Heat shock proteins: novel therapeutic tools for HIV-infection?
Bibliographic record
Abstract
Heat shock proteins (Hsps), cyclophilins (Cyps) and FK binding proteins (FKBPs) form a family of intracellular chaperone molecules that facilitate protein folding and assembly. These stress proteins are selectively expressed in cells in response to a range of stimuli, including heat, lymphokine and microbial/viral infections. This review discusses the role of stress proteins in the HIV-1 viral life cycle, with regard to the development of specific Hsp-based therapeutic strategies against HIV-1 infection. Cumulative findings are cited implicating CypA, Hsp27, Hsp70 and FKBPs in host cell and viral activation, viral entry, assembly or formation of infectious virions. Biological response modifiers that show specific high-affinity interactions with Cyp, FKBPs and Hsps, including cyclosporins, FK-506 and cyclopentenone prostaglandins respectively, may block HIV-1 replication and infection, providing novel HIV-1 therapeutic strategies. Moreover, Hsp binding to viral complexes can enhance antiviral immunity, including natural killer (NK), antibody-dependent (ADCC), gamma delta T-cell and cytotoxic T-lymphocyte (CTL) activities against HIV-1 infected cells. The ability of Hsps to interact with HIV-1 viral proteins, combined with their inherent adjuvant and immunogenic properties indicates that Hsps may also serve as vehicles for antigen delivery and the design of AIDS vaccines.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".