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Record W3117829822 · doi:10.3390/biom11010010

Quercetin as a Natural Therapeutic Candidate for the Treatment of Influenza Virus

2020· review· en· W3117829822 on OpenAlex

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.

Bibliographic record

VenueBiomolecules · 2020
Typereview
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPandemicVirusQuercetinMedicineOutbreakIntensive care medicineCoronavirus disease 2019 (COVID-19)VirologyDiseaseInfectious disease (medical specialty)BiologyInternal medicine

Abstract

fetched live from OpenAlex

The medical burden caused by respiratory manifestations of influenza virus (IV) outbreak as an infectious respiratory disease is so great that governments in both developed and developing countries have allocated significant national budget toward the development of strategies for prevention, control, and treatment of this infection, which is seemingly common and treatable, but can be deadly. Frequent mutations in its genome structure often result in resistance to standard medications. Thus, new generations of treatments are critical to combat this ever-evolving infection. Plant materials and active compounds have been tested for many years, including, more recently, active compounds like flavonoids. Quercetin is a compound belonging to the flavonols class and has shown therapeutic effects against influenza virus. The focus of this review includes viral pathogenesis as well as the application of quercetin and its derivatives as a complementary therapy in controlling influenza and its related symptoms based on the targets. We also touch on the potential of this class of compounds for treatment of SARS-COV-2, the cause of new pandemic.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.162
GPT teacher head0.471
Teacher spread0.309 · 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