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Record W1988232405 · doi:10.1517/14712598.1.1.67

Heat shock proteins: novel therapeutic tools for HIV-infection?

2001· review· en· W1988232405 on OpenAlexaff
Bluma Brenner, Zev A. Wainberg

Bibliographic record

VenueExpert Opinion on Biological Therapy · 2001
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsHeat shock proteinBiologyCytotoxic T cellVirologyBiochemistryIn vitro

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.244
GPT teacher head0.442
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations40
Published2001
Admission routes1
Has abstractyes

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