay method is applied to the quantification of A2 protein, a highly immunogenic biomarker for the pathogen. Quantification of A2 was performed in the ng/ml range, analysis by ELISA suggested that a limit of 0.1ng/ml of A2 is approximate to 1 pathogen per ml and the sensing system shows the potential to deliver a similar level of quantification. Significant reduction in assay complexity as further enzyme linked enhancement is not required when applying a plasmonic methodology to an immunoassay. The basic instrumentation required for a portable device and potential dual optical readout where both plasmonic and photoluminescent response are assessed and investigated including consideration of the application of the device to testing where non-literate communication of results is considered and issues of performance are addressed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".