Cell-free DNA as a complementary diagnostic tool for neglected tropical diseases towards achieving the WHO NTDs elimination by 2030
Bibliographic record
Abstract
Neglected Tropical Diseases (NTDs) continue to ravage the poorest regions of the world, with over 600 million people being affected in Sub-Saharan Africa. The global burden of NTDs within these regions is staggering, particularly post-COVID-19 pandemic, where the emerging infection intercepted the existing eradication efforts and protocols such as the Mass Drug Administration (MDA). This further complicated the approaches laid down to achieve the endgame program of eliminating the neglect and transmission of NTDs. To compensate for the detriment of COVID-19's interruption, accurate and timely diagnoses play a vital role in attaining the objectives of the WHO's goal of NTD elimination by 2030. To this effect, alternative approaches in diagnostics are urgently needed, particularly with the inadequacy of current diagnostic strategies for NTDs. Cell-free DNA (cfDNA) has shown great promise in detecting NTDs. Several studies have demonstrated its potential for diagnosing diseases such as malaria, leishmaniasis, and schistosomiasis. However, the adoption of cfDNA in NTD research faces several challenges, including the cost of the procedure, standardization, and technical expertise. Proper capacity building and training can mitigate some of these challenges. However, despite these limitations, the affordability of cfDNA detection is improving due to increased awareness of the approach and researchers' integration considerations into current diagnostic routines. In conclusion, while there are challenges to adopting cfDNA in NTD research, it remains a promising alternative strategy to be considered in the fight against NTDs.
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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 | 0.008 | 0.011 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".