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Record W3125919268 · doi:10.4103/2212-5531.307128

Surgical or homemade masks and medical clothing for inactivation of SARS-CoV-2 (COVID-19)

2021· article· en· W3125919268 on OpenAlexaff
Rozhina Ghanavi, Seyed Alireza Nadji, Poopak Farnia, Jalaledin Ghanavi

Bibliographic record

VenueInternational Journal of Mycobacteriology · 2021
Typearticle
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakClothingVirologyPersonal protective equipmentSars virusMedicineHistoryOutbreakPathologyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Aims &Objective: SARS-CoV-2 spreads when you cough, sneeze, talk, sing, laugh, or breathe out rapidly. Therefore, to protect the individuals or health-care workers, the WHO and CDC have given their standard precautions to use the different types of masks depending on the load of SARS-CoV-2 in their working area. We made a mask that not only protect ourselves from SARS-CoV-2 through respiration, but also give us a protection through inactivation of attached SARS-CoV-2. Methods: The spray made by modified molecule of macrocycle (Ghanavi, United States patent; 10147951). The clothing was sprayed and after using in contaminated area was observed under scanning electron microscopy. The virus load was counted using RTPCR. Result: the Load of virus showed reduction up to 64 fold (94%) in sprayed clothing. The inactivation of COVID-19 were observed under scanning electron microscopy Conclusions: By this technology, the spread and transmission of virus from contaminated to clean area can be reduced or controlled.

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)0.003
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 score0.606
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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)0.0000.000

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 head0.069
GPT teacher head0.406
Teacher spread0.337 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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