Leishmania infections: Molecular targets and diagnosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Progress in the diagnosis of leishmaniases depends on the development of effective methods and the discovery of suitable biomarkers. We propose firstly an update classification of Leishmania species and their synonymies. We demonstrate a global map highlighting the geography of known endemic Leishmania species pathogenic to humans. We summarize a complete list of techniques currently in use and discuss their advantages and limitations. The available data highlights the benefits of molecular markers in terms of their sensitivity and specificity to quantify variation from the subgeneric level to species complexes, (sub) species within complexes, and individual populations and infection foci. Each DNA-based detection method is supplied with a comprehensive description of markers and primers and proposal for a classification based on the role of each target and primer in the detection, identification and quantification of leishmaniasis infection. We outline a genome-wide map of genes informative for diagnosis that have been used for Leishmania genotyping. Furthermore, we propose a classification method based on the suitability of well-studied molecular markers for typing the 21 known Leishmania species pathogenic to humans. This can be applied to newly discovered species and to hybrid strains originating from inter-species crosses. Developing more effective and sensitive diagnostic methods and biomarkers is vital for enhancing Leishmania infection control programs.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it