HIV cure research contributions from Africa in the last three decades
Bibliographic record
Abstract
Introduction: The HIV epidemic in Africa is characterized by extensive viral subtype diversity and human genetic heterogeneity which influence disease outcomes; amidst the co-morbidities that modulate HIV reservoirs and immune responses. This paper provides an overview of the quantity and spectrum of HIV cure research in context of the contributions made by African scientists toward HIV cure related research in Africa. Methods: Using a hybrid environmental scan, we searched the Treatment Action Group website to identify registered HIV cure-related observational and interventional studies between 1995-2024. To identify published papers related to HIV or SIV latency, we searched PubMed for articles with HIV or SIV in the title PLUS terms related to virus latency in the title or in medical subject headings (MeSH); and downloaded results in PubMed format in a text file. We used an R script which checked NCBI to identify articles which cited the original paper that first described the HIV reservoir in 1995, restricting to only those within the query result. This was repeated using loop functions until we obtained all articles directly or indirectly linked to the original paper. Results: Overall, we show an increasing trend of HIV cure-related observational and interventional studies globally; with the least number of studies in Africa. The PubMed query retrieved 7122 HIV cure-related published articles, as at 23 July 2024; of which 2820 were directly or indirectly linked to understanding the HIV reservoir. Of the 2916 articles with first author affiliation country determined, only 52 (0.02%) had affiliations from African institutions. Of the 1955 articles with last author affiliation country determined, only 43 (0.02%) had affiliations from institutions in Africa. The majority of articles with first or last authors from African institutes were descriptive clinical studies of HIV infection, with less than ten studies specifically addressing HIV latency. Conclusion: Scale up of HIV cure research in Africa remains critical to hasten achievement of the global goal of an end to the AIDS epidemic by 2030. There is a need to bridge the technical, infrastructural and technological divides and address constraints in funding and capacity; to promote discovery, characterization and application of promising innovative therapies including immunotherapies and cell and gene therapies towards attaining an effective, durable, affordable and scalable HIV cure.
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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 | 0.030 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.027 | 0.035 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".