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Record W3047697090 · doi:10.3389/fmolb.2020.00197

Combination Therapy Using Inhalable GapmeR and Recombinant ACE2 for COVID-19

2020· article· en· W3047697090 on OpenAlexaff
Navin Kumar Verma, Mobashar Hussain Urf Turabe Fazil, Shane P. Duggan, Dermot Kelleher

Bibliographic record

VenueFrontiers in Molecular Biosciences · 2020
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsUniversity of British Columbia
FundersMinistry of Education, IndiaMinistry of Education - Singapore
KeywordsRecombinant DNARNAVirologyBiologyIntracellularCoronavirusChemistryCoronavirus disease 2019 (COVID-19)Cell biologyBiochemistryMedicineGene

Abstract

fetched live from OpenAlex

Here we report our perspective on applying GapmeR technology in combination with recombinant angiotensin-converting enzyme 2 (ACE2) in the treatment of COVID-19 patients. GapmeR is a cell-permeating antisense single-stranded DNA molecule that can be designed to specifically target intracellular severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Once internalized into host cells, such as lung alveolar cells, GapmeR molecules can bind to the viral RNA. This RNA/DNA hybrid will then be degraded by the RNase H enzyme abundantly present in the host cells. GapmeRs can be delivered to COVID-19 patients through inhalation or via nebulization. SARS-CoV-2-targeted GapmeR can also be given to frontline healthcare workers as a prophylactic protection. The recombinant ACE2 protein, the efficacy of which is being evaluated in clinical trials, will bind to the spike (S) glycoprotein of extracellular SARS-CoV-2 and potentially block viral infectivity. We propose that combining inhalable SARS-CoV-2-targeted GapmeRs with recombinant ACE2 could provide a viable and rapidly implementable more effective therapeutic approach for eradicating SARS-CoV-2 and save millions of lives.

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)0.000
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.310
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.068
GPT teacher head0.362
Teacher spread0.295 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

Explore more

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