Editorial: Combatting tropical diseases: a multi-level approach
Bibliographic record
Abstract
in hepatocytes. It is important to understand from these studies that in silico screening coupled with in vitro assays of natural products can help identify new therapeutic agents against infectious diseases.In addition to viruses, parasites are also known to cause disease and are responsible for different neglected diseases. One such parasite is Toxoplasma gondii, which causes toxoplasmosis. The disease affects both humans and livestock and can even lead to death. Sun et al. developed a recombinant GRA12 protein encapsulated PLGA nanoparticle as a vaccine against acute toxoplasmosis and demonstrated the efficacy of the vaccines in mice (Sun et al., 2023). The PLGA-encapsulated GRA12 vaccine was able to increase the IFN-gamma and IL-10 levels while decreasing IL-4 levels in mice, indicating the efficacy of the vaccine. This vaccine candidate holds tremendous potential against acute toxoplasmosis.In conclusion, this special issue summarizes the advances in the development of new drugs and vaccines against infectious diseases. Encompassing viruses and parasites affecting both humans and livestock. The articles underscore the urgency for innovative strategies to address neglected tropical diseases worldwide. As editors, we emphasize the need for new and innovative approaches to tackle tropical diseases neglected in the world. We believe that the special issue will help researchers understand that a combined in silico-in vitro approach is essential for the development of drugs and vaccines. Moreover, the need for governmental intervention in the fight against tropical diseases should be highlighted. We extend our gratitude to the editorial team and authors for their invaluable contributions, which have greatly contributed to the success of this issue.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".