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Plants secondary metabolites and antiviral properties

2023· article· en· W4366495356 on OpenAlexvenueno aff
Ali Jaber, Mohamad El Hajj, Mohammed Zein

Bibliographic record

VenueDiscovery Phytomedicine - Journal of Natural Products Research and Ethnopharmacology · 2023
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug-Induced Hepatotoxicity and Protection
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Virology2019-20 coronavirus outbreakAdjuvantSAFERViral replicationBiologyVirusMedicinePharmacologyImmunologyInfectious disease (medical specialty)DiseaseInternal medicine

Abstract

fetched live from OpenAlex

There are between three and five million viral infections annually. And normally viral infections are being managed therapeutically through available antiviral regimens with unsatisfactory clinical outcomes. So chemically synthesized drugs can be replaced by secondary metabolites from medicinal plants, which are healthier, safer, and cheaper alternatives. Phytomolecules have been employed as antiviral medicines against a variety of viruses since they can inhibit them directly through blocking their entry or throughout the replication phases, in addition to their use as adjuvant treatments in respiratory infections. For instance, the newly emerged COVID-19 is causing one of the most disruptive pandemics in this century. Thus, phytomolecules offer a ray of hope for human health amid pandemics such as COVID-19. This review provides updated data on the secondary metabolites with different antiviral activities.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.243
GPT teacher head0.478
Teacher spread0.235 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations0
Published2023
Admission routes1
Has abstractyes

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Same venueDiscovery Phytomedicine - Journal of Natural Products Research and EthnopharmacologySame topicDrug-Induced Hepatotoxicity and ProtectionFrench-language works237,207