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Record W3216341188 · doi:10.1038/s41598-021-02753-7

Denaturation of the SARS-CoV-2 spike protein under non-thermal microwave radiation

2021· article· en· W3216341188 on OpenAlexafffund
Pooya Afaghi, M Lapolla, Khashayar Ghandi

Bibliographic record

VenueScientific Reports · 2021
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakMicrowaveSpike (software development)VirologyComputational biologyComputer scienceBiologyMedicineOutbreakTelecommunicationsInfectious disease (medical specialty)Pathology

Abstract

fetched live from OpenAlex

SARS-CoV-2, the virus that causes COVID-19, is still a widespread threat to society. The spike protein of this virus facilitates viral entry into the host cell. Here, the denaturation of the S1 subunit of this spike protein by 2.45 GHz electromagnetic radiation was studied quantitatively. The study only pertains to the pure electromagnetic effects by eliminating the bulk heating effect of the microwave radiation in an innovative setup that is capable of controlling the temperature of the sample at any desired intensity of the electromagnetic field. This study was performed at the internal human body temperature, 37 °C, for a relatively short amount of time under a high-power electromagnetic field. The results showed that irradiating the protein with a 700 W, 2.45 GHz electromagnetic field for 2 min can denature the protein to around 95%. In comparison, this is comparable to thermal denaturation at 75 °C for 40 min. Electromagnetic denaturation of the proteins of the virus may open doors to potential therapeutic or sanitation applications.

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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.031
GPT teacher head0.322
Teacher spread0.291 · 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

Citations23
Published2021
Admission routes2
Has abstractyes

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