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Record W3011747051 · doi:10.3389/fimmu.2020.00361

Advances in Developing CAR T-Cell Therapy for HIV Cure

2020· review· en· W3011747051 on OpenAlexafffund
Jinxin Qi, Chengchao Ding, Xian Jiang, Yong Gao

Bibliographic record

VenueFrontiers in Immunology · 2020
Typereview
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsAntiretroviral therapyHuman immunodeficiency virus (HIV)MedicineCell therapyVirologyIntensive care medicineBiologyCellViral loadGenetics

Abstract

fetched live from OpenAlex

Acquired immune deficiency syndrome (AIDS), which is caused by HIV infection, is an epidemic disease that has killed millions of people in the last several decades. Although combination antiretroviral therapy (cART) has enabled tremendous progress in suppressing HIV replication, it fails to eliminate HIV latently infected cells, and infected individuals remain HIV positive for life. Lifelong antiretroviral therapy is required to maintain control of virus replication, which may result in significant problems, including long-term toxicity, high cost, and stigma. Therefore, novel therapeutic strategies are urgently needed to eliminate the viral reservoir in the host for HIV cure. In this review, we compare several potential strategies regarding HIV cure and focus on how we might utilize chimeric antigen receptor-modified T cells (CAR T) as a therapy to cure HIV infection.

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 score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.039
GPT teacher head0.357
Teacher spread0.317 · 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

Citations61
Published2020
Admission routes2
Has abstractyes

Explore more

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