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Record W4399553403 · doi:10.1016/j.ajt.2024.05.019

Virus-specific T cell therapy to treat refractory viral infections in solid organ transplant recipients

2024· review· en· W4399553403 on OpenAlexfundno aff
Lorne Schweitzer, Pawel Muranski

Bibliographic record

VenueAmerican Journal of Transplantation · 2024
Typereview
Languageen
FieldMedicine
TopicCytomegalovirus and herpesvirus research
Canadian institutionsnot available
FundersFonds de recherche du Québec – Nature et technologiesFonds de Recherche du Québec - SantéRoyal College of Physicians and Surgeons of Canada
KeywordsMedicineImmunosuppressionImmunologyOrgan transplantationVirusTransplantationRefractory (planetary science)Intensive care medicineVirologyInternal medicineBiology

Abstract

fetched live from OpenAlex

Solid organ transplant recipients require ongoing immunosuppression to prevent acute rejection, which puts them at risk of opportunistic infections. Viral infections are particularly challenging to prevent and treat as many establish latency and thus cannot be eliminated, whereas targets for small molecule antiviral medications are limited. Resistance to antivirals and unacceptable toxicity also complicate treatment. Virus-specific T cell therapies aim to restore host-specific immunity to opportunistic viruses that is lacking due to ongoing immunosuppressive therapy. This minireview will provide a state-of-the-art update of the current virus-specific T cell pipeline and translational research that is likely to lead to further treatment options for viral infections in solid organ transplant recipients.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.045
GPT teacher head0.382
Teacher spread0.337 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations10
Published2024
Admission routes1
Has abstractno

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