Editorial: Novel pharmacological targets and strategies to treat neglected global diseases (NGDs): an LMIC perspective
Bibliographic record
Abstract
Neglected Global Diseases (NGDs) are infectious diseases and other conditions that cause physical and cognitive impairments, contribute to mother and child illness and death, and make it difficult to earn a living, thereby disproportionately affecting the world's poorest populations. They include maternal, infant, and child health conditions, neglected tropical diseases (NTDs), as well as HIV/AIDS, tuberculosis, and malaria. The World Health Organization (WHO) has a list of 21 NTDs that cause substantial illness for more than one billion people globally (1). These diseases thrive in conditions of extreme poverty, areas that generally have unsafe water, poor sanitation, substandard housing, and limited access to health care or essential medicines. In some countries, this includes most rural areas, urban slums, or conflict zones. However, it is increasingly recognized that emerging middle-income countries contain the highest numbers of people affected.NGDs impact daily life for many, predominantly in countries with weak health and social support systems, they cripple families and contribute to the harsh cycle of poverty. While some NTDs are fatal without treatment, most of them are debilitating and stigmatizing. Children are particularly vulnerable. Overall, NGDs cause great suffering and are a leading cause of chronic disability, diminished quality of life, and premature death. Despite their impact, funding for NTDs research and treatment fades compared to "developed world impacted or supported diseases." Unfortunately, particularly in the past 20 years, the development of novel pharmacological targets and new interventions based on these targets to treat NGDs has been limited (2). However, in recent years there has been a resurgence of research in these areas from low-or middle-income countries (LMICs) researchers themselves. Unlike similar Research Topics in this area, the purpose of this Research Topic was to highlight the novel work of LMIC researchers in developing new targets and interventions to treat NGDs.We received five contributions from colleagues highlighting everything from Global Access Principles to research on leishmaniasis.peptides for potential use against malaria and various bacterial, viral, and fungal infections; a portable vaccine cooler; a diagnostic technology to detect severe sepsis; and an SMS Messaging System to monitor and support patients with HIV, TB and COVID-19. The article identified
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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 | 0.003 | 0.012 |
| 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 | 0.005 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.030 | 0.017 |
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".