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Record W2579943635 · doi:10.1142/s0218339017500061

JOINT IMPACTS OF THERAPY DURATION, DRUG EFFICACY AND TIME LAG IN IMMUNE EXPANSION ON IMMUNITY BOOSTING BY ANTIVIRAL THERAPY

2017· article· en· W2579943635 on OpenAlex
Hongying Shu, Lin Wang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Biological Systems · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsUniversity of New Brunswick
FundersTongji UniversityNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsImmunityImmune systemBoosting (machine learning)ImmunologyPhase lagLagMedicinePharmacotherapyArtificial intelligenceComputer scienceInternal medicineMathematics

Abstract

fetched live from OpenAlex

Antiviral drug therapy that targets on boosting virus-specific immune response has become very promising in controlling the virus, especially when completely eradicating the virus from the host turns out to be difficult. Using a concrete viral infection model that incorporates the time lag needed for the expansion of immune cells, we numerically explored the joint impacts of the duration of therapy, the efficacy of the drugs and the time lag in immune expansion on immunity boosting for a single phase of therapy. Our findings reveal that a single phase of therapy can establish sustained immunity if the therapy is stopped in a suitable range of timing and large time lag in the expansion of immune cells and too strong or too weak therapy would lead to a failure in immunity boosting. Our findings may provide some insights on designing efficient and rational therapy strategies in boosting sustained immunity.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.369
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.032
GPT teacher head0.268
Teacher spread0.236 · 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