HIV and the gut microbiome: future research hotspots and trends
Bibliographic record
Abstract
Background: The use of highly active antiretroviral therapy has transformed AIDS into a chronic infectious disease, but issues of chronic inflammation and immune system activation persist. Modulating the gut microbiome of patients may improve this situation, yet the specific association mechanisms between HIV and the gut microbiome remain unclear. This study aims to explore the research hotspots and trends of the HIV and the gut microbiome, providing direction for future research. Methods: We conducted a search of the Web of Science Core Collection database up to April 30, 2024 to retrieve articles related to the relationship between the HIV and the gut microbiome. The scientific achievements and research frontiers in this field were analyzed using CiteSpace, VOSviewer, and Bibliometrix statistical software. Results: As of April 30, 2024, a total of 379 articles met the inclusion criteria. The number of publications in this field peaked in 2023, and the number of articles published after 2020 declined. The country with the highest number of publications was the United States (184 articles), and the institution with the most publications was the University of Colorado (USA) (21 articles). The author with the most publications was Routy Jean-Pierre (Canada) (14 articles). High-frequency keywords, aside from the key terms, included "HIV," "inflammation," "immune activation," "gut microbiota," and "translocation." Keyword burst results indicated that short-chain fatty acids, T cells and obesity might become the focus of future research. Conclusion: The research hotspots in this field should prioritize examining the role of the primary gut microbiome metabolite, short-chain fatty acids, in reducing immune system activation and inflammation. Another emerging area of interest could be the investigation into the annual increase in obesity rates within this field. Furthermore, understanding the metabolic mechanisms of short-chain fatty acids in T cells is essential. Additionally, multi-omics analysis holds potential.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".