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Record W3011210268 · doi:10.1097/coh.0000000000000621

Shared immunotherapeutic approaches in HIV and hepatitis B virus: combine and conquer

2020· review· en· W3011210268 on OpenAlexaff
Mala K. Maini, Dimitra Peppa

Bibliographic record

VenueCurrent Opinion in HIV and AIDS · 2020
Typereview
Languageen
FieldMedicine
TopicHepatitis B Virus Studies
Canadian institutionsInstitute of Infection and Immunity
FundersMedical Research CouncilWellcome Trust
KeywordsImmunologyImmune systemHepatitis B virusMedicineImmunityHuman immunodeficiency virus (HIV)Hepatitis BInnate immune systemAcquired immune systemVirologyVirus

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The aim of this study was to identify similarities, differences and lessons to be shared from recent progress in HIV and hepatitis B virus (HBV) immunotherapeutic approaches. RECENT FINDINGS: Immune dysregulation is a hallmark of both HIV and HBV infection, which have shared routes of transmission, with approximately 10% of HIV-positive patients worldwide being coinfected with HBV. Immune modulation therapies to orchestrate effective innate and adaptive immune responses are currently being sought as potential strategies towards a functional cure in both HIV and HBV infection. These are based on activating immunological mechanisms that would allow durable control by triggering innate immunity, reviving exhausted endogenous responses and/or generating new immune responses. Recent technological advances and increased appreciation of humoral responses in the control of HIV have generated renewed enthusiasm in the cure field. SUMMARY: For both HIV and HBV infection, a primary consideration with immunomodulatory therapies continues to be a balance between generating highly effective immune responses and mitigating any significant toxicity. A large arsenal of new approaches and ongoing research offer the opportunity to define the pathways that underpin chronic infection and move closer to a functional cure.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.954
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.182
GPT teacher head0.373
Teacher spread0.191 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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