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Record W4224303988 · doi:10.3390/covid2040037

A Short Overview: Marine Resources as Potential Interventions for the Omicron SARS-CoV-2 Variant

2022· article· en· W4224303988 on OpenAlexaff
Sarah Geahchan, Hermann Ehrlich, M. Azizur Rahman

Bibliographic record

VenueCOVID · 2022
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 detection and testing
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiologyVirologySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Human immunodeficiency virus (HIV)Computational biologyCoronavirus disease 2019 (COVID-19)DiseaseMedicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

In November of 2021, a recently evolved variant of SARS-CoV-2, omicron, was discovered. In just one month, omicron has spread to more than 89 countries resulting in a rapid rise in cases and a new wave of infections. With over 46 mutations, omicron brings concern to the public health and may be able to infect at a greater capacity than previous strains. Although able to infect double vaccinated and previously infected individuals, the booster vaccine may prove promising. However, more research is needed to fully elucidate the key function of each mutation and to better develop effective drugs. Marine resources may be a promising drug discovery avenue to investigate. For example, compounds such as natural bromotyrosines and inorganic polyphosphate have been shown to have multiple mechanisms of action against viruses, like SARS-CoV-2. Through viral entry blockade and preventing viral replication and protein synthesis, metabolites produced from marine organisms may be promising against the evolving SARS-CoV-2. The present review highlights key features of the omicron SARS-CoV-2 variant, summarizes key studies and reports on omicron viral infection and examines the potential for intervention using renewable marine resources.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.097
GPT teacher head0.368
Teacher spread0.271 · 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.

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

Citations2
Published2022
Admission routes1
Has abstractyes

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