Current trends and future directions in probiotics research for HIV/AIDS
Bibliographic record
Abstract
Aim: This study aims to comprehensively and systematically review the current status of research on probiotics and HIV/AIDS, while also exploring future research hotspots and trends in this domain. Methods: The Web of Science (WoS) Core Collection database was queried up until May 13, 2024, to retrieve relevant literature on probiotics and HIV/AIDS. Utilizing CiteSpace, VOSviewers, and Bibliometrix software, scientific achievements and research frontiers in this field were analyzed. Results: As of May 14, 2024, a total of 90 articles was included in. The publication output in this area peaked in 2017, with a subsequent decline in the number of articles post-2019. The United States emerged as the leading country in terms of article count (32 articles), with The University of Western Ontario being the institution with the highest publication output. Dr. Reid G contributed the most articles (12 articles). In addition to key terms, high-frequency keywords included immune activation, inflammation, and microbial translocation. The burst analysis of keywords suggests that vaccines may become a focal point of future research. Conclusion: Future research hotspots and trends should focus on elucidating the types of probiotics, intervention timing, and optimal strains (in terms of mixing ratios) in the context of HIV/AIDS. Furthermore, exploration into the role of probiotic metabolites, such as short-chain fatty acids, in vaccine development is warranted.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".