Proteomics as a novel HIV immune monitoring tool
Bibliographic record
Abstract
PURPOSE OF REVIEW: There is still a fundamental lack of understanding of what protected vaccinee's in the moderately successful RV144 Thailand trial. It is clear that better tools are needed to identify and study correlates of protection and immune responses to vaccine challenge. Quantitative mass spectrometry (MS) has evolved considerably to become a useful tool in biomarker discovery; however, until recently it has been scarcely used to define host responses to HIV exposure and/or viral infection. In this review we discuss current quantitative MS techniques, their application in current HIV studies as well as novel approaches that could be used to better examine innate or adaptive immune responses in HIV vaccine or microbicide trials. RECENT FINDINGS: Several recently published studies have allowed researchers to utilize quantitative MS as part of a systems biology approach to better understand the HIV-affected host's interaction with HIV and/or vaccine challenge. Proteomics has shown it can play a major role in studies to demonstrate insight into HIV replication, early stages of pathogenesis, and identify potential correlates of mucosal protection. SUMMARY: Novel advances in quantitative proteomic techniques are allowing the opportunity to profile and evaluate HIV specific innate and adaptive immune responses, and will increase our understanding of HIV pathogenesis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".